Deciphering the Role of Cep55 As a Potent Oncogene and a Potential Therapeutic Target Against Triple Negative Breast Cancer

Deciphering the Role of Cep55 as a Potent Oncogene and a Potential Therapeutic Target against Triple Negative Breast Cancer

Author Sinha, Debottam

Published 2017-11

Thesis Type Thesis (PhD Doctorate)

School School of Natural Sciences

DOI https://doi.org/10.25904/1912/425

Downloaded from http://hdl.handle.net/10072/374768

Griffith Research Online https://research-repository.griffith.edu.au DECIPHERING THE ROLE OF CEP55 AS A POTENT ONCOGENE AND A POTENTIAL THERAPEUTIC TARGET AGAINST TRIPLE NEGATIVE BREAST CANCER

Debottam Sinha Masters of Biotechnology

School of Natural Sciences Griffith Sciences, Griffith University

Submitted in the fulfilment of the requirements of the degree of Doctor of Philosophy

November 2017

Abstract

CEP55 (also known as c10orf3 and FLJ10540) is a highly coiled coil protein of 55 kDa and was initially identified by our laboratory, as a pivotal component of cell abscission, the final stage of cytokinesis in somatic cells. Various independent studies over the past decade have highlighted the critical role of CEP55 in recruiting ESCRT machinery at the midbody and facilitating equal segregation of cytoplasmic contents between the daughter cells. CEP55 is regulated in a phosphorylation-dependent manner by CDK1, ERK2 and PLK1 for timely recruitment to midbody. Conversely, in germ cells, CEP55 in partnership with TEX14 has also been shown to be an integral component of the intercellular bridge prior to meiosis. Notably, CEP55 overexpression has been linked to tumorigenesis of various major organs including those of the breast, lung, colon and liver. Its expression has been significantly correlated with aggressiveness, tumor stage, metastasis and poor prognosis across multiple tumor types. CEP55 binds to and stabilizes the catalytic subunit, p110 of PIK3CA and increases AKT signaling. Independently, studies have shown the interplay between CEP55 and FOXM1 in the cancer context which is negatively regulated by TP53, although the mechanisms underlying this theory are not well defined. Despite significant in vitro studies, the role of CEP55 in development and promoting tumorigenesis remain incompletely understood.

In order to decipher the mechanism by which CEP55 promotes tumorigenesis in vivo, we developed a novel “knock-in” transgenic mouse model that ubiquitously overexpresses Cep55 (Cep55Tg/Tg). Unexpectedly, I found that Cep55Tg/Tg male mice were sterile and suffered severe and progressive defects in spermatogenesis due to spermatogonial stem cell (SSC) dysfunction. Thus, in the first research chapter, we have characterized this male-specific phenotype and shown that Cep55 overexpression results in hyper-activation of PI3K/Akt signaling in testis. In line with this, we observed increased phosphorylation of Foxo1, and suppression of its nuclear retention. Independently, I observed that Cep55 amplification favored upregulation Plzf, Ret and Gfra1, factors required for SSC maintenance. Consistent with this data, I also observed selective down-regulation of genes associated with germ cell differentiation in Cep55 overexpressing testes, including Erg4 and Sohlh1. Thus, Cep55 amplification leads to a shift towards the initial maintenance of SSC stemness while blocking SSC differentiation and entry into meiosis. However, in the long term, it results in progressive germ cell loss. Collectively, in this chapter, we have shown that Cep55 overexpression

iii inactivates Foxo1 resulting in degeneration of germ cells and the manifestation of a Sertoli cell only (SCO)-like phenotype similar to that seen in many azoospermic men.

In the second research chapter, I demonstrated that the Cep55Tg/Tg mice were susceptible to a wide spectrum of neoplasias arising at approximately 15 months post birth. The tumor spectrum varied from lung adenoma and carcinoma, papillary adenocarcinoma, B-cell and T- cell lymphoma, myelogenous leukemia, haemangiosarcoma and lipoma suggesting Cep55 to be a broad-spectrum oncogene. This is consistent with the overexpression of CEP55 observed in a wide- variety of human cancers. I have observed that Cep55Tg/Tg mice were prone to a higher incidence of lymphomas and sarcomas mimicking Trp53-/- phenotype. Notably, we observed reduced tumor latency in bi-transgenic Cep55wt/Tg Trp53wt/- mice compared to Cep55wt/wt Trp53wt/- mice, indicating loss or suppression of Trp53 function might be an important event in de novo tumorigenesis observed in Cep55Tg/Tg mice. In addition, I have observed that Cep55 amplification in vivo caused hyperproliferation due to upregulation of the PI3K/Akt pathway and also the Foxm1-Plk1 pathway. Further, I have also demonstrated that Cep55 overexpression promotes genomic instability in vivo and protects polyploid cells during perturbed mitosis. Collectively, I have characterized the oncogenic potential of Cep55 in vivo and showed that Cep55 amplification in mice leads to de novo tumorigenesis through acquisition of genomic instability.

In the third research chapter, I have used Breast Cancer (BC) as a model to study the role of CEP55 in regulating the fate of aneuploid cell populations during perturbed mitosis. I have shown that high CEP55 mRNA expression associates with aggressive breast cancer subtypes with poor clinical outcomes. Moreover, I have demonstrated that depletion of CEP55 impacts cell proliferation, anchorage-independent growth and tumor forming capacity in vivo. I have also illustrated that CEP55 overexpression promotes aneuploid cell survival during perturbed mitosis by evading apoptosis. Collectively, these findings highlight the clinical implications of deregulated CEP55 and how this can be exploited for therapy development.

In the fourth and final research chapter, I have demonstrated that CEP55 is transcriptionally controlled by an ERK1/2-MYC axis in BC. Notably, I have shown that inhibition of MEK1/2 using a small molecule inhibitor can mimic depletion of CEP55 in vivo and CEP55- depleted cells cannot tolerate mitotic stress caused by anti-mitotic drugs such as PLK1 inhibitors. Here, I explore the rationale of synergistically targeting the CEP55-dependent PLK1/ERK2 pathways using small the molecule inhibitors AZD6244157 (MEK1/2 inhibitor) and BI2536158

(PLK1 inhibitor) against TNBC. This combination of MEK1/2-PLK1 blockade resulted in selective tumor cell growth inhibition and apoptosis in vitro as well as significant growth retardation and regression in multiple in vivo basal-like breast cancer xenografts models. Mechanistically, I have demonstrated that the dual combination resulted in unscheduled CDK1/Cyclin B activation and favored CDK1-Caspase 3-dependent mitotic catastrophe. Therefore, I have provided preclinical evidence of a novel therapeutic strategy of a MEK1/2-PLK1 dual combination for selectively targeting CEP55 over-expressing BC in the clinic.

In summary, these findings illustrate the physiological and oncogenic role of CEP55 in vivo and broaden our understanding of CEP55 function with respect to spermatogenesis and genomic instability. The findings also demonstrate that precise regulation of CEP55 levels are necessary for regular homeostasis, and highlight the therapeutic potential of targeting this protein in aggressive breast cancer.

Statement of originality

This work has not been previously submitted for a degree or diploma in any University. To the best of my knowledge and belief, the thesis contains no material published or written by another person except where due reference is made in the thesis itself. All sources of information have been fully acknowledged.

------

Debottam Sinha

Date: 28th November 2017

Table of Contents

Chapter - 1 ...... 1

Literature Review ...... 1 1.1 Introduction ...... 2

1.2 Cause of Cancer ...... 4

1.3 Abnormal cell proliferation gives way to cancer ...... 6

1.4 Deregulated signaling pathways in cancer ...... 7

1.5 The Cell Cycle ...... 10

1.5.1 Entry into mitosis and regulation of CDK1 ...... 13 1.5.2 Spindle Assembly Checkpoint (SAC) ...... 17 1.5.3 Deregulation of mitotic regulators in human cancers ...... 19 1.6 Cytokinesis ...... 19

1.7 Centrosomal protein CEP55 ...... 22

1.7.1 Role of CEP55 in Cytokinesis ...... 22 1.7.2 CEP55 recruits downstream machinery to the midbody ...... 25 1.7.3 The structure of CEP55 and ESCRT complex at the midbody ...... 26 1.7.4 Phosphorylation of CEP55 ...... 27 1.8 Role of CEP55 in germ cell function ...... 28

1.9 CEP55 as regulator of stemness ...... 29

1.9.1 Midbody fate ...... 29 1.9.2 Exosomes ...... 29 1.10 Role of CEP55 as a regulator of the PI3K/AKT pathway ...... 30

1.11 CEP55 and its connection with cancer ...... 32

1.11.1 FOXM1 ...... 33 1.11.2 TP53 ...... 34 1.11.3 NF-ĸB ...... 34 1.12 Association of CEP55 with other human diseases ...... 35

1.13 Role of CEP55 as a marker of malignancy risk, prognosis and therapy outcome ...... 36

1.14 Aims and hypothesis ...... 40

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Chapter – 2 ...... 41

Materials and Methods ...... 41 2.1 Animal husbandry...... 42

2.1.1 Ethics ...... 42 2.1.2 Generation of the targeting construct of Cep55 overexpressing transgenic mice ...... 42 2.1.3 Generation of the ubiquitous Cep55 knock-in mouse model ...... 42 2.1.4 Animal housing ...... 43 2.1.5 Time mating and organ collections ...... 43 2.1.6 GonadoSomatic index analysis ...... 43 2.2 Cell biology ...... 43

2.2.1 Cell culture ...... 43 2.2.2 Generation of mouse embryonic fibroblast (MEFs) ...... 44 2.2.3 Generation of mouse tumor lines from primary tumor ...... 44 2.2.4 Cell culture of breast cancer cell lines ...... 45 2.2.5 NIH-3T3 proliferation assay ...... 45 2.2.6 MTS (cell viability) assay ...... 45 2.2.7 Doubling time assay ...... 46 2.2.8 Cell proliferation assay ...... 46 2.2.9 Cell cycle analysis ...... 46 2.2.10 Colony formation assay ...... 46 2.2.11 Gene silencing ...... 47 2.2.12 Immunoflorescence ...... 47 2.2.13 Microscopy ...... 50 2.2.14 Karyotyping ...... 50 2.3 Biochemistry and Molecular Biology ...... 50

2.3.1 Genomic DNA extraction ...... 50 2.3.2 Polymerase chain reaction ...... 50 2.3.3 RNA extraction and reverse transcription ...... 51 2.3.4 Quantitative real-time PCR (qRT-PCR) ...... 52 2.3.5 Immunoblotting ...... 53 2.3.5.1 Cell and tissue lysis ...... 53 2.3.5.2 Protein quantification ...... 53

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2.3.5.3 Polyacrylamide gel electrophoresis ...... 53 2.3.5.4 Western blotting ...... 54 2.4 Histology ...... 54

2.4.1 Fixation for histology ...... 54 2.4.2 Haematoxylin and eosin or PAS staining ...... 54 2.4.3 Immunohistochemistry ...... 55 2.4.4 Coverslipping ...... 55 2.4.5 Microscopy and analysis ...... 55 2.5 Bioinformatic analysis ...... 55

2.6 Statistical analysis...... 56

Chapter - 3 ...... 58

Aim1.1: To study the role of CEP55 over-expression in mammalian physiology and spermatogenesis in vivo ...... 58 3.1 Introduction ...... 59

3.1.1 The process of spermatogenesis and characteristics of SSCs ...... 59 3.1.2 The origin of SSCs and the role of its precursors ...... 61 3.1.3 The molecular cross talk governing SSC cell fate ...... 63 3.1.4 Role of CEP55 in spermatogenesis ...... 64 3.2 Results ...... 67

3.2.1 Generation of the ubiquitously expressing Cep55 “Knock-in” transgenic mice model ...... 67 3.2.2 Overexpression of Cep55 leads to male specific infertility ...... 70 3.2.3 Cep55 overexpression leads to Sertoli cell-only (SCO) syndrome ...... 73 3.2.4 Overexpression of Cep55 leads to blockage of SSCs at early differentiation ...... 74 3.2.5 Cep55 overexpression favors proliferation and mitotic activity of the SSC population ...... 75 3.2.6 Cep55 overexpression modulates Foxo1 inactivation via hyper-activation of PI3K-Akt-Pdk1 axis ...... 79 3.2.7 Cep55 overexpression results in a failure to trigger spermatogonial differentiation ...... 83 3.3 Discussion ...... 89

3.4 Conclusion ...... 92

Chapter – 4 ...... 93

Aim1.2: To study the prospective of CEP55 as a potent oncogene in vivo and characterizing the molecular mechanism via which CEP55 over-expression induces spontaneous tumor formation ...... 93

4.1 Introduction ...... 94

4.1.1 Aberration of Mitotic Checkpoint ...... 95 4.1.2 Centrosome amplification...... 97 4.1.3 Role of CEP55 in cancer and CIN ...... 97 4.2 Results ...... 99

4.2.1 CEP55 is overexpressed in a comprehensive range of human cancers...... 99 4.2.2 Cep55 overexpression promotes spontaneous tumorigenesis in vivo ...... 102 4.2.3 Cep55 overexpression promotes cell proliferation advantage in vivo ...... 106 ...... 108 4.2.4 Cep55 loss leads to proliferation defect in vivo ...... 109 ...... 112 4.2.5 Cep55 overexpression facilitates polyploidy in vivo ...... 112 4.2.6 Cep55 overexpression impacts Foxm1-Plk1 axis ...... 116 4.2.7 Cep55 overexpression promotes faster tumorigenesis upon Trp53 depletion ...... 119 4.3 Discussion ...... 123

4.4 Conclusion ...... 126

Chapter - 5 ...... 127

Introduction to Breast Cancer...... 127 5.1 Introduction ...... 128

5.2 Heterogeneity of Breast Cancer ...... 128

5.2.1 Classification of breast cancer ...... 129 5.2.2 Genomic Architecture of Breast Cancer ...... 131 5.2.3 Luminal Subtype ...... 131 5.2.4 HER2/ NEU ...... 132 5.2.5 Basal Subtype ...... 132 5.3 Triple Negative Breast Cancer ...... 134

5.3.1 Classification of TNBC subtypes ...... 135 5.3.2 Targeting the receptor tyrosine kinase (RTKs) and non-RTKs signaling pathway ...... 136 5.3.3.1 Inhibitors for VEGFR and PDGFR ...... 138 5.3.3.2 Inhibitors for EGFR...... 138 5.3.3.3 Inhibitors against Survival and Proliferative pathways ...... 140

5.3.4 Inhibitors against regulators of cell cycle ...... 141 5.4 Conclusion ...... 144

Chapter – 6 ...... 145

Aim 2.1: To investigate the impact of CEP55 over-expression or depletion on aneuploid basal like breast cancer cell survival during perturbed mitosis...... 145 6.1 Introduction ...... 146

6.1.1 Role of mitotic proteins in driving genomic instability in cancer ...... 146 6.1.2 Cytokinesis failure results in CIN in breast cancer ...... 147 6.1.3 Role of CEP55 in breast cancer tumorigenesis ...... 148 6.2 Results ...... 149

6.2.1 CEP55 overexpression is associated with poor outcome in breast cancer ...... 149 6.2.2 Differential expression of CEP55 regulates breast cancer cell proliferation and survival ...... 151 6.2.3 High levels of CEP55 are protective in aneuploid cells ...... 153 6.2.4 CEP55 overexpression provides survival advantage during perturbed mitosis ...... 155 6.2.5 Loss of CEP55 dictates cell fate following anti-mitotic drug treatment ...... 155 6.3 Discussion ...... 161

6.4 Conclusion ...... 163

Chapter – 7 ...... 164

Aim 2.2: To characterize the dual combination of MEK1/2-PLK1 inhibition in selectively eliminating basal-like breast cancer in a CEP55-dependent manner...... 164 7.1 Introduction ...... 165

7.1.1 The current scenario of TNBCs in the clinic ...... 165 7.1.2 Targeting mitotic genes is a better option against TNBCs ...... 166 7.1.3 Synthetic lethality could prove to be a better answer to target TNBCs ...... 167 7.1.4 Involvement of CEP55 in TNBC ...... 169 7.2 Results ...... 170

7.2.1 MAPK controls CEP55 levels in BC ...... 170 7.2.2 MAPK transcriptionally controls CEP55 levels through MYC ...... 170 7.2.3 CEP55 expression determines sensitivity to combined inhibition of MEK1/2 and PLK1 ...... 171 7.2.4 CEP55 expression determines specificity to targets TNBCs ...... 176 7.2.5 Dual combination induces mitotic catastrophe in TNBCs...... 179

7.2.6 Combined MEK1/2-PLK1 inhibition impedes aggressive tumor formation in vivo ...... 180 7.3 Discussion ...... 184

7.4 Conclusion ...... 187

Chapter 8 ...... 188

General Discussion and Future Directions ...... 188

References ...... 194

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List of Figures

Figure 1.1: Hallmarks of cancer and their therapeutic targeting...... 2

Figure 1.2: Models of Carcinogenesis...... 4

Figure 1.3: Signal transduction and therapeutic targeting of PI3K and MAPK pathway...... 9

Figure 1.4: The eukaryotic cell cycle...... 11

Figure 1.5: G1/S transcriptional regulation...... 13

Figure 1.6: The molecular overview of mitosis...... 14

Figure 1.7: Regulation of cyclin B-CDK1 complex and progression into mitosis...... 15

Figure 1.8: Role of PLKs in cell cycle...... 16

Figure 1.9: Spindle assembly checkpoint and role of APC...... 18

Figure 1.10: Stages of cytokinesis...... 20

Figure 1.11: The role of CEP55 in cytokinesis...... 24

Figure 1.12: Additional roles of CEP55...... 31

Figure 3.1: Overview of the Spermatogonial niche...... 62

Figure 3.2: Molecular player of Spermatogenesis...... 65

Figure 3.3: Generation of the ubiquitously expressing Cep55 “knock-in” transgenic mice model. ... 68

Figure 3.4: Validation and characterization of the ubiquitously expressing Cep55 “knock-in” transgenic mice model...... 70

Figure 3.5: Cep55 overexpression leads to male specific infertility...... 73

Figure 3.6: Cep55 overexpression leads to Sertoli-cell like syndrome...... 76

Figure 3.7: Overexpression of Cep55 leads to blockage of SSCs at early differentiation...... 78

Figure 3.8: Cep55 overexpression favors proliferation and mitotic activity of SSC population...... 83

Figure 3.9: Cep55 overexpression impacts on sub-cellular localization of Foxo1 via hyper-activation of PI3k-Akt-Pdk1 axis...... 86

Figure 3.10: Cep55 overexpression favors spermatogonial cyst formation...... 87

Figure 3.11: Cep55 overexpression results in a failure to trigger spermatogonial differentiation...... 88

Figure 4.1 Mechanism of genomic instability...... 96

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Figure 4.2:CEP55 is overexpressed in a broad range of cancers independent of a proliferation- associated effect...... 100

Figure 4.3: CEP55 overexpression is not cell cycle dependent...... 101

Figure 4.4: CEP55 overexpression in vivo promotes tumorigenesis...... 104

Figure 4.5: Anomalies associated with Cep55Tg/Tg mice in vivo during tumorigenesis...... 105

Figure 4.6: Cep55 overexpression promotes cell proliferation advantage in vivo...... 107

Figure 4.7: Cep55 overexpression leads to hyper-phosphorylation of PI3K/Akt pathway...... 108

Figure 4.8: Loss of Cep55 downregulates PI3K/Akt pathway and cell proliferation in vitro...... 111

Figure 4.9: Loss of Cep55 impacts tumorigenesis in vivo...... 112

Figure 4.10: Association of CEP55 overexpression with CEP55...... 114

Figure 4.10: Cep55 overexpression causes genomic instability...... 116

Figure 4.12: Cep55 overexpression favors genomic instability during perturbed mitosis...... 118

Figure 4.13: Loss of Cep55 leads to mitotic catastrophe...... 119

Figure 4.14: Cep55 overexpression leads to upregulation of FoxM1 and Plk1...... 121

Figure 4.15: Loss of Trp53 leads to early tumor latency in Cep55 overexpressing mice...... 122

Figure 5.1: Molecular classification of breast cancer ...... 130

Figure 5.2: Classification of TNBCs...... 137

Figure 5.3: Overview of the molecular targets identified in TNBCs and their inhibitors that currently in clinical trials...... 139

Figure 6.1: Clinical correlation of CEP55 mRNA expression in breast cancer datasets...... 150

Figure 6.2: CEP55 regulates human breast cancer cell survival...... 153

Figure 6.3: CEP55 regulates genomic instability and aneuploidy...... 154

Figure 6.4: CEP55 overexpression provides survival advantage during perturbed mitosis...... 156

Figure 6.5: Loss of CEP55 cause premature G2/M entry and mitotic cell death...... 158

Figure 6.6: Loss of CEP55 cause premature CDK1/Cyclin B activity and mitotic catastrophe...... 160

Figure 7.2: CEP55 is a downstream effector of MAPK signaling...... 173

Figure 7.3: MAPK transcriptionally controls CEP55 levels through MYC...... 174

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Figure 7.4: CEP55 expression determines sensitivity to combined inhibition of MEK1/2 and PLK1...... 176

Figure 7.5: Combined MEK1/2-PLK1 inhibition specially kills basal-like breast cancer lines...... 178

Figure 7.6: Dual combination induces mitotic catastrophe in TNBCs ...... 179

Figure 7.8: Combined MEK1/2-PLK1 inhibition impedes aggressive tumor formation in vivo using MDA-MB-231 xenograft model...... 182

Figure 7.7: Combined MEK1/2-PLK1 inhibition impedes aggressive tumor formation in vivo using syngeneic 4T1.2 model...... 182

List of tables

Table 1.1: Summary of the multiple studies with predictive gene signatures containing CEP55 ...... 39

Table 2.1A: List of siRNA sequences used for knock down the respective genes in murine cells. .... 48

Table 2.1B: List of siRNA sequences used for knock down the respective genes in human cells...... 48

Table 2.2: List of shRNA sequences used for knock down the respective genes in murine cells...... 49

Table 2.3: List of shRNA sequences used for knock down the respective genes in human cells...... 49

Table 2.4: List of antibodies used for immunofluorescence and immunohistochemistry...... 49

Table 2.5: List of PCR primers sequences used for genotyping PCR for mouse DNA...... 51

Table 2.6: List of qRT_PCR primers sequences for transcript analysis...... 53

Table 2.7: List of antibodies used for immunoblotting in the study...... 57

Table 4.1: Incidence of tumors in Cep55 over-expressing and control mice ...... 125

Table 4.2: Incidence of tumors in Cep55/Trp53 bi-transgenic mice model over-expressing ...... 125 and control mice ...... 125

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Abbreviations

AIHW Australian Institute of Health and Welfare ALIX ALG2-interacting protein X ANOVA Analysis of Variance APC/C Anaphase Promoting Complex/Cyclosome ASCAT Allele-Specific Copy Number Analysis of Tumors ATCC American Type Culture Collection ATM Ataxia Telangiectasia Mutated ATR Ataxia Telangiectasia and Rad3-related protein AURKA Aurora A AURKA Aurora B BAD Bcl-2-Associacted Death Promoter BC Breast Cancer BC Breast Cancer BER Base Excision Repair BL Basal Like BLBC Basal Like Breast Cancer BRCA Breast Cancer susceptibility gene BSA Bovine Serum Albumin BUB Budding Uninhibited by Benzimidazoles BUBR1 Budding Uninhibited by Benzimidazoles Receptor 1 CAK CDK Activating Kinase CAL Chromosome Arm-Level CCNB1 Cyclin B1 CCP Cell-Cycle Progression CD Cluster of Differentiation CDC Cell Division Cycle CDKs Cyclin Dependent Kinases cDNA Complementary DNA CENPF Centrosomal Protein F CEP Centrosomal Protein CHFR Checkpoint With Forkhead and Ring finger domains CHK1/2 Checkpoint Kinase CHMP Charged multivesicular body protein CIN Chromosomal Instability CK CytoKeratin CLs Cancer Lines CML Chronic Myeloid Leukaemia CNA Copy Number Alterations CNA Copy Number Alterations COADREAD Colorectal Adenocarcinoma CSCs Cancer Stem Cells

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CTA Cancer Testes Antigen DCIS Ductal Carcinoma In Situ DISC1 Disrupted In Schitzophrenia 1 protein DMEM Dulbecco’s Modified Eagle’s Medium DNA DeoxyriboNucliec Acid DUSP4 Dual-Specific Phosphatase-4 EABR ESCRT and ALIX-Binding Region EGFR Epidermal growth factor receptor ER Estrogen Receptor ERK Extracellular signal-Regulated kinase ESCRT Endosomal Sorting Complex Required for Transport ESCs Embryonic Stem Cells FBS Fetal Bovine Serum FGFR Fibroblast Growth Factor Receptor FOXM1 Forkhead box M1 FOXO Forkhead box protein O FRET Florescence Resonance Energy Transfer GIN Genomic Instability GLUT4 Glucose Transpoter 4 GSI GonadoSomatic Index GSK-3β Glycogen Synthase Kinase-3 Beta H&E Hematoxylin and eosin HCC HepatoCellular Carcinoma HDAC Histone DeACetylase HER2 Human Epidermal Growth Factor Receptor-2 HGFR Hepatocyte Growth Factor Receptor HMECs Human Mammary Epithelial Cells HR Homologous Repair HSP90 Heat Shock Protein 90 IGF-1R Insulin-like Growth Factor-1 Receptor INPP4B Inositol Polyphosphate-4 Phosphatase Beta JAK Janus Activated Kinase k-MT kinetochore-MicroTubule LAMB1 Laminin subunit Beta-1 LAR Luminal Androgen Receptor Expression LATS1 Large Tumour Suppresor Kinase 1 LIHC Liver Hepatocellular Carcinoma LOH Loss Of Heterozygosity LUAD Lung AdenoCarcinoma MAD Mitotic Arrest Deficient MAPK Mitogen Activated Protein Kinase Multinucleated neurons, Anhydramnios, Renal dysplasia, Cerebellar hypoplasia and MARCH Hydranencephaly

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MBds MidBody derivatives MCL-1 Myeloid leukamia Cell differentiation protein-1 MEFs Mouse Embryonic Fibroblasts METABRIC Molecular Taxonomy of Bresat Cancer International Consortium MKLP1 Mitotic Kinesin-Like Protein MKS Meckel-like syndrome MMP-2 Matrix MetalloProteinase-2 MPS1 Mono Polar Spindle 1 mRNA messenger RNA MSL Mesenchymal Stem-Like MTMR Myotubularin-related protein mTORC Mammalian Target of Rapamycin Complex MYT1 Myelin Transcription factor 1 NAC NeoAdjuvant Chemotherapy NEKs NIMA Related Kinase 1 NF1 NeuroFibromin NHEJ Non-Homologous End Joining NPM1 Nucleophosmin 1 NUMA1 Nuclear Mitotic Apparatus Protein 1 OCSCC Oral Cavity Squamous Cell Carcinoma OS Overall Survival PARP Poly (ADP-Ribose) Polymerase PAS Periodic Acid-Schiff PB1 Polar Body 1 PBDs Polo Box Domains PBS Phosphate Buffered Saline PCM PeriCentriolar Matrix PCM1 PeriCentriolar Material 1 pCR pathological Complete Response PCR Polymerase Chain Reaction PDGFR Platelet-Derived Growth Factor Receptor

PDK1 Phosphoinositide-dependent kinase-1 PFA ParaFormAldehyde PGCs Primordial Germ Cells PI3K Phosphoinositide-3-kinase PIN1 Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 PIP2 Phosphatidylinositol 4,5-bisphosphate PIP3 Phosphatidylinositol 3,4,5 trisphosphate PLK Polo Like Kinases PR Progesterone Receptor PRC1 Protein Regulator of cytokinesis 1 PTEN Phosphatase and tensin homolog PTTG Pituitary tumor transforming gene

xix qRT-PCR Quantitative Realtime PCR RAF Rapidly Accelerated Fibrosarcoma RAS Rat Associated Sarcoma RB1 Retinoblastoma 1 RIPA RadioImmunoPrecipitation Assay RNA Ribonucleic acid RNAi RNA interference ROR Risk Of Relapse RPKM Reads Per Kilobase of transcript per Million RPM Revolutions Per Minute RR Response Rate RSEM RNA-Seq by Expectation Maximization RTKs Receptor Tyrosin Kinases SAC Spindle Assembly Checkpoint SCNAs Somatic Copy Number Aberrations SCO Sertoli-Cell-Only SD Standard Diviation SEM Standard Error and Mean shRNA Short Hairpin RNA siRNA Small Interfereing RNA SSC Spermatogonial Stem Cell STAT3 Signal Transducer and Activator of Transcription 3 STR Short Tandem Repeats TAA Tumor Associated Antigen TCGA The Cancer Genome Atlas TEX14 Testes Expressed 14 TNBC Triple Negative Breast Cancer TPX2 Targeting protein for Xklp2 TSC Tuberous sclerosis protein TSG101 Tumor Susceptibility Gene 101 UbiC Ubiquitin C VEGF-A Vascular endothelial growth factor-A WC Whole-Chromosome WES Whole-Exome Sequencing ZBTB16 Zinc Finger and BTB Domain-16

ACKNOWLEDGEMENT

First and foremost, I would like to express my sincere gratitude to my principle supervisor Prof. Kum Kum Khanna, for providing me with the opportunity to complete my project work, by allowing me to join her laboratory and work under her able guidance and support. Thank you for all your guidance and support. I would also like to thank my co-principle supervisor, Prof. Alejandro J. Lopez for your constant backing and encouragement during my PhD tenure. The project work that is included in this PhD thesis has been carried out at QIMR Berghofer Medical Research Institute, Brisbane from May, 2014- November, 2017. I am obliged to the Institute for giving me the excellent facility and provide all the necessary requirements to complete my research. I wish to thank Prof. Frank Gannon (Director, QIMR Berghofer) for giving me an opportunity to carry on with my project work at this reputed Institution. I would like to express my gratitude to management and administration of Griffith University for providing the necessary scholarships and other opportunities to develop my career.

I would like to express my profound indebtedness to my associate supervisor and mentor, Dr. Murugan Kalimutho, for his constant contribution and guidance during the entire tenure of my project work. I thank him sincerely for his critical comments and valuable suggestions which I strongly believe not only helped me during this period but will also do in my future. I would like to express my gratitude to my co-associate supervisor, Dr. Amanda Bain, for guidance and continuous support throughout my PhD. I would also like to thank Dr. Deepak Mittal for his help, mentorship and support during my PhD. A special thanks to the members of Khanna laboratory, especially Stephen Miles, for providing technical support alongside encouragement and friendship. It has been a great pleasure to work with the friendly people of the laboratory. I would also like to take the opportunity to sincerely thank the technical team of QIMR Berghofer for providing all the necessary facilities and training for necessary equipment which were required in order to complete my research.

I also devote a special thanks to Ms. Purba Nag, my partner, for her love and constant support in and outside the laboratory throughout my PhD. Lastly, I convey my hearty thanks and regards to my parents, Mr. Dwijen Sinha and Mrs Samita Sinha and my family for their constant support with loads of affection which gave me the strength to let me work with utmost sincerity.

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Statement of contribution by others in the thesis

Cep55 Floxneo targeted mice were generated as a contracted service by Ozgene, WA. The Southern blot to validate gene targeting as seen in Figure 3.4A was performed by Ozgene.

Dr. Murugan Kalimutho performed the in vitro validation of MDA-MB-231 shRNA clones as shown in Figure 6.2 alongside Karyotyping Figure 6.3 B, polyploidy validation in Figure 6.4A-D and biochemical analysis of mitotic cell fate Figure 6.6A.

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Publications and presentations

Publications directly arising from and is part of the thesis 1. Debottam Sinha, Murugan Kalimutho, Josephine Bowels, Jo Mariner, Ai-leen Chan, Amanda Bain, Jacinta Simmons, Alejandro Lopez, Raimundo Freire, Robin Hobbs, Moira o’ Bryan, Kum Kum Khanna. Overexpression of Cep55 leads to male specific sterility by inactivating Foxo1 through up-regulation of Pi3k-Akt pathway. FASEB 32 (9). (Paper accepted on 4th April, 2018).

2. Debottam Sinha*, Murugan Kalimutho*, Jessie Jeffery, Katia Nones, Sriganesh Srihari, Winnie C. Fernando, Pascal H.G. Duijf, Deepak Mittal, Jodi M. Saunus,Sunil R. Lakhani, J. Alejandro López, Kevin J. Spring, Brian Gabrielli, Nicola Waddell, Kum Kum Khanna. Combination therapy targeting PLK1 and the ERK1/2-MYC-CEP55 axis potently inhibits aggressive basal-like breast cancer. Submitted to EMBO Molecular Medicine on 8th October, 2017. (Chapter 6 and 7 is adopted from this paper).

3. J Jeffery, D Sinha, S Srihari, M Kalimutho, KK Khanna. Beyond cytokinesis: the emerging roles of CEP55 in tumorigenesis. Oncogene 35 (6), 683-690; 2016. (Section 1.7-1.13 has been adopted from this paper).

Publications not relevant but arising at the time of the thesis 1. D Mittal, D Sinha, D Barkauskas, A Young, M Kalimutho, K Stannard, F Caramia, B Haibe- Kains, J Stagg, KK Khanna, S Loi, MJ Smyth. Adenosine 2B receptor expression on cancer cells promotes metastasis. Cancer Research 76 (15), 4372-82; 2016.

2. M Kalimutho, AL Bain, B Mukherjee, P Nag, D Nanayakkara, S Harten, JL Harris, GN Subramaniam, D Sinha, S Shirasawa, S Srihari, S Burma, KK Khanna. Extrapolating genetic interactions from S.cerevisiae identifies enhanced dependency of KRAS mutant colorectal cancer cells on RAD51-dependent homologous recombination repair. Molecular Oncology 11(5), 470-490; 2017.

Conference presentation 1. July 10, 2015. QIMR Berghofer, Brisbane, Australia; Invited speaker at the Annual Student Symposium, titled “CEP55: a potent biomarker in treatment of TNBCs”. 2. September 12, 2015. QIMR Berghofer, Brisbane, Australia; Invited speaker at the Student Retreat, titled “Overexpression of Cep55 is associated with male specific sterility” 3. February 14-17, 2016. Lorne, Victoria, Australia; Poster presentation at 37th Lorne Genome Conference. “CEP55 as a potential therapeutic target in TNBC” (Presenting Author). 4. March 20-24, 2016. Melbourne, Victoria, Australia; Poster presentation at 14th International Workshop on Radiation Damage to DNA. “Characterization of CEP55 as a key regulator of genomic instability” (Presenting Author). 5. June 24, 2016. QIMR Berghofer, Brisbane, Australia; Invited speaker at the Early Career Research Seminar (a mini-conference series), titled “Overexpression of Cep55 leads to spontaneous tumour formation in vivo”.

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6. July 22, 2016. QIMR Berghofer, Brisbane, Australia; Invited speaker at the Annual Student Symposium, titled “Cep55: a potent oncogene and key regulator of genomic instability.” 7. April 1-5, 2017. Washington D.C., USA; Poster presentation at 108th AACR Annual Meeting, 2017. “Dual inhibition of MEK1/2 and PLK1specifically targets aggressive breast cancer cell population with CEP55 elevated expression” (Presenting Author). 8. September 8-12, 2017. Madrid, Spain; Poster presentation at ESMO Congress, 2017. “Synergistic inhibition of CEP55 induces mitotic catastrophe and specifically targets aggressive breast cancer” (Presenting Author).

Conference abstracts 1. Debottam Sinha, Murugan Kalimutho, J. Alejandro Lopez, Kum Kum Khanna. Dual inhibition of MEK1/2 and PLK1 specifically targets aggressive breast cancer cell population with CEP55 elevated expression; Cancer Res 2017;77(13 Suppl). doi:10.1158/1538- 7445.AM2017-1144. 2. Debottam Sinha, Murugan Kalimutho, J. Alejandro Lopez, Kum Kum Khanna. Synergistic inhibition of CEP55 induces mitotic catastrophe and specifically targets aggressive breast cancer; Annals of Oncology, Volume 28, Issue suppl_5, 1 September 2017. doi: mdx390.033. 3. P Raninga, M Kalimutho, D Sinha, A Bain, K Tonissen, KK Khanna. Targeting thioredoxin reductase 1 in novel combination therapies in p53 mutant triple negative breast cancer; Annals of Oncology, Volume 28, Issue suppl_5, 1 September 2017. doi: mdx390.035.

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A note on thesis structure

At the commencement of the project, the focus of the project was solely to decipher the oncogenic potential of CEP55 and thus the in vivo mouse model was generated. However, the unexpected findings within the project significantly expanded the understanding of the physiological role of Cep55 in spermatogenesis apart from cancer biology. In addition, the current emphasis of the Signal Transduction laboratory has been to characterize novel molecular pathways in TNBCs and identify potential therapeutic targets. Thus breast cancer (BC) cell lines were used as a model to study the mechanism via which CEP55 regulates aneuploidy in humans and it’s potential as a novel candidate for target specific therapy. Therefore, the structure of the thesis has been adopted on the chronological accuracy to highlight the role of CEP55 in spermatogenesis (Developmental biology) and cancer biology.

The introductory literature review solely focuses on an overview of CEP55 and also highlights its association with multiple molecular pathways that govern cell cycle and are associated with tumorigenesis. BC, being a heterogeneous disease, is a broader field of study. Therefore, a second introductory chapter was introduced to discuss the classification of BC and the importance of studying TNBC as a model. Independently, each result chapter introduces and discusses the respective findings in the context of their specific fields. The general discussion then elaborates the discipline-specific findings in the context of broader cellular function of CEP55.

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A note on nomenclature

Within the thesis, genes and proteins are named as per the standard nomenclature. Human gene symbol are all in uppercase and italicized (example:-CEP55) and human gene products are all in uppercase, without italicization (CEP55). Mouse gene symbols are all in italicized with only the first letter in uppercase (Cep55) while the mouse gene products are not-italicized, with only the first letter in uppercase (Cep55).

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Chapter - 1

Chapter - 1 Literature Review

Chapter - 1

1.1 Introduction Cancer is defined as a disease caused by dynamic changes in the genome which can affect the majority of the living cells of the body in both genders among humans (Doll, 1999). It is a disease in which normal cells of the body overcome the instructions which dictate their controlled life span and become abnormal as they pursue uncontrolled proliferation. Research over the past few decades has identified definitive “hallmarks” of the disease such as surviving in the absence of growth signals; evading apoptosis; genomic instability and mutation; resistance to anti-growth signals; sustaining angiogenesis; having uncontrolled proliferation and to invade and metastasis (Figure 1.1) (Hanahan and Weinberg, 2000) (Hanahan and Weinberg, 2011). With the increase in abnormal cell population, the ability of these malfunctioning cells to invade into the surrounding tissues and metastasis to distant organs is considered to be the primary cause of mortality in the majority of patients (Liotta and Stetler-Stevenson, 1991, Valastyan and Weinberg, 2011, Mehlen and Puisieux, 2006, Tarin, 2011).

Figure 1.1: Hallmarks of cancer and their therapeutic targeting. Schematic representation of the hallmarks of cancer and illustration of drugs developed and are currently in clinical trials to interfere with each of the acquired capabilities required for tumor pathogenesis (adapted from (Hanahan and Weinberg, 2011)).

Chapter - 1

Cancer is second only to cardiovascular disease with respect to mortality rate in Australia reported by AIHW (Australian Institute of Health and Welfare) in 2011. According to the statistics from AIHW, it is estimated that about 126,800 new cancer cases would be diagnosed in 2015 in Australia. In their subsequent survey of 2016, AIHW had reported to have 43,039 death cases due to cancer among the Australian population (Welfare, 2016). This rate was predicted to increase in 2015 to 46,570 indicating the urgency for better therapies against the disease (Luo et al., 2009, Dietel and Sers, 2006). However, the road to successful treatment against cancer faces a number of challenges (Dietel and Sers, 2006, Schilsky et al., 2010). Amongst them is the specific targeting of therapies against pathogenetically discrete tumor subtypes with high efficiency but with minimal toxicity (Golub et al., 1999, Huang et al., 2006). Identification of molecular biomarkers or features underlying the disease are of key importance to rationalize a mechanistic approach for target dependent therapy (Henry and Hayes, 2012) (Chen et al., 2013).

Cancer biomarkers are influenced by multiple factors such as environmental (Tilburt et al., 2011), chemical agents (Landrigan and Baker, 1995) (exogenous factors) or genetic impairment (Jones and Baylin, 2002) (endogenous factors) which result in initiation, promotion and progression of the disease (Doll and Peto, 1981, Davis and Hoel, 1990). The cancer incidence rates vary among individuals based on their genetic constitution (Bishop, 1987) and also the influence of environmental factors and lifestyle (Armstrong and Doll, 1975, Jemal et al., 2010). Initiation ensues with the damage occurring at the DNA level giving way to mutations and providing a doorway to genomic instability (Negrini et al., 2010). These mutations such as deletion, insertions, chromosome translocation, amplifications, transpositions (Greenman et al., 2007, Pleasance et al., 2010) of proto- oncogenes and tumor suppressor genes, are key players in the regulation of tumorigenesis (Anderson et al., 1992). With the initiation occurring in one cell, it soon proceeds further leading to promotion of the disease. This notion is based on the “theory of clonal origin” of cancer which states that cancer is a multi-gene, multi-step disease originating from a single abnormal cell with an altered genomic sequence. Abnormal proliferation of these uncontrolled cells leads to gain of further mutations which ultimately result in tumor heterogeneity and increase in tumor mass ( Figure 1.A) (Pleasance et al., 2010, Cox et al., 2014). In recent times, the theory of CSCs (Cancer Stem Cells) has also been proposed which proposes that tumors arise from CSCs which in turn arise from mutations gained by embryonic stem cells (ESCs) or progenitor cells ( Figure 1.B) (Shipitsin and Polyak, 2008, Bradshaw et al., 2016). In both cases, clonal expansion facilitates tumor progression until critical volume capacity at the site of origin, after which these abnormal cells break free from the basal membrane

Chapter - 1 barrier of the tissue and begin to metastasise to distant sites (Friedl and Wolf, 2003). The entire process takes times to originate and get detected, as the mutation frequency occurring at the DNA level is approximately 1 in 20 million per locus per cell cycle (Drake, 1991).

A B Clonal Origin Cancer Stem Cell Theory Theory

Figure 1.2: Models of Carcinogenesis. (A) The clonal evolution model illustrates the gain of a series of mutation originating from a normal cell (blue) which undergoes clonal expansion over time to form tumor cells (orange). (B) The cancer stem cell theory suggests that cancer arises from mutation in pluripotent stem cells to form CSCs (red) which continue to self-renew the tumor niche (adopted from (Bradshaw et al., 2016)).

1.2 Cause of Cancer The root cause of cancer has been pinned down to both genetic and epigenetic alteration arising by chance in a normal cell which enables them to escape homeostatic control. It is well known that mutation in genes which play a critical role in cell proliferation and regulate homeostasis in systematic development of an individual, are also prone to initiate tumorigenesis de- novo. These genes can be broadly characterized into oncogenes (growth promoting genes) and tumor suppressor genes (Lee and Muller, 2010).

Chapter - 1

Generally, the genes involved in the regulation of normal cell growth and division, are known as proto-oncogenes which upon mutation become oncogenes (Croce, 2008, Mayr and Bartel, 2009). Oncogenes are associated with the active promotion of cell proliferation as the dominant mutations allow these genes to amplify the activity level of their encoded products (Tuveson et al., 2004). This phenomenon is known as the “gain of function mutation” wherein mutation of only one copy of the gene has the capability of promoting tumorigenesis (Kralovics et al., 2005). A prominent examples oncogenes are the mutations among the RAS gene (Guerrero et al., 1996) fa family which encode HRAS, NRAS, KRAS, found in multiple human tumors including pancreatic tumors (Fleming et al., 2005, Li et al., 2004) carry a KRAS mutation (Han et al., 2006).

Independently, to control the activity of proto-oncogenes, normal cells have tumor suppressor genes, which serve to regulate their proliferative capability (Weinberg, 1991, Sager, 1989). These genes act as “brakes” for the cell cycle, and must not undergo mutation that would inactivate the inhibitory action of these genes (Nigro et al., 1989). As long as one functional copy of the inhibitory gene exists, the regulation of uncontrolled proliferation can be performed; therefore, both copies need to be mutated and these type of recessive mutations are known to be “loss of function mutation” (Levine, 1993, Perry and Levine, 1993). Thus, in a pre-malignant cell comprised of a dominant and a recessive copy of the tumor suppressor gene (heterozygous) the dominant allele is lost due to mutation indicating the loss of heterozygosity, determining tumor progression (Lundberg et al., 1987, Yokota et al., 1987).

An example of loss of function mutation is the TP53 gene which is located on chromosome 17p13.1 and mutated in about 50% of human tumors (Wahl et al., 1996) (Whibley et al., 2009). Homozygous loss of TP53 is common in tumors of the breast (Jonkers et al., 2001), lung (Takahashi et al., 1989) and colon (Rodrigues et al., 1990) which are well known for their high mortality rates, illustrating the importance of this gene in tumorigenesis (Ventura et al., 2007). Certain individuals have been shown to inherit a mutated copy of TP53 gene and they are more prone to develop tumors than others as it is much easier to lose the second copy over time. This syndrome, known as Li-Fraumeni, is characterized by developing malignant tumors by the age of 50(Lang et al., 2004). Loss of TP53 impairs the mechanism of apoptosis and activation of endogenous cell cycle inhibitors like p21 which control the activity of CDKs to regulate the cell cycle. TP16 gene, a cell cycle inhibitor of CDK4-Cyclin D complex (Aprelikova et al., 1995) is known to undergo deletion (Cairns et al., 1995) to inactivate the function of RB1 protein, in human tumors such as 75%

Chapter - 1 pancreatic cancer (Caldas et al., 1994, Ohtsubo et al., 2003, Huang et al., 1996); 40-70% glioblastoma (Schmidt et al., 1994, Hartmann et al., 1999); 50% oesopharengeal cancers(Igaki et al., 1994) and 20% non-small cell lung carcinomas (Reed et al., 1996, Washimi et al., 1995).

In addition to genetic alterations, environmental factors also play a role in the promotion of tumorigenesis (Higginson and Muir, 1977). Smoking, tobacco consumption, radiation exposure, nuclear warfare, inappropriate diet and lack of exercise, occupational hazards, stress and pollution are some of the well-known causes of cancer in the modern industrial world (Clapp and Ozonoff, 2004). One specific examples of this is melanoma caused by to exposure to UV radiation, which is a dominant form of cancer in Australia as the ozone layer depletion is elevated near the South Pole (Maddodi and Setaluri, 2008).

1.3 Abnormal cell proliferation gives way to cancer Limitless abnormal proliferation and self-sufficiency in growth signal are the two unique “hallmarks of cancer” (Coussens et al., 2000). Restricted accessibility to growth factors and other feedback mechanisms in addition to contact inhibition generally governs controlled proliferation in normal cells. Driver mutations in somatic cells results in short circuiting of the overtly steady requirements of their exogenous mitotic signals (Stratton et al., 2009). These mutations further lead to autocrine production of normally limited mitogens, and mutation of the RTKs or G-protein transducers such as RAS (Cox et al., 2014). These mutations also play a vital role in disrupting other interdisciplinary signaling pathways or signal transducer molecules which are associated with regulating cell cycle homeostasis. Therefore, accumulation of these events eventually leads to abnormal proliferation of transformed cells and tumorigenesis (Hunter, 2000). A well-known example of such a phenomena can be observed in the late-G1 cell cycle checkpoint regulated by pRB1 (Dunn et al., 1988, Nevins, 2001). The anomalies observed in RB gene include deletion alongside deregulation of the CDKs causing their hyper-activation or through genetic loss of their endogenous inhibitors (Malumbres and Barbacid, 2009). CDKs regulate RB function by phosphorylation and functionally inactivate RB in regular G1 progression upon mitotic stimulation (Harbour et al., 1999, Lundberg and Weinberg, 1998). Deregulation RB pathway due to CDK hyperactivity therefore enabling self-sufficient signaling to undergo rapid proliferation and abnormal cell cycle (Harbour and Dean, 2000). Another primer example is the uncontrolled expression of MYC

Chapter - 1 which leads to a major deregulation of the cell cycle (Albert et al., 1994, Raffeld et al., 1995). MYC, a potent transcription factor, is known as a pleiotropic controller of proliferation and regulates both cell cycle progression and cell growth (Bouchard et al., 1998, Burgin et al., 1998). MYC expression is firmly controlled by availability of mitogens in normal somatic cells however, in cancer context, its expression increases exponentially (Pelengaris et al., 2000).

1.4 Deregulated signaling pathways in cancer The genetic alterations discussed above also lead to distortion of major signaling pathways such as MAPK and PI3K/AKT signaling pathways which in turn fuel cancer progression (De Luca et al., 2012). Gene amplification is generally one of the well characterized phenomena associated with deregulation of such a pathway, accompanied by mutations including truncation, point mutations and fusions. Examples of oncogenic gain from mutation have also been observed in other pathways including the p53 pathway, DNA repair pathways, RTK pathways (EGFR, HER2) and components of development-associated signaling pathways (WNT, Hedgehog, Hippo and Notch). Anomalies in these pathways can further affect nuclear receptor signaling pathways including estrogen receptor signaling and can also affect transcription factors (FOXO transcription factor family and NF-ĸB), chromatic remodelers and cell cycle effectors (cyclins).

Both the MAPK and PI3K/AKT pathway are transiently activated in response to growth factors or ligand binding in normal cells to carry out regular signal transduction and maintenance of a normal cell cycle (De Luca et al., 2012). PI3Ks are family of proteins which are associated with regulation of cell growth, vesicle trafficking, metabolism, glucose homeostasis and proliferation (Engelman et al., 2006). However, in cancer settings, PI3K pathway components such as p110 (α-Catalytic subunit) of PI3K, PDK1 and AKT are known to undergo gene amplification or “gain of function mutation” (Sever and Brugge, 2015). In addition, tumor suppressors such as PTEN, TSC1 and TSC2 have been shown to undergo deletion or inactivation mutations to enhance hyper activation of other downstream effectors like mTORC1 (Richardson et al., 2004, Fruman et al., 2017). As such, AKT phosphorylates a plethora of downstream effector molecules such as p27 causing it inactivation to enable cell cycle progression and proliferation. Further, it inactivates GSK3β, to prevent degradation of cyclin D1 (Diehl et al., 1997) and enables translocation of GLUT4

Chapter - 1 to the plasma membrane therefore maintaining a strong grip on cell growth and metabolism (Miinea et al., 2005). Additionally, AKT also coordinately regulate the stability of FOXO transcription factors in a phosphorylation dependent manner enabling their nucleus to cytoplasmic localization and hence controlling stem cell proliferation and differentiation (Tran et al., 2003). More importantly, AKT signaling exerts an anti-proliferative effect by inhibition of pro-apoptotic proteins such as BAD and BIM (Franke et al., 1997). Multiple feed-back loops (both positive and negative) including oncogenes and upstream RTKs are able to actively stimulate PI3K/AKT signaling in cancer. As upregulation of this pathway is considered a hallmark of cancer, the PI3K/AKT pathway has been a target of intense research to developing novel therapeutics and targeting this pathway is currently being tested in the clinic (Figure 1.3) (Fruman and Rommel, 2014, Slomovitz and Coleman, 2012).

Mitogen activated protein kinase (MAPKs) signaling pathway shares the similar fate to that of PI3K pathway and has been implicated in pathogenesis of cancer. MAPKs are serine- threonine kinases that are involved in various intracellular signaling processes such as cell proliferation, differentiation, death, survival and transformation (Torii et al., 2006). Several downstream effectors of this pathway, including p38, ERK, c-JUN are commonly hyper-phosphorylated by upstream effectors like RAS and B-RAF in cancers. RAS and B-RAF are known to gain driver mutations as observed many human cancers like melanoma, lung and colon (Dhillon et al., 2007). KRAS mutation has been associated with 50% colon cancer and about 70-90% of pancreatic tumors (Fleming et al., 2005, Li et al., 2004). The mutant form of KRAS (G12D) in transgenic mice has demonstrated MEK dependent hyper-phosphorylation of colonic epithelial tissue (Haigis et al., 2008, Calcagno et al., 2008). The epidermal growth factor receptor (EGFR) is prone to undergo mutation frequently in lung (~80% cases of non-small cell lung cancer) and colon cancer. In-frame deletion in the tyrosine kinase domain causes activation of downstream signaling network leading to hyper activation of ERK1/2 signaling and PI3K/AKT signaling (Dy and Adjei, 2009). Independently, upregulation of MEK1/2 has been associated with activating matrix metalloproteases allowing anchorage independent growth and metastasis (Voisin et al., 2008). On the other hand, B-RAF mutation is common in ~66% cases of malignant melanomas where-in the specific mutation (V600E) is the major culprit in upregulation of ERK1/2 and other downstream effectors of MAPK (Halilovic and Solit, 2008, Wan et al., 2004). These downstream effectors include myosin light chain kinase, calpain, focal adhesion kinase and paxillin, phosphorylation of which results in promoting tumor cell migration. Similar to AKT, ERK1/2 regulates activity of pro-apoptotic proteins such as BIM and anti-apoptotic protein MCL-1, aiding cancer cell survival (Balmanno and Cook, 2009). ERK1/2

Chapter - 1 directly phosphorylates MCL-1 at Threonine-163 leading to its stabilization which in turn favors resistance to anticancer drugs and poor prognosis. Therefore, the MAPK signaling pathway is considered a prominent therapeutic target for the development of novel small molecule inhibitors. Examples of this include Sorafinib (RAF inhibitor); Tramatinib (MEK1/2 inhibitor); Gefitinib and Erlotinib (EGFR inhibitors) which are currently FDA-approved and are regularly used in association with adjuvant and neoadjuvant settings in the clinic (Figure 1.3) (Roberts and Der, 2007, Morkel et al., 2015).

PI3K/AKT Pathway MAPK Pathway

Figure 1.3: Signal transduction and therapeutic targeting of PI3K and MAPK pathway. (Left panel) Graphical representation of the PI3K/AKT/mTORC pathway and various inhibitors developed with the objective of direct or indirect inhibition of the molecular targets (adopted from (Slomovitz and Coleman, 2012). (Right panel) Highlights the signaling pattern of the MAPK pathway alongside the various inhibitors developed against this pathway (adopted from (Morkel et al., 2015))

Chapter - 1

1.5 The Cell Cycle Cell division requires careful orchestration of multiple molecular factors including cyclins, CDKs and coordination of checkpoints for monitoring the order, integrity and fidelity of major events in the cell cycle (Evans et al., 1983, Hartwell et al., 1994). Multiple positive- and negative- feedback loops exist to ensure the successful completion of these events (Elledge, 1996). The process of cell division (Evan and Vousden, 2001) can be broadly classified into karyokinesis and cytokinesis (Foe and Alberts, 1983). Karyokinesis involves the replication of the entire genetic material and segregation of the DNA into separate nuclei during mitosis followed by cytokinesis during which the cytoplasm is equally divided among two daughter cells. Karyokinesis can also be termed as the mitotic cell cycle in eukaryotic cells, which includes four stages: Growth 1 (G1) (Weinberg, 1995, Massague, 2004), Synthesis (S) (Celis and Celis, 1985), Growth 2 (G2) (Agarwal et al., 1995) (together known as interphase) and Mitosis (M) phase. There also exists a quiescent stage, G0, where certain cells (e.g. stem cells) rest over a period of time (Figure 1.4A) (Hartwell and Kastan, 1994, Collins et al., 1997). G1 is the stage where the cell synthesizes the required proteins for DNA replication and once this has occurred (Cells generally spend the most amount of time in G1), the cell enters S phase. G2 follows S phase wherein the cell prepares for the segregation of the replicated chromosome and ultimately enters M phase (Mitotic phase). Mitosis consists of four stages: prophase, metaphase, anaphase and telophase. During prophase, the spindle begins to assemble and the chromatin condenses to form chromosomes. At prometaphase, the nuclear envelope breaks and the chromosomes attach to the spindle at the kinetochore. Following this, at metaphase, the sister chromosomes are aligned at the spindle midzone. In anaphase, the sister chromosomes are pulled to opposite poles by centrioles at each pole. Telophase is the final stage where two daughter nuclei are formed prior to initiating cytokinesis (Figure 1.4B) (Nigg, 2001).

The CDKs are the serine/threonine protein kinases that phosphorylate key substrates which in turn promote DNA synthesis and mitotic progression. CDKs require binding of specific cyclins which are periodically destroyed for regulation of the kinase activity of the CDKs in a timely manner. Though there exist multiple loci encoding various CDKs and cyclins (13 and 25 loci, respectively), only a certain subset of CDK-cyclin complexes play a major role in regular progression of the cell cycle. These involve ten cyclins belonging to four different classes (A, B, D and E) in conjunction with three interphase CDKs (CDK4, CDK6 and CDK2) and a mitotic CDK (CDK1 also called CDC2) (Figure 1.4A) (Malumbres and Barbacid, 2005). The progression of the

Chapter - 1 cell cycle is monitored by checkpoints that act as accelerators and brakes and serve as a surveillance mechanism to sense possible defects or unfavorable conditions including DNA damage and chromosome missegregation errors to modulate cell cycle arrest by regulating CDK activity. Three major checkpoints serve to guard the eukaryotic cell cycle, namely: the G1/S checkpoint, G2/M checkpoint and the SAC (spindle assembly checkpoint) (Kastan and Bartek, 2004).

A B Eukaryotic Cell Cycle Overview of Mitotic phase of Cell Overview Cycle

Figure 1.4: The eukaryotic cell cycle. (A) Graphical representation of the eukaryotic cell cycle illustrating phases of the cell cycle, the function of CDKs and their associated cyclins and also the G1checkpoint monitoring the progression of the cell cycle from G1 to S phase (adopted from (Collins et al., 1997)). (B) Graphical representation of the principle events happening in the M phase of the animal cell cycle (adopted from (Nigg, 2001)).

The G1/S checkpoint verifies the presence of optimum environmental factors and mitogen signals favoring cell proliferation alongside attainment of optimal cell size to initiate genome replication. The presence of mitogen stimuli increases synthesis of D-type Cyclins which combines with CDK4 and CDK6 and leads to phosphorylation and inactivation of RB (Figure 1.5) (Morgan, 1997, Murray, 2004). Inactivation of RB results in disassociation of E2F, a transcription factor, enabling transcription of several factors including Cyclin E, required for DNA synthesis

Chapter - 1

(Figure 1.5) (Sears and Nevins, 2002, Stevaux and Dyson, 2002). Cyclin E binds to CDK2 which in turn can phosphorylate RB as a positive feedback loop and initiates S phase (Figure 1.5) (Bertoli et al., 2013). However, in majority of the human tumors, RB is often mutated which enables rapid proliferation of the cancerous cells (Sherr, 2004). In vivo studies have demonstrated that cyclin E can rescue mice deficient of all Cyclin D genes that die at mid/late gestation suffering from anemia indicating the interdependency of D- and E- type Cyclins in phosphorylating RB and a major rate limiting factors in G1 to S transition (Kozar et al., 2004). Other mechanisms involved in governing premature entry into S phase of the cell cycle include endogenous inhibitors of the cyclin-CDK complex, such as p27Kip1, p15Ink4b, p16Ink4a, p21Cip1/WAF1 and p57Kip2 (Pavletich, 1999). An example this is the role of p27 in silencing Cyclin E-CDK2 in resting (G0) cells or in early G1 phase which is liberated in presence of mitogenic stimulus by suppressing the transcription, translation and stability of nuclear p27 (Blain et al., 2003). Some studies have proven that enough activation of CDK2 induces a negative feedback loop to block p27 sequestration by polyubiquitination (Montagnoli et al., 1999).

Once the G1/S checkpoint is satisfied, the cell cycle progresses through to S phase enabling genome duplication following which it enters M phase. Post replication phase, cells require transcription factors such as FOXM1 for transcription of genes in G2 for mitotic entry (Laoukili et al., 2005, Leung et al., 2001, Costa, 2005) a gene which is highly expressed in different aggressive carcinomas (Radhakrishnan and Gartel, 2008, Koo et al., 2012, Raychaudhuri and Park, 2011, Laoukili et al., 2007). G2 checkpoint proteins involve ATM (Hurley and Bunz, 2007), ATR (Alderton et al., 2006) known for detecting DNA damage (Jazayeri et al., 2006) with checkpoint kinase (CHK) 1 and 2 (Liu et al., 2000, Hirao et al., 2000) trigger cascade of signals along via the p53 pathway to control G1/S and G2/M transition (shown in Figure 6) (Agarwal et al., 1995). The entry to M phase is guarded by the G2/M checkpoint which regulates the levels of CDK1. At G2 phase, CDK1 levels remain low but an increased activity of CDK1 in conjunction with B type Cyclins is required for the cell cycle to enter mitosis. Therefore, activation of CDK1 is the focal point of many signaling pathways that control the commitment of a cell to mitosis (Nurse, 2000, Porter and Donoghue, 2003).

Chapter - 1

Positive Feedback Loop

Negative Feedback Loop

Figure 1.5: G1/S transcriptionalControl regulation.of cell cycle transcription Graphical during representation G1 and S phases illustrating the inactivation of RB by phosphorylation initially by cyclin D and the CDK4/6 complex to release transcription factor E2F which activates transcription of cyclin E. Cyclin E in association with CDK2 and further leads to phosphorylation of RB in a positive feedback loop. This network of activating cyclin E and other transcription factors is favored in the presence of positive mitotic stimuli. However, in absence of the stimuli or in the presence of unfavorable conditions, endogenous inhibitors such as p27 inhibits cyclin E and CDK2 complex formation in a negative feedback loop and hence inhibits the progression of cell cycle through the G1/S checkpoint (adopted from (Bertoli et al., 2013)).

1.5.1 Entry into mitosis and regulation of CDK1

Activation of CDK1 results in phosphorylation of 100 different substrates including numerous downstream effector kinases at the S/TPx(x)R/K consensus to initiate the necessary events required to progress through mitosis (Figure 1.6) (Malumbres and Barbacid, 2009). These phosphorylation events happen post binding to the cyclin B following which CDK1 undergo specific structural changes enabling binding of ATP to the terminal γ phosphate and hence target serine or threonine residue of CDK1 substrates. However, this requirement alone lacks robust CDK1 activity as to attain full activation, CDK1 requires phosphorylation at a threonine residue (T14) near the active site which is catalyzed by CDK-active kinase (CAK) enabling further conformational

Chapter - 1 rearrangement to gain active confirmation of CDK1. Function of CAK is regulated solely on the basis of availability of Cyclin B and its binding with CDK1 (Morgan, 1995). The full activation of CDK1 and the entry of cell cycle in M phase are restrained by WEE1 and MYT1, the endogenous inhibitors of CDK1 (Harvey et al., 2005, Mueller et al., 1995). These kinases phosphorylate CDK1 on a tyrosine residue (Y15) and adjacent threonine residue (T14) upon binding to cyclin B by disabling the confirmation change required to actively bind the downstream substrates (Gould and Nurse, 1989, Featherstone and Russell, 1991, Mueller et al., 1995). Thus, upon binding of CDK1 with Cyclin B and phosphorylation by CAK and WEE1, CDK1 is primed but requires dephosphorization of Y15 and T14 to initiate entry into mitosis (Figure 1.7) (Rhind and Russell, 2012) .

The subcellular localization of the protein complex plays a vital role in regulation of G2/M transition as inhibition of the CDK1 complex by WEE1 maintains the complex in the cytoplasm in G2 phase. CDC25, a dual-specificity protein phosphatase, enables the dephosphorylation of CDK1 at Y15 and T14 allowing it’s translocation into the nucleus enabling G2/M transition (Gautier et al., 1991, Kumagai and Dunphy, 1991, Strausfeld et al., 1991). Experimental studies suggest that CDC25 and WEE1 together determine the fate of cells in mitosis and resemble a “switch-like” activation of CDK1 provided precipitance of favorable conditions and availability of mitogenic signal (Nurse, 1975, Russell and Nurse, 1986, Russell and Nurse, 1987).

Figure 1.6: The molecular overview of mitosis. Graphical representation of the molecular players of M phase of the cell cycle which dictate the major changes of the chromosome and the spindle structure for successful mitosis. These players are also prone to undergo alteration in human cancers. The cyclin B-CDK1 complex phosphorylate a major number of these molecular players to initiate mitosis (adopted from (Perez de Castro et al., 2007)) . 14

Chapter - 1

Figure 1.7: Regulation of cyclin B-CDK1 complex and progression into mitosis. Graphical representation of the signaling cascade associated with the regulation of CDK1 at the G2/M transition. The phosphorylation of CDK1 at Y15 by WEE1 and dephosphorylation by CDC25 is known to be the rate limiting step dictating a cell’s entry into mitosis. Once activated, CDK1 by feedback mechanism also regulates its own fate by both phosphorylating WEE1 and CDC25 directly or indirectly through PLK1. This figure also highlights the regulation of CDC25 by DNA damage and the replication checkpoint upon detecting damage during interphase (adopted from (Rhind and Russell, 2012)).

Chapter - 1

PLKs are comprised of a unique C-terminal protein-protein interacting domain, called the Polo-box domain (PBDs) which is essential for its activity in controlling different processes like cell cycle, differentiation and survival (Barr et al., 2004, Elia et al., 2003). There exist 5 PLKs (Strebhardt, 2010) of which PLK1 (Strebhardt and Ullrich, 2006) is of importance because of its higher expression in mitotic cells and plays a critical role in the overall cell cycle such as mitotic entry, centrosome duplication, metaphase to anaphase transition and cytokinesis (Figure 1.8) (Glover et al., 1998, Zitouni et al., 2014). Alongside PLK1, many other mitotic kinases namely Aurora A interact with CDK1 (Barr and Gergely, 2007, Murata-Hori et al., 2002) to phosphorylate histones H1 and H3 and other proteins like topoisomerase II (TOP2A) involved in chromosome condensation (Malumbres and Barbacid, 2005, Perez de Castro et al., 2007). Moreover, the phosphorylation of these kinases is further controlled by other phosphatases such as protein phosphatase (PP) 1 and 2 or proline directed phosphatases CDC14A and CDC14B (Trinkle-Mulcahy and Lamond, 2006).

Figure 1.8: Role of PLKs in cell cycle. Schematic representation of the numerous functions of individual PLKs (represented by different colored boxes) at different stages of the cell cycle indicating the dynamic role of the PLK family in cell cycle progression (adopted from (Zitouni et al., 2014)).

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1.5.2 Spindle Assembly Checkpoint (SAC) The SAC (Lew and Burke, 2003), also called mitotic checkpoint, is known for arresting mitotic progression during cell cycle dysfunction to ensure proper alignment and segregation of chromosomes (Rudner and Murray, 1996). The SAC is activated in each cell cycle instantly upon entry into M phase wherein, it’s main function is to delay anaphase until accurate attachment of each chromosome at the metaphase plate (Figure 1.9) (Lew and Burke, 2003, Rudner and Murray, 1996, Rhind and Russell, 2012). Upon improper attachment, an inhibitory signal arises from kinetochore to induce recruitment of proteins machinery involving MAD2, BUBR1, BUB3 and MPS1 (also called TTK) (Ditchfield et al., 2003, Musacchio and Salmon, 2007). The recruitment of these proteins is due to tension at the kinetochore sensed by AURKB, survivin and INCENP (Hauf et al., 2003, Ditchfield et al., 2003, Pinsky and Biggins, 2005). BUBR1 gets activated upon binding to CENPE (a kinetochore protein) and recruits MAD1-MAD2 heterodimers in association with the HEC1 and ZW10-ZWINT-ZWILCH complex which prevents APC/C-CDC20 complex activation (Lin et al., 2006, Karess, 2005). But the process is altered in properly segregated chromosome wherein MAD2 is released from the complex to activate the APC/C complex which degrades Cyclin B1 causing CDK1 inactivation and therefore allows cells to exit mitosis (Shah and Cleveland, 2000).

The anaphase-promoting complex/cyclosome (APC/C) is the major mitotic ubiquitin ligase that controls timely degradation of numerous regulators of mitosis including Cyclin B, AURORA kinase and PLKs (Pines, 2006). APC/C depends on CDC20 and CDH1 for substrate specificity and its activity is controlled heavily by the SAC. The APC/C-CDH1 complex gets activated in late mitosis after all chromosomes contact bipolar spindle and degrades B-type Cyclins and PTTG1 (securin) (Peters, 2006). Degradation of PTTG1 results in cleavage of cohesins by seperase (ESPL1) which results in separation of sister chromatids. The other substrates of APC/C- CDH1 complex that are degraded at late anaphase involves PLK1, AURKA, survivin, NEK2, CDC20 and SKP2 (Nakayama and Nakayama, 2006). The regulation of APC/C-CDH1 is controlled by cell cycle dependent phosphorylation of CDH1 by CDK in S, G2 and M phase to inhibit premature activity of APC/C. However, in late M phase, CDC14 dephosphorylates CDH1 enabling its binding to APC/C and activation of the APC/C-CDH1 complex (Zachariae et al., 1998, Jaspersen et al., 1999, Visintin et al., 1998).

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Figure 1.9: Spindle assembly checkpoint and role of APC. Schematic representation of the molecular players involved in SAC activation involving BUB1, BUB3, CDC20, MAD1, MAD2 and many others as shown. Signaling pathways associated with these proteins are initially responsible for the sensing unattached kinetochores and delaying cytokinesis until proper segregation of the sister chromatids. The graphical representation also shows the regulation of APC/C and its function at various stages of mitosis (adopted from (Rhind and Russell, 2012)).

The exit from mitosis involves the spatial and temporal coordination of multiple processes involving degradation of Cyclin B and deactivation of CDK1 and cytokinesis (Bosl and Li, 2005). One major pathway, the NoCut pathway, requires AURKB activity delays cytokinesis until all chromosome arms are pulled out of the cleavage plane (Norden et al., 2006). The SAC also plays a major role in initiating timely cytokinesis as it requires PRC1 (protein regulator of cytokinesis 1) the central spindlin complex which is comprised of the RHO family member GAP, RACGAP1and kinesin like protein KIF23 (Glotzer, 2005, Piekny et al., 2005). Inactivation of CDK1 activates PRC1 while AURKB and PLK1 associate with KIF20A to initiate furrow formation at the central spindle and in turn initiate cytokinesis (Gruneberg et al., 2004, Neef et al., 2003).

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1.5.3 Deregulation of mitotic regulators in human cancers Theodore Boveri first predicted that alteration in chromosomes might be associated with cancer development and progression. A significant number of genetic alterations of mitotic regulators have been demonstrated both in vitro and in vivo to contribute to tumorigenesis. One of the best characterized of these is Cyclin D1 which is amplified in breast, esophageal, bladder, lung, squamous cell carcinoma and certain B-cell lymphomas (Bodrug et al., 1994, Lovec et al., 1994, Wang et al., 1994b, Wang et al., 1994a). Moreover, molecular studies have demonstrated that mutations lead to genetic instability and results in human tumors with chromosomal instability phenotype (Kops et al., 2005). These genetic alterations include DNA amplification as observed in AURKA and also its regulators FOXM1, TPX2 and CENPF; chromosomal translocation and deregulated expression of NUMA1, CEP110, Ninein, NUP98, PCM1 and NPM1; and deletion of NPM1 and LATS1, which has been demonstrated to be associated with specific tumor types (Perez de Castro et al., 2007). In addition, point mutations observed in NPM1 and PLK1 alongside SAC regulators such as BUB1 and its receptor BUBR1, TTK, KNTC1, ZW10, ZWILCH, MAD1 and MAD2. Other mitotic gene alterations include molecules associated with pre-mitotic events such as B-type cyclins, CHFR, CDK1, NEK2, CDC20, PRC1, ECT2 and H2FAX (Perez de Castro et al., 2007).

1.6 Cytokinesis After division of the nuclear membrane following segregation of duplicated chromosomes, in eukaryotic cell division, cytokinesis is carried out to divide a single cell into two daughter cells (Steigemann and Gerlich, 2009, Glotzer, 2005). This process begins with the construction of a cleavage furrow intervened with actomyosin ring contraction (Mullins and Biesele, 1977, Elia et al., 2011, Guizetti et al., 2011). Post anaphase, the central spindle assembly in association with PRC1 (activated by CDK1 deactivation, as discussed previously) and the kinesin- like protein (KIF23) form a complex called the centralspindlin complex (Glotzer, 2005). On the other hand, AURKB and PLK1 combine with KIF20A to support centralspindlin complex formation and function in furrow formation to initiate cytokinesis (Gruneberg et al., 2004, Neef et al., 2003). The spindle midzone formed during anaphase gets transformed into an intercellular bridge also known as the midbody (a platform where all molecules regulating cytokinesis are assembled), which is the last remaining connection between the two about to be formed daughter cells (Figure 1.10) (Mullins and

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Biesele, 1977, Lee et al., 2008, Chen et al., 2012). This midbody is comprised of arrays of antiparallel microtubules meeting at the midbody, which is responsible for providing an anchor to the intervening cleavage furrow (Barr and Gruneberg, 2007). The midbody remains intact until the very last step of cytokinesis, where it is severed during a process termed cell abscission (Roberts et al., 2002).

Figure 1.10: Stages of cytokinesis. Schematic representation of the step-wise processes that occur in a eukaryotic cell during cytokinesis. Post chromosomal segregation, the formation of the cleavage furrow begins at the middle of the mother cell and slowly the furrow constricts to form the midbody which recruits the necessary proteins to separate the cytoplasm to form new daughter cells (adopted from (Chen et al., 2012))

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The centrosome is an organelle present near the nucleus which serves as a microtubule organization center and plays an essential part in cell polarity and cell cycle progression (Bornens, 2002, Doxsey, 2001, Lange, 2002). It consists of a pair of barrel-shaped structures called centrioles which are surrounded by the pericentriolar material (PCM) (Loncarek et al., 2008), at which proteins like the γ-tubulin ring complex are recruited during mitosis (Zheng et al., 1995). The centrosome undergoes various structural and functional modifications during mitosis to form bipolar mitotic spindles with each spindle at one pole (Hinchcliffe and Sluder, 2001, Hinchcliffe et al., 2001, Walczak and Heald, 2008). These spindles allow re-organization of the microtubule cytoskeleton which attaches to the kinetochore of the chromosomes in metaphase and pulls the aligned centrosome towards each pole during anaphase, to ensure separation of daughter cells. This process is functionally guarded by signaling of proteins like Aurora A, PLK and NEKs like kinases which also plays a part in chromosome duplication, maturation and segregation (Bischoff and Plowman, 1999, Meraldi et al., 2004, Chen et al., 2002, Barr et al., 2004, Glover et al., 1998). Centrosomal amplification is being associated with chromosomal instability and is known to be one of the “hall marks of cancer” (Jallepalli and Lengauer, 2001). Therefore centrosomal proteins can serve as good therapeutic options against treatment of many cancers. The family of CEP (Centrosomal proteins) proteins forms the active component of the centrosome and are present in the PCM which is crucial for centriole biogenesis and cell cycle progression. The family is composed of ~31 proteins of which proteins like CEP57, CEP63, CEP152, CEP164 are potential molecular targets for drug discovery. One such centrosomal protein, CEP55, was first discovered in our laboratory a decade ago, as a key regulator of cytokinesis. Since then, many reports have suggested its association with cancer development and progression, which will be further discussed in the following section.

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1.7 Centrosomal protein CEP55 Prof. Khanna’s laboratory was the first to identify and characterize CEP55 (also known as c10orf3 and FLJ10540), a decade ago, as a centrosome and midbody-associated protein that also plays a critical role in the regulation of cytokinesis (Fabbro et al., 2005). Subsequent studies have described the cooperation of CEP55 with members of Endosomal Sorting Complex Required for Transport (ESCRT) machinery in enabling intracellular bridge constriction to facilitate abscission (Carlton and Martin-Serrano, 2007, Carlton et al., 2008, Lee et al., 2008). Recently, research has implicated CEP55 as a modulator of the PI3K/AKT pathway, in the regulation of stemness, as a promotor of tumorigenesis as well as in an autosomal recessive disorder (discussed later in detail). Collectively, these studies have deciphered physiological roles of CEP55 independent of its role in cytokinesis. From here on, I will focus on discussing the major function of CEP55 across various effector pathways and its roles as a biomarker and driver of tumorigenesis.

1.7.1 Role of CEP55 in Cytokinesis As discussed previously, the process of cytokinesis occurs when the cytoplasm is equally distributed among two daughter cells after the mother genetic material is separated at the opposite ends of the cell. The plasma membrane of the mother cell initiates ingression at the center of the cell, the place where the midbody (also known as Flemming body) is formed (Green et al., 2012). The midbody serves as a platform for sequential recruitment of protein complexes which causes cytokinetic bridge constriction on either side of the midbody and also results in a severing of microtubules within the bridge. Mierzwa et al. had demonstrated that during cell abscission, the cytokinetic bridge gets ruptured at the midbody to enable physical separation of the daughter cells (Mierzwa and Gerlich, 2014).

Fabbro et al. had initially named CEP55 to be a centrosome- and midbody-associated protein of approximately 55kDa in size comprising of 464 amino acids (Figure 11A) (Fabbro et al., 2005). The pericentriolar matrix (PCM), which is composed of a cloud of electron-dense material, surrounds the centrosomes (Kumar et al., 2013) and harbors a number of coiled-coil proteins including key structural proteins CG-NAP and Pericentrin B (Takahashi et al., 2002). CEP55, similar to other centrosomal proteins, is also a highly coiled coil protein (Figure 1.11A) (Fabbro et al., 2005) and localizes primarily to the PCM but is also allied with the mother centriole. The N-terminus of

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CEP55 contains a homodimerization domain which comprises of a hinge region present within the coiled-coil structure termed as the ESCRT and ALIX-Binding Region (EABR region) (Figure 1.11A & B) (Lee et al., 2008, Martinez-Garay et al., 2006). The EABR region is critical for normal functioning of CEP55 in cytokinesis while the C-terminus ports the phosphorylation sites that regulate the localization of the protein between both the centrosome and the midbody during the cell cycle (Fabbro et al., 2005, Martinez-Garay et al., 2006). In addition, studies have also demonstrated that CEP55 is recruited to the mitotic spindle during mitosis but it remains associated with centrosome throughout the interphase of the cell cycle (Martinez-Garay et al., 2006). Consistent with this, Sauer et al. have identified CEP55 signature in the human mitotic spindle proteome (Sauer et al., 2005). Independent studies have validated the localization of CEP55 to the central spindle during anaphase and to the midbody during cytokinesis (Fabbro et al., 2005, Zhao et al., 2006). In addition, Zhao et al. have also reported CEP55’s role in microtubule-bundling activity in vitro (Zhao et al., 2006), although the significance of this remains debatable.

During interphase, CEP55 remains associated with the mother centriole but localizes primarily to the PCM. However, it does not require the microtubules for its centrosomal localization. In the PCM, CEP55 interacts with the PCM proteins CG-NAP and Pericentrin B though it is not required for the localization of CG-NAP or Pericentrin B to the centrosome or microtubule nucleation in interphase cells (Fabbro et al., 2005, Martinez-Garay et al., 2006). However, CEP55’s localization to the central spindle and midbody is dependent on the interaction between centralspindlin complex (comprised of MKLP1 and MgcRacGAP) by direct contact with MKLP1 (Zhao et al., 2006). Studies have highlighted the importance of this association in MKLP1-depleted cells, where CEP55 could not localize to the midbody (Zhao et al., 2006). Recently, Mondal et al. have demonstrated the role of the breast and ovarian cancer susceptibility gene, BRCA2 in modulating the recruitment of CEP55 to the midbody (Mondal et al., 2012). In addition, Neto et al. have shown that Syntaxin16, a protein involved in membrane fusion, regulate cytokinesis by recruiting endosomes, exocyst components and CEP55 to the midbody (Neto et al., 2013).

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B C

Figure 1.11: The role of CEP55 in cytokinesis. (A) Schematic representation illustrating the domains of CEP55 which is comprised of a coiled-coil region (CC1 and CC2). The coiled-coil region harbors a hinge region known as the ESCRT and ALIX-binding region (EABR). The N-terminal region of CC1 region contains a homodimerization region (white dotted region), while the C-terminus (colored blue) in CC2 represents the functional domain which is activated by phosphorylation during mitosis. CEP55 is phosphorylated at S425, S428 and S436 at C-terminal region during mitosis (red circles). (B) Schematic representation of CEP55 structure at the midbody. CEP55 exist as homodimers and each homodimer can bind one molecule of either ALIX or TSG101 at the EABR domain. Upon recruitment, ALIX recruits ESCRT-III complexes directly while TSG101 recruits ESCRT-III complexes by recruitment of ESCRT-I. (C) Schematic representation of the recruitment of ESCRT-III complexes at the constriction site in a CEP55-dependent manner promotes the formation of 17nm ESCRT-III filaments (pink). ESCRT-III complexes then recruit Spastin which cleaves the microtubules (orange) (adopted from (Jeffery et al., 2015b)).

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1.7.2 CEP55 recruits downstream machinery to the midbody CEP55 plays a critical role for precise midbody assembly as CEP55-depleted cells exhibited an abnormal midbody structure that lacked visible midbody under light microscope (Zhao et al., 2006). They also demonstrated the interaction of CEP55 with an orchestra of structural and regulatory proteins essential for accomplishment of cytokinesis. These proteins include Aurora B, MKLP2, PRC1, ECT2, Anillin and Syntaxin 2 which are recruited to the midbody thus implying the importance of CEP55 being indispensable for midbody structure maintenance (Zhao et al., 2006). Carlton et al. and Morita et al. have independently demonstrated that CEP55 coordinates the final stage of cell abscission. They have showed that at midbody, CEP55 recruits proteins involved in vesicular fission events through direct interaction with ALIX and TSG101 and vesicular fusion events (Endobrevin) (Figure 1.11B & C) (Carlton and Martin-Serrano, 2007, Morita et al., 2007). ALIX is an ESCRT-III associated protein while TSG101 is a component of ESCRT-I (Carlton and Martin-Serrano, 2007). Both of these proteins localize to centrosomes and are well known for their function in membrane fission events of cytokinesis as well as retroviral budding (Carlton and Martin- Serrano, 2007, Morita et al., 2007). Carlton et al. have demonstrated that depletion of ALIX or TSG101 leads to defective cytokinesis resulting in an increased percentage of multinucleated cells arrested at midbody stage, resembling the similar phenotype observed in CEP55-depleted cells (Carlton and Martin-Serrano, 2007). Markedly, CEP55 depletion did not have any defect in viral budding unlike ALIX or TSG101 depletion, signifying the role of CEP55 as an adaptor than a component of ESCRT pathway in during cytokinesis (Carlton and Martin-Serrano, 2007).

Recently, Mondal et al. demonstrated that BRCA2, independent of its DNA repair function, facilitates CEP55 in recruiting TSG101 and ALIX to the midbody (Mondal et al., 2012), although the direct involvement of BRCA2 in cytokinesis remains elusive (Lekomtsev et al., 2010). However, one controversial report suggests the lack of universal role of CEP55 in regulation of cytokinesis in mammalian cells as depletion of CEP55 in HEK293 does not hinder their continuous cell division (Morita et al., 2007). Further characterization is required to disseminate the role of CEP55 as a direct (facilitates actual abscission) or indirect (regulates recruitment of vesicular trafficking proteins) regulator of cytokinesis.

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1.7.3 The structure of CEP55 and ESCRT complex at the midbody

As discussed previously, CEP55 forms a complex with ALIX and TSG101 at the midbody to recruit ESCRT machinery to the midbody for facilitating membrane fission events required to assist separation of two daughter cells (Carlton and Martin-Serrano, 2007, Morita et al., 2007). Among many other ESCRT complexes (such as 0, I, II and III), ESCRT-III plays critical role in regulating cytokinesis as it forms long filamentous structures to allow construction of the constriction site (Morita et al., 2007, Carlton and Martin-Serrano, 2007, Guizetti et al., 2011). In the context of CEP55, ALIX enables recruitment of ESCRT-III whereas TSG101 first recruits ESCRT-I which in turn recruits ESCRT-III (Figure 1.11B) (Carlton and Martin-Serrano, 2007, Morita et al., 2007).

CEP55, like other centrosomal proteins is a highly coiled coil protein and is known to exist as a homodimer (Martinez-Garay et al., 2006). Initially, Morita et al. demonstrated that CEP55 is comprised of two coiled-coil region joined by a hinge region that enabled binding of ALIX and TSG101 (Morita et al., 2007). However, Lee et al. demonstrated that the hinge region is part of the coiled coil region with the probability of being a continuation of N-terminal domain (Figure 1.11B) (Lee et al., 2008). In addition, Lee et al. also demonstrated that each homodimer of CEP55 binds only one copy of either ALIX or TSG101 illustrating recruitment of multiple homodimers of CEP55 to midbody during cell abscission (Lee et al., 2008). They also showed that ALIX and TSG101 compete for binding to the EABR region of CEP55 with similar affinity and stoichiometry (Lee et al., 2008). The EABR region comprises of a GPPX3Y motif of which ALIX binds at the 797 to 809 residues while TSG101 binds to 154 to 166 residue of EABR (Lee et al., 2008, Carlton and Martin- Serrano, 2007).

CEP55 acts a scaffold protein at the midbody for recruitment of various ESCRT components such as CHMP2A, CHMP4A, CHMP5 and VPS4 that are recruited in a sequential manner that is dependent on CEP55, TSG101 and ALIX (Morita et al., 2010). Guizetti et al. demonstrated that the members of ESCRT-III localize to the cortical side alongside constriction zone of the midbody while CEP55 and ALIX are restricted to the midbody (Guizetti et al., 2011). Morita et al. have independently demonstrated that depletion of these multiple ESCRT-III components also resulted in cytokinesis failure as observed by depletion of CEP55 (Morita et al., 2010, Morita et al., 2007). ESCRT-III upon recruitment to the midbody, oligomerizes to form filament like structure which protrudes out of the midbody to generate a constriction site, the place where abscission occurs (Figure 1.11C). The ESCRT filaments in turn recruits the microtubule-severing protein, Spastin,

Chapter - 1 which leads to cleavage of the remaining microtubules binding two daughter cells (Guizetti et al., 2011).

1.7.4 Phosphorylation of CEP55

CEP55 is phosphorylated on serine 425 and 428 by CDK1 and ERK2 during mitosis (Figure 1.11A) (Fabbro et al., 2005). These phosphorylation events enable association of CEP55 with PIN1 (van der Horst and Khanna, 2009). The interaction of CEP55 with PIN1 favors stabilization of CEP55 to enable its phosphorylation at serine 436 by PLK1 (van der Horst et al., 2009, van der Horst and Khanna, 2009). The phosphorylation of these three serine residues is required for successful completion of cytokinesis as mutants of Cep55 which are not able to be phosphorylated results in multinucleation and midbody arrest (Fabbro et al., 2005).

It was subsequently demonstrated that PLK1 dependent phosphorylation of CEP55 regulates the timing of CEP55’s recruitment to the midbody induced by inhibition of its interaction with MKLP1 (Figure 1.11A) (Bastos and Barr, 2010). Furthermore, PLK1 phosphorylation at serine 436 prevents premature recruitment of CEP55 to the midbody. In addition, the recruitment of CEP55 to the midbody is limited until late anaphase during which PLK1 is degraded due to mitotic exit. PLK1 inhibition or expression of CEP55 mutated at PLK1 phosphorylation site (S436A) leads to premature localization and higher CEP55 level at the midbody. The premature recruitment of Cep55 to the midbody affects structure of the midbody and results in cytokinesis failure. It illustrates the critical importance of spatio-temporal recruitment of CEP55 for maintenance of the integrity of both the midbody structure and successful abscission (Bastos and Barr, 2010).

Recently, St-Denis et al. demonstrated the interaction of CEP55 with phosphatases MTMR3 and MTMR4 during early mitosis, independent of their role in lipid regulation (St-Denis et al., 2015). They showed that MTMR3 and MTMR4 heterodimers interact directly with CEP55 using the GPPX3Y motif of MTMR4, same as the one used by ALIX and TSG101, as discussed previously (St-Denis et al., 2015). The MTMR3 and MTMR4 interaction with CEP55 have also been shown to prevent premature recruitment of CEP55 to the midbody, similar to the phosphorylation on S436 by PLK1 (St-Denis et al., 2015). Furthermore, depletion of either MTMR3 or MTMR4 resulted in cytokinesis failure. Notably, St-Denis et al. also demonstrated that MTMR3-MTMR4 interact with PLK1 to form a network of molecules regulating the temporal recruitment of CEP55 to the midbody and its interaction with TSG101 and ALIX (St-Denis et al., 2015).

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1.8 Role of CEP55 in germ cell function

In adult tissue at regular condition, CEP55 has been shown to be highly expressed in the testes followed by the thymus (Fabbro et al., 2005, Martinez-Garay et al., 2006). Studies in germ cells have shown that CEP55 localizes to the intercellular bridges that are composed of regular cytokinesis components in addition to other germ-cell specific factors such as TEX14 (Greenbaum et al., 2011, Chang et al., 2010). Iwamori et al. demonstrated that TEX14 is an inactive kinase responsible for maintenance of stable intercellular bridges during meiosis and serves as an endogenous inhibitor of CEP55 by inhibiting the interaction between CEP55 with ALIX and TSG101 (Iwamori et al., 2010). Greenbaum et al. independently demonstrated the importance of intercellular bridges in vivo by generating Tex14-/- mice which resulted in the absence of intercellular bridges and male sterility (Greenbaum et al., 2006). Intercellular bridges are an essential structure for spermatogonial stem cell (SSC) function as they allow nuclear cross- talk between the connected SSCs (Burgos and Fawcett, 1955). CEP55 acts as an adaptor protein in recruiting TEX14 and other components of intercellular bridge in germ cells. Kim et al. showed that TEX14 has strong affinity for binding to the EABR region of CEP55 as it competes with ALIX to facilitate formation of the intercellular bridge which connects differentiating spermatogonia in males (Kim et al., 2015). Although these studies have indicated how TEX14 is regulating the function of CEP55 to form intercellular bridges, the molecular function of CEP55 in overall testes development has not been fully characterized. In oocytes, Xu et al. demonstrated that Cep55 remains associated with spindle poles during metaphase as depletion using siRNA resulted in defective spindle formation and chromosomal misalignment (Xu et al., 2015). This resulted in accumulation of Cyclin B1 and activation of SAC proteins leading to metaphase I arrest and failed to produce first polar body (PB1) extrusion in mice (Xu et al., 2015). Further studies on CEP55 function in testes and germ cell development and its role in cell cycle progression apart from its contribution to the formation, maintenance, and/or stability of intercellular bridge are necessary to elucidate it’s physiological role in overall development.

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1.9 CEP55 as regulator of stemness

1.9.1 Midbody fate Independent studies have demonstrated that CEP55 might directly impact stemness through regulation of midbody remnants (also known as midbody derivatives or MBds), though the concept remains debatable (Kuo et al., 2011, Ettinger et al., 2011). Kuo et al. demonstrated that post cytokinesis completion, the MBds are inherited in an asymmetric fashion and they are degraded by autophagy in a CEP55 dependent manner (Kuo et al., 2011). The autophagy receptor, NBR1 interacts with CEP55 to degrade MBds as both CEP55 overexpression and NBR1 depletion resulted in decreased MBd degradation resulting in uncoupling receptor-mediated entry into autophagy (Kuo et al., 2011). As accumulation of MBds is associated with increase in stemness, CEP55 overexpression in multiple cancer, might endorse stemness by retracting the degradation of MBds. Independently, CEP55 has also been linked to cause an alternate midbody fate called midbody release, a condition where midbody is released in the extracellular space between the two daughter cells (Ettinger et al., 2011). Ettinger et al. demonstrated that both knockdown of CEP55 and its binding partners, ALIX and TSG101 leads to impairment of this process (Ettinger et al., 2011). Moreover, the report also highlighted the fact that impaired midbody release resulted in cellular differentiation (Kuo et al., 2011). Therefore, these reports confirm the apparent association of CEP55 with midbody fate and stemness though the implications of this are currently unclear.

1.9.2 Exosomes Exosomes are cell organelles comprised of mRNAs, proteins, miRNAs, non-coding RNAs and other molecules encapsulated by a lipid composed membrane (Raposo and Stoorvogel, 2013). Exosomes, unlike intracellular organelle, are released into the extracellular space and are taken up by donor cells enabling transmission of these materials (Simons and Raposo, 2009). Zhang et al. and Azmi et al. have independently demonstrated, in a cancer settings, that exosomes suppress the immune system and promote angiogenesis, metastasis, stemness, tumor invasiveness and drug resistance (Zhang and Grizzle, 2011, Azmi et al., 2013). Hong et al. have purified CEP55 mRNA from exosomes of colorectal cancer cell lines (Hong et al., 2009). They demonstrated that addition of these exosomes to endothelial cells promoted their cell proliferation. However, the individual contribution of CEP55to this phenotype remains elusive (Hong et al., 2009).

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1.10 Role of CEP55 as a regulator of the PI3K/AKT pathway

PI3K/AKT pathway is one of the major pro-survival pathways. PI3K is responsible for phosphorylation of phosphatidylinositol 4,5-bisphosphate (PIP2) for converting it to phosphatidylinositol 3,4,5 trisphosphate (PIP3) which in turn results in phosphorylation and activation of AKT (Hemmings and Restuccia, 2012). PTEN acts as a negative regulator as it reverses the phosphorylation of PIP3 to PIP2. AKT is known to have diverse roles in cell cycle, cell survival, angiogenesis, protein synthesis and metabolism (Manning and Cantley, 2007). Mutation of PI3KCA (gene for p110), the catalytic subunit of PI3K results in constitutive activity or loss of PTEN expression leads to consequential increase in PIP3 pool and hence AKT activation, thus causing dysregulation of the PI3K/AKT pathway (Cheung and Testa, 2013).

Chen et al. first reported the association of CEP55 with p110 in vitro (Figure 1.12A) (Chen et al., 2007). They showed that the interaction between CEP55 and p110 enables stability of the catalytic subunit causing increased AKT activation by increase in S473 phosphorylation (Chen et al., 2007). Chen et al. independently also demonstrated that the CEP55/PIK3CA interaction might be facilitated by VEGF-A which enables CEP55’s localization to the plasma membrane harboring the PI3K complex (Chen et al., 2009b). Hwang et al. reported that Fibulin-5, an extracellular matrix protein, increases AKT activation in a CEP55-dependent manner (Hwang et al., 2013). However, an interplay between VEGF-A and Fibulin-5 in promoting CEP55-dependent AKT activation is yet to be determined. Though, such interplay will be context-dependent as VEGF-A expression is oncogenic wherein it promotes angiogenesis (Goel and Mercurio, 2013). On the other hand, Fibulin- 5 amplification has been reported to be oncogenic in some cancer contexts while in others, its activity has been shown to be tumor-suppressive as it antagonizes angiogenesis (Yanagisawa et al., 2009).

In addition, our laboratory recently validated the importance of CEP55 in the regulation of PI3K/AKT pathway by describing a cep55 mutant zebrafish line that mimicked a Cep55 knockout model (Jeffery et al., 2015a). Jeffery et al. demonstrated that the mutant zebrafish (designated e48) embraced a (c115C>T) mutation which resulted in a premature stop codon at residue 39 (Jeffery et al., 2015a). However, the heterozygote mutants were viable and fertile and were indistinguishable compared to their wildtype siblings. Contrarily, the homozygous mutants displayed abnormal development in hand with larval lethality due to immensely increased apoptosis through AKT destabilization. Notably, our laboratory provided the first phenotypic and molecular

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proof with respect to CEP55 being indispensable for embryonic development through mediating regulation of PI3K/Akt signaling (Jeffery et al., 2015a). Surprisingly, the phenotype observed in cep55 homozygous mutant zebrafish differed from the phenotype observed in human cancer lines. In the cancer context, CEP55 regulates AKT activation (Chen et al., 2007) while in zebrafish it enabled Akt stability at embryonic developmental stage of zebrafish (Jeffery et al., 2015a). Further in vivo investigation is required for understanding the mechanisms mediating the different relationships between AKT protein/activation levels and CEP55 in human cells versus zebrafish.

A B

Figure 1.12: Additional roles of CEP55. (A) Schematic representation of the direct interaction of CEP55 with the PI3K catalytic subunit, p110, to promote its stability and its subsequent activation. An increase in PI3K activation leads to an increased pool of PIP3 and results in a successive increase of AKT activation (marked by S473 phosphorylation). An increase in CEP55 level and its localization to the plasma membrane is promoted by presence of VEGF-A. (B) Interdependency and feedback loop controlling regulation of CEP55, PLK1 and FOXM1 observed in vitro. PLK1 phosphorylates CEP55 and FOXM1 which transcriptionally control CEP55 and PLK1 while CEP55 has been reported to promote FOXM1 expression. TP53 has been shown to negatively suppress expression of all the three proteins in vitro (adopted from (Jeffery et al., 2015b)).

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Recently, consistent with our zebrafish model, CEP55 has been linked to vertebrate neural function (Tsai et al., 2015). Tsai et al. using mevinphos injection into the rostral ventrolateral medulla of rats, as a model for organophosphate poisoning, illustrated that CEP55 induced an increase in the activation of the PI3K/AKT pathway. They demonstrated that hyperactivity of PI3K/AKT pathway resulted in upregulation of the nitric oxide synthase II pathway, a reduction of smooth muscle tension in the blood vessels and progressive hypotension (Tsai et al., 2015). Collectively, this report suggests a definitive role of CEP55 in brain-stem mediated cardiovascular regulation.

In the cancer setting, Chen et al. demonstrated that CEP55 overexpression is associated with hepatocellular carcinoma (HCC) and lung adenocarcinoma and results in promoting invasion and cell migration via upregulation of the PI3K/AKT pathway (Chen et al., 2007, Chen et al., 2009b). As discussed previously, VEGF-A promotes CEP55 expression and hence the PI3K/AKT pathway in a dose dependent manner in lung cancer cell lines. Notably, VEGF-A stimulate translocation of CEP55 to the plasma membrane from cytoplasm (Chen et al., 2009b). In addition, VEGF-A is a compelling stimulator of endothelial cell vascular permeability, motility, proliferation and angiogenesis with well-established role in cancer (Goel and Mercurio, 2013). Consistently, patient samples demonstrated concomitant overexpression of VEGF-A, CEP55 and phospho-AKT (Chen et al., 2009b). Similarly, Tao et al. demonstrated CEP55 overexpression in gastric carcinoma also causes upregulation of the PI3K/AKT pathway which results in inhibition of p21 WAF1/Cip1 expression and dysregulated cell cycle (Tao et al., 2014). Thus these reports illustrates that CEP55 might alter tumor cell behavior via activation of the pro-survival PI3K/AKT pathway.

1.11 CEP55 and its connection with cancer CEP55 has been identified as a CTA (cancer testes antigen) and TAA (tumor associated antigen) (Inoda et al., 2009a). CTAs are proteins that are normally expressed chiefly in testes, however in the cancer setting they become more widely expressed as seen in the case of CEP55 (Fratta et al., 2011). Thus CEP55 is also a TAA as, in cancer context, its expression is aberrant. This property of CEP55 makes it ideal to be a vaccine therapy candidate. Preliminary studies have suggested the effectiveness of CEP55-based immunotherapy vaccines with higher potential in the treatment of chemotherapy-resistant colon cancer stem cells and tumor-initiating cells

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(Inoda et al., 2011a, Inoda et al., 2009a). Further, the association of CEP55 with the PI3K/AKT pathway provides definite proof of its link with cancer. Apart from its association with PI3K/AKT, studies have shown the interplay of CEP55 with many other key player of tumorigenesis (discussed below).

1.11.1 FOXM1 CEP55 has been shown to be a direct transcriptional target of FOXM1 (Figure 1.12B) (Gemenetzidis et al., 2009, Waseem et al., 2010). Laoukili et al. have previously demonstrated the activation of a FOXM1 transcriptional program during the G2/M phase of the cell cycle enabling regular progression through mitosis in a timely fashion (Laoukili et al., 2005). This program also induces activation of other cell cycle genes such as AURKB, PLK1, CCNB1, CENP-F, and NEK2. In addition, CEP55 was also identified in a screen for genes that are highly expressed in the G2/M stage of cell cycle and its expression in tumor tissue has been correlated with other known cytokinesis regulators such as PLK1, MKLP1, MKLP2, AURKA and AURKB (Zhao et al., 2006). Overexpression of FOXM1 has been implicated in many cancers and is associated with chromosomal instability (CIN) (Laoukili et al., 2007, Myatt and Lam, 2007). Waseem et al. have showed that FOXM1 overexpression is recurrently observed in head and neck squamous cell carcinoma and correlates with overexpression of CEP55 in lymph node metastasis (Waseem et al., 2010). In line with this, a recent study by Chen et al. demonstrated that CEP55 is associated with FOXM1 expression in oral cavity squamous cell carcinoma (OCSCC) lines (Chen et al., 2009a). They also showed that CEP55 controls FOXM1 in a dose-dependent manner and induces an increased transcription of MMP-2 enabling cell migration and invasion (Chen et al., 2009a). Concomitantly, the expression of FOXM1, CEP55 and MPM2 were amplified in OCSCC patient samples that might impact response to therapy. However, other transcriptional targets of FOXM1 were not functionally validated thus the specificity of CEP55 targeting upregulation of FOXM1 is unclear. In summary, these studies illustrate the presence of a positive feedback loop between and CEP55 and FOXM1 wherein CEP55 promotes expression of FOXM1 which results in increased expression of CEP55 (Figure 1.12B). However, such a feedback loop is not unique as comparable loops exist between PLK1 and FOXM1 during mitosis wherein, PLK1 phosphorylates FOXM1 to increase its’s transcriptional activity (Fu et al., 2008). Thus, the mechanism by which CEP55 increases FOXM1 expression levels might be via reciprocal regulation between the two proteins, however further molecular characterization is warranted.

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1.11.2 TP53 Chang et al. have shown that p53, a potent tumor suppressor, negatively regulates CEP55 expression (Figure 1.12B) (Chang et al., 2012). It is well-known that p53 is inactivated or mutated among many human cancers and negatively regulates various transcription factors such as FOXM1 and c-MYC alongside multiple cell cycle regulatory genes such as PLK1, CCNB1 and many other processes (Levine et al., 1991, Sachdeva et al., 2009, Pandit et al., 2009, McKenzie et al., 2010, Innocente et al., 1999). Therefore, p53 might indirectly affect CEP55 expression by regulating FOXM1 levels. However, as discussed previously, Fabbro et al. have shown that PLK1 directly regulates CEP55 stability via phosphorylation of CEP55 to promote cell abscission post mitosis (Figure 1.12B) (Fabbro et al., 2005). Chang et al. also demonstrated that p53 negatively regulates PLK1 thus creating a p53-PLK1-CEP55 axis; hence directly modulating CEP55 stability and cytokinesis completion (Chang et al., 2012). They have also provided direct evidence of the axis as observed by a decrease in the level and half-life of CEP55 post induction of p53 contrary to the increase in level and half-life of CEP55 upon PLK1 overexpression. However, further work is required to characterize the role of this axis in tumor development and progression.

1.11.3 NF-ĸB Peng et al. recently demonstrated the involvement of NF-ĸB signaling in the regulation of CEP55 in pancreatic cancer (PANC) (Peng et al., 2017). They demonstrated that CEP55 overexpression is associated with progression of PANC patients while in vitro higher CEP55 levels favored proliferation, migration, and invasion of PANC cells. They illustrated using in vivo models, that CEP55 overexpression in association with upregulation of MMP2, MMP9 and Cyclin D1 accelerated tumorigenicity of PANC xenografts. Importantly, they demonstrated the activation of NF-ĸB/IĸBα signaling pathway in CEP55 transduced PANC cells (Peng et al., 2017). Taken together these studies highlight the need to identify the exact mechanism regulating CEP55 expression in the cancer context. However, the existence of a complex interplay among Cep55 and other regulators of the cell cycle signify that cancer cells might select for high CEP55 expression.

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1.12 Association of CEP55 with other human diseases CEP55 overexpression has been associated with a complex phenotype observed in a patient with Xp22/10q24 translocation, which resulted in acquiring three copies of CEP55 (Martinez- Garay et al., 2006, Martinez-Garay et al., 2002). The patient had brain irregularities (reduced white matter, enlarged ventricles, motor retardation, absence of the corpus callosum and the disturbance of retinal pigment epithelium) in addition to facial anomalies and stato- and psychomotor retardation. Martinez-Garay et al. showed that the complex rearrangement also impacts other genes (such as the FAM9 family on chromosome Xp22.3); therefore further investigation is required to characterize their precise contribution of Cep55 levels to this phenotype (Martinez-Garay et al., 2002). Several centrosomal genes such as CENPJ, DISC1 (psychotic and bipolar spectrum disorders) and CDK5RAP2 (autosomal recessive primary microcephaly) have been implicated in neuronal disorders (Palo et al., 2007, Bond et al., 2005). Although the study implicated the contribution of CEP55 to the phenotype at the genetic level; however further functional validation such as determining AKT hyperactivity in patient-derived fibroblast would have shed light on the in vivo role of CEP55 and the impact on other signaling cascades.

More recently, loss of CEP55 function due to the presence of a truncation mutation in the C-terminal region of CEP55 (p.S425X) has been shown to cause a novel autosomal recessive syndrome affecting neuronal mitosis called MARCH (Multinucleated neurons, Anhydramnios, Renal dysplasia, Cerebellar hypoplasia and Hydranencephaly) (Frosk et al., 2017). Hydranencephaly is a congenital anomaly wherein the cerebral hemisphere of the brain is replaced by a fluid-filled cyst. Frosk et al. reported the presence of a homozygous nonsense mutation in CEP55 which resulted in truncation of CEP55 mRNA in patient sample. They also expressed the truncated form of CEP55 in COS-7 cells and observed that the truncated protein (~40 amino acid short), fails to localize to the midbody, resulting in multinucleated daughter cells due to abscission failure. Further, they replicated the truncated mutation observed in patients, using the CRISPR/CAS9 system in zebrafish to recapitulate the presence of renal dysplasia, cerebellar hypoplasia and craniofacial abnormalities (key features of MARCH) and rescued the phenotype by expressing full length human CEP55 (Frosk et al., 2017).

Another study by Bondeson et al. has demonstrated the association of CEP55 with Meckel-like syndrome (MKS) which is characterized by presence of renal cystic dysplasia, occipital encephalocele and polydactyly (Bondeson et al., 2017). In a genetic screen of 2 affected fetuses using whole-exome sequencing (WES), they demonstrated the presence of a homozygous nonsense

Chapter - 1 mutation at the C-terminal of c.256C>T (p.Arg86*) in the CEP55 locus which results in an autosomal recessive lethal ciliopathy in the affected fetus. Thus, these studies elaborate the association of CEP55 with human embryology and developmental disease.

1.13 Role of CEP55 as a marker of malignancy risk, prognosis and therapy outcome CEP55 has been identified in the prognostic signature for multiple cancers as demonstrated in Table 1.1. Carter et al. have shown that CEP55 is part of the top 70 genes identified as responsible for CIN (CIN70 signature) from analysis involving 12 cancer types including lung, breast, glioma, medulloblastoma, mesothelioma and lymphoma (Carter et al., 2006). CIN70 along with its subset CIN25 significantly correlate with clinical outcome and distant metastasis. In addition, CEP55 is part of a CIN signature of 10 genes whose high expression leads to drug resistance, chromosomal instability and cell proliferation (Zhou et al., 2013). A separate genetic screen in prostate cancer highlighted CEP55 to be part of the 31-gene cell-cycle progression (CCP) signature that strongly correlates with actively proliferating prostate cancer cells (Cuzick et al., 2011). They also illustrated that in combination with other clinical parameters such as tumor stage, margin status and prostate-specific antigen (PSA) levels results in a ‘combined score’ that matched the highly predictive ten-years fatality risk of patients (Cuzick et al., 2011).

Montero-Conde et al. have shown CEP55 to be included in a set of 23 genes whose overexpression is associated with poor prognosis of thyroid cancer (Montero-Conde et al., 2008). An independent study has shown that CEP55 overexpression is part of a 39 gene signature and linked to distant metastasis in renal cancer (Jones et al., 2005). Similarly, another study has listed CEP55 as part of 100 gene signature for CIN in ovarian, breast and colon cancer (Cheng et al., 2013). Consistently, Al-Ejeh et al. have shown that CEP55 is part of a 206 gene signature representing genes enriched in promoting CIN, associated with aggressiveness in triple negative breast cancer (TNBC) (Al-Ejeh et al., 2014b). In addition, multiple studies have shown that expression of CEP55 is associated with poor survival in breast cancer (BC) (Montero-Conde et al., 2008, Cheng et al., 2013, Ma et al., 2003, Naderi et al., 2007, Hu et al., 2012, Coutant et al., 2011). This include the association of CEP55 with an 85 gene signature upregulated in invasive ductal carcinoma (IDC) in comparison to matched ductal carcinoma in situ (DCIS) pairs (Ma et al., 2003). In addition, another study has reported that CEP55 is part of a 16-gene signature that is overexpressed in invasive IDC

Chapter - 1 compared to pre-invasive DCIS in young women (Colak et al., 2013). Moreover, a separate study has shown CEP55 to be part of a malignancy risk signature that predicts cancer invasion risk and progression (Chen et al., 2010a). This signature involves upregulation of 109 genes including CEP55 while downregulation of 31 genes which was generated comparing the expression profiles of normal breast tissue with IDC.

CEP55 overexpression has been identified using micro-array expression profiling in the mRNA isolated from multiple human cancers. Pateint sample analsysis has revealed deregulation of various pathways in CEP55 overexpressing tumors (Ma et al., 2003, Naderi et al., 2007, Jones et al., 2005, Martin et al., 2008, Kikuchi et al., 2003). Though increased multimucleation in these samples was not observed, the overall pateint data highlighted the importance of CEP55 in tumorigenesis. CEP55 upregulation has been proposed to be an early event in tumorigenesis. It has been demonstrated in the BC settings that organized growth-arrested human mammary epithelial cells (HMECs) correlate with better prognosis while disorganized proliferative cells correspond to tumors with poor prognosis. Fournier et al. have showed that downregulation of CEP55, which is part of 60 genes involved in transition from a disorganized proliferative state to an organized growth- arrested state, leads to proliferation loss (Fournier et al., 2006). Sakai et al. have demonstrated that enhanced expression of CEP55 is associated with premalignant lesion of colon and colorectal cancer (Sakai et al., 2005).

A majority of these signatures and risk-scores have been developed into patented prognostic ‘kits’ for therapy selections, malignancy risks and prognosis in clinical settings (Martin et al., 2010, Carter et al., 2005, Perou et al., 2012, Wagner et al., 2011, Stone et al., 2010, Chen and Yeatman, 2011). An example is the CIN 70 gene signature (Carter et al., 2006) which has been patented to predict the treatment outcomes of patients suffering from solid tumors such as glioma, medulloblastoma and lymphoma (Carter et al., 2005). Another example is the patenting of the mitotic CIN signature (Cheng et al., 2013) for diagnosis and treatment of BC (Anastassiou and Cheng, 2012). CEP55 is part of the 22-gene ‘3D signature kit’ that predicts response to chemotherapy in BC patients, wherein high expression of these genes predicts low pCR (pathological Complete Response) (Martin et al., 2010). Similarly, CEP55 is part of a 21- gene signature that predicts ROR (risk of relapse) and response to taxane therapy (Perou et al., 2012). At the commercial level, signature of CEP55 is part of Myriad Genetics Inc for predicting response to therapy and prognosis of lung cancer alongside prognosis of other cancer namley prostate, brain and bladder (Wagner et al., 2011,

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Stone et al., 2010). Also its signature is utilised in the commercially available PAM50 classification for predicting BC prognosis (Perou et al., 2008).

In summary, studies over the last decade have deciphered the mechanism by which CEP55 regulate cytokinesis. Nevertheless, CEP55 has been shown to be dispensable for cytokinesis in normal cells alongside its limited expression other than in testes raises questions as to its physiological role. Further, recent reports illustrating its involvement in human autosomal recessive disease warrant validation of the mechanism via which CEP55 influences homeostasis in the body. Notably, the association of CEP55 with the regulation of the PI3K/AKT pathway, midbody fate and stemness raise the possibility that CEP55 might serve as a potent oncogene as its involvement has been linked to multiple cancers. Recent work also highlights that CEP55 is a good marker of poor prognosis, metastasis, ROR, CIN and therapy resistance in many cancer types. However, the potential mechanism via which it drives tumorigenesis remains elusive. Detailed mechanistic investigation including in vivo model systems are needed to understand its regulation and function during cancer development and progression for designing therapeutic strategies that might yield a major impact on a number of human malignancies with CEP55 overexpression.

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Malignancy Signature function Number of Genes Reference

Breast cancer Progression 16 (Colak et al., 2013)

Breast cancer Malignancy risk 109 (Chen et al., 2010a)

Breast cancer Survival, time to distant 70 (Naderi et al., 2007) metastasis Triple negative Aggressiveness 206 (Al-Ejeh et al., breast cancer 2014b)

ER+ breast cancer Progression 6 (Martin et al., 2008)

ER+ breast cancer Distant metastasis free survival 9 (Hu et al., 2012)

ER+ breast cancer p53 status, prognosis with or 39 (Coutant et al., without adjuvant tamoxifen therapy, chemotherapy 2011) sensitivity Breast, ovarian and CIN 100 (Cheng et al., 2013) colon cancer Multiple cancers CIN, prognosis 70 (Carter et al., 2006)

Multiple myeloma CIN, drug resistance, relapse 10 (Zhou et al., 2013)

Prostate cancer Prognosis 31 (Cuzick et al., 2011)

Renal cell cancer Progression, distant metastasis 31 (Jones et al., 2005)

Thyroid cancer Prognosis 23 (Montero-Conde et

al., 2008)

Table 1.1: Summary of the multiple studies with predictive gene signatures containing CEP55

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1.14 Aims and hypothesis

The aim of my PhD project is to decipher the mechanism by which CEP55 promotes CIN and regulates aneuploid cell fate in tumorigenesis which results in multiple cancers. Lack of appropriate mouse models to study genomic instability and polyploidy has often impeded our understanding of such cellular processes during tumorigenesis, hampering efforts to develop specific effective therapies and/or disease biomarkers. Therefore, I have utilised the only Cep55 ubiquitously over-expression mouse model generated by Prof. Khanna’s laboratory to study the role of Cep55 on mammalian physiology, genomic instability and the mechanism via which Cep55 over-expression promotes tumorigenesis in vivo. In addition to these studies, I have also investigated the functional aspects of CEP55 and its dependent pathways as a potential biomarker to target Triple-Negative Breast cancer treatment.

My hypothesis is that

CEP55 is a ‘new player and guardian of polyploid cell survival’ in advanced stage cancers and thus can be targeted for therapy development.

To address my hypothesis, I propose the following aims:-

Aim 1: To characterize the functional role of CEP55 over-expression with perspective of overall development and tumorigenesis in vivo using novel “knock-in” mouse models.

Aim1.1: To study the role of CEP55 over-expression in mammalian physiology and spermatogenesis in vivo. Aim 1.2: To study the prospective role of CEP55 as a potent oncogene in vivo and characterize the molecular mechanism via which CEP55 over-expression induces spontaneous tumor formation.

Aim 2: To characterize CEP55 as a potential therapeutic target for the treatment of aggressive breast cancer.

Aim 2.1: To investigate the impact of CEP55 over-expression or depletion on aneuploid basal like breast cancer cell survival during perturbed mitosis. Aim 2.2: To characterise the dual combination of MEK1/2-PLK1 inhibition selectively eliminating basal-like breast cancer in a CEP55-dependent manner.

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Chapter – 2

Materials and Methods

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2.1 Animal husbandry

2.1.1 Ethics All animal work was approved by the QIMR Berghofer Medical Research Institute, Animal Ethics Committee (number A0707-606M) and was performed in strict accordance with the Australian code for the care and use of animals for scientific purposes.

2.1.2 Generation of the targeting construct of Cep55 overexpressing transgenic mice Generation of Rosa26-UbiC-Cep55 floxed mice was a contracted service performed by Ozgene Pty Ltd. (Perth, WA, Australia). To generate the Cep55 “Knock-in” model, a targeting vector was designed comprised of Cep55 cDNA, (accession number AK004655) preceded by a human Ubiquitin C (UbiC) promoter and lox-P flanked polyadenylation (pA+) “Stop” region, with a downstream FRT-flanked neomycin resistance cassette (PGK-NEO) for ES cell selection. Genomic targeting of the construct was achieved in wild-type BALB/C embryonic stem cells using standard homologous recombination and blastocyst manipulation techniques. Gene manipulation was verified by Southern blot, with probes against both the endogenous coding region and neomycin selection cassette following restriction digest of genomic DNA using the EcoRV restriction enzyme.

2.1.3 Generation of the ubiquitous Cep55 knock-in mouse model Genomic targeting of the construct was achieved in wild-type BALB/C embryonic stem cells using standard homologous recombination and blastocyst manipulation techniques. Gene manipulation was verified by Southern blot, with probes against both the endogenous coding region and neomycin selection cassette following restriction digest of genomic DNA using the EcoRV restriction enzyme. Cep55 knock-in mice were generated by crossing heterozygous Rosa26Ubiq-polyA- flTg(Neo)/wt mice with FLPe mice to excise the PGK-Neo cassette, followed by backcrossing to C57BL/6 wild-type mice to remove the FLP transgene. Rosa26Ubiq-polyA-flTg(Neo)/wt mice were subsequently crossed to Rosa26EIIA-Cre mice (Ozgene) to excise the (pA+) “stop” region, allowing over-expression of Cep55 cDNA from the Rosa26 locus (Rosa26Tg/Tg). These mice were then crossed to BALB/C wild-type mice to segregate the Rosa26EIIA-Cre and Rosa26Tg/Tg alleles. Resulting Cep55 heterozygous (Cep55wt/Tg) mice were inter-crossed to generate three genotypes: wild-type (Cep55wt/wt), heterozygous (Cep55wt/Tg) and homozygous (Cep55Tg/Tg) mice.

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2.1.4 Animal housing All experimental animals were maintained on a mixed (129SV/E X C57BL/6) strain and were housed at the Queensland Institute of Medical Research Animal Facility in OptiMICE® caging (Centennial, Colorado, USA) on a 12-hour light-dark cycle at 25°C.

2.1.5 Time mating and organ collections Mice pregnancy was accessed on the basis of a copulation plug on the following morning post mating date, designated as embryonic day. Such assessment was done for isolating juvenile testes for P7-P21 and also for MEF isolation on E13.5. For organ and MEF isolation from mice, the mice were anaesthetized with AttaneTM Isoflourene (Biomac Pty Ltd, Hornsby, NSW) and were culled by cervical dislocation. The organs were isolated and were either washed in ice-cold PBS (Phosphate Buffered Saline) and fixed for histology (discussed later) or were snap frozen for protein or mRNA isolation. The testes of the juvenile mice were carefully isolated using the Nikon SMZ45stereo dissecting microscope (Nikon Inc, Tokyo, Japan). In case of MEF’s, the embryos from the uterine horns of the mother was isolated and washed in ice-cold PBS prior to processing.

2.1.6 GonadoSomatic index analysis Testes from age-matched mice of each genotype were dissected and weighed. The gonadoSomatic index (GSI) was determined according to the formula GSI = (weight of the right testis + weight of the left testis)/total body weight * 100 as previously described (Boucher et al., 2015).

2.2 Cell biology

2.2.1 Cell culture The culture of mammalian cell lines was maintained by incubating at 37ºC with 20% oxygen levels and 5% CO2. All tissue culture plasticwares was purchased from Corning® Stone Staffordshire, UK (flasks and plates) and Costar® Washington DC, USA (plastic pipettes).

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2.2.2 Generation of mouse embryonic fibroblast (MEFs) To generate the MEFs, the embryos from the uterine horns of the mother of respective genotypes were isolated into sterile petri-dish with ice-cold PBS. Embryos were dissected into individual dishes and washed twice in ice-cold sterile PBS. The heads of each embryo were isolated and were used for PCR genotyping while the embryonic liver including internal viscera was removed. Eviscerated embryos were placed in 1mL of trypsin-EDTA (Sigma Aldrich®, St Louis, USA) and were dispersed into small pieces by mechanical shearing using a sterile scalpel blade. The dispersed tissues were allowed to digest at 37ºC/20 % oxygen/5 % CO2 for 20 minutes and the digested cell lysate transferred to a 10 mL falcon tube. Undigested tissues were removed from the cellular suspension using two rounds of sedimentation and collection of the supernatant. The single suspension cells were seeded into 25cm2 flasks (Corning®) and allowed to adhere overnight. Primary MEFs were maintained in DMEM (Dulbecco’s Modified Eagle’s Medium) (Life Technologies TM, Carlsbad, CA, USA) containing 20% Fetal Bovine Serum (FBS) (SAFC BiosciencesTM, Lenexa, USA) and 1% penicillin-streptomycin (Life Technology TM). Primary MEFs prior to passage 6 were used for all experiments apart from NIH-3T3 assays and other long term assays.

2.2.3 Generation of mouse tumor lines from primary tumor To generate the primary tumor lines from tumors originating in mice, mice were anaesthetized with AttaneTM Isoflourene (Biomac Pty Ltd) and culled by cervical dislocation. The tumor was isolated using a sterile scalpel blade and was washed (PBS, 2 times) under sterile conditions on a petri-dish. After washing, a small section (2-4 mm2) of the tumor was surgically removed followed by mechanical disaggregation using a sterile scalpel blade and then incubation in 0.1% collagenase (Sigma Aldrich®) in 10 mL of DMEM (Life TechnologiesTM) containing 20% FBS (SAFC BiosciencesTM) and 1% penicillin-streptomycin (Life TechnologyTM). The cells were then incubated in 37ºC water bath for a minimum of two hours with agitation every 10-20 minutes. After the incubation, an equivalent volume of DMEM was added after which the cell suspension was passed through an 85 µm nylon mash cell strainer (Becton Dickinson (BD) Falcon™, San Jose, CA, USA) and the undigested tissues were squeezed mechanically. The cellular suspension was then centrifuged at 2500 RPM for 10 minutes at room temperature and washed (PBS, 3 times). Cell viability was measured using the trypan blue dye and cells were plated in a 25 cm2 tissue flask (Corning, Stone Staffordshire, UK). The media compositions used for maintaining (at initial stages)

Chapter - 2 the cells includes : 100 µL (100 µg/mL) of EGF, 500 µL (10mg/mL) of insulin and 1% Sodium pyruvate (Life TechnologyTM) in regular DMEM (Life Technologies TM) containing 20% Fetal Bovine Serum (SAFC BiosciencesTM) and 1% penicillin-streptomycin (Life Technology TM).

2.2.4 Cell culture of breast cancer cell lines The breast cancer cell lines used in this study were purchased from the American Type Culture Collection (ATCC, Manassas, Virginia, USA), otherwise stated in acknowledgment, cultured and maintained as per ATCC recommendations and as described previously (Anderson et al., 2017). All the cell lines were regularly tested for Mycoplasma infection and authenticated using short tandem repeats (STR) profiling by scientific services at QIMR Berghofer Medical Research Institute.

2.2.5 NIH-3T3 proliferation assay For NIH-3T3 proliferation assay, primary MEFs at passage 3 were plated at a density of 5×105 cells in a 10 cm dish in duplicate for three biological repeats per genotypes. The cells were trypsinized every three days and were counted using the trypan blue dye in the Countess® automated cell counter (Life TechnologiesTM) and re-plated at a density of 5×105 cells per dish. The additive change in rate of growth was calculated as a function of passage number.

2.2.6 MTS (cell viability) assay Cell viability assay was performed using the CellTiter 96® AQueous one cell viability assay reagent (Promega, WI, USA) for 3 biological replicates per genotype in triplicate. Briefly, cells were plated at a density of 1000 cells per well in an overall media volume of 200 uL on a 96-well tissue-culture plate (BD FalconTM). Post 24 hours of plating, drug treatment was performed on respective wells and was cells maintained at 37ºC/20 % oxygen/5 % CO2 for six days. On the sixth day, 100 µL of media containing 10% MTS reagent was added to each well and the plate was incubated at 37ºC for 1hour at 490nm on a Biotek PowerwaveTM XS2 microplate spectrophotometer (Winooski, VT, USA) (Al-Ejeh et al., 2014b).

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2.2.7 Doubling time assay Cells at a density of 1×105 cells per well, were plated in a 6-well plate in duplicate for each genotypes in an overall media volume of 2 mL with replicates for 12 hour intervals until 60 hours. Post every 12 hour interval, the cells from each genotype were collected and the overall cell density was counted using the Countess® automated cell counter (Life TechnologiesTM).

2.2.8 Cell proliferation assay For cell proliferation assay, the cells at a density of 2.5×105 cells per well, were plated in 6-well plate in duplicate and were imaged in realt time using The IncuCyte® S3 Live-Cell Analysis system (Essen BioSciences Inc, USA) following which data analysis was performed using the in-build IncuCyte® S3 software.

2.2.9 Cell cycle analysis For cell cycle analysis, the cells at a density of 1×105 cells per well, were plated in 6- well plate in duplicate and were harvested at respective time-point using 1mL of trypsin-EDTA (Sigma Aldrich®, St Louis, USA). The cells were then washed twice using the freshly prepared ice- cold FACS medium (1%FBS in PBS) and was fixed using 100% Ethanol for 24hrs. After 24hrs, the fixed cells were further washed twice using the ice-cold FACS medium (1%FBS in PBS) and were suspended in 360µL staining solution (150uL of 1mg/mL of propidium iodide (Sigma Aldrich®) and 15mg/mL RNAse A) and were incubates at 37ºC for 30 minutes. Cell cycle perturbations and the subG1 apoptotic fractions were determined using flow cytometry analysis of cells stained with propidium iodide and analyzed using ModFit LT 4.0 software as described previously (Kalimutho et al., 2011).

2.2.10 Colony formation assay Five hundred to one thousand cells were seeded on 12 well plates and incubated for additional 14 days to determine colony viability. The colonies were fixed with 0.05% crystal violet for 30 minutes, washed and quantified for crystal violet intensity after destaining using Sorenson’s buffer (0.1M sodium citrate in 50% Ethanol, pH 4.2) at 590 nM absorbance using PowerWave HT Microplate Spectrophotometer (BioTeK, Winooski, Vermont, USA).

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2.2.11 Gene silencing Transient gene silencing was performed by reverse transfection using 5-10 nM of respective small interfering RNAs (siRNAs) (See Table 2.1 for sequence) for 48hrs. The siRNAs were manufactured by Shanghai Gene Pharma. The transfection was performed using Lipofectamine RNAiMAX (Life TechnologiesTM). Mouse small hairpin RNAs (shRNAs) for Cep55 (pLKO plasmids, (Sigma Aldrich®, St Louis, USA)) clones (See Table 2.1 for sequence) were established using lentiviral packaging using Poly (ethyleneimine) solution (Sigma Aldrich®, St Louis, USA). For human cell line MDA-MB-231-luciferase tag cells were established using (See Table 2.2 & 2.3 for sequence) the lenti viral Invitrogen BLOCK-iTTM Pol II miR RNAi Expression vector kit (Life Technologies). In addition, constitutively CEP55-knockdown in Hs578T cells was achieved using previously reported plasmids (Fabbro et al., 2005) (See Table 1 for sequence). Selection of MDA- MB-231 clones was performed using 50 μg/mL Zeocin and 5 μg/mL Blastocydin (Life TechnologyTM). Other cloned were selected using 5µg/mL of Puramycin (Life TechnologyTM).

2.2.12 Immunoflorescence For tissue immunofluorescence, the slides were dewaxed, and rehydrated according to standard technique as described by Hobbs et al. (Hobbs et al., 2012). For whole-mount immunofluorescence, testes were detunicated and seminiferous tubules teased apart and rinsed in PBS on ice. Tubules were fixed with 4% paraformaldehyde (PFA) for 6 hours at 4°C and washed in PBS prior to blocking in 0.3% Triton X-100 in PBS (PBSX) supplemented with 10% FBS and 2% BSA (Bovine Serum Albumin), as previously described (Hobbs et al., 2012). Tubules were incubated overnight at 4°C with primary antibodies diluted in PBSX containing 1% BSA. Samples were washed in PBSX and primary antibodies detected with Alexa Fluor-conjugated secondary antibodies (Jackson ImmunoResearch, Bar harbor, Maine, USA and Thermo Fisher Scientific, Waltham, Massachisettes, USA). The slides were mounted and imaged using Vectashield (Vector Labs) before analyzing.

For immunofluorescence assay in cell lines, MEFs were cultured up to 70% confluency on a sterile glass coverslips. The cells were washed once in PBS and was fixed in ice- cold 4% paraformaldehyde (PFA) (Sigma Aldrich®, St Louis, USA) followed by washing again in 1xPBS and permeabilized in 0.2% TritonX-100 (Sigma Aldrich®) for 10 minutes at room temperature. Next the coverslips were blocked with filtered 3% BSA (Sigma Aldrich®,) in PBS for

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1hour in a humidified chamber at room temperature. Antibodies used for immunofluorescence are detailed in Table 2.4. Following primary antibody incubation, cell were washed (PBS, 3 times) and incubated in secondary antibody as well as DAPI nuclear stain (Sigma Aldrich®) diluted in 3% BSA (1:1000) for 30 minutes at room temperature in a humidified chamber in the dark. Following secondary antibody incubation the coverslips were washed (PBS, 3 times) and mounted using Prolong® gold anti-fade mounting medium (Life Technology TM).

No Gene of Sense (5’-3’) Anti-sense (5’-3’) . interest 1. Cep55_Scr CAAUGUUGAUUUGGUGUCUGCA UGAAUAGGAUUGUAAC 2. Cep55_SEQ CCAUCACAGAGCAGCCAUUCCCA AGUGGGAAUGGCUGCUCUGUGAUG 1 CT GUA

3. Cep55_SEQ AGCUACUGAGCAGUAAGCAAACA AAUGUUUGCUUACUGCUCAGUAGC 2 UU UUU 4. FoxM1_SCR CAAUGUUGAUCAAGUCUGCA UGAAUAGUUGUAAC 5. FoxM1_SEQ AAGGUGUUGCUAUCCAGUGAA UUCCACAACGAUAGGUCACUU 1 6. FoxM1_SEQ AGGACCACUUCCCUUACUUU UCCUGGUGAAGGGAAUGAAA 2

Table 2.1A: List of siRNA sequences used for knock down the respective genes in murine cells.

No. Gene of Sense (5’-3’) Anti-sense (5’-3’) interest 1. c-MYC_5 AUGUAAACUGCCUCAAAUUGG AAGUCCAAUUUGAGGCAGUUUAC ACTT AUUA 2. c-MYC_6 GCGACGAGGAGGAGAACUUCU UGGUAGAAGUUCUCCUCCUCGUC ACCA GCAG 3. ETS-1_5 CCCAGAGAUGCCUUAACCUUU CAACAAAGGUUAAGGCAUCUCUG GUTG GGAA 4. ETS-1_1 CCAGAAGAGAGGAAUGACUUG CCUUCAAGUCAUUCCUCUCUUCU AAGG GGAA 5. ERK2/MA CCAGGAUACAGAUCUUAAAU GACAAAUUUAAGAUCUGUAUCCU PK1_1 UUGTC GGCU 6. ERK1/MA AUAAACGGAUCACAGUGGAGG GCUUCCUCCACUGUGAUCCGUUU PK3 AAGC AUUG

Table 2.1B: List of siRNA sequences used for knock down the respective genes in human cells.

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No. Gene of Sense (5’-3’) interest 1. Cep55_Scr CCGGCGCTGTTCTAATGACTAGCATCTCGAGATGCTAGTCATTAGAAC AGCGTTTTTT 2. Cep55_sh#1 CCGGCCGTGACTCAGTTGCGTTTAGCTCGAGCTAAACGCAACTGAGTC ACGGTTTTTG 3. Cep55_sh#2 CCGGCAGCGAGAGGCCTACGTTAAACTCGAGTTTAACGTAGGCCTCTC GCTGTTTTTG 4. Cep55_sh#3 CCGGCGTTTAGAACTCGATGAATTTCTCGAGAAATTCATCGAGTTCTA AACGTTTTTT 5. Cep55_sh#4 CCGGGAAGATTGAATCAGAAGGTTACTCGAGTAACCTTCTGATTCAAT CTTCTTTTTT

Table 2.2: List of shRNA sequences used for knock down the respective genes in murine cells.

No. Cell Line Gene of Sense (5’-3’) interest 1. MDA MB CEP55_Scr AAGTCGACGTACGTCGCCTTAGGGAATTCCAAAGGTTATT 231 GTTACTGAAA 2. CEP55_#2 TGCTGTAAGCATTCTTCTCCTTCTCAGTTTTGGCCACTGACT GACTGAGAAGGAAGAAT CTTA 3. CEP55_#8 TGCTGTCTTCCAGCTGTTCAAGCAATGTTTTGGCCACTGAC TGACATTGCTTGCAGCTGGAAGA 4. HS578T CEP55_Scr AAGTCGACGTACGTCGCCTTAGGGAATTCCAAAGGTTATT GTTACTGAAA 5. CEP55_Sh1 TGCTGTCTTCCAGCTGTTCAAGCAATGTAGCTGGCCACCCT AGACATTGCTTGCAGCTGGAAGA

Table 2.3: List of shRNA sequences used for knock down the respective genes in human cells.

Antibody Specificity Company Cat. No Dilution Cep55 In House developed (aminoacids - 1:300 55-250) Plzf R&D Systems AF2944 1:100 Ki67 Novacastra NCL-ki67p 1:500 Phospho-Histone H3(S10) Cell Signaling Technology 4499 1:200 Foxo1 Cell Signaling Technology 2880 1:500 c-Kit R&D Systems AF1356 1:250 γ-Tubulin Sigma Aldrich T5192 1:400 α-Tubulin Sigma Aldrich T9026 1:300

Table 2.4: List of antibodies used for immunofluorescence and immunohistochemistry.

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2.2.13 Microscopy For tissue immunofluorescence, the slides were analyzed using Zeiss Invert LSM780 confocal microscope. For cell lines, the analysis was performed using a Delta-Vision personalDV deconvolution microscope (Applied Precision, GE Healthcare, Issaquah, WA). The analysis of the images was performed using Fiji ImageJ software (Java3D, Minnesota, USA).

2.2.14 Karyotyping For karyotyping, cells were incubated in media containing Colcemid (0.05µg/mL) for 2 hours and harvested using trypsin-EDTA (Sigma Aldrich®) and washed (PBS, 2 times). The cells were then treated with freshly prepared 0.56% KCl for 1 hour at 37°C following which they were fixed with freshly prepared ice-cold fixing solution (Methanol/Acetic acid 3:1). The cell suspension was dropped from a height on a slide placed on a heating block at 42°C. Slides were air-dried overnight, stained with Giemsa (Sigma Aldrich®) and images taken using a bright field microscope.

2.3 Biochemistry and Molecular Biology

2.3.1 Genomic DNA extraction Mouse ear clips were digested in DirectPCR® tail lysis buffer (Viagen Biotech Inc, Los Angeles, CA) containing 0.25 mg/mL Proteinase K overnight at 55°C with agitation. The samples were heated the following day at 85ºC for 45 minutes in order to denature Proteinase K activity. One µL of crude lysate was used for genotyping PCR (Polymerase Chain Reaction).

2.3.2 Polymerase chain reaction Genotyping PCR was performed using the extracted DNA from ear clips in a final volume of 25µL (Master mix) containing the following components: 2.5 µL of AmpliTaq® Gold

10X PCR buffer, 2 mM MgCl2, 0.2 mM dNTPs (dATP, dTTP, dCTP and dGTP), 10 picomoles of each primer and 0.3µL AmpliTaq® Gold polymerase. Reaction specificity was determined by a water-only negative control for each run of genotyping reactions. PCR reactions were performed on a

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GeneAmp® PCR system 9700 (Applied Biosystems™, Waltham, Massachusetts, USA) PCR machine with one cycle of 95ºC for 12 minutes, followed by 35 cycles of the following: 95ºC for 30 seconds, 55ºC for 30 seconds and 72ºC for 30 seconds, and a final elongation step of 72ºC for 4 minutes. PCR products were electrophoresed in Tris-Acetate EDTA on a 1.5% agarose gel at 100V and viewed on a Bio-Rad Molecular Imager® Gel Doc™ XR system trans-illuminator (Bio-Rad Laboratories Inc, Hercules, USA) using Quantity One® 1-D analysis software (Bio-Rad Laboratories Inc). Primers used for PCR are detailed in Table 2.5.

No. Sequence Name Forward (5’-3’) Reverse (5’-3’) 1. P1 (WT allele) ACGTTTCCGACTTGAGTTGC GCAACTCAAGTCGGAAACGA 2. P2 (WT allele) CTTATCGTCGTCATCCTTGT ACAAGGATGACGACGATAAG 3. P3 (Transgenic CCGACAAAACCGAAAATCTG CAGATTTTCGGTTTTGTCGG allele)

Table 2.5: List of PCR primers sequences used for genotyping PCR for mouse DNA.

2.3.3 RNA extraction and reverse transcription RNA was extracted either from mouse tissue or cell lines using the QIAgen RNeasy® kit (Valencia, CA, USA) as per manufacturer’s instructions. Cells were harvested using trypsin- EDTA (Sigma Aldrich®) and washed (PBS, 2 times) while the tissues (<5mg) were mechanically sheared using sterile scalpel blade after which appropriate volume of RLT buffer at 4ºC (supplied in the kit) was added as indicated by the manufacturer. A DNAse digestion step was performed using the DNAse enzyme provided in the iScript™ cDNA kit (Bio-Rad Laboratories Inc) after RNA extraction. The RNA quality and quantity was assessed using Nanodrop ND-1000 spectrophotometer (Thermo-Scientific). Reverse transcription was performed using Superscript®III Reverse Transcriptase First Strand synthesis (Life TechnologiesTM) as per manufacturer’s instruction. The cycle condition used were: Priming at 25ºC for 5 minutes, reverse transcription at 46ºC for 20 minutes and reverse transcriptase inactivation at 95ºC for 1 minute.

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2.3.4 Quantitative real-time PCR (qRT-PCR) qRT-PCR was performed in a 96 well plate using a Light Cycler 480 using Sybr green mastermix (Roche Applied Science, Basel, Switzerland). Primers used for qRT-PCR were designed using qPrimerDepot (online database) are outlined in Table 2.6. The composition of the mastermix in an overall volume of 10µL included: 5µL of Sybr green, 10 picomoles of each primer, 1µL of diluted cDNA and 3µL of H2O for three biological replicates performed in duplicates. The cycle conditions used were: 95ºC for 5 minutes, followed by 40 cycles of the following: 95ºC for 10 seconds, 58ºC for 10 seconds and 72ºC for 5 seconds, and a final elongation step of 72ºC for 5 minutes. Ct values were calculated using the accompanying Light cycler 480 software, version

1.5.0.39 following which analysis was performed using the ΔΔCt method, with values normalized to β-Actin, Gapdh and Tbp for mouse or ATCB and HPRT1 for human cell lines. For each biological cDNA sample analysis, a negative control containing cDNA solution without reverse transcriptase added was used to ensure no genomic DNA contamination. Alongside, a regular negative control containing only H2O was included for each primer set. Taqman PCR was performed using Taqman master mix (ABI) and qRT-PCR was carried out on ABI Prism 7500 or ViiA7 machines. Endogenous control gene used for normalization was Tbp (encoding TATA box binding protein, Mm00446973_m1). Taqman gene expression sets used were: PLZF (Zbtb16; Mm01176868_m1; GRFA1 (Mm00439086_m1); Pcna (Mm00448100_g1); Kit (Mm00445212_m1); Foxo1 (Mm00490671_m1); Oct3/4 (Mm00658129_gH); Ngn3 (Mm00437606_s1); Id4 (Mm00499701_m1); Nanog (Mm02384862_g1).

No. Gene Name Forward (5’-3’) Reverse (5’-3’) 1. Cep55 CAAATAACACAGTTGGAATCCTTG TTCAGGTTCTCTCTGGAGTGG 2. Sohlh1 TGACAACGCGCTCTGGCG TGCCTCAGTTTGATGGCC 3. Lhx8 CCTGCAGTTCTGAAACCACAC GGCACACGAGCTGCTACATTA 4. Ret CATAGAGCAGAGGTGTGCCA GGCTGAAGCTGATTTTGCTC 5. Erg4 GCAGGAGTCTGTTAAGTCCCC CTGCCTGCTAGGGACGCT 6. Sall4 CAGGGGAGTTCACTGGAGC AGCACATCAACTGGGAGGAG 7. Actin GGCTGTATTCCCCTCCATCG CCAGTTGGTAACAATGCCATGT 8. ETS1 TCATTTCTTTGCTGCTTGGA CTCACCATCATCAAGACGGA 9. CEP55 TGGCTCCAAACTGCTTCAAC ACTTCCCGCTGCTGATCATA 10. MYC ACCGAGTCGTAGTCGAGGT TTTCGGGTAGTGGAAACCA 11. ACTB CCCAGAGCAAGAGAGAGG GTCCAGACGCAGGATG

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12. HPRT1 CCTGGCGTCGTGATTAGTGAT AGACGTTCAGTCCTGTCCATAA

Table 2.6: List of qRT_PCR primers sequences for transcript analysis.

2.3.5 Immunoblotting

2.3.5.1 Cell and tissue lysis In order to collect the cell lysate, the adherence cells were harvested by scraping into scraping buffer (PBS containing Sodium Fluoride (100mM; Sigma Aldrich®) and Sodium Ortho- Vanadate (100; Sigma Aldrich®). The harvested cells were centrifuged at 2500 RPM for 5 minutes and were then re-suspended in RIPA buffer (25mM Tris-Hcl (pH 7.6), 150mM NaCl, 1% NP40, 1% Sodium deoxycholate and 0.1% SDS). The samples were incubated in the lysis buffer for 75 minutes on ice. Cell debris was removed by centrifugation at 4ºC for 30 minutes at 13,000 RPM and the supernatant was collected in a fresh tube. For tissue lysate, the organs were diced using a sterile blade followed by lysing in Urea lysis buffer (8M urea, 1% SDS, 100mM NaCl, 10mM Tris (ph7.5) and incubated for 30 minutes on ice after which the samples were sonicated for 10 seconds on a Branson Sonifier 450 (Branson Ultrasonic Corporation, Danbury, CT, USA). The tissue debris was removed by centrifuging at 4ºC for 30 minutes at 13,000 RPM and the supernatant was collected in a fresh tube.

2.3.5.2 Protein quantification Protein quantification was performed using the Pierce BCA Protein Assay Kit. Post quantification, 30 µg of protein was aliquoted for each immunoblot (unless mentioned) to which 1X Laemmli buffer (1 mL Glycerol, 10% SDS, 0.5 M Tris HCl (pH 6.8), 2.5 mL β-mercaptoethanol, and 0.25gm Bromophenol blue; 5X solution) was added and samples heated to 95ºC for 5 minutes prior to SDS-PAGE.

2.3.5.3 Polyacrylamide gel electrophoresis Acrylamide gel was poured as per the protocol described by (Mahmood and Yang, 2012). Protein samples were then subjected to electrophoresed by SDS-PAGE using the Bio-Rad Mini-PROTEAN® Tetra system at 120V in SDS running buffer (25mM Tris-HCl, 192mM glycine,

Chapter - 2

0.1% SDS (v/v)) After the electrophoresis, the gel was transferred onto Amersham Hybond C nitrocellulose membrane (GE Healthcare, Waukesha, WI, USA) using the Invitrogen Xcell SureLock™ transfer system in 1X transfer buffer (50mM Tris, 40mM Glycine, 20% methanol) at 35V for 90 minutes. Membranes were blocked in BLOTTO (5% Skim milk powder (Diploma Brand) in TBS containing 0.5% Tween-20) for 1 hour on a shaker at room temperature. Blocked membranes were incubated with primary antibodies (listed in Table 2.6) overnight at 4ºC. The following day, the membranes were incubated in respective secondary antibody (listed in Table 2.6) for 1 hour after washing thrice in TBST.

2.3.5.4 Western blotting Western blotting was performed as described earlier (Mittal et al., 2016). Protein detection was performed using Super Signal chemiluminescent ECL-plus (PerkinElmer) on an LAS- 4000 imaging system (Fujifilm Life Sciences, Stamford, CT, USA) film developer.

2.4 Histology

2.4.1 Fixation for histology For histologic examination tissues were collected and fixed in 4% formaldehyde in PBS after washing (PBS, 3 times) and was stored in 70% ethanol prior to processing. The tissues were then embedded in paraffin blocks, and 5-μm-thick sections prepared for staining. For PAS staining, whole testes were removed from male mice and fixed in Bouin’s fixative (Sigma Aldrich®) for 24 hr. Tissues were embedded in paraffin and 4 μm sections mounted onto Superfrost plus slides using the Sakura Tissue-Tek® TEC™ (Sakura Finetek, Tokyo, Japan).

2.4.2 Haematoxylin and eosin or PAS staining Paraffin-embedded sections were de-waxed with a Leica Autostainer XL prior to staining. The slides were then stained with H&E (Hematoxylin and eosin) or PAS (Periodic-Acid Schiff) stain by following standard protocol.

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2.4.3 Immunohistochemistry Immunohistochemistry was performed in assistance with the QIMR Berghofer Medical Research Institute in-build facility. Antigen retrieval was performed using 2.94g tri- Sodium Citrate in 1L MQ (pH 6.0) buffer and microwaved. Tissue sections were permeabilized in 0.2% Triton X-100/PBS for 5 min, followed by 0.05% Triton X-100/PBS for 10 min. Tissue sections were treated with 3% (vol/vol) H2O2 before immunostaining using the Dako EnVision™ (Agilent, system Waukesha, WI, USA) and counterstaining with haematoxylin. Table 2.4 demonstrates the list of antibodies used for IHC.

2.4.4 Coverslipping Slides were coverslipped using the Leica CV5030 (Leica Biosystems, Wetzlar, Germany) glass coverslipper and Shandon Consul-Mount mounting media (Life TechnologyTM).

2.4.5 Microscopy and analysis Slides were scanned on the Aperio® Scanscope® XT (Aperio®, Vista, USA) using 20X or 40X objecting. Histology images were observed using the Aperio® ImageScope™ software v10.0.36.1805. Cross section area was calculated using the Scanscope® console software v101.0.0.18 while the immunohistopositive cells were analyzed using the Positive Pixel count v9 algorithm to score immunopositive cells in testes sections.

2.5 Bioinformatic analysis Whole-chromosome (WC) and chromosome arm-level (CAL) somatic copy number aberrations (SCNAs) were inferred from TCGA processed (Level 3) Affymetrix Genome Wide SNP6.0 Array data for the indicated cancer types, as previously described (Thangavelu et al., 2017). Using the same datasets, ASCAT2.4 (Van Loo et al., 2010) was used to compute the ploidy level for each sample. Samples with ploidy between 1.9 and 2.1 were considered diploid, samples with ploidy lower than 1.9 or between 2.1 and 2.5 were called near-diploid aneuploid and samples with ploidy>2.5 were considered aneuploid and having undergone at least one whole-genome doubling (WGD). Clinical annotations, processed values of gene expression and CNA profiles were

Chapter - 2 downloaded from METABRIC (Curtis et al., 2012) (Discovery n = 995) and TCGA (Cancer Genome Atlas, 2012) (n=492). To calculate CNA for both datasets, we used the available copy-number value overlapping genes in a given tumor sample. We computed an average across ~20000 genes to obtain average CNA for each tumor (herein defined as average copy number of genome/tumor). These average values were used to estimate Pearson correlation between CEP55 mRNA expression levels and average CNA across tumors and are the values used in Figure 4.15A (discussed later). MYC transcription factor activity was inferred from TCGA tumor samples using their protein expression profiles and a previously described trained affinity regression model (Osmanbeyoglu et al., 2017). Proliferation-adjusted CEP55 expression was calculated by dividing the CEP55 mRNA level by the KI67 mRNA level, all expressed in RPKM (Reads Per Kilobase of transcript per Million) and RSEM (RNA Sequencing by Expectation Maximization)-normalized.

2.6 Statistical analysis Student’s t-test or one-way or two-way RPKM and RSEM with Bonferoni post hoc or Mann-Whitney U test testing (specified in figure legend) was performed using GRAPHPAD PRISM v6.0 (GraphPAd Software, LaJolla, CA, USA) and the p-values were calculated as indicated in figure legends. Asterisks indicate significant difference (*p<0.05, **p<0.01, ***p<0.001 and ****p<0.0001), ns= not significant.

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Antibody Name Company Cat. No Dilution CEP55 (mouse and human) In-house (RB1) - 1:4000 γ-Tubulin Sigma Aldrich T5192 1:1000 β-Actin BD Pharmingen 612656 1:2000 COX-IV Millenium Science Pty Ltd LCR-926-42214 1:2000 P53 Santa Cruz Sc-126 1:1000 β-Catenin Cell Signaling Technology 9582 1:1000 ZEB/TCF Cell Signaling Technology 3396 1:1000 Vimentin Cell Signaling Technology 5741 1:1000 pSTAT3(Y705) Cell Signaling Technology 9145 1:1000 STAT3 Cell Signaling Technology 9139 1:1000 pAKT (S473) Cell Signaling Technology 4060 1:1000 AKT Cell Signaling Technology 9272 1:1000 pERK1/2(T202/Y204) Cell Signaling Technology 4370 1:2000 ERK1/2 Cell Signaling Technology 4695 1:2000 pEGFR (Y1068) Cell Signaling Technology 2234 1:1000 PARP Cell Signaling Technology 9542 1:1000 Cleaved Caspase-3 Cell Signaling Technology 9664 1:500 pAKTS473 Cell Signaling Technology 4060 1:1000 AKT Cell Signaling Technology 9271 1:1000 pPDK1(S241) Cell Signaling Technology 3438 1:1000 PDK1 Cell Signaling Technology 3062 1:1000 pFoxo1 Cell Signaling Technology 9461 1:1000 PLK1 Cell Signaling Technology 4513 1:1000 MYC (Y69) Abcam Ab32072 1:1000 AURKA Cell Signaling Technology 4178 1:1000 MPM2 Upstate biotechnology 05-368 1:500 Cyclin B1 Abcam Ab7957 1:1000 p-MEK(T286) Cell Signaling Technology 9127 1:1000 p-CDK1(Y15) Cell Signaling Technology 4539 1:1000 WEE1 Cell Signaling Technology 4936 1:1000 CDC25B Sigma Aldrich Sc-5619 1:250 p-MCL1(S159/T163) Cell Signaling Technology 4579 1:1000 p-H3 (S10) Cell Signaling Technology 9706 1:1000 BCL2 Cell Signaling Technology 2876 1:1000 BCL-XL BD Pharmingen 51-9000093 1:1000 BAK Pro Sci Incorporated 3347 1:1000 BIM Cell Signaling Technology 2933p 1:1000 Survivin GeneTex Inc GTX100441 1:1000 Rabbit Secondary Sigma Aldrich A0545 1:4000 (peroxidase) Mouse Secondary Sigma Aldrich A9044 1:4000 (peroxidase)

Table 2.7: List of antibodies used for immunoblotting in the study.

Chapter - 3

Aim1.1: To study the role of CEP55 overexpression in mammalian physiology and spermatogenesis in vivo

Chapter - 3

3.1 Introduction Spermatogenesis is a complex process that requires an appropriate balance between spermatogonial stem cell (SSC) renewal and commitment to meiosis, and ultimately leads to sperm production (Figure 3.1A ) (He et al., 2009). SSCs are specialized stem cells that reside at the basement membrane of seminiferous tubules and are competent for both self-renewal and differentiation (Kanatsu-Shinohara et al., 2004, Seandel et al., 2007, Guan et al., 2006b, DE ROOIJ, 1998). SSCs are contained within a population of undifferentiated spermatogonia and are composed of isolated A-type single cells (As) plus cysts of cells that remain interconnected by cytoplasmic bridges upon cell division due to incomplete cytokinesis (Figure 3.1A & B) (Aponte et al., 2005).

Two cell cysts are referred to as Apaired (Apr) while cells within cysts of 4 or more undifferentiated cells are referred to as Aaligned (Aal) (Figure 3.1B, right hand side panel). The majority of undifferentiated cells, especially Aal, act as committed progenitors while steady-state self-renewal is restricted to the fraction expressing Gfra1, which are mainly present as As and Apr (Hara et al., 2014,

Oatley et al., 2007). Committed undifferentiated cells convert into A1-differentiating spermatogonia that undergo a series of mitotic divisions to generate type B spermatogonia, which enter meiosis and ultimately produce mature spermatozoa (Aponte et al., 2005, de Rooij and Russell, 2000). A plethora of molecular factors, such as Gdnf (Meng et al., 2000), Foxo1 (Goertz et al., 2011), Kit (Schrans- Stassen et al., 1999), Plzf (Buaas et al., 2004, Filipponi et al., 2007), Id4 (Oatley et al., 2011), Nanos2 (Sada et al., 2009), Neuregulin-1 (Hamra et al., 2007), Redd1 (Hobbs et al., 2010), Ret (He et al., 2007, Jijiwa et al., 2008) and Stra8 (Anderson et al., 2008) regulate the switch between spermatogonial self-renewal and differentiation (reviewed by Jan et al.2012 (Jan et al., 2012), He et al. 2009 (He et al., 2009)). These factors are the products of paracrine and autocrine signaling cascades of both somatic and germs cells and play critical roles in maintenance of spermatogonial homeostasis (Jan et al., 2012).

3.1.1 The process of spermatogenesis and characteristics of SSCs The process of spermatogenesis occurs in a specialized organ called the testes located within the scrotum of mammals. The testes are comprised of a pair of oval glands and evolve from the genital ridge under the influence of male specific hormones during embryonic development. Spermatogenesis leads to the production of haploid sperm which upon fusion with a secondary oocyte generated from an ovary produces an embryo (Deprimo et al., 2007). Spermatogenesis occurs in the seminiferous tubules of the testis and requires the microenvironment provided by the somatic Sertoli cells for continuous generation of spermatozoa throughout the mammalian lifespan (Brinster

Chapter - 3 and Zimmermann, 1994). The entire process comprises of an initial mitotic phase followed by meiotic phase after which haploid spermatids mature and are released into the lumen of seminiferous tubules as spermatozoa (Brinster and Zimmermann, 1994, Russell, 1990).

SSCs are the foundation of spermatogenesis and male fertility and represent about 0.03% of all germ cells among the mammalian testicular niche (Figure 3.1A) (Tegelenbosch and de Rooij, 1993). SSCs are unique as they have the capability of balancing self-renewal and differentiation cycles to govern the production of millions of sperm per day (He et al., 2009). The SSCs are primitive diploid germ cells and can be broadly characterized into three different types based on their nuclear morphology studied in murine models. Type A or the As cells are undifferentiated SSCs and are the most primitive due to lack of heterochromatin. These cells can undergo mitotic self-renewal to maintain the stem cell pool or differentiate into spermatogonia which can undergo further differentiation into spermatozoa (Figure 3.1B) (Nagano et al., 2002, DE ROOIJ,

1998, Guan et al., 2006a). Once the differentiation of As cells initiates, the process of cytokinesis remains incomplete, resulting in the generation of two differentiating spermatogonia, Apr cells which are connected together by an intercellular bridge (Aponte et al., 2005). Apr cells undergo clonal expansion to give rise to long chains of 4,8, 16 and 32 (Aal) cells (Figure 3.1B) (Phillips et al., 2010, de Rooij and Russell, 2000). From this stage, the final differentiation begins with formation of B- type spermatogonia via mitosis and the process continues to produce more differentiating intermediate spermatogonia and finally form primary spermatocytes. These spermatocytes undergo the first meiotic division to form secondary spermatocytes which subsequently enter a second meiotic division to give rise to haploid round spermatids and ultimately mature into spermatozoa (Handel and Schimenti, 2010, O'Donnell et al., 2011). The entire process comprises of 16 steps with spermatid formation in the steps 1-8 step and maturation into spermatozoa in steps 9-16 (Figure 3.1B, right hand side panel) (Ahmed and de Rooij, 2009).

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3.1.2 The origin of SSCs and the role of its precursors Germ cell fate is assimilated at the proximal region of the mammalian epiblast cells that modulates towards formation of PGCs (Primordial Germ Cells) (Richardson and Lehmann, 2010, Saitou, 2009). Research in murine models has illustrated that this event occurs by embryonic day (E) 6 wherein the transcriptional regulator Prdm1 (also known as Blimp1) dictates the PGC specification and its lineage At E7.5-8, the PGCs located at the posterior primitive streak start to migrate towards the embryonic ectoderm and finally reach the genital ridge by E13.5 where they undergo continuous proliferation (Tam and Snow, 1981). This process is heavily regulated by signaling cascades through receptor tyrosine kinases within the PGCs, namely c-KIT, SCF (Gu et al., 2009), CXCR4 (a G-protein-coupled receptor) and its ligands SDF1 (Ara et al., 2003) as loss of these factors has been shown to hinder the migration of PGCs (Richardson and Lehmann, 2010). Upon arrival at the genital ridge, the PGCs are encircled by the somatic Sertoli cells following which development of seminiferous cords is initiated. The somatic cells synthesize male-determinant signals which dictate the PGCs (known as gonocytes by this stage) to advance towards a male developmental fate (Durcova-Hills and Capel, 2008, Maatouk and Capel, 2008).

The gonocytes continue to proliferate until E15-16 during which they enter the stage of quiescence in mice and remain dormant until birth of the fetus (Kluin and de Rooij, 1981, Vergouwen et al., 1991). Factors such as PP2A, a cell cycle regulatory protein, which plays a crucial role in directing the proliferation of gonocytes are down-regulated along with several other factors like Activin A and TGFβ (van den Ham et al., 2003, Mendis et al., 2011, Moreno et al., 2010). These gonocytes resume proliferation post birth at approximately postnatal day (P) 3 and mature to produce spermatogonia (Nagano et al., 2000, Kluin and de Rooij, 1981).

Chapter - 3

A B

Figure 3.1: Overview of the Spermatogonial niche. (A) Schematic representation of the cross-section of a mouse seminiferous tubule showing the basal membrane comprising of SSCs (As spermatogonia) present close to the basement membrane, surrounded by Sertoli cells. The schematic also describes the presence of other types of spermatogonia apart from primary spermatogonia and round spermatids more towards the lumen of the tubule with elongated spermatids nearest to the lumen which will shed into the lumen as spermatozoa. (B) Schematic representation of SSC proliferation and self-renewal alongside differentiation. SSCs (As cells marked in purple) can either self-renew (left hand side panel) by forming two new daughter cells and replenish their population or can initiate differentiation during which the daughter cells, (Apr cells, marked in pink) lack cytokinesis and remain connected by cytoplasmic bridge. The Apr cells continue to divide in to Aal4 cells which in turn divide into Aal8 and this process continue for a total of 9-10 times prior to produce spermatocytes (Adopted from (de Rooij and Mizrak, 2008)).

Chapter - 3

3.1.3 The molecular cross talk governing SSC cell fate The tightly controlled process of spermatogenesis critically relies upon homeostasis between self-renewal and differentiation as a shift in balance in either direction results in azoospermia and infertility (Flannigan and Schlegel, 2017, Singh et al., 2011). To maintain this balance, multiple signaling networks must converge while tight regulation must be persisted for regular spermatogonial homeostasis (He et al., 2009). One of the key regulators of spermatogenesis is Gdnf, which is synthesized from the Sertoli cells and is essential in the induction of a signaling cascade leading to SSC proliferation (Katoh-Semba et al., 2007, Spinnler et al., 2010). Gdnf binds to its receptor Gdnf-family receptor α1 (Gfrα1) to activate the c-Ret receptor which in turn phosphorylates down-stream targets including PI3K/Akt, Mek and Scr kinases (Lee et al., 2007a, Oatley et al., 2007). Gdnf also plays a critical role in regulating Id4 (Figure 3.2) and it has previously been shown that overexpression of Gdnf causes accumulation of undifferentiated spermatogonia in mice (Figure 3.2) (Oatley et al., 2011, Oatley et al., 2006, Jan et al., 2012). Additionally, downregulation of Id4 results in loss of SSC proliferation and in vivo depletion leads to progressive germ cell exhaustion causing male sterility.

Germ cell progenitor and pluripotency associated genes like Oct-4 and Nanos3 critically control the PGCs population as in vivo studies have demonstrated the loss of these genes causes a decrease in PGC number and absence of mature germ cells (Tsuda et al., 2003, Kehler et al., 2004). Alongside, Nanos2 also plays a crucial role in stem cell state of SSCs as both over-expression and depletion results in accumulation and fatigue of undifferentiated spermatogonia respectively. In the same way, independent studies from Buass et al. and Costoya et al. have shown that Plzf (also known as ZBTB16 in humans) is a critical factor in maintenance of stem cell state of SSCs as loss of Plzf leads reduction of undifferentiated spermatogonia (Figure 3.2). They have also demonstrated that loss of Plzf in vivo causes higher levels of differentiating cells indicating its role in promoting self-renewal over differentiation. Plzf is expressed from the gene Zfp145 during embryogenesis (E17.5), and its expression peaks at the first week of post-natal life but decreases with the onset of differentiation. Plzf expression post-birth allows gonocytes to exit quiescence and enter the proliferative stage (Buaas et al., 2004, Costoya et al., 2004). Additionally, it acts as a transcription factor for negatively regulating c-Kit expression which triggers the process of differentiation. Independent studies have shown that mutation of c-Kit ligand, Scf or heterozygous mutation of c-Kit results in aberration of differentiating spermatogonia (Figure 3.2) (de Rooij et al., 1999, Jan et al., 2012). Similarly, Sohlh1 in mice has been shown to be expressed in differentiating spermatogonia as

Chapter - 3 ablation of Sohlh1 leads to differentiation obstruction (Ballow et al., 2006). A recent study by Goertz et al elaborated the role of Foxo transcription factors in regulating the self-renewal and differentiation, specifically Foxo1 which transcriptionally regulate c-Kit, c-Ret and many other targets which are key in triggering differentiation (Goertz et al., 2011). They have also genetically demonstrated the control of Foxo1 by a PI3K-Akt-Pdk1 signaling axis, wherein the up-regulation of Pdk1 or deletion of Pten results in hyper-activation of the Akt pathway which impairs the Foxo1 transcription factor, therefore hindering the self-renewal capability of spermatogonial cells (Goertz et al., 2011).

3.1.4 Role of CEP55 in spermatogenesis CEP55 is a highly coiled-coil protein that is required for somatic cell abscission during cytokinesis, the final stage of cell division (Fabbro et al., 2005). Using HeLa cells, Fabbro et al. initially illustrated that CEP55 remains associated with the centrosome throughout interphase and by the end of anaphase it is recruited to the midbody to initiate cell abscission (Fabbro et al., 2005). The process of cytokinesis is a highly controlled process that involves numerous proteins and requires localization of the ESCRT machinery to the midbody to ensure abscission and equal segregation of cytoplasmic contents between the two daughter cells (Carlton and Martin-Serrano, 2007, Green et al., 2012, Mierzwa and Gerlich, 2014). Recently, independent studies in somatic cells have demonstrated the essential role of CEP55 in recruitment of the ESCRT machinery to the midbody (Lee et al., 2008). CEP55 upon localization to the midbody recruits an array of proteins including ALIX and TSG101 (components of ESCRT machinery), which binds to the EABR of CEP55, a hinge region present between the two coiled-coil domains in the N-terminal of the protein (also explained previously in Chapter 1; Section 1.7) (Carlton and Martin-Serrano, 2007, Carlton et al., 2008, Morita et al., 2007).

Chapter - 3

Figure 3.2: Molecular player of Spermatogenesis. Schematic illustration of the key signaling cross-talk associated with regulation of self-renewal and differentiation in SSCs (adopted from (Jan et al., 2012).

Chapter - 3

Independently, CEP55 is required for intercellular bridge formation in germ cells through interaction with its biochemical partner TEX14, an intercellular bridge forming factor (Chang et al., 2010). TEX14 is an inactive kinase that acts as an endogenous inhibitor of CEP55 in germ cells to disable cell abscission enabling formation of a syncytium (Iwamori et al., 2010). Mechanistically, TEX14 has strong affinity for binding to the EABR region of CEP55 (Kim et al., 2015). It competes with ALIX to facilitate formation of the intercellular bridge which connects differentiating spermatogonia in male mice. Intercellular bridges are an essential structure for spermatogonial function as they allow nuclear cross-talk between the connected germ cells (Huckins and Oakberg, 1978, Fawcett, 1959). Although these studies have indicated how TEX14 antagonises the function of CEP55 to allow formation of intercellular bridges, the molecular function of CEP55 in spermatogenesis has not been characterized, despite high expression of CEP55 in the testes (Fabbro et al., 2005).

Over the last few years, CEP55 overexpression has been linked to poor prognosis in multiple cancers including lung (Chen et al., 2009b), hepatocellular (Chen et al., 2007), breast (Colak et al., 2013, Chen et al., 2010b), gastric (Tao et al., 2014) and oral squamous cell carcinoma (Chen et al., 2009a) (Schippling et al., 2016). Using preclinical models of hepatocellular and lung cancer, Chen et al. showed that CEP55 binds to and stabilizes the catalytic subunit, p110 of PIK3CA and increases AKT signaling (Chen et al., 2009b, Chen et al., 2007). In line with this, we recently showed cep55’s role in embryonic development through characterization of a cep55 mutant (nonsense mutation in cep55) zebrafish model. We demonstrated that homozygous mutant zebrafish developed hydrocephaly and underwent extensive apoptosis post-fertilization with defects in the cell cycle (Jeffery et al., 2015a). Furthermore, we showed that Akt was destabilized in the homozygous mutants, which was partially rescued by providing either constitutively-activated PI3KCA or AKT1 expression. In addition, two recent studies have emphasized the connection between CEP55 loss and genetic diseases like MARCH (Frosk et al., 2017) and MKS (Bondeson et al., 2017) characterized by the presence of multiple congenital anomaly.

In this study we have described, for the first time, a novel “knock-in” mouse model that ubiquitously overexpress Cep55. Cep55Tg/Tg mice exhibit male-specific sterility due to a block in postnatal germ cell differentiation. We have shown that Cep55 regulates a molecular switch between self-renewal and differentiation of SSCs via modulation of the PI3K-PKB/Akt axis. Cep55 overexpression resulted in inactivation of Foxo1 and impeded spermatogonial differentiation. In addition, we observed prolonged high expression of Plzf in association with Ret and Gfra1 and

Chapter - 3 concomitant down-regulation of Erg4 and Sohlh1 in Cep55Tg/Tg testes thereby validating that the defect lies in germ cell differentiation. This phenotype was accompanied by progressive germ cell loss and a SCO-like phenotype in adults. Our data demonstrates that precise regulation of Cep55 expression is required to maintain the process of spermatogenesis in mice.

3.2 Results

3.2.1 Generation of the ubiquitously expressing Cep55 “Knock-in” transgenic mice model The gene of murine Cep55 is located in chromosome 19 (Martinez-Garay et al., 2006). Overexpression of CEP55 has been previously shown to correlate with tumorigenesis in multiple cancer types (Schippling et al., 2016). To define whether Cep55 amplification would be sufficient to promote de-novo tumorigenesis, we engineered a transgenic knock-in allele allowing overexpression of flag-tagged Cep55 from the ubiquitously expressed Rosa26 locus under the control of the human Ubiquitin C (UBC) promoter (Figure 3.3). Two independent transgenic mouse lines were generated post-selection of embryonic stem cells and inter-crosses of chimeras with wildtype mice.

Correct targeting of Cep55 alleles was confirmed by Southern blotting and genotyping PCR (Figure 3.4A & B). The resultant genotypes hereafter will be referred to as wildtype (Cep55wt/wt), heterozygous (Cep55wt/Tg) and homozygous (Cep55Tg/Tg). Mice of each genotype were born overtly normal and at near-Mendelian ratio and had no apparent change in body weight at P2 (Figure 3.4C & D). To validate the overexpression of Cep55 in the transgenic mouse line, we investigated the expression level of the Cep55 transgene in multiple tissues of two month old mice at both the transcript and protein level. We observed substantially higher levels of Cep55 transcription in various organs of transgenic mice compared to wildtype siblings, though the expression varied among different organs (Figure 3.4E). Consistently, we observed a marked upregulation of Cep55 at a protein level ranging from 2 to 5 fold increase in expression when compared to wildtype (Figure 3.4F). Taken together, these data indicated that the transgenic mice ubiquitously overexpress Cep55 in multiple tissue types.

Chapter - 3

Figure 3.3: Generation of the ubiquitously expressing Cep55 “knock-in” transgenic mice model. Schematic representation of the approach used for generation of the Cep55 “knock-in” transgenic mice. Using the homologous recombination site at the Rosa 26 locus, a flag-tagged Cep55 cDNA (Tg) with human Ubiquitin C (UbiC) promoter was introduced in the mice genome for ubiquitous expression. A poly-Adenylation (pA+) sequence, flanked by loxP sites, was present between the UbiC and the Tg. The construct also contained a neomycin resistance cassette (pGk- Neo-pA+) flanked by FRT sites (Neo) to facilitate clonal selection of Neo-expressing mouse embryonic stem cells (ESCs) in vitro. The insertion of the plasmid resulted in generation of Rosa26Ubiq-polyA-flTg(Neo)/wt mice which were first bred with FLPe mice followed by backcrossing to wild type so as to exclude the FLPe gene. This resulted in generation of Rosa26Ubiq-polyA-Tg/wt which was then backcrossed to ubiquitous Rosa26 EIIA-Cre mice to cause deletion of the poly-Adenylation sequence. The littermates were backcrossed to wild type so as to exclue the Cre-recombinase and to ultimately generate the heterozygous Rosa26UbiqTg allele on a mixed genetic background (129SV/E X C57BL/6). Black arrows comprising of P1, P2 and P3 denote genotyping PCR primers while arrows comprising of P4 and P5 denote qRT- PCR primers.

Chapter - 3

Figure 3.4: Validation and characterization of the ubiquitously expressing Cep55 “knock-in” transgenic mice model. (A) Southern blot confirming correct genomic targeting of Cep55 flneo mice following EcoRV restriction digest. Samples were probed with a 3’ probe (top) detecting a wild-type band of 11.5 kb and transgenic band of 9.2kb, or a neo probe (bottom) detecting a band of 8.5kb for the transgenic allele (see also Figure 3.3). Neo sample (lane 11) designates an unrelated neomycin transgenic mouse used as a positive control. (B) PCR genotyping showing Cep55 wild type (primer 1; P1 and primer 2; P2 in Cep55wt/wt) and Cep55 transgenic (primer 1; P1, primer 2; P2 and primer 3; P3 in Cep55wt/+ while primer 1; P1 and primer 3; P3 in Cep55Tg/Tg). (C) Average weight of 2 day old pups ± SEM (n=6 per group) of the indicated genotypes. (D) Relative body weight of indicated genotypes including male (left) and female (right) littermates. Error bars represents ± SEM (n=6 per group) over 30 weeks. (E) Graphical representation of transgenic expression of Cep55 transcripts in various major organs of two month old mice (average ± SEM, n=3 mice per genotype, technical duplicates) of the indicated genotype (primer pairs P4 and P5 were used in this qRT-PCR analysis). Values were normalized to Actin and Gapdh. (F) Immunoblot analysis of extracts of the indicated organs as discussed in (E). Gapdh served as loading control. ns = not statistically significant, *p < 0.05, **p < 0.01, ***p<0.001 and ****p<0.0001.

3.2.2 Overexpression of Cep55 leads to male specific infertility Live Cep55 transgenic mice were indistinguishable in terms of appearance and behavior from wildtype siblings. Cep55Tg/Tg mice were overtly normal compared to wildtype and heterozygous counterparts as indicated by no significant difference in relative body weight, irrespective of their gender (Figure 3.4D). However, the Cep55Tg/Tg male mice were sterile at all ages tested, indicating the presence of a reproductive defect in these mice. Interestingly, gross morphological analysis showed that Cep55Tg/Tg testes were much smaller than the testes of age- matched wildtype and Cep55wt/Tg counterparts (Figure 3.5A). In addition, we observed a significant reduction (P<0.0001) in gonado-somatic index (GSI score) (Figure 3.5B) and the circumference of two and six month old Cep55Tg/Tg testes (Figure 3.5C).

Histological analysis revealed severe spermatogenesis defects in the seminiferous tubules in Cep55Tg/Tg testes of two and six old month mice (Figure 3.5D). Within Cep55Tg/Tg testes spermatogenesis was arrested prior to the pachytene stage of meiosis. Many spermatocytes of Cep55Tg/Tg testes contained pyknotic nuclei suggestive of apoptosis (indicated by black arrow in Figure 3.5D). As a result, Cep55Tg/Tg testes completely lacked haploid germ cells including mature sperm. By contrast, wild type mice seminiferous tubules contained all spermatogenic stages (Figure 3.5D). Moreover, Cep55Tg/Tg seminiferous epithelium contained a large number of vacuoles, suggestive of the loss of germ cells (Figure 3.5D). There was no overt difference in the histology of

Chapter - 3

Leydig cells, intertubular immune cells or peritubular cells between genotypes. These data show that Cep55 overexpression leads to a germ cell arrest during meiosis and male sterility as a result of a failure to produce functional sperm.

A closer analysis of the spermatogonial population within Cep55Tg/Tg mice showed that spermatogonial number was qualitatively normal at two months of age, but by six months of age, there was a notable reduction in spermatogonial number and concomitant worsening of the spermatogenic phenotype (Figure 3.5E). These data are suggestive of a stem cell defect. Consistent with the loss of germ cells observed histologically, there was a significant reduction (P<0.0001) in the cross sectional area of seminiferous tubules of Cep55Tg/Tg testes (Figure 3.5F). Notably, there was no difference in the relative weight or histology of other organs at same time point among the three genotypes confirming the phenotype is testis-specific (Figure 3.5G & H).

Chapter - 3

2 Months 6 Months 2 Months 6 Months

Chapter - 3

Figure 3.5: Cep55 overexpression leads to male specific infertility. (A) Gross morphology analysis of adult testes of two month (upper panel) and six months (lower panel) old littermate mice of indicated genotypes. (B) Gonadosomatic index (GSI score) of indicated genotypes among two month (upper panel) and six month (lower panel) old littermate mice ± SEM (n=6 per group). (C) Graphical representation of difference in circumference (mm) per each testes of two months (upper panel) and six months (lowe panel) old mice ± SEM (n>3 per group) of the indicated genotypes performed using Aperio histology software. (D) Histological sections of two month (upper panel) and six month (lower panel) old testes of indicated littermate genotypes stained with hematoxylin and eosin (H&E) to show histology of seminiferous tubules. Cep55Tg/Tg tubules demonstrate vacuole like structure (indicated by V) and pyknotic cells (black arrow) in their lumen with no differentiating spermatocytes but retain the basal layer with intact spermatogonial population. Scale bar = 200 μm. (E) Higher magnification inset of individual tubules from respective genotypes are shown to display the severity of the phenotype observed in Cep55Tg/Tg testes. Scale bar = 50 μm. (F) Graphical representation of cross section area (in mm2) per 100 tubules ± SEM (n=3 per group) of two month (upper panel) and six month (lower panel) old testes of the indicated genotypes performed using Aperio histology software. (G) Graphical representation of the relative weight (with respect to the body weight) of liver (left) and lung (right) of two month and six month old littermates of indicated genotypes ± SEM (n=6 per group). (H) Sections of two month old liver (top) and lung (bottom) of indicated genotypes stained with H&E to show histology of the respective organs. Scale bar = 100 μm. ****p<0.0001; ns = not statistically significant.

3.2.3 Cep55 overexpression leads to Sertoli cell-only (SCO) syndrome The above data suggested that Cep55Tg/Tg testes exhibited a defect in both spermatogonial differentiation and long-term SSC maintenance, resulting in an absence of mature spermatozoa and sterility resembling azoospermia. Azoospermia is the term used to describe the complete absence of sperm in the ejaculate (Seshagiri, 2001). While azoospermia can be associated with a range of histological presentations, one of the most frequent is the complete loss of germ cells, termed SCO syndrome (Silber et al., 1995, McLachlan et al., 2007). To further investigate the SSC phenotype, we performed PAS (Periodic Acid Schiff) staining in six month old littermates and observed that the Cep55Tg/Tg testes demonstrated chronic atrophy (Figure 3.6A). To assess the progression of the phenotype, we first investigated the loss of SSCs cells at this period. We observed a lower percentage of Ki67 positive cells in Cep55Tg/Tg testes compared to wildtype littermates (Figure 3.6B). To assess if Cep55 overexpression ultimately leads to a SCO-like phenotype, we performed co-immunofluorescence for Mvh (mouse vasa homologue protein, a marker of all germ cell types) and Sox9 (Sertoli cell marker) on two and six month old testes of different Cep55 genotypes. Co-staining demonstrated the presence of an inflated spermatogonial population, as observed by higher intensity of Mvh staining in two month old Cep55Tg/Tg testis samples (Figure 3.6C, upper panel). Conversely, many tubules of six month old Cep55Tg/Tg testis samples contained

Chapter - 3

Sertoli cells but had lost all germ cells (Figure 3.6C, lower panel). Moreover, we also observed a reduction in the mitotic activity of the spermatogonial population in Cep55Tg/Tg testes, as marked by phosphorylated Histone H3 (Serine 10), demonstrating age-dependent germline degeneration (Figure 3.6D). Notably, we observed greater degeneration of testes in 16 month old Cep55Tg/Tg testes by PAS staining indicating the absence of germ cells (Figure 3.6E). Taken together, the data illustrate that Cep55 amplification leads to a severe and progressive collapse of spermatogenesis in Cep55Tg/Tg mice causing a SCO phenotype and azoospermia.

3.2.4 Overexpression of Cep55 leads to blockage of SSCs at early differentiation To delineate which aspects of spermatogenesis were disrupted by amplification of Cep55, we investigated the histology of the Cep55Tg/Tg testes, and their wildtype counterparts, across several ages during the establishment of full adult spermatogenesis (Figure 3.7A). It has been well documented that during the embryonic period the PGCs, the predecessors of spermatogonia, are specified and migrate towards the genital ridge, a process that is completed by E11.5. During this period, the PGCs undergo rapid proliferation (Saitou, 2009, Richardson and Lehmann, 2010). Upon reaching the genital ridge, the PGCs mature into gonocytes (GCs) which are surrounded and supported by Sertoli cells (Durcova-Hills and Capel, 2008, Maatouk and Capel, 2008) (shown in (Figure 3.7A).

Analysis of the E14.5 testes demonstrated no perceptible variance in the development of the seminiferous cords between genotypes (Figure 3.7B). As Cep55wt/Tg testes were phenotypically similar to wildtype at all developmental periods analyzed, from here on we have chosen to focus only on the analysis of Cep55Tg/Tg compared to wildtype littermates. Immuno-fluorescence using Mvh, a marker of GCs and stem cell populations and Sox9, a Sertoli cell marker, revealed no substantial difference with respect to migration and localization of PGCs in embryonic testes and normal development of Sertoli cell at this time point indicating regular development of seminiferous cords irrespective of Cep55 amplification (Figure 3.7C).

GCs continue to proliferate until E16.5 after which they become quiescent (Kluin and de Rooij, 1981, Moreno et al., 2010) but resume proliferation post birth and generate SSCs and other undifferentiated spermatogonia plus differentiating cells that participate in the first wave of

Chapter - 3 spermatogenesis (Nagano et al., 2000, Vergouwen et al., 1991) (Figure 3.7A). Consistent with E14.5, we observed no significant difference in morphology between Cep55Tg/Tg and wildtype testes at P7 (Figure 3.7D; upper panel) suggesting no anomaly associated with Cep55 amplification at these periods of development. As the first wave of differentiation is triggered by P7-P10 (Figure 3.7A), we next investigated the histology of P10 testes. PAS staining at this period of testicular development revealed fewer meiotic cells in Cep55Tg/Tg testes (Figure 3.7E). This histological divergence was very pronounced in P21 (Figure 3.7D; lower panel) and adult males (two month old) (Figure 3.7F) respectively wherein dramatically fewer meiotic cells were observed with lack of haploid germ cells. Consistent with the H&E analysis (Figure 3.5D), the majority of the germ cells appeared arrested prior to initiation of meiosis and severely lacked the trigger of differentiation in contrast to those of wildtype testes. Furthermore, we found that the GSI score from each of the above developmental time points indicated a chronic defect in testicular development post initiation of differentiation in Cep55Tg/Tg testes (Figure 3.7G). Collectively, this phenotype illustrates an initial block in germ cell maturation during early meiosis followed by a progressive defect in SSC function.

3.2.5 Cep55 overexpression favors proliferation and mitotic activity of the SSC population In order to explore whether an abnormality in SSCs contributes to the phenotype we examined expression of the transcription factor Plzf (promyelocytic leukaemia zinc finger), a well- known marker of undifferentiated spermatogonia, during embryogenesis and post-natal life between the genotypes (Costoya et al., 2004). In wild type mice, Plzf expression has been shown to peak during the first week of postnatal life during which time the GCs exit quiescence, migrate to the basement membrane, differentiate into spermatogonia and enter a proliferation phase (Buaas et al., 2004). Plzf negatively regulates expression of Kit, encoding a receptor tyrosine kinase essential for spermatogonial differentiation (Costoya et al., 2004, Filipponi et al., 2007).

As P10 Cep55Tg/Tg testes demonstrated fewer meiotic cells, we chose this period of juvenile testicular development for further molecular analysis. Consistent with our hypothesis, immunohistochemistry for Plzf illustrated a significantly higher proportion of positive cells in Cep55Tg/Tg mice (P<0.0001) (Figure 3.8A), while c-Kit staining was reduced compared to control mice (Figure 3.8B). Collectively, these data suggest that in the presence Cep55 amplification, there is an aberrant expansion of the undifferentiated SSC pool with a partial block in the commitment of spermatogonia to the spermatogenic program.

Chapter - 3

Figure 3.6: Cep55 overexpression leads to Sertoli-cell like syndrome. (A) PAS stained histological sections of six months old Cep55wt/wt (upper panel)) and Cep55Tg/Tg (lower panel) mice (‘V’ indicates the presence of a vacuole like structure in the lumen of the seminiferous tubule). (B) IHC with Ki67 antibody (left) on testes sections of indicated six month old genotypes; graphical representation (right) of difference in percentage of positive nuclei count per 100 tubules ± SD (n=2 per group). (C) Immunofluorescence representing co-expression of Sox9 (green) and Mvh (red) of the indicated genotypes of two month old (Top) and six months old (Bottom) littermates (a and b represents the presence of Sertoli cells and vacuoles in seminiferous tubules, respectively). (D) Immunofluorescence representing co-expression of phosphorylated histone H3 (Serine 10) (green) and Mvh (red) of the indicated genotypes of two month old (upper panel) and six months old (lower panel) littermates. (E) PAS stained histological sections of sixteen month old Cep55wt/wt (Top) and Cep55Tg/Tg (Bottom) mice. Scale bar = 100 μm. **** p<0.0001.

Chapter - 3

Figure 3.7: Overexpression of Cep55 leads to blockage of SSCs at early differentiation. (A) Schematic representation of the time-line of testis development with respect to maturation of SSCs, their self-renewal and different stages of differentiation observed regularly in wildtype testes. (B) Histological sections of E14.5 testes of indicated genotypes stained with H&E. The black dotted lines represent regular seminiferous cords formed at this stage of testes development. Scale bar = 200 μm. (C) Immunofluorescence representing co-expression of Sox9 (green) and Mvh (red) of the indicated phenotypes illustrating presence of PGCs (Mvh+) surrounded by Sertoli cells (Sox9+) suggesting regular development of seminiferous cords. Scale bar = 200 μm. (D) PAS stained histological sections of 7 day old (upper panel) and 21 day old (lower panel) Cep55wt/wt (left) and Cep55Tg/Tg (right) mice. Scale bar = 50 μm. (E) Histological sections of 10 day old Cep55wt/wt (Top) and Cep55Tg/Tg (Bottom) mice stained with PAS. (F) Histological sections of two month old Cep55wt/wt (Top) and Cep55Tg/Tg (Bottom) mice stained with periodic acid-Schiff stain (PAS) demonstrating the presence of spermatozoa and mature lumen in Cep55wt/wt seminiferous tubule the while tubule of Cep55Tg/Tg demonstrates presence of only spermatogonial population at the basal layer, lacking differentiated spermatozoa. Scale bar = 50 μm. (G)Variation in GSI score of littermate mice of indicated genotypes at indicated developmental stages ± SEM (n=6 per group); ns = not statistically significant. ***p<0.001 and ****p<0.0001.

Chapter - 3

As rapid proliferation of SSCs and A type spermatogonia has been linked with testicular degeneration (Handel et al., 1999), we wanted to investigate the level of proliferation and mitotic activity of the Cep55Tg/Tg testes at adulthood (two months old mice). Ki67 and phosho-histone H3(S10) staining revealed the presence of significantly higher nuclear (P<0.0001) staining of SSCs in Cep55Tg/Tg testes (Figure 3.8C & D). Furthermore, we found co-localization of Ki67 with Plzf in P10 Cep55Tg/Tg testes indicating an active proliferation of undifferentiated spermatogonia at this stage of development compared to wildtype littermates (Figure 3.8E). In addition, immunostaining with Ki67 antibody showed a significantly higher percentage (P<0.0001) of proliferating germ cells in two month old Cep55Tg/Tg testes (Figure 3.8F). Further, we found a significantly higher number (P<0.0001) of mitotic germ cells stained by phosphorylated Histone H3 (Serine 10) in the seminiferous tubules of Cep55Tg/Tg testes compared to wildtype littermates (Figure 3.8G). Taken together, these data indicate Cep55 overexpression favors proliferation and mitotic activity of SSCs over differentiation, illustrating disruption of the normal dynamics of spermatogenesis.

3.2.6 Cep55 overexpression modulates Foxo1 inactivation via hyper-activation of PI3K-Akt-Pdk1 axis Recent studies in vivo have highlighted the importance of Foxo transcription factors in spermatogenesis (Goertz et al., 2011). Foxo1 is required for both SSC self-renewal and differentiation as it transcriptionally regulates a network of downstream targets such as Kit and Ret (Goertz et al., 2011). Foxo1 activity is regulated by Akt-dependent phosphorylation which is associated with translocation of the transcription factor from the nucleus to the cytoplasm resulting in its functional inactivation and degradation (Brunet et al., 1999). To this end, Goertz et al. showed the importance of PI3K signaling in regulating SSCs differentiation through the coordination of Foxo1 stability and subcellular localization upon conditional knockout of Pdk1 and Pten mice (Goertz et al., 2011). As CEP55 is known to stabilize the catalytic subunit, p110 of PI3KCA which in turn leads to hyper-phosphorylation of AKT (Chen et al., 2007), we hypothesized that Cep55 overexpression may reduce Foxo1 stability by disabling its’ nuclear retention and subsequently perturbing spermatogonial differentiation.

In order to prove this hypothesis, we investigated the timing of Foxo1 expression, and its down-stream targets, in the presence of Cep55 overexpression. Immunostaining demonstrated the presence of significantly fewer Foxo1 positive spermatogonial cells (P<0.0001) in Cep55Tg/Tg testes

Chapter - 3 compared to wildtype (P<0.0001) (Figure 3.9A). In contrast, immunofluorescence data demonstrated that there were significantly more (p<0.001) Plzf positive spermatogonia per tubule (Figure 3.9B & C), thus indicating the presence of a higher proportion of undifferentiated spermatogonia at P10 in Cep55 overexpressing testes. Also consistent with the hypothesis, we found a significant increase in the proportion of Plzf positive spermatogonia displaying cytoplasmic, and thus functionally inactive, Foxo1 (p<0.01) in Cep55Tg/Tg testes (Figure 3.9D) consistent with IHC data (Figure 3.9A). This striking change in Foxo1 localization was accompanied by increased phosphorylation of components of the PI3K-Akt signaling pathway in transgenic testis protein lysate (Figure 3.9E). Collectively these data show that Foxo1 activity is inhibited in spermatogonia of Cep55 overexpressing testis through aberrant activation of the PI3K/Akt axis.

Next we chose to investigate the cellular fate of c-Kit in Cep55Tg/Tg testes. Consistent with our immunohistochemistry (Figure 3.9A) and western blot (Figure 3.9E) data, we observed the loss of nuclear Foxo1, in Cep55Tg/Tg testes was associated with a significantly lower (P<0.01) number of c-Kit positive spermatogonia, compared to wildtype (Figure 3.9F & G). This data indicated a block in the transition to spermatogonial differentiation and commitment to the spermatogenesis program at P10. Consistent with our previous results, Foxo1 was predominantly cytosolic in Cep55Tg/Tg undifferentiated spermatogonia while wildtype-undifferentiated spermatogonial cells primarily demonstrated nuclear Foxo1 (Figure 3.9F & G). Collectively, our data suggested that Cep55 amplification results in hyper-activity of the PI3K/Akt pathway and concomitant inactivation of Foxo1, resulting in impaired spermatogonial differentiation.

Chapter - 3

Figure 3.8: Cep55 overexpression favors proliferation and mitotic activity of SSC population. (A) Plzf immunostaining showing the presence of a higher number of undifferentiated spermatogonia in Cep55Tg/Tg testes (left panel); graphical representation of difference in percentage of positive nuclei count per 100 tubules ± SEM (n=3 per group) (right panel). (B) c-Kit immunostaining showing lower cytoplasmic c-Kit expression in P10 Cep55Tg/Tg testes. (C) Ki67 immunostaining representing presence of a higher number of proliferating spermatogonia in Cep55Tg/Tg testes (left panel); graphical representation of difference in percentage of positive nuclei count per 100 tubules ± SEM (n=3 per group) (right panel). (D) Phosphorylated histone H3 (Serine 10) immunostaining showing presence of higher number of mitotically active spermatogonia in Cep55Tg/Tg testes (left panel); graphical representation of difference in percentage of positive nuclei count per 100 tubules ± SEM (n=3 per group) (right panel). (E) Immunofluorescence representing co-expression of Ki67 (green) and Plzf (red) of the indicated phenotypes illustrating presence of higher number of proliferating spermatogonial cells in P10 Cep55Tg/Tg testes. Inset represents presence of co-localization of Ki67 and Plzf in undifferentiated spermatogonia in Cep55Tg/Tg testes. (F) Immunohistochemistry (IHC) with Ki67 antibody (left panel) on testes sections of indicated two month old genotypes; graphical representation (right panel) of difference in percentage of positive nuclei count per 100 tubules ± SD (n=2 per group). (G) IHC with Histone H3 (Serine 10) antibody (left panel) of same sections as (C) with graphical representation (right panel) of difference in percentage of positive nuclei count per 100 tubules ± SD (n=2 per group). Scale bar = 50 μm. **p < 0.01 and ****p<0.0001.

3.2.7 Cep55 overexpression results in a failure to trigger spermatogonial differentiation As discussed previously, the connection between CEP55 in the male germline has been limited to the formation and maintenance of intercellular bridges (Greenbaum et al., 2006). As cyst formation at initial stages of spermatogonial development is critical for spermatogonial differentiation, we wanted to determine the potential impact of Cep55 over-expression on spermatogonial cyst formation. We performed immunofluorescence of whole mount seminiferous tubules from two-month-old mice of each genotype. Molecular investigation demonstrated the presence of a “ring-like” structure stained by Cep55 antibody connecting Plzf positive spermatogonia in each genotype, indicating that intercellular bridge formation was not disrupted in Cep55Tg/Tg testis (Figure 3.10A). As anticipated, the intensity of Cep55 staining in Cep55Tg/Tg testes was substantially higher compared to wildtype littermates (Figure 3.10B). Further, we observed no difference in Tex14 and Tsg101 expression at the mRNA level (Figure 3.10C). Together, these data indicated that Cep55 over-expression does not impact spermatogonial cyst formation.

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Goertz et al. have previously demonstrated the role of Foxo1 in transcriptionally dictating the switch between SSC self-renewal and differentiation (Goertz et al., 2011). To further assess the potency of this block we assessed the in vivo expression of the reported Foxo1 transcriptional targets associated with spermatogonial self-renewal and differentiation including Ret (Goertz et al., 2011), Sall4 (Zhang et al., 2006), Lhx1 (Tanaka et al., 2010) and differentiation markers such as Egr4 (Tourtellotte et al., 1999) and other genes regulating spermatogonial function (Goertz et al., 2011). To determine the impact of Cep55 amplification upon these modulators of spermatogenesis, we performed qRT-PCR on P10 testes tissue mRNA. Interestingly, we observed that Cep55Tg/Tg testes demonstrated significant upregulation (P<0.01) of the SSC maintenance gene Ret (Figure 3.11A), wherein Ret co-receptor for the key SSC maintenance factor Glial cell-line- derived neurotrophic factor (Gdnf) (Naughton et al., 2006, Jain et al., 2004). These data further confirm the shift in the balance of spermatogonia development toward the maintenance of undifferentiated SSCs at P10. As Foxo1 is required for maintenance of Ret expression in spermatogonia and Foxo1 function is suppressed in Cep55Tg/Tg testis, the observed upregulation of Ret is Foxo1-independent. In addition, we observed significant upregulation (P<0.001) of Sall4 (Figure 3.11A) in Cep55Tg/Tg testes. The pluripotency factor Sall4 (Sal like protein 4) plays essential autonomous roles in male germline integrity and is abundantly expressed in Plzf positive spermatogonia (Hobbs et al., 2012). Upregulation of Sall4 is consistent with an increased relative abundance of spermatogonia in Cep55Tg/Tg testes (Figure 3.11A). Furthermore, we observed significant upregulation of Ccnd1 (Beumer et al., 2000) (P<0.01) and Pcna (P<0.01) in Cep55Tg/Tg testes indicating more active proliferation of spermatogonia compared to wildtype at this period of development (Figure 3.11A).

Additionally, we observed a significant down-regulation of Erg4 (P<0.01) which is a marker of meiotic germ cells (Figure 3.11A). Tourtellotte et al. demonstrated that Erg4 is critical for germ cell maturation and regulates genes associated with early stages of meiosis (Tourtellotte et al., 1999). Thus, down-regulation of Erg4 in Cep55Tg/Tg testes confirms that increased Cep55 expression inhibits germ cell differentiation and maturation. We also observed significant downregulation (P<0.001) of Sohlh1 (spermatogenesis and oogenesis specific basic helix-loop-helix transcription factor) in Cep55Tg/Tg testes (Figure 3.11A). Sohlh1 is essential for spermatogonial differentiation and selectively expressed in committed progenitors and differentiating spermatogonia in testis (Ballow et al., 2006). Therefore, downregulation of Sohlh1 is consistent with a disruption in spermatogonial differentiation (Figure 3.11A). As reported by Ballow et al., for the Sohlh1-/- model and in agreement

Chapter - 3 with reduced Sohlh1 expression, Lhx8 was also significantly down-regulated (P<0.001) in Cep55Tg/Tg testes (Ballow et al., 2006) (Figure 3.11A).

In agreement with expansion of self-renewing spermatogonia and disrupted differentiation, we observed significant upregulation of genes associated with SSC function including Gfra1, Ret, Plzf, and Sall4 in two month old Cep55Tg/Tg testis (Figure 3.11B). Additionally, we also observed significant upregulation of Ccnd1 and Pcna at this time point, indicating continued active proliferation of spermatogonia at this period of development (Figure 3.11B). Unexpectedly, we also observed upregulation of c-Kit at this age (Figure 3.11B) potentially due to a block of germ cell differentiation at early meiotic stages (Greenbaum et al., 2006). Importantly, we also observed continued upregulation of the PI3K-Akt signaling pathway in one and two-month-old Cep55Tg/Tg testes by immunoblotting, consistent with analysis of P10 samples (Figure 3.11C). Significant reductions of Erg4, Sohlh1 and Lhx8 confirmed a block in germ cell differentiation at this age (Figure 3.11D). Taken together, Cep55 overexpression continues to favor SSC proliferation and maintenance of undifferentiated A type spermatogonia and is associated with a failure of spermatogonial differentiation at adulthood (Figure 3.11E).

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Figure 3.9: Cep55 overexpression impacts on sub-cellular localization of Foxo1 via hyper- activation of PI3k-Akt-Pdk1 axis. (A) Foxo1 immunostaining showing presence of a higher number of undifferentiated spermatogonia in Cep55Tg/Tg testes (left panel); graphical representation of difference in percentage of positive nuclei count per 100 tubules ± SEM (n=3 per group) (right panel). (B) Immunofluorescence representing co-expression of Foxo1 (green), Plzf (red) and DAPI (blue) of the indicated genotypes of 10 day old littermates. Scale bar = 50 μm. (C) Graphical representation of relative Plzf positive cells with respect to cytoplasmic Foxo1 of indicated genotypes ± SEM (n=3 per group). (D) Graphical representation of relative nuclear Foxo1 expression of indicated genotypes ± SEM (n=3 per group). (E) Immunoblot analysis of 10 day old littermates of indicated genotypes showing overexpression of Cep55 leads to hyperactivity of PI3k- Akt pathway. β-Actin was used as loading control. (F) Immunofluorescence representing co- expression of Foxo1 (green), c-Kit (red) and DAPI (blue) of the indicated genotypes of 10 day old littermates. Scale bar = 50 μm. (G) Graphical representation of relative c-Kit negative cells with respect to phosphorylated Foxo1 of indicated genotypes. *p < 0.05, **p < 0.01, ***p<0.001 and ****p<0.0001. 86

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Figure 3.10: Cep55 overexpression favors spermatogonial cyst formation. (A) Representative whole-mount images of two months old seminiferous tubules of indicated genotypes stained for Cep55 (Red), Plzf (Green) and Gilz (Blue). Higher magnification inset shows representative higher expression of Plzf in Cep55Tg/Tg testes compared to Cep55Wt/Wt. Dotted lines in whole-mounts indicate the seminiferous tubule profile. Scale bar = 50 μm. (B) Graphical representation CEP55 expression level from (A) represented as mean intensity values obtained from analysis using Fiji ImageJ. Mean values ± SD (n=3) is shown and 4 independent fields were analyzed for 50 cells for each indicated proteins. (C) Relative mRNA expression level of indicated genes in wild-type versus Cep55Tg/Tg testes of two months old littermates ± SEM (n=3 per group). The normalizing genes used were Actin and Gapdh. ns = not statistically significant

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Figure 3.11: Cep55 overexpression results in a failure to trigger spermatogonial differentiation. (A) Relative mRNA expression level of indicated genes in wild-type versus Cep55Tg/Tg testes of 10 day old littermates ± SEM (n=3 per group). The normalizing genes used were Actin and Gapdh. (B) Relative mRNA expression level of indicated genes in two month old testes (wild-type versus Cep55Tg/Tg littermates) representing markers of undifferentiated and actively proliferating SSCs (mean ± SEM, n=3 per group). (C) Immunoblot analysis of one month and two month old littermates of indicated genotypes showing overexpression of Cep55 leads to hyperactivity of PI3k-Akt pathway. β-Actin was used as loading control. (D) Relative mRNA expression level of indicated genes in two month old testes (wild-type versus Cep55Tg/Tg littermates) representing markers of differentiating spermatogonia (mean ± SEM, n=3 per group). The normalizing genes used in B and C were Actin and Gapdh. (E) Schematic representation of the deregulation of regular spermatogenesis in Cep55Tg/Tg mice demonstrating initial maintenance of SSCs with active proliferation rate, their self-renewal and progressive degeneration. *p < 0.05, **p < 0.01, ***p<0.001 and ****p<0.0001. 88

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3.3 Discussion Infertility is one the fastest growing reproductive health problems of the modern era and approximately 13-18% of couples suffer from this disease (Thoma et al., 2013). With advancement in the field, the number of patients seeking infertility treatment is increasing. Male infertility accounts for approximately 50% of infertility cases and is most commonly associated with reduced sperm function, morphology and number (Hart et al., 2015, Flannigan and Schlegel, 2017). For the majority of these men, no precise molecular diagnosis can be made. Clearly, the maintenance of an appropriately functioning SSC pool for mature sperm production is critical to long term male fertility and its failure leads to azoospermia. Of the men presenting with azoospermia only 15-20% can be given a precise genetic diagnosis (de Kretser, 1997, Ferlin et al., 2007). Such underlying genetic causes include chromosomal abnormalities (Gonzalez-Merino et al., 2007), microdeletion of the Y chromosome (Bhasin et al., 1997), X-linked and autosomal gene mutations (Schultz et al., 2003). Moreover, with development of molecular genetics, the importance of autosomal genes such as ZBTB16, C-KIT, SALL4, STRA8, LHX1 has gained importance in testicular development as their gain or loss severely impacts the homeostasis of spermatogenesis (He et al., 2009, Jan et al., 2012).

In this study, we have demonstrated that balanced expression of Cep55 is critical for the maintenance of spermatogenesis. Our data illustrated that the Cep55Tg/Tg males were infertile and suffered from chronic atrophy restricted to testes. Therefore, for the first time we have provided in vivo evidence to support the role of Cep55 in regulating the function of germ cells and spermatogenesis in healthy male mice. At the molecular level, we have also characterized the association of Cep55 with Foxo1 through the PI3K-Akt pathway in the mammalian system and have demonstrated that upregulation of the Cep55-dependent PI3K-Akt pathway can cause male-specific sterility. Moreover, our data provided the first developmental evidence that Cep55 overexpression causes impairment of testicular expansion post birth arising at P10, the stage where spermatogonia differentiation is triggered. However, embryonic processes, such as PGCs specification, proliferation and migration to germinal ridge and maturation into gonocytes were unaffected, suggesting a less important functional role for Cep55 during early testis development in mice.

Molecular characterization of Cep55Tg/Tg testes at P10 revealed the presence of a significantly higher number of undifferentiated and mitotically active spermatogonial cells than in wildtype testes, implying deregulation in the regular spermatogonial homeostasis. Goertz et al. have demonstrated a critical role for Foxo1 in spermatogonial self-renewal and differentiation and that the PI3K-Akt pathway is a key negative regulator of this transcription in the male germline in vivo

Chapter - 3

(Goertz et al., 2011). Conversely, Lee et al. have previously described an important role of Akt in SSC self-renewal in vitro indicating that a balance in Akt activity is required for maintaining SSC function (Lee et al., 2007b). Although CEP55 has been reported to bind to the catalytic subunit of PI3KCA in vitro enabling increased phosphorylation of AKT at S473 (Chen et al., 2007), our study provides the first molecular data that Cep55 interacts with PI3K in the mammalian system, to regulate Foxo1 stability and subcellular localization via the Akt and Pdk1 axis which in turn hinders spermatogonial differentiation. Therefore, this notion elaborates the logical explanation behind the presence of a higher number of c-Kit negative spermatogonia alongside down-regulation of other differentiation markers such as Erg4, Sohlh1 and Lhx8. As a consequence of this, we observed upregulation of Ret, Sall4 and Gfra1 indicating maintenance of undifferentiated spermatogonia with active proliferation and SSC self-renewal both in juvenile and adult mice.

Foxo transcription factors have been known to regulate tissue homeostasis in addition to adult stem cell function in mammals throughout life (Kenyon, 2010). Goertz et al. demonstrated the biological importance of Foxo1 in maintenance of undifferentiated SSCs as Foxo1- /- demonstrated severe defects in conserving the SSC pool (Goertz et al., 2011). They provided experimental evidence to demonstrate the importance of subcellular localization of Foxo1 from the cytoplasm to nucleus at P3 for maturation of GCs to SSCs (Goertz et al., 2011). Interestingly, our phenotype did not resemble the conditionally inactivated Foxo1-/- mice which demonstrated severe reduction SSCs at P7. An explanation could be that the presence of a minimum level of Foxo1 retention in the nucleus across various stages of development drives sufficient c-Kit expression for differentiation. Furthermore, the transcript data from two month old mice demonstrated higher expression of Foxo1 in Cep55Tg/Tg testes indicating the presence of undifferentiated spermatogonia. In accordance with this, our phenotype partly resembled the conditionally inactivated Pten mouse model (Goertz et al., 2011) wherein Pten loss led to hyper-activation of Akt which led to cytoplasmic localization of Foxo1. Contrastingly, Pten-/- mice demonstrated severe germ cell loss by 4 weeks and deterioration of the testes structure by 14 weeks compared to our phenotype which demonstrated a progressive degeneration post 24 weeks. Justification of this could be the level of hyper-activation of PI3K-Akt pathway in Pten-/- testes might be much greater compared to level observed in Cep55Tg/Tg testes with intact Pten.

CEP55 is a cancer testis antigen and has been linked to various human solid tumors (Schippling et al., 2016). Additionally, we have previousy demonstrated the importance of cep55 in embryonic development by introduction of a non-sense mutation in zebra fish wherein loss

Chapter - 3 of cep55 post-fertilization lead to immense apoptosis besides cell cycle defect. Very recently, two independent studies demonstrated the association of CEP55 with two human autosomal recessive genetic disorders. Frosk et al. identified the presence of a homozygous non sense mutation at the S425 phosphorylation site of CEP55 among MARCH patients (Frosk et al., 2017). Using a zebra fish model, they demonstrated loss of function of cep55 mimicked the similar features of MARCH observed in human patients, therefore establishing a strong connection of the protein with a novel multiple congenital anomaly syndrome. Bondeson et al. showed that loss of Cep55 is associated with a ciliopathic lethal autosomal recessive syndrome called Meckel-Gruber syndrome (MKS) characteristics of which involve renal cystic dysplasia, occipital encephalocele and polydactyly (Bondeson et al., 2017).

In line with this, our data provides significant evidence for annotating deregulated Cep55 expression with the growing list of modulators with crucial roles in dictating SSC fate and the maintenance of spermatogenesis. However, further molecular characterization should be performed to demonstrate the interplay of Cep55-PI3K-Akt axis within the network of transcriptional factors that regulate spermatogenesis. Our study provides preliminary evidence that the consequence of Cep55 overexpression is to generate symptoms such as progressive atrophy and degeneration of testes that resembles azoospermia. As Cep55 amplification results in SOC-like syndrome, our data indicates that when Cep55 expression reaches above a critical threshold, it can disrupt germline integrity. These data raise the possibility that CEP55 overexpression might contribute to human male infertility. It will therefore be of great interest in future to assess the copy number variations of CEP55 in azoospermic men (Flannigan and Schlegel, 2017).

Chapter - 3

3.4 Conclusion

In conclusion, this study broadens our understanding of the functional role of Cep55 in the maintenance of regular spermatogonial homeostasis. It illustrates for the first time that Cep55 overexpression leads to male-specific sterility. Further studies using a Cep55 knock-out model would provide a better landscape of Cep55 biology in regulating the process of spermatogenesis and male sterility. The present study highlights that critical governance of Cep55 expression is needed for SSC self-renewal and differentiation as overexpression results in triggering phosphorylation of Foxo1 and its cytoplasmic sequestration which hinders the differentiation process of SSCs. This loss of differentiation of mitotic SSCs leads to a progressive severe atrophy, which mimics the clinical symptoms of azoospermia. The study also focuses on the association of Cep55 with well-known list of transcription factors such as Foxo1 and Plzf, which trigger the switch between SSC self-renewal and differentiation. However, further dissection of the functional interplay between Cep55 and these factors alongside their directive of interaction is warranted.

Chapter - 4

Chapter – 4 Aim1.2: To study the prospective of CEP55 as a potent oncogene in vivo and characterizing the molecular mechanism via which CEP55 over-expression induces spontaneous tumor formation

Chapter - 4

4.1 Introduction Genomic instability (GIN) is a well-defined hall mark of cancer which facilitates procurement of evolutionary somatic mutations causing tumor plasticity and heterogeneity (Baas et al., 2006, Dowsett et al., 2010). It is a condition characterized by accumulation of genetic alteration caused due to improper chromosome segregation, failure of check point control and perturbed mitosis, defect in telomere maintenance or failure to repair damaged DNA and genome doubling (Metzger-Filho et al., 2013, Sansregret and Swanton, 2017). Chromosomal instability (CIN), is a type of GIN that represents the rate of increased chromosomal missegregation during mitosis. Both GIN and CIN are well documented to provide phenotypic variation to accommodate clonal expansion of cancer cells, resistance to chemotherapy and tumor relapse (Tabchy et al., 2011, Zahreddine and Borden, 2013).

One of the main consequence of CIN is aneuploidy which is defined to be the condition related to gain or loss of whole chromosome leading to genomic imbalance (Zahreddine and Borden, 2013). The concept of aneuploidy, a phenomenon of abnormal chromosome constitution and cancer, has been well recognised for over a century now. Features of aneuploidy include abnormalities in chromosome numbers, which arise from persistent chromosome segregation errors during mitosis and contributes to oncogenic clonal evolution (Horiuchi et al., 2016). A number of cellular mechanisms leading to aneuploidy have now been uncovered and it is evident that aneuploidy can result from the perturbation of a variety of pathways that normally ensure faithful segregation of chromosomes during mitosis (Zahreddine and Borden, 2013). Exploring how mitotic genes facilitate these processes in tumor evolution has the potential to uncover a major mechanism of tumor cell heterogeneity, thus offering potential aneuploidy-specific targets in the cancer therapeutics armamentarium (Perez de Castro et al., 2007). From a clinical perspective, aneuploid cancers are well annotated with poor clinical outcomes, associated with metastasis and therapeutic resistance.

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4.1.1 Aberration of Mitotic Checkpoint GIN has been reported to initiate cancer, boost progression, drug resistance, and metastasis to distant sites while impacting the overall survival of patients. As discussed previously, the SAC plays a critical role in sensing correct attachment of kinetochore to the mitotic spindles during mitosis (Figure 4.1A). Improper attachment of chromatids results in activation of SAC and halts cell cycle progression at M phase (discussed in detail previously). In vivo studies using mice models of the SAC associated genes have demonstrated high level of CIN or specifically aneuploidy (Perez de Castro et al., 2007, Simon et al., 2015). These studies have demonstrated the proportion of increased aneuploidy being linked to spontaneous tumor formation (either conditional mouse model or crossed to sensitized model) (Figure 4.1B). Notably, genetic studies on human tumors have highlighted that the SAC-associated genes are prone to often undergo amplification instead of mutation (Sansregret and Swanton, 2017) (also shown in Figure 4.1C). A prime example is the Mad2 overexpression which resulted in a highly aneuploid tumor phenotype by hyper-activation of the SAC (Sotillo et al., 2007, Kato et al., 2011). Mad2 overexpression leads to tetraploidization and hyperstabilization of the k-MT (kinetochore-MicroTubule) attachments which drives chromosomal instability leading to spontaneous tumor formation. A separate study using conditional Bub1 overexpressing mice demonstrated that Bub1 overexpression causes chromosomal misalignment and lagging chromosomes leading into aneuploidy and causing spontaneous tumor formation over time (Ricke et al., 2011). They also demonstrated that Bub1 overexpression results in overexpression of Aurora B which impaired regular chromosomal segregation, suppression of which resulted in normal mitosis.

Correct k-MT attachment is critical for accurate chromosome segregation where kinetochores of sister chromosomes should attach to opposite poles (Figure 4.1A). AURKB plays an important role in establishing correct (amphotelic) attachment at each pole during prometaphase- metaphase. It also destabilizes syntelic (both sister kinetochores attach to same pole) and merotelic (one kinetochore attached to both poles) attachment (Sansregret and Swanton, 2017, Gonzalez- Loyola et al., 2015). Merotelic attachment has been shown to be a major driver of CIN as it results in lagging chromosomes at anaphase. Thus, aberration of the mitotic checkpoint is a major driver in CIN and plays a key role in tumor progression and can also be targeted for treatment of genomically unstable solid tumors.

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B C

Figure 4.1 Mechanism of genomic instability. (A) Schematic representation of events occurring at mitosis. The schematic also illustrates the various types of k-MT attachment which involves activation of SAC. (B) Schematic showing compromised activity of SAC leading to perturbed mitosis and numerous attachment defects. (C) Schematic showing improper k-MT attachment leading to lagging chromosomes at anaphase onset. (D) Schematic of centrosome amplification leading to multipolar spindle formation causing tetraploidy. (E) Representation of presence of extra chromosomes leading to multipolar kinetochore attachement causing daughter cells lacking multiple chromosomes randomly (adopted from (Sansregret and Swanton, 2017)).

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4.1.2 Centrosome amplification Centrosomal amplification is one of the wide spread features of human cancer and is one of the key factors in promoting CIN (Nigg and Stearns, 2011). In general, a centrosome gets duplicated into two daughter centrioles during S-phase, localizes to each pole during M phase, harbours spindle assembly and plays a critical role in chromosome segregation (Nigg and Stearns, 2011). Recent studies, have identified a number of defects such as over duplication of centrosomes and aborted mitosis leading to genome duplication (tetraploidy) which results in multiple centrosomes (Kwon et al., 2008). Extra centrosomes attach randomly to chromosomes at early stages of mitosis and causes formation of multiple spindles and cluster at two poles (Figure 4.1 D & E). Michelle et al. have demonstrated the role of Plk4 in regulating centrosome duplication as they observed that Plk4 over-expression promoted centrosome duplication, which resulted in spontaneous tumor initiation in vivo. The irregular clustering of centrosomes results in merotelic attachment of chromosomes and generation of lagging chromosomes. Tetraploid cells are the result of cytokinesis failure and they harbour supernumerary centrosomes which lead to CIN (Ganem et al., 2007). The failure to cluster the extra chromosomes gives way to multipolar division causing generation of abnormal karyotypic changes of the genome (Nigg and Stearns, 2011).

4.1.3 Role of CEP55 in cancer and CIN CEP55 is a centrosomal protein and plays a critical role in cytokinesis (Fabbro et al., 2005). Over the last decade, studies have identified the association of CEP55 with multiple cancers (Jeffery et al., 2015b). Experimental studies in HCC and lung adenocarcinomas have demonstrated the interaction of CEP55 with the catalytic subunit of PI3KCA causing increased AKT activation to promote invasion and cell migration (Chen et al., 2007, Chen et al., 2009b). In addition, they also illustrated that VEGF-A facilitates CEP55/PIK3CA interaction in a dose dependent manner in vitro (Chen et al., 2009b). In gastric carcinoma, CEP55 overexpression leads to inhibition of p21 WAF1/Cip1 expression and dysregulated cell cycle via upregulation of the PI3K/AKT pathway (Tao et al., 2014). Notably, in head and neck squamous cell carcinoma FOXM1 overexpression correlates with CEP55 amplification leading to lymph node metastasis (Waseem et al., 2010). Recently, Chen et al. demonstrated in oral cavity squamous cell carcinoma (OCSCC) lines that CEP55 control FOXM1 in a dose dependent manner and induces an increased transcription of MMP-2 enabling cell migration and invasion (Chen et al., 2009a). In addition, Chang et al. demonstrated that wild type Trp53 suppresses CEP55 expression by negatively regulating PLK1 creating a p53-PLK1-CEP55

Chapter - 4 axis; hence directly modulating CEP55 stability and cytokinesis completion (Chang et al., 2012). Further, in a recent study, Peng et al. reported the involvement of NF-ĸB signaling in the regulation of CEP55 in pancreatic cancer (PANC) (Peng et al., 2017). They demonstrated that CEP55 overexpression is associated with disease progression of PANC patients while in vitro higher CEP55 level favored proliferation, migration, and invasion of PANC cells (discussed in detail in Chapter 1; Section 1.10 and 1.11) Though these in vitro studies have established the link between CEP55 with cancer, the mechanism by which CEP55 promotes tumorigenesis in vivo remains elusive.

In this chapter, we have demonstrated that overexpression of Cep55 in vivo leads to spontaneous tumor initiations and instigate a wide spectrum of tumor at late latency, mimicking the tumor spectrum observed in Trp53-/- mice. Further, we also genetically validated the involvement of Trp53 loss to by creating a bi-transgenic mouse model (Cep55Tg/Tg ; Trp53-/-) which demonstrated similar latency as that observed in Cep55Tg/Tg mice. The tumors observed in Cep55Tg/Tg demonstrated a high incidence rate in comparison to Cep55wt/Tg and Cep55wtwt mice with the tumors being significantly larger and more penetrant, proliferative and metastatic in nature. In addition, we demonstrated that Cep55 amplification leads to hyper proliferation alongside favoring genomic instability. We also elaborated the association of Cep55 with polyploidy and demonstrated that Cep55 overexpression protects polyploid subpopulation upon perturbed mitosis. Additionally, we provided preliminary mechanistic evidence of Cep55 overexpression leading to upregulation of Foxm1 and Plk1 axis which in turn might initiate tumorigenesis. Collectively, our data proves the oncogenic potential of Cep55 overexpression in vivo and demonstrated that Cep55 overexpression can be an important stimulus in the initiation and progression of different cancer subtypes.

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4.2 Results

4.2.1 CEP55 is overexpressed in a comprehensive range of human cancers We analyzed the expression of CEP55 mRNA in human cancers using the Oncomine database (Rhodes et al., 2007). Comparing 7403 tumor samples to 1467 normal control samples of matched tissue type, we observed the expression of CEP55 to be significantly higher than in matched normal tissue (p<0.05; t-tests) across multiple types of human cancers (Figure 4.2A). Of all studies that showed a significant CEP55 expression difference compared to normal tissue (n=212), 193 (91%) showed significant overexpression and 19 showed significant underexpression (9%) (Figure 4.2B). Further, using the NCI-60 datasets, we observed the variation of CEP55 expression across major cancer cell lines (Figure 4.2C). Moreover, some highly proliferative cancers, such as leukaemia, frequently show underexpression of CEP55 (Figure 4.2B), suggesting tissue-dependent expression of this protein.

In addition, using the TCGA lung adenocarcinoma, liver hepatocellular carcinoma and colon adenocarcinoma datasets, we observed that CEP55 is significantly upregulated (p<0.0001, Mann-Whitney U test) in tumors in comparison to normal control tissue (Figure 4.3A). Next, we wanted to validate whether the expression of CEP55 is cell cycle dependent. As CEP55 is known to be associated with the centrosomes, including during S phase, increased CEP55 expression in tumors could simply be a reflection of increased proliferation of cancer cells. However, we observed that the expression of CEP55 was significantly higher in tumors than in normal tissue even after compensation for the expression of the cell proliferation markers Ki67 or PCNA (all p<0.0001, Mann-Whitney U test) (Figure 4.3B & C). In addition, tumors that express Ki67 at levels in the same range as normal samples show significantly elevated CEP55 expression levels than the normal samples (Figure 4.3D). Taken together, these data indicate that CEP55 is overexpressed in a broad range of human tumors and this is not purely due its increased expression in cycling cells.

Chapter - 4

Figure 4.2:CEP55 is overexpressed in a broad range of cancers independent of a proliferation-associated effect. (A) Statistical representation of CEP55 expression in multiple data sets of each cancer subtypes. A total of 212 datasets were identified showing statistically significant dysregulated expression of CEP55 in tumors compared to matched normal control tissues (p<0.05, t-test). Each box represents a dataset. Studies showing significant CEP55 overexpression are shown in red, those showing significant underexpression in blue. Fold over- or underexpression is shown as indicated. (B) Pie charts derived from data in (A). Proportions of studies showing significant overexpression (red) and underexpression (blue) are shown. P-values were calculated using Fisher’s exact tests. (C) Statistical representation of CEP55 mRNA expression (represented as Z-Score) amongst NCI-60 dataset including cell lines from different types of cancer (represented by dots of respective shapes)

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Figure 4.3: CEP55 overexpression is not cell cycle dependent. (A) RSEM-normalised CEP55 expression levels in lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LIHC) and or colorectal adenocarcinoma (COADREAD) samples compared to matched normal control samples. Data are from TCGA (Cancer Genome Atlas Research et al., 2013). (B) Ratios of CEP55/MKI67 RSEM-normalised expression levels in LUAD, LIHC and COADREAD of datasets as in (A). (C) Ratio of CEP55/PCNA RSEM-normalised expression levels in LUAD, LIHC and COADREAD of datasets as in (A). (D) CEP55 expression levels in all normal samples and in the tumor samples that express MKI67 in the same range as the MKI67 level in the normal samples. P-values: Mann-Whitney U test. ****p<0.0001.

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4.2.2 Cep55 overexpression promotes spontaneous tumorigenesis in vivo To assess the tumorigenic potential of CEP55 in vivo, we created a novel “knock-in” mouse model that ubiquitously overexpresses Cep55, as described in the previous chapter. We monitored a cohort of Cep55Tg/Tg (n=50) and Cep55wt/Tg (n=40) in comparison to Cep55wt/wt (n=40) mice for 24 months to define whether Cep55 overexpression is sufficient to initiate tumorigenesis. We observed that the Cep55Tg/Tg mice developed tumors at long latency (~15 months), and succumbed to cancer significantly earlier than Cep55wt/Tg and Cep55wt/wt littermates (Figure 4.4A). Further, we observed that approximately 70% of the Cep55Tg/Tg mice developed tumors compared to 20% of Cep55wt/Tg and 2.5% of Cep55wt/wt mice (Figure 4.4B). The Cep55Tg/Tg mice of both genders developed a wide spectrum of tumors in a range of organs such as lung adenoma and carcinoma, hepatocellular carcinoma, papillary carcinoma, hemangiosarcoma, fibrosarcoma, B- and T- cell lymphoma and myelogenous leukemia (Figure 4.4C-E and Table 4.1).

The tumor burden observed in Cep55Tg/Tg mice varied between 1-3 tumors per animal in comparison to Cep55wt/Tg which uniformly developed only adenomas in the lung (Figure 4.4E). Cep55Tg/Tg mice were susceptible to a high rate of adenomas in the lung (Figure 4.4F and Table 4.1) though some of them harbored other tumors at the time of death. Cep55Tg/Tg mice were also prone to a higher rate of lymphoma; in particular, the incidence of B-cell lymphoma was significantly elevated by more than 3.5- fold in comparison T-cell lymphoma (Figure 4.4G). Moreover, we stained the lymphoma samples using B220 (B-Cells marker) and CD8 (T-Cell marker) to annotate the cell type specificity of lymphoma observed in Cep55Tg/Tg mice (Figure 4.4G).

Apart from lymphoma, Cep55Tg/Tg demonstrated a higher incidence of lung adenocarcinomas (60%), which was 3-fold higher compared to other carcinomas (Figure 4.5A). The Cep55Tg/Tg mice demonstrated significant loss (p<0.001) of body weight comparted to their littermate counterparts (Figure 4.5B). Further, Cep55Tg/Tg mice exhibited significant loss (p<0.001) of body weight in comparison to their body weight measured six months prior to tumor incidence (Figure 4.5C). The primary cancers observed in the Cep55Tg/Tg mice were highly aggressive with increased proliferation rate as perceived by mass of the organs in which these tumors were originated (Figure 4.5D-F). In addition, we observed ~16% of the tumors were susceptible to metastasis in the lungs and liver (Figure 4.5G) alongside elevated levels of inflammation and lymphocyte infiltration. Collectively, these data highlight that Cep55 overexpression in vivo, promotes the development of a wide spectrum of cancers and cancer associated abnormalities such as inflammation and metastasis.

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Figure 4.4: Cep55 overexpression I promotes tumorigenesis. (A) Kaplan Meier Survival analysis of mice of indicated genotypes showing Cep55Tg/Tg mice are more susceptible to form tumors and with a shorter latency period (~14 months) compared to their control counterparts. (B) Statistical representation of cancer incidence rate among mice of indicated genotypes. (C) Gross morphology (left panel) and H&E stained microscopic images of selected sections from Cep55Tg/Tg mice (Scale bars, 1cm). (D) Cancer penetrance per major indicated tumor types. (E) H&E stained microscopic images of major indicated tumor types from Cep55Tg/Tg mice. (F) Statistical representation of tumor burden observed in the mice of each genotype. (G) Statistical representation of lymphoma burden observed in Cep55Tg/Tg mice. (H) B220 and CD8 immunostaining used to categorise the type of lymphomas. B220+ve and CD8-ve were classified as B-Cell lymphoma while CD8+ve and B220-ve were classified as T-Cell lymphomas. Scale bar = 200μm. P-values: Mann- Whitney U test. ****p<0.0001.

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Figure 4.5: Anomalies associated with Cep55Tg/Tg mice in vivo during tumorigenesis. (A) Statistical representation of adenocarcinoma burden observed in Cep55Tg/Tg mice. (B) Statistical representation of the body weight observed at the end of tumor survival of each genotype (n>40 per group). (C) Statistical representation of the body weight of Cep55Tg/Tg mice observed at the time of death (time point when the mice became moribund as per body score) in comparison to that observed at the start of the cohort monitoring (6 months prior to tumor incidence) (n>40 per group). (D) Spleen mass, (E) Liver mass, (F) Lung mass represented as statistical figure indicating the level of hyper-proliferation and aggressiveness observed in Cep55Tg/Tg mice in comparison to its wild type counterpart (n>10 per group). (G) Statistical representation of metastasis incidence observed in Cep55Tg/Tg mice. Error bars represent the ± SEM; P-values: One way Anova and Mann-Whitney U test. ***p<0.001, ****p<0.0001.

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4.2.3 Cep55 overexpression promotes cell proliferation advantage in vivo Upregulation of CEP55 has been shown to promote rapid cell proliferation in human cancer lines. To illustrate whether overexpression of Cep55 in vivo provides proliferation advantage, we isolated cells from mouse embryonic fibroblasts (MEFs) of each genotype at E13.5 (Figure 4.6A). Immunoblot on cell extracts from MEFs of each genotype validated higher protein expression of Cep55 in the Cep55Tg/Tg MEFs compared to control counterparts (Figure 4.6B). To determine growth potential and time to senescence in these MEFs, we performed an NIH-3T3 assay and observed that primary Cep55Tg/Tg MEFs had a significantly higher (p<0.0001) proliferation rate in comparison to Cep55wt/Tg and Cep55wt/wt MEFs (Figure 4.6C). However, no significant difference was observed in the proliferation rates between Cep55wt/Tg and Cep55wt/wt (Figure 4.6C). Similarly, cell viability and cell proliferation assays demonstrated that Cep55Tg/Tg MEFs possess a proliferation advantage over their control counterparts (Figure 4.6D & E). Further, the Cep55Tg/Tg MEFs exhibited a faster doubling time (Figure 4.6F). In addition, consistent to the doubling time data, Cep55Tg/Tg MEFs demonstrated faster cell-cycle progression (Figure 4.6G & H).

CEP55 has been shown to upregulate AKT phosphorylation and enhance cell proliferation in vitro (Chen et al., 2009b, Chen et al., 2007). On the basis of this phenomenon, we wanted to validate whether the proliferation of Cep55Tg/Tg MEFs is facilitated by activation of the PI3K/Akt pathway. Western blot analysis on cell lysates from the MEFs of each genotype demonstrated hyper-phosphorylation of the PI3K/Akt pathway (Figure 4.7A). In addition, we also observed upregulation of the MAPK pathway as illustrated by hyper-phosphorylation of Egfr, Erk1/2 and c-Myc alongside PCNA in Cep55Tg/Tg MEFs (Figure 4.7A). Similarly, we also observed upregulation of both of these pathways in tissue lysates isolated from Cep55Tg/Tg mice in comparison to wildtype counterparts (Figure 4.7B). Further, to define whether the upregulation of these pathways is a consequence of increased proliferation, we serum starved the MEFs of each genotype and observed continued hyper-activation of both PI3K and MAPK pathway in Cep55Tg/Tg MEF’s until 48hrs (Figure 4.7C). However, post 72hrs of starvation, we observed neutralization of phosphor- Erk1/2 and p-Akt(T308) indicating cessation of cellular proliferation and highlighting the self-mitogen gaining capability observed in the Cep55Tg/Tg MEF’s. Taken together, these data highlight that Cep55 amplification provides cell proliferation advantage via multiple cell signaling pathways in vivo.

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Figure 4.6: Cep55 overexpression promotes cell proliferation advantage in vivo. (A) PCR genotyping (see Materials and Methods for primer details) of DNA isolated from the primary MEFs of each indicated genotype showing the presence of amplicons of the expected size for each genotype. #1 and #2 denotes biologically independent DNA samples of each genotype which were used for future experiments. (B) Immunoblot analysis of Cep55 expression in cell lysates from the primary MEFs of each genotype. (C) Graphical representation of NIH-3T3 proliferation assay measured as a function of passage number (indicated as CPD) observed in primary Cep55Tg/Tg MEFs in comparison to control counterparts (n=3 per group). (D) Statistical representation of cell viability of immortalized MEFs of each genotype as indicated in (C) measured each day over a period of 6 days. (E) Statistical representation of cell proliferation of immortalized MEFs of each genotype as indicated in (C) performed using IncuCyte. (F) Statistical representation of relative fold change in the doubling time of immortalized MEFs from each genotype as indicated in (C). (G) Cell cycle profile of primary MEFs of indicated genotypes at 24 hours. (H) Comparison of cell cycle profile of immortalized MEFs of indicated genotypes over 48 hours measured at 12 hours intervals. Error bars represent the ± SEM from three independent experiments. P-values: One way Anova

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Figure 4.7: Cep55 overexpression leads to hyper-phosphorylation of PI3K/Akt pathway. (A) Immunoblot analysis of cell lysates from immortalized MEFs of indicated genotypes showing overexpression of Cep55 in vivo leads to hyperactivity of PI3K/Akt and MAPK pathway. (B) Immunoblot analysis of indicated tissue lysates of 6 months old littermates of indicated genotypes showing Cep55 overexpression leads to hyperproliferation (PCNA expression) and increased activity of the PI3K/Akt pathway. (C) Immunoblot analysis of cell lysates from serum starved immortalized MEFs of indicated genotypes showing overexpression of Cep55 in vivo leads to hyperactivity of PI3K/Akt and MAPK pathway, independent of cell proliferation. β-Actin was used as loading control.

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4.2.4 Cep55 loss leads to proliferation defect in vivo To complement the above data, we next wanted to delineate the consequence of Cep55 loss on cell proliferation both in vitro and in vivo. Cep55 depletion using siRNA in Cep55Tg/Tg MEFs resulted in downregulation of p-Akt(S473) (Figure 4.8A) which in turn significantly delayed (P<0.001) cell proliferation (Figure 4.8B). Independently, we established tumor cell lines from fibrosarcoma observed in the liver (Figure 4.4C). The morphology of these cells demonstrated heterogeneity as observed by presence of mono-nucleated and multinucleated nuclei (Figure 4.8C). In comparison to Cep55wt/wt and Cep55Tg/Tg MEF’s, the cancer lines (CLs) illustrated hyper-activation of Cep55 alongside components of cell survival pathway (Figure 4.8D). We depleted Cep55 in the CLs using siRNA and observed that similar to Cep55Tg/Tg MEFs, downregulation of Cep55 lead to downregulation of Akt pathway (Figure 4.8E & F) and significantly (p<0.01) impacted cell proliferation (Figure 4.8G).

To further evaluate the impact of Cep55 on cellular proliferation and survival in vitro and in vivo, we established Cep55 depleted clones in the CLs by stable transduction using shRNAs that target different regions in the Cep55 transcript (Figure 4.9A). Of all the clones, cells with partial knockdown of Cep55 (sh#S2) or near complete knockdown (sh#S4) of Cep55 were used in further studies (Figure 4.9B). Cep55 depleted sh#4 clone demonstrated delayed long-term proliferation (Figure 4.9D) and anchorage-independent colony formation (Figure 4.9D). Moreover, engraftment of the partial Cep55-depleted CLs clone (sh#S2) showed a significant reduction (p<0.0001) in tumor growth while a near complete loss of Cep55 (sh#S4) abolished tumor formation in NOD/SCID mice (Figure 4.9E), suggesting that high levels of Cep55 are critical for tumor formation. Collectively, these data signify that loss of Cep55 causes proliferation defects in vivo.

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Figure 4.8: Loss of Cep55 downregulates PI3K/Akt pathway and cell proliferation in vitro. (A) Immunoblot analysis to assess levels of indicated signaling molecules following transient depletion of Cep55 using siCep55 (10 nM) in Cep55Tg/Tg MEFs. (B) Effect of Cep55 depletion using siCEP55 (10 nM) on the proliferation of Cep55Tg/Tg MEFs. Error bars represent the standard error of the mean from two independent experiments. (C) Cell morphology (left panel-bright field image; right panel- fluorescence image wherein α-tubulin (green) marks the cytoplasm while DAPI (blue) marks the nucleus) of the CLs isolated from the tumor cells. (D) Immunoblot analysis of cell lysates of the primary CLs in comparison to Cep55wt/wt and Cep55Tg/Tg MEFs indicated persistence of Cep55 overexpression and hyperactivity of PI3K/Akt and MAPK pathway. Albumin is used as a marker for cancerous origin in the liver (fibrosarcoma in liver). β-Actin was used as loading control. (E) Immunoblot analysis to assess levels of indicated signaling molecules following transient depletion of Cep55 using siCep55 (10 nM) in the CLs. (F) Immunoblot analysis to assess levels of functional activity of the si Cep55 (10 nM) over different time points in the CLs. (F) Effect of Cep55 depletion using siCep55 (10 nM) on cell proliferation of the CLs. Proliferation was assessed using the IncuCyte ZOOM® live cell imager and the percentage of cell confluence was determined using an IncuCyte mask analyzer. Error bars represent the standard error of the mean from two independent experiments. β-Actin was used as a loading control. Scale bar = 50μm. One Anova **p < 0.01 and ***p<0.001

Figure 4.9

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Figure 4.9: Loss of Cep55 impacts tumorigenesis in vivo. (A) Immunoblot analysis to validate extent of Cep55 depletion with indicated shCep55 sequences (indicated as #S1- #S4 with Sh_Scr being used as control) in the CLs. (B) Immunoblot analysis of Cep55 expression in Cep55-depleted CLs. Two isogenic lines (sh#S2 and sh#S4) were obtained using two different shRNA sequences. β- Actin was used as loading control. (C) Effect of Cep55 depletion on cell proliferation in CLs assessed using the IncuCyte ZOOM® live cell imager (phase-only processing module). The percentage of cell confluence was determined using an IncuCyte mask analyzer. (D) Representative images of colony formation at 14 days determined using crystal violet staining in control and Cep55- depleted CLs. (E) Six-week-old female NOD/Scid cohorts of mice were injected subcutaneously with the control and Cep55-depleted cells. Growth rate (area, mm2) of the tumors was measured using digital calipers. Growth rate (area, mm2) of the tumors was measured using digital caliper. Differences in growth were determined using Student’s t-test, n = 5 per group.*p < 0.05 and ****p<0.0001.

4.2.5 Cep55 overexpression facilitates polyploidy in vivo

Fabbro et al. initially showed that a fine balance of CEP55 expression is critical for faithful cytokinesis (Fabbro et al., 2005). They demonstrated that both complete depletion of CEP55 using siRNAs and overexpression of CEP55 in Hela cells resulted in cytokinesis failure leading to multi-nucleation. Above, we reported that human cancers mostly show CEP55 overexpression, rather than underexpression (Figure 4.2B). Consistent with these observations, we found that tumors that have undergone whole-genome doubling (WGD), a direct consequence of cytokinesis failure, typically express significantly higher levels of CEP55 than diploid and near diploid tumors (Figure 4.10A). Also, as discussed previously, multi-nucleation or polyploidy leads to genomic instability, a well-known ‘hallmark of cancer’, that promotes tumor progression, metastasis, aggressiveness and drug resistance. In line with this, CEP55 is part of a 70-gene signature associated with chromosomal instability (CIN70) in various types of cancer (Carter et al., 2006). We found that CEP55 expression and CIN70 score strongly correlate in various cancer types (total n=1764, all r>0.73, all p<0.0001, Pearson correlation) (Figure 4.10B). In addition, CEP55 expression was increased in tumors showing structural or numerical aneuploidy, including whole-chromosome aneuploidy, chromosome arm- level aneuploidy, or aneuploidy as computationally inferred from somatic copy number aberrations (Figure 4.10C & D). Thus, these data highlight the connection between CEP55 and genomic instability.

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Next, we wanted to investigate whether Cep55 amplification in Cep55Tg/Tg promotes polyploidy. To answer this question, we performed immunofluorescence in the MEFs of the three genotype using α-tubulin (cytoskeleton marker) and γ-tubulin (centrosome marker) (Figure 4.11A & B). We observed that the Cep55Tg/Tg MEFs have 3-fold higher percentage (p<0.0001) of binucleated cells (Figure 4.11C) and multinucleated cells (Figure 4.11D). Similarly, sh#S4 clone of CLs with reduced Cep55 expression demonstrated a significant reduction (p<0.0001) in multinucleated cells compared to its counterparts (Figure 4.11E & F).

To support the above data, we performed cell cycle analysis by fluorescence-activated cell sorting (FACS) firstly on the immortalized MEFs (p<0.001) of each genotype and found that the polyploid subpopulation in Cep55Tg/Tg MEFs was significantly increased (P value) compared to its counterparts (Figure 4.12A). Likewise, freshly isolated primary Cep55Tg/Tg MEFs also demonstrated a significant increase in polyploid subpopulation (p<0.001), though the percentage was significantly lower (p<0.001) than immortalized MEFs (Figure 4.12B & C). To understand the effect of Cep55 on polyploid subpopulation, we challenged the immortalized MEFs with the microtubule- depolymerizing drug nocodazole which blocks cells in mitosis and causes perturbed mitotic progression. We observed that Cep55Tg/Tg MEFs generated a high percentage of (p<0.0001) polyploidy with significantly reduced (p<0.0001) cell death (identified as the sub-G1 fraction) during perturbed mitosis (Figure 4.12D & E). Likewise, we observed similar phenotypes in the primary MEFs (Figure 4.12F & G). Notably, we also observed a significant increase (p<0.0001) in polyploidy among various organs of Cep55Tg/Tg mice than their littermate counterparts (Figure 4.12H).

Similarly, we also observed that the Cep55-depleted (sh#S4) clone of CLs demonstrated a significantly reduced (p<0.0001) polyploid subpopulation (Figure 4.13A). Upon nocodazole treatment, sh#4 demonstrated efficient mitotic arrest that control cells (Figure 4.13B). Complementing the MEF data, we noted a significant decrease (p<0.0001) in polyploidy following nocodazole treatment in Cep55-depleted clones (Figure 4.13C). Also, sh#S4 showed a significant increase in cell death during perturbed mitosis (Figure 4.13D). In summary, these data suggest that Cep55 amplification facilitates polyploidy which in turn results in overcoming perturbed mitosis by enabling mitotic slippage (further discussed in later chapters) causing genomic instability and hence tumorigenesis.

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Figure 4.10: Association of CEP55 overexpression with aneuploidy. (A) Boxplots showing CEP55 expression in tumor whose genomes are diploid, near-diploid aneuploid or aneuploid after whole-genome doubling (WGD). Data are from the TCGA liver hepatocellular carcinoma (LIHC), lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD) and colorectal adenocarcinoma (COADREAD) datasets. P values: Mann-Whitney U tests. (B) Scatter plots showing CEP55 expression and CIN70 scores in tumors as above. R2 and p values: Pearson correlations (Carter et al., 2006). (C) Boxplots showing CEP55 expression in indicated tumors with whole-chromosome (WC)-euploid and WC-aneuploid genomes, determined as previously described P values: Mann-Whitney U tests (Thangavelu et al., 2017). (D) Boxplots as in (C) but at the chromosome arm level (CAL).

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Figure 4.11: Cep55 overexpression causes genomic instability. (A) Immunofluorescence showing genomic instability observed in Cep55Tg/Tg MEFs as indicated by the presence of multiple nuclei (marked by DAPI (Blue)) staining compared to other counterparts. The figure also shows amplification of centrosomes (marked by γ-Tubulin (Red)) in Cep55Tg/Tg MEFs. The entire cell (cytoplasm) is marked by α-Tubulin (Green). (B) Higher magnification of the immunofluorescence image of Cep55Tg/Tg MEFs indicating the level of genomic instability observed in (A). (C) Graphical representation showing the percentage of binucleated cells observed in respective MEFs. (D) Graphical representation indicating the percentage of multinucleated cells observed in respective MEF genotypes. Error bars represent the ± SEM from three independent experiments. (E) Immunofluorescence showing genomic instability observed among CL shRNA clones as indicated by the presence of multiple nuclei (marked by DAPI (Blue)) staining compared to counterparts. The entire cell (cytoplasm) was marked by α-Tubulin (Green). (F) Graphical representation indicating the percentage of multinucleated cells observed in respective CLs shRNA clones. Error bars represent the ± SEM from three independent experiments. One way Anova ****p<0.0001

4.2.6 Cep55 overexpression impacts Foxm1-Plk1 axis FOXM1 is a potent transcription factor which has been well-documented to be associated with tumorigenesis as it contributes to oncogenic transformation and favors tumor initiation, growth and progression alongside genomic instability (Myatt and Lam, 2008). In cancer settings, CEP55 has been shown to control FOXM1 in a dose dependent manner (Chen et al., 2009a). FOXM1 has been shown to transcriptionally control mitotic proteins such as PLK1 and CEP55. Separately, PLK1 has been shown to regulate both CEP55 and FOXM1 via phosphorylation (discussed previously in Chapter 1; Section 1.11). Thus, on the basis of this theory, we hypothesized that upregulation of the FOXM1-PLK1-CEP55 axis might impact cell proliferation and hence tumorigenesis in vivo.

To test our hypothesis, we accessed the TCGA data sets of solid cancers to observe that CEP55 expression levels to linearly co-relate with FOXM1 (p<0.001) (Figure 4.14A). To support this data, we performed western blot analysis on the MEFs of the three genotypes to observe upregulation of Foxm1 and Plk1 in Cep55Tg/Tg MEFs (Figure 4.14B). Moreover, immunoblotting data illustrated upregulation of Foxm1 and Plk1 in Cep55Tg/Tg tissue samples (Figure 4.14C). In addition, we also depleted Cep55 using siRNA in the Cep55Tg/Tg MEFs to observe downregulation of Foxm1 and Plk1 (Figure 4.14D) which leads to delayed cell proliferation (Figure 4.14E). To complement this data, we also depleted Foxm1 using siRNA in the Cep55Tg/Tg MEFs and observed downregulation of Cep55 and also significantly delayed (p<0.0001) proliferation (Figure 4.14F & G). Similarly, we also depleted Cep55 and Foxm1 using siRNA in the CLs and observed comparable effects as seen in the MEFs (Figure 4.14H-K). Taken together, these data highlight interplay between

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Cep55-Plk1-Foxm1 axis in cancer.

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Figure 4.12: Cep55 overexpression favors genomic instability during perturbed mitosis. (A) Statistical representation of polyploidy analysis (>4N DNA contents) determined using FACS in immortalized MEFs. (B) Statistical representation of polyploidy analysis (>4N DNA content) determined using FACS in primary MEFs. (C) Statistical representation of polyploidy analysis (>4N DNA content) comparison determined using FACS between primary and immortalized MEFs. (D) Percentage of polyploidy (>4N) following nocodazole (75 µg/ml) treatment for 24hrs determined using FACS in indicated immortalized MEFs. (E) Apoptotic fraction (Sub-G1 population) determined by propidium iodide staining of cells treated with Nocodazole as described in (D). (F) Percentage of polyploidy (>4N) following nocodazole (75 µg/ml) treatment for 24hrs determined using FACS in primary MEFs. (G) Apoptotic fraction (Sub-G1 population) determined by propidium iodide staining of cells treated with Nocodazole as described in (F). (H) Statistical representation of polyploidy analysis (>4N DNA contents) determined using FACS in indicated tissues of respective age-matched genotypes (n=3). Error bars represent the ± SEM from three independent experiments. One way Anova ***p < 0.001 and ****p<0.0001.

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Figure 4.13: Loss of Cep55 leads to mitotic catastrophe. (A) Statistical representation of polyploidy analysis (>4N DNA content) determined using FACS in indicated CL shRNA clones. (B) Statistical representation of cell cycle profile comparison of DMSO and nocodazole (75 µg/ml) treatment after 24hrs in indicated CLs shRNA clones. (C) Percentage of polyploidy (>4N) following nocodazole (75 µg/ml) treatment for 24hrs determined using FACS in indicated CLs shRNA clones. (D) Apoptotic fraction (Sub-G1 population) determined by propidium iodide staining of cells treated with Nocodazole as described in (C). Error bars represent the ± SEM from three independent experiments. One way Anova ****p<0.0001.

4.2.7 Cep55 overexpression promotes faster tumorigenesis upon Trp53 depletion From the above data, it was clear that Cep55 overexpression in vivo has a string interconnection between Foxm1 and Plk1. As Chang et al. have demonstrated previously that TP53 suppresses CEP55 expression indirectly, by negatively regulating PLK1 (Chang et al., 2012). Thus, we suspected that Cep55 amplification might impact Trp53 function in vivo which results in tumorigenesis as Cep55Tg/Tg demonstrated high percentage of lymphomas and sarcomas mimicking tumor phenotype observed in Trp53-/- mice (Donehower et al., 1992). We next assessed whether there is a relationship between TP53 status and CEP55 expression in human tumor samples. In both lung adenocarcinoma and hepatocellular carcinoma, CEP55 levels were significantly higher in tumors that suffered allelic TP53 copy number loss than in TP53 diploid tumors (both p<0.0001, Mann-Whitney U test) (Figure 4.15A). Also, TP53 mutant tumors showed significantly elevated CEP55 expression in these tumor types (both p<0.0001, Mann-Whitney U test) (Figure 4.15B). However, this was not the case in colorectal adenocarcinomas (Figure 4.15A & B). This suggests that impaired the p53 pathway function may cause CEP55 overexpression in lung and liver carcinomas and also explains their incidence than colorectal adenocarcinomas in Cep55Tg/Tg mice.

Next we wanted to validate whether amplification of Cep55 in combination with one allele of Trp53 loss would accelerate tumorigenesis in mice. We crossed the female Cep55Tg/Tg mice with Trp53-/- male mice to obtain a bi-transgenic mouse model of the genotype Cep55wt/Tg;Trp53wt/- (n=15) which we monitored alongside Cep55wt/wt;Trp53wt/- (n=17), Cep55wt/Tg;Trp53wt/wt (n=11) and Cep55wt/wt;Trp53wt/wt (n=10). We observed that the single allele of Cep55wt/Tg;Trp53wt/- mice succumbed to cancer at significantly earlier latency (~14 months; p<0.0001) than Cep55wt/wt;Trp53wt/- (close to 18 months) while no tumors were observed in other groups (Figure 4.15C). Further, the incidence rate of tumorigenesis observed in Cep55wt/Tg;Trp53wt/- mice was significantly higher

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(~85%; p<0.0001) (p) in comparison to Cep55wt/wt;Trp53wt/- (50%,p) (Figure 4.15D). However, we did not see any differences in the tumor spectrum between the two groups (John Finnie’s report; Table 4.2). Taken together, these data indicate that loss of a single Trp53 allele could accelerate the tumor incidence in Cep55 overexpressing mice and could be the potential additional genomic alteration gained by Cep55Tg/Tg mice for de novo tumorigenesis.

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Figure 4.14: Cep55 overexpression leads to upregulation of Foxm1 and Plk1. (A) Scatter plot representation of Cep55 expression levels in correlation with Foxm1 and Plk1 for TCGA data sets of respective carcinomas. Sample size (n), linear regression (R) of respective datasets is indicated. p= Pearsons’s co-relation coefficient. (B) Immunoblot analysis of cell lysates from immortalized MEFs of indicated genotypes showing overexpression of Cep55 leads to upregulation of indicated mitotic proteins. (C) Immunoblot analysis of cell lysates from respective tissues of indicated genotypes showing overexpression of Cep55 in vivo leads to upregulation of mitotic players. (D) Immunoblot analysis to assess levels of indicated signaling molecules following transient depletion of Cep55 using siCep55 (10 nM) in Cep55Tg/Tg MEFs. (E) Effect of Cep55 depletion using siCep55 (10 nM) on cell proliferation of MEFs. (F) Immunoblot analysis to the assess levels of indicated signaling molecules following transient depletion of Foxm1 using siFoxm1 (10 nM) in Cep55Tg/Tg MEFs. (G) Effect of Foxm1 depletion using siFoxm1 (10 nM) on the cell proliferation of MEFs. (H) Immunoblot analysis to the assess levels of indicated signaling molecules following transient depletion of Cep55 using siCep55 (10 nM) in primary CLs. (I) Immunoblot analysis to assess levels of indicated signaling molecules following transient depletion of Foxm1 using siFoxm1 (10 nM) in primary CLs. (J) Effect of Cep55 depletion using siCep55 (10 nM) on cell proliferation in primary CLs. (K) Effect of FoxM1 depletion using siFoxm1 (10 nM) on cell proliferation of the indicated MEFs. Proliferation was assessed using the IncuCyte ZOOM® live cell imager and the percentage of cell confluence was determined using an IncuCyte mask analyzer. Error bars represent the ± SEM from three independent experiments. One way Anova **p<0.01, ***p < 0.001 and ****p<0.0001.

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Figure 4.15: Loss of Trp53 leads to early tumor latency in Cep55 overexpressing mice. (A, B) Boxplots showing CEP55 expression in indicated tumors or matched normal samples with TP53 copy number and/or mutation status as indicated. Numbers of samples for each column are shown above the x-axis. P values: Mann-Whitney U tests. (C) Tumor free survival curve of the mice of indicated genotypes demonstrating that the Cep55wt/Tg; Trp53wt/- mice are more susceptible to forming tumors with a shorter latency period (~14 months) compared to control counterparts. (D) Statistical representation of cancer incidence rate among mice of indicated genotypes. Mann- Whitney U test. ****p<0.0001.

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4.3 Discussion Genomic instability is identified to be a state during which the tendency to gain alteration in the genome increases during perturbed cell cycle (Negrini et al., 2010). Experimental studies using mouse models have demonstrated that genomic instability is one of the major driving forces for tumorigenesis (Shen, 2011). Chromosomal instability (CIN) is a type of GIN that represents the rate of increased chromosomal missegration during mitosis. These studies have demonstrated the role of abnormal expression of mitotic proteins as the primary cause of CIN and to correlate with poor prognosis (Perez de Castro et al., 2007). Interestingly, these mitotic genes are rarely mutated in cancer settings (Cahill et al., 1999, Hernando et al., 2001) though they show high copy number alterations, an example of which is PLK1 (Degenhardt and Lampkin, 2010, Strebhardt and Ullrich, 2006). CEP55 is a mitotic scaffold protein which plays a critical role in cytokinesis and recent reports have linked its association with cancer. CEP55 has been identified in prognostic signatures for multiple cancers and is part of the CIN70 signature which correlated significantly with clinical outcome and distant metastases (Jeffery et al., 2015b). Despite several reports demonstrating CEP55 overexpression in cancer, the oncogenic potential and the mechanism via which CEP55 promotes tumorigenesis in vivo is currently elusive.

In this chapter, we have demonstrated that CEP55 overexpression is widespread in human cancers (both solid and non-solid tumors) in a cell cycle independent manner. Consistent with this, we demonstrated that Cep55 overexpressing mice develop a wide spectrum of tumors such as lung adenoma and carcinoma, hepatocellular carcinoma, papillary carcinoma, hemangiosarcoma, fibrosarcoma, B- and T- cell lymphoma and myelogenous leukemia. The Cep55Tg/Tg mice had an increased incidence of pre-malignant lesion, overall tumor burden and metastasis potential of tumors relative to Cep55wt/Tg mice. Cep55Tg/Tg developed tumors at ~15 months (long latency), a characteristic of CIN mouse models which develop tumors at a latency of more than one year (Duijf and Benezra, 2013, Pfau and Amon, 2012). Notably, we have also demonstrated that Cep55 amplification leads to hyperactivation of the PI3K/Akt pathway which in turn might upregulate the MAPK pathway to cause rapid cell proliferation in vitro and in vivo. However, we did not observe an high levels of neoplasia in Cep55wt/Tg (only showed lung adenoma after 2 years) or Cep55wt/wt mice indicating that a critical threshold level of Cep55 overexpression is required for tumor initiation.

In addition, we have demonstrated that Cep55 overexpression causes multinucleation and protects a polyploid subpopulation of cells against mitotic drugs. We suspect that Cep55 overexpression might favor bypassing mitotic cell death by prematurely exiting mitosis. Further, we

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Chapter - 4 have also demonstrated that CEP55 overexpression is associated with polyploid tumors, similar to what is observed with other mitotic proteins such as Mad2 (Sotillo et al., 2007), Bub1 (Ricke et al., 2011), Aurora-A (Fu et al., 2007), Eg5 (Yu and Feng, 2010), PLK1 (Weichert et al., 2005, Ito et al., 2004), and NEK2 (Zhong et al., 2014). Notably, loss of Cep55 expression enabled susceptibility to mitotic poisons illustrating the importance of Cep55 overexpression in deciding the fate of tumor cells during perturbed mitosis. However, additional screening is required for validating the role of Cep55 in determining mitotic fate and spindle biology in cancer settings. CEP55 is a cancer testes antigen, highly expressed in cancers of different histological types but not in normal tissues except the testes (Inoda et al., 2009a). Therefore, overexpression of CEP55 might be the first oncogenic stimuli involved in inducing genomic instability and eventually triggering tumor initiation.

Recent reports have suggested that cells procure specific genomic alteration prior to malignant transformation (Manning et al., 2014). The Cep55Tg/Tg displayed higher incidence of lymphomas and sarcomas, mimicking the phenotype observed in Trp53-/- mice. As p53 negatively controls CEP55 expression (Chang et al., 2012), we demonstrated using genetic analysis that Cep55 overexpression in association with loss of single allele of Trp53 in vivo causes early tumor latency in comparison to sole loss of Trp53. In addition, we also demonstrated upregulation of Foxm1 and Plk1 correlating with Cep55 amplification both in vivo and in vitro. These data elaborate the involvement of the Trp53-Plk1-Foxm1 axis induced upon Cep55 overexpression over time to initiate chronic tumorigenesis. As upregulated expression of FOXM1 and PLK1 alongside p53 loss has been shown to be involved in human tumor pathology, we provide the first preliminary evidence of the involvement of Cep55 amplification to modulate the Trp53-Plk1-Foxm1 axis in vivo. Interestingly, no anomaly in overall morphology of Cep55Tg/Tg was observed within the first year of birth. Thus, it elaborated the concept of “clonal origin” in cancer which signifies that cancer is a multi-gene, multi- step disease originating from a single abnormal cell having altered genomic sequence over time.

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No. Cancerous Lesions Cep55wt/wt Cep55wt/Tg Cep55Tg/Tg # % # % # % 1. Lymphoma 0 0 0 0 18 35.29 B-Cell Lymphoma - - 11 21.56 T-Cell Lymphoma - - 7 13.72 2. Sarcoma 0 0 0 0 9 16.73 Fibrosarcoma - - 4 44.44 Haemagiosarcoma - - 4 44.44 Leiomyosarcoma - - 1 11.12 3. Lung Adenocarcinoma 0 0 0 0 6 11.76 4. Alveolar-Bronchiolar Lung 0 0 10 22.22 15 29.78 Adenoma 5. Hepatocellular Carcinoma 0 0 0 0 3 5.89 6. Hepatoma 0 0 2 4.34 2 3.92 7. Myelogenous Leukaemia 0 0 0 0 7 12.80 8. Hyperplasia 0 0 4 8.69 8 15.68 Follicular Hyperplasia 0 0 4 8.69 5 9.80 Endometrial Hyperplasia 0 0 0 0 3 5.88 9. Lypoma 0 0 0 0 1 1.96

Table 4.1: Incidence of tumors in Cep55 over-expressing and control mice

No. Cancerous Lesions Cep55wt/wt;Trp53wt/wt Cep55wt/Tg;Trp53wt/wt Cep55wt/wt;Trp53wt/- Cep55wt/Tg;Trp53wt/- # % # % # % # % 1. Lymphoma 0 0 7 70 9 64.28 B-Cell Lymphoma 0 0 4 40 5 35.71 T-Cell Lymphoma 0 0 3 30 4 28.57 2. Sarcoma 0 0 5 50 6 42.85 Fibrosarcoma 0 0 4 40 4 28.57 Haemagiosarcoma 0 0 1 10 2 14.28 Leiomyosarcoma 0 0 0 0 0 0 3. Lung Adenocarcinoma 0 0 0 0 2 14.28 4. Alveolar-Bronchiolar 0 1 10 0 0 6 42.85 Lung Adenoma 5. Hepatocellular 0 0 0 0 Carcinoma 6. Hepatoma 0 0 0 0 7. Myelogenous Leukaemia 0 0 0 2 14.28 8. Hyperplasia 0 0 0 5 35.71 Follicular Hyperplasia 0 0 0 0 Endometrial Hyperplasia 0 0 0 0 9. Lypoma 0 0 0 0 0 0 0 0

Table 4.2: Incidence of tumors in Cep55/Trp53 bi-transgenic mice model over-expressing and control mice

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4.4 Conclusion In summary, Cep55Tg/Tg mice model could be valuable tool is studying the mechanism of chromosomal instability as it retains the characteristics similar to that observed in CIN mouse models. Further, the concept that Cep55 protects polyploid cells during perturbed mitosis could have clinical implication and must be further investigated in treatment of aneuploid cancers. The Cep55Tg/Tg mice edifies the characteristics of Cep55 to be a potent oncogene, when overexpressed beyond a critical level, as these mice succumb to a wide spectrum of tumors irrespective of cancer subtype. Thus these phenotypic data validate the gene signature studies that have identified Cep55 overexpression in multiple tumors. The Cep55wt/TgTrp53wt/- demonstrated reduced latency compared to Cep55wt/wtTrp53wt/- mice highlighting the loss of p53 could be a potential mechanism in initiating tumorigenesis observed in the Cep55Tg/Tg mice as they mimic the Trp53-/- phenotype in terms of tumor spectrum. Additionally, upregulation of Foxm1 and Plk1 support the concept of Trp53-Plk1- Foxm1 modulation which might also facilitate de novo tumorigenesis upon Cep55 overexpression in vivo. However, advanced genetic studies using well characterized potent oncogenic mouse model to better understand the mechanism via which Cep55 drive tumor progression and validate the effect of oncogenic stimuli on tumor latency upon Cep55 overexpression is warranted.

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Chapter - 5 Introduction to Breast Cancer

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5.1 Introduction Breast cancer (BC) is a heterogeneous disease encompassing striking genetic and phenotypic diversities (Stingl and Caldas, 2007, Neve et al., 2006, Polyak, 2007). With an estimated 1.7 million diagnosed cases in 2012, BC is considered to be the leading cause of death among women worldwide (Stewart, 2014). As per 2008 census, an increase by 20% in cancer incidence rate with 14% rise in mortality rate has been recorded highlighting the sharp upsurge in BC cases in recent years. BC is the third most commonly diagnosed cancer recorded in Australia. According to 2012 census, BC was known to be the leading cause of mortality among women (Kalimutho et al., 2015). As per the 2016 survey, the projected number of diagnosed cases of BC raised to 15,740 from 14,568 of 2011 demonstrating 7.44% increase over past 4 years with age-standardized case to be 59 per 100,000 persons (Welfare, 2016).

From research conducted over the past decade, it is now clear that BC is a heterogeneous group of diseases with variation in proliferation rate, metastatic potential, invasive capability (Bloom and Richardson, 1957, Skibinski and Kuperwasser, 2015), fluctuations in the presence of specific driver mutations (Wood et al., 2007, Stephens et al., 2012) and also their response to varying anti-cancer compounds (Hayward et al., 1977). Classification and prognosis of BC have historically relied upon tumor morphology and expression of three classical receptors namely: the estrogen receptor (ER), progesterone receptor (PR) and the human epidermal growth factor receptor 2 (HER2). Notably, expression profile analysis in the beginning of the 21st century identified five major transcriptional subtypes of BC, namely: luminal A, luminal B, HER2-enriched, basal-like and normal like (Perou et al., 2000, Sorlie et al., 2001, Sorlie et al., 2003). Individually, each subtype exhibits discrete histological features, disease progression and clinical outcome apart from therapeutic response (Fan et al., 2006, Paik et al., 2004, Dowsett et al., 2010, Metzger-Filho et al., 2013).

5.2 Heterogeneity of Breast Cancer Tumor heterogeneity is one of the prominent areas of active research in the field of BC diagnosis and has been linked to the mammary hierarchy. BC as a heterogeneous disease exhibits a high degree of diversity between and within the tumor among cancer patents which contributes to

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Chapter - 5 the risk of disease progression and resistance to therapy (Polyak, 2011). Theories have stated that the accumulation of oncogenic hits in normal precursor cells in the mammary hierarchy results in the rise of abnormal cells in breast. Breast tumors differ in nature depending on either variation in these genetic mutations or on the identity of the initiating cells, known as the “cell of origin” theory (Polyak, 2011, Torres et al., 2007, Visvader, 2011). Further, the particular collection of genetic mutations accumulated in a single precursor cell that drives de novo tumorigenesis has a major role in determining the tumor phenotype. With the expansion in the field of DNA-sequencing technologies, techniques like whole exome analysis of patient samples highlighted the comprehensive molecular portraits of BC which shows the correlation of mutated genes with particular tumor subtype (Banerji et al., 2012, Ellis et al., 2012). These advancements in the field have proven to be a powerful tool with respect to diagnosis, prognosis and the treatment of BC as they have clarified the concept of inter- and intra-tumor heterogeneity observed in a clinical setting (Malinowsky et al., 2010, Becker et al., 2007, van 't Veer et al., 2002, Patsialou et al., 2012). Delineating the genetic mapping of tumors from individual patients has provided the backbone for conceptualizing the molecular classification of BC (as discussed in the next section). Conversely, though these developments have provided a paradigm shift, the clinical application of the knowledge gained from the data analysis with respect to treatment is yet to be adopted.

5.2.1 Classification of breast cancer In order to understand the disease based on its heterogeneity, researchers and clinicians have upgraded the BC classification system. This system has undergone many modifications over time with the expansion of knowledge in the field and can now be implemented for rationalizing therapeutic strategies (Perou et al., 2000, Voduc et al., 2010). BC can be broadly classified into in situ and invasive (infiltrating) carcinoma based on histological classification (Malhotra et al., 2010). Although the histological classification schemes have been a valuable asset for decades, they lack the integration of molecular marker data which determines prognostic significance.

Based on immunohistological profiling, clinicians grouped BC into hormone receptor positive and negative tumors which formed the basis for the development of endocrine therapies aiming to target hormone receptors, the best example being tamoxifen which targets the estrogen receptor (Howell et al., 2000, Baum et al., 2003, Osborne, 1998). Other examples are aromatase

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Chapter - 5 inhibitors, raloxifene (Black et al., 1994) and many other estradiol analogues like fulvestrant which are a group of selective estrogen receptor modulators (SERMs) (Mitlak and Cohen, 1999, Jordan and O'Malley, 2007). Usage of these compounds in adjuvant settings have provided a lower incidence of contralateral BC and overall survival. Conversely, about half of overall hormone receptor positive BC is resistant to endocrine treatment which can be either due to heterogeneity of the cells or acquired resistance developed over time. It has therefore proven a major challenge to categorize patients into groups that might or might not benefit from the specific adjuvant therapy (Osborne, 1998, Johnston et al., 1995, Sorlie, 2004, Sotiriou et al., 2003). As a result, modern research has focused on validating potential predictive biomarkers to enable a “personalized treatment” strategy for BC patients (Dietel and Sers, 2006, Malinowsky et al., 2010). Gene expression analysis with the help of hierarchical clustering has enabled BC to be characterized into six broad subtypes, based on expression of the respective receptors, termed as intrinsic subtypes (discussed in Section 5.1). These subtypes illustrate variation in major characteristics such as patient outcome, over-all survival, disease free survival, therapy response or ethnicity (Figure 5.1) (Perou et al., 2000, Sorlie et al., 2001, Sotiriou et al., 2003, Sorlie, 2004). Thus, the intrinsic subtype underlines the importance of treating BC, not as a single entity but as a heterogeneous disease.

Figure 5.1: Molecular classification of breast cancer. Schematic of molecular classification in BC on the basis of intrinsic molecular biomarkers identified by microarray analysis of patient tumor samples (Malhotra et al., 2010)

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5.2.2 Genomic Architecture of Breast Cancer Archiving the data from The Cancer Genome Atlas Network (TCGA) illustrated the identification of about 30,626 somatic mutations which comprise of 28,319 point mutations, 2302 insertions/deletions and 4 dinucleotide mutations. Among the mentioned point mutations were 19,045 missense, 6,487 silent and 1,437 nonsense mutations and 506 splice-site mutations, and 819 mutations among RNA genes (Stephens et al., 2012, Cancer Genome Atlas, 2012). Some of the well- known genes which have been affected due to these mutations include TP53, GATA3, CDH1, RB1, PTEN, PIK3CA, AKT1, MLL3, MAP3K1 (Cancer Genome Atlas, 2012, Perou et al., 2000) and CDKN1B along with some novel genes like CCND3, NF1, SF3B1, PTPN22, PIK3R1, TBX3, RUNX1, CBFB, PTPRD and AFF2 (Cancer Genome Atlas, 2012, Bamshad et al., 1997). Several identified somatic mutations relate to the above-mentioned intrinsic subtypes of BC as these vary among different subtypes. For example, within the Luminal A subtype, the most frequently mutated genes are PIK3CA (45%) (Loi et al., 2010) along with GATA3 (Usary et al., 2004), TP53 (Sorlie et al., 2001, Banerji et al., 2012), CDH1 (Lombaerts et al., 2006), MAP3K1 (Johnson and Lapadat, 2002) while Luminal B subtype commonly has TP53 (Sorlie et al., 2001) and PIK3CA (29%) (Loi et al., 2010) mutations at significantly higher frequency. In contrast, the basal subtype predominantly harbors TP53 mutation >85% frequency basal subtype which predominantly harbor TP53 mutation >85% frequency (Cancer Genome Atlas, 2012). The HER2 subtype exhibits a different pattern of mutation with highest amplification of HER2 (Cheang et al., 2009) (80%) followed by mutation in TP53 (72%) (Kim et al., 2006) and PIK3CA (39%) (Cancer Genome Atlas, 2012, Stemke-Hale et al., 2008). This diversification of mutation level among the subtypes illustrates the need to understand the concept of mRNA- and clinical-subtype specificity of the intrinsic subtype related to the type of mutation. Studies have also shown the presence of deleterious germ line variation among different genes in about 10% of sporadic BC namely: RAD51C, CHEK2, BRIP1, NBN, PTEN, BRCA1, BRCA2, and ATM. Therefore, it becomes important to understand each subtype specifically (Walsh et al., 2010).

5.2.3 Luminal Subtype The luminal subtype is mainly characterized by the presence of ER and PR expression and has a similar profile to that of luminal cells of the breast epithelium (Paik et al., 2004, Parker et al., 2009). The Luminal A subtype is known to be less aggressive than the luminal B, HER2/neu or basal-like subtype (Sotiriou et al., 2003). On the contrary, the Luminal B subtype has HER2

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Chapter - 5 expression (varies among patients) but not as amplified as the HER2/neu subtype along with ER and PR expression. The luminal B subtype also shares some genomic characteristics similar to that of ER negative tumors, an example being TP53 mutation (Sorlie et al., 2003, Langerod et al., 2007). Patients diagnosed with this subtype are generally known to have poor overall survival and a shorter disease-free survival rate (Cheang et al., 2009). Studies have also demonstrated that Luminal A subtypes are prone to much lower TP53 mutation frequency with Luminal A (12%) in comparison to Luminal B (29%) (Creighton et al., 2010, Majumder et al., 2004). Consequently, Luminal B also shows other alterations such as ATM, FOXM1, CDKN2C and MDM2 while Luminal A can be alterations in the RB1 pathway which results in amplification of Cyclin D1(Cancer Genome Atlas, 2012).

5.2.4 HER2/ NEU As stated earlier, the defining characteristic of this subtype being high amplification of the HER2 (human epidermal growth factor 2) gene along with HER2-amplicon-associated genes, such as GRB7 (growth factor receptor-bound protein) (Seidman et al., 2001, Vinatzer et al., 2005). Two different sub-classifications of the HER2 subtype exist. The first is a HER2+ subtype which is ER negative and highly express FGFR4, EGFR, HER2 and harbors high TP53 mutation. The second subtype is an ER positive HER2+ subtype, which expresses GATA3, BCL2 and ESR1 (Lal et al., 2005). HER2 amplified tumours can be treated with small molecule inhibitors such as Lapatinib (Geyer et al., 2006), or Trazstumab, which gives a better clinical result with improved patient outcome (Piccart-Gebhart et al., 2005). Other therapeutic targets are PIK3CA, which has a very high mutation frequency in this subtype with low PTEN mutation (Cancer Genome Atlas, 2012). EGFR has also gained interest as a druggable target with monoclonal antibodies like Pertuzumab (Baselga et al., 2010, Baselga and Swain, 2010) in combination with Trastuzumab (Kurokawa et al., 2000).

5.2.5 Basal Subtype This subtype, was first described by Moll et al. and is the most aggressive type of BC (Moll et al., 1982) and is known to possess distinctive clinical features, response to chemotherapy and patient outcome with precise site of distant relapse (Livasy et al., 2006, Cheang et al., 2008). This subtype is characterized by lack of ER, PR and HER2 expression which makes it very distinctive from Luminal and HER2 subtypes and is termed as BLBC (basal like breast cancer) (Nielsen et al., 2004). The expression of basal cytokeratins like CK5/6, CK14, CK17 CNN1

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(calponin 1), CAV1 (caveolin) LAMB1 (laminin) (Moll et al., 1983) alongside high expression of EGFR (Cheang et al., 2008) represent the key features of this subtype. Gene signature studies by Perou et al. have highlighted that the above molecular could be useful as potential diagnostic markers in the clinic (Perou et al., 2000, Sorlie et al., 2001). BLBC accounts for 12.3-36.7% of breast cancer cases (Badve et al., 2011) with incidence being highest amongst young African and African- American patients. Unlike other BC, BLBC is common in women in their early age of menarche (Millikan et al., 2008) and is inversely proportional to duration of lactation.

The BLBC subtype demonstrates the highest level of TP53 (85%) (Carey, 2006) mutation frequency along with conditional alteration in the TP53 signaling pathway, strongly indicating that loss of p53 is essential for the progression of this subtype (Sorlie, 2004, Cancer Genome Atlas, 2012). Genetic analysis has also suggested that loss of other tumor suppressors- in particular RB1 and BRCA1- is strongly associated with the basal-like subtype. The PIK3CA pathway is hyperactivated in BLBC, either through loss of PTEN (Stemke-Hale et al., 2008, Saal et al., 2008) and INPP4B (Fedele et al., 2010) or activating mutations in PIK3CA which occur at a lower frequency (9%) compared to luminal subtypes (Herschkowitz et al., 2008, Jiang et al., 2010). PARADIGM analysis (Vaske et al., 2010) comparing the basal to luminal subtype has confirmed the hyper-activation of FOXM1 (Prat et al., 2013) a potent oncogene that is well known to act as a transcriptional enhancer for the expression of keratins 5, 6, 17 alongside many other genes associated with cell proliferation. PARADIGM analysis has also identified the hyper-activation of MYC (Chandriani et al., 2009) and HIF1-a/ARNT (Cancer Genome Atlas, 2012), which are important characteristics of the basal subtype; other modulators include ATM mutation, BRCA1 and BRCA2 inactivation (Campeau et al., 2008) and cyclin E1 amplification (Foulkes et al., 2004). As described above, BLBCs have high TP53 mutation, which leads to impairment of the DNA damage checkpoint resulting in genomic instability by avoiding programmed cell death (Popova et al., 2012, Chin et al., 2006). EGFR expression being around 39-54% (Carey et al., 2006, Nielsen et al., 2004, Reis-Filho et al., 2006a, Gilbert et al., 2008) with loss of PTEN boosts p-AKT pathway activation (Marty et al., 2008). It allows evasion of apoptosis and higher cell proliferation through EGFR mediated Ras/MAPK Kinase pathway activation. α-β-Crystallin (Moyano et al., 2006, Sitterding et al., 2008), which is expressed highly in BLBC (45%) is associated with resistance chemotherapy and worse survival of BC patients. BLBCs are prone to have low Cyclin D1 (Reis-Filho et al., 2006b) expression but higher E2F3 and Cyclin E gene expression (Toft and Cryns, 2011).

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5.3 Triple Negative Breast Cancer In recent years, researchers have classified a subgroup of BLBC known as triple negative breast cancer (TNBC) which forms about 15% of the overall BC population and are subjected to conventional chemotherapy and radio therapy as mode of treatment in clinic (Anders and Carey, 2009, Cortazar et al., 2014). TNBCs share multiple similar characteristics to that of BLBC; however, not all BLBCs are TNBC (only about 70% similarity) and vice versa and these terms are not mutually exclusive (Parker et al., 2009, de Ronde et al., 2010). The mutation spectrum observed among TNBCs include TP53 (85%), Myeloid Cell Leukemia 1 (MCL1) amplification (54%), c-MYC amplification (35%), mutation or loss of RB1 (20%), PTEN (35%) and PIK3CA (7%) mutation with loss of INPP4B (30%) and high EGFR expression (Kalimutho et al., 2015, Shah et al., 2012, Dent et al., 2007). Well known CIN observed in TNBC includes the frequent amplification of 1q, 3q, 8q and 12q with loss of 4q, 5q and 8q chromosome arms (Kalimutho et al., 2015). Similar to BLBCs, TNBC incidence is reported to be dominant in younger African-American or Hispanic women (~40 years of age) (Carey et al., 2010, Foulkes et al., 2010). TNBCs are a class of IDC characterized by poor differentiation, bigger tumor size and higher proliferative capability with metastatic potential mostly to lung and brain in contrast to other soft tissues and bone, characteristics of other BC subtypes (Fulford et al., 2007, Dent et al., 2009). The management of this form of aggressive BC with a target specific treatment is a significant challenge.

To counter these challenges and understand the features of TNBC, consortia like The Cancer Genome Atlas (TCGA) (Cancer Genome Atlas, 2012) and METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) (Gatza et al., 2010) in a collaborative effort have put forth the hypothesis of sub-grouping of patients based on targetable vulnerabilities (target specific treatment for specific gene) for improving the efficacy of treatment. However, heterogeneity at the intra- and inter-tumor level along with inherent and acquired resistance, still remains a significant hurdle to overcome in the development of targeted therapies for TNBC patients. Next generation sequencing studies have illustrated at the range of 4-6 specific molecular subtypes, elements of definable signaling pathways, within the heterogeneous TNBC subtype which provide potential molecular target to overcome the above mentioned challenges (Lehmann et al., 2011, Podo et al., 2010, Shah et al., 2012, Burstein et al., 2015). Chemotherapy still remains the mainstay to treat TNBC with the hope of disrupting the cancer cell homeostasis, examples of which include anthracyclines (Mulligan et al., 2014), taxanes (Liedtke et al., 2008) and platinum compounds

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(Petrelli et al., 2014). Although there have been reports of high response rates (RR) to neoadjuvant chemotherapy with these compounds at initial stages, eventual recurrence leads to poor overall survival (OS). Due to this lack of pCR among TNBC patients post chemotherapy, it becomes important to determine the choice and nature of chemotherapy to be administered. For example, platinum compounds like carboplatin in combination with standard chemotherapy have enhanced the rates of pCR in some TNBC patients but those who don’t reach pCR undergo worse outcomes (Kalimutho et al., 2015, Masuda et al., 2013). This failure illustrates the need for effective therapeutic strategies for TNBC patients. It is therefore important to understand the recently identified subtypes of TNBCs.

5.3.1 Classification of TNBC subtypes Lehman et al. have recently classified TNBCs on the basis of meta-analysis of gene expression from 21 BC datasets into 7 classes (six definable and one unstable class). These subtypes of TNBC are known as basal-like (BL1 and BL2, known for their myoepithelial origin), mesenchymal-like (M), mesenchymal stem-like (MSL), luminal androgen receptor expression (LAR) and immunomodulatory (IM) (Figure 5.2) and they show distinctive therapeutic response upon neoadjuvant chemotherapy (NAC) settings (Lehmann et al., 2011). The BL1 class has a high proliferative rate with a high percentage of Ki67 staining and a signature of cell cycle and DNA- damage response (DDR) genes. They show the highest pCR (52%) post NAC as they have a good response to antimitotic agents like taxanes (examples are paclitaxel and docetaxel) and cisplatin. The BL2 class show enhanced proliferative gene activity with over-expression of metabolic genes and survival-mediated RTKs with patients in this class hardly accomplish pCR (Lehmann et al., 2011).

The mesenchyme-like (M) and mesenchyme stem-like (MSL) classes have the characteristic property of expressing epithelial-mesechymal transition markers like Snail, Slug, Twist and Vimentin along with growth factors and both the classes are susceptible to PI3K/mTOR (mammalian target of rapamycin) inhibitors and sensitive to sarcoma family kinase (SRC) inhibitor. In contrast to the M class, MSL shows high levels of transforming growth factor β (TGF-β) receptor type III (TGF-βRIII) (Jovanovic et al., 2014) which give the characteristic to invade/migrate in vivo but low proliferative gene signature with lower mitotic index thus have moderate pCR rate (23-31%). The luminal androgen receptor expression (LAR) class shows a very poor response to chemotherapy (about 10% pCR) and is exhibits a high level of androgen receptor signaling with PI3K signaling,

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Chapter - 5 targeting of which provides better response (Lehmann et al., 2014). Lastly, the Immunomodulatory (IM) class shows a moderate pCR and has a high immune response due to infiltrating T cells. In the following section, we will discuss in more detail the strategies and therapies available in the market for the treatment of TNBC (Abramson et al., 2015, Mayer, 2013).

5.3.2 Targeting the receptor tyrosine kinase (RTKs) and non-RTKs signaling pathway Elevated levels of RTKs have been shown to be associated with many cancers as they are important elements in signal transduction pathways. They are also associated with autocrine signaling and cell-cell communication and essential for cell proliferation and differentiation, regulation of cell growth and metabolism in conjunction with promotion of cell survival and apoptosis (Zhang and Hochwald, 2013, Gschwind et al., 2004). Examples of some well-known RTKs include VEGFR (vascular endothelial growth factor receptor) (Greenberg and Rugo, 2010, Dent, 2009); PDGFR (platelet-derived growth factor receptor); EGFRs (Song et al., 2013, Ueno and Zhang, 2011); FGFR (fibroblast growth factor receptor) (Sharpe et al., 2011, Turner et al., 2010); IGF-1R (insulin-like growth factor-1 receptor) (Litzenburger et al., 2011), HGFR/c-MET (Gujral et al., 2013, Sohn et al., 2014, Gaule et al., 2014) and AXL (Gujral et al., 2013, Meyer et al., 2013). It has been recognized that non-RTKs like SRC (sarcoma family kinase) (Tryfonopoulos et al., 2011) and common signaling intermediates like PI3K/AKT/mTOR, RAF/MEK/ERK pathways, janus family of kinases/signal transducer and activator of transcription (JAK/STATs) as well as Wnt/b- catenin (CTNNB1) signaling and have been used as targets in the treatment of TNBCs (Schlessinger, 2014, Krejci et al., 2012). Unfortunately, these targets have shown promising outcomes only in-vitro as single agents but has failed to counter the aggressive nature of TNBCs in clinical settings. However, with planned combination strategies, these targets is expected provide promising results in clinic trials (Figure 5.3).

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Figure 5.2: Classification of TNBCs. Schematic of Lehman’s classification of six distinct molecular subtypes of TNBC. The classification was performed on the basis of integrative gene- expression and copy number profiling screens elaborating the variation in the signaling pathways leading to robust diversification. The diagram also highlights the percentage of incidence and pCR of each subclass alongside potential molecular targets which can be used for targeted therapeutics (adopted from (Kalimutho et al., 2015)).

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5.3.3.1 Inhibitors for VEGFR and PDGFR To date, the monoclonal antibody bevacizumab (Kumler et al., 2014) and small molecule inhibitor sunitinib (Chinchar et al., 2014) have been shown to inhibit VEGFR signaling and is known to suppress angiogenesis in TNBC xenograft. Studies have shown the enhanced response rate with a combination of bevacizumab and paclitaxel chemotherapy even though no improvement of overall survival (OS) was observed (Yadav et al., 2014). Recently completed clinical trial CALGB 40603 trial (NCT00861705) illustrated the improved pCR rates of patients treated with combination of carboplatin along with bevacizumab to neoadjuvant (paclitaxel followed by doxorubicin and cyclophosphamide) vs patients treated with neoadjuvant chemotherapy (Sikov et al., 2015). PDGFRα and PDGFRβ (Carvalho et al., 2005, Schlessinger, 2014, Coltrera et al., 1995) are the receptors for the ligands PDGF -A, -B, -C and –D, dimerization of which regulates cell survival, proliferation, migration of cancer cells and self-renewal of cancer stem cells. Sunitinib (Chinchar et al., 2014), also shown to block dimerization of PDGF ligands to their receptor but imatinib (Smith, 2011), approved by the FDA for the treatment of chronic myeloid leukaemia (CML) is known to be more potent as blocks the phosphorylation of PDGFRβ (Figure 5.3).

5.3.3.2 Inhibitors for EGFR EGFR (HER1) is well known to be over-expressed in many cancers apart from TNBCs and is associated with activation of the BRAF (Banerji et al., 2012, Cancer Genome Atlas, 2012) signaling pathway resulting in poor OS (Corkery et al., 2009). Monoclonal antibodies like panitumumab and cetuximab along with small molecule inhibitors like gefitinib and erlotinib (Imai and Takaoka, 2006, Matar et al., 2004) have been clinically approved for use in lung cancer. However, in vivo studies have shown no significant change in proliferation rates of TNBC xenografts post cetuximab treatment (Sergina et al., 2007) as AKT and HER3 is now known to act as feedback loops to stimulate the pathway hence causing resistance (Kalimutho et al., 2015). Combination of platinum compounds like carboplatin or cisplatin (Carey et al., 2012, Baselga et al., 2013) cetuximab provided uncertain efficacy in phase II clinical trial as compared to the control group; patients treated with combination therapy did show good response rate but OS rate did not improve significantly. In contrast, combination of cetuximab-coupled radionuclide with chemotherapy and PARP inhibition (Al-Ejeh et al., 2013) (see further discussion on PARP inhibitors in later sections) exterminated the entire BC stem cell population with no relapse of primary or

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Figure 5.3: Overview of the molecular targets identified in TNBCs and their inhibitors that currently in clinical trials. Schematic of the multiple signaling cascades associated with TNBCs and corresponding specific inhibitors currently in clinical trials or being screened in preclinical experiments. The diagram also suggests possible strategies that could be implemented at the same time for viable treatment tactics against aggressive disease (adopted from (Kalimutho et al., 2015)).

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5.3.3.3 Inhibitors against Survival and Proliferative pathways The PI3K-AKT-mTOR pathway is well characterized to be the survival pathway and is associated with cell proliferation, metabolism and mortality in cross talk with the MAPK pathway (Fruman and Rommel, 2014). While mTOR controls the regulation of metabolism through controlling glucose uptake and energy generation while AKT acts as a regulator of apoptotic pathway (Bcl2 family members) and cellular regulation including translation, protein turnover, cell growth. Hyper-activation of PI3K (Cancer Genome Atlas, 2012) (60%) through mutations or deletions of PTEN and LOH of INPP4B rather than mutation of PIK3CA (8%) and negative feedback loops from RTKs (Fedele et al., 2010, Gewinner et al., 2009, Gordon and Banerji, 2013, Yunokawa et al., 2012) (as discussed earlier) and MAPK signaling is known to be associated with TNBC patients and is responsible for poor prognosis. The synergetic effects of dual inhibition with targeting of these regulators in preclinical studies represents a promising therapeutic strategy (Figure 5.3).

The JAK/STAT pathway is also well characterized to regulate multiple cellular functions including proliferation, survival, differentiation, apoptosis and migration (Furth, 2014). JAK tyrosine kinases are the docking sites for STATs, and both have been shown to play a crucial role in TNBC with molecular profiling demonstrating the oncogenic role of STAT3 in promoting uncontrolled growth and angiogenesis. Consistent with this, an increase in phosphorylation of STAT3 is observed in 50% of BC and is associated with poor prognosis (Montero et al., 2014, Britschgi et al., 2012, Marotta et al., 2011). Preclinical studies in mouse models have shown that the suppression of STAT3 alone leads to reduced tumor growth with increased susceptibility of these tumors to chemotherapeutic targets. IL6/JAK2/STAT3 signaling is associated with CD44+/CD24+ basal like cancer stem cells inhibition of which via BSK805 (JAK2 inhibitor) (Baffert et al., 2010) diminished xenograft tumor growth and eliminated the stem-cell (Shields et al., 2013). At present, ruxolitinib (Verstovsek et al., 2012) known for its effects for the treatment of myelofibrosis is currently in Phase II clinical trial in combination with paclitaxel (Figure 5.3).

Anomalies in the MAPK pathway are known to be associated with the expansion and progression of TNBCs and contributing to malignant transformation with abnormal cell proliferation to oppose apoptosis (Roberts and Der, 2007, Dhillon et al., 2007). TCGA data indicates the presence of low mutation frequency in RAS and RAF (2%) (Cerami et al., 2012). However somatic mutations of neurofibromin (NF1) (Wallace et al., 2012) and hypermethylation of DUSP4 (Dual-Specific Phosphatase-4) (Balko et al., 2012), negative regulator of RAS pathway, are driving RAS and RAF

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Chapter - 5 activation in metastatic TNBC. Deregulation of these factors in turn leads to hyperactivation of mitogen-activated protein kinase kinase 1/2 (MEK1/2) and mitogen-activated protein kinase (ERK1/2) (Bhatt et al., 2010, Stephens et al., 2012). Post NAC, BLBC xenografts demonstrated elevated levels of RAS signaling which when treated with combination of selumetinib (MEK1/2 inhibitor) (Balko et al., 2012, Davies et al., 2007) and docetaxel strongly synergized tumor growth inhibition. However, preclinical studies of this inhibitor have failed in Phase I primarily due to kinome reprogramming after MEK1/2 inhibition via gaining resistance through RTK pathway (Robert et al., 2013, Ascierto et al., 2013, Farley et al., 2013, Kirkwood et al., 2012, Duncan et al., 2012). Cross talk between the MAPK and PI3K pathways is well established (Mendoza et al., 2011), and co-inhibition of both these pathways may facilitate tumor growth inhibition, a clinical trial of which is ongoing with use of GSK1120212 (Gilmartin et al., 2011) in combination with other chemotherapeutic agents. Kinome profiling has brought forth the hyper-activation of non-canonical MAPK pathway which involves mitogen-activated protein kinase 7 (MAPK7/ERK5) and HSP90 (heat shock protein 90) (Al-Ejeh et al., 2014a) as potential therapeutic option in treatment against TNBC (Figure 5.3).

5.3.4 Inhibitors against regulators of cell cycle TP53, known as the “guardian of the genome” is responsible for sensing DNA damage and arresting the cells for repair in G1 of the cell cycle or to undergo apoptosis if repair fails to maintain genomic stability. Along with TP53, there exists other regulators like DNA damage checkpoint kinases ATM, ATR, and CHK1/2 (checkpoint kinase 1/2) which play a crucial role in regulating genomic stability by coordinating cell cycle arrest with DNA repair (Perry and Levine, 1993). However, in TNBC, the mutation frequency of TP53 is very high along with germ line mutation of BRCA1 and BRCA2. On the other hand, CHK1/2 are neither amplified nor mutated and are overexpressed in genomically unstable cells like TNBC, thus CHK1 is gaining importance in the treatment of aggressive BC having TP53 mutation (Dent et al., 2011). CHK1 functions by arresting the cell cycle in response to DNA damage (double stranded breaks) by phosphorylating cell division cycle 25A (CDC25A) protein phosphatase. CHK1 inhibitor UNC-01 (Takahashi et al., 1987) boosted cisplatin sensitivity 60 fold in the preclinical setting, by opposing the DNA-damage dependent G2 checkpoint. However, in the clinical setting this drug has not shown significant efficacy due to poor bioavailability (Bunch and Eastman, 1996, Dees et al., 2005). The concept however gave way to other CHK1 inhibitors LY2606368 (Eli Lily), SCH900776 (Schering Plough), PF-477736 (Pfizer)

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Chapter - 5 and AZD7762 (Astra Zeneca) which are, either being used as a single agent or in combination with other chemotherapies, currently in Phase I trials with advanced stage solid tumors and Phase II in TNBC patients with germ line BRCA1/2 mutation (Figure 5.3) (Castedo et al., 2004b). Interestingly, several independent studies have shown up-regulation of ERK1/2 acting as a resistance mechanism to CHK1 inhibitors, which are compensated when combined with MEK1/2 inhibitor (Dai et al., 2001, McKinstry et al., 2002).

One of the recently developed rationales is the inhibition of PARP (Poly ADP ribose polymerase) in the treatment of BRCA1-mutant cancer via synthetic lethality (process of cell death due to loss of two genes which fails in deletion of either gene individually). Due to mutation or inactivation of BRCA1 and BRCA2, tumor cells have impaired HR (homologous repair) and thus rely on PARP to repair DNA damage via alternative NHEJ (Non-Homologous End Joining) and BER (Base Excision Repair) (Lips et al., 2013, Seong et al., 2014, Couch et al., 2015, Audeh, 2014). However, BRCA1/BRCA2-deficiency does not synergise with other components of the BER pathway, suggesting that the above mentioned mechanism might not explain observed synergistic interaction of PARPi in BRCA-deficient cells. Consistent with this, the synergistic interaction is shown to be dependent on ability of PARPi to trap PARP in chromatin, resulting in replication- associated breaks, which are repaired by the HR pathway (Brown et al., 2017). Therefore to target PARP in BRCA deficient tumors is a good strategy, as inhibition of PARP will force cells to enter apoptosis upon DNA damage (Farmer et al., 2005) (Figure 5.3). Veliparib and Olaparib are inhibitors of PARP1 which have demonstrated reduced tumor growth in BRCA1 deficient mice with low toxicity levels (To et al., 2014). With this success, these inhibitors were trailed in synergistic combination with chemotherapeutic agents in the pre-clinical setting which showed higher apoptosis, an example of which is the combination of olaparib and paclitaxel l (Shi et al., 2014). In the clinical setting, the PARP inhibitor Iniparib, in combination with chemotherapy, showed improvement in median OS and progression -free survival in metastatic TNBC patients, compared to chemotherapy alone in phase I/II clinical trials (O'Shaughnessy et al., 2011). However, results from the recently completed phase III clinical trial were not that promising as expected (O'Shaughnessy et al., 2014). This may be due to the discovery that Inaparib is not a specific PARP inhibitor, as it functions by non-selectively modifying cysteine-containing proteins (Liu et al., 2012). Olaparib in combination with chemotherapy for the treatment of metastatic TNBC patients has also had moderate efficacy (Dent et al., 2013). Currently many phase I and II studies are under progress with PARP inhibitors and are expected to achieve favorable results.

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Cell cycle regulators like CDKs in association with cyclins promotes progression of the cell cycle and hence are valuable targets of TNBCs. This is because CDKs are not mutated in TNBCs but there exists mutation among endogenous inhibitors of CDKs like INK4 class inhibitor p16 enabling uncontrolled proliferation (Kalimutho et al., 2015). Selective inhibition of CDK1 and CDK2 has demonstrated synthetic lethality in conjunction with MYC amplification in TNBC, through BIM activation (Horiuchi et al., 2012). The CDK1 inhibitor Flavopiridol is known to repress the transcription of apoptosis inhibiting genes and binds directly to other CDK inhibitors like CDK2, CDK4 and CDK6 (Konig et al., 1997). CYC202, a CDK2 inhibitor is thought to have good output against cell line derivatives of multiple tumors including TNBC (Konig et al., 1997). In association with PIK3CA inhibition, the CDK4/6 inhibitor LEE-001whose sensitivity is determined by RB1 dephosphorylation has shown promising results in xenograft models (Vora et al., 2014). Even UNC-01 is also associated with the inactivation of CDK2 via de-phosphorylation thus limiting progress of cells into S phase (Akiyama et al., 1997). Notably, studies have shown that overexpression of PLK1 is associated with poor survival indicating that high PLK1 expression is closely associated with cancer aggressiveness including TNBC (Maire et al., 2013b). Wide varieties of human tumors have susceptibility to PLK1 inhibition but on the contrary, 80% reduction of PLK1 expression had no effects on primary human cells or organs of PLK1 RNAi mice. Inhibition of PLK1 acts similarly to that of other mitotic poisons such as nocodazole or taxanes as it blocks the cells in G2-M phase of cell cycle and ultimately leads to apoptosis (Steegmaier et al., 2007, Lenart et al., 2007). Thus targeting PLK1 is of high importance and specificity for the treatment of many cancers, and the use of inhibitors against PLK1 for the treatment of TNBC is under investigation.

Clinically approved HDAC (Histone DeACetylase) inhibitors as monotherapy (NCT02623751) or in combination with cisplatin (NCT02393794) are currently under examination in TNBC. Recent studies have identified many novel molecular targets with strong preclinical data such as PIM1 kinase (Braso-Maristany et al., 2016, Horiuchi et al., 2016), WEE1 (Zheng et al., 2017, Garimella et al., 2012), TTK/hMPS1 (Maire et al., 2013a), BET bromodomain inhibitors (Shu et al., 2016) and MELK4. These molecular targets represent attractive therapeutic options for initiating clinical trials for the treatment of TNBC. It will be interesting to see their efficacy both as an individual agents and in adjuvant or neoadjuvant settings in human patients. Further, targeting the hypoxia-inducible factor 1-α (HIF1α) pathway (Hu et al., 2013), c-Met (NCT01738438) (Sameni et al., 2016) and integrins (such

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Chapter - 5 as integrin β5) (Bianchi-Smiraglia et al., 2013) that are involved in angiogenesis is also being investigated for treating TNBC patients.

5.4 Conclusion BC is not a uniform cancer entity but represents several different subtypes with varying molecular landscapes, biological behavior, aggressiveness and risk profiles, thus posing an enormous challenge for clinical management. Among all subtypes, BLBC or more precisely, TNBCs account for a small percentage of diagnosed cases, but are associated with causing a disproportionate number of deaths among patients. Molecular profiling using modern technologies such as whole exome sequencing and proteomics has revealed valuable insights in identifying putative targets specific to each subtypes of TNBC. This data has been supported by promising preclinical analysis with new therapies. However, selecting the correct set of patients for clinical trials for these therapies will be critical. Drug resistance poses a significant problem in the clinic as tumor heterogeneity enables tumor cells to adapt and rewire their signaling circuitry to take advantage of redundancy and feedback mechanisms. Data from systems biology has highlighted the importance of synthetic lethality wherein simultaneous inhibition of multiple ‘hubs’ within an oncogenic pathway may be a good strategy to overcome alternate rewiring. Although, the emergence of immunotherapy is shaping to be a boon in determining the clinical outcome of cancer patients, unfortunately the approach is not that effective against TNBC patients. Thus, conventional chemotherapy still remains the mainstay for most patients with an urgent need for improved and affordable targeted therapeutics against TNBC.

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Chapter – 6

Aim 2.1: To investigate the impact of CEP55 over-expression or depletion on aneuploid basal like breast cancer cell survival during perturbed mitosis.

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6.1 Introduction Genomic instability (GIN) (as discussed previously Chapter 4; Section 4.1), is a well- known characteristic of most cancers and is defined as the tendency of gaining increased alteration by a cell during the cell cycle (Baas et al., 2006, Dowsett et al., 2010). In a regular cell division, factors such as high-fidelity DNA replication during S-phase, proper SAC activation and controlled monitoring of chromosomal segregation, coordinated cell cycle progression and error free DNA damage response minimize the level of GIN (Negrini et al., 2010). Chromosomal instability (CIN) is a major form of GIN which represents the higher rate at which the structure and number of chromosomes of a normal cell changes over time (Shen, 2011). There exists variation in the level of numerical (nCIN) and structural (sCIN) chromosomal instability between cell populations which results in tumor heterogeneity, which in turn paves the way for clonal expansion of cancer cells, resistance to chemotherapy and tumor relapse (Tabchy et al., 2011, Zahreddine and Borden, 2013).

Breast cancer (BC) is an umbrella term representing a broader class of heterogeneous diseases encompassing striking genetic and phenotypic diversities (Stingl and Caldas, 2007, Neve et al., 2006). Gene expression analysis with the help of hierarchical clustering has enabled BC to be characterized into five broad subtypes, based on expression of the different markers, termed as intrinsic subtypes, namely: luminal A, luminal B, HER2/Neu over-expressing, basal-like and normal breast-like tumor subtype, which vary based on patient outcome, over-all survival, disease free survival, therapy response or ethnicity (Perou et al., 2000, Sorlie et al., 2001, Sotiriou et al., 2003, Sorlie, 2004). Thus, the intrinsic subtype underlines the challenge of treating BC, not as a single entity but as a heterogeneous disease, displaying variation at the histopathological and clinical perspective, which can be well appreciated by understanding the genomic architecture of the disease.

6.1.1 Role of mitotic proteins in driving genomic instability in cancer Patients with BC frequently display both nCIN and sCIN chromosomal changes (Cancer Genome Atlas, 2012, Duijf et al., 2013, Zack et al., 2013). Gain or loss of whole chromosomes is referred as nCIN while gain or loss of chromosome fragments or chromosomal rearrangements, including insertions, amplifications, deletions and translocations is referred as sCIN (Gollin, 2005). As discussed previously, during anaphase, SAC activation ensures that the chromosome copies are not parted, until all kinetochores on the sister chromatids are engaged by microtubules originating from centrosomes at opposite poles of the cell. Upon accomplishment of

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Chapter - 6 this phenomena, the checkpoint gets satisfied and a series of reactions are initiated that ultimately leads to cleavage of the cohesins, thus enabling the sister chromatids to separate during anaphase (discussed previously in Chapter 1) (Musacchio and Salmon, 2007). SAC activation during mitosis is critical for faithful chromosome segregation as studies have demonstrated that defects in SAC activation results in promotion of nCIN in cancer cells (Perez de Castro et al., 2007). Interestingly, though SAC is weakened for promoting nCIN in vitro and in vivo, mitotic checkpoint genes are rarely mutated (Tighe et al., 2001, Perez de Castro et al., 2007, Gascoigne and Taylor, 2008). As an alternative, mitotic checkpoint genes, such as BUB1, BUB1B, CDC20, MAD1, MAD2L1, TRIP13 and TTK, are often overexpressed in breast and other cancers. Amplification of these genes is associated with hyper-activation of SAC causing prolonged mitosis, which results in “mitotic slippage”, and ultimately nCIN (Gascoigne and Taylor, 2008, Schvartzman et al., 2010, Al-Ejeh et al., 2014b, Carter et al., 2006). Vitale et al. have demonstrated that cancer cells develop strategies to perpetuate aneuploidy by uncoupling mitotic death linked regulation of mitotic-checkpoints. However, they have emphasized that molecular mechanisms responsible for causing mitotic aberration, cell death and genomic stability are still largely elusive (Vitale et al., 2011). In recent years, the role of epigenetics has been linked to be one of the mechanisms to avoid cell death after mitotic catastrophe, in particular aberrant methylation of genes such as BUB3, AURKA, DYNC1I1, DCTN2 and CHFR genes involved in chromosomal segregation and spindle assembly (Krause et al., 2016).

6.1.2 Cytokinesis failure results in CIN in breast cancer As discussed previously, cytokinesis the last step of cell division which results in physical separation of the cells. A number of genes such as AURKA, AURKB, BRCA2, CEP55, FOXM1 and KIF20A control this process and are often mutated or overexpressed in breast cancer (Carter et al., 2006, Al-Ejeh et al., 2014b, Jeffery et al., 2015b, Daniels et al., 2004). Failure to undergo cytokinesis results in generation of tetraploid binucleate cells. One of the causes of multinucleation is endoreduplication, i.e., genome duplication without undergoing mitosis and cytokinesis, or mitotic slippage (also known as endomitosis). This phenomenon often arises in cells, exit mitosis without undergoing cytokinesis following prolonged mitotic arrest due to mitotic checkpoint hyperactivation (Edgar et al., 2014). Recent findings from whole exome sequencing in BC patient samples have identified that tetraploidy is a prime transitional step to aneuploidy during breast cancer development (Zack et al., 2013). Notably, it has been well documented that tetraploidy

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6.1.3 Role of CEP55 in breast cancer tumorigenesis CEP55 is a coiled coil centrosomal protein originally identified as an indispensable regulator of cytokinesis, the final stage of cell division that results in the physical separation of two daughter cells (discussed in detail in Chapter 1; Section 1.7). CEP55 is expressed at low levels in most normal human tissues except testis, and its dysregulation is linked to multiple disease states (Jeffery et al., 2015b, Inoda et al., 2009a). It is associated with aggressive behavior in in vivo xenograft models, is an independent marker of poor clinical outcome in various malignancies, and has been recognized as a strong candidate for vaccine development in breast and colorectal cancers (Inoda et al., 2009b, Inoda et al., 2011c, Inoda et al., 2011b). As discussed previously (Chapter 1; Section 1.12), in breast cancer, overexpression of CEP55 has been implicated in the progression from ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) (Ma et al., 2003). CEP55 was also identified as part of a 19-gene signature that stratified breast cancer patients into good versus poor prognosis (Fournier et al., 2006). The level of expression of CEP55 was higher in patients who died within 5 years of diagnosis compared to patients who survived (Fournier et al., 2006). Therefore, it appears that CEP55 plays a pivotal role in tumor initiation and progression, potentially through the emergence of aneuploidy in cancers. However, the mechanism of how CEP55 mediates genomic instability and tumorigenesis has remained elusive.

In this chapter, we provide the first experimental evidence that links overexpression of CEP55 to genomic instability and onset of aneuploidy in breast cancer. Using large breast datasets with clinical follow-up information, we confirmed that a high level of CEP55 mRNA is associated with poor clinical outcomes. We found that CEP55-depleted breast cancer cells proliferate at a slightly reduced rate and have significantly reduced primary tumor growth in athymic nude mice. In addition, we demonstrated that knockdown of CEP55 in breast cancer cells in vitro significantly reduced the number of aneuploid cells, induced cell death during perturbed mitosis and sensitized cells to anti-mitotic agents such as PLK1 inhibitor (BI2536). We also demonstrated that loss of CEP55 leads to premature CDK1/Cyclin B1 activation and induces mitotic catastrophe upon perturbed mitosis. Collectively, we have shown that CEP55 is a protector of the aneuploidy state in

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BC during perturbed mitosis and might be a major player in causing resistance against mitotic inhibitors in the clinic.

6.2 Results

6.2.1 CEP55 overexpression is associated with poor outcome in breast cancer

Although CEP55 is ubiquitously overexpressed in many human tumor types (Jeffery et al., 2015b) (also discussed previously), a detailed molecular understanding of its role in tumorigenesis has remained elusive. We analyzed CEP55 expression using the publically available Gene expression-based Outcome for Breast cancer Online (GOBO) database (n=1881) (Ringner et al., 2011). We found that CEP55 mRNA expression is associated with the PAM50 breast cancer molecular subtypes (Luminal A, Luminal B, HER2 and Basal-like), with the basal-like subtype exhibiting significantly higher expression of CEP55 compared to other subtypes (p<0.0001; Figure 6.1A). This increased expression of CEP55 was also associated with high-grade tumors (p<0.0001; Figure 6.1B) and Estrogen Receptor (ER) status (p<0.0001; Figure 6.1C). High CEP55 expression was significantly associated with poor overall survival (p=0.00102), relapse free survival (p<0.00001) and distant-metastasis free survival (p=0.01135) (Figure 6.1D-F).

CEP55 is part of a proliferation/mitotic gene signature suggesting that the observed differences in patient survival could be due to its association with proliferation. To rule out this possibility, we normalized the expression of CEP55 with a key proliferation marker, KI67 using the TCGA dataset (n=492) (Cancer Genome Atlas, 2012). This confirmed that CEP55 expression was significantly higher in breast cancer patients compared to normal breast tissue independent of proliferation and subtypes (p<0.0001; Figure 6.1G). Collectively, these data provide compelling evidence that high expression of CEP55 mRNA is associated with poor clinical outcomes in breast cancer and therefore could be a novel target for therapeutic intervention.

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Figure 6.1: Clinical correlation of CEP55 mRNA expression in breast cancer datasets. Relationship between CEP55 mRNA expression (Log2 expression) and (A) breast cancer intrinsic molecular subtypes, (B) histological grade, and (C) Estrogen Receptor (ER) status in the GOBO dataset. Association of CEP55 expression with clinical outcome for (D) overall survival, (E) relapse- free survival and (F) distant metastasis-free survival determined using the GOBO dataset. (G) CEP55 expression using the breast cancer TCGA dataset after normalization to Ki67. ****p<0.0001.

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6.2.2 Differential expression of CEP55 regulates breast cancer cell proliferation and survival To help select suitable models for functional work, we first analyzed CEP55 in a published breast cancer cell line gene expression array dataset (n=51 lines) (Neve et al., 2006). Similar to clinical samples, CEP55 mRNA expression was higher in basal-like, triple-negative cell lines, particularly those with mesenchymal and invasive phenotypes (Figure 6.2A-C). Western analysis showed a similar trend toward higher protein expression in basal-like lines (Figure 6.2D).

To evaluate the impact of CEP55 on cellular proliferation and survival, we depleted CEP55 in MDA-MB-231 cells, (shows highest protein level in the panel of cell lines (Figure 6.2D) by stable transduction with doxycycline-inducible shRNAs that target different regions in the CEP55 transcript. The CEP55 shRNA selected expressing clones, hereafter referred as sh#2 (partial depletion of CEP55) and sh#8 (near complete depletion of CEP55) demonstrated lower expression of CEP55 compared to scrambled shRNA-transfected cells (hereafter referred to as control) (Figure 6.2E). Both lines with reduced CEP55 exhibited significantly delayed long-term proliferation (p<0.0001; Figure 6.2F) and anchorage-independent colony formation (Figure 6.2G). Independently, to confirm this was a bona fide effect and exclude the possibility of cell line or shRNA sequence- specific loss of aneuploidy, we depleted CEP55 in another well-characterized aneuploid line, Hs578T (Hackett et al., 1977), using constitutively-expressed shRNAs that target different regions of the CEP55 transcript than those used for the MDA-MB-231 model (Figure 6.2H, Upper panel). Analysis of cell proliferation and anchorage-independent growth showed a very similar pattern to the CEP55-depleted MDA-MB-231 lines (Figure 6.2H (lower panel) & I). Furthermore, engrafting a partial CEP55-depleted MDA-MB-231 derivative (sh#2) showed a significant reduction in tumor growth while a near complete CEP55 reduction (sh#8) abrogated tumor formation in NOD/scid mice (Figure 6.2J), suggesting CEP55 is essential for tumor formation. Taken together, these data provide very strong proof that CEP55 overexpression favors cell proliferation and survival of BC cells.

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Figure 6.2: CEP55 regulates human breast cancer cell survival. (A) Subtype-specific CEP55 mRNA expression (Log2 expression) in breast cancer lines assessed using the Neve dataset. (B, C) Interim analyses of CEP55 mRNA expression (Log2 expression) in basal-like vs. non-basal-like breast cancer cell lines (TNBC: triple-negative breast cancer; HR pos: Hormone receptor positive). (D) Immunoblot analysis of CEP55 expression in a panel of human breast cancer lines. Tubulin served as a loading control. TP53 mutant status of the cell lines is highlighted with asterisks (*). (E) Immunoblot analysis of CEP55 expression in CEP55-depleted MDA-MB-231 cells. Two isogenic lines (sh#2 and sh#8) were obtained using two different shRNA sequences as described in the methods section. COX-IV served as a loading control. (F) Effect of CEP55 depletion on cell proliferation in MDA-MB-231 cells assessed using the IncuCyte ZOOM® live cell imager (phase-only processing module). The percentage of cell confluence was determined using an IncuCyte mask analyzer. (G) Representative images of colony formation at 14 days was determined using crystal violet staining in control and CEP55-depleted MDA- MB-231 cells. (H) Immunoblot analysis of CEP55 expression in CEP55-depleted Hs578T using a constitutive shRNA construct. COX-IV was used as a loading control (upper panel). Effect of CEP55 depletion on cell proliferation in Hs578T cells was assessed using IncuCyte ZOOM® live cell imager phase-only processing module for 120 hours. Percentage of cell confluence was determined using an IncuCyte mask analyzer (lower Panel). (I) Evaluation of colony formation at 14 days was determined using crystal violet staining in both control and CEP55-depleted Hs578T cells. For colony formation assays, 1000 cells were seeded. (J) Six-week-old female NOD/Scid cohorts of mice were injected in the 4th inguinal mammary fat-pad with the control and CEP55-depleted cells. Growth rate (area, mm2) of the tumors was measured using digital caliper. Differences in growth were determined using Student’s t test, n = 5 per group. ****p<0.0001.

6.2.3 High levels of CEP55 are protective in aneuploid cells CEP55 is part of the 70-gene chromosomal instability (CIN) signature that was associated with aneuploidy in several cancer types (Carter et al., 2006), thus we questioned whether high expression could confer tolerance to or promote aneuploidy (causal effect). We hypothesized that CEP55-depletion will result in reduction of aneuploidy in TNBC cell lines. To test our hypothesis, we performed cell cycle analysis by fluorescence-activated cell sorting (FACS) on the established CEP55-depleted MDA-MB-231 surviving cells, and found that the polyploid subpopulation was significantly reduced compared to the control cells (p<0.0001; Figure 6.3A). To confirm this, we performed metaphase spread analysis and found that both the mean and spread of the chromosome number were significantly reduced in sh#8 compared to control cells (p<0.0001, t- test and p<0.0001, F-test; Figure 6.3B). Similarly, we found that the Hs578T aneuploid subpopulation was significantly reduced in CEP55-depleted cells compared to control (p<0.0001; Figure 6.3C), suggesting that the reduced aneuploid subpopulation was neither cell line- nor shRNA sequence-specific, but rather was a direct consequence of CEP55 depletion.

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To investigate the potential clinical significance of these observations, we analyzed CAN (copy number alterations) and expression data from TCGA. Gains at chromosome 20 are very common in breast cancer, particularly the 20q arm (Hodgson et al., 2003, Tabach et al., 2011). Using TCGA data, we found that 20q gain is significantly more frequently gained than lost in breast tumor samples (15.6 vs. 1.1%, p<00.0001; Figure 6.3D) and tumors with 20q gain are significantly associated with higher CEP55 expression (p<0.0001; Figure 6.3E). Moreover, we found that high CEP55 expression is associated with high average copy number of genome in the METABRIC Discovery dataset (n=995 tumors; p<0.0001; Figure 6.3F). Overall, the data suggest that high CEP55 expression may support tolerance of genomic instability and aneuploidy.

Figure 6.3: CEP55 regulates genomic instability and aneuploidy. (A) Polyploidy analysis (>4N DNA contents) determined using FACS in control and CEP55-depleted MDA-MB-231 cells (left panel). Corresponding cytogram showing different phases of cell cycle and the polyploidy subpopulation analyzed using ModFit LT 4.0 software (right panel). Yellow peaks represent subpopulation of polyploidy/aneuploidy. (B) Analysis of chromosome number in indicated MDA- MB-231 clones by Giemsa staining of metaphase spreads n= number of metaphase spreads counted. Error bars represent the standard error of the mean from two independent experiments. (C)Percentage of aneuploidy in both control and CEP55-depleted Hs578T cells consistent to (A). (D) Percentage of breast cancer TCGA tumors with and without chromosome 20q gain and loss, P<0.0001, Chi-square test. (E) CEP55 expression in TCGA tumors that were stratified with and without chromosome 20q gain. (F) Correlation between average copy number alterations of genome (CNAs)/tumor and CEP55 mRNA expression in the METABRIC dataset. ****p<0.0001.

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6.2.4 CEP55 overexpression provides survival advantage during perturbed mitosis Next, we investigated mechanisms by which CEP55 overexpression could promote GIN. Since CEP55 is a microtubule associated centrosomal protein that efficiently bundles microtubules (Zhao et al., 2006) and CEP55-depleted cells exhibit reduced proliferation (Figure 6.2F & H), we challenged CEP55-depleted cells with nocodazole or the PLK1 inhibitor (PLK1i) B12536. Both of these agents blocks cells in mitosis by inhibiting signaling cascades necessary for centrosome separation, microtubule-kinetochore attachments and normal mitotic progression. We noticed an increase in polyploidy following both nocodazole and PLK1i in control, but not in CEP55-depleted cells (Figure 6.4A & B), suggesting that CEP55 overexpression may facilitate premature exit during mitotic arrest by impairing cell death induced by these agents (as observed in the previous chapter). Indeed, CEP55-depleted MDA-MB-231 cells showed a significant increase in cell death (identified as the subG1 fraction) during perturbed mitosis, which could prevent the generation of polyploidy (Figure 6.4C & D). Similar results were obtained in CEP55-depleted Hs578T cells treated with nocodazole and PLK1i (Figure 6.4E). Collectively, the above data suggests that overexpression of CEP55 blocked mitotic cell death in TNBC cell lines during perturbed mitosis.

6.2.5 Loss of CEP55 dictates cell fate following anti-mitotic drug treatment To examine specifically whether anti-mitotic drug-induced apoptosis in CEP55- depleted cells occurs during mitosis, we followed their fates using time-lapse microscopy after PLK1i treatment. There was no significant baseline difference in the average time spent in mitosis following CEP55 depletion in growing cells (Figure 6.4F), but we found that upon PLK1 inhibition control cells indeed spent more time in mitosis (Figure 6.4G). This behavior correlated with cell fate, as PLK1i-treated parental cells exhibited prolonged mitotic arrest and underwent slippage, while CEP55-depleted cells died in mitosis (p<0.0001; Figure 6.4H). To study if cells were dying by caspase-dependent apoptosis, we pretreated them with the pan-caspase inhibitor Z-VAD, and found that this rescued the phenotype (p<0.0001; Figure 6.4I). This indicates that the mitotic cell death observed with CEP55 depletion and PLK1i treatment was caspase-dependent. Taken together, these data provide significant evidence on the fact that CEP55 loss enhances death in mitosis after antimitotic treatment.

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Figure 6.4: CEP55 overexpression provides survival advantage during perturbed mitosis. (A) Percentage of polyploidy (>4N) following nocodazole (75 µg/ml) in control and CEP55-depleted MDA-MB-231 cells. (B) Percentage of polyploidy (>4N) following B12536 (5 nM) in control and CEP55-depleted MDA-MB-231 cells. (C, D) Apoptotic fraction (Sub-G1 population) determined by propidium iodide staining of cells treated with PLK1i or Nocodazole as described in (A & B). (E) Percentage of sub-G1 fraction following B12536 (10 nM) or nocodazole (75µg/ml) in control and CEP55-depleted Hs578T cells. (F) Average time spent in mitosis of growing both control and CEP55-depleted MDA-MB-231 cells. Time taken to complete mitosis was defined as the time from nuclear envelope breakdown until two daughter cells were observed. (G) Average time spent in mitosis and (H) mitotic outcomes in control and CEP55-depleted MDA-MB-231 cells following treatment with the BI2536 (5 nM). (I) Cells were pre-treated for two hours with 50 µM of the pan Caspase inhibitor Z-VAD-FMK, followed by treatment with B12536 (5 nM), before assessment of cell fate. Error bars represent the standard error of the mean from two independent experiments. For each experiments n=50 mitotic cells were counted per condition using Olympus Xcellence IX81 time-lapse microscopy. ****p<0.0001

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6.2.6 Loss of CEP55 primes an unscheduled CDK1/Cyclin B activation and apoptosis in the presence of anti-mitotic drugs Several studies have demonstrated that unscheduled CDK1/Cyclin B activation is required for anti-mitotic drug efficacy (Yu et al., 1998, Castedo et al., 2002). To test the hypothesis that this also occurs in our models, we first measured the kinetics of G2/M entry in both control and CEP55-depleted cells. Cells were synchronized by double-thymidine block, released into culture medium and collected at 2-hour intervals for DNA content analysis. Notably, there was no significant difference in the kinetics of G2/M entry, suggesting the CEP55-depleted cells were cycling in a similar manner to control cells (Figure 6.4F) and spending an equal average time in mitosis (Figure 6.4G). However, when cells were released into PLKi or nocodazole, the CEP55- depleted cells cycled faster, (Figure 6.5A-C) with significantly increased cell death (Figure 6.5D). They entered G2/M more rapidly, reflected by early induction of Cyclin B, CDC25B and pMEK1T286 (a marker of CDK1/Cyclin B activation), Wee1 destabilization and dephosphorylation of CDK1Y15 as well as accumulation of phospho-Ser/Thr-Pro MPM-2 (Figure 6.6A). These findings suggest rapid and unscheduled entry of CEP55-depleted cells into mitosis, alongside mitotic cell death instead of prolonged cell arrest (Figure 6.6A; cleaved PARP). Moreover, CDK1 activity was monitored using a CDK1-FRET biosensor activity probe as described previously (Vennin et al., 2017, Gavet and Pines, 2010). CDK1 activity is low when FRET (florescence resonance energy transfer) signal is low (high mCerulean lifetime) vs. CDK1 activity is high when FRET signal is high (low mCerulean lifetime) (Figure 6.6B). We found significantly lower lifetime, indicating higher CDK1 activity within 8-10 hours post release of synchronized CEP55–depleted cells into nocodazole (P<0.001, Figure 6.6C & D), suggesting unscheduled entry of these cells in mitosis. Collectively, our data suggest that in the absence of CEP55, treatment with anti-mitotic agents induces an apoptotic threshold breach through premature CDK1 activation.

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Figure 6.5: Loss of CEP55 cause premature G2/M entry and mitotic cell death. (A) Control and CEP55-depleted MDA-MB-231 cells were synchronized by double-thymidine block and released into culture medium. Cells were then collected every 2 hour interval for cell cycle profiling. (B, C) Similar to experiment in (A), synchronized control and CEP55-depleted MDA- MB-231 cells were released into either B12536 (5 nM) or nocodazole (75 µg/ml) and phases cell cycle distribution. Error bars represent the standard error of the mean from two independent experiments

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Figure 6.5: Loss of CEP55 cause premature G2/M entry and mitotic cell death (cont.). (D) Apoptotic fraction (Sub-G1 population) determined by propidium iodide staining of cells treated with Nocodazole (upper panel) and PLK1i (lower panel) as described in (B& C). Error bars represent the standard error of the mean from two independent experiments. *p < 0.05, **p < 0.01, ***p<0.001 and ****p<0.0001

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Figure 6.6: Loss of CEP55 cause premature CDK1/Cyclin B activity and mitotic catastrophe. (A) Cells were synchronized using double thymidine then released into nocodazole (75µg/ml), and protein lysates were collected at the indicated time points. Immunoblot analysis was then performed to determine the expression and activity of mitotic regulators as indicated. Levels of phospho-MEKT286 and dephosphorylation of phospho-CDK1Y15 served as markers of Cdk1 activation/mitotic entry. COX- IV served as a loading control. (B) Schematic representation of the mCerulean-CDK1-FRET biosensor. CDK1 activity is low when FRET signaling is low (high lifetime reading) vs. CDK1 activity is high when the FRET signaling is high (low lifetime reading) (adopted from (Vennin et al., 2017)). (C) Representative mCerulean lifetimes maps of Hs578T control and CEP55-depleted cells upon nocodazole (75µg/ml) treatment at indicated time points. Cells were synchronized using double thymidine for 16 hours prior to nocodazole treatment. (D) Quantification of mCerulean lifetimes of control and Hs578T CEP55-depleted cells and in response to treatment with nocodazole at indicated time points. Ns= not significant, **p < 0.01 and ****p<0.0001 160

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6.3 Discussion Elevated levels of mitotic proteins significantly contributes to early cellular transformation and hence tumor formation in organs (Kops et al., 2005). Although no high prevalence gene mutations of such mitotic proteins have been reported in cancers so far (Cahill et al., 1999, Hernando et al., 2001), these genes are known to be affected by copy number changes in multiple cancers; for example amplification of PLK1 (Degenhardt and Lampkin, 2010, Strebhardt and Ullrich, 2006), Aurora-A (Zhou et al., 1998, Wang et al., 2006), Survivin (Altieri, 2003), Cyclin B (Malumbres and Barbacid, 2009), NEK2 (Zhong et al., 2014) and others, which renders benefit and survival advantage during tumorigenesis. One such event that involves dysregulated expression of mitotic proteins is during cytokinesis which can significantly contribute to aneuploidy and genomic instability in tumors (Sagona and Stenmark, 2010). Since our first seminal report on the physiological role of CEP55 during cytokinesis a decade ago, numerous reports have been linked to the pathophysiological role of high CEP55 expression in cancers. However, the link between overexpression of CEP55 and genomic instability has not been studied. Here using breast cancer as an experimental model, we provide the first description of the role of CEP55 in regulating genomic instability and aneuploidy in cancer. Consistent with existing reports (Jeffery et al., 2015b), using multiple microarray gene expression datasets we found that CEP55 is highly expressed in breast cancer, particularly in aggressive breast cancer subtypes. Moreover, we report that CEP55 overexpression is significantly associated with clinicopathological parameters including in ER- negative and tumors associated with poor clinical outcomes, further suggesting that CEP55 promotes aggressive breast cancers at various stages during tumor development. Consistent with this, Inoda et al. (Inoda et al., 2009b) first described CEP55 as a cancer antigen and CEP55-derived peptides as a potent vaccine therapy in breast and colorectal cancers.

We found that depletion of CEP55 in aggressive breast cancer cell lines impedes long- term cell proliferation, colony formation, migration and invasion capacities and hence tumor forming capability in athymic nude mice in a dose-dependent manner. These findings illustrate that CEP55 attains oncogenic properties by regulating multiple functions in breast cancer at various stages, like other mitotic proteins such as Aurora-A (Fu et al., 2007), Eg5 (Yu and Feng, 2010), PLK1 (Weichert et al., 2005, Ito et al., 2004), and NEK2 (Zhong et al., 2014) which affect multiple functions during tumorigenesis. Polyploidization is an end product of failure or aberrant mitosis which triggers genomic instability and aneuploidy in most solid tumors(Vitale et al., 2011). Consistent with this, chromosome 20 has been well studied and linked to initiation and progression

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Chapter - 6 of multiple cancers including breast cancer (Tabach et al., 2011, Hodgson et al., 2003). Particularly, chromosome 8 and 20 q amplicons are shown to harbor a number of genes including MYC and AuroraA/TPX2, respectively, which are significantly overexpressed in multiple cancers suggesting that overexpression of CEP55 significantly contributes to cellular transformation and genomic instability by causing chromosomal imbalance.

One major obstacle in the clinic is the frequent development of resistance towards anti-mitotic drugs such as taxol, a cornerstone treatment in most cancer types (Chan et al., 2012). Even though most tumors showed some degree of mitotic indices/arrest, tumor cells often bypass mitotic arrest and prematurely exit mitosis, a phenomenon known as mitotic slippage. Mitotic slippage has been shown to be a bone fide resistance mechanism of taxol and other mitotic agents (Chan et al., 2012, Burgess et al., 2014). Silencing of CEP55 significantly impaired premature exit in cells treated with docetaxel and other antimitotic agents. In an essence it shows that CEP55 overexpression provides a survival advantage during mitotic insults and such ablation of overexpressed protein could sensitive cells providing better efficacy. This strongly suggests that CEP55 could be a potent biomarker of taxol and/or other anti-mitotic drug efficacy to stratify patients with low CEP55 expression for better response.

One simple mechanism in which CEP55-depleted cells are sensitized to anti-mitotic agents was through premature activation of CDK1/Cyclin B, which was associated with the activation of the cell death pathway. Mitosis is a fine balance between cell survival and death (Burgess et al., 2014, Castedo et al., 2004a). Persistent arrest will cause acute activation of cell death signaling due to impaired CDK1/Cyclin B degradation mediated by an E3 ubiquitin ligase APC/C (Castedo et al., 2002). Exemplifying this, anti-mitotic agents such as taxol activate SAC, which block the degradation of Cyclin B and Securin maintaining CDK1 activation. Persistent CDK1 activation in-turn triggers cell death signals. We found that after treatment with anti-mitotic agents CEP55- depleted cells prematurely enter mitosis and acutely die due to the inability to promote slippage, thus suggesting that CEP55 dictates cell fate during perturbed mitosis and facilitates genomic instability by facilitating mitotic exit. Thus, we have shown for the first time that the centrosomal protein CEP55 could provide a survival advantage to aneuploid cells by providing stability and tolerance during mitotic insults. Targeting mitotic proteins has long been known to be effective in highly proliferating tumor cells (Chan et al., 2012). Understanding such a pathway that regulates genomic instability and aneuploidy due to aberrantly expressed proteins such as CEP55 during mitosis

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6.4 Conclusion CEP55 is a mitotic protein, which has been shown to be overexpressed in the cancer context. CEP55 has been identified in prognostic signatures of multiple cancers and is also associated with a CIN signature of 10 genes which conferred drug resistance, and cell proliferation upon high expression (Jeffery et al., 2015b). In this chapter, we have revealed the role of CEP55 as a new regulator of aneuploidy cell fate in TNBCs, a subtype of breast cancer with a high level of genomic instability. We have mechanistically demonstrated that CEP55 upregulation might favor resistance to mitotic poison in the clinic as it favors mitotic slippage upon perturbed mitosis. However, loss of CEP55 induces premature CDK1/Cyclin B activity which leads to mitotic catastrophe when challenged by mitotic inhibitors. These findings prvide a rationale for targeting CEP55 in the treatment of TNBCs making it a potential aneuploidy-specific target and significantly impact clinical practice.

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Aim 2.2: To characterize the dual combination of MEK1/2-PLK1 inhibition in selectively eliminating basal-like breast cancer in a CEP55-dependent manner.

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7.1 Introduction Triple negative breast cancer (TNBCs), a recent derivative of the BLBC subtype, constitutes 15% of the overall BC population (Parker et al., 2009, de Ronde et al., 2010). TNBC, like BLBC are common among young African and African-American women patients in their early age (Millikan et al., 2008). TNBCs are characterized by poor differentiation, bigger tumor size and higher proliferative capability with metastatic potential mostly to lung and brain in contrast to other soft tissues and bone (Fulford et al., 2007, Dent et al., 2009). Majority of TNBC patients display a poor outcome with 29-45% cases demonstrate a pathological complete response (pCR). The first 5 years survival rate is predicted to be about 70%, lower by 10% in comparison to other BC subtypes (Masuda et al., 2013, Liedtke et al., 2008, Petrelli et al., 2014). Chemotherapy still remains the mainstay to treat TNBC with the hope of disrupting the cancer cell homeostasis, examples of which include anthracyclines (Mulligan et al., 2014), taxanes (Liedtke et al., 2008) and platinum compounds (Petrelli et al., 2014). TNBCs show high response rates (RR) to neoadjuvant chemotherapy with these compounds compared to other BLBC at initial stages but the in longer term (>5 years survival) have poor overall survival. Due to the lack of a better pCR among TNBC patients post chemotherapy, it becomes debatable to determine the choice over the nature of chemotherapy to be administered (discussed previously in Chapter 5; Section 5.3).

7.1.1 The current scenario of TNBCs in the clinic The failure to combat TNBC in the clinic questions the need for an effective therapeutic strategy for these patients. Heterogeneity displayed by TNBCs (discussed previously) allows attainment of resistance, either innate or acquired, to targeted therapies which poses substantial difficulties with respect to durable response as the tumor cells adapt by altering signaling network to gain advantage via feedback mechanism (Logue and Morrison, 2012). One of the well- characterized resistance mechanisms has been in response to RTK inhibitors which show redundancies with reprogramming of the kinome function to effectively bypass the inhibition of the target (Holohan et al., 2013, Ellis and Hicklin, 2009). One reason behind this phenomenon can be due to gain of survival-enhancing mutations which leads to altered crosstalk or feedback loop inhibition. The innate or de novo resistance is the mechanism where the drug, which has demonstrated efficacy in preclinical studies, shows no measurable response in clinical settings (Ellis and Hicklin, 2009, Holohan et al., 2013). The mechanism of acquired resistance refers to the failure to respond following continuous administration of a drug which was initially effective. Research has

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7.1.2 Targeting mitotic genes is a better option against TNBCs Mitosis is well known to be vulnerable to injuries, and mitotic cells have been a target of therapeutic exploitation in the clinical setting (Gonzalez-Angulo et al., 2011). Mitotic genes are rarely mutated in cancer (Cahill et al., 1999, Hernando et al., 2001), but rather their levels are highly elevated; for example elevated levels of PLK1 (Degenhardt and Lampkin, 2010, Strebhardt and Ullrich, 2006), Aurora-A (Zhou et al., 1998, Wang et al., 2006), Survivin (Altieri, 2003), Cyclin B (Malumbres and Barbacid, 2009). In addition, many other mitotic genes are currently being investigated as potential biomarkers to specifically target cancer cells over normal cells. The advantage of targeting mitotic proteins using small molecule inhibitors in the treatment of solid tumors is reduced side effects like neurotoxicity caused due to alteration of microtubule dynamics upon treatment with traditional chemotherapy (Gonzalez-Angulo et al., 2011). However, as single agents these mitotic inhibitors have failed to demonstrate better efficacy in clinic.

TNBCs are genomically unstable and are resistant to mitotic poisons, hence represents a significant clinical problem in the management of breast cancer patients (Chan et al., 2012). Resistance to anti-mitotic drugs like taxol (Chan et al., 2012) represents a significant clinical problem in the management of breast cancer patients. Although most but not all tumors show some degree of chemotherapy response, tumor cells often bypass mitotic arrest and prematurely exit mitosis, a phenomenon known as mitotic slippage. Research has identified mitotic slippage to be a genuine mechanism of resistance to taxol and other mitotic agents (Chan et al., 2012, Burgess et al., 2014). It is a condition during which cancer cells initially show mitotic arrest upon treatment with mitotic inhibitors which interfere with microtubule dynamics, but they are not able to tolerate prolonged arrest in the presence of these drugs which eventually leads to premature mitotic exit and

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7.1.3 Synthetic lethality could prove to be a better answer to target TNBCs Targeted therapies on the basis of molecular profiling of patient samples have revealed numerous targets which have shown promising preclinical results with several candidates presently under clinical trials. However, the initial response has been modest with the primary reason being intra- and inter-tumor heterogeneity enabling tumor cells to acclimatize and rewire their signaling circuitry using redundancy and feedback mechanism (Polyak, 2007, Marusyk and Polyak, 2010). With development in the field of systems biology, researchers have hypothesized targeting of multiple signaling pathways will provide maximize drug efficacy and overcome signal rewiring. This type of strategy is precisely known as synthetic lethality, wherein simultaneous inhibition of genes from independent pathways by small molecule inhibitors or RNAi leads to cellular death, but individual inhibition is compatible with viability (Nijman, 2011). The concept of synthetic lethality has been developed by studying yeast and fruit-flies which involves interdependency of two genes wherein perturbation of both genes simultaneously results in loss of viability but not individually (Lucchesi, 1968, Kaiser and Schekman, 1990). In the cancer context, gene overexpression, especially oncogenes, is one of the common alterations observed during cellular transformation. Targeting overexpressing genes via targeting its interacting partner genes leads to synthetic lethality and has proved to be a valuable tactic for specifically targeting tumor cells (Figure 7.1) (O'Neil et al., 2017).

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B C D

Figure 7.1: Model of synthetic lethality. (A) Schematic representation of the interdependency of two genes in overall cell viability. Mutation (indicated by yellow star) of the either genes (Gene A or B) or amplification of both the genes does not impact the overall cell viability. (B) Presence of mutation of both the interdependent genes leads to lethality. (C) Single mutation of one gene in combination with inhibition of the other gene using small molecule inhibitors or monoclonal antibodies or RNAi technology induces lethality. (D) Inhibition of one of the codependent partner gene which is overexpressed using small molecule inhibitors or monoclonal antibodies or RNAi technology also induces synthetic lethality (adopted from (O'Neil et al., 2017)).

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7.1.4 Involvement of CEP55 in TNBC Expression of centrosomal proteins has been particularly well defined for significant correlation with poor survival in breast cancer (BC) (Ma et al., 2003, Naderi et al., 2007). Elevated expression of CEP55, a mitotic scaffold protein has been shown to be associated with multiple cancers (elaborated in Chapter 1). Precisely, it is known to be one of the top genes in CIN70 gene signature and has been reported to be linked to BC (discussed in Chapter1 & 6). In the previous chapter, we have demonstrated that CEP55 is highly overexpressed in TNBCs and is associated with poor prognosis of TNBC and regulates the fate of the aneuploid cell population. Notably, we demonstrated that shRNA-depleted CEP55 cells were highly sensitive to mitotic cell death in the presence of mitotic poisons such as PLK1 inhibitor (BI2536) and leads to unscheduled CDK1/Cyclin B activation and favors CDK1-Caspase 3-dependent mitotic catastrophe. These findings highlight the fact that CEP55 could serve as a potential therapeutic candidate for treatment of TNBCs. However, due to the presence of the coiled-coil structure (described in Chapter 1; Section 1.7) of the CEP55 protein, it is not readily druggable. Previously, our laboratory has previously shown that CDK1/ERK2- and PLK1-dependent phosphorylation of CEP55 primes early events in cytokinesis (Fabbro et al., 2005). Thus, we hypothesis that targeting upstream or downstream effectors of growth/survival pathways might impact level of CEP55 in vivo.

In this chapter, we have demonstrated that targeting the MAPK pathway using small molecules modulates the level of CEP55 in vitro and could be used to mimic CEP55 depletion in vivo. In the previous chapter, we have demonstrated that loss of CEP55 enables sensitization of TNBC cells to mitotic poisons and induces mitotic catastrophe. Using these two concepts, we have streamlined the notion to target CEP55 in vivo by synthetically targeting PLK1/ERK2 pathways using small molecule inhibitors AZD6244157 and BI2536158 to see possible combinatorial effects. Notably, we found that CEP55 is a downstream effector of mitogen-activated protein kinase (MAPK)-MYC signaling. We demonstrated that inhibition of the MEK1/2 pathway leads to reduces CEP55 in a dose-dependent manner. Furthermore, we demonstrated dual inhibition of MAPK signaling (MEK1/2 inhibition) and the mitotic pathway (PLK1 inhibition) synergistically reduced the growth of both murine and human breast cancer in vitro and in vivo. Collectively, we have provided a rationale for clinically targeting CEP55-dependent pathways in genomically unstable basal-like, triple-negative breast tumors for better treatment efficacy.

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7.2 Results

7.2.1 MAPK controls CEP55 levels in BC To overcome the technical difficulties in directly targeting CEP55, we speculated that indirectly targeting upstream or downstream effectors may be a more effective way to manipulate CEP55 levels. Treating MDA-MB-231 cells with a number of small molecule inhibitors of EGFR/HER2 signaling revealed that CEP55 protein was markedly suppressed following inhibition of MEK1/2 and to a lesser extent with AKT and PI3K/mTOR, but not with upstream receptor tyrosine kinases (RTKs) EGFR, HER2, VEGFR or PDGFR (Figure 7.2A). The suppression of CEP55 by MEK1/2 inhibitors occurred at both the mRNA and protein level (Figure 7.2B & C), and was dose- and time-dependent (Figure 7.2B & D). We also observed that this effect was not caused due to G1 cell cycle arrest (Figure 7.2E), suggesting that MAPK signaling regulates CEP55. The promoter activity of CEP55 was also significantly reduced with MEK1/2 inhibitor treatment and ERK1/2 siRNA (Figure 7.2F & G). In addition, EGF stimulation of growth arrested MDA-MB-231 cells (in 0.1% foetal calf serum) markedly increased CEP55 and MYC mRNA and protein levels (Figure 7.2H & I). Taken together, these data provide evidence that CEP55 is regulated by the MAPK pathway in vitro.

7.2.2 MAPK transcriptionally controls CEP55 levels through MYC From the above data, we observed that upregulation of CEP55 is also causing upregulation of MYC upon MAPK stimulation, both at the protein and transcript level. Upon mitogenic stimulation, MAPK signaling activates an array of downstream effectors including the transcription factor MYC, which is associated with genomic instability and progression in multiple cancers (Sears et al., 2000, Prochownik, 2008), and is a known consequence of MEK1/2 blockade in breast cancer cells (Duncan et al., 2012). Therefore we wondered if MYC regulates CEP55. RNAi- mediated depletion of MYC in MDA-MB-231 cells reduced CEP55 promoter activity, mRNA and protein levels with and without prior to EGF stimulation of cells (Figure 7.3A-C), while depletion of ETS-1 (another transcription factor downstream of MAPK (Foulds et al., 2004, Ohtani et al., 2001)) did not show any significant difference (Figure 7.3A-C). Moreover, we observed that upon exogenous activation of MYC with 4-hydroxytamoxifen (4OHT) in MCF10A-MYCER cells (a published in vitro model of conditionally overexpressing MYC upon 4OHT stimulation) (Sato et al.,

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2015), markedly increased CEP55 levels (Figure 7.3D & E). Finally, in the TCGA dataset, CEP55 mRNA levels correlated with predicted MYC activity (p<0.0001; Figure 7.3F). Collectively these data suggest that MAPK controls CEP55 expression through MYC.

7.2.3 CEP55 expression determines sensitivity to combined inhibition of MEK1/2 and PLK1 There is currently no specific way to target aneuploid cancers, though several lines of evidence suggest that CEP55 could be a candidate target for therapeutic development. First, it is a cancer-testis antigen, with expression restricted to the testis in healthy adults, or malignant cells in some cancer patients (Morita et al., 2007, Inoda et al., 2009a). Second, high CEP55 expression is an indicator of chromosomal instability (Carter et al., 2006) and poor clinical outcome (Jeffery et al., 2015b). Evidence presented in the previous chapter demonstrates that CEP55 promotes genomic instability and resistance to anti-mitotic drugs and even partial depletion of CEP55 was sufficient for aneuploid breast cancer cell elimination in vitro (Chapter 6; Figure 6.4 & 6.5). Since MEK1/2 inhibitors target CEP55 through MYC (Figure 7.2B), we tested if a selective MEK1/2 inhibitor (AZD6244) has activity against CEP55-high cells in combination with the anti- mitotic drug PLK1, BI2535. We found that CEP55-depleted MDA-MB-231 and Hs578T cells were sensitive to PLK1 inhibition (Figure 7.4Ai & Bi). But upon addition of AZD6244 (1 µM), we observed no alteration in the sensitivity of CEP55-depleted clones of each cell lines (Figure 7.4Aiii- iv & Biii). However, the control cells, with high CEP55 expression, profoundly responded to the combination treatment (Figure 7.4Aii, & Bii). In addition, we also observed increased apoptosis in control cells having higher CEP55 levels as observed by a higher subG1 population in control cells (Figure 7.4C & D) alongside PARP and cleaved Caspase-3 as observed in western blot (Figure 7.4E & F). To our surprise, CEP55- loss tends to exhibit a reduced cell death phenotype to this combination treatment (Fig. 5K, Fig. S8C). Collectively, these data illustrate the dependency of MEK1/2 and PLK1 inhibitor on high CEP55 expression for inducing synthetic lethality in TNBC cell lines.

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Figure 7.2: CEP55 is a downstream effector of MAPK signaling. (A) Immunoblots analysis was performed to determine CEP55 levels in MDA-MB-231 cells treated with multiple inhibitors targeting the EGFR/HER2 pathway for 24 hour. The following inhibitors were used: MEK1/2i (AZD6244 (1 µM)), the AKT, PI3K/mTORi (BEZ235 (0.5 µM), AKTi VIII (1 µM)), EGFRi and HER2i (Erlotinib (1 µM), Afatinib (0.25 µM), Lapatinib (1 µM), Trastuzumab (10 µg/ml) or the pan VEGFR, PDGFR and RAF kinases (Sorafinib, (1 µM)). COX-IV servedas a loading control. (B) Immunoblots analysis of MDA-MB-231 cells treated with three different MEK1/2 inhibitors at various concentrations (Selumetinib AZD6244, Trametinib GSK1120212 and Binimetinib MEK162) after 24h. COX-IV served as a loading control. (C) Relative fold changed of CEP55 and MYC mRNA levels following different MEK1/2 inhibitors treatment at indicated dose and time. Fold changed was calculated relative to untreated control cells. (D) Immunoblot showing impact AZD6244 (0.5 µM) on CEP55 and MYC levels in MDA-MB-231 cells at indicated time points. COX-IV served as a loading control. (E) Quantitation of cell cycle distribution of MDA-MB-231 cells treated with different MEK1/2 inhibitors (selumetinib (1 µM) or Trametinib (0.5 µM)) for indicated time points. (F) Relative CEP55 promoter luciferase activity in MDA-MB-231 cells either treated with AZD6244 (1 µM) for 6 hours or ERK1/2 depletion for 24 hours. PGL3 basic reporter plasmid was used to normalize basal CEP55 promoter activity. (G) Relative CEP55 promoter luciferase activity upon 10 nM ERK1/2 siRNA determined using DualGlo assay in MDA-MB- 231cells similar to (F). PGL basic vector was used to normalize CEP55-promoter activity. (H) Immunoblots analysis showing MYC and CEP55 expression in MDA-MB-231 cells upon transfection with siRNA against MYC, and ETS1 (10 nM), with and without EGF (100 ng/mL) stimulation as indicated time points. COX-IV served as a loading control. (I) Relative fold changed of CEP55 and MYC mRNA levels following EGF stimulation in MDA-MB-231 cells cultured in 0.1% fetal bovine serum at indicated time points. Relative fold changed was calculated to untreated control cells. Error bars represent the ± SEM from three independent experiments*p < 0.05 and **p < 0.01.

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Figure 7.3: MAPK transcriptionally controls CEP55 levels through MYC. (A) Relative CEP55 promoter luciferase activity upon MYC siRNA was determined using DualGlo assay in MDA-MB- 231 cells. PGL3 basic reporter plasmid was used to normalize basal CEP55 promoter activity. (B) Immunoblots analysis showing MYC and CEP55 expression in MDA-MB-231 cells upon transfection with siRNA against MYC, and ETS1 (10 nM), with and without EGF (100 ng/mL) stimulation as indicated time points. COX-IV served as a loading control. (C) Relative basal and EGF induced fold changed of CEP55, MYC and ETS1 mRNA levels at indicated time points in MDA-MB-231 cells transfected with siRNA against 10 nM CEP55, MYC or ETS1 for 24 hour. (D) Immunoblots analysis showing CEP55 and MYC levels following 4-hydroxytamoxifen (4OHT) (0.5 µM) induction in MCF10A MYCER cells cultured in 0.1% fetal bovine serum contained media at indicated time points. COX-IV served as a loading control. (E) Analysis of Pearson correlation coefficients (PCC) of CEP55 vs. MYC levels with R=0.9982, p=0.0016. Protein band intensities were measured using ImageJ software. (F) Analysis of Pearson correlation coefficients (PCC) of CEP55 vs. MYC transcriptional activity in breast cancer TCGA tumor data with R=0.4157. Error bars represent the ± SEM from three independent experiments *p < 0.05.

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Figure 7.4: CEP55 expression determines sensitivity to combined inhibition of MEK1/2 and PLK1. (A) Statistical representation of control and CEP55-depleted MDA-MB-231 cells were exposed with different concentrations of PLK1 (BI2536) alone (i) or in combination with AZD6244 (1 µM) (ii- iv), and cell viability was determined after 6 days. The dose-response curve was generated by calculating cell viability relative to untreated control and plotted against drug concentration. (B) Statistical representation of control and CEP55-depleted Hs578T cells were exposed with different concentrations of BI2536 alone (i) or in combination with AZD6244 (1 µM) (ii-iii), and cell viability was determined after 6 days. The dose-response curve was generated by calculating cell viability relative to untreated control and plotted against drug concentration (left panel). Immunoblots showing CEP55 expression in control and CEP55-depleted Hs578T cell lines. COX-IV served as a loading control (right panel). (C) Percentage of sub-G1 population identified using propidium iodide staining and quantified by FACS following single and combination treatment with AZD6244 (1 µM) and BI2536 (2.5 nM) inhibitors after 96h. (D) Percentage of sub-G1 population identified using propidium iodide staining and quantified by FACS following single and combination treatment with AZD6244 and BI2536 inhibitors after 96h in control and CEP55-depleted Hs578T cells. Error bars represent the standard error of the mean from two independent experiments. (E) Immunoblots analysis of both control and CEP55-depleted MDA-MB-231 cells treated with single and combination treatments after 96h and cleaved PARP, Caspase-3 along with MYC, ERK1/2 and CEP55 were determined. COX-IV served as a loading control. (F) Immunoblots analysis of both control and CEP55-depleted Hs578T cells treated with single and combination treatment with AZD6244 (1 µM) and BI2536 (5 nM) inhibitors after 96h. Cleaved PARP, Caspase-3 along with MYC, ERK1/2 and CEP55 were determined. COX-IV served as a loading control. Error bars represent the ± SEM from three independent experiments *p < 0.05 and ****p<0.0001.

7.2.4 CEP55 expression determines specificity to targets TNBCs Having established that combination treatment targeted CEP55-dependent cells, we treated a panel of 21 breast cancer lines with MEK1/2 and PLK1 inhibitors and found mixed responses, but basal-like lines specifically responded to the combination regimens (Figure 7.5A). Additionally, near-normal MCF10A and D492 lines exhibited non-responsive effect following combination treatment, consistent with the idea that CEP55 activity is important in certain cancers but not normal cells. The combined treatment synergistically induced apoptosis in basal-like, triple- negative lines, reflected by increased subG1 population and induction of cleaved caspase-3 and PARP (Figure 7.5B & C), and completely eradicated colony formation in all basal-like, triple- negative lines tested, but not in ER+ (MCF7) or HER+ (SKBR3) lines (Figure 7.5D). In summary, these data illustrates that, the dual combination specifically targets TNBC cells lines which comprises of higher CEP55 expression level but does not respond to hormone receptor positive cell lines.

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Figure 7.5: Combined MEK1/2-PLK1 inhibition specially kills basal-like breast cancer lines. (A) Heat map showing relative cell viability of a panel of human breast cancer lines treated individually with single and combination treatment with AZD6244 (1 µM) and BI2536 (2.5 nM) inhibitors and cell viability was determined after 6 days. DMSO treated control was used to calculate percentage of cells affected by individual or combination treatment. NN: near normal. (B) Immunoblot analysis was performed on MDA-MB-157 and SUM159PT cells treated with single or in combination with AZD6244 and BI2536 inhibitors after 96 hours and cleaved PARP and Caspase-3 were determined along with CEP55, MYC and both phosphorylated and total ERK1/2. COX-IV as a loading control (left panels). Percentage of sub-G1 population identified using propidium iodide staining and quantified by FACS following single and combination treatment with AZD6244 and BI2536 inhibitors after 96h (middle panels). Representative images of colony formation at 14 days determined using crystal violet staining in cells treated with single and combination inhibitors (middle panels). CI values for combination treatment (right panels). (C) Immunoblot analysis, percentage of sub-G1 population and colony formation at 14 days was performed on MDA-MB-436 similar to (B). (D) Immunoblot analysis, percentage of sub-G1 population and colony formation at 14 days was performed on MCF7 (luminal cell line) and SKBR3 (HER2 amplified cell line) similar to (B). Error bars represent the ± SEM from three independent experiments ****p<0.0001.

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7.2.5 Dual combination induces mitotic catastrophe in TNBCs Next we wanted to define the mechanism via which the combination MEK1/2 and PLK1 inhibitors lead to synthetic lethality in TNBCs. Using time-lapse microscopy, we found that the combined MEK1/2-PLK1 treated cells underwent significant cell death in mitosis compared to individual drug treated cells which either underwent slippage or cell division (Figure 7.6A). In addition, similar to knockdown experiments (Chapter 6; Figure 6.7A), we found that combination treatment markedly reduced CEP55 level and accelerated G2/M entry due to unscheduled CDK1/Cyclin B activation (phospho-MEKT286, accumulation of Cyclin B1, and dephosphorylation of CDK1Y15) as early as 9 h, causing cell death at 12 h in both MDA-MB-231 and SUM159PT cells (Figure 7.6B), which was associated with reduced expression of anti-apoptotic protein (MCL1, BCL2 and Survivin). Collectively, these data suggest that combined MEK1/2-PLK1 inhibition leads to premature CDK1/Cyclin B activation causing early entry into mitosis and also induces mitotic catastrophe (death in mitosis) by inhibiting of anti-apoptotic protein in CEP55-dependent triple- negative breast tumors.

Figure 7.6: Dual combination induces mitotic catastrophe in TNBCs. (A) Percentage of mitotic outcomes determined using time-lapse microscopy of MDA-MB-231 cells treated with single or in combination drugs of BI2536 (2.5 nM) and AZD6244 (1 µM). (B) Immunoblot analysis was performed to determine the expression and activity of mitotic and apoptotic regulators upon single and combination treatment with AZD6244 and BI2536 inhibitors as indicated time points in both MDA-MB-231 and SUM159PT cells. Levels of phospho-MEKT286 and dephosphorylation of phospho-CDK1Y15 served as markers of CDK1 activation/mitotic entry. COX-IV served as a loading control. Error bars represent the ± SEM from three independent experiments and ****p<0.0001.

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7.2.6 Combined MEK1/2-PLK1 inhibition impedes aggressive tumor formation in vivo Based on the persuasive in vitro data, we wanted to further investigate the effect of MEK1/2-PLK1 combination therapy in syngeneic and xenograft models of breast cancer. Immunocompetent BALB/c mice were inoculated with high CEP55-expressing aggressive syngeneic cell line 4T1.2, which also showed promising effect upon combination treatment (Figure 7.7A, upper panel) using mammary fat pad injections. Treatments were initiated when tumors reached 25mm2: 12.5mg/kg BI6727 (similar activity as BI2536 and under active clinical investigation (Maire et al., 2013b, Zhang et al., 2016)), thrice-weekly and 12.5mg/kg AZD6244 BID for two weeks. As observed in vitro (Figure 7.7A, upper panel), tumors exhibited modest responses to individual agents, but had pronounced outgrowth inhibition in the combination arm (Figure 7.7B), without noticeable toxicity on their body weight (not shown). Immunoblot analysis of tumor lysates showed reduced CEP55 and enhanced cleaved Caspase-3 (Figure 7.7C). Next, we pretreated mice with combined inhibitors for four days before grafting 4T1.2 cells, followed by a further 10 days of treatment. While mice treated with vehicle or single agents died within 30 days, combination therapy prolonged survival (median 55 days; Figure 7.7D & E). Here also we did not observe toxicity in any of the treatment groups (not shown). Furthermore, NOD/SCID fat pad xenografts of MDA-MB-231 and its metastatic variant HM-LNm5 (both aneuploid lines) exhibited profound tumor regression and longer survival with combination therapy (Figure 7.8A-D). In summary, these data show that blocking MEK1/2/PLK1 substantially impacts tumor growth in multiple models of basal like, triple-negative breast cancer, and represents a novel candidate strategy for treating aneuploid breast tumors that exhibit high expression of CEP55.

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Figure 7.7: Combined MEK1/2-PLK1 inhibition impedes aggressive tumor formation in vivo using syngeneic 4T1.2 model. (A) Immunoblot analysis, percentage of sub-G1 population and colony formation at 14 days was performed on 4T1.2 similar to Figure 7.5 (B). (B) Left, Five week-old female BALB/c cohorts of mice were injected in the 4th inguinal mammary fat pad with the Cep55-ovexpressing mammary carcinoma cell line 4T1.2. Tumor size (area, mm2) was measured using a digital caliper and mean tumor size of each cohort is presented. Mice were treated with vehicle, AZD6244 (12.5mg/kg BID), BI6727 (12.5mg/kg thrice weekly), or combined AZD6244/BI2536 treatment. Right, representative excised tumor images are shown. (C) Immunoblot analysis was performed on tumor lysate to determine target inhibition following single and combined treatments as indicated in (B). Cleaved Caspase-3 was probed to determine apoptosis. GAPDH served as a loading control. (C) Growth rate (mean tumor size, area, mm2) of pre-treated female BALB/c cohorts of mice bearing the 4T1.2 mammary tumor linenas indicated in (B) for 4 days, followed by 4T1.2 cell were injected in the 4th inguinal mammary fat pad with 4T1.2 line. (D) Five week-old female BALB/c cohorts of mice were pre-treated with single and combination BI6727/AZD6244 inhibitors Survival of the mice was monitored over the indicated period and analysed by log-rank test (P=0.0005). Error bars indicate standard error of mean; n = 6 per group. Differences in growth were determined using Student’s t test and by calculating subsequent p values. ***p < 0.001, ****p<0.0001.

Figure 7.8: Combined MEK1/2-PLK1 inhibition impedes aggressive tumor formation in vivo using MDA-MB-231 xenograft model. (A) Immunoblot analysis, percentage of sub-G1 population and colony formation at 14 days was performed on MDA-MB-231-HM_LN5 (a derivative of MDA-MB-231 cells which has high potential to metastasis into brain and lymph node) similar to Figure 7.5 (B). (B) Left, Five week-old female BALB/c Nude cohorts of mice were injected in the 4th inguinal mammary fat pad with the CEP55-ovexpressing mammary carcinoma cell line MDA-MB-231-HM_LN5. Tumor size (area, mm2) was measured using a digital caliper and mean tumor size of each cohort is presented. Mice were treated with vehicle, AZD6244 (12.5mg/kg BID), BI6727 (12.5mg/kg thrice weekly), or combined AZD6244/BI2536 treatment. Right, representative excised tumor images are shown. (C) Growth rate (mean tumor size, area, mm2) of MDA-MB-231 xenografts in BALB/c nude mice treated with vehicle, AZD6244, BI6727, or combined AZD6244/BI6727 treatment as indicated in (B). (D) Survival of the mice post treatment as mentioned in (C) was monitored over the indicated period and analyzed by log-rank test (P<0.0001). Error bars indicate standard error of mean; n = 6 per group. Differences in growth were determined using Student’s t test and by calculating subsequent p values. ****p < 0.0001.

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Figure 7.8

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7.3 Discussion TNBCs represent a small percentage of diagnosed BC cases though they are associated with high mortality rates among patients. They are highly heterogeneous and aggressive in nature and exhibit low pCR (30-45%) and overall survival rates in comparison to other BC subtypes. Though clinical data have demonstrated their initial response to chemotherapy, in the long-term they exhibit worse outcomes than other subtypes. In addition, their heterogeneous nature enables them to acquire resistance by signaling network rewiring making them on of the biggest challenges to treat in the clinical settings. Thus, there exists an urgent need for better strategies for the treatment of TNBCs (Kalimutho et al., 2015). Advances in system biology have provided the concept of combination treatments against a hub of oncogenes which are dependent on their neighbor genes for survival. Target inhibition of these interdependent genes would provide the effect of synthetic lethality and could prove to deliver a better overall survival (OS) and better clinical response.

In this chapter, we demonstrated that ERK1/2 transcriptionally controls CEP55 mRNA and due to the lack of a specific small molecule inhibitor against CEP55, inhibition of MEK1/2 using the small molecule inhibitor Selumetinib, can mimic depletion of CEP55 in vivo. In the previous chapter, we showed loss of CEP55 sensitized the TNBC cells to mitotic inhibitor BI2536, leads to unscheduled CDK1/Cyclin B activation and favor CDK1-Caspase 3-dependent mitotic catastrophe. On the basis of this concept, we rationalized the usage of a MEK1/2 inhibitor in combination with a PLK1 inhibitor across a series of BC cell lines. We observed a higher synergistic response in basal like subtypes which harbor higher genomic instability alongside higher CEP55 expression. In addition, the combination induced premature entry of these cells into mitosis in the presence of antimitotic drugs due to depletion of CEP55 and inhibited tumor growth in vivo enabling better survival. Therefore, we provided the first preclinical evidence of a novel treatment strategy of MEK1/2 -PLK1 dual combination for selectively targeting CEP55 over-expressing BC in the clinics.

CEP55, a novel coiled-coil protein, has been characterized by our laboratory to participate in the final stages of cell cleavage to form two daughter cells. Inoda et al. described CEP55 as a cancer testis antigen and postulated that CEP55-derived peptides could be used as vaccine-based therapy in breast and colorectal cancers (Inoda et al., 2009a). CEP55 has been shown to regulate cell proliferation, migration and invasion in multiple cancer cell line models (Jeffery et al., 2015b). Overexpression of CEP55 has been shown to be involved in BC progression (Ma et al., 2003) and has been particularly well defined for its significant correlation with poor survival in breast cancer (Ma et al., 2003, Naderi et al., 2007). In the previous chapter, we provided the first

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Chapter - 7 evidence that CEP55 regulates the fate of the aneuploid cell population during perturbed mitosis. We demonstrated, that challenging the control TNBC cells expressing high CEP55 levels with the anti- mitotic agent PLK1 induces an inefficient spindle assembly checkpoint resulting in mitotic slippage. Conversely, loss of CEP55 leads to mitotic catastrophe by inducing premature CDK1/Cyclin B activity during perturbed mitosis. Taken together, these results indicate that CEP55 plays a significant role in facilitating genomic instability and thus likely contributes to the aneuploidy state of breast and other tumors.

TNBCs are highly associated with genomic instability and aneuploidy due to 5q loss and gains at 8q, 10p, and 12p (discussed previously). They are heavily reliant on mitotic genes for tumor progression, levels of which are highly elevated but are rarely mutated in cancer (Cahill et al., 1999, Hernando et al., 2001). CEP55, being as part of the CIN70 signature, is associated with genomic instability in TNBC patients and might fuel resistance to mitotic inhibitors (i.e. docetaxel, taxol and PLK1i) in the clinical setting. Hence, CEP55 can be potentially targeted for therapy development against genomically unstable cancers. However, the coiled-coil structure of CEP55 makes it undruggable, thus proving a bigger challenge to specifically inhibit its expression in vivo.

Our laboratory initially showed that ERK2/PLK1 critically regulates the functional role of CEP55 via phosphorylation of CEP55 at particular stages of mitosis, allowing it to localise to the midbody for accurate cytokinesis. Thus we hypothesized if targeting upstream or downstream effectors of growth/survival pathways impact CEP55-dependent cell survival. We found that CEP55 is a downstream effector of MAPK signaling through MYC, which has a central role in transformation, tumorigenesis and genomic instability and MYC is deregulated in many cancers (Sears et al., 2000, Prochownik, 2008). In breast cancer, deregulation of MYC is most frequent in basal-like tumors, and is associated with resistance to adjuvant chemotherapy (Xu et al., 2010), hence targeting its dependent pathways could be a good therapeutic strategy. Thus, we rationalized the usage of a MEK1/2 inhibitor in combination with a PLK1 inhibitor across a series of BC cell lines. We observed synthetic lethality, specifically among the aggressive hormone receptor negative cell lines, which expressed higher expression of CEP55 compared to normal like and receptor positive BC cell lines with lower CEP55 levels. Further, we also demonstrated that loss or lower expression of CEP55 does not facilitate the synergism of the combination treatment indicating that higher CEP55 expression is required for synthetic lethality upon dual combination. Notably, combining inhibitors of MEK1/2 (targets the MYC-CEP55 axis) and PLK1 (mitosis) inhibited the outgrowth of aggressive basal-like syngeneic and human breast cancer xenografts. The effects of

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Chapter - 7 similar combination strategy have also been reported in two other studies: MEK1/2i with docetaxel (a microtubule poison) reduced mammary tumor growth in vivo (Yacoub et al., 2006), and a MEK1/2-PLK1 inhibition caused regression of NRAS mutant melanoma xenografts (Posch et al., 2015). A major obstacle in the clinic is the relatively shallow armamentarium of systemic agents for treating triple-negative breast cancer (TNBC). Identifying targetable pathways that these tumors are dependent on for survival is a high priority in the field. Multiple deregulated pathways have been identified but targeting these with individual agents has produced disappointing results in preclinical and clinical trials (Wali et al., 2017, Kalimutho et al., 2015). For example, MEK1/2 blockade activates RTK-mediated resistance within hours of treatment, while co-blockade of RTKs and MEK1/2 (Sorafenib+AZD6244) was more effective in killing TNBC tumors (Duncan et al., 2012). High rate of proliferation and genomic instability are features common to many TNBCs, and while cell cycle-targeting agents have generally failed in previous trials, accurate analysis of efficacy was potentially confounded by trial design issues. More recently, the possibility of targeting mitosis and mitotic exit has received more attention. Inhibitors of cyclin-dependent kinase (CDK) 1, CDK 4/6, Auroras, Polo kinases or spindle kinesins are being actively investigated for better therapeutic efficacy in appropriately selected patient groups (Dominguez-Brauer et al., 2015, Manchado et al., 2012, Lim et al., 2016).

Unlike other spindle checkpoint proteins (e.g. MPS1 (Daniel et al., 2011), BUB1 (Ricke et al., 2011) and MAD2 (Kops et al., 2004)), centrosomal proteins have not been previously implicated in aneuploid cell survival. This study is the first to demonstrate that CEP55 can confer a survival advantage by evading apoptosis during perturbed mitosis. Targeting mitotic proteins has long been known to be an effective therapeutic strategy in highly proliferating tumor cells (Chan et al., 2012). However, strategies to target mitotic proteins have failed in the clinic due to various reasons, including unselected patient cohorts, lack of companion biomarkers, toxicity and drug resistance(Chan et al., 2012). To address these hindrances, we provide strong preclinical evidence in favor of a novel therapeutic approach targeting aneuploidy and genomic instability, which are fundamental and recurrent features of TNBCs. Since various PLK1 inhibitors are already being assessed individually in clinical trials and the MEK1/2 inhibitor trametinib was recently approved by the FDA for treating patients with metastatic melanoma, our data suggest that a combination strategy could have better efficacy in basal-like, TNBCs that exhibit highly aberrant copy-number profiles.

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7.4 Conclusion CEP55, a cytokinesis regulator, is over-expressed in various cancers including TNBCs and has been linked to genomic instability. Here, we mechanistically demonstrated that the MAPK pathway regulates CEP55 which is a downstream effector of the ERK1/2-MYC axis in vitro. Further, we established that co-inhibition of MEK1/2-PLK1 induced premature CDK1/cyclin B activity and forced mitotic catastrophe to reduce growth and regression of TNBCs in vivo in a Cep55 dependent manner. Thus, these findings highlight the rational of a novel treatment strategy of MEK1/2 -PLK1 dual combination for selectively targeting CEP55 over-expressing BC in the clinic.

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Chapter 8

General Discussion and Future Directions

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CEP55 was first identified and characterized by our laboratory as a key regulator of cytokinesis (Fabbro et al., 2005). We initially demonstrated that depletion as well as overexpression of CEP55 leads to cytokinesis failure and causes multinucleation. Further, we delineated the molecular mechanism through which CEP55 regulates cytokinesis and its regulation by CDK1, ERK2 and PLK1-dependent phosphorylation. Over the last decade, numerous experimental studies have validated that CEP55 regulates midbody fate and recruits components of the ESCRT machinery including ALIX and TSG101 to the midbody to perform accurate cell abscission (Carlton and Martin-Serrano, 2007, Green et al., 2012, Mierzwa and Gerlich, 2014).

Alongside its role in the regulation of cytokinesis in mitotic cells, the gene signature of CEP55 has been reported to be associated with worse prognosis in multiple tumors(Jeffery et al., 2015b). Reports have illustrated that CEP55 overexpression in cancer has dual but independent functions in activation of PI3K/AKT and FOXM1-dependent pathways; thus highlighting the potential mechanism underlying CEP55-associated tumor phenotypes in vitro (Janus et al., 2011, Tao et al., 2014, Chen et al., 2009b, Chen et al., 2007). In addition to its role in cancer, recent studies have also documented the essential role of CEP55 in embryonic development through regulation of the PI3K/AKT pathway independent from its role in stemness. Two recent studies have linked the involvement of genetic inactivation of CEP55 with the autosomal recessive human disorders, MARCH (Frosk et al., 2017) and MKS (Bondeson et al., 2017), wherein mutation in CEP55 has led to multiple congenital anomalies and embryonic lethality. At the whole organism level, CEP55 expression is highest in the testes wherein Cep55 has been shown to associate with TEX14, a protein involved in maintenance of the intercellular bridge between differentiating germ cell (Chang et al., 2010, Greenbaum et al., 2006). Collectively, these studies have emphasized the prospect of a comprehensive role of CEP55 in promoting cell proliferation, growth and survival at several levels. In summary, these findings shed light on the platitude of physiological role of CEP55 in overall development apart from cytokinesis.

In this thesis, we have aimed to comprehensively characterize the physiological role of CEP55 overexpression in vivo, specifically in mammalian physiology, and have also investigated the mechanism by which CEP55 overexpression promotes tumorigenesis. CEP55 overexpression has been predicted to be an early event in tumorigenesis and thus to investigate the potential role of CEP55 overexpression in vivo, our laboratory engineered the novel transgenic ‘knock-in’ mouse model overexpressing Cep55 from the ubiquitously expressed Rosa26 locus. Interestingly, prior to the development of a broad spectrum of tumors, we observed that the Cep55Tg/Tg male mice

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Chapter - 8 presented with chronic atrophy in the testes and were infertile. Notably, the Cep55wt/Tg male were completely fertile though a significant increase in Cep55 expression was observed in their testes. These observations have illustrated the critical importance of precise regulation of Cep55 levels for the maintenance of mammalian spermatogenesis, though embryonic processes including generation of progenitor germ cells (PGCs) and gonocyte function and maturation were unaffected.

Interestingly, our molecular characterization of Cep55 overexpression in the testes suggested a possible connection of Cep55 with Foxo1 through the PI3K-Akt pathway and have demonstrated that upregulation of the Cep55-dependent PI3K-Akt pathway can cause male-specific sterility. Further, we demonstrated that Cep55 overexpression shifts the ontogeny of spermatogonial Stem Cells (SSCs) towards increase proliferation and maintenance rather than differentiation and meiosis which ultimately results in total loss of SSCs and a sertoli-cell only tubule phenotype in the testes. It would be interesting in future studies to cross the Cep55 overexpressing mice with conditional Akt1 or Foxo1-KO mice to validate the direct involvement of Cep55 with Akt in spermatogonial homeostasis.

In the physiological context, we have provided the first physiological evidence of the critical role that Cep55 plays in mammalian spermatogenesis and annotate deregulated Cep55 expression with the growing list of modulators with crucial roles in dictating SSC fate and the maintenance of spermatogenesis. Infertility is a growing issue in modern society, with malfunction in spermatogenesis one of the contributing factors. Malfunction in the process of spermatogenesis often results in male-specific sterility, with symptoms including reduced sperm function, morphology and number (Thoma et al., 2013, Hart et al., 2015, Flannigan and Schlegel, 2017). The term azoospermia refers to the condition wherein a complete loss of mature sperm production occurs due to improper functioning of the SSC pool (de Kretser, 1997, Ferlin et al., 2007). The phenotype observed in Cep55Tg/Tg males resembled azoospermia with definitive symptoms of SCO-like syndrome; hence providing the first experimental evidence of Cep55 amplification being associated with disruption of germline integrity in vivo. These observations provide a rationale for further investigating the copy number variations of CEP55 in azoospermic men in future clinical studies.

In the cancer context, elevated CEP55 has been shown to be associated with CIN-70 and also its subset, the CIN-25 gene signature (Carter et al., 2006, Cuzick et al., 2011). Over the last 10 years, CEP55 has been identified as a marker of poor prognosis, metastasis, risk of relapse, CIN and therapy resistance in many cancer types. However, these data are correlative, and a causal role

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Chapter - 8 for CEP55 in tumorigenesis had not been established. In this study, we provide the first evidence that Cep55Tg/Tg mice (both genders) exhibited a high tumor incidence in an age-monitored cohort, thus providing direct evidence for the oncogenic potential of overexpressed Cep55 in vivo. We showed that Cep55 amplification leads to a highly penetrant induction of a broad spectrum of tumors in mice including lung adenomas and carcinomas, hepatocellular carcinoma, papillary carcinoma, hemangiosarcoma, fibrosarcoma, B- and T- cell lymphoma and myelogenous leukemia. Notably, the Cep55Tg/Tg mice were susceptible to tumor formation with a longer latency period and high tumor burden. These primary tumors were highly aggressive with increased proliferative capacity and prone to metastasis. This phenotype shares similarities with other CIN mouse models, signifying the importance of the Cep55 mouse model in studying the concepts of CIN and aneuploidy.

To further validate the tumorigenic potential of Cep55 overexpression at the molecular level, we performed functional characterization studies on Cep55Tg/Tg MEFs. In support of our in vivo data, Cep55Tg/Tg MEFs had increased proliferative ability compared to control, with concomitant upregulation of PI3K/Akt pathways and higher fraction of polyploid cells in vitro and in vivo. Further, we observed that Cep55 favors the persistence ofa polyploid state during perturbed mitosis to provide a cell survival advantage by facilitating mitotic slippage. Conversely, Cep55 depletion in single cell suspensions from spontaneous tumors in-vitro showed loss of polyploidy with delayed proliferation and tumor initiation in vivo. Additionally, we also observed the upregulation of FoxM1 and Plk1, major players of chromosomal instability in Cep55Tg/Tg mice which might dictate cellular transformation in vivo. Interestingly, the tumor spectrum observed in the Cep55Tg/Tg mice closely mimicked the spectrum observed in Trp53-/- mice. Moreover, the level of CEP55 among TP53 mutated and TP53 loss human tumor samples was highly increased compared to wildtype TP53 tumors. As TP53 has been show to negatively regulate CEP55 in vitro (Chang et al., 2012), the Cep55wt/Tg;Trp53wt/- mice revealed high incidence rate (~85%) and shorter latency to tumor formation in comparison to Cep55wt/wt;Trp53wt/- mice (~50%). It would be interesting to perform further genomic analyses using SNP-CGH for copy number changes in these tumors, which would shed light on the genetic landscape of Cep55Tg/Tg tumors and might implement the additional oncogenic gains or tumor suppressor loss acquired over time for tumor initiation. In summary, these findings signify the oncogenic contribution of Cep55 in determining CIN cell fate during tumorigenesis in vivo. Thus, the Cep55 overexpressing mice model could prove to be a useful tool to study the mechanisms of CIN; however, further genetic studies using other potent oncogenic mouse

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Chapter - 8 models would help to better characterize the machanism via which CEP55 promotes genomic instability to initiate tumorigenesis.

Since the expression of CEP55 is elevated in BC and given the Signal Transduction Laboratory’s expertise in the field, we chose BC as an experimental model to further study the role of CEP55 in tumorigenesis. In this thesis, we provide the first description of CEP55’s function in regulating genomic instability in cancer and have highlighted its potential as a therapeutic target. We demonstrated using bioinformatic analysis of multiple microarray gene expression datasets that TNBCs, known to have high CIN, show a high level of CEP55 expression. We also demonstrated that CEP55 overexpression is significantly associated with worse prognosis of ER-negative tumors associated with poor clinical outcomes, further suggesting that CEP55 promotes aggressive breast cancers at various stages during tumor development. We found that CEP55-depletion in TNBC lines impeded long-term cell proliferation, colony formation and cancer forming competency in athymic nude mice in a dose-dependent manner. Further, we also reported that CEP55 overexpression in vitro significantly protected the aneuploid cell population through mitotic slippage during perturbed mitosis. In contrast, CEP55-depleted clones underwent induced cell death by mitotic catastrophe. Notably we observed premature activation of CDK1/Cyclin B in CEP55-depleted cells leading to activation of cell death pathways upon being challenged with mitotic poisons such as PLK1 inhibitors. These findings signify that CEP55 overexpression may provide a survival advantage to the polyploid population of BC by providing stability and tolerance during mitotic insults. Understanding these pathways that control genomic instability and aneuploidy due to aberrantly expressed proteins such as CEP55 during mitosis will be critical in the development of novel targeted therapies against advanced stage malignancies.

TNBCs are one of the greatest challenges to treat in the clinical setting due to their highly heterogeneous nature (Podo et al., 2010) which fuels resistance towards anti-mitotic drugs such as taxol, a cornerstone treatment for many cancer types (Chan et al., 2012). Although TNBC tumors exhibit a degree of mitotic arrest after treatment with anti-mitotic drugs, the majority often bypasses mitotic arrest and undergoes mitotic slippage. As CEP55 silencing significantly impaired premature exit in TNBC cells treated with a PLK1 inhibitor, CEP55 could be a potential therapeutic target in the clinical settings. We demonstrated in TNBCs that the MAPK pathway transcriptionally controls CEP55 in a MYC-dependent manner. Thus, MEK1/2 inhibition using the small molecule inhibitor Selumetinib, can mimic depletion of CEP55 in vivo. As our laboratory has previously demonstrated the functional role of CEP55 in mitosis to be ERK2/PLK1-dependent, we rationalized

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Chapter - 8 specific targeting of TNBCs on the basis of CEP55-dependent ERK2/PLK1 pathways using small molecule inhibitors AZD6244157 (MEK1/2i) and BI2536158 (PLK1i). We observed a synergistic effect of combined MEK1/2 and PLK1 inhibition in terms of marked suppression of growth of aggressive basal-like syngeneic tumors and human breast cancer xenografts. These effects mimicked the effects observed in two independent studies: MEK1/2i with docetaxel (a microtubule poison) reduced mammary tumor growth in vivo (Yacoub et al., 2006), and a MEK1/2-PLK1 inhibition caused regression of NRAS mutant melanoma xenografts (Posch et al., 2015). Therefore, we provide strong preclinical evidence in favor of a novel therapeutic approach targeting aneuploidy and genomic instability, which are fundamental and recurrent features of TNBCs. However, to further ascertain the efficacy of combination treatment, patient-derived xenograft models should be tested prior to the initiation of clinical trials.

In conclusion, the thesis provides a broader perspective on the multifaceted roles that CEP55 plays in both development and tumorigenesis. We have generated and characterized the first mouse model with overexpression of Cep55, allowing us to study the effects of Cep55 overexpression at the physiological role, and providing the first evidence that Cep55 overexpression is sufficient for de-novo tumorigenesis. Moreover, we have used this model to illustrate that Cep55 levels must be tightly regulated during testes development, and may be an interesting candidate for further examination in cases of azoospermia. The Cep55 overexpression mouse model characterized in this thesis will prove useful in future studies of this protein with respect genomic instability. Although not within the scope of this thesis, it would be interesting to complement these studies with the use of a Cep55 knockout mouse model to further define the role of this protein to the processes of spermatogenesis and tumor formation at the whole-organism level. Our findings outlined in this thesis also highlight the importance of CEP55 in targeting genomically unstable cancers. The effective management of TNBCs requires rationalized strategies to eliminate aneuploid cells, but drugs specifically targeting aneuploid cells are relatively underdeveloped. Our characterization of the role of CEP55 in promoting CIN in aggressive BC lays a foundation for developing novel therapeutic strategies to treat recurrent disease. The thesis also provides a rationale for using combined MEK1/2 and PLK1 inhibitors to target CEP55-dependent aneuploid tumors. In conclusion, these results lay the foundation for future work in the field of CEP55 biology in disease and mammalian physiology.

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