Imperial College London Department of Medicine

A Genetic Screening for the Tumour Suppressive Anticancer Genes

A thesis submitted for the degree of Doctor of Philosophy

Qize Ding

December 2016

Supervisor: Professor Eric Lam

Declaration of Originality

I, Qize Ding, hereby declare that I am the sole author of this thesis, the whole of which contains my original research work conducted at the Department of Medicine,

Imperial College London from 2012 to 2015. Contents such as figures, data and material from other sources are appropriately cited and acknowledged in the text and a full list of reference is shown at the end of my thesis. I declare that this is the true copy of my thesis and the content of this thesis has not been and will not be submitted in any form for a higher degree from any other university or institution.

Acknowledgements

I would like to express my sincere gratitude to my current main supervisor Professor Eric

Lam, as well as my former main supervisor Professor Stefan Grimm who supervised my project for the first two and half years. Their knowledge on the research areas and valuable inputs, directions and guidance have been of great value to me throughout the project. I would also like to say many thanks to my co-supervisor Dr Mona A El-Bahrawy, who advised me on the thesis write-up and corrections.

Many thanks go to my colleagues, the PhD student Motasim Masood, who was helpful and supported the MTT, clonogenic assay and apoptosis assay. Another former PhD student,

Ming Hwang who helped with some Western blotts. I also warmly thank the former MSc student Nazhif Zaini for efficient and hard work for 4 months under my supervision in the lab.

My deep gratitude also goes to all other members of the lab, Dr Ana Gomes, Dr Stefania

Zone, Dr Chun Gong, Upekha Karunarathna, Catherine Yao, as well as my former lab members Dr Ming Hwang, Dr Bevan Lin, Dr Wanwisa Chaisaklert, Dr Christoph Datler, Dr

Evangelos Pazarentzos, for their enthusiasm to help and advice on technical expertise and knowledge.

The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commerical No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commerical purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers much make clear to others the licence terms of this work.

Abstract

Here, using a gain of function genetic screen approach I investigated 377 human apoptosis inducer genes that were previously isolated in HEK293T cells, and performed several rounds of genetic screens to isolate 22 human anticancer genes that only induce cell death in transformed but not normal cells. Later, using the genetically well-characterised cell line

HEK293T as well as establishing transformed variants overexpressing individual oncogene, mutated genes or by knocking down tumour suppressor genes, I performed a separate genetic „synthetic lethal screen‟ to identify a number of transformation scenarios in which these anticancer genes are targeted. Of these 22 anticancer genes, 16 showed a pronounced cell death inducing effect against c-Myc upregulation. c-Myc overexpressing cells were characterised to show phenotypic differences compared to their normal counterpart. On further validation, c-Myc specificity for these 16 genes was confirmed in a different cellular background in which some of these genes show inverse correlation to c-

Myc expression. The last part of my project is to explore the molecular mechanism by which these 16 c-Myc specific anticancer genes induced cell death in c-Myc overexpressing cells.

For the gene 1A3 (TMEFF2), my data indicated that it inhibited NF-B activity and CDK5 by inducing c-Myc specific cell death. In addition, 3 other genes 3B6, 4G3 and 4G4 also showed to induce c-Myc specific cell death through inhibition of FOXK2 in c-Myc overexpressing cells. In conclusion, I demonstrate that this „gain of function‟ genetic screening strategy is useful for the isolation of tumour suppressor genes, but the establishment of their potential „anticancer‟ functions would require further understanding of their molecular modes of action.

Publications and Conferences

Publications

Dominant Suppression of Inflammation via Targeted Mutation of the mRNA Destabilizing Protein Tristetraprolin. Ross EA, Smallie T, Ding Q, O'Neil JD, Cunliffe HE, Tang T, Rosner DR, Klevernic I, Morrice NA, Monaco C, Cunningham AF, Buckley CD, Saklatvala J, Dean JL, Clark AR. J Immunol. 2015 Jul 1;195(1):265-76. doi: 10.4049/jimmunol.1402826.

The anticancer gene ORCTL3 targets stearoyl-Coa desaturase-1 for tumour-specific apoptosis. AbuAli G, Chaisaklert W, Stelloo E, Pazarentzos E, Hwang MS, Qize D, Harding SV, Al-Rubaish A, Alzahrani AJ, Al-Ali A, Sanders TA, Aboagye EO, Grimm S.Oncogene. 2015 Mar 26;34(13):1718-28. doi: 10.1038/onc.2014.93.

IκΒα inhibits apoptosis at the outer mitochondrial membrane independently of NF-κB retention. Pazarentzos E, Mahul-Mellier AL, Datler C, Chaisaklert W, Hwang MS, Kroon J, Qize D, Osborne F,

Al-Rubaish A, Al-Ali A, Mazarakis ND, Aboagye EO, Grimm S. EMBO J. 2014 Dec 1;33(23):2814-28. doi: 10.15252

Selectin ligand sialyl-Lewis x antigen drives metastasis of hormone-dependent breast cancers. Julien S, Ivetic A, Grigoriadis A,QiZe D, Burford B, Sproviero D, Picco G, Gillett C, Papp SL, Schaffer L, Tutt A, Taylor-Papadimitriou J, Pinder SE, Burchell JM. Cancer Res. 2011 Dec 15;71(24):7683-93. doi: 10.1158/0008-5472.CAN-11-1139.

Anti-inflammatory effects of selective glucocorticoid receptor modulators are partially dependent on up-regulation of dual specificity phosphatase 1. Joanny E, Ding Q, Gong L, Kong P, Saklatvala J, Clark AR. Br J Pharmacol. 2012 Feb;165(4b):1124-36. doi: 10.1111/j.1476-5381.2011.01574.x.

Conference

21 March 2012 High Throughput/High Content Technology Symposium,

Imperial College London

Table of Contents

Declaration of Originality 2

Acknowledgement 3

Abstract 5

Publications and Conference Attendence 6

Table of contents 7

Abbreviations 11

Chapter 1. Introduction 17

1.1 Cell death 17

1.1.1 Apoptosis 17

1.1.2 Necrosis 19

1.1.3 Autophagy 21

1.1.4 Apoptosis pathways 21

1.1.5 Other apoptosis pathways 24

1.1.6 Caspase cascade 25

1.1.7 Imbalance and disruption of apoptotis 26

1.1.8 Apoptosis and Cancer therapy 28

1.2 Anticancer genes 32

1.3 Synthetic lethality 40

1.3.1 Background information 40

1.3.2 BRCA and PARP inhibition mediated synthetic lethality 42

1.3.3 Mutated P53, Rb and Ras related synthetic lethality 43

1.3.4 Myc related synthetic lethality 45

1.4 Myc 50

1.4.1 History of Myc 50

1.4.2 My family proteins 51

1.4.3 Functions of Myc 52

1.4.4 Regulation of Myc 54

1.4.5 Roles of Myc in Cancers 55

1.4.6 Therapy against Myc-driven Cancers 57

1.5 Roles of Forhead box protein FOXM1, FOXK2 and FOXO3 in human cancers and apoptosis 58

1.6 Role of CDK5 in Cancers and apoptosis 64

1.7 Hypothesis and Objectives 66

Chapter 2. Methods and Materials 69

2.1 Reagents and Materials 69

2.2 Molecular Biology 72

2.2.1 Molecular Cloning 72

2.2.2 Plasmid DNAs Section 72

2.2.3 Bacterial culture 73

2.2.4 Transformation of Plasmids 73

2.2.5 Ultra-pure Silica oxide large scale plasmid DNA isolation 74

2.2.6 DNA isolation with commercial kits 75

2.2.7 Quantification of DNA concentration 75

2.2.8 Restriction enzyme reactions 76

2.2.9 DNA Gel electrophoresis 76

2.3 Cell culture and Transfection 77

2.3.1 Mammalian cell culture 77

2.3.2 Xfect transfection 77

2.3.3 Other commercial transfection kits 78

2.3.4 Production of stable transfected cell lines 78

2.4 Gene expression measurement 78

2.4.1 RNA extraction 79

2.4.2 Reverse transcription 79

2.4.3 Running of quantitative Polymer chain reaction 79

2.4.4 All Primers 80

2.5 Cell death measurement 82

2.5.1 Propidium iodide staining (PI) 82

2.5.2 DIOC6 staining 82

2.6 Functional assays 83

2.6.1 Cell cycle distribution assay 82

2.6.2 CPRG assay 82

2.6.3 MTT assay 85

2.6.4 Clonogenic cell surivial assay 85

2.6.5 Cellular transformation assay 85

2.6.6 Mitochondrial reactive oxygen species measurement

(MitoSox assay) 85

2.7 Protein SDS-PAGE Gel electrophoresis 86

2.7.1 Preparation of cell lysates 86

2.7.2 Protein quantification 86

2.7.3 SDS-PAGE Gel electrophoresis 87

2.7.4 Western Blotting 87

2.7.5 Antibody list 88 9

2.8 Statistical Analysis 89

Chapter 3 Synthetic lethality screen-Results and Discussion 90

3.1 Background information 89

3.2 Transfection in CV1 cells 92

3.2.1 Commercial transfection reagents 92

3.2.2 Non-commercial transfection reagents 95

3.3 Optimization of in vitro cell death assay (CPRG assay) 97

3.4 Implementation of genetic screens 102

3.4.1 First round of screen (Primary screen) 105

3.4.2 Second round of screen (Primary screen) 107

3.4.3 Third round of screen (Primary screen) 107

3.4.4 Validation of candidate genes in CV1 normal cells 111

3.4.5 Validation of candidate genes in HEK293T cells 113

3.4.6 Identifying genetic changes linked to tumour specific effects (Secondary screen) 115

3.5 Discussion 123

Chapter 4 Functional validation of 22 putative anticancer genes -Results and Discussion 130

4.1 Background Information 129

4.2 Cell death validation in different cellular systems 129

4.3 Cell proliferation 131

4.4 Long term impact on cell growth 133

4.5 Characterisation of c-Myc overexpressing cells 134

4.5.1 Change of cell morphology 134

4.5.2 Loss of cell - cell contact inhibition 135

4.5.3 Cell cycle and cell proliferation 137

4.5.4 Increased NF-B activity 139

4.5.5 Increase in reactive oxygen species (ROS) levels in

c-Myc overexpressing cells 141

4.6 Validation of c-Myc induced specificity on cell death 143

4.7 Expression levels of Myc specific Anticancer genes in relation to c-Myc expression 147

4.8 Discussion 149

Chapter 5.Identification and validation of potential targets for c-Myc specific putative Anticancer Genes- Results and Discussion 154

5.1 Background information 154

5.2 Testing of inhibition of already known synthetic lethal targets against

c-Myc 154

5.3 Characterisation of Fibulin 5 (Gene 1B8) 156

5.4 CDK5 and NF-kB as potential targets for TMEFF2 (Gene 1A3) 162

5.5 Identifying FOXK2 as a potential target for 3 Anticancer Genes 168

5.6 Discussion 174

Chapter 6. Final conclusion and Future directions 182

References 187

Appendices 219

Abbreviations

ARK5 AMPK-related kinase 5

AIDS Acquired immune deficiency syndrome

AMPK Amp-activated protein kinase

ANT1 Adenine nucleotide translocator

ATG Autophagy related genes

APC/C Anaphase-promoting complex/cyclosome

Apoptin Apoptosis-inducing protein

BCR-ABL Break point cluse gene – Abelson tyrosine kinase gene

β-Gal β-galactosidase

BSA Bovine serum albumin

BRCA Breast cancer susceptibility gene

BAP-1 BRCA-1 associated protein 1

BCA Bicinchoninic acid

Bcl-2 B-cell lymphoma -2

Bax Bcl2-associated X protein

Bcl-xL B-cell lymphoma-extra large

BH Bcl-2 homology

Bid BH3 interacting domain death agonist

CPRG Chlorophenol red-β-D-galactopyranoside

CDK Cyclin dependent kinase

CCPC Chromosomal passenger protein complex

ChIP Chromatin immunoprecipitation

CHK2 Checkpoint kinase 2

CDC45 Cell division cycle protein 45

CARD Caspase recruitment domain

Caspases Cysteine ASPartate-Specific ProteASEs cDNA Complementary DNA

Ced Cell death

DED Death effector domain

DiOC6 3,3‟- dihexylocarbocyanine iodide

DNA Deoxyribonucleic acid

DMEM Dulbecco‟s Modified Eagle‟s Medium

E1A Early region 1A

E1B Early region 1B

E4ORF4 E4 open reading frame 4

ER Endoplasmic reticulum

EDTA Ethylenediaminetetraacetic acid

EGF Epithelial growth factor

ERK Extracellular signal–regulated kinases

ER Endoplasmic reticulum

FCS Foetal calf serum

FADD Fas-Associated protein with Death Domain

FAC Fluorescece activated cell sorter

FCS Fetal calf serum

FLIP FLICE-like inhibitory protein

GFP Green fluorence protein

HAMLET Human Alpha-lactalbumin Made Lethal to Tumour cells

HEK293T Human embryonic kidney 293T 13

HeLa Human cervical carcinoma

HMEC Human mammary epithelial cells

HIV Human Immunodeficiency Virus

IL-24 Interleukin-24

IKK IκB kinase

IGF Insulin-like growth factor

IPSC Inducible pluripotent stem cells

JNK c-Jun N-terminal Kinase kDa kilo Dalton

LB Broth Luria-Bertani broth mTORC1 Mammalian target of rapamycin complex 1

MAPK Mitogen-activated protein kinases

MDA-7 Melanoma differentiation associated gene 7 mRNA Messenger RNA

MFI Mean fluorescence intensity

MTT Methylthiazol Tetrazolium

MycBP C-Myc binding protein

NaCl Sodium chloride

NaOH Sodium hydroxide

NRK Normal rat kidney

NF-ƙB Nuclear Factor- ƙB

NS1 Nonstructural protein 1

ORCTL Organic cation transporter like

PARP (Poly ADP-ribose) polymerase

PEI Polyethylenimine 14

PAGE Polyacrylamide Gel Electrophoresis

Par-4 Prostate apoptosis response-4

PBS Phosphate buffered saline

PDGF Platelet-derived growth factor

PI Propidium iodide

PRKDC Protein kinase, DNA-activated, catalytic polypeptide

PVDF Polyvinylidene difluoride qRT-PCR Quantitative real-time PCR

Rb Retinoblastoma protein

RIPK1 Receptor interacting protein kinase 1

RIPA Radioimmunoprecipitation assay

RIP Receptor Interaction Protein

RNAi RNA interference

RNAP II RNA polymerase II

SAE1/2 SUMO-activating enzyme 1/2

SCD-1 Stearoyl-CoA desaturase 1

SDS Sodium dodecyl sulfate siRNA Short Interfering RNA shRNA Short Hairpin RNA

Smac Second mitochondria derived activator of caspase

SV40 Simian virus 40

TAE Tris acetate EDTA t-Bid Truncated Bid

TE buffer Tris-EDTA buffer

TNF Tumour necrosis factor 15

TNFR TNF Receptor

TRADD Tumour necrosis factor receptor type 1-associated DEATH domain protein

TRAIL TNF-Related Apoptosis-Inducing Ligand

TRAILR TNF-Related Apoptosis-Inducing Ligand Receptor

WT1 Wilm‟s tumour 1

XRCC1 X-ray repair cross-complementing protein 1

Chapter 1 Introduction

1.1 Cell death

Multicellular organisms must balance cell survival and cell death for their development and tissue homeostasis. Imbalance of cell death and survival leads to pathological consequences and diseases. The principal modes of cell death are apoptosis, cell death- associated autophagy and necrosis. These modes of cell death have been described as type

I cell death with characteristic clearance by phagocytic activity, type II cell death with characteristic regulated self degradation, and type III cell death with no regulated clearance, respectively (ref.1).

1.1.1 Apoptosis

Apoptosis is a Greek word meaning: „falling off of leaves‟. This natural phenomenon was described by Karl Vogt in 1842 in the study of development (ref.2). In 1964, the concept of

“programmed cell death” was introduced by Lockshin and Williams in the study of cell death during insect metamorphosis (ref.2). In 1972 the term ‘apoptosis’ was introduced by Kerr, Wyllie and Currie with descriptions of morphological changes and physiological processes associated with programmed cell death (ref.3). Since then, the molecular mechanisms of apoptosis have been intensely studied. In 1999, the cell death associated molecules CED3 and CED4 were cloned from Caenorhabditis elegans. In this functional study, these two genes were found to initiate apoptosis, and the loss of function of these two molecules caused defects in cell death (ref. 4, 5). In addition, the finding of a novel gene,

CED9, which was found to inhibit cell death, led to the discovery of a homologous mammalian gene, namely Bcl-2 with related structure and function. This exciting finding

suggested that the mechanism of apoptosis has been conserved between nematodes and mammals during evolution (ref. 6).

Highly complex biological systems regulate their growth, cell differentiation and cell survival in many different ways. Apoptosis is a key mechanism by which cells control their fate between life and death. Apoptosis is also known as programmed cell death and is a genetically controlled form of cell death (ref.9). Apoptosis is a widespread phenomenon that occurring in nematodes (ref.12), insects (ref.11), and plants (ref.13). In mammals, it plays equally important roles in different biological mechanisms ranging from embryogenesis to ageing and maintenance of tissue homoeostasis. Highly complex developmental processes, like metamorphosis and mammalian embryogenesis, utilise apoptosis to dispose of unwanted cells; for example, sculpturing the shape of fingers (ref.19). Also, in our daily life, death of 10 billion cells is tightly controlled in combination with proliferation of new cells to maintain cell numbers for the proper function of our biological system. At the same time, defects in apoptosis are associated with many human diseases (ref.8). Thus, apoptosis remains an important area of research in biomedical sciences.

Apoptosis is characterised by an orderly succession of changes in cell morphology as the process of programmed cell death progresses. Firstly, cells start to shrink, and show chromatin condensation, nuclear fragmentation and DNA cleavage to 200 base pairs. This is followed by the formation of apoptotic bodies. Other characteristics of apoptosis include pronounced fragmentation of the Golgi apparatus, the endoplasmic reticulum (ER) and the mitochondrial network, with protein releasing from those cellular compartments (Fig.1)

(ref.21,22). The presence of apoptotic bodies would result in inflammation, which affects

neighbouring cells, local tissues or organs, and a process called phagocytosis is mediated principally by macrophages to remove the apoptotic bodies and cellular debris to avoid disruption of essential physiological processes and inflammatory response (ref.8). However, if apoptotic bodies are not engulfed by macrophages, then a process called secondary necrosis will occur and an inflammatory response will be induced in the cell surroundings.

The mechanism regulating the balance of live and dead cells is strictly controlled at the cellular level (ref. 20).

Moreover, when cells are infected by pathogens and experience excessive physical damages, our immune systems can utilise apoptosis to selectively eliminate the defective cells to ensure the survival of healthy cells (ref.9). Similarly, diseased cells or cells subject to cellular stress can also undergo apoptosis. Dysregulation of this process can result in disease with defective apoptosis, resulting in autoimmune diseases such as systemic lupus erythematosus and cancer, while excessive apoptosis occurs in diseases, such as AIDS and neurodegenerative disorders (ref.10).

1.1.2 Necrosis

Necrosis is another form of cell death distinct from apoptosis. Morphologically, cells experiencing extreme stress start chromatin clumping, along with swelling of organelles and flocculent mitochondria, followed by disintegration and release of intracellular contents

(Fig.1). One of the unique features in terms of physiological outcomes of necrosis is its ability to trigger inflammatory responses upon cell death. Previously, researchers thought this process is caused by accident as an uncontrolled process (ref. 23). However, recent studies suggest that necrosis might be controlled by signalling pathways initiated by 19

receptors, such as Fas/CD95, TRAIL-R, TLR3, leading to necroptosis. Necrosis can be blocked by the inhibition of RIPK1 by necrostatin 1 (ref. 24, 25). More recently, a number of studies suggest other emerging pathways of regulated necrosis, including ferroptosis, oxytosis, ETosis, NETosis, cyclophilin D mediated regulated necrosis and parthanatos (ref.

27).

Live cells

Apoptosis Necrosis

Figure 1. Difference between Apoptosis and Necrosis. Morphological difference between apoptosis and necrosis in each step of process of cell death. This picture is adapted from (ref. ApoReview - Introduction to

Apoptosis by Andrew Gewies 2003)

1.1.3 Autophagy

Autophagy is derived from the Greek language, meaning a process of self-cannibalisation under a condition in which cells retain their own organelles and cellular compartments, and consume them in lysosomes to generate energy and new molecules, including proteins and membranes (ref.28). Autophagy is an evolutionarily conserved process and physiologically a cellular mechanism to keep cells alive under stress conditions. Morphologically, autophagy is characterised by the absence of chromatin condensation, autophagic vacuolisation, and phagocytositic clearance. (ref. 30). However, excessive autophagy can lead to cell death. So far, three types of autophagy, namely macroautophagy, microautophagy and chaperone mediated autophagy, have been identified. Autophagy is triggered by nutrient deprivation, hypoxia, ER stress and reactive oxygen species (ROS). It consists of multiple steps that include nucleation, elongation and autophagosome and autolysosome formation. A series of highly conserved genes have been found to be involved in the execution of these processes, called autophagy related genes (ATGs) (ref. 29). Autophagy is characterised by recycling damaged cellular materials within autophagosomes, a vesicle that contains damaged and degraded organelles for degradation. In the autolysomes, the content is further degraded by enzymes such as acidic lysosomal hydrolases (ref.30). At the molecular level, some molecules are found to associate with and regulate the autophagy related pathways, namely mTORC1, PI3K, AKT, Beclin-1 and p53 (ref.31).

1.1.4 Apoptosis pathways

Apoptosis is a very complex and sophisticated system and one of most crucial cellular processes. It is genetically programmed and regulated, and executed in response to external or internal cellular stimuli. Apoptosis involves activation of a cascade of molecular events by

a family cysteine proteases called caspases (ref.32). The outcome of this process shifts the equilibrium towards pro-apoptotic pathways, leading to a number of biological processes including cleavage of cellular proteins, breakdown of the nucleus, and formation of apoptotic bodies (ref. 33). In addition, activation of caspases is triggered by two well-known pathways, namely the intrinsic and extrinsic pathways (ref. 34). Recently, there is a less understood pathway suggested in the scientific community and is called the ER pathway (ref. 35). There is also cross talk between these three pathways.

The extrinsic pathway is triggered upon physical binding between death receptors and ligands e.g tumour necrosis factor receptor 1 (TNFR1) and tumour necrosis factor (TNF),

Fas receptor and Fas ligand (ref. 35,36). Binding causes the activation of intracellular domains of the death receptor, which then form a binding site for recruitment of adaptor proteins e.g TRADD and FADD. Then, the death inducing signalling complex is formed and recruits caspase initiators e.g Caspase 8 and 10, to further activate downstream caspases, leading to cell death (Fig.2) (ref. 37, 38, 39).

The intrinsic pathway begins at the mitochondria, and is initiated by a range of excessive cellular stresses, such as DNA damage, heat shock, cytotoxic drugs, radical oxygen species, and nutrient deprivation (ref. 40). This disrupts the balance of signals from pro-apoptotic and anti-apoptotic molecules in the cells. The balance shifts towards the pro-apoptotic signals.

As a result, the pro-apoptotic Bcl-2 family members, including Bim, Bax and Bad, are localised on the outer mitochondrial membranes through formation of pores initiating the pathway (ref. 41). The activities of anti-apoptotic proteins of the Bcl-2 family, such as Bcl-2 and Bcl-xL, which act through blocking the binding of these proapoptotic molecules to their

mitochondrial binding sites, are downregulated (ref.41). In the presence of higher levels of proapoptotic Bcl-2 family member proteins and lower levels of antiapoptotic Bcl-2 proteins, mitochondrial outer membrane permeability is compromised causing the release of cytochrome-c molecules, which binds with a protein called apoptosis protease-activating factor -1 (Apaf-1). This compex, in turn, interacts and activates procaspase-9 to form a complex called the apoptosome, which then activates the downstream pro-caspase-3, leading to cell death (Fig.2) (ref. 42).

The extrinsic and intrinsic pathways can cross-talk through a molecule called Bid. Inactive

Bid is activated by caspase-8 from the extrinsic pathway. Activated Bid, called t-Bid, then interacts with another pro-apoptotic protein, Bax, forming a complex which translocates to the mitochondrial outer membrane and triggers apoptosis (ref. 43, 44, 45)

Figure 2. Schematic diagramme of instrinic and extrinsic apoptosis pathways. Picture is adapted from

(ref 363.)

1.1.5 Other apoptosis pathways

There are other pathways in apoptosis that involve direct disruption of mitochondria. For instance, the proteases Granzyme A and B can directly disrupt the mitochondrial membrane to trigger apoptosis. Apoptosis mediated by the release of serine proteases Granzyme A and

B by immune cells like CD8+ cytotoxic T cells and NK cells to eliminate the target cells upon activation (ref. 46). This activated Granzyme B is also able to cleave a number of pro- apoptotic molecules such as Bid and other effector caspases to promote activation of apoptosis in a caspase-dependent fashion. In addition, Granzyme A can activate caspase independent pathways once in a cell by triggering DNA nicking through cleavage of ER SET

complex, causing apoptotic DNA degradation. (ref. 47, 48). These two granzymes work together to trigger cell death in target cells.

1.1.6 Caspase cascade

Caspases are normally present in all cells in inactive forms known as procaspases. At present, fourteen caspases have been identified and they share high levels of homology with the C. elegans cell death gene Ced-3 (ref. 51). Caspases are all aspartate specific cysteine proteases that share a conservative sequence in pentapeptide active site "QACXG" (X=R, Q, or D). Structurally, caspases have two domains, the caspase recruitment domain (CARD) and the death effector domain (DED). These two protein domains are responsible for protein interaction to initiate functions (ref. 49). Caspase 1 was the first human caspase to be discovered (ref. 50). Caspases are divided into two groups, namely initiator and effector caspases. Caspases 2, 8, 9 and 10 are initiator caspases that can start the proteolytic cleavage of other caspases, whereas caspases 3, 6 and 7 are the effector caspases which cleave their downstream substrates in apoptosis pathway (ref. 49). Activation of the extrinsic and intrinsic pathways of apoptosis can both ultimately lead to activation of procaspases 8, 9 and 10. Downstream procaspases include effector caspases such as caspase 3, 6 and 7 (ref.

49). The active form of these caspases can subsequently cleave specific target substrates, such as PARP [(poly ADP-ribose) polymerase], lamin and Bcl-2 and ultimately, leading to apoptosis (ref. 51). Executioner caspases can further amplify the activation of initiator caspases in a positive feedback loop. However, there are caspases which are not involved in apoptosis, such as Caspase 1, 4, 5 and 12. These caspases participate in immune responses through regulation of inflammatory cytokines such as IL1β and IL18 (ref. 49).

1.1.7 Imbalance and disruption of apoptosis

Under physiological conditions, well-defined mechanisms have been established to regulate and maintain the appropriate cellular composition of tissues and organs in biological systems.

There are many mechanisms which strictly control and maintain the balance between live and dead cells. For example, elimination of virus infected cells or cells experiencing excessive damage by the immune system is induced to ensure that only healthy cells can survive (ref.9 ). However, imbalance of apoptosis would lead to dysfunction of the system and disease will be the inevitable consequence (ref. 10). A broad range of proteins play an important role in maintaining the balance of cell proliferation and cell death. For instance,

FAS receptor and FAS ligand play a very important role in regulation of immune cells in particular activated T cells.

There are a number of categories of diseases associated with excessive apoptosis (Fig.3).

These are typically, neurodegenerative diseases and AIDS. Neurodegenerative diseases, such as Alzheimer‟s and Parkinson‟s, show elevated levels of caspase 3 and increased apoptosis of neuronss (ref. 52). In AIDS, HIV infection is associated with decreased numbers of CD4 T lymphocytes and immunodeficiency. There is evidence that HIV is capable of inducing apoptosis in infected CD4 T lymphocytes by cleaving and inactivating Bcl-2 and activating procaspase 8 (ref. 53).

In addition, there are several categories of disease associated with defects in apoptosis

(Fig.3). Two typical examples are autoimmunity disease and cancer. In autoimmunity, defective clearance of dead cells causes exposure of potential autoantigens contributing to generation of autoreactive T and B cells ( ref. 54). Also, defective apoptosis of activated

immune cells leads to disease onset. One example is that mice with defective FAS signalling pathway produce a large number of autoantibodies in a disease that has very similar features to human systemic lupus erythematosus (SLE) (ref. 55). In cancer, evading apoptosis has been described as one of six hallmarks of cancer (ref. 56). Cancer cells can undergo proliferation in an uncontrolled manner and have defects in apoptosis. Over time, cancer cells accumulate and form tumours at the primary tumour site, followed by metastasis to other parts of the body and, ultimately leading to death (Fig.3). Furthermore, recent evidence shows that cancer cells have the ability to acquire resistance to apoptosis through mutations or changes of expression of both pro-apoptotic and anti-apoptotic molecules.

Upregulation of anti-apoptotic molecules, such as Bcl-2, is an example of causes for tumorigenesis in many human cancers (ref. 57).

(A)

(B)

(C)

Figure 3. Level of cell death and cell proliferation is important under normal physiological and disease conditions. (A) Balance of cell death and cell proliferation under physiological homeostasis. (B) Less cell death results in different diseases e.g cancer and autoimmunity. (C) More cell death results in different diseases e.g AIDS and neurodegenerative disease.

1.1.8 Apoptosis and cancer therapy

Evasion of apoptosis or cell death is described as one of six hallmarks of cancer. The balance of live and dead cells is altered in cancer, and cancer cells have defects in apoptotic pathways. In the cellular transformation process, balance of cell death shifts towards oncogenes that promote cell survival, whereas tumour suppressor genes that inhibit cell survival lose their functions. Also, oncogenes that promote cell proliferation are able to cooperate with those that inhibit cell death, ultimately leading to tumorigenesis (ref. 58).

Most of the clinically available chemotherapeutic agents are known to target proteins, DNA and pathways required for a number of cellular functions, such as cell division, cell proliferation, DNA repair. There is evidence that these drugs can create additional cellular stress to cancer cells so that stress responses exceed a threshold level required for cell survival (ref. 59). However, these chemotherapeutic agents are not specific enough in targeting only cancer cells and in occasions, they potentially promote the natural selection of 28

cancer cells. Eventually, cancer cells are able to develop resistance against these chemotherapeutic agents and acquire a survival advantage and evade the therapy.

According to previous studies, some of these chemotherapeutic agents induce cell death or apoptosis through disruption of the mitochondrial outer membrane (ref. 60). However, this brings considerable collateral cytotoxic effects to normal cells with intact apoptosis mechanisms. In other words, these chemotherapeutic agents may also inadvertently eliminate normal cells. The most pronounced side effects happen to organs and tissues, such as skin, gastrointestinal tract and bone marrow, with proliferative cells. Therefore, it is increasingly important to identify cancer cell specific genetic alterations, so that we can develop targeted therapies which avoid the collateral damage associated with current drugs .

New therapeutic agents attempt to re-activate extrinsic and intrinsic pathways as these pathways are defective in cancer cells. One typical example of molecules involved in intrinsic pathways are the Bcl-2 family member proteins. There are two categories of proteins, namely pro-apoptotic Bcl-2 family proteins such as the BH-3 only proteins Bax and Bak, and anti-apoptotic Bcl-2 family proteins such as Bcl-2 and Bcl-xL. In B cell lymphomas, Bcl-2 is shown to be upregulated. Transgenic mouse models with overexpression of Bcl-2 develop different forms of lymphoproliferative disorders (ref. 61). In addition, Bcl-2 has been found to be upregulated in other cancers, such as hodgkin's lymphoma (ref. 62), breast cancer (ref.

63), non-small and small cell lung carcinoma (ref. 64), and renal cell carcinoma (ref. 65).

Mutations to pro-apoptotic Bcl-2 family member proteins, such as Bax and Bak, have been found in gastric and colon carcinomas (ref. 66). Studies on targeting anti-apoptotic Bcl-2 family member proteins for inducing apoptosis in cancer cells are active and have been ongoing for many years and the focus is now on BH-3 only proteins. The rationale to design small molecule compound BH-3 mimics to antagonise Bcl-2 protein, is to model the pro- 29

apoptotic functions of BH-3 only proteins to restore apoptosis functions in cancer cells.

Some of these compounds are already in phase I/II clinical trials (ref. 67, 68). So far, the only therapeutic approach targeting anti-apoptotic Bcl-2 family member proteins that has advanced the most is the antisense oligonucleotide targeting of anti-apoptotic protein Bcl-2, which is in phase III clinical trial for the treatment of chronic lymphocytic leukaemia (ref. 54).

Furthermore, as previously mentioned, activation of death receptor upon binding to death ligand leads to activation of downstream signalling pathways involving caspase 8 cascade.

TRAIL is known as an apoptosis inducing member of the TNF superfamily and its receptor is called TRAIL receptor 1 and 2. CD95 is a known Fas receptor and its ligand is FasL. These are two typical examples involved in initiating extrinsic pathways. In TRAIL, in vitro and in vivo evidence has highlighted its importance in suppressing tumour growth (ref.69). TRAIL knockout mice develop haematopoietic malignancies (ref. 69). While TRAIL receptor 1 and

2 are often lost or mutated in 20% of human cancers, including head and neck cancer, non-

Hodgkin lymphomas and breast cancer (ref. 54). Moreover, tumours develop resistance against TRAIL by upregulating decoy receptors, such as decoy receptors 2 and 4 in breast cancer. In vitro siRNA knockdown of decoy receptor experiments can re-sensitise MCF7 cells to TRAIL treatment (ref.70). For CD95, this death receptor is downregulated or mutated in cancer cells. CD95 has been found to be lost in hepatic carcinoma, (ref.71) and downregulated in colonic (ref.72), ovarian, cervical, and lung cancers as well as melanoma

(ref.73). Therapeutic approaches developed towards targeting death receptors by agonists are attractive, but their toxicity to normal cells remains a considerable concern. So far,

TRAIL based therapy has advanced the most to phase II clinical trial and is the only death ligand that possesses the unique capability to trigger cell death only in cancer cells while sparing normal cells, both in vitro and in vivo. However, there are a few challenges 30

associated with this therapy. Some cancer cell lines or primary cancer cells are resistant to this TRAIL based therapy. Sensitisers such as CDK9 inhibitor, SMAC and BH-3 mimetics are needed in combination with TRAIL to restore the therapeutic effect in these cells (ref.74).

In addition, recent studies showed that genetic backgrounds of cancer cells become an important factor in determining therapeutic efficacy. In mouse models, K-ras mutated cancer cells not only develop resistance against this TRAIL based therapy but also utilise TRAIL as a metastasis promoting factor (ref.75).

Several small molecule compounds used in conventional chemotherapy can eliminate tumour cells in many ways. One mechanism is to promote cell death or apoptosis by activating intrinsic pathways and disrupting the integrity of the outer mitochondrial membrane.

However, the lack of specificity of these cancer treatments involving inhibition of other proteins and pathways required for the normal physiological functions, leads to significant side effects in patients. This drawback remains a significant concern in clinical application, and limits the maximum administration dose and hence, therapeutic efficacy and benefits, as well as patients‟ quality of life (ref.76). Potential solutions to overcoming this problem are to develop targeted therapy that either uses of selective chemical compounds for triggering cancer specific apoptosis or finding alternative targets such as genes that can induce cancer specific cell death. In the latter approach, appropriate vectors are needed to deliver these genes to cancer cells. The recent emergence of the concept of “anticancer genes” can contribute to advancement of this approach.

1.2 Anticancer genes

Recent studies have identified a new class of genes and their encoded proteins that can promote the induction of cell death or apoptosis only in cancer cells, while leaving normal cells unharmed. This new area of research was established by my former supervisor

Professor Stefan Grimm and Dr Mathieu Noteborn. This specific anticancer activity is achieved upon the ectopic expression of these genes in cancer cells by suitable vectors.

However, the underlying mechansims by which these anticancer genes induce their effect are largely unknown. To date, there are 10 anticancer genes discovered to have cancer specific activity. A few of them have reached phase I/II clinical trials such as TRAIL and

HAMLET, whereas others are still in the preclinical stage of research (ref.77).

Compared with simple structure of small molecular chemical compounds, the advantage of anticancer genes is that genes and encoded proteins are structurally complex. It is much easier to identify the molecular targets of these genes or encoded proteins using modern molecular biology techniques compared to small molecule compounds which can target multiple proteins and pathways and lack specificity as previously mentioned. In addition, conventional chemotherapeutics block the activities of proteins and pathways essential for cancer cell growth and proliferation. Anticancer genes, however, can actively initiate signals in a dominant way upon ectopic overexpression. These signals generated by anticancer genes can create conflicts with other signals generated by malignant transformation e.g oncogenes, ultimately leading to transformation specific cell death. In contrast to anticancer genes, tumour suppressor genes are only expressed at the endogenous level to prevent tumour formation. Tumour formation occurs when two alleles of tumour suppressor genes are inactivated or when the activity of the tumour suppressors are not sufficient to repress tumorigenesis. The evidence highlights that overexpression of a well-known tumour 32

suppressor gene, p53, leads to induction of apoptosis in both normal and tumour cells

(ref.78). Some data suggested that some anticancer genes are able to act as tumour suppressor genes (ref.77).

Apoptin

This is the first identified anticancer gene derived from chicken anaemia virus (ref.79). It has been shown to induce apoptosis in a range of tumour cell lines but not in normal cells (ref.80).

Over years of studies on this molecule, apoptin has been shown to be selectively phosphorylated only in tumour cells (ref.81,82). The ceramide, which has tumour suppressor activity, is increased in tumour cells for apoptin induced cell death (ref.83). More importantly, apoptin was shown to physically interact with the anaphase promoting complex/cyclosome

(APC/C) complex to cause cell cycle arrest at G2/M phase and p73/PUMA mediated apoptosis (ref.84,85). Apoptin induced apoptosis is p53 independent and can not be blocked by Bcl-2 or Bcl-xL upregulation (ref.86), but caspase activation and cytochrome-c release are involved in this process.

HAMLET

HAMLET stands for human α-lactalbumin made lethal to tumour. It is a protein complex, which consists of partially unfolded α-lactalbumin and five molecules of oleic acid, that kills tumour cells in a specific manner, while leaving normal cells unharmed (ref.87). In vivo model studies showed significant tumour regression in skin paillomas and bladder cancer.

Importantly, the papillomatous skin tumour disappeared completely with no toxic side effects to healthy cells reported in this study (ref.88). The mechanism of action for tumour specific

cell death inducing effect is still unclear. However, some studies have provided leads to explain this effect. One study revealed that translocation of HAMLET to tumour cell nuclei occurs and specifically causes chromatin disruption and loss of transcription, leading to cancer cell death (ref.89). Another study showed that bovine HAMLET accumulates in the endolysosomal compartment of tumour cells and triggers the leakage of cathepsins into the cytosol in cancer cells (ref.90). The latest study suggested that macroautophagy is involved in HAMLET induced cell death as cell death can be reduced when macroautophagy is inhibited because cancer cells alter macroautography pathways (ref.91). Cell death induced by HAMLET cannot be inhibited by upregulation of Bcl-2 or blockade of Fas pathways, and the pathways are p53 independent. The drawback of HAMLET based therapy is that it is only effective with local administration. So an appropriate delivery system is needed for achieving the therapeutic purpose. HAMLET has now reached phase I /II clinical trials.

Mda-7 (IL-24)

Melanoma differentiation associated-7 (Mda-7) [also termed interleukin-24 (IL-24)] protein was classified as a tumour suppressor gene because its expression level is shown to be significantly reduced in advanced melanomas (ref.92). At its physiological level, Mda-7 plays a role in modulating immune responses such as causing proinflammatory cytokine production, but the tumour specific effect occurs only with high levels of Mda-7 (ref.77). Mda-

7 has been studied extensively and implicated in inducing cell death with elevation of ceramide only in cancer cells (ref.93). Ceramide is a lipid molecule composed of sphingosine and a fatty acid that has been shown to induce cellular apoptosis in tumours (ref.93). In addition, inhibition of Bcl-2 expression and upregulation of ROS are two other mechanisms by which Mda-7 induces cell death in a prostate cancer model (ref.94). One study suggests

that secreted mda-7/IL-24 can stabilise mda-7/IL24 mRNA in the neighbouring cells to cause tumour specific apoptosis (ref. 95); however, the mechanism is unclear. Furthermore, combination therapy of breast cancer using Mda-7 with Herceptin has shown enhanced antitumour activity as compared to monotherapy (ref.96).

Brevinin-2R

Brevinin-2R is a novel defensin molecule that is comprised of 25 amino acids and isolated from the skin of the frog Rana ridibunda. Its physiological function is anti-microbial activity but its anticancer activity was demonstrated in a range of cancer cell lines including MCF-7

(breast adenocarcinoma), A549 (lung carcinoma), Jurkat (T cell leukaemia), SW742 (colon carcinomas) and L929 (fibrosacroma) (ref.97). There is little information about the mechanism of action of this gene. So far, one study shows that its anticancer activity is through association with the endosome and that it activates a lysosomal-mitochondrial death pathway leading to autophagy like cell death (ref.97).

E4orf4

This is a viral gene derived from adenovirus E4 open reading frame 4 (E4orf4) and its encoded protein has demonstrated tumour cell specific killing activity in tumour cell lines

(ref.77). It is a relatively small, 114 residue polypeptide in adenovirus 2 and 5 that may play a role in both early and late stages of the infectious cycle (ref.84). There is evidence that this gene is able to induce tumour specific cell death through inhibition of the APC/C complex in a similar manner as the other anticancer gene, Apoptin (ref.98). The cell death pathways are

P53 and caspase independent and blockage of Bcl-2 cannot inhibit cell death; so, it might

work through activation of a non-apoptotic pathway (ref.99). In addition, its physical interaction with protein phosphatase 2A (PP2A), which is crucial in cancer development, was shown to involve cell death induction (ref.100,101).

Noxa

Noxa is a BH-3 only protein in the Bcl-2 family. It is one of the anticancer genes and its tumour specific effect might be overlooked as there is a lack of further evidence to show untransformed cells being unharmed. So far, the only evidence to demonstrate its selective anticancer activity is from an in vitro study in which breast cancer cells were selectively killed but normal mammary epithelial cells remain unharmed (ref.102).

Parvovirus–H1 NS1

Again, NS1 is a viral protein that has been shown to eliminate tumour cells in a specific manner. This protein can physically interact with casein kinase II (CKIIα) and modulate the substrate specificity of this kinase. This leads to an altered pattern of tropomyosin phosphorylation, leading to cell death (ref.103). Upon inhibition of casein kinase II, the tumour selective cell death activity is inhibited (ref.103).

Par-4

Par-4 stands for prostate apoptosis response -4, a leucine zipper protein that has shown tumour suppressor gene because its inactivation contributes to the development of spontaneous tumours (ref.104). Interestingly, the apoptotic pathways of Par-4 are p53

independent, and Bcl-2 blockage cannot rescue the cells from cell death but Par-4 action requires the involvement of Fas receptor and inhibition of NF-B (ref.105). Par-4 has been shown to inactivate Akt1 through direct physical interaction and this may provide an explanation for its specific apoptosis effect on tumour cells (ref.106). In addition, there is evidence to suggest the involvement of the extrinsic pathway in Par-4 induced apoptosis mediated through activation of cell death receptor (ref.107). This gene, which was suggested as a tumour suppressor gene, can also be classified as an anticancer gene.

TRAIL

TRAIL stands for TNF-related apoptosis-inducing ligand and is one of the anticancer genes that has advanced the most into phase I/II clinical trials. Its anticancer activity has been demonstrated in many tumour cells but not normal cells (ref.77). The mechanism of action by which TRAIL induces tumour specific cell death is still unclear. So far, one possible explanation is that overexpression of c-Myc in cancer cells upregulates TRAIL receptor DR5 and downregulates inhibitor of downstream pro-apoptotic pathway, FLIP , leading to specific killing of cancer cells (ref.108).

ORCTL3

ORCTL3 stands for organic cation transporter like 3 molecule, which is a distinct gene identified in the late Professor Stefan Grim‟s laboratory. It is the most recently isolated anticancer gene. However, its tumour specific activity is not directly connected to its function as a urate and high-affinity nicotinate transporter in the cells (ref.109). Again, ORCTL3 has shown pronounced cell death effect only in a range of transformed cells but no harmful effect

in normal cells (ref.110). In tumour cells, a specific ER stress effect occurs through activation of pro-apoptotic factor ATF4, instead of anti-apoptotic Grp78, causing the induction of apoptosis (ref.110).

ORCTL3 was isolated in a two stage genetic screen, namely primary and secondary screens.

In the primary screen, the transfections were conducted in HEK293T cells and the secondary screen was performed in both normal rat kidney (NRK) cells and in NRK cells transformed by mutated H-ras oncogene (Fig.4) (ref.110). Interestingly, there are no H-ras mutations reported in HEK293T cells and there are no E1A or E1B55K viral oncogenes present in NRK transformed cells. As a result, ORCTL3 is not able to induce cell death in normal NRK cells but is able to induce cell death in H-ras transformed NRK cells. This shows that ORCTL3 can induce apoptosis in transformed cells with different transformation scenarios regardless of the genetic backgrounds of these transformed cells (ref.110).

Figure 4: Schematic diagram of the workflow for genetic screening in the isolation of ORCTL3. This is a high-throughput genetic screen with two different robots, one of which is responsible for DNA-isolation and

another one is for transfection and readout procedures all in the 96 well format. The DNAs isolated from bacteria in 96 well plate were transfected into HEK293T cells in the primary screening in first step. Only those apoptosis inducers isolated in the primary screen were then transfected into NRK normal cells (NRK WT) and H-Ras transformed NRK cells to assess the apoptosis in the secondary screening in order to isolate Anticancer genes, in this case ORCTL3. (Picture is adapted from ref.110)

After the isolation of putative “anticancer genes”, a former colleague carried out a study on the mechansim of action of ORCTL3 (ref.111). Stearoyl-CoA desaturase-1 (SCD-1), which is an enzyme involved in synthesis of unsaturated fatty acids, turns out to be the target of ORCTL3 in transformed cells (ref.111). SCD-1 has been shown to be upregulated in transformed cells and its elevated expression is correlated with tumours (ref.111).

Table 1. Summary of Anticancer genes and their cancer specific activities

This table is adapted from (ref. 77)

Overall, these anticancer genes targeting tumour cells in a specific manner bring hope that therapeutic benefits can be enhanced and side effects reduced compared to conventional chemotherapy. Different anticancer genes are located in different cellular compartments and trigger apoptosis through either extrinsic or instrinsic pathways, P53 or Caspases dependent or independent pathways (Table.1). For therapeutic application, these anticancer genes can be

developed in combination with viral or non-viral gene delivery systems, or fusion with cell- penetrating peptides to deliver them into cells and induce cancer specific cell death.

1.3 Synthetic lethality

1.3.1 Background

The development of anticancer drugs is based on the idea that an understanding of cancer biology will allow us to target cancer cells specifically with targeted therapy by the so called

“magic bullets”. Identification of novel genes and finding out their functions and roles in cancer development is a crucial part of this process. Promising approaches, which only focus on targeting tumour cells and not their normal counterparts, will have a positive impact on the therapeutic benefit of cancer treatment. One breakthrough in the field of targeted therapy against cancer was the discovery of Imatinib (Gleevec), which is tyrosine kinase inhibitor of a fusion protein called BCR-ABL, encoded by a chimeric oncogene, for the first line treatment of chronic myelogenous leukaemia (ref.113). This small molecule selectively inhibits and eradicates BCR-ABL expressing cancer cells but leaves normal cells unharmed.

As a result, this drug shows an extremely high and very positive clinical response rate

(ref.112).

Later, this concept was also applied to finding treatments for a range of other human cancers. In the last decade, a promising concept called synthetic lethality was developed for identifying potential targets associated with cancer development for cancer therapy (Fig.5). It is based on the principle that loss of function of two or more genes, instead of one gene, leads to cell death (ref.114). Initially, the concept of synthetic lethality originated from studies 40

in Drosophila models. This approach was then utilised in the screening of chemical compound libraries that selectively eliminate yeast deletion mutants with defects in cellular functions such as DNA repair and cell cycle (ref. 115, 116). Subsequently, it became a strategy to identify potential targets in mammalian cells that are associated with tumour transformation scenarios, such as a mutation of a tumour suppressor gene, or overexpression or an activating mutation of a cellular oncogene (ref.117). The critical advantage of utilising a synthetic lethality approach is to exploit the cancer specific cellular changes so that only cancer cells are targeted and eliminated but not normal cells. The second advantage is that this approach can be extended to pharmacologically undruggable targets such as overexpressed Myc, mutated oncogene Ras, mutated tumour suppressor genes Rb and p53 (ref.118,119). These deregulated oncoproteins and mutated tumour suppressor proteins commonly contribute to the development of cancer. The synthetic lethality approach can potentially identify molecules or interactions that are important in supporting and maintaining viability of cancer cells when these deregulated proteins are present. Also, approaches can be extended to different areas of cancer biology such as tumour associated hypoxia and metabolic cellular processes (ref.120).

Figure 5. Illustration of synthetic lethality. In normal cells, loss of either gene A or B is not able to trigger cell death because there might be other genes that can compensate the loss of function of gene A or B. However, in cancer cells, a mutation or loss function of gene B contributed to cancer transformation, in combination with loss of function of gene A will cause cell death in cancer cells only. This figure is adapted from (Ref: 121)

1.3.2 BRCA and PARP inhibition mediated synthetic lethality

One of the pioneering studies using synthetic lethality was in in breast cancer and revealed that dysfunction of two important genes BRCA1 and BRCA2 sensitised cancer cells to the

PARP inhibitor because cancer cells which are deficient in homologous recombination pathways involved in double strand DNA break repair (ref.122). This proof of concept of synthetic lethality in mammalian cells was confirmed in a preclinical study. Women with heterozygous germline mutations carrying one wild type and one mutated BRCA1/2 gene have higher risk of getting breast or ovarian cancer (ref.122). BRCA1 and BRCA2 are responsible for repair of double strand damaged DNA through homologous recombination

(ref.123). PARP is an enzyme that is activated by DNA damage and is responsible for the repair of single strand DNA breaks (ref.124,125,126). In normal cells, when DNA damage occurs and causes single-strand breaks, the protein x-ray repair cross-complementing

protein 1 (XRCC1) interacts with PARP to form a complex in the repair of single strand breaks. If the single strand DNA breaks cannot be repaired due to inhibition of PARP, accumulation of DNA damage leads to double strand breaks and collapse of the replication fork. At this point, functional BRCA protein repairs double strand breaks using homologous recombination pathways so that cells will not undergo apoptosis. In cancer cells with defects in BRCA function where the homologous recombination pathway is deficient, inhibition of

PARP by the small molecule inhibitor leads to accumulation of excessive single and double strand breaks, ultimately leading to cell death (ref.127). Cancer cells with defects in BRCA have shown 1000 fold higher sensitivity to PARP inhibitor compared to cells with functional

BRCA (ref.128,129). In the clinical trial, PARP1 inhibitor Olaparib, achieved the proof of concept and showed a favourable therapeutic index and response rate of 41% among patients with defects in BRCA1/2 (ref.130) and has now entered phase II clinical trials.

1.3.3 Mutated p53, Rb and Ras related synthetic lethality p53 is described as the guardian of human genome stability and it is mutated in 50% of human cancers. p53 is involved in a broad range of cellular functions, including apoptosis, cell cycle regulation, cell proliferation and DNA repair (ref.131). Unsurprisingly, mutation of p53 contributes to cell transformation (ref.132 ). Pharmacological targeting of mutant forms of p53 or direct restoration of p53 can lead to elimination of p53-deficient cells.

Identification of synthetic lethal targets that cross-talk with the loss of function of p53 is crucial for development of a novel, and broadly applicable anticancer therapy. This will also be important for a deeper understanding of p53 function. Original attempts to screen small molecules against cells devoid of p53 started at National Cancer Institute in the USA. So far, there are 11 synthetic lethal targets identified for transformed cells with loss of function of

p53. These 11 targets are known molecules (Fig. 6), 8 of which fall into 4 major cellular functions such as G2/M checkpoint, microtubules, Rho pathway and Actin biosynthesis, while the functions and pathways of two remain unknown (ref.133).

Retinoblastoma protein (Rb) is a well-known tumour suppressor gene in some human cancers and its role in controlling cell cycle progression is well established. One study showed that S-phase kinase associated protein (SKP2) is one of synthetic lethal targets to

Rb inactivation (Fig. 6)(ref.133). SKP2 is an E3 ubiquitin ligase for phosphorylation of Thr187 in p27, either cells with SKP2 knock-out or p27T187A knock-in are synthetically lethal with

Rb inactivation (ref.133). However, the mechanism of action in Rb inactivation mediated synthetic lethality is still unclear.

Figure 6. p53 and Rb mutatnt mediated synthetic lethality targets. Loss of function mutation in both p53 and Rb leads to inactivation of tumour suppressor proteins. This picture was modified from review (ref.117)

Ras, which is a small GTP binding proteins, plays a role in numerous cellular functions.

Unlike Rb and p53, gain of function mutations in Ras contributes to the transformation of

cells. Gain of function mutations in oncogenes such as Ras can also transform cells. It is estimated that 20% of all human cancers have N-Ras, K-Ras and H-Ras activating mutations, where constitutive activation of Ras proteins leads to a transformed phenotype

(ref.134). So far, Ras proteins themselves have proved impossible to inhibit selectively by small molecule compounds. The synthetic lethality approach in combination with genetic screening using an RNAi library or small molecule compounds has led to identification of 7 potentially synthetic lethal targets associated with the activating mutant forms of Ras (Fig.7).

This area of research is active and work is still ongoing.

Figure 7. Ras mutant mediated synthetic lethality targets. Gain of function mutation in Ras leads to constitutive activation of proteins. This picture was modified from review (ref.117)

1.3.4 Myc related synthetic lethality c-Myc is known as a proto-oncogene and is involved in transcription of 15% of human genes under normal physiological conditions (ref.181). Amplification and overexpression of c-Myc contribute to cell transformation and tumorigenesis. Like Ras, Myc is difficult to target and inhibit pharmacologically as an oncoprotein because of its involvement in normal cellular functions. A few groups have performed small molecule screenings using c-Myc overexpressing tumour cells and proposed that candidates such as chloroquine and topoisomerase inhibitors are synthetic lethal to c-Myc. However, the mechanism of action of 45

these inhibitors is still unknown (ref.135,136). Based on known functions of c-Myc, several candidates were identified such as DR5, CDKs and Aurora B kinase. The alternative approach, which is the high throughput genetic screening in combination with synthetic lethality, was used to isolate targets against deregulated c-Myc in mammalian cells (ref.146).

DR5

For the first method, based on current understanding of involvement of c-Myc in inducing apoptosis, c-Myc overexpression sensitises cells to apoptosis (ref.137,138). The plasma membrane bound TRAIL receptor DR5 is a c-Myc regulated gene. The level of DR5 is upregulated in c-Myc overexpressing cells, leading to sensitisation to cell death (ref.139).

Later, the same group performed siRNA based genetic screen which identified GSK3β as a synthetic lethal target to c-Myc in c-Myc overexpressing cells. GSK3β is involved in regulating c-Myc stability by phosphorylating Tyr58 of c-Myc that contributes to c-Myc destabilisation. Knockdown of this molecule leads to increased c-Myc through stabilisation and upregulation of DR5, leading to apoptosis induction (ref. 140). Recently, another group using a genome wide shRNA screen identified the F-box protein, FBXW7, a component of a

SCF-like ubiquitin ligase complex which targets c-Myc for proteasomal degradation, as a synthetic lethal gene to c-Myc upregulation. Interestingly, FBXW7 works closely with GSK3β to regulate the c-Myc protein level (ref.141).

CDKs c-Myc dysregulation impacts cell cycle regulation and genomic stability. CDK1 inhibition mediates sensitisation of c-Myc overexpressing cells to cell death through depletion of

survivin (ref.142). In neuroblastoma, CDK2 is overexpressed and shows a synthetic lethal relation to n-Myc amplification when CDK2 is inhibited (ref.143).

Aurora B kinase

Pharmacological agents inhibiting aurora B kinase such as VX-680 selectively eliminate c-

Myc overexpressing cells (ref.144). This study demonstrates the synthetic lethal interaction of aurora B kinase to c-Myc. Selective killing is mediated through disabling of chromosomal passenger protein complex (CCPC) and is independent of P53 (ref.144). However, VX-680 can also inhibit Aurora A and C kinase, further studies are needed to validate the specificity to aurora B kinase (ref.145).

Genetic screen isolated synthetic lethal targets

High throughput genome-wide screening has been used to isolate synthetic lethal targets against many oncogenes or mutated tumour suppressor genes involved in cell transformation in mammalian cells. The conventional approach is to utilise RNAi technology combined with bioinformatic tools for conducting large-scale screens to identify synthetic lethal interactions. Five such screens for c-Myc have been published and individual studies have proposed novel synthetic lethal targets against c-Myc upregulation (ref.146; 147;148;

149; 150).

CSNK1E siRNA based genetic screening was performed by Toyoshima using isogenic human foreskin fibroblast cell lines (HFFs), namely control HFF and c-Myc overexpressing HFF cells. Cell death measurement took place at 96 hours upon transfection of siRNAs against individual genes. A panel of 3000 “druggable” human genes was used in this screen and they identified 140 positive hits as synthetic lethal targets against c-Myc upregulation (ref.

148). These genes are functionally relevant to transcriptional machinery, cell cycle, chromatin modification, metabolism, mitotic control and apoptosis. CSNK1E was then chosen for further in vivo validation and shown a strong correlation with Myc expression level in neuroblastoma and progression of this disease. Inhibition of CSNK1E by a small molecule compound selectively kill Myc overexpressing cells in vitro and cause tumour regression in vivo (ref.148). However, the function of this protein and its mechanism of action in synthetic lethality are unknown.

SAE1/2 (SUMO-activating enzyme 1/2)

In the study of identifying SAE as a synthetic lethal target against c-Myc upregulation, human mammary epithelial cells (HMEC) expressing an inducible MYC expression system,

Myc-ER, were used (ref.146). A human retroviral shRNA library containing 74905 constructs targeting 32293 unique transcripts was transduced into HMEC. They identified 403 positive hits as c-Myc synthetic lethal genes from which they focused on studying SAE2. SAE2 protein is a component of the SUMOylation pathway. Loss of SAE2 activity in Myc overexpressing cells triggered mitotic catastrophe and ultimately cell death (ref.146).

ARK5 (AMPK-related kinase 5)

In this study, a small panel of siRNAs against 800 kinases was used in human osteosarcoma U2OS cells expressing an inducible Myc-ER system. Individual siRNAs were transfected into cells by transfection reagents and indirect immunofluorescnce of PARP cleavage as a measure of cell viability. Liu and colleagues identified two positive hits, namely ARK5 and AMPK. ARK5 is an upstream regulator of AMPK and is involved in the regulation of mitochondrial respiratory chain complexes and mTORC1 signalling pathway.

Inhibition of these two molecules leads to collapse of ATP levels in Myc overexpressing cells

(ref.146). This finding provides evidence of a connection of c-Myc to metabolic pathways and its potential therapeutic application in this area.

PRKDC (Protein Kinase, DNA-activated, Catalytic polypeptide)

This study exploited an shRNA based genetic screen using a lentiviral human kinome shRNA library containing 500 kinase genes in human lung fibroblast non-transformed cells

WI-38 and the isogenic cell line WI-38 overexpressing c-Myc. Cells were harvested for analysis at 14 days post-transduction, in combination with deep sequencing efforts which identified 18 synthetic lethal genes in cells overexpressing c-Myc. They chose PRKDC for further study and showed that its inhibition leads to further DNA damage in Myc overexpressing cells, as well as down-regulation of Myc mRNA and protein expression levels (ref.149).

Spliceosome

This study used shRNA based genetic screening of human mammary epithelial cells engineered with an inducible Myc-ER system. They identified that targeting the core spliceosome is a promising approach for treating Myc driven cancer. Depletion or inhibition of one important component of core spliceosome, BUD31, leads to synthetic lethality in Myc overexpressing cells. As Myc overexpression increases transcription and makes cells dependent on the core spliceosome, loss of spliceosome function due to inhibition of BUD31 increases intron retention and causes accumulation of pre-mRNA, leading to Myc specific cell death (ref.150).

So far, the dataset containing all c-Myc synthetic lethal genes isolated from these three genetic screens were analysed using functional proteomics. There are 3 main pathways in which these c-Myc synthetic lethal genes are involved, namely transcription, regulation of the

Myc-MAX network and protein ubiquitination and sumoylation (ref.151).

1.4 Myc

1.4.1 Background information of Myc

The Myc gene was discovered in the 1970s from the acute avian oncogenic retrovirus MC29

(ref.152) and named v-gag-Myc. Subsequently, based on protein sequence, homologs named c-Myc were identified in the chicken and human genomes (ref.153,154). Thereafter, in a study on Burkitt‟s lymphoma, the human c-Myc gene was found to be deregulated via chromosomal translocations to the immunoglobulin heavy chain locus (ref.155-157). This was the first indication that human c-Myc is a proto-oncogene and that its deregulation could

cause neoplastic transformation. Today, c-Myc is estimated to be deregulated in almost 50% of all human malignancies (ref.158). The first evidence to indicate the ability of Myc to transform cells was shown in primary rat embryo fibroblasts, possibly in cooperation with other cellular oncogenes (ref.159). In cancers, deregulated expression of Myc is sufficient to initiate cell transformation in different tissues (ref.160, 161, 162). Also, cancer cells with deregulated expression of Myc tend to develop “Myc-addiction” for maintaining oncogenic properties (ref.163, 164,165, 166). However, Myc has its essential roles in a range of cellular functions under normal physiological conditions. Myc knockout mice showed embryonic lethal features implying it has a critical role in embryogenesis and development (ref.167).

1.4.2 Myc family proteins

Myc is known as a transcription factor comprised of 439 amino acids and it belongs to a family that also includes MYCL (L-Myc) and MYCN (N-Myc) (Fig.8) (ref.168,169). These three family members share a similar protein structure containing a basic helix-loop-helix leucine zipper (bHLH-LZ) domain at the C-terminus responsible for sequence specific DNA binding and dimerisation with a binding partner such as Max (ref.170). The physical interaction between Myc and Max is crucial for Myc driven oncogenesis (ref.171). At the N- terminus, the transactivation domain (TAD) consists of regions covering Myc box I and II responsible for transcription of target genes (ref.172). In addition, the functions of Myc Box III and IV are less characterised and some evidence suggests that they might be involved in induction of transcription and apoptosis (ref.173). In terms of tissue and organ distribution, c-

Myc expression is observed in most cell types with MYCN expression being restricted to the brain (ref.174). MYCN and c-Myc are functionally redundant to some extent in the murine model (ref.175). Interestingly, MYCN amplification is only observed in neuroblastomas, but

MYCN and c-Myc overexpression or amplification are observed in breast and small cell lung cancers (ref.176,177,178). The role of MYCL in cancer development is less well understood but there is evidence to show its amplification in small cell lung cancer and ovarian cancer

(ref.179,180).

Figure 8. Schematic presentation of structure of Myc family proteins including c-Myc, N-Myc and L-Myc. This picture is adapted from (ref. 364.)

1.4.3 Functions of Myc

Myc has been studied extensively and known to function as a transcription factor involved in transcriptional activation and repression of target genes in cells. According to genome-wide studies with chromatin immunoprecipitation (ChIP) approach, Myc is involved in the transcription of 10-15% of human genes (ref.181). Most target genes participate in important cellular functions such as nucleotide metabolism, ribosome biogenesis, cell cycle, RNA processing and DNA replication (ref.182,183,184,185). Interestingly, Myc is able to upregulate or downregulate expression a range of microRNAs and long non-coding RNAs, contributing to Myc overexpression induced cellular transformation in cancers (ref.186,187).

As a transcriptional activator, Myc physically interacts with Max through dimerisation to bind to sequence specific DNA binding motifs called E-boxes (5‟-CACGTG-3‟), which are located in the enhancer or promoter regions of target genes (ref.188). Upon binding, Myc recruits a number of proteins including TRRAP, TIP60, GCN5, TBP, P300/CBP and PCAF to form protein complexes, leading to histone acetyltransferase (HAT) activity (ref.189,190). An increase in HAT activity results in a relaxed, open chromatin state that facilitates transcription initiation of target genes by transcription initiation complexes (ref.191). Most target genes transcribed and upregulated by Myc transcriptional activation partially contribute to cellular transformation such as cyclin-dependent kinase 4 (CDK4), cylins D1,

D2 and E1 (ref.192,193,194,195). Other genes upregulated by Myc are involved in cell metabolism and protein synthesis, for example, through lactate dehydrogenase and ribosomal protein EIF4E (ref.190).

As a transcriptional repressor, Myc recruits proteins to form histone deacetylase (HDAC) complexes, leading to a closed chromatin state that represses transcription of a large number of genes (ref.196). In addition, there is further transcriptional repression mechanism by which Myc physically interacts with transcription activators MIZ1 and SP1 to trans-repress their transcription promoting activity of p15INK4b and p21Cip1/Waf1 (ref.197,198). Other genes repressed by Myc are those involved in cell adhesion and migration such as E-cadherin, N- cadherin and some integrins, which are mediated through regulation of miRNA miR-9 (ref.

190,199).

In addition to transcriptional functions, Myc also plays important roles in other cellular functions such as transcription amplification, DNA replication and cell differentiation. In

transcription amplification, Myc plays a role in amplifying transcription in a general, rather than in a sequence specific fashion. Recent ChIP-seq studies showed that Myc tends to bind to already active promoters, which are occupied by RNA pol II (RNAP II) complexes and active chromatin marks (ref. 200). Moreover, Myc has a transcription independent function in

DNA replication. One of the proposed mechanisms for this activity is that Myc recruits cell division cycle protein 45 (CDC45), which is a replication factor, to replicative origins to direct origin activation (ref.201). In cell differentiation, Myc is one of four genes which can be introduced to reprogramme fibroblasts to become pluripotent stem cells (IPSC) (ref.202,203).

This suggests that Myc has an essential role in cell self-renewal at different stages of cell differentiation.

1.4.4 Regulation of Myc

As previously mentioned, Myc regulates 10%-15% of human genes. With such a broad function, Myc activity and regulation are under strict control in the cell (ref.181). Myc activity and regulation can be broken down to two levels, namely at the levels of gene and protein.

At the gene expression level, Myc is regulated through a number of signaling pathways and mediators, including as TGF-β, NOTCH, hedgehog, PDGF, EGF and NF-kB (ref.204). In normal cells, these pathways are under tight control through both proliferative and anti- proliferative signals. Myc has very short lived mRNA, with a half-life of about 20 minutes.

This is partly regulated by a number of microRNAs, such as miR-34, miR-145 and let-7

(ref.205,206,207). Interestingly, Myc can auto-regulate its own expression in a way that introduction of exogenous Myc will lead to downregulation of endogenous Myc, but the mechanism is not clear (ref.208). At the protein level, Myc can be modified by phosphorylation, ubiquitination, O-linked glycosylation and acetylation (ref.209). Myc protein

level is also under tight regulation with an average half-life also around 20 minutes. Myc protein level is kept as low as 1000-6000 molecules per cell in normal cells (ref.210,211).

Tumorigenesis is likely to occur if Myc protein level exceeds a threshold level, which is about

2 or 3 fold increase above those at normal cells (ref.212). The Myc protein has two well characterised phosophorylation sites, namely serine 62 (S62) and threonine 58 (T58), which are required for its activity, stability and proteasomal degradation (ref.213). The S62 site is phosphorylated by kinases such as CDK1, JNK, MAPK and ERK (ref. 189). Whereas, the

T58 site can be phosphorylated only by GSK3β upon phosphorylation of the S62 site

(ref.214). After phosphorylation of the T58 site, phosphorylated Myc is recognised by ubiquitin ligases and degraded through the ubiquitin proteasomal system. In Burkitt- lymphoma, mutation of these two sites result in a stable mutant form of Myc protein which resists degradation (ref.215). Furthermore, Myc protein has 8 acetylation sites that are implicated in transcription initiation by Myc, and Myc-acetylation is mediated by proteins such as P300/CBP, GCN5 and TIP60 (ref.189).

1.4.5 Roles of Myc in cancer

In normal cells, relatively low Myc levels are needed to maintain its physiological cellular functions. In mice, the Myc knockout is embroynic lethal (ref.167). This suggests Myc is essential in development and survival of normal cells. Up to 50% of human cancers show significantly elevated Myc expression which can play a number of different roles in cancer development (Fig.9). These mechanisms include: 1) chromosomal translocation of the Myc locus to immunoglobulin promoter, 2) amplification of the Myc locus, 3) mutations of the Myc gene, and 4) mutations or loss of function of genes that regulate Myc activity and stability

(ref.204,216). Knockdown of Myc in some cancer cell lines appears to reduce cell

proliferation and growth, and even induce apoptosis in some cases (ref.217;218;219). In addition to genes transcribed and controlled by Myc, in vitro studies also showed that Myc overexpression leads to increased genomic instability through induction of ROS

(ref.220,221).

Burkitt lymphoma is a type of haematopoietic cancer and the first human cancer connected to aberrant Myc expression. Myc is disregulated through chromosomal translocation between chromosome 8 and the immunoglobulin IgH and IgL loci are on chromsomes 14, 2 and 22 (ref.222). This chromosomal translocation results in the Myc gene coming under the control of immunoglobulin gene promoter, leading to aberrant expression. In addition, Myc rearrangements are also related to other haematological cancers, such as multiple myeloma, diffuse large B-cell lymphoma, acute lymphocytic leukaemia (ref.176). Mutations at the S62 and T58 phosphorylation sites are also found in Burkitt lymphoma. Activating mutation of

NOTCH1 is found to cause overexpression of Myc, contributing to development of T cell acute lymphocytic leukaemia (ref.223). In solid tumours, Myc is deregulated mainly through amplification and overexpression. This is observed in a range of human cancer such as lung, breast, colon, liver, cervical and prostate cancer as well as neuroblastoma (ref.224).

However, Myc rearrangements and translocations have not been identified in solid tumours.

In addition, FBXW7 is known as a tumour suppressor gene that polyubiquitinates and targets

Myc for proteasome mediated degradation to control Myc protein expression levels.

Mutations of FBXW7 occur in both T cell acute lymphoblastic leukaemia, as well as colon and pancreatic cancer (ref. 225, 226).

Figure 9. Schematic presentation of cancer relevant Myc functions. This picture is modified from (ref.364).

1.4.6 Therapy against Myc-driven cancer

As previously mentioned, Myc is vital in tumorigenesis and cancer development. This makes

Myc an attractive target for cancer therapy. However, small molecules directly targeting Myc have proved difficult to develop because Myc is undruggable due to two main reasons. First,

Myc is important for physiological functions in normal cells. Second is that Myc lacks a classical activation site. Therefore, therapeutic strategies directed towards defects in Myc deregulated cells, including Myc target genes and synthetic lethal target genes, may be a strategy to identify therapeutic opportunities for treatment of Myc overexpressing cancers.

The dominant negative mutant Myc, Omomyc, is able to cause tumour regression in mouse models of non-small cell lung cancer, which gives hope that directly targeting Myc in overexpressing tumour cells without affecting healthy cells can be achieved (ref.212). In addition, a small molecule called 10058-F4 is capable of recognizing Myc amino acid 57

residues 402-412 and disrupting the physical interaction between Myc and Max in order to exert its anti-tumorigenic effect in cancer cell lines (ref. 227). However, improved versions of this molecule are still in development and in vivo data is not available. Moreover, targeting metabolic pathways in Myc-upregulated cells is another active area of research. A small molecule inhibitor developed against glutaminase (GLS), BPTES, shows promising results in

B-cell lymphoma in both in vitro and in vivo models (ref.228). Myc overexpressing cells depend on exogenous glutamine and inhibition of GLS starves cells of glutamine, leading to

Myc-specific cell death (ref.229). Several small molecule inhibitors are being developed to block these synthetic lethal targets against Myc upregulation for the treatment of Myc- overexpressing human cancers (Table 2) (ref.229).

Table 2: Chemical compounds currently developed to treat Myc-dependent Tumours

This table is adapted from (ref.158)

1.5 Roles of Forkhead box proteins FOXM1, FOXK2 and FOXO3 in human cancers and apoptosis

FOX proteins are important in embryonic development and adult tissue homeostasis by regulating genes involved in cell cycle, apoptosis, metabolism, cell proliferation and cell differentiation (ref.233). So far, there are 50 forkhead proteins have been identified in the

human genome, which can be catergorised into 19 subgroups based on the sequence homology of their forkhead domains (ref.230;231;232). Deregulation of FOX proteins is implicated in tumorigenesis and cancer development (ref.231). In here, I will mainly focus on three important Fox proteins, namely FOXM1, FOXO3 and FOXK2 and discuss their association with human cancer.

FOXM1

FOXM1 is a potent oncogene and its overexpression is observed in many human cancers

(ref.234). The oncogenic roles of FOXM1 are manifold and were established in mouse models for colon cancer, lung cancer and liver cancer, in which FOXM1 knockout significantly reduces its tumorigenic potential (ref.235,236,237). FOXM1 regulates the expression of a range of target genes by binding the consensus sequence TAAACA

(ref.238,239). Deregulated FOXM1 has been shown to perturb cell cycle progression through affecting mitotic regulators, such as CDC25B, Cyclin B, Aurora B kinase and PLK1, leading to loss of control over cell cycle checkpoints in G1/S and G2/M phases (ref.233).

This partially confers proliferative advantages during cellular transformation. FOXM1 expression is induced when cells are in the G1 phase of the cell cycle, followed by sustained expression levels in S, G2 and M phases (ref.240). FOXM1 expression is positively regulated by two well known oncoproteins, namely Myc and K-ras, and negatively regulated by tumour suppressor proteins, such as p53 and checkpoint kinase 2 (CHK2)

(ref.240,241,242,243). In addition, other oncogenic properties of FOXM1 are reflected in promoting angiogenesis, cell migration, cell proliferation, drug resistance, vascular repair and metastasis through upregulation of a range of molecules (ref.244,245). For example,

FOXM1 upregulates caveolin, which then reduces expression of E-cadherin, which is in turn

involved in cell-cell contact (ref.246). FOXM1 knockdown or depletion by siRNA in a range of cancer cell lines (eg, prostate, liver, breast, lung and cervix cell lines) leads to loss of anchorage independent and colony formation in soft agar (ref.247). Moreover, FOXM1 also influences stem cell maintenance by modulating the WNT-β-catenin pathway and promoting stem cell like properties in cancer cells (ref.248,249,250). In tumour angiogenesis, FOXM1 transcriptionally activates VEGF expression and metastasis through binding to the VEGF promoter, while FOXM1 knockdown significantly reduces expression of MMP2 and MMP9, which were implicated in tumour invasion and metastasis (ref.251,252,253). Moreover, overexpression of FOXM1 in many human cancers is associated with resistance to chemotherapeutics, such as herceptin and paclitaxel, siRNA based knockdown of FOXM1 is able to increase the sensitivity of cancer cells to these drugs (ref. 254). So far, there are two inhibitors identified from high throughput screens, namely siomycin A and thiostrepton.

These have been shown to reduce the transcriptional activity of FOXM1, decreasing the expression of FOXM1 regulated transgenes, including CENPB and Cdc25B, leading to anti- proliferative and pro-cell death effects on tumour cells. Since FOXM1 expression is not upregulated in normal cells, the toxic effects associated with use of these two small molecule inhibitors are small, making them very attractive in the development of targeted cancer therapy (ref.255,256,257).

FOXO3

FOXO subfamily of proteins include FOXO1, FOXO3, FOXO4 and FOXO6 are transcription factors which function in the nucleus. FOXO proteins bind DNA as monomers to consensus recognition sequence (G/C)(T/A)AAA(C/T)A (ref.258,259,260). Foxo3 null mice are viable but show abnormal ovarian follicular development and encounter fertility problems (ref.261).

It is known as a tumour suppressor gene that has been shown to be deregulated in a number of human cancers, such as glioblastoma, rhabdomyosarcoma, prostate cancer, breast cancer and leukaemia (ref.262,263). Downregulation of FOXO3 transcriptional activity caused by AKT, IKK and ERK mediated phosphorylation is observed in breast cancer, whereas overexpression of FOXO3 is able to inhibit breast tumour growth and size

(ref.264,265). FOXOs are regulated by various growth factors, such as insulin-like growth factor (IGF), epidermal growth factor receptors (EGFR), cytokines, nutrients and oxidative stress. These different factors control protein expression, cellular localisation and transcriptional activity of FOXO proteins (ref.262). PI3K/AKT kinase pathway is constitutively active in many human cancers, when its upstream inhibitory phosphatase PTEN is mutated, negatively regulates FOXO-mdiated transcriptional activity (ref.262). In addition, other kinase pathways such, as stress-activated c-Jun-NH2-kinase , AMP-activated protein kinase, casein kinase 1 (CK1), IkB kinase β and ERK1/2 have shown to regulate FOXO activity

(ref.266,265). Some of these kinase pathways exert inhibitory effects by phosphorylating

FOXO3 and causing FOXO3 to translocate from the nucleus to the cytoplasm followed by degradation, leading to loss of its transcriptional activity (ref.262). Recent evidence suggests that FOXO3 can be downregulated by ERK pathway via MDM-2 mediated proteasome degradation (ref.265). A few antibody-based therapies or small molecule inhibitor therapies targeting these pathways are able to restore and activate FOXO3 levels. For example, blocking antibody against EGFR inhibits the PI3k/AKT pathway, while the ERK inhibitor

AZD6244 targets the ERK mediated inhibitory pathway (ref.267). Restoration of FOXO3 in cancer cells can trigger apoptosis through upregulation of two pro-apoptotic BH3- only proteins, such as Bim and BNIP3 (ref.268,269,270). In prostate cancer cells, upregulation of

FOXO3 causes apoptosis, as well as increasing expression of TRAIL through binding to

TRAIL promoter (ref.271). In addition to inducing apoptosis in cancer cells, FOXO3 can

transactivate cell cycle inhibitory genes, such as p27Kip1 and p21Cip1/Waf1, which play important roles in inducing G1 cell cycle arrest (ref.272,273). More recently, the reciprocal regulation of

FOXO3 and Myc revealed that FOXO3 can reduce ROS production in mitochondria through inhibition of Myc, because Myc overexpression is able to increase ROS level in cells

(ref.274,275). In addition, the inverse correlation between FOXO3 and Myc function is also revealed in genome-wide ChIP-seq studies of FOXO3 (ref.276).

FOXK2

There are two FOXK transcription factors, namely FOXK1 and FOXK2. FOXK1 has been identified as a regulator involved in myogenic stem cell proliferation (ref.277). However, the function of FOXK2 is not clear. FOXK2 expression is constant throughout cell cycle, but its phosphorylation status varies depending on the phase of cell cycle with maximal phosphorylation being observed during mitosis (ref.278). Phosphorylation status affects

FOXK2 protein stability, transcriptional activity and repressive activities. The DNA binding specificity of FOXK2 is quite similar to other FOX proteins because they both have

GTAAACA as a consensus binding sequence (ref.279). One study suggested that FOXK2 and FOXO3 have extensive overlap in genome-wide binding profiles. These shared

FOXK2/FOXO3 binding regions can be strongly bound by these two proteins but are not mutually exclusive (ref.280). Another study showed that FOXK1 and FOXK2 can function antagonistically to FOXO3 by affecting the activity of genes in the autophagy pathway

(ref.281). However, the function and mechanism of action of FOXK2 have not been studied extensively. Recently, association between FOXK2 and the transcription factor AP-1 to regulate gene transcription was demonstrated and FOXK2 promotes AP-1 mediated transcriptional control (ref.282). BRCA-1 associated protein 1 (BAP-1), a tumour suppressor

protein, was suggested to interact with FOXK2 to repress its transcripional activity (ref.283).

Using ChIP, the binding genomic regions occupied by FOXK2 and a range of genes transcribed and regulated by FOXK2 were identified. These included genes involved in controlling a range of cell sigalling pathways, apoptosis and cell migration (ref.282).

Interestingly in this study, L-Myc, N-Myc and c-Myc binding protein (MycBP) showed positive correlation with expression of FOXK2, meaning these genes are regulated by FOXK2.

However, it is unclear if FOXK2 acts as an activator or a repressor (ref.282).

FOXK2 was identified as a regulator of Interleukin 2 (IL-2) during the transcription process

(ref.279). Later, its ability to interact with other viral oncoproteins such as adenovirus E1A and papillomavirus E6 was revealed. More recently, the connection of FOXK2 to cell cycle regulation has been shown and its activity linked to apoptosis can be regulated by CDK- cyclin complexes including CDK1-cyclin B and CDK2-cyclin A (ref.284,285,286). Surprisingly, expression of a mutant form of FOXK2 with two point mutations in serines 368 and 423 causes apoptosis in cells as stable cell lines expressing this FOXK2 mutant cannot be generated. Furthermore, FOXK1 and FOXK2 both share a common function in repressing expression and activity of p21Cip1/Waf-1, which is an inhibitory molecule in the cell cyle

(ref.286). Another study showed that knockdown of FOXK2 has two major effects in vitro.

One such effect is increased cell death due to upregulation of the pro-apoptotic proteins

Noxa and Puma, and the other is a reduced number of proliferating cells (ref.287). These two emerging findings reinforced the idea that FOXK2 deficiency leads to cell proliferation and survival, indicating FOXK2 is linked to apoptosis.

More recently, the first connection between FOXK proteins and cancer was established showing that FOXK 1 and 2 are overexpressed in a number of human cancers such as cervical cancer, colon cancer, head and neck cancer, liver cancer and lymphoma (ref.288).

FOXK proteins act as oncoproteins to activate Wnt/β-catenin signalling by facilitating

Dishevelled (DVL) protein nuclear translocation in both in vitro and in vivo models (ref.288).

Deregulation of the Wnt/β-catenin pathway is implicated in tumorigenesis and cancer development (ref.289,290). Furthermore, the connection between FOXK2 and chemotherapy drug resistance was uncovered in our group, deregulation of FOXK2 to confer drug resistance was observed in drug resistant cells (ref.291).

1.6 Role of CDK5 in cancer and apoptosis

Cyclin-dependent kinases (CDKs) are a family of serine/threonine proline kinases. CDKs play central roles in regulation of cell cycle, transcription, and other cellular functions such as neuronal differentiation and metabolism (ref.292,293). In cancer, many CDKs and cyclins demonstrate different changes including overexpression, amplification, leading to tumorigenesis and cancer development. To date, there are 20 CDKs discovered in mammalian cells (ref.294). Some of them have cell cycle dependent functions and others have cell cycle independent roles. CDK4 and CDK6 are two molecules known to be overexpressed and associated with cyclin D1, thereby driving G1 to S phase of cell cycle in cancer cells (ref.295).

CDK5 is an unconventional molecule in the CDK protein family. Previous studies have shown that CDK5 is ubiquitously expressed but is only functionally active in post-mitotic neurons. In addition, CDK5 activation requires activators p35/p25 and p39 instead of cyclins 64

(ref.296,297). The roles of CDK5 has been studied extensively in the neuron system involving neuronal migration, synapse formation and synaptic activities in mature neurons

(ref.298,299). Upon calcium stimulation, there is calpain dependent cleavage of p35 or p39 to produce p25, which is the activator for CDK5 (ref.300). The cellular function of activated

CDK5 is dependent on its localisation. In the nucleus, activated CDK5 interacts with p27Kip1 and causes cell cycle arrest by disturbing the DP1-E2F1 transcriptional dimer, whereas in the cytoplasm it is involved in inducing cell death (ref.301,302). CDK5 hyperactivity is associated with a number of neurodegenerative disorders, such as Alzheimer‟s disease and

Parkinson‟s disease (ref.303,304).

In human cancer, CDK5 deregulation also plays important roles. CDK5 hyperactivation contributes to the development of glioblastoma and neuroblastoma (ref.305). Amplification and overexpression of CDK5 has been observed in a range of human tumours such as head and neck, breast, ovarian, prostate, sacroma, bladder, myeloma and lung cancers (ref.306).

For example, CDK5 is involved in regulating cell motility, migration and metastasis in prostate cancer (ref.307). Consistently, decreased methylation of the CDK5 promoter resulting in upregulation of CDK5 was observed in lymphoma (ref.308). In pancreatic cancer, mutation of K-Ras leads to CDK5 and its activators, p39 and p35, are hyperactive (ref.309).

In breast cancer, TGF-beta upregulation induces the overexpression of CDK5 and p35, leading to epithelial-mesenchymal transition (ref.310). In neuronsal cells, CDK5 has been shown to suppress cell apoptosis by phosphorylating c-Jun-N-terminal kinase 3 (JNK3), thereby reducing c-Jun phosphorylation (ref.311). Moreover, CDK5 has been shown to maintain survival of pancreatic β-cells through the FAK/AKT/PI3K pathway (ref.361). In a number of cancers, such as ovarian, melanoma, liver and prostate cancers, CDK5 knockdown or inhibition can make cancer cells more sensitive to chemotherapeutic agents 65

(ref.312,313,314). However, there is contradictory evidence to suggest that CDK5 can stabilise and activate p53 to induce neuronal cell death (ref.315). The contribution of CDK5 to cell death depends on the cellular context.

1.7 Hypotheses and objectives

Hypotheses

Based on previous work on isolation of the anticancer gene ORCTL3, a primary screen was conducted using human embryonic kidney 293 T (HEK293T) cells as a first cell line. Again, in a separate project for isolating general apoptosis inducer, HEK293T cell was used by a former colleague Bevan Lin in the genetic screen experiments (Unpublished data). HEK293T cell is a transformed cell line generated by transformation using sheared adenovirus 5 DNA, producing a 4-kbp fragment containing the E1A and E1B oncogenes which stably integrated into chromosome 19 (ref.316). Subsequently, HEK293T was generated by further transformation using SV40 large T antigen (ref.317,318). In combination, the E1A, E1B and

SV40 large T antigen viral oncogenes induce the transformed phenotype of the original human kidney embryonic cells. HEK293T has relevance to human cancer because E1A is known to physically interact with tumour suppressor protein Rb, while E1B55 repress the transactivation of tumour suppressor p53 and inhibit the activity of the tumour suppressor protein Wilm‟s tumour 1 (WT1) (ref.320,321,322,323). Furthermore, the SV40 large T antigen also targets Rb, p53 and WT1 (ref.320,321,322,323). Loss of function of these tumour suppressor genes has been reported to contribute to tumorigenesis. Recently, much more information about the genetic backgrounds of HEK293T has been revealed by genomic sequencing of HEK293T cells. For example, amplification of other oncogenes, such as Myc and microRNA17-92 cluster, have also been identified in HEK293T cells (ref.319).

In the previous genetic screen, only one anticancer gene, ORCTL3, was discovered using the human HEK293T in the first round screen, and the rat NRK normal and p53-Ras transformed NRK cells in the subsequent screen. These two screens were performed in cells with different genetic backgrounds. Based on the knowledge of precise genetic backgrounds of HEK293T only, I hypothesised that more anticancer genes would exist in order to counteract the multiple genetic changes associated with cellular transformation events in

HEK293T cells.

Objectives

The previous study used two genetically different cell lines for isolation of ORCTL3 gene, namely NRK and HEK293T (ref.100). NRK is a rat kidney cell line and HEK293T is a human transformed kidney cell lines. The genetic differences partially led to isolation of only one anticancer gene. Another reason is that Ras mutation is not present in HEK293T cells. With the approach in the previous angesstudy, the anticancer gene isolated must target both genetic changes associated with transformation of HEK293T cells, as well as mutant Ras driven transformation. Therefore, in this study we used genetically close primary kidney cell to human HEK293T cell, namely CV1. CV1 has no disease related mutations and considered as a disease-free cell type (ref.ATCC database). More importantly, CV1 is known to be transfected easily. With these advtanges, we can use CV1 in combination with HEK293T cells to isolate more than one anticancer gene.

Because we already knew the genetic changes associated with transformation of HEK293T cells, we hypothesised that there are many more anticancer genes are available in HEK293T cells. To test this hypothesis, I used a pool of 377 apoptosis inducer genes previously isolated from HEK293T cells by a former laboratory colleague to set up a genetic screen

(Unpublished data). To this end, the first step was to screen anticancer genes in a primary screen using a cell line considered as normal, namely the green monkey kidney fibroblast

CV1 cells. Those genes negative in the cell death read-out represent potential anticancer genes and share one common feature to ORCTL3, being inactive in inducing cell death of normal cells. The second step was then to set up a secondary genetic screen to identify the genetic changes associated with cellular transformation events which these individual anticancer genes are targeting. In order to accomplish that, transformed cells generated by overexpressing a known oncogene or mutating a tumour suppressor gene in the parental

CV1 cells would be used for a secondary screen and subsequently to assess cell death. If those genes turned out to be positive and were able to induce cell death in any one of transformed cells, I could then conclude that these genes are capable of inducing tumour specific cell death by targeting particular genetic changes associated with cellular transformation. Once the connection between individual anticancer genes and this particular transformation scenario was established, I then sought to identify the specific molecules and/or events targeted by the anticancer genes in the transformed cells. In the case of the previously isolated anticancer gene ORCTL1, SCD-1 was identified as a target and shown to be upregulated only in transformed cells.

Chapter 2 Materials and methods

2.1 Reagents and Materials

Following are the materials and reagents acquired from different suppliers:

Sigma Aldrich (Poole, UK)

2-mercaptoethanol (M7522), 40% Acrylamide (A7168), DMEM (D0819), Ampicillin sodium salt (A9518), Bovine serum albumin (BSA, A7906), Bicinchoninic acid kit (BCA1-1KT), Propidium iodide solution (P4864), Dimethyl sulfoxide (D2650), Earle's Balanced Salt Solution (E2888), Fetal calf serum (F7524), L-glutamine (G7513), Polybrene® (H9268), Polyethylenimine Branched (MW 25,000) (408727), Puromycin dihydrochloride (P8833), Triton X-100 (T8532), TWEEN® (P5927) and Yeast extract (Y1625), LB-broth (L3022), Tris Buffered Saline (94158-10TAB), Agarose (A9539), phosphatase inhibitor cocktail (Cat. No. P5726), Purvalanol A(P4484), Poloxin (SML0469), Aurora kinase-A inhibitor I (SML0882), IC261(I0658)

Invitrogen (Renfrew, UK)

10x PBS (14200-067), DMEM (21969-035), Penicillin/streptomycin (15070-063), PicoGreen

(P7589), Earle's Balanced Salt Solution (24010-043), DH5αTM chemocompetent bacteria

(18263-012), PureLinkTM HiPure plasmid filter purification kit (K2100-07), Halt™ Protease and Phosphatase Inhibitor Cocktail (100X, 78440), DiOC6 (D273), Trypsin-EDTA solution

(0.5%, 15400-54) and UltraPure™ DNA Typing Grade® 50X TAE Buffer (24710-030). All primers were ordered from Invitrogen.

Biotium (Cambridge, UK)

GelRedTM Nucleic Acid Gel Stain, 10,000X in DMSO (41002)

Bio-Rad Laboratories (Hemel Hempstead,UK)

0.5M Tris-HCl pH 6.8 solution (161-0799), 1.5M Tris-HCl pH 8.8 solution (161-0798), 10x

TGS buffer (161-0772) and SDS solution (10% w/v, 161-0416), nitrocellulose membrane

0.45µm (9004-7000)

GE Healthcare (Little Chalfont Bucks, UK)

HyperfilmTM high performance chemoluminescent film (28906837)

Promega (Southampton, UK)

Wizard® SV Gel and PCR Clean-Up System (A9282)

Fluka (Gillingham,UK)

Polyvinyl alcohol mounting medium with DABCO (10981)

Polysciences (Hirschberg an der Bergstrasse, Germany)

Polyethylenimine, Linear (MW 25,000) (23966-2)

New England Biolabs (Finnzymes) (Hitchin,UK)

Phusion High-Fidelity DNA Polymerase (F-530L)

OXOID Ltd. (Basingstoke ,UK)

LB-agar (852323)

Fermentas (Renfrew, UK)

5x protein loading buffer (R0891), 50x TAE buffer (#B49), 6X DNA Loading Dye (R0611),

Bradford (500-0006), GeneRuleTM 1KB Plus DNA Ladder (SM1333), GeneRuleTM 1KB

DNA Ladder (SM0314), FastAP Thermosensitive Alkaline Phosphatase (EF0651),

PageRulerTM prestained protein ladder (SM1811 or SM0671), ProteoBlockTM protease inhibitor cocktail (R1321) and T4 DNA Ligase HC (EL0013)

All FastDigest restriction enzymes used were purchased from Fermentas.

Pierce (Renfrew, UK)

Enhanced chemoluminescent reagent (#32106)

Applied Biosystem (Renfrew, UK)

Power SYBR Green Master Mix (4368708)

Thermoscientific (Paisley, UK)

Nuclease free water at molecular biology grade (SH30538.1), Mitosox (M36008)

QIAGEN (Manchester, UK)

Effectene Transfection Reagent (301425).

Clontech (Manchester, UK)

Xfect Transfection Reagent (631318).

2.2 Molecular Biology

2.2.1 Molecular Cloning

This is adapted from the manufactuer‟s protocol and our lab‟s protocol.The Phusion high fidelity DNA polymerase (Finnzymes) was used to perform the polymerase chain reaction

(PCR). The reaction mix is composed of 1 l of dNTP (10 mM), 10 l Phusion HF buffer,

7.5l of each of forward and reverse primers (10M), 0.5l of Phusion polymerase (1 unit/50l reaction), 1l of DNA template (less than 50ng), 1.5 l of DMSO and an appropriate volume of water to make up the final reaction volume to 50 l. The PCR reaction begins with the denaturation stage at 98°C for 59 s, followed by 40 cycles of amplification at

98°C for 30 s, then annealing at 50°C for 20 s, then extension at 72°C for 30 s per 1000 bases, followed by a final extension at 72°C for 5 min.

After gel electrophoresis to verify the size of PCR products, the appropriate fragment was cut and purified using Wizard® SV Gel and PCR Clean-Up Kit (Promega), which was used specifically for restriction digestion. The plasmid vector was cut with the appropriate restriction enzymes followed by addition of 1l of Alkaline Phosphatase (1.0 unit/l)

(Fermentas) for incubation of 5 min at 37°C. In the ligation step, the ratio of 1:5 between vector and insert was used with 1 unit of T4 ligase and 2 l of 10X T4 ligase buffer

(Fermentas) and appropriate water to make up to a final volume of 20 l mixuture. The ligation reaction took place at 4°C overnight followed by heat inactivation at 70°C for 5 min.

At this stage, the ligation mix was ready to be used in the bacterial transformation step.

2.2.2 Plasmid DNAs Section

The standard mammalian plasmid DNA utilized is the pcDNA3.1 vector with the CMV promoter upstream of transgene (Invitrogen). Two control plasmids, namely pcDNA3-

Luciferase and pcDNA3-β-galactosidase were also used in the expeirment. In the NITE

(National Institute of Technology and Evaluation) library containing 30000 full length and fully sequenced cDNAs, each cDNA was cloned using the pME18SFL3 vector, which is an SV40- based promoter driven expression system. Apoptosis positive and negative controls including GFP, RIPK1 and Caspase-2 and GFP were cloned in the pME18SFL3 vector. The reproter plasmid pNFkB-hrGFP (Stratagene,AgilentTechnologies,UK) was used to determine the NF-kB activity by measuring the GFP expression level by flow cytometry.

2.2.3 Bacterial culture

This is adapted and followed from our lab‟s established protocol (ref.326)

Bacteria carrying the plasmid of interest was grown in LB media containing 2% LB and 1%

Yeast extract with 100µg/ml ampicillin for high copy number plasmids. The volume of bacteria culture depends on the scale of DNA isolation according to the instructions given in the commercial kits. Bacteria was inoculated and incubated at 37°C and 250 xg for growth between 16 and 20 hours.

Glycerol stocks of individual bacterial clones containing cDNA were stored in -80°C for long term storage. Individual cDNA clones carrying bacteria were generated by our former member of our group and placed in 96 well plates. To grow the bacterial culture for individual bacterial clone, 1.4 ml of LB media per well and 4 l of glycerol stocks were used in 96 well

plates. The plates were then sealed by gas permeable membrane (ABgene, AB-1450) followed by 20 hours of growth in the shaker at the speed of 250 rpm.

2.2.4 Transformation of Plasmids

This is adapted and followed from manufacturer's protocol.

Escherichia coli DH5α was used for transformation. According to the manufactuer‟s protcol,

1-100 ng of plasmid DNA was combined with 50 l of competent cells and incubated on ice for 15 min. The mixture was heat-shocked by incubating at a water bath at 42°C for 45 s, then followed by returning to ice for an incubation for 2 min. 900 l LB medium was then added to each reaction and the cells allowed to recover at 37°C and 260 rpm for 1 hour in the absence of any antibiotics. The cells were then pelleted and fresh media was added and plated onto LB agar plate containing the appropriate antibiotics for selection.

2.2.5 Ultra-pure Silica oxide large scale plasmid DNA isolation

This protocol was adapted from Professor Stefan Grimm‟s previous work (ref.326).

For large scale, high throughput, cost effective DNA isolation for screening purpose, this protocol was chosen to use in the experiment. This protocol was previously established in the lab. It has been adapted so it can be directly used in the high throughout setting in a cost-effective manner, with ultra-pure plasmids obtained at the end. This protocol allows us to yield high quality supercoiled and endotoxin-free plasmids, which are suitable to any transfection protocols in mammalian cells.

Upon overnight growth of bacterial culture in 1.4 ml of LB medium in the DWP plate, the plate was centrifuged at 4300 rpm (Sigma Robotic centrifuge 4K15) and the pellet

suspended in 250 l of P1 buffer (10mM EDTA, 50mM Tris-HCL, 100ug/ml RNase A), followed by addition of 250 l of P2 lysis buffer (200mM NaOH and 1% SDS) and incubation at room temperature for 5 min. Then 250 l of chilled P3 buffer (3.0M potassium acetate at pH 5.5) was added and the mixture was incubated for 5 min at room temperature, followed by spinning at 4300 rpm for 15 min. The supernatant was then transferred to a new DWP plate, followed by addition of 120 l of P4 buffer (2.5% SDS in isopropanol) and incubated for 15 min at 4°C, then -20°C for another 15 min for precipitation of endotoxin. After that, 150

l of silica solution (50mg/ml dissolved in water) was added and incubated at room temperature for 15 min. The mixture was again spun down and washed with acetone twice to remove contaminants. Finally, the plate was left to air-dry for 30 – 45 min, followed by the elution of plasmid DNA by 100l of nuclease free water (HyClone water, Thermo scientific).

2.2.6 DNA isolation with commercial kits

Plasmid DNA preparation was performed using Mini, Midi and Maxi HiPure plasmid filter purification kits (Invitrogen). The detailed protocols were followed, according manufacturer‟s instruction.

2.2.7 Quantification of DNA concentration

The concentration of plasmid DNA harvested through the kits was quantified by NanoDrop

(Thermoscientific) at wavelengths 260 nm and 280 nm and the protocol performed in accordance with manufacturer‟s instruction.

For the plasmid DNA harvested from the high throughout DNA isolation protocol, PicoGreen

(Invitrogen) was used to quantify the concentration. The reason is that plasmid DNA isolated

from the high throughput protocol has a small trace of silica oxide, which would interfere with the conventional way of measurement namely NanoDrop due to the fact that silica oxide has similar absorbance wavelength as DNA at these wavelenghts. Therefore, a method with higher degrees of sensitivity is required for determining plasmid DNA isolated from by the high throughput method. PicoGreen is a fluorescent nucleic acid stain which can act as a molecular probe for DNA molecules and gives very sensitive readings in the spectrometer.

100ul of 200 times diluted Picogreen was mixed with 100 times diluted plasmid DNA, followed by 5 min incubation at room temperature. The measurement of DNA was determined by fluorescence signal detected by FLUOstar OPTIMA (BMG labtech) with excitation at 480 nm and emission at 520 nm.

2.2.8 Restriction enzyme reactions

The restriction enzyme digestions were performed according to the manufacturer‟s instructions. 1 g of plasmid DNA was mixed with 1l of enzyme (1 unit/l )and 2 l of

FastDigest reaction buffer and 16 l of nuclease free water (HyClone water, Thermo scientific) to make up total volume of 20 l mixture. The incubation was for 5-10 min and followed by gel electrophoresis.

2.2.9 DNA Gel electrophoresis

Different percentages of agarose gels were prepared according to the different molecular weights of genes of interest. Usually, for genes with molecular weights between 500 bp and

10 Kbp, 1% agarose gel was used. DNA samples were mixed with DNA loading buffer at a volume ratio of 5 to 1 before loading onto the gel. Meanwhile, 1 KB plus DNA ladder

(Fermentas) was loaded in a separate lane and was run with samples in TAE buffer for 60 minutes at 100V.

2.3 Cell culture and Transfection

2.3.1 Mammalian cell culture

Human embryonic kidney cell line HEK293T, human cervical carcinoma Hela cells, green monkey kidney fibroblast CV1 cells, human breast adenocarcinoma cell line MCF7 were cultured in high glucose Dulbecco‟s eagle medium (DMEM) with supplements of heat inactivated foetal calf serum (FCS, Sigma-Aldrich) containing final working concentration of

2mM sodium pyruvate (Sigma- Aldrich) and 100U/ml penicillin-streptomycin (Sigma-Aldrich).

These cells were normally kept in T75 flasks or 10cm cell culture dishes in a humidified incubator with 5% atmospheric CO2 at 37°C.

For long term storage of cells, cells were re-suspended along with freezing medium containing 20% FCS and 10% DMSO and kept at -80°C.

2.3.2 Xfect transfection

This is adapted and followed from manufactuer‟s protocol (Clontech).In 96 well format in the high throughput screening setting, optimal volume of 10 l of gene candidate isolated from the high throughput DNA isolation method were mixed with 250 ng of β-Gal plasmids and

0.12 l of Xfect solution in the Xfect reaction buffer to make up the transfection mixture up to a final volume of 20 l. After incubation for 10 min at room temperature, the transfection mixture was gently added to cells in the wells with 100ul of OptiMem medium (Life

Technologies). The medium in each well was replaced with fresh media upon 4 h of incubation in the incubator. 77

2.3.3 Other commercial transfection kits

Commerically available transfection kits were used to transfect other cell lines, with conditions previously optimized in the lab. QIAGEN Effectene was used for Hela cell transfection, Xfect for MCF-7 cell transfection, JetPEI, linear and branched PEI was used for

HEK293T cells transfection. These kits were used in accordance with manufacturer‟s protocol.

2.3.4 Production of stable transfected cell lines

This protocol is followed and adapted from lab‟s established protocol. Desired stable cell lines were generated in 6-well plates upon transfection with expression plasmids carrying genes of interest along with antibiotic resistance genes. After 2 days, the appropriate antibiotics were used for selection and the duration of selection depended on the antibiotics.

G418 selection usually took about 2 weeks and puromycin around 4 and 5 days to complete.

The concentrations of different antibiotics for different cell types were optimized by establishing the kill curve prior to the experiment. After selection, surviving cells were pooled and maintained for further use in the presence of reduced concentration of antibiotics.

2.4 Gene expression measurement

The manufacturer's protocol is followed for the following assays.

2.4.1 Quantitative polymer chain reaction (qPCR) RNA extraction

RNA extraction was performed using the QIAshredder and RNeasy. Cell pellets were harvested and RNA isolated from cells using the protocol provided by the kit (Qiagen). RNA

was treated with DNase I (Qiagen) for 15 minutes at room temperature to remove DNA. RNA extraction quantity and quality were assessed by gel electrophoresis and NanoDrop respectively.

2.4.2 Reverse transcription

1 g of RNA from each sample was taken for cDNA synthesis. Reverse transcription of RNA was performed using QuantiTec Reverse Teranscription Kit (Qiagen) and followed the protocol provided in the kit.

2.4.3 Running of quantitative Polymer chain reaction

For each well in 384-well plate (ABI biosystem), 10 l of SYBR green (Life technologies) and

7.5 l of nuclease free water (HyClone water, Thermo scientific) and 0.5 l of 10 nm of each primer, plus 2 l of cDNA template. Transcript levels were quantified using the standard curve method. The qPCR conditions were as follows: 10 min at 95°C, 40 cycles of 15 s at

95°C and 60 s at 60°C, then 15 s at 95°C and 60 s at 60°C. L19 served as a house-keeping gene in the qPCR experiment for normalization. Applied Biosystem® 7500 Real-Time PCR

System was used to perform the reaction. For data analysis, by using standard curve method, the quantity mean of each gene was determined and used in calculation. Then, each gene‟s quantity was normalized to house-keeping gene L19 by dividing quantity mean of each gene by quantity mean of L19. The normalised number is the relative measurement of quantity of each gene relative to L19 and can be used for comparsion with same gene from other cells such as wild type MCF7 and stable Myc knockdown MCF7 cells.

2.4.4 Primers

Table 3. All primers used

2.5 Cell death measurements

The following protocols were adapted from our lab‟s established protocols.

2.5.1 Propidium iodide staining (PI)

The cells medium were harvested with trypsin, and spun down at 1500rpm for 5 minutes, and the cell pellet was then resuspended in 100 l of PBS containing propidium iodide (20 g/ml). Flow cytometry (FACSCalibur) using FL-3 channel was used to quantify the percentage of dead cells as propidium iodide would stain DNA under the condition that the cell membrane is permeable, hence it can be used to estimate the late stage of cell death.

2.5.2 3,3-dihexaoxacarbocyanine iodide staining (DIOC6)

The cells and medium were harvested with trypsin, followed by centrifuging at 500 xg for 5 min; then cells were resuspended in 100 l of PBS containing 40 nm DIOC6 and 6 g/ml for

30 min in the incubator and 40 minutes at the room temperature in the dark. Finally, cell death was quantified by flow cytometry using FL-1 channel for DIOC6 and FL-3 for PI staining. DIOC6 stains the membrane of mitochondria and serves as a marker for early stage cell death. During cell death, the outer membrane of mitochondria breaks down and results in decreased levels of DIOC6 staining. The combination of DIOC6 and PI staining allow simultaneous quantification of cell death when cells are in the process of undergoing cell death and distinguishing between early and late stages.

2.6 Functional assays

These protocols are adapted and followed by our lab‟s established protocols

.2.6.1 Cell cycle distribution assay

Cell cycle was assessed by flow cytometry analysis using PI staining. CV1 normal or CV1

Myc overexpressing cells were seeded in a 24-well plate at a density of 20,000 cells and cultured in complete DMEM medium for 72 h. Then, cells were trypsinized and harvested, followed by centrifuging at a speed of 500 xg for 5 min. Cells were then resuspended in

150l PBS with equal volume of 2 times concentrated lysis buffer (20 g/ml propidium iodide,

0.1% sodium citrate and 0.1% triton and PBS) (Sigma-Aldrich) and incubated in the dark for

10 minutes at room temperature. In the flow cytometry analysis, FL-2 channel was used to quantify different phases of cell cycle for cells and data was interpreted using a cell cycle platform with Flowjo 7.6.2 software. The cell cycle distribution was shown as G1, S and

G2/M phases.

2.6.2 CPRG assay

This is a sensitive assay for cell death that was established by Professor Stefan Grimm in the lab (ref.365). CPRG is a substrate of an enzyme called β-galactosidase (β-Gal) and the conversion of CPRG by β-gal to a product will result in change of colour, which is easily measured by spectrophotometer (Flurostar-Optima, BMG batch). Only dead cells with breaking down cell membrane can be entered by CPRG, whereas CPRG cannot enter the intact and healthy cells (Fig 10).

In the high throughput screening process, 42 h post-transfection of β-gal and candidate gene into cells into 96 well plates, 10 l of CPRG (13mM, Roche) were added to each well containing 200 l of phenol-red free medium (Sigma-Aldrich). The duration of turnover of

CPRG substrate by β-gal in CV1 cells was 8 h after which the measurement 1 (CPRG1 reading) was taken in a spectrometer at 590 nm. Then, 20 l of 1% (v/v) Triton lysis buffer 82

was added and the the second measurement (CPRG2 reading) was taken the following day in a spectrometer at 590 nm. The ratio using CPRG1 reading dividing CPRG2 reading is calculated and checked for the cell death.

Equation for calculation of CPRG ratio:

CPRG ratio= (CPRG reading 1)/(CPRG reading 2)

Live cells Dead cells

CPRG Reading 1 (Before lysis)

Lysis buffer

CPRG Reading 2 (After lysis)

Figure 10. Mechanism of β-Gal CPRG assay. CPRG as a substrate of β-galactosidase can only enter cells when cell membrane becomes permeablised in the late stage of apoptosis and converted into products to generate signal. CPRG reading 1 is used to measure the cell death signal in dead cell population. CPRG reading

2 is used to measure signal in transfected cell population upon cell lysis.

2.6.3 Colorimetric methylthiazol tetrazolium (MTT) assay

This protocol is adapted and followed from our lab‟s established protocol.

MTT assay was used to determine the proliferation status of cells. Cells were seeded in a 96 well plate at a density of 5000 cells per well in 100 l complete DMEM medium for CV1 and

CV1 myc overexpressing cells, for MCF7 cells the cell density was 3000 cells per well in 100

l complete DMEM medium (Sigma-Aldrich). Cell proliferation was measured by adding 20

l of 5 mg/ml MTT solution (Sigma-Aldrich) dissolved in PBS in each well and incubated for

3.5 h. Then, the MTT solution was replaced with 150 l of MTT solvent (4mM HCl, 0.1%

NP-40) (Sigma-Aldrich) in isopropanol. The plate was placed in a shaker at 250 rpm for 15 min at room temperature, followed by the measurement of optical density at 590 nm in the plate reader (Flurostar-optima). The blank in which there was no cell at all was used as background for analysis. The cells with no transfection served as a benchmark, and the cells with transfection were normalized to the benchmark. Triplicates were used in the experiment for transfection of each candidate gene.

2.6.4 Clonogenic cell survival assay

This protocol is adapted and followed from our lab‟s established protocol.

Clonogenic cell survival assay was used to assess the roles of gene candidates to proliferative ability of cells upon transfection in both CV1 normal, CV1 Myc and MCF7 cells.

5000 cells of MCF7 or 10,000 of CV1 normal or CV1 Myc cells were seeded in three wells of a 6-well plate and cultured for 8 days for MCF7 cells, 9 weeks for CV1 and CV1 Myc cells in complete DMEM medium for colony formation. The colonies were then fixed with 4% paraformaldehyde (Thermo scientific) followed by three washes with PBS. The crystal violet

(Sigma-Aldrich) solution was used to stain the cells for 30 min at room temperature. Cells were left overnight for drying. Crystal violet was dissolved in 33% acetic acid (Sigma-Aldrich) dissolved in water. The samples were then taken to a plate reader for quantifying at a wavelength of 590 nm.

2.6.5 Cellular transformation assay (In-Vitro soft agar assay)

This protocol is adapted and followed from our lab‟s established protocol.

This assay was used to detect the in-vitro cellular transformation of CV1 normal and CV1

Myc cells. This transformation is associated with phenotypic changes, namely anchorage independence. According to the previous studies, there is a very good correlation between in vitro transformation and in vivo tumorigenesis. The soft agar plate consists of 3 ml of base agar (0.5% agar and 10% FCS containing DMEM medium) and 3 ml of top agarose (0.7% agarose and 10% FCS). The base agar and top agarose were made separately and mixed altogether. 5000 cells were resuspended in the top agarose. For each cell type, there were triplicates of samples in petri dishes (40 mm × 12 mm) (Thermo scientific) for growth at 37°C for 4 weeks. On the day of measurement, 0.5ml of 0.005% crystal violet (Sigma-Aldrich) were used to stain the cells for 1 h at room temperature. Then, the cell colonies were counted. In this experiment, a soft agar plate with base agar and top agarose but no cells served as a background control for cell quantification, whereas the HEK293T cells with known colony formation ability served as a positive control in the experiment.

2.6.6 Mitochondrial reactive oxygen species measurement (MitoSox assay)

This protocol is followed from manufacturer‟s protocol.

Cells were harvested and collected in PBS, followed by staining of 5 µM of MitoSox for 10 minutes at 37°C. Stained cells were quantified by flow cytometry. In addition, the non- stained cells were used to quantify the background. Flow cytometry data was analyzed by flowjo 7.6.3.

2.7 Protein SDS-PAGE Gel electrophoresis

The following protocols are adapted and followed from our lab‟s established protocols or manufacturer's instructions.

2.7.1 Preparation of cell lysates

Cells were trypsinized, harvested and spun followed by spinning down at 500 xg for 5 min and addition of RIPA lysis buffer in the presence of protease inhibitor (complete Mini, Roche) and phosphatase inhibitor (Sigma Aldrich). The cell lysate was incubated on ice for 20 minutes, and then was span down at 13000 xg for 20 minutes at 4°C. The supernatant was transferred to a new Eppendorf for further use.

2.7.2 Protein Quantification

Protein concentration was quantified by Bicinoninic acid assay kit (Sigma-Aldrich). Ninety- five l of BCA solution were mixed with 5 µl of protein sample in 96-well plates and incubated for 30 min at room temperature at the shaker. Meanwhile, the samples for establishing standard curves need to be prepared according to manufacturer‟s instruction

(Sigma-Aldrich). Protein absorbance was measured in the spectrometer at 590 nm and concentration was determined using the standard curve.

2.7.3 Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE)

30 g of cell lysate was used and resolved in SDS-PAGE (Stacking gel: 4.8% polyacrylamide, 125mM Tris-HCL pH6.8, 0.1 % SDS, 0.1 % APS, 0.1 % TEMED; Resolving gel: 10-15 % polyacrylamide, 375 mM Tris-HCL pH8.8, 0.1 % SDS, 0.1 % APS, 0.05%

TEMED) diluted in the TGS running buffer (25mM Tris-HCL, 192mM Glycin, 0.1% SDS). The protein ladder PageRuler (Fermentas) was used to determine the molecular weight of proteins of interest.

Transferring was performed using nitrocellulose membrane (Biorad) or PVDF (Millipore) in the transfer buffer (23% ethanol in TGS buffer) in the conventional liquid system at 105 mA for 16 h for one gel.

2.7.4 Western Blotting

After transferring, the membrane was blocked by buffer containing 5 % of milk (Sigma-

Aldrich) in Tris buffered saline tween (20mM Tris, 0.9 % NaCl, pH 7.4, 0.1 % Tween-20) for

30 min. Then, the blocking buffer was discarded and replaced by the primary antibody solution at 4°C for desired duration according to the manufacturer‟s protocol. Three washes by TBS-tween, each for 10 min at room temperature, were performed after incubation with primary antibody. The secondary antibody, the horseradish peroxidise conjugated antibody was added and incubated with the membrane for 60 min. The membranes were then washed three times with TBS-tween. Finally, enhanced chemoluminescent reagent (Pierce)

was added to the membrane, the HRP reaction took place and high performance chemoluminescence film (GE Healthcare) was used to capture the signal from the membrane.

2.7.5 Antibody list

Table 4. All primary and secondary antibodies used

Antigen Source Company Cat. No. Dilution FOXO3a Rabbit Millipore DAM1739172 1 in 2000 FOXK2 Rabbit Bethyl A301-729A 1 in 1000 FOXM1 Rabbit Santa Cruz biotechnology Sc-502 1 in 1000 GAPDH Mouse Santa Cruz biotechnology Sc-32233 1 in 6000 c-Myc Mouse Sigma-Aldrich M4439 1 in 1000 β-tubulin Rabbit Santa Cruz biotechnology sc-9104 1 in 1000 P53 Mouse Santa Cruz biotechnology sc-126 1 in 1000 Rb Mouse Cell Signalling 9309 1 in 1500 E1A Mouse Millipore 05-599 1 in 500 E1B55 Mouse From Dr. Arnold J. Levine 2A6 clone 1 in 1000 H-ras Rabbit Santa Cruz biotechnology Sc-520 1 in 200 WT1 Rabbit Sigma-Aldrich SAB2102716 1 in 1500 Mouse-HRP Goat Invitrogen G21040 1 in 3000 Rabbit- HRP Goat Sigma-Aldrich A0545 1 in 6000

2.8 Statistical Analysis

Two-tailed student t-test was used to perform the statistical analysis. The data is considered to be statistically significant if p value is smaller than 0.05.

Chapter 3 Synthetic Lethality Screens

3.1 Background

The concept of synthetic lethality is based on the principle that simultaneous perturbation of two genes results in cell death. It can be used to identify novel genes involved in cell death and their mechanisms of action. Highly conserved pathways such as those regulating cell death can be studied in simpler eukaryotic cells such as yeast through to human cells. Identifying such novel gene functions has contributed to characterisation of genetic interaction maps in human cancer.

This in turn identifies novel drug targets for the discovery of a new generation of anticancer drugs and therapies.

In this study, I used the synthetic lethality approach to perform a genetic screen in order to identify new anticancer genes, which are capable of triggering cell death only in transformed cells but leaving normal cells unharmed. Previously, our group identified one anticancer gene called ORCTL3 (ref.110). ORCTL3 is known as a nicotinate transporter, which physically interacts and blocks the target molecule SCD-1 for inducing cell demise in different transformation scenarios, irrespective of the genetic background of transformed cells. Based on the results of this work carried out by a former colleague, SCD-1 upregulation has been shown in a number of transformed cells (ref.111).

In the previous study on the isolation of ORCTL3, the primary screen was performed in the

HEK293T cell line and a secondary screen conducted in the rat kidney cell line NRK, as well as

NRK cells transformed by the mutant H-ras oncogene (ref.110). ORCTL3 is inactive in normal

NRK cells but can induce apoptosis in H-ras transformed NRK cells and HEK293T cells.

Moreover, ORCTL3 has been shown to induce apoptosis in v-src transformed cells (ref.110). It 89

was also found that OCTRL3 exhibited little toxicity to a range of other cell lines which are considered normal in contrast to transformed cell lines, including CV1 normal cells. This indicates that ORCTL3 can induce apoptosis in different transformation scenarios potentially regardless of the genetic backgrounds of the transformed cells.

Hence, in my project we applied the same logic and set up a genetic screen for novel anticancer genes, which can promote tumour-specific cell death specifically in transformed cells. As the first step, we started with 377 apoptosis inducers which have been isolated in HEK293T cells and conducted a small scale screen in the normal CV1 cell line, derived from a male adult African green monkey. From the result of this screen only those genes showing negative read-out in cell death assays represent candidates for anticancer genes with similar features to ORCTL3 in triggering cell death in transformed cells but not in normal CV1 cells. This approach would allow us to identify novel anticancer genes.

The second step is to screen for anticancer genes against genetic changes associated with cellular transformation by overexpressing oncogenes or mutated tumour suppressor genes, selected on the basis of the characterised genetic background of HEK293T cells. It is known that human adenovirus 5 oncogenes, E1A and E1Bs drive transformation of HEK293T cells (ref.316).

Adenovirus E1A protein has been shown to drive the cell cycle and proliferation by sequestering the well known tumour suppressor gene, retinoblastoma protein (Rb), a member of the E2F family of transcription factors. Thus, E2F is able to transactivate genes required for the entry into

S phase (ref. 320). E1A has been shown to cooperate with E1B-19k and E1B-55k in inducing cell transformation in HEK293T cells. The E1B-19k molecule works as a BCL-2 homologue that inhibits E1A induced apoptosis (ref.321,322), On the other hand, E1B-55k physically binds to the

well known tumour suppressor, p53 protein, to inhibit it from transcribing genes required for apoptosis induction (ref.322), as well as wilm‟s tumour 1 (ref.323). In addition, previous work on

HEK293T cells showed c-Myc gene amplification, which contributed to cellular transformation

(ref.319). Therefore, we exploited the known genetic changes relevant for cell transformation in

HEK293T cells and performed a genetic screen, which utilises the same principle as the synthetic lethality screen, to identify which genetic change each anticancer gene is targeting. In the transformed cells, we expected to see cell death resulting from conflicting signals created by individual transformation events and an anticancer gene. This individual transformation scenario could be either overexpression of an oncogene such as c-Myc, or loss of function of a tumour suppressor gene such as wilm‟s tumour 1 or loss of Rb and p53.

3.2 Transfection of CV1 cells

The 377 positive apoptosis inducers isolated from the NITE (National Institute of Technology and

Evaluation) library, a collection of 30000 full length and fully sequenced cDNAs, by the former lab member Bevan Lin were used to transfect CV1 normal cells to identify novel anticancer genes. As decribed, only candidate genes that are not able to induce cell death in CV1 normal cells represent potential anticancer candidates. The CV1 kidney fibroblast cell line is known to be resistant to transfection and has been shown to translocate transfected plasmids to the nucleus about three times less efficiently compared to Hela cells (ref.324). In order to efficiently transfect the cDNA representing individual candidate genes into CV1 cells, I needed transfection reagents which could achieve efficient transfection with minimal toxicity. Thus, I searched for the most suitable transfection reagent from both commerical and non-commerical sources.

3.2.1 Commerical transfection reagents

A few of the commerical transfection reagents, which have been shown to efficiently transfect

CV1 cells, were experimentally tested. From these, Xfect achieved a reasonable transfection efficiency of around 60% (Fig. 11). In addition, no significant toxicity was observed under the light microscope (Fig. 12).

Figure 11. Flow cytometry analysis of transfection efficiency for different commercial transfection reagents at 48 hours post transfection in CV1 normal cells. 1 µg of GFP expressing plasmid was transfected to CV1 normal cells. Different transfection protocols were used according to the manufactuers‟ protocols. Flowjo® were used in this analysis.

Untransfected β-Gal Transfection GFP Transfection

Figure 12. Assessment of toxicity for Xfect transfection in CV1 normal cells. Phase and contrast images were taken at 48 hours post-transfection for each transfection reagent. The first one is the untransfected CV1 normal cells and the second one is the β-galactosidase (β-Gal) expression plasmid transfected CV1 normal cells as control, the third one is the green fluorence protein (GFP) expression plasmid transfected CV1 normal cells. 1 µg of expression plasmids was used in the transfection with incubation time of 4 hours in the presence of complete medium with 10% FCS.

Xfect was chosen for further optmisation of transfection in CV1 cells. By changing a number of parameters such as cell density, volume of medium, incubation time for mixing transfection reagent and cells, the transfection conditions for Xfect was optimised to achieve a transfection efficiency of 68% (Fig. 13A). In addition, the ratio of Xfect polymer to plasmid DNA was varied to achieve optimal transfection. By using the same amount of Xfect polymer and varying the amount of DNA, the optimal ratio was found that 1 to 3, namely 0.3 µl of Xfect for 1 µ g of plasmid

DNA. This is in line with the product description from the manufacturer (Clontech) and could not be further improved (Fig. 13B).

(A) (B)

Figure 13. Quantification and optimisation of transfection efficiency for Xfect transfection reagent in CV1 normal cells in the presence of complete medium (with 10% FCS). (A) Flow cytometry analysis of transfection efficiency in CV1 normal cells at 48 hours post transfection. Untransfected cells, 1 µg of control plasmid β- galactosidase (β-Gal) and 1 µg of green fluorence protein (GFP) were transfected into CV1 normal cells in the presence of complete medium with 10% FCS. (B) Optimisation of transfection condition by varying ratio of Xfect polymer to plasmid DNA. Different ratios of Xfect polymer and plasmid DNA were generated by mixing the same amount of Xfect transfection reagent with different amounts of plasmid DNA, followed by 10 minutes incubation at room temperature before incubating with CV1 normal cells. Both transfection efficiency and mean fluorence intensity (MFI) were measured by flow cytometry in FL-1 channel.

Previous studies have shown that Optimem medium improved transfection efficiency of CV1 cells as compared to foetal calf serum containing medium (ref.325). Surprisingly, serum reduced medium Otpimem significantly enhanced protein expression of the transfected genes indicated by mean fluorence intensity, while the transfection efficiency and the toxic effect remained the same (Fig.14A and B)..

(A)

(B)

Figure 14. Optimisation of Xfect transfection efficiency in CV1 normal cells with different cell culture medium.

(A) No toxic effects were observed when CV1 normal cells were placed in Optimem or complete medium (with 10%

FCS), phase and contrast pictures were taken under the light microscope. (B) Flow cytometry analysis of transfection efficiency in CV1 normal cells at 48 hours post-transfection. One µg of control plasmid β-galactosidase (β-Gal) and 1

µg of green fluorence protein (GFP) were transfected into CV1 normal cells in the presence of Optimem medium or complete medium (with 10% FCS). Both transfection efficiency and mean fluorence intensity were determined in Fl-1 channel in flow cytometry.

3.2.2 Non- commercial transfection reagent

Meanwhile, the non-commercial transfection reagents, namely linear-PEI and branched PEI were tested and optimised for transfecting CV1 cells by varying the same parameters as for the commercial transfection reagent Xfect. Linear and branched PEI are structurally different

(Fig.15A). As PEIs had been used to transfect SV40 large T antigen transformed CV1 cells (COS cells) efficienctly in the past and CV1 is the parental cell type, I therefore next assayed its efficiency in CV1 cells. Under the optimal conditions of PEI based transfection protocol, both linear and branched PEI polymers diluted from the stock concentration of 2 mg/ml with dilution factors of 1000x and 2000x, respectively, showed higher toxicity when examined under phase contrast microscopy (Fig.15B). Also, the transfection efficiencies for 1000x L-PEI and 2000x B-

PEI were around 30% and 40%, respectively, in CV1 normal cells. However, the protein expression levels of transfected genes measured by mean fluoresence intensities from both PEIs were not as strong as Xfect (Fig.15C). For linear PEI, protein expression level was only about half of the protein expression level seen in branched PEI and 25% of protein expression level in

Xfect. This could be explained by different amounts of plasmid being taken up by the cells with the different transfection reagents. Xfect was found to be more efficient in delivering expression plasmids into each cell compared to branched and linear PEI.

(A)

(B)

(C)

Figure 15. Analysis of non-commerical transfection reagents linear PEI (L-PEI) and branched PEI (B-PEI) in CV1 normal cells. Transfection conditions have been optimised for these two non-commerically available transfection reagents, namely 1000 times dilution for linear-PEI and 2000 times dilution for branched PEI. The stock concentrations of both linear and branched PEI are 2 mg/ml. One µg of DNA was used to transfect CV1 cells under the optimal conditions for each of transfection reagents. (A) Structures of linear and branched PEI polymers, this picture is adapted from (ref.366) (B) Phase and contrast pictures for assessing the toxicity. (C) Meaurements of transfection efficiency and mean fluorence intensity (MFI). Each transfection experiment was performed separately at least three times (N=3). Xfect transfection was used as a positive control.

Overall, for the reagents tested, Xfect showed little toxicity, as well as the highest transfection efficiency and protein expression levels as measured by mean fluorence intensity (MFI).

Therefore, Xfect was chosen for the following screening experiments, in combination with the use of Optimem medium, is desirable.

3.3 Optimisation of in vitro cell death assay (CPRG assay)

Once transfection was optimised, the next step was to apply the same transfection conditions in optimising the in vitro cell death assay. This cell death assay is also called β-galactosidase (β- gal) CPRG assay. The assay is conducted by co-transfection of the β-gal reporter plasmid with the individual candidate gene in the 96 well format followed by addition of β-gal substrate CPRG at defined times upon transfection. β-galactosidase at defined times upon transfection catalyzes the hydrolysis of a disaccharide substrate to its product of monosaccharides. Chlorophenol red- ophegalactopyranoside (CPRG) is one of the substrates of β-galactosidase and had been used in the genetic screening experiments in our laboratory in the past. This assay is based on the rapid and sensitive measurement of the turnover of the substrate CPRG by β-Gal. The colour of the medium then turns from yellow to pink and can be easily measured by spectrometry using a plate reader. When cells undergo any forms of cell death, CPRG as a substrate for β-gal can pass through the permeable cell membrane and will be converted into products that can be detected by a plate reader at the wavelength of 590nm. On the other hand, the cellular membranes of healthy cells will remain intact, are not permeable and there will be no hydrolysis of CPRG. For the screening purpose, this in vitro cell death assay needed to be sensitive, rapid and easy to operate in a 96 well plate format.

In this assay, two measurements of absorbance at wavelength of 590nm were taken, the first one (reading 1) representing the population of apoptotic cells and the second one (reading 2) upon the addition of a 1% Triton lysis buffer to determine the total enzymatic activity from all transfected cells. CPRG ratio was then calculated by using reading 1 divided by reading 2. As previously demonstrated by a former colleague Bevan Lin, CPRG ratio provide more statistical power than other calculated indicators in this cell death assay because the variation of results

from independent experiments is little and results are more consistent (ref.Ph.D Thesis titled

Using the RISCI Genetic Screening Platform for Elucidating Apoptosis by Lin Kuan Chee Bevan).

For my project with this assay, the first issue I needed to address was to determine whether β- gal was stable and active in CV1 cells undergoing apoptosis. Hence, different known apoptosis inducing control plasmids were used and co-transfected individually with the β-Gal expression plasmid at the weight ratio of 1:1 in a 96-well plate, and a total of 500 ng of DNA/well was transfected into cells by Xfect. The 1% triton lysis buffer and CPRG were added to the cells 48 hours post-transfection and absorbance measured at every hour to monitor the kinetics of the assay. The signals from all cells transfected with the apoptosis inducers were not reduced compared with the signal from the control, which was β-gal and GFP (Fig.16A). This indicated that β-gal is stable, functional and enzymatically active after cells underwent apoptosis. However, the reason why signals from Caspase 2 and RIP were higher compared to the control remains unknown.

The second issue with this cell death assay I needed to address was the incubation time of

CPRG with cells at which the cells transfected with reporter β-Gal expression plasmid and negative cell death inducer control e.g GFP, started to show significant increased absorbance indicative of cell death in reading 1. As a result, I co-transfected different apoptosis inducers along with reporter β-gal expression plasmid, then at 48 hours post-transfection I monitored absorbance by incubating CPRG with transfected cells for 24 hours. It was found that the incubation time of 7 hours was the optimal time point at which I did not observe a significant increase in cell death in cells transfected with negative cell death inducer e.g GFP (Fig. 16B).

(A)

(B)

Figure 16. Quantification of cell death using in vitro cell based CPRG assay in CV1 normal cells. (A) To determine if β-Galactosidase is sensitive, stable and functional upon cell death induction, 250 ng of non apoptosis inducer GFP and 250ng of different apoptosis inducers, namely Caspase 2 (Cas2), Caspase 8 (Cas8), adenine nucleotide translocator (Ant1) and receptor-interacting serine/threonine-protein kinase 1 (RIP1) were co-transfected individually along with 250ng of β-Gal to CV1 normal cells under the optimized transfection condition. GFP alone –ve is negative control, which is the transfection of GFP expression plasmid alone without the β-Gal expression plasmid. At 48 hours post transfection, 1% triton lysis buffer and CPRG were added and absorbance was measured by plate reader at wavelength of 590nm at every hour. (B) To determine incubation time of CPRG with transfected cells at which the co-transfection of β-Gal with GFP starts to show significant increase of cell death as background in reading 1 at 48 hours post transfection.

The next step was to find out if this condition gave the highest sensitivity in the cell death measurement. In other words, the difference in reading 1 between negative apoptosis inducer and positive apoptosis inducers was the greatest. I knew that the incubation time of CPRG with transfected cells could not exceed 7 hours, otherwise the background cell death would start to increase significantly. Therefore, experiments were performed by using different time points for adding the CPRG and finally the time point at 42 hours post transfection gave the best sensitivity among different time points (Fig. 17A,B). Also, the CPRG ratio was calculated based the equation of reading 1 dividing reading 2 under this optimal condition (Fig. 17C).

(A)

(B)

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(C)

Figure 17 Optimised conditions for an in vitro cell based CPRG assay to measure cell death under the optmised transfection condition in CV1 normal cells. (A) Schematic diagram to illustrate the cell death assay. CV1 cells were transfected with reporter β-Gal plasmid with individual gene by Xfect transfection reagent in Optimem medium and CPRG was added at 42 hours post-transfection and incubated with cells for 7 hours, first reading was measured by plate reader, followed by the addition of cell lysis buffer and the second reading was measured in the following day. (B) Optimal conditions of assay for measurement of cell death in absorbanced illustrustred by co- transfecting β-Galactosidase (β-Gal ) along with individual non-apoptosis inducer control, such as GFP, and apoptosis inducers, such as Caspase 2 and RIP1. (C) CPRG ratios were calculated based on the equation of reading 1 dividing by reading 2 for cells transfected with different apoptosis control expression plasmids. Triplicates were performed for each of the transfections and means and standard deviation were calculated and plotted in this graph.

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3.4 Implementation of screens

Figure 18. Overview of Screens in both CV1 normal and transformed cells, as well as HEK293T cells. The genetic screen was serparated into two steps. The first step was to isolate novel anticancer genes in CV1 normal cells, and the second step was to identify the genetic changes associated with cellular transformation which can be targeted by individual anticancer genes. DNA was isolated using silica oxide method in the first step of screen, and commercially available miniprep DNA isolation in the second step of genetic screen.

The genetic screen was set up to isolate novel anticancer genes that can induce tumour specific cell death in cells with genetic changes relevant for tumour associated transformation. There were two steps in the genetic screen, the first one being isolation of novel anticancer genes and the second one being identification of specific genetic changes associated with tumour transformation, which individual anticancer genes could target (Fig.18). This screen began by using those positive apoptosis inducers that my former colleague Bevan Lin had isolated in

HEK293T cells and conducted on a small scale in CV1 normal cells. The CV1 cell line is a kidney fibroblast cell derived from a male adult African green monkey and can be considered as a

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normal cell line in contrast to transformed CV1 cells with stable transfection of individual oncogenes or mutated tumour suppressor genes.

The first step was to screen and isolate anticancer genes in a primary screen. Those genes shown negative in the read-out would represent the potential anticancer genes and share one common feature with ORCTL3, which is the ability to induce cell death in transformed cells such as HEK293T cells and being inactive in normal cells such as CV1 cells. The second step was to identify the transformation scenarios which these anticancer genes target. In the secondary screen, I needed to understand which oncogene or mutated tumour suppressor gene the individual anticancer gene was targeting. In order to accomplish that, transformed cells, which were generated by overexpressing individual oncogenes or mutated tumour suppressor genes in

CV1 cells, were used for the secondary screen. If some genes were positive in the read out and were able to induce cell death in transformed CV1 cells, I could conclude that these genes were capable of inducing tumour specific cell death by targeting a particular oncogene or mutated tumour suppressor gene. For the secondary screen, I already knew some of the genetic background of HEK293T cells and that was these cells expressing two known oncogenes E1A and E1B55k. E1A is known to inhibit the tumour suppressor gene product Rb and E1B55k suppresses transactivation of p53 and inhibits the other tumour suppressor gene wilm‟s tumour 1

(ref.320,322). In addition HEK293T has gene amplification of the well known oncogene c-Myc and microRNA17-92 gene cluster as shown in the previous study (ref.319). Therefore, I could generate transformed cells by using CV1 normal cells as parental cells with stable overexpression of individual oncogenes such as E1A, E1B55, c-Myc, mutant P53, a combination of E1A and E1B55, or stable knockdown of WT1 or Rb.

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The screen was initiated by using the silica oxide DNA isolation method, which was previously established in our lab to conduct robotic, high throughput DNA isolation for a cDNA library

(ref.326). This approach was easy to use, less time consuming and relatively cheap to implement for many genes in a human cDNA library format. In the primary screen, I used this approach to isolate the plasmids of 377 candidate genes in a 96 well plate format. In total, there were 4 plates of candidate genes. Individual candidate genes were transformed into competent bacterial cells.

Colonies were picked for bacterial glycerol stocks and grown in a 96 deep well plate overnight, followed by the established protocol of silica oxide DNA isolation. For transfecting each candidate gene in each well of the 96 well plate, the optimal volume of 15 µ l of isolated individual candidate gene (100-250 ng of isolated cDNA) was mixed with 250 ng of β-Gal expression vector, as well as 0.15 µl of the commercially available transfection reagent Xfect. At 42 hours post- transfection, CPRG was added as a substrate to the cells and incubated for 7 hours. The colour change of CPRG in the medium would give rise to reading 1 which was determined by a plate reader at the wavelength of 590nm. The lysis buffer was added at 49 hours post-transfection and reading 2 was measured the following day. This assay exploited the disturbance of plasma membrane integrity in cells during the late stage of cell death. Reading 1 measured the population of dead cells, reading 2 reflected the transfection efficiency. The ratio of reading 1 over reading 2 indicated the percentage of cell death among the transfected cells in each well.

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3.4.1 First round of screen (Primary screen)

(A)

(B) Plate 1

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(D) Plate 3

(E) Plate 4

Figure 19. Representative results of first step of the screen to short-list gene candidates from 377 positive apoptosis inducers in a total of 4 plates of 96 well format in CV1 parental cells. The Y-axis shows the CPRG ratio determined and x-axis shows the individual gene candidates. Co-transfection of individual gene candidate along with β-Gal in each well of 96 well plate. Three negative controls, namely non-apoptosis inducers such as GFP, empty vector PcDNA3 and luc were used to determine the threshold level and two positive controls such as Caspase 2 and RIP1 were used to show this assay is valid. The red line represents the threshold level. Gene candidates with CPRG ratio equal to or below the threshold level are the potential hits. For each plate, two repeats of experiments were conducted.

To begin with, 377 apoptosis inducers previously isolated in our laboratory, in the form of cDNA and were individually transfected along with reporter plasmid β-Gal into CV1 normal cells for isolation of anticancer genes. For each candidate gene, CPRG ratio was calculated as preivously described. Only those candidate genes with CPRG ratios lower than the upper range of the ratio

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of the negative control transfected cells were considered to be hits. Anticancer genes have an important characteristic which is not to induce cell death in normal cells, namely CV1 normal cell in this case. In the first step of the screen, two repeats were performed for each plate, and I identified 240 genes that were inactive in inducing cell death in CV1 normal cell (Fig.19).

3.4.2 Second round of screen (Primary screen)

(A)

(B)

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Figure 20. The representative results of second round of the screen from 240 short-listed gene candidates obtained from the first round of screen in CV1 normal cells. (A) Overview of second round screen (B) Second round screen result. The Y-axis shows the CPRG ratio determined and x-axis shows the individual gene candidates. Co-transfection of individual gene candidate along with β-Gal in each well of 96 well plate. Again, three negative controls, namely non-apoptosis inducers, such as GFP, empty vector pcDNA3 and luc, were used to deteremine the thresholds and two positive controls such as Caspase 2 and RIP1 were used to validate this assay. The red line represents the threshold level. Gene candidates with CPRG ratio equal to or below the threshold level are the potential hits. For each plate, two repeats of experiments were conducted.

The 240 previously short-listed genes were isolated and transfected into CV1 normal cells under the same conditions. The same selection criteria were applied to screen for those genes with

CPRG ratio equal or lower than ratios of negative controls. Finally, 86 genes out of 240 turned out to be inactive in inducing cell death (Fig.20). These genes were chosen to enter into the third round of screening and further testing.

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3.4.3Third round of screen (Primary screen)

(A)

(B)

Figure 21. The representative results of third round of the screen from 86 short-listed candidate genes originated from the second round of screen in CV1 normal cells. (A) Overview of third round screen (B) Third round screen result. The Y-axis shows the CPRG ratio determined and x-axis shows the individual gene candidates.

Co-transfection of individual gene candidate along with β-Gal in each well of the 96 well plate. Again, three negative controls, namely non-apoptosis inducers such as GFP, empty vector pcDNA3 and luc, were used to deteremine the thresholds and two positive controls, such as Caspase 2 and RIP1, were used to show this assay is valid. The red line

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represents the threshold level. Gene candidates with CPRG ratio equal to or below the threshold level are the potential hits. For each plate, two repeats of experiments were conducted.

Eighty-six short-listed genes from second round of screening were selected for testing in the third round. Again, the same selection criteria applied to these genes. In this third round screen, 78 genes turned out to be truly negative in inducing cell death in CV1 normal cells (Fig. 21).

In the last three rounds of the screen, the silica oxide-based DNA isolation method was employed. In my screen, this approach was used to isolate individual candidate genes genes and the number of candidate genes genes was narrowed down from 377 to 78 genes. However, this approach had two drawbacks. One drawback was the variation of the amount of DNA isolated and transfected into cells. Moreover, the concentrations of isolated plasmid DNA ranged from 10ng/µl to 50ng/µ l . The other drawback was the presence of small traces of silica oxide in the isolated plasmid DNA that could cause interference with binding of the transfection reagent

Xfect to isolated plasmid DNA. Thus, so far the 78 candidate genes isolated were negative in the screen and needed further validation by another DNA isolation approach. Accordingly, we chose the commerically available miniprep kit for the second part of screen.

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3.4.4 Validation of candidate genes in CV1 normal cells

(A)

(B)

(C)

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(D)

(E)

Figure 22. Validation of 78 candidate genes from previous three rounds of screen in CV1 normal cell. The (A) Overview of validation screen. (B), (C), (D) and (E) Third round screen results. The Y-axis shows the CPRG ratio determined and x-axis shows the individual candidate genes. Co-transfection of individual gene candidate along with ß-Gal in each well of 96 well plate. Three negative controls, namely non-apoptosis inducers such as GFP, empty vector PcDNA3 and luc, were used to deteremine the threshold level and two positive controls such as Caspase 2 and RIP1 were used to validate this assay. The red line represents the threshold level. Candidate genes with CPRG ratio equal to or below the threshold level are the potential hits. For each plate, two repeats of experiments were conducted. Student 2 tailed t-test was performed and p<0.05.

These 78 short-listed candidate genes consistently showed negative cell death inducing effect in

CV1 normal cells with the silica oxide DNA isolation method. To further validate these genes, with miniprep DNA isolation method, only 43 genes remained non-cell death inducers (Fig.22).

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The remaining 35 genes showed significantly high CPRG ratios compared to negative control genes clearly indicating a cell death inducing effect in CV1 normal cells. These genes were therefore removed from the candidate list.

3.4.5 Validation of candidate genes in HEK293T cells

(A)

(B)

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Figure 23. Validation of candidate genes as apoptosis inducers in HEK293T cells. (A) Overview of validation screen (B) validation screen result. The Y-axis shows the CPRG ratio determined and x-axis shows the individual candidate genes. Co-transfection of individual gene candidate along with β-Gal were carried out in each well of 96 well plate. Three negative controls, namely non-apoptosis inducers such as GFP, empty vector pcDNA3 and luc, were used to deteremine the threshold level and two positive controls such as Caspase 2 and RIP1 were used to show this assay is valid. The red line represents the threshold level. Candidate genes with CPRG ratio significantly higher than the threshold level are the potential hits. For each plate, two repeats of experiments were conducted. Student 2 tailed t-test was performed and p<0.05.

These 43 validated genes in CV1 normal cells have shown to have negative effects in inducing cell death. Again, we needed to further validate the cell death inducing effect in HEK293T cells, which was the cell line used in the isolation of 377 candidate genes genes as apoptosis inducers.

With the miniprep plasmid DNA, these 43 candidate genes genes were transfected to HEK293T using linear PEI. In the transformed cells, only 22 genes turned out to be effective in activating cell death as they exhibited significantly higher CPRG ratios than the negative control genes

(Fig.23), while showing no such effect in the normal CV1 cells. Accordingly these genes have the criteria to be defined as anticancer genes.

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3.4.6 Identifying genetic changes linked to tumour specific effects (Secondary screen)

(A)

(A) CV1 normal cell (Control cell 1)

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(B) pcDNA3 stable CV1 cell (Control cell 2)

(D) Recombinant E1A and E1B55 overexpressing CV1 cell

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(E) Recombinant E1A overexpressing CV1 cell

(F) E1B55 overexpressing CV1 cell

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(G) Dominant negative p53 overexpressing CV1 cell

(I) Stable Rb knockdown CV1 cell

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(J) Mutant H-ras transformed CV1 cell

(K) Stable Wilm’s tumour 1 (WT1) knockdown CV1 cell

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(L) Stable c-Myc overexpressing CV1 cell

Figure 24. Representative screen data for different CV1 transformed cells in identifying genetic changes associated with the tumour specific effect. Three control cells, namely CV1 normal cell, pcDNA3 stably transfected cells and shRNA knockdown control construct stable CV1 cells were generated and used in the screen as controls. Eight different transformed cells were generated by either overexpressing c-Myc, mutant form of p53 (R175H), activating mutant H-ras (G12V), recombinant E1A, E1B55 alone, combined E1A and E1B55, or stable knockdown of tumour suppressor gene such as Rb, wilm‟s tumour 1. (A) Overview of screen (B) (C) (D) (E) (F) (G) (H) (I) (J) (K) (L) indicate the screen results on different CV1 cells including both control CV1 normal cells as well as transformed CV1 cells. In addition, western blotting data show the level of protein of interest in CV1 cells. The Y-axis shows the CPRG ratio determined and x-axis shows the individual gene candidates. A 96 well plate format was used to co-transfect individual candidate genes with β-Gal . Three negative controls, namely non-apoptosis inducers such as GFP, empty vector pcDNA3 and luc were used to deteremine the threshold level and two positive controls such as Caspase 2 and RIP1 to indicate the expected level of apoptosis. The red line represents the threshold level. Gene candidates with CPRG ratio significantly higher than the threshold level represent potential hits. For each plate, two independent experiments were conducted. Student 2 tailed t-test was performed (significant p<0.05).

Those 22 candidate genes which were confirmed to be anticancer genes through the characteristic of showing cell death inducing activity only in transformed cell HEK293T cells but not in CV1 normal cells. The next step was to find out the genetic changes associated with

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transformation that these anticancer genes are targeting. As previously described, the genetic background of HEK293T is known from previous studies (ref.319). Therefore, I next generated stable transformed CV1 cells by either overexpressing individual oncogenes or dominant negative forms of tumour suppressor genes, or to knockdown tumour suppressor genes directly.

Following the transfection of individual candidate genes into differently transformed CV1 cells would allow us to find out which genetic change was directly linked to the tumour specific cell death effect. We generated two additional control cells, namely stable pcDNA3 CV1 cells and shRNA control construct CV1 cells. These control CV1 cells further confirmed that these 22 anticancer genes were not cytotoxic to normal cells. Using a number of transformed CV1 cells, we identified the genetic changes associated with this tumour specific effect for these 22 anticancer genes (Fig. 24). The most promising effect was observed in the c-Myc overexpressing

CV1 cells, as the majority of genes were showing a relatively strong cell death inducing effect compared to other types of transformed CV1 cells. The summary of the genetic changes targeted by different anticancer genes is shown in table 5. Some anticancer genes can target more than one genetic change associated with transformation, and they include clones 3D5 and

4F6. Moreover, some anticancer genes were unique in targeting only one genetic change, such as 4D9 and 4E10 against recombinant E1A. Information on individual candidate genes is shown in table 6. Candidate genes 3A3, 4E11 and 4E8 are not targeting any of the known genetic changes asssociated with transformation of HEK293T cells.

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Table 5. Summary of all 22 Anticancer genes against different genetic changes

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Table 6. Functions of 22 Anticancer genes.

3.5 Discussion

The 377 apoptosis inducers were isolated by a former colleague in HEK293T cells (Unpublished data). Taking advantage of our understanding of the genetic changes associated with transformation of HEK293T cells, we hypothesised that some of these apoptosis inducers could target these transformation scenarios leading to cell death of HEK293T. Only those inducers that trigger cell death only in transformed cells fall into the category of anticancer genes. In this genetic screen, we identified 22 anticancer genes that have no cytotoxic effect upon transfection in normal cells, namely CV1 cells, but target a range of genetic changes associated with

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transformation occuring in HEK293T cells identified using CV1 cells transformed by different oncogenes. These 22 anticancer genes can induce cell death with different efficiencies in different transformation scenarios in the transformed CV1 cells. Transformation driven by c-Myc elicits the most promising cell death effect for 16/22 anticancer genes, suggesting that c-Myc is very likely the common genetic change targeted by these 16 anticancer genes. Interestingly, some anticancer genes can target more than one genetic change associated with transformation and some genes are unique in targeting only one transformation scenario of those examined.

More importantly, three anticancer genes failed to induce cell death in the transformation scenarios tested. These could potentially target other transformation scenarios operating in the in

HEK293T cells used.

CV1 normal cells were used in the study of ORCTL3, which was a new anticancer gene isolated in our lab, and which showed no cytotoxic effect in CV1 cells. The transformed CV1 cells were generated by constitutively overexpressing individual oncogenes or mutated tumour suppressor genes. The advantage of our approach was establishing an isogenic cell type which constitutely expressed the gene of interest and the genetic background could be altered according to the function of the introduced gene.

In this genetic screen, a commerically available transfection reagent Xfect was used in the screen of CV1 cells because in this cell type it provided a good transfection efficiency with minimal toxicity compared to other commerically and non-commerically available transfection reagents. In the transfection protocol of Xfect, optimem medium was used instead of complete medium with 10% FCS. This protocol was adopted from the previous study using the calcium phosphate transfection approach in CV1 cells (ref.324). By using optimem medium, no significant

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toxic effect was observed but protein expression levels were significantly enhanced. This was indeed very important because one of most crucial features of an anticancer gene is not to induce cell death in normal cells. Under the determined optimal transfection conditions, such high protein expression levels could potentially raise the threshold and criteria of screen for the anticancer genes, ultimately leading to fewer potential anticancer candidate genes being isolated.

This would ensure the quality of genetic screen and candidate genes that meet the strict criteria for being defined as anticancer genes.

In the screen, the in vitro CPRG cell death assay was employed to efficiently distinguish the negative and positive controls under the optimal transfection condition. CPRG ratio offered better statistical power in quantifying cell death. CPRG ratio represents a proportion of dead cells within the total transfected cell population. Since we started with a relatively large number of candidate genes known as apoptosis inducers, we employed the ultra-pure DNA preparation method known as silica oxide DNA isolation. This method enabled us to obtain high quality plasmid DNA and yield, but the drawback of this approach was the presence of small traces of silica oxide that could interfere with the transfection leading to potential false positive readouts. As a result, I had to use the commerically available DNA isolation kit Miniprep to obtain pure DNA for the validation screen in HEK293T, in CV1 normal cells in the primary screen and a range of transformed CV1 cells in the secondary screen. Moreover, with the silica oxide DNA isolation protocol I performed the screen at least twice in each round for each candidate gene, to ensure consistent data. For the selection criteria of candidate genes, as the first step for the general screen with silica oxide

DNA isolation appraoch I included as many candidate genes as possible so I used the upper limit of standard deviation of CPRG ratio for the negative control plasmid transfected cells as a selection threshold. Hence, only those candidate genes showing CPRG ratio equal or less than

CPRG ratio of cells transfected with negative control plasmid were considered as the anticancer 125

genes. In the validation step with commerically available miniprep approach, we used the student t-test in the screen to verify potential candidate genes. We also applied the same statistical test in the screen for checking the genetic changes which these anticancer genes were targeting.

Twenty-two anticancer genes were identified from this genetic screen as they have shown to be able to trigger the inability of triggering cell death in CV1 normal cells. In normal cells, the second signal created by either overexpression of oncogenes or mutated tumour suppressor genes is missing. The first activation signal created by individual anticancer genes alone cannot cause the conflict of signals leading to cell death. When the second signal is present, conflict of signals occurs so that transformed cells undergo cell death. I generated isogenic cells, namely CV1 normal cells stably transfected with one gene, which could be an oncogene or dominant negative version of tumour suppressor gene or shRNA construct targeting one particular tumour suppressor gene. There are a number of reasons why we used CV1 cells and the main reason is that it is considered as a “clean” system in which there is no defect in tumour suppressor genes or overexpression of oncogenes that would drive cellular transformation. In such a

“known” genetic background, I believe that the isogenic cells created represent a robust system that allows me to truly isolate anticancer genes. By utilising the concept of synthetic lethality, we performed genetic screens to identify the genetic changes associated with transformation that are targeted by these anticancer genes. This is unlike the conventional approach in two ways. Firstly, we performed a genetic screen with gain of function, an activating signal is induced by introducing and overexpressing an exogenous gene to the cells, whereas the conventional synthetic lethality approach employed loss of function genome-wide screens utilising siRNA or shRNA to specifically knockdown individual genes. Secondly, we used a

“clean” cell system, namely normal cells and isogenic cells based on their parentally normal cell genetic background, whereas the conventional approach uses a cancer cell line and its 126

isogenic cancer line created by stable knockdown for the screen. There are a number of advantages in performing this genetic screen this way. The activating signal by overexpressing an exogenous gene cannot be functionally compensated or replaced by any endogenous gene in this gain of function screen.However, in the loss of function screen, a targeted gene or gene product could be functionally compensated or replaced by other endogenous genes. In addition, the actual knockdown level of a targeted gene leading to synthetic lethal effect is a concern in the loss of function screen.

Table 6 lists the transformation scenarios which individual anticancer genes can target. These transformation scenarios are based on the current understanding of genetic backgrounds in

HEK293T cells, mainly focused on E1A and E1B55k. However, three anticancer genes 4E8, 3A3 and 4E11 are not targeting any of the known transformation mechanisms associated with E1A and E1B55k. This suggests that there are other genetic changes present in HEK293T cells these three anticancer genes may target. SV40 Large T antigen, which is not included in the genetic screen, could be the potential transformation driver these three anticancer genes can target, because SV40 large T antigen is known to suppress the function of well-known tumour suppressor genes, including Rb and p53. There is evidence that SV40 large T antigen can inhibit other molecules such as p300, CBP, phosphatase PP2A, to contribute to transformation (ref.323).

I know from previous work in our lab that H-ras is not mutated in HEK293T cells. As a consequence, activating mutant H-ras (G12V) transformed CV1 cells were used as a control transformed cell in this study with the expectation of not finding an anticancer gene against activating mutation of H-ras. Surprisingly, there were 9 anticancer genes isolated and identified against H-ras driven transformation and all of these 9 genes also targeted c-Myc driven transformation. Therefore, the only possible explanation is that these 9 genes could target c-Myc upregulated by the activating mutation of H-ras. Indeed, Ras mutation has been shown to 127

transform cells through many different pathways, one of which is to activate ERK mediated upregulation of c-Myc (ref.327)

Most of these putative anticancer genes identified do not have a defined role in cancer or apoptosis.However, a few of them are known as tumour suppressor genes and they include

TMEFF2 (gene 1A3), HYAL2 (gene 2H3), Fibulin 5 (gene 1B8). In my data, they also exhibited strong effect as tumour suppressor. More validation in other normal cell lines is needed to confirm that they do not show cytotoxic effect so that they can fall into the category of

“anticancer gene”. There is little information about the signalling pathways and mechanisms mediated by these 22 anticancer genes. Even for those genes with known function, there is no connection to apoptosis pathways or genetic changes associated with cellular transformation in previous studies. However, in my studies of these genes that have been identified with a putative novel function as anticancer genes which specifically kill transformed cells when ectopically overexpressed. Their new roles connect to cell death pathways in cancer. For example, ORCTL3, the gene previously isolated in our lab, was initially identified as a cation/anion transporter but its anticancer role in targeting SCD-1 has no relation to its initially identified role as a transporter.

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Chapter 4 Functional validation of 22 putative anticancer genes

4.1 Background

The twenty-two putative anticancer genes were isolated in HEK293T cell in the first cellular model, which is a transformed human embryonic kidney cell line, and showed strong ability to induce apoptotis in HEK293T cells but not normal CV1 cells. CV1 cells were used as a normal cell model in this study because they are not transformed and are genetically close to human cells, and the results obtained are thus relevant to human cancer. After identifying the putative 22 candidates as potential anticancer genes, the next step was to assess their

“anticancer” function, namely cytotoxic effect in cancer cells, and to evaluate their effect on cell proliferation and the long-term impact on cancer cell growth.

4.2 Cell death validation in different cellular systems

Transformed cells with different genetic backgrounds were used to assess the “anticancer” effect, namely the apoptotic effect in transformed cells. Thus, two well-known and the most commonly used cancer cell lines were used to test for cell death upon transfection of individual anticancer genes. HeLa is a human cervical cancer cell line and MCF-7 a human breast cancer cell line. It was found that all 22 genes are positive in Hela cells and only gene

4F6 failed to induce apoptotis in MCF-7 cells (Fig.25).

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(A) MCF-7 cells

(B) Hela cells

Figure 25. Validation of anticancer function for all putative anticancer genes in MCF-7 and some c-Myc specific anticancer genes in Hela cells. 1 µg of plasmid DNA for each gene was transfected to Hela and MCF- 7 cells by effectene and Xfect transfection reagent respectively according to the transfection protocol provided by manufactuers, followed by 8 hours incubation and change of medium. Upon 48 hours post-transfection, all transfected Hela and MCF-7 cells were harvested, collected and stained with cell death early stage marker DIOC6 and late stage marker propidium iodide (PI) for 1 h. (A) Cell death measurement sin MCF-7 cells were performed upon transfection of 22 putative anticancer genes. (B) Cell death measurement in Hela cells upon transfection of 16-Myc specific putative anticancer genes. Flow cytometry was used to quantify cell death by FL-1 and FL-3 channels. This experiment was repeated at least three times to verify the effects. (The experiment data

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shown in figure A for MCF-7 cells was shared with a fellow PhD student colleague Motasim Masood because the Motasim Masood and Qize Ding both made equal contribution to this experiment)

4.3 Cell proliferation

Since these 22 putative anticancer genes are able to eradicate transformed cells at 48 h upon transfection, it is likely they are inducing apoptosis and blocking cell proliferation.

These 22 putative anticancer genes were then used to evaluate their impact on cell proliferation. The MTT assay was performed to measure cell proliferation on MCF-7 cells upon transfection of individual anticancer genes. The yellow MTT is reduced to form purple formazan by the mitochondria of living cells, this reduction can happen only when mitochondrial reductase enzymes are active, the degree of conversion of colour is thus directly proportional to the number of live cells. In the results, transfected cells with individual anticancer genes all showed different degrees of reduction in cell proliferation compared to control cells transfected with GFP only. This data from cell proliferation assays showed is consistent with the results obtained in cell death measurement by flow cytometry analysis

(Fig.26).

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Figure 26. Representative data evaluating 22 Anticancer genes on cell proliferation of MCF-7 cells. Cell proliferation was measured by MTT assay. 30µg of each gene candidate including negative controls such as GFP, luciferase and PcDNA3.0, positive controls such as Caspase 2 (Cas2), Caspase 8 (Cas8) and tBid were transfected into MCF-7 cells by Xfect transfection reagents for 6 h in a 10-cm dish. Then, MCF-7 cells were tryspinized and counted, 3000 cells were seeded into individual wells in triplicates with 100 µl/well in 96 well plates. After 4 hours, once cells had attached to the plate, 20µl of 5mg/ml MTT was added to each well, followed by incubation for 3.5 hours at 37°C. Then, cell medium was removed, and 150µl of MTT solvent was added to each well. The plate was placed on shaker for 15 minutes and absorbance was measured at 590nm in plate reader. Then, cell proliferation at different time points were determined to monitor the effect of these Anticancer genes onto MCF-7 cells. (This MTT assay was performed with a fellow PhD student colleague Motasim Masood so the data is shared between Motasim Masood because Motasim Masood and Qize Ding both made equal contribution to this experiment) 132

4.4 Long term impact on cell growth

The next question that needed to be addressed was the long term impact on cell growth. We knew that these genes were capable of inducing cell death in HEK293T, Hela and MCF-7 cells. Transformed cells have the ability to form colonies over a period of time in vitro. The clonogenic assay is used to assess cell survival based on the ability of transformed cells to form colonies. Thus, I used this approach to indirectly validate the cell death/senescence- inducing effect in transformed cells. Transiently transfected MCF-7 cells with individual anticancer genes were seeded in plates and cell survival determined by colony formation after a few days. The results were that 16 genes showed significantly reduced numbers of colonies compared to controls and the other 6 genes did not exhibit any reduction in colony formation (Fig. 27).

Figure 27. Clongenic assay to assess the long term impact of 22 putative Anticancer genes to cell growth of MCF-7 cells. 30µg of each gene candidate, also including negative controls such as GFP, luciferase and PcDNA3.0, positive controls such as Caspase 2 (Cas2), Caspase 8 (Cas8) and tBid were transfected to MCF-7 cells by Xfect transfection reagents for 6 hours in a 10-cm dish. Then MCF-7 cells were tryspinized and counted, 1000 cells were seeded into individual well in triplicates with 1ml each well in 6 well plate. Medium was replaced every two days. After 10 days of incubation, cell medium was removed and cells were washed three times with

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PBS, followed by 15 minutes incubation with 4% paraformaldehyde to fix the cells. Then, fixed cells were washed with PBS three times, and 0.5% crystal violet used to stain the cells for 20 min at room temperature, followed by soaking in tap water for washing. The plate was left and exposed to air overnight. Pictures of the plates were taken the following day. Then, the crystal violet staining of cells from each well was solubilized with 1ml of 10% acetic acid on the shaker for 20 minutes, absorbance was measured for each plate at a wavelength of 590nm. (This clonogenic assay was performed with a fellow PhD colleague Motasim Masood so the data is shared between Motasim Masood and Qize Ding).

4.5 Characterisation of c-Myc overexpressing Cells

Among the putative 22 anticancer genes, most were able to target genetic changes associated with cellular transformation. The cell death inducing effect against the well-known oncogene c-Myc was strongest compared to other genetic changes. C-Myc is a transcription factor involved in regulation of 15% of human genes (ref.181) and it plays a very important role under normal physiological condition. However, there are numerous human cancers known to overexpress c-Myc, supporting its role in cellular transformation, tumorigenesis and cancer development. Currently, small molecule inhibitors of c-Myc have significant toxicity to normal cells and, for this reason, are not used in cancer treatment. Thus, utilising the synthetic lethality approach has the potential to identify a molecular target whose inhibition will cause cell death in cancer cells with overexpression of c-Myc.

4.5.1 Change of cell morphology

In my previous screens, 16 anticancer genes targeted genetic changes associated with overexpression of c-Myc. More importantly, the cell death effect is very strong and reproducible. Therefore, I studied the effect of anticancer genes on c-Myc overexpression.

First, I characterised the phenotype of c-Myc overexpressing CV1 cells by looking at the morphology of the cells in comparison to the parental CV1 normal cells. Normal CV1 cells

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have a thin and elongated appearance, whereas c-Myc overexpressing CV1 cells are round and bigger in size (Fig. 28).

(A) CV1 normal cells

(B) CV1 c-Myc overexpressing cells

Figure 28. Comparison of the morphology between normal CV1 cells and c-Myc overexpressing CV1 cells. The phase and contrast pictures were taken in different areas of cell flask by light microscope. (A) CV1 normal cells. (B) c-Myc overexpressing CV1 cells. C-Myc overexpressing CV1 cells were generated by transfecting pcDNA3.0 expression plasmid encoding c-Myc using Xfect transfection reagent, at 48 h post transfection, pools of transfected cells were selected using 2.5mg/ml G418 antibiotic for approximately 2 weeks. After selection, cells were then cultured and maintained in 1.5 mg/ml G418 containing full medium.

4.5.2 Loss of cell - cell contact inhibition

One important characteristic of cellular transformation is the loss of cell - cell contact inhibiton. Normal cells continue to proliferate until they encounter other cells and then cell proliferation stops. However, in vitro cancer cells tend to proliferate and divide in an uncontrolled manner even when they encounter other cells, then cancer cells will grow on top of each other and form more than one single cell layer. This important characteristic was

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also observed in c-Myc overexpressing cells (Fig. 29). At week 4, c-Myc overexpressing

CV1 cells formed more than one layer of cells in the well but CV1 normal cells showed only one compact layer in the well. This strongly suggested that these Myc overexpressing CV1 cells lose cell-cell contact inhibition.

(A)

Figure 29. Loss of cell-cell contact inhibition in c-Myc overexpressing CV1 cells. Phase contrast pictures of cells were taken by light microscope weekly for 4 weeks. c-Myc overexpressing CV1 cells were generated by transfecting c-Myc expression plasmid in CV1 normal cells. Xfect transfection reagent was used in this experiment, and selection started at 48 h post-transfection for 2 weeks. For the G418 antibiotic resistance gene containing construct, we used 2.5 mg/ml of G418 for selection. After the selection process, surviving cells was collected and expanded for further use. These stably transformed cells were maintained in long term culture with 1.5 mg/ml of G418.

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4.5.3 Cell cycle and cell proliferation c-Myc is known to drive the cell cycle by regulating molecules involved in the cell cycle and proliferation. In order to confirm these roles in our c-Myc overexpressing CV1 cells, I compared cell proliferation between CV1 normal cells and c-Myc overexpressing CV1 cells.

By performing the MTT assay, c-Myc overexpressing CV1 cells were shown to proliferate significantly faster than CV1 normal cells (Fig.30A). In serum-free medium, c-Myc overexpressing CV1 cells proliferated much faster than CV1 normal cells (Fig.30B). This result is in line with a previous study on the role of c-Myc in supporting cell proliferation in the absence of serum (ref.328)

(A) (B)

Figure.30. c-Myc overexpressing CV1 cells proliferate faster than CV1 normal cells, and continue to grow in FCS negative medium. 5000 normal CV1 and cMyc overexpressing CV 1 Cells were seeded in 96-well plates in triplicate in complete DMEM. Cell proliferation was determined by MTT assay at each time point. 10 mg/ml of

MTT solution was added to 100 l of DMEM full medium and incubated for 3.5 hours. Then, MTT containing medium was removed and MTT solvent was added, followed by 15 min shaking and absorbance was finally measured by plate reader at the wavelength of 590 nm. (A) Cell proliferation MTT assay was performed to compare cell proliferation beteen normal CV1 and c-Myc overexpressing CV1 cells in FCS containing medium. (B)

Cell proliferation MTT assay was performed to compare cell proliferation beteen normal parental CV1 and c-Myc overexpressing CV1 cells in FCS negative medium. Results are representative of at least three independent experiments. Data includes means and standard deviation.

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From previous studies on cell proliferation, c-Myc overexpressing CV1 cells demonstrated faster proliferation in both serum positive and negative medium. c-Myc is known to drive cell cycle progression from G1 to S phase by upregulating ceel cycle regulators such as cyclin

D1 (ref.172). I hypothesised that c-Myc overexpressing CV1 cells would have a relatively high percentage of cells in the S phase of the cell cycle. By performing the cell cycle distribution analysis, c-Myc overexpressing CV1 cells showed a significant increase in the percentage of cells in S phase, and Myc stable knockdown MCF-7 cells showed significant reduction in the percentage of cells in S phase (Fig.31). Thus, in this experiment the correlation between cell proliferation and cell cycle was present, the faster cell proliferation being driven by overexpressing c-Myc.

(A) CV 1 cells (B) MCF-7 cells

Figure 31. Cell cycle distribution of CV1 normal cells and c-Myc overexpressing CV1 cells, Wild type MCF-7 and stable Myc knockdown MCF-7 cells. The same number of normal CV1 and c-Myc overexpressing CV1 cells, wild-type MCF-7 and stable Myc knockdown MCF-7 cells were seeded in 24 well plates and left for 72 hours in complete DMEM, were then harvested, fixed and cell death was quantified by PI staining and analysed by flow cytometry. Analysis of cell cycle for G1 phase, S phase and G2/M phase was conducted in flowjo 7.6.3. Results are representative of at least three independent experiments. Data includes means and standard deviation.

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Moreover, based on our observation under the light microscope, c-Myc overexpression in

CV1 cells did not exhibit significantly increased apoptotic cells under normal cell culture conditions. This could suggest that c-Myc overexpression promotes cell survival instead of cell death in this cellular model.

4.5.4 Increased NF-B activity

Furthermore, NF-B, a well-known transcription factor, plays an important role in a number of cellular functions, including promoting cell survival in cancer. Some previous studies suggest that c-Myc can block NF-B activity and NF-B could be one of the downstream targets of c-Myc (ref.329). However, c-Myc overexpression had no effect on the level of NF-

B protein expression (unpublished data). Therefore, it was interesting to investigate the effect of c-Myc overexpression on NF-B activity in CV1 and MCF-7 cells. We performed a co-transfection experiment in which an NF-B reporter plasmid with a NF-B binding site in the promoter region was used reporting via GFP fluorescence. This indirect assay showed that NF-B activity was significantly higher in c-Myc overexpressing cells than CV1 normal cells (Fig.32A). In MCF-7 cells, stable c-Myc knockdown MCF-7 cells showed significant decreased level of NF-B activity compared to wild type MCF-7 cells (Fig. 32B). This indicated that c-Myc overexpression increased NF-B activity given that NF-B protein expression level remained the same. Furthermore, since NF-B has been suggested to be a downstream target of c-Myc (ref.329), upregulation of NF-B activity induced by c-Myc overexpression could play an important role in promoting cell survival. Thus, the NF-B inhibitor was used to treat c-Myc overexpressing CV1 cells and to assess cell death by flow cytometry. The results showed that c-Myc overexpressing CV1 cells were more sensitive to

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the NF-B inhibitior and significantly increased cell death was observed by flow cytometry

(Fig.32B). This experiment confirmed that NF-B activity was upregulated by c-Myc overexpression and c-Myc overexpressing CV1 cells were more sensitive to NF-B inhition leading to cell death (Fig.32C)

(A)

(B)

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(C)

Figure 32. c-Myc overepxressing CV1 cells show higher NF-B activity and are more sensitive to NF-kB inhibitor leading to cell death. (A) Both normal CV1 and c-Myc overexpressing CV1 cells were co-transfected 500ng of control plasmid pcDNA3 along with 500 ng of control plasmid β-gal or an NF-kB reporter plasmid

(pNFkB-hrGFP). The NF-B reporter plasmid has a NF-B promoter binding site fused with the GFP gene. NF-

B activity is reflected in the percentage of shifting cell population expressing GFP compared to control. In this experiment, cells transfected with pcDNA3.0 and β-gal served as the control, whereas cells transfected with pcDNA3.0 and reporter plasmid were the experimental sample. (B) NF-B activity level in both Scramble plasmid stable MCF-7 and stable cMyc knockdown MCF-7 cells. The percentage of shift in the cell population has been normalised against the transfection efficiency. (C) NF-B inhibitor (N-Oleoyldopamine) was used to test the sensitivity of c-Myc overexpressing CV1 cells in response to NF-B inhibition by measuring cell death. A range of concentrations of NF-B inhibitor was used in the treatment of both CV1 normal cells and c-Myc overexpressing CV1 cells for 24 hours. Then all cells were harvested and stained with propidium iodide for 30 minutes. Cell death was quantified by flow cytometry and signals detected in FL-3 channel. Analysis of cell death was performed in flowjo 7.6.3. (This data is produced by Nazhif Zaini and shared with me as he was under my supervision, as a master student)

4.5.5 Increase in reactive oxygen species (ROS) levels in c-Myc overexpressing cells c-Myc overexpression has been shown to induce DNA damange before S phase through enhancement of ROS levels without triggering apoptosis (ref.330). I therefore expected to

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see an enhancement of ROS levels in c-Myc overexpressing CV1 cells. ROS levels were determined with the MitoSox reagent, which permeates live cells and selectively targets mitochondria. MitoSox is then quickly oxidised by superoxide, generating signals detected by flow cytometry. The results showed significantly increased ROS levels in c-Myc overexpressing CV1 cells in contast to CV1 normal cells (Fig.33A). This ROS level could be tolerated in c-Myc overexpressing cells and the cellular threshold level of ROS was still not reached as no induction of apoptosis was observed. When ROS inducing chemicals were used with c-Myc overexpressing CV1 cells, more cell death was observed than with CV1 normal cells (Fig. 33B). These findings suggest that c-Myc upregulates ROS levels in the

CV1 cellular system but is not enough to reach the threshold level at which cells undergo apoptosis. This finding on Myc elevating ROS levels is in line with the findings in previous studies.

(A)

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(B)

Figure 33. c-Myc overexpressing CV1 cells show higher levels of ROS and are more sensitive to ROS inducing drugs compared to CV1 parental cells. (A) Both CV1 normal and c-Myc overexpressing CV1 cells were harvested and collected in PBS, followed by staining of 5 µM of MitoSox for 10 minutes at 37°C. Stained cells were quantified by flow cytometry. In addition, the non-stained cells were used to quantify the background. Flow cytometry data was analyzed by flowjo 7.6.3. (B) Two well-known ROS inducing chemical compounds were used to treat both CV1 normal and c-Myc overexpressing CV1 cells for 24 hours. A range of concentrations of two compounds were used to assess the cell death by phase and contrast pictures. (This data is produced by Nazhif Zaini and shared with me as he was under my supervision as master student)

4.6 Validation of c-Myc induced specificity of cell death

In the genetic screen, the previously established in vitro CPRG assay for cell death was used to screen for putative anticancer genes, as well as the genetic changes that these putative anticancer genes target. However, this in vitro cell death assay was developed and established in house and is not a commonly used method for assessing cell death. In order to validate the cell death inducing effect of these genes, I needed to employ a more commonly used cell death assay, namely PI staining for detecting late stage cell death. c-

Myc overexpression in cells was reported to sensitise cells for cell death by upregulating pro-

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apoptotic genes (ref.331). Therefore, it was very important to address this question before proceeding to the next stage of validation. Firstly, I transfected both negative control and positive control expression plasmids individually into both normal CV1 and c-Myc overexpressing CV1 cells. After 48 hours, cell death was measured by PI staining and flow cytometry analysis. There was no difference in the extent of cell death induced in both cell types and this meant that c-Myc overexpression did not sensitise CV1 cells to cell death upon transfection (Fig.34A, B).

(A) (B)

Figure 34. Sensitivity of c-Myc overexpressing CV1 cells to cell death remains the same. To compare normal CV1 with c-Myc overexpressing CV1 cells under the same optimal transfection conditions in the presence of optimem medium, 1 µg of negative control expression plasmids such as PcDNA3 and luciferase, positive control expression plasmids such as RIP1, p20, Caspase 8 and tBid were transfected to both normal CV1 and c- myc overexpressing CV1 cells and were then harvested at 48 h post transfection. Cells were collected and stained with propidium iodide for 30 minutes. Cell death assessment was conducted by flow cytometry and FL-3 channel was used. Flow cytometry result was conducted with flowjo 7.6.3. (A) Cell death measurement in normal CV1 normal (B) Cell death measurement in c-Myc overexpressing CV1 cells.

Once the sensitivity of cell death upon transfection in both cell types was confirmed, the 16 putative anticancer genes against c-Myc upregulation were transfected individually into both

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normal CV1 and c-Myc overexpressing CV1 cells and cell death measured by PI staining 48 h post-transfection by flow cytometry. The c-Myc specific cell death inducing effects of these

16 anticancer genes were again confirmed (Fig.35)

(A) CV1 normal cells

(B) CV1 c-Myc overexpressing cells

Figure 35. Validation of c-Myc specificity of the 16 putative anticancer genes in CV1 normal and c-Myc overexpressing cells. 1 g of plasmid DNA for each gene was transfected to both normal CV1 and c-Myc overexpressing CV1 cells in 300 µ l of Optimem medium. After 48 hours of transfection, cells were harvested, collected and stained with propidium iodide for 30 minutes. Cell death was measured by flow cytometry and FL-3 channel was used. Flow cytometry data was analysed with flowjo 7.6.3. (A) Cell death measurement in normal CV1 cells. (B) Cell death measurement in c-Myc overexpressing CV1 cells.

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Similarly, c-Myc specificity was tested and confirmed in MCF-7 cells, which is a different cellular system with a completely different genetic background. MCF-7 is a breast cancer cell line, which is known to overexpress c-Myc, so it is an ideal cell model for this study. Thus, c-

Myc stable knockdown MCF-7 cells was established and used in the study of c-Myc specificity (Fig. 36A). Cell death was measured using the standard staining method with

DIOC6 and PI at 48 h post-transfection followed by flow cytometry. The results showed that c-Myc knockdown cells transfected with individual c-Myc specific anticancer genes exhibited significally reduced degrees of cell death upon transfection (Fig. 36B). This result again confirmed the c-Myc specificty of these 16 anticancer genes in a cellular system other than kidney CV1 cells.

(A)

(B)

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Figure 36. Validation of c-Myc specificity of the 16 Anticancer genes in MCF-7 cells. MCF-7 control cells (MCF-7 ctrl) and MCF-7 c-Myc stable knockdown cells (MCF-7 c-Myc KD) were generated by transfecting the shRNA scramble construct and shRNA construct against c-Myc into MCF-7 cells respectively by Xfect transfection reagent in complete DMEM medium for 8 hours, followed by change of medium and incubation for an additional 40 hours. At this point, 1 µg/ml of puromycin in complete medium was used for selection for around 6 days. After that, 0.4 µg/ml of puromycin was used for long-term maintainance. Both the shRNA scramble and shRNA against c-Myc contain a puromycin resistance gene. (A) Stable knockdown of c-Myc was confirmed by RT-qPCR analysis of c-Myc mRNA levels in both MCF-7 c-Myc stable knockdown cells and MCF-7 scramble control stable cells. C-Myc gene expression was normalised against the house keeping gene L19. (B) Evaluation of c-Myc specificity of 16 Anticancer genes in MCF-7 cells. 1 µg of plasmid DNA for each gene was transfected to scramble stable and c-Myc knockdown stable MCF-7 cells by Xfect transfection reagent, followed by 8 hours incubation and change of medium. 48 h post-transfection, all transfected MCF-7 cells were harvested, collected and stained with cell death early stage marker DIOC6 and late stage marker propidium iodide (PI) for 1 hour. Flow cytometry was used to quantify cell death by FL-1 and FL-3 channels. This experiment was repeated at least three times to verify the effects.

4.7 Expression levels of c-Myc specific putative anticancer genes in relation to c-Myc expression

Hitheto, the 16 putative anticancer genes had been shown to induce c-Myc specific cell death and a number of them have previously been shown to be regulated by c-Myc, but there was no expression profile and functional study shown of these genes in relation to their regulation by c-Myc. Hence, it was important to know the endogenous expression levels of these genes in response to c-Myc expression level changes. Real time-quantitative PCR

(RT-qPCR) was performed to detect the endogenous gene expression levels of individual anticancer genes in isogenic cell lines with c-Myc overexpression. Although the experiment was only performed once, I could observed the trend that most of genes were showing inverse relation to c-Myc expression, apart from genes 4F4 and 3B3 (Fig.37).

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Figure 37. Study on correlation of gene expression level of individual Anticancer genes to c-Myc expression in both wild type MCF-7 and c-myc stable knockdown MCF-7 cells. Cells were transfected with 5 µg of individual Anticancer gene expression plasmids by Xfect transfection reagent in the 6-well plate. At 24 h post-transfection, total RNA was isolated following RNA extraction kit instructions. RNA then underwent reverse transcription to produce cDNA synthesis. cDNAs were used as templates for qPCR. For each gene, one forward primer and one reverse primer were used for gene amplification with SYBR green, and relative gene expression was normalised against the house keeping gene L19. Standard curve was established for each gene and quantity mean corresponding to CT value was calculated based on the standard curve in the SDS2.3 programme.

Overall, I observed an inverse correlation with c-Myc expression of expression for most of the putative anticancer genes tested. However, the interactions and pathways to explain these effects are still unknown. For example, fibulin 5 (gene 1B8) and TMEFF2 (gene 1A3) have been reported to negatively correlate with expression of c-Myc in the literatures

(ref.332).

4.8 Discussion

The 22 putative anticancer genes were able to eliminate transformed cells in both HEK293T cells and CV1 transformed cells. However, these two cell systems are both derived from kidney of different species. Therefore, their “anticancer” features needed to be validated in transformed cells derived from different human tissues. To this end, I used two commonly used cancer cell lines, namely Hela and MCF-7 cells for a validation study. In MCF-7 cells,

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the 22 putative anticancer genes again showed strong and promising “anticancer” functions by efficiently killing transformed cells. In Hela cells, all the tested 16 anticancer genes demonstrated the same effect. Moreover, the cell proliferation MTT assay indirectly reflected the extent of cell death/senescence in transfected MCF-7 cells by measuring the relative cell proliferation. There was a stronger cell death effect and less numbers to proliferate leading to slower proliferation rates. The MTT cell proliferation assay further supports the cell death measurement by flow cytometry analysis, because cells transfected with individual anticancer genes showed significant decreases in cell proliferation within 72 hours of transfection. However, another approach, namely clongenic assay, which assesses longer term impact on cell growth and indirectly determines the degree of cell death in transfected

MCF-7 cells, does not fully correlate with the results in cell death measurement by flow cytometry analysis. The clonogenic assay indicated only half of the genes had a negative effect on the long-term growth of transformed cells. There are a number of reasons to potentially explain why the clonogenic assay did not demonstrate the correlation with cell death as measured by flow cytometry and cell proliferation. Firstly, colony formation was assessed at 10 day-post transfection, by which point, the products of these anticancer genes were not present at very high levels, diminishing the effect at day 10. Expression levels from transient transfection started to decline from 72 h post transfection. Usually, clonogenic assay is used to assess the impact of small chemical compounds or stablely transfected genes on cell growth based on the colony formation. In these experiments, clonogenic assays may not have been the ideal experiment to determine the longer-term impact on cell growth. Senescence will also play a role in cell proliferation in longer terms. Secondly, from a technical perspective, this assay quantifies absolute colony formation, so the number of cells seeded in day 1 of the experiment is very critical.

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Furthermore, c-Myc overexpressing cells have shown the strongest and most promising cell death effect upon transfections of the 16 putative anticancer genes. Only a few synthetic lethal targets have been found against c-Myc upregulation in previous studies. We believe that finding more potential synthetic lethal targets against c-Myc overexpression could be interesting and contributes towards development of a novel cancer therapy. By characterising c-Myc overexpressing CV1 cells using different approaches, we found this cell type was distinct from the parental normal CV1 cells. Firstly, our c-Myc overexpressing CV1 cells showed very similar morphological changes that is in line with the previous study on the impact of c-Myc upregulation, involving change of cell size and shape (ref.333). Secondly, loss of cell-cell contact inhibition is known as one of important features of cancer cells. Our established transformed CV1 cells including c-Myc overexpressing CV1 cells clearly demonstrated this feature. Thirdly, c-Myc is known to upregulate genes required to drive cells to enter S phase of cell cycle. Our study showed that c-Myc overexpressing cells proliferated faster than CV1 normal cells. In addition, c-Myc overexpressing CV1 cells were able to grow and proliferate in the absence of serum in the cell culture medium, whereas

CV1 normal cells are unable to do so. Again, this phenomon had also been reported previously (ref.328). Surprisingly, c-Myc overexpressing CV1 cells showed a significant increase of S phase in cell cycle distribution compared to CV1 normal cells, whereas Myc knockdown MCF-7 cells showed significant reduction of S phase in cell cycle distribution in contrast to wild-type MCF-7 cells. Fourthly, NF-kB activity was upregulated and inhibition of

NF-B sensitises c-Myc overexpressing CV1 cells to cell death. NF-B is known to be aberrantly activated in a range of human cancers and promotes cancer cell survival by upregulating anti-apoptotic genes (ref.334,335). My data suggests that NF-B plays an anti- apoptotic role in supporting survival of c-Myc overexpressing CV1 cells. NF-B might serve as a potential synthetic lethal target against c-Myc upregulation but more validation is

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needed to support this concept. Previous studies showed impaired function of NF-B by c-

Myc upregulation, which is not in line with our findings described here. Furthermore, ROS levels are elevated significantly in c-Myc overexpressing CV1 cells, which is in line with the preivous study. By using ROS inducing chemical compounds, c-Myc overexpressing CV1 cells were more sensitive to cell death, since induced ROS levels exceeded the threshold for these cells. This could induce further DNA damage responses (DDR) that in turn could trigger cell death (ref.336).

More importantly, the c-Myc specific effect of the 16 putative anticancer genes was demonstrated with a commonly used approach, namely PI staining in CV1 cellular system followed by flow cytometry analysis. Again, this was verified in a different cellular system, in

MCF-7 cells, with the widely used method for staining cells with DIOC6 and PI to determine cell death. Future work could include a different transformed cellular system with known c-

Myc upregulation to further validate this c-Myc specific function.

Although Rt-qPCR to measure endogenous levels of the anticancer genes, was performed only once, most tested anticancer genes showed an inverse correlation with the expression level of c-Myc. Fibulin 5 (gene 1B8) and TMEFF2 (gene 1A3) have been shown to have an inverse relation with with expression of c-Myc in the previous studies. TMEFF2 is hypermethylated and its expression is negatively regulated by c-Myc gene expression because the TMEFF2 promoter has binding sites for c-Myc (ref.332). However, the mechanisms of how c-Myc regulates expression of fibulin 5 and the other putative anticancer genes remain unknown.

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These genes do show endogeous expression at relatively low levels in c-Myc upregulated cancer cells. Some of them are known as tumour suppressor genes. My data suggests that they do show strong effects as tumour suppressor in transformed cells. They could be the potential candidates of “ anticancer genes”. In order to meet strict requirement of

“anticancer genes”, more validation is needed to confirm that they do not show cytotoxic effect in normal cell line or primary cells.

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Chapter 5 Identification and validation of potential targets for c-Myc specific putative anticancer genes

5.1 Background

The c-Myc specificty of these 16 putative anticancer genes was characterised in two cell lines with different genetic backgrounds. The next step was to identify the target associated with the c-Myc specific activity. In the previous study on characterisation and target identification of ORCTL3, SCD1 was identified as the target of ORCTL3, because SCD1 was upregulated in a range of transformed cells. Moreover, the inhibition of SCD1 could lead to the death of transformed cells, while normal non-cancer cells would be spared. In my study, I applied the same logic of attempting to identify the anticancer gene target associated with genetic changes driven by c-Myc upregulation. It is anticipated that the target for the putative anticancer candidate gene would be (i) deregulated by c-Myc overexpression, (ii) contributing to cellular transformation and maintaining cell surivial and (iii) targeted by this anticancer gene to mediate c-Myc specific cell death. Therefore, I started with a number of anticancer genes against c-Myc upregulation based on our knowledge of them and hoped to find potential leads to identify the target.

5.2 Testing of inhibition of already known synthetic lethal targets

As previously mentioned, there are a few synthetic lethal targets against c-Myc upregulation, such as Aurora kinase A and B (ref.144), cyclin-dependent kinase 1 and 2 (ref.142,143) and

CSNK1E (ref.148). These molecules have been shown to be upregulated in c-Myc 154

overexpressing cells and inhibition of these molecules can selectively kill c-Myc overexpressing cancer cells in in vitro and in vivo models. So, as a starting point for identifying the potential targets for these 16 potential anticancer genes against c-Myc upregulation, I first tested these synthetic lethal targets in the CV1 cell system.

(A) Aurora kinase B inhibitor (AZD1152) (B) Aurora kinase A inhibitor (Aurora-A inhibitor I)

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(E) CDK inhibitor (Purvalanol A)

Figure 38. Cell death analysis of normal CV1 and C-Myc overexpressing CV1 cells upon inhibition of different already known c-Myc specific synthetic lethal targets by small molecule inhibitors. Different concentrations of different inhibitors were used to treat both CV1 normal and c-Myc overexpressing CV1 cells for 24 hours. Cells were then harvested, collected and stained with propidium iodide for 30 minutes. Cell death assessment was conducted by flow cytometry using FL-3 channel. Flow cytometry data was analysed with flowjo 7.6.3.

In the assay, only inhibition of synthetic lethal targets CSNK1E and CDKs showed significant cell death in c-Myc overexpressing CV1 cells compared with CV1 normal cells (Fig.38).

However, inhibition of CSNK1E also demonstrated significant cell death in CV1 normal cells at low inhibitor concentrations. In contrast, the 16 putative c-Myc specific anticancer genes did not induce cell death in normal CV1 cells even when at high expression levels. As a result, targets like CSNK1E would not be a suitable candidate to pursue in future studies of this kind. However, it would be reasonable to pursue CDKs, as inhibition of CDKs only leads to cell death in c-Myc overexpressing CV1 cells without any cytotoxic effects in CV1 normal cells.

5.3 Characterisation of Fibulin 5 (Gene 1B8)

The gene product of clone 1B8, Fibulin 5, is a secreted protein that has a number of functions in the extracellular matrix and a tumour suppressor role in a number of human

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cancers (ref.337,378). This gene was selected for further study because this is the only secreted protein among the 16 anticancer gene products. Potentially, Fibulin 5 could be further developed as a recombinant therapeutic protein for targeted therapy of c-Myc overexpressing tumours. I hypotheised that Fibulin 5 induces c-Myc specific cell death through targeting plasma membrane bound proteins or receptors, which could be potential targets of interest. A subset of integrins, including α5β1, αvβ3 and αvβ5, have been shown to bind to Fibulin 5 (ref.377,378). Hence, I used commercially available recombinant Fibulin

5, which is a full-length protein. This was added to Myc overexpressing CV1 cells. However, the recombinant Fibulin 5 protein failed to induce cell death at 48 h post-treatment, while transfection of, Fibulin 5 was able to demonstrate cell death inducing effects in c-Myc overexpressing CV1 cells (Fig.39).

Figure 39. Assessment of cell death in c-Myc overexpressing cells upon treatment of recombinant Fibulin 5 protein. Phase contrast pictures were taken for assessing cell death of cMyc overexpressing CV1 cells after 48 h post transfection of Fibulin 5 expression plasmid or treatment with recombinant Fibulin 5 protein. This experiment was performed in a 24-well plate format. For the incubation experiment, different concentrations of recombinant Fibulin 5 and vehicle control (BSA) were used to treat c-Myc overexpressing CV1 cells. For the transfection experiment, 1 µg of Fibulin 5 expression plasmid or 1 µg of GFP expression plasmid used as a control was transfected into c-Myc overexpressing CV1 cells, The commerically available transfection reagent Xfect was used under optimal transfection conditions.

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In parallel, I tested whether the cell death inducing effect could be abrogated if neutralising antibody against Fibulin 5 was added to cell medium at 33 h post transfection of Fibulin 5 expression plasmid. However, Fibulin 5 neutralizing antibody could not inhibit cell death of c-

Myc overexpressing cells induced by overexpressing Fibulin 5 gene (Fig. 40).

(A) Fibulin 5 transfection (B) Caspase 8 transfection

Figure 40. Cell death measurement of c-Myc overexpressing CV1 cells upon transfections of Fibulin 5. One µg of Fibulin 5 expression plasmid was transfected to c-Myc overexpressing CV1 cells using 0.3 µl of Xfect, different dilutions of control antibody (c-Myc ab) and Fibulin 5 antibody (Fibulin 5 ab) were added to cells at 33 h post-transfection and PI staining was used to assess the percentage of cell death at 48 h post tranfection by flow cytometry. As for the control, cells transfected with caspase 8 were also incubated with c-Myc antibody and Fibulin 5 antibody under the same experimental condition. Control antibody (c-Myc ab) was the isotype control for the Fibulin 5 antibody. FL3- channel was used to analyse the data and flowjo 7.6.3 for analysis. (A) Cell death assessment upon transfection of Fibulin 5 expression plasmid and at 33 h post-transfection, control antibody (c- Myc ab) and Fibulin 5 antibody were added and incubated with cells for additional 16 hours. (B) Quantification of cell death upon transfection of caspase 8 expression plasmid and at 33 hours post-transfection, control antibody (c-Myc ab) and Fibulin 5 antibody were added and incubated with cells for an additional 16 hours.

In the first experiment, I did not observed inhibition of cell death upon addition of recombinant Fibulin 5 protein. Then I tried to incubate recombinant Fibulin 5 with cells in a different way by coating the protein on the plate first before seeding the c-Myc

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overexpressing CV1 cells. The results showed again that recombinant Fibulin 5 protein failed to induce cell death in c-Myc overexpressing CV1 cells (Fig.41).

(A)

(B)

Figure 41. Prior coating with recombinant Fibulin 5 does not induce cell death in c-Myc overexpressing CV1 cells. In the 24-well plate, the same concentrations and amounts of recombinant Fibulin 5 and control protein (BSA) were coated onto a 24-well plate overnight at 4 °C for one day prior to cell seeding. On the following day, 20,000 cells were seeded and incubated in the presence of the coated recombinant Fibulin 5 protein for 24 h, followed by PI staining for 30 min and flow cytometry analysis of cell death. (A) Cell death assessment of c-Myc overexpressing cells at 24 h post-treatment with proteins by phase contrast microscopy. (B) Flow cytometry analysis of cell death at 24 h post-treatment with proteins.

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I next used two methods to incubate recombinant Fibulin 5 protein with c-Myc overexpressing cells, but did not observe any induction of cell death. Moreover, the neutralising Fibulin 5 antibody also failed to inhibit the cell death induction by tranfection of fibulin 5 plasmid. These results suggest two possible scenarios. First, Fibulin 5 protein is highly expressed only in the proximity of the Fibulin 5 transfected cells and not in most other distant cells. In the second scenario, Fibulin 5 is not targeting any plasma membrane bound proteins but is instead functioning intracellularly for cell death induction. To test the first scenario, I co-transfected control plasmid pcDNA3.0 and Fibulin 5 expression plasmid separately with GFP into c-Myc overexpressing CV1 cells and counted the number of dead and live cells surrounding the Fibulin 5 and GFP transfected cells, as well as the non- transfected cells. In this experiment, I expected to observe a significantly increased number of dead cells surrounding the transfected cells compared to non-transfected cells (Fig.42).

(A) Percentage of dead and live cells surrounding the transfected live cells

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(B) Percentage of dead and live cells surrounding the non-transfected live cells

Figure 42. Testing of hypothesis 1 that highly concentrated Fibulin 5 expression is present only in proximity to the transfected c-Myc overexpressing CV1 cells. c-Myc overexpressing CV1 cells were co- transfected with 0.9 µg of expression plasmid DNA pcDNA3 or Fibulin 5 with 0.1 µg of GFP. (A) At 48 h post- transfection, cell counts were performed, focusing on the transfected live cells (green) and counting the non- green dead and live cells surrounding the transfected green and live cells. (B) At 48 h post-transfection, cell counts were performed focusing on the non-transfected live cells (non-green) and counted the non-green dead

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and live cells surrounding the non-transfected live cells. Dead cells showed the small and round morphology in phase contrast images and were those with small and bright features in fluorescence images.

With this experiment, I failed to observe significantly higher levels of cell death in cells close to Fibulin 5-transfected cells (green cells) compared with cells close to untransfected cells

(non-green cells). This result suggested that it is unlikely that Fibulin 5 is inducing c-Myc specific cell death through targeting plasma membrane bound proteins. The second scenario, whereby Fibulin 5 is functioning intracellularly to induce c-Myc specific cell death is a plausible explanation for the killing function of Fibulin 5 as previous studies have shown that

Fibulin 5 is present in the cytoplasm as well as the nucleus, despite its unknown intracellular roles (ref.339).

5.4 CDK5 and NF-B as potential targets for TMEFF2 (Gene 1A3)

CDK1 inhibition has been shown to induce cell death only in c-Myc upregulated cancer cells

(ref.142). A previous study has also demonstrated that CDK2 inhibition induced a sythetic lethal effect against c-Myc overexpression in triple negative breast cancer cells (ref.143).

Moreover, the CDK1 inhibitor, Purvalanol A, has been shown to induce cell death only in cells with c-Myc upregulation (ref.142). Interestingly, according to the manufacturer, this inhibitor could block a range of CDKs‟ activity, particularly CDK1, -2, -4, -5 and -6. It is therefore possible that this CDK inhibitor can induce c-Myc specific cell death through blocking a number of CDKs, or one of them other than CDK1. As a consequence, I wanted to validate the c-Myc specific cell death effect in our c-Myc overexpressing CV1 cells. In this experiment, only c-Myc overexpressing CV1 cells were eliminated upon CDK inhibition, whereas CV1 normal cells were completely unharmed (Fig. 43A). A different cancer cell 162

model system, MCF-7, also displayed the same trend with regard to c-Myc specificity (Fig.

43B).

(A) CV 1 cells

(B) MCF-7 cells

Figure 43. c-Myc overexpressing and parental CV1 cells and c-Myc knockdown MCF-7 cells are sensitive to CDK inhibition and are eliminated by the CDK inhibitor (CDKi) Parvalanol A. Different concentrations of CDK inhibitor Purvalanol A were used to treat both normal CV1 and c-Myc overexpressing CV1 cells, as well as MCF-7 wild type and stable MCF-7 expressing knockdown of c-Myc for 24 hours. Moreover, Purvalanol A was dissolved in DMSO so the same volume of DMSO was added to cells under the same conditions of treatment. Then, the CV1 cells were harvested, collected and stained with propidium iodide for 30 minutes. MCF-7 cells were stained with DIOC6 and PI for one hour prior to flow cytometry analysis. Cell death assessment was conducted by flow cytometry using FL-1 channel for DIOC6 and FL-3 channel for PI. Flow cytometry result was analysed with flowjo 7.6.3.

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The CDK inhibitior Parvalanol was suggested to cause synthetic lethality against c-Myc through blocking CDK1 activity (ref.142). CDK1 is known to be the only universal CDK that can compensate for the loss of function of other CDKs and drive the cell in different phases of the cell cycle (ref.340). Xenopus CDK1 has been shown to have almost the same activity as human CDK1 in a previous study (ref.341). Therefore, expression plasmid encoding

Xenopus CDK1 was used in a co-transfection experiment along with individual anticancer genes against c-Myc overexpression. The results showed that three anticancer genes elicited a strong inhibitory effect on cell death when co-transfected with Xenopus CDK1

(Fig.44).

Figure 44. Identification of 3 putative anticancer genes targeting CDK1. Cell death measurement of c-Myc overexpressing CV1 cells upon co-transfections of individual Anticancer genes along with Xenopus CDK1 (xCDK1) at 48 h post-transfection. 0.5 µg of xCDK1 and 0.5 µg of individual Anticancer expression constructs were transfected to c-Myc overexpressing CV1 cells using Xfect for 48 hours in optimem medium. At 48 hours post-transfection, PI was used to stain cells for 30 minutes followed by flow cytometry analysis of cell death. FL3 channel was used.

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As previously mentioned, CDK1 can compensate for loss of function of other CDKs at different phases of the cell cycle. It is possible that these three anticancer genes are actually targeting other CDKs, but CDK1 replaces the function of the targeted CDKs to inhibit the induction of cell death. Moreover, Purvanol A is a pan-CDK inhibitior and known to target a number of CDKs, including CDK1, CDK2, CDK4 and CDK5. The manufacturer‟s data sheet gives IC50 for inhibiting CDK1 as 4nM and a range of concentrations of this compound were tested for assessing the sythetic lethal effect against CDK1 in c-Myc overexpresing CV1 cells. The concentration for observing this synthetic lethal effect was almost 10 µM, which is the same concentration that showed synthetic lethal effect in the previous study (ref.142).

This suggested that Purvanol A is likely to target other CDKs rather than CDK1 alone to show this synthetic lethal effect against c-Myc. It was important to test this idea by co- transfecting individual CDKs along with these three anticancer genes. We used expression plasmids for four different human CDKs, namely CDK2, CDK4, CDK5 and CDK6, and performed co-transfection experiments to identify those CDKs whose inhibition leads to synthetic lethality in c-Myc overexpressing cells. Among the four CDKs tested, only CDK5 showed inhibition of cell death induced by the anticancer gene TMEFF2 (1A3 gene) (Fig.45).

Figure 45. CDK5 is a potential target for putative anticancer gene 1A3 in c-Myc overexpressing CV1 cells. Cell death analysis by flow cytometry upon co-transfecting 0.5 µg of putative nticancer gene TMEFF2 (1A3) 165

along with 0.5 µg of individual human cyclin dependent kinase (CDK), including CDK2, CDK4, CDK5 or CDK6, into c-Myc overexpressing CV1 cells. At 48 h post-transfection, all cells were harvested and stained with propidium iodide (PI) for 30 min before cell death analysis by flow cytometry.

In parallel, this anticancer gene TMEFF2 (1A3) was tested for its correlation with NF-B activity. This was based on the fact that ORCTL3, which is the most recently identified and characterised anticancer gene from our group, has been shown to downregulate the NF-B activity in a previous study (unpublished data), and that could contribute to the cell death induction by the putative anticancer genes. In my previous experiment, NF-B inhibition with the small molecule inhibitor in c-Myc overexpressing CV1 cells showed greater sensitivity to cell death induction. I then wanted to test if the anticancer gene TMEFF2 (1A3) shared a similar effect in inducing cell death through restricting NF-B activity. To this end, I co- transfected the NF-B reporter plasmid along with the anticancer gene TMEFF2 (1A3). The result showed a significant reduction of NF-B activity in cells transfected with TMEFF2 (1A3) compared to cells transfected with the negative control plasmid in c-Myc overexpressing

CV1 cells (Fig. 46A). Moreover, under different experimental conditons where NF-kB level was highly upregulated by adding the cytokine TNFα in CV1 normal cells, co-transfection of the anticancer gene TMEFF2 (1A3) was again able to limit NF-B activity. ORCTL3 was employed as a positive control in this experiment. In contrast, the other anticancer genes isolated from the screen did not show this effect and served as negative controls for 4E6.

This suggests that the putative anticancer gene, 1A3, could induce cell death through suppressing NF-B activity.

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(A)

(B)

Figure 46. Inhibitory effects on NF-B activity by the putative anticancer gene TMEFF2 (1A3) in CV1 cells. (A) c-Myc overexpressing CV1 cells were transfected with 500 ng of TMEFF2 and 500 ng of NF-B reporter plasmid for 24 hours. At 24 h post-transfection, at which point there was no significant cell death observed, cells were harvested, followed by NF-B activity evaluation by flow cytometry. Absolute NF-B activity was determined by measuring the percentage by which the cell population shifted by flow cytometry. The relative NF-kB activity was determined by normalising to the NF-B activity in the negative control which is the co-transfection of luciferase expression plasmid and NF-B reporter plasmid. (B) Normal CV1 cells were transfected with 500ng of gene of interest and 500ng of NF-B reporter plasmid for 48 hours. At 24 h post transfection, cells were treated with 20 ng/ml of TNF for an additional 24 hours. Then cells were harvested and absolute NF-B activity determined by measuring the percentage of the cell population which shifted, by flow cytometry. Positive control expression plasmid mouse ORCTL3 (mORCTL3) and negative control luciferase expression plasmid were used in this experiment.

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Overall, TMEFF2 could induce cell death in cells with c-Myc overexpression through inhibition of CDK5 or NF-B activity. Whether inhibition of each of CDK5 and NF-B activity is strong enough to trigger cell death in c-Myc overexpressing cells is currently unknown and further validation is needed.

5.5 Identification of FOXK2 as a potential target for three anticancer genes

I next aimed to identify potential targets, which are connected to c-Myc transcribed genes. c-

Myc is known to transcribe 15% of human genes, making it almost impossible to test all of them due to limited time and resources. Accordingly, I selected cyclin D1, as well as candidates involved in regulating c-Myc expression, such as FOXM1 (ref.342), FOXO3 and

FOXK2 (ref.274,275). I started with these four genes at the time to evaluate the gene expression profile in relation to c-Myc expression in MCF-7 cells. I transfected individual anticancer genes into both control MCF-7 and c-Myc stable knockdown MCF-7 cells and performed the RTq-PCR to assess the gene expression pattern. For cyclin D1, knockdown of c-Myc leading to downregulation of cyclin D1 was confirmed (Fig.47A). In addition, cyclin

D1 mRNA level in most of anticancer gene transfected cells was unaffected, apart from 4G3 and 1A3 with a very small degree of reduction. FOXO3, is known as a tumour suppressor gene and has been shown to downregulate c-Myc activity/expression (ref.274,275), but in our experiment in the context of breast cancer cell MCF-7, FOXO3 mRNA level was almost unaffected when c-Myc was downregulated (Fig.47D). There were no changes in FOXO3 mRNA levels upon transfection of individual anticancer genes. This suggested that these anticancer genes did not promote cell death through upregulating FOXO3. However, more

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than half of the anticancer genes showed a trend of reducing expression of FOXM1 and

FOXK2 at the mRNA levels upon transfection in control cells compared to c-Myc stable knockdown MCF-7 cells (Fig.47B, C). This strongly suggested that these anticancer genes work upstream of FOXM1 and FOXK2, and that the inducation of cell death might be mediated through decreasing FOXM1 and FOXK2 expression at the mRNA levels (Fig.47).

FOXM1 and FOXK2 are both transcription factors and can regulate a number of genes involved in cellular functions, such as proliferation and apoptosis (ref.233,287).

(A) cyclinD1

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(B)FOXK2

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(D) FOXO3

Figure 47. Quantifying gene expression levels of cyclin D1, FOXM1, FOXK2 and FOXO3 upon transfection of individual c-Myc specific putative anticancer genes in MCF-7 cells. Cells were transfected with 5 µg of individual putative anticancer gene expression plasmids by Xfect transfection reagent in a 6 well plate format. At 24 hour post-transfection, total RNA was isolated by following RNA extraction kit instructions. RNA then underwent reverse transcription to produce cDNA synthesis. cDNAs were used as templates for RTq-PCR. For each gene, one forward primer and one reverse primer were used for amplification with SYBR green, and relative gene expression was normalized against the house keeping gene L19. For each gene, relative expression for scramble construct stably MCF-7 (Scr-wt) transfected with luciferase sample has been normalized as 1, and other datas normalised against it. Standard curves was established for each gene and quantity mean

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corresponding to CT value was calculated based on the standard curve in the SDS2.3 programme. Representative data on relative gene expression of (A) cyclin D1 (B) FOXK2 (C) FOXM1 (D) FOXO3

From the RTq-PCR data, there were 8 anticancer genes that demonstrated inhibitory effects on gene expression of both FOXK2 and FOXM1. These 8 anticancer genes were transfected into control MCF-7 cells and c-Myc stable knockdown MCF-7 cells. Cells were harvested, lysed and levels of FOXK2 and FOXM1 proteins determined by Western blotting.

Surprisingly, most of these 8 anticancer genes do not show correlation between their mRNA and protein expression profiles. At the protein level for FOXM1 expression, only anticancer genes 4G4 and 4E6 showed the same expression pattern as the RTq-PCR data, in which

FOXM1 was downregulated in c-Myc knockdown stable cells but not in control cells when empty vector was transfected. Furthermore, FOXK2 downregulation occurred when c-Myc was knocked down. This is a novel observation as there was no previous study demonstrating this correlation. Only the anticancer genes 3B6, 4G3 and 4G4 showed very similiar expression patterns in both RTq-PCR and Western blotting, in which FOXK2 level was reduced significantly when these putative anticancer genes were transfected into control

MCF-7 cells. However, FOXK2 protein levels did not decrease in c-Myc stable knockdown

MCF-7 cells. This supported the notion from the preivous study that FOXK2 is required for cell survival, and cells could not survive as FOXK2 expression is reduced substantially

(Fig.48).

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Figure 48. Regulation of FOXM1 and FOXK2 protein expression by 8 shortlisted putative anticancer genes in the absence or presence of c-Myc upregulation in MCF-7 cells. Control MCF-7 cells (c-Myc +ve ) and c-Myc stable knockdown MCF-7 cells (c-Myc –ve) were transfected either with empty vector pcDNA3.0 or individual putative anticancer genes by Xfect transfection reagent for 8 hours. At 24 hour post transfection, cell were harvested and lysed. Thirty µg of protein were loaded, separated on SDS-PAGE gels and immunoblotted with antibodies against the different proteins. Change of protein expression of FOXM1 and FOXK2 in each cell type upon transfection of individual putative anticancer gene was compared to the same cell type transfected with empty vector PcDNA3.0. EV: empty vector.

5.6 Discussion

A number of potential anticancer gene candidates were identified from the secondary genetic screens as synthetic lethal targets against c-Myc upregulation. Instead of using loss of function genetic screen, which many researchers use nowadays, my strategy of genetic

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screening was based on a gain of function approach. Using this scheme, our group has previously successfully identified the anticancer gene, ORCTL3. The follow-up mechanism study revealed that SCD1 was a target of ORCTL3 in inducing specific cell death in transformed cells. In this project, the 16 putative c-Myc specific anticancer genes I identified were capable of exerting their synthetic lethal effects by activating signals that creat a conflict with another signal created by c-Myc upregulation. These 16 putative anticancer genes were further validated in cells with different genetic backgrounds. Uncovering the cellular targets and modes of action of these putative anticancer genes is important for confirming that these candidates are indeed bona fide „anticancer‟ genes.

Fibulin 5 is the only identified candidate gene whose gene product is a secreted protein. This protein is a vascular ligand for some integrin receptors and plays a suppressor role in lung cancer invasion and metastasis through the MAPK and ERK pathways (ref.343). In a similar manner, TRAIL, as a secreted protein, is a well-known example of an anticancer gene, which can exert its tumour specific effect through binding to its receptor, TRAIL receptor

(ref.77). A previous study showed that Fibulin 5 could target cell membrane bound proteins e.g integrin, to control cellular ROS levels (ref.365). Hence, our original hypothesis was that

Fibulin 5 might target cell membrane bound proteins to exert its c-Myc specific cell death effect. However, despite using different ways of delivering recombinant Fibulin 5 protein to the cellular membrane, as well as the neutralising antibody, I failed to modulate cell viability in c-Myc overexpressing cells. These observations led me to propose that Fibulin 5 may not induce cell death through targeting cell membrane bound proteins. It is possible that Fibulin

5 can only work at very high concentrations in proximity to Fibulin 5 transfected cells.

However, this idea turned out to be invalid because my results showed that there were no significant differences in the numbers of both dead and live cells surrounding transfected 175

and untransfected cell populations. Chen and colleagues showed that Fibulin 5 expression is inversely proprotional to c-Myc expression in lung cancer, and transfected Fibulin 5 could inhibit Wnt/β-catenin signalling and suppress Matrix Metallopeptidase 7 (MMP7) in lung cancer cells, through binding to intergin via RGD motif (ref.344). However, in this study there is still a lack of evidence to support the notion that Fibulin 5 functions as a secreted protein.

For example, no integrin blocking antibody has been used to verify the interaction between secreted Fibulin 5 and the extraceullar domain of integrin. Moreover, there is evidence that

Fibulin 5 is localised in both cytoplasm and nucleus, but its intracellular role is unknown

(ref.345). As a consequence, it is possible that Fibulin 5 might have an intracellular role in exerting its c-Myc specific cell death effect. As part of future studies, one could tag Fibulin 5 with a fluorescent protein to characterise its cellular localisation and explore how this correlates with its cell death inducing role. Previous studies showed that expression of

Fibulin-5 can be silenced by promoter hypermethylation in the majority of lung cancer cell lines and primary tumours, and oncoproteins such as c-Myc are involved in this process

(ref.343). Consistently, my RTq-PCR results showed that Fibulin 5 expression is inversely proportional to c-Myc gene expression in MCF-7 cells. This suggests that its tumour suppressor role is connected to the oncogenic properties of c-Myc. In future studies, it would be interesting to investigate how overexpressing Fibulin 5 can induce the synthetic lethal effect in c-Myc upregulated cancer cells. Conversely, there is evidence showing an oncogenic role for Fibulin 5 in human cancers, such as nasopharyngeal carcinoma (ref.346).

The contexts of cancers and cell types used in these studies seems to lead to different conclusions drawn on the roles of Fibulin 5 in cancer. More studies on its mechanism of action are required to fully characterise the role of Fibulin 5 in different types of cancer.

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Moreover, inhibition of CDK 1 and 2 with a small molecule compound or siRNA knockdown constructs was shown to cause synthetic lethal effect in c-Myc overexpressing cells

(ref.142,143). In the CV1 cellular system, the same CDK inhibitor, purvanol A at the same concentration was able to show a synthetic lethal effect in c-Myc overexpressing CV1 cells, but not in normal CV1 cells. In addition, c-Myc knockdown in MCF-7 cells showed less cell death with the CDK inhibitor compared with wild-type MCF-7 in my experiments. As a result,

I hypothesised that this CDK inhibitor targets either a combination of CDKs or only one CDK other than CDK1 or 2. Firstly, the co-transfection experiment data suggested that CDK5 could be a potential target of the anticancer gene 1A3 (TMEFF2). The other two anticancer genes, 4E6 (FAM83B) and 1B8 (Fibulin 5), which demonstrated inhibition of cell death by co- transfecting xCDK1, could not be inhibited by co-transfecting CDK5. Moreover, TMEFF2 could suppress the NF-B activity in the same manner as the preivously identified anticancer gene, ORCTL3. The inhibition of NF-B with small molecule chemical compound could sensitise cell death in c-Myc overexpressing CV1 cells. Together these data suggest that inhibition of NF-B could be a second target of TMEFF2. TMEFF2, a transmembrane protein with a short cytoplasmic tail and extracellular EGF-like domain, is known as a tumour suppressor gene in vitro and in vivo and its gene expression level is inversely correlated to c-Myc expression (ref.322). In a range of human cancers, hypermethylation of TMEFF2 has been described but there has been no evidence of mutation associated with disease

(ref.347). Hitherto, TMEFF2 is known to mediate tumour suppression through a number of mechanisms, one of which being decreasing cell proliferation and blocking cell cycle progression through SHP-1 (ref.348). Secondly, TMEFF2 can modulate AKT and ERK signalling pathways to suppress growth, migration and invasion of prostate cancer (ref.349).

Thirdly, TMEFF2 activates STAT1 pathways and upregulates a large number of associated interferon-inducible genes to inhibit growth of colon cancer cells (ref.350). Furthermore,

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TMEFF2 can interact with sarcosine-dehydrogenase (SARDH), which catalyses sarcosine conversion to glycine (ref.351), to modulate sarcosine levels that can affect cellular invasion

(ref.352). However, none of these studies showed evidence of how TMEFF2 overexpression can mediate apoptosis. My data on co-transfection experiments with TMEFF2 in c-Myc overexpresssing cells suggests that TMEFF2 induces c-Myc specific cell death through inhibition of CDK5, NF-B, or both. Moreover, CDK5, which is best known for its involvement in signalling pathways in Alzheimer‟s disease, and in other disease models, such as diabetes. CDK5 has been implicated in maintaining cell survival of pancreatic β-cells through activating FAK/AKT/PI3K pathway. Recently, CDK5 has been shown to mediate AKT activation. In concordance, CDK5 downregulation leads to decrease in AKT phosphorylation and cell cycle arrest in prostate cancer (ref.362). Moreover, CDK5 is also overexpressed in breast cancer cell lines, including MCF-7, and cancerous breast tissues (ref. 353). MCF-7 is known to overexpress c-Myc and the CDK5 inhibitor, Roscovitine, has been demonstrated to induce apoptosis and proliferative arrest of MCF-7 cells (ref.354). Consistently, my results suggest a connection between c-Myc and CDK5 could exist. In future studies, one should perform RTq-PCR and Western blotting to directly correlate the level of protein expression and activity of c-Myc and CDK5. Our co-transfection results suggest that TMEFF2 induces c-

Myc specific cell death through inhibition of CDK5. Whether inhibition of CDK5 alone, or in combination with suppression of NF-B activity by overexpressing TMEFF2 leading to c-Myc specific cell death remains a question to be addressed in the future.

In addition to gene characterisation and target identifications for anticancer genes Fibulin 5 and TMEFF2, I have focused my study on 3 Forkhead box proteins, the transcription factors

FOXM1, FOXK2 and FOXO3. FOXM1 is a well-known oncogene involved in a broad range of cellular functions including cell cycle, cell proliferation and apoptosis (ref.233). FOXK2 has

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been implicated in cancer cell proliferation and survival (ref.287). FOXO3 is known as a tumour suppressor gene and its inactivation is often observed in many human cancers

(ref.233). c-Myc has previously been shown to upregulate FOXM1 expression and downregulate FOXO3 expression (ref.342,274,275), but its expression and regulation in relation to FOXK2 is unknown. I am interested in exploring a possible interaction of these anticancer gene candidates with FOXM1, FOXK2 and FOXO3 in the context of c-Myc upregulation. Firstly, upon transfection of the 16 anticancer genes against c-Myc upregulation background of wild-type MCF-7 and c-Myc stable knockdown MCF-7 cells, I studied the expression of these three molecules by RTq-PCR and found that 8 genes cause significant reductions in mRNA levels of both FOXM1 and FOXK2 but have no effect on

FOXO3 expression. I also examined the protein levels of FOXM1 and FOXK2 upon transfection of these 8 short-listed genes in both wild type MCF-7 and c-Myc stable knockdown MCF-7 cells to determine if the expression levels of these two Forkhead proteins were consistent with their mRNA levels observed with RTq-PCR. The results showed that

4E6 and 4G4 overexpression caused a reduction in FOXM1 expression at both the mRNA and protein levels. Ectopic expression of genes 3B6, 4G3 and 4G4 also decreased FOXK2 mRNA and protein expression. Gene 4G4 was the only candidate anticancer gene that was able to consistently decrease both FOXM1 and FOXK2 expression at both the mRNA and protein levels.

FOXM1 mRNA level was decreased in c-Myc stable knockdown cells, but its protein level remained unchanged compared to wild-type MCF-7 cells. However, FOXM1 protein level was reduced in stable c-Myc knockdown MCF-7 cells upon transfection of genes 4E6 and

4G4. Nevertheless, the discordance between the FOXM1 mRNA and protein levels in control and c-Myc knockdown MCF-7 cells indicates that the studies on FOXM1 expression in these 179

cells needs to be repeated. FOXM1 is upregulated in many human cancers, and its downregulation by siRNA or inhibition using compounds, such as Siomycin A has been shown to induce cancer cell death (ref.255). Reduction of FOXM1 protein expression only occurs in cells transfected with genes 4G4 and 4E6 in wild-type MCF-7 cells but not c-Myc stable knockdown cells. To further validate this effect in future experiments, co-transfection of FOXM1 along with 4G4 or 4E6 in c-Myc upregulated cells can be performed to assess the effects on cell viability. If FOXM1 is the target of 4G4 or 4E6, one would expect to see blockage of cell death induced by 4G4 or 4E6 when FOXM1 is overexpressed.

The positive correlation between FOXK2 and c-Myc expression is a novel finding from this study. Other members of the group have also observed the same effect when transiently overexpressing c-Myc in CV1 cells in their experiments (unpublished data). FOXK2 inhibition has been shown to cause an increase in cell death and cell proliferative arrest (ref.287).

Moreover, a FOXK2 mutant containing two point mutations (S368A/S423A) exhibited a dominant negative effect and was able to promote apoptosis (ref.286). Consistent with these studies, our finding that anticancer genes 3B6, 4G3 and 4G4 induce cell death through inhibition of FOXK2. Moreover, microarray data from a previous study has also demostrated the positive correlation of FOXK2 with N-Myc and L-Myc expression (ref.282). Our data suggests that FOXK2 is upregulated by c-Myc overexpression, and its inhibition leads to suppression of cell growth and induction of apoptosis. However, there is contradictory evidence showing that overexpression of FOXK2 could suppress tumour growth in breast cancer (ref.355). Our understanding of FOXK2 is incomplete because of the limited published work. In consequence, further validation is needed to support my finding that

FOXK2 inhibition can cause synthetic lethal effect to c-Myc upregulation. These genes have shown strong cell death inducing functions in transformed cells, but not in CV1 normal cells. 180

Further studies in other non-cancerous cell lines or primary normal cells are necessary to confirm that these genes do show differential toxicity in cancer and non-cancerous cells. The work is still ongoing in the lab. So far, based on the data described here, these anticancer gene candidates have shown strong effects as tumour suppressor genes.

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Chapter 6 Final Discussion and Future directions

In the present study, I performed a gain of function genetic screen to isolate 22 putative anticancer genes whose overexpression can cause tumour specific cell death. Most of the functions of these genes in cancer and apoptosis is not clearly understood. Interestingly, novel functions of these 22 genes and their linked to tumour suppression and apoptosis have been established in this study. The previously defined roles of the majority of these genes do not appear to be connected to cancer and cell death. Using this gain of function approach, unlike the conventional RNAi based genetic screen, enables us not only to isolate anticancer genes or tumour suppressor genes, but also helps to unveil novel gene functions and cell signalling pathways against genetic changes associated with cell transformation.

Notably, the results obtained from gain of function studies can be complemented with other data, such as microarray, cancer genomics, proteomics, and even the data from RNAi genetic screen, to gain a comprehensive understanding of the role of cellular molecules, their associated signalling pathways, and interaction partners and to identify targets for treatment of disease and as biomarkers. One typical example of integrating genetic screen and other powerful „omic‟ tools and animal models is that Zender and colleagues used cancer genomics data to shortlist candidate genes and then performed RNAi genetic screen, followed by mouse model work to identify tumour suppressor genes (ref.356).

The 377 apoptosis inducers were firstly isolated by a former colleague Bevin Lin in transformed HEK293T cells, the “anticancer” effect was further validated in cancer cell lines,

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such as the MCF-7 breast and the Hela cervix cancer cells. Future work in this area will require additional transformed cells or cancer cell lines with different genetic backgrounds e.g lung, prostate, and liver cancer should be included to demonstrate additional or universal

“anticancer” effects. Since these 22 putative anticancer genes exhibit tumour suppressor activity, it is reasonable to conduct bioinformatic analysis by integrating microarray data and evaluation of whether these genes are downregulated in various human cancers. One can also find out that if the downregulation of these genes is correlated with patient prognosis.

As previously mentioned, the most important feature of an anticancer gene is to induce cell death only in transformed cells, while sparing normal cells. Moreover, the African green monkey kidney fibroblast CV1 cell line is the only normal cell type I used in the genetic screen and the subsequent validation experiments. Thus, in order to further verify the non- toxic effects of these 22 anticancer genes, one should also use other normal human cell type, such as the breast epithelial cell line MCF-10A, lung fibroblast cell line WI38 or skin fibroblast cell line HFF1. Of the 22 putative anticancer genes, 16 showed very pronounced cell death inducing effects against c-Myc upregulation cell backgrounds. Our data, so far, has confirmed strong “anticancer” traits in these tumour suppressor genes. The c-Myc specific killing effect was demonstrated by these genes in the c-Myc-CV1 and MCF-7 cell systems.

The drawback of using MCF-7 cells is that they are a breast cancer cell line with an unknown transformed genetic background. Non-cancerous cell systems with different genetic backgrounds, like MCF10-A, HFF-1 or WI38 cells should also be included to generate c-Myc overexpressing cells in the validation. Cell death will be determined in c-Myc overexpressing cells and compared with their normal counterparts upon transfection of these 16 c-Myc specific putative anticancer genes. In this way, one will definitively be able to verify the non- cytotoxic and cytotoxic effects of these genes in non-cancerous cell types other than CV1 and also the c-Myc overexpressing transformed cells, retrospectively. Furthermore, in order

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to identify the molecular mechanism of action of these c-Myc specific genes, the alternative solution is to integrate c-Myc related pathways with H-ras and Rb pathways. In combination with analysis with bioinformatic tools, one could potentially identify the crosstalk signalling nodes of c-Myc, H-ras and Rb pathways, which could be novel potential molecular targets for these c-Myc specific genes.

My data suggests that TMEFF2 is one putative anticancer gene candidate and could inhibit both CDK5 and NF-B, to contribute to c-Myc specific cell death. Obviously, further validation using RNAi technology is needed to confirm this. However, it is not clear if inhibition of two molecules at the same time or only one of them is potent enough to induce cell death in c-Myc overexpressing cells. As part of the future work, one can use siRNA or shRNA to individually, or in combination knockdown CDK5 and NF-B, in c-Myc upregulated cells to assess their ability to induce cell death. Previous studies have showed that selective targeting of molecules involved in the interactions between the NF-B and the JNK pathways may be a feasible approach to trigger cancer specific cell death (ref.357). Interestingly, it has been demonstrated that the CDK inhibitors Roscovitine and Purvalanol can casue the inhibition of both CDK5 and NF-B activity (ref.358,359). In another study, knockdown of

CDK5 in combination of blocking NF-B activity with a small molecule inhibitior can also induce strong cytotoxic effects in myeloma cell lines and primary tumour cells (ref.360).

Equally, overexpression of CDK5 can enhance cell survivial through FAK/AKT/PI3K pathway in pancreatic β-cells (ref.361). Collectively, these studies suggest that inhibition of both

CDK5 and NF-B can trigger cancer cell death, but its relation to c-Myc upregulation remains unknown. My data with the analysis of TMEFF2, so far, indicates a connection between NF-B, CDK5 and Myc possbily exists. With NF-B, it would be interesting to investigate how TMEFF2 might affect the activity of NF-B without changing its level of 184

protein expression. To further validate if inhibition of CDK5 is the key CDK responsible for c-

Myc specific cell death, one can use tansient knockdown of a range of CDKs using siRNA or shRNA alone and in combination in c-Myc upregulated cells and assess their effects on cell death. In addition, RTq-PCR and Western blotting can be used to determine if gene and protein expression levels and the activity level of CDK5 or its activator p39/p35 are upregulated when c-Myc is overexpressed.

For the future direction of this project, the 3 anticancer genes against FOXK2 in c-Myc overexpressing cells, more experiments are needed to confirm that FOXK2, indeed, is the target of these 3 anticancer genes in c-Myc upregulated cells. The first experiment is to co- transfect the 3 individual anticancer genes along with the FOXK2 plasmid in a c-Myc upregulated cell line to determine if FOXK2 overexpression can block cell death induced by one or more of these genes. In parallel, one would also transfect siRNA or shRNA against

FOXK2 into c-Myc upregulated cells to assay if this can induce substantially higher levels of cell death in c-Myc overexpressing cells in comparison to control cells. The next question one can ask is whether these 3 genes induce p53 and Caspase-dependent apoptosis by performing Western blotting experiment upon transfection of each candidate anticancer gene.

Moreover, one would expect, like some of other published anticancer genes, such as E4orf4 and HAMLET, overexpression of the anti-apoptotic protein Bcl-2 could not rescue cells from cell death. To test this conjecture, one can co-express Bcl-2 with each of these 3 anticancer genes to determine if they share this characteristic with the established anticancer genes. If

FOXK2 is confirmed to be the target of the candidate anticancer genes, one would then look into the connection between the gene, FOXK2 and c-Myc. There are several questions which one can ask. First, at the molecular level, is inhibition of FOXK2 alone strong enough to induce cell death in c-Myc upregulated cells? How do these 3 anticancer gene candidates 185

cause downregulation of FOXK2 in c-Myc upregulated cells? What are the pathways linking these 3 putative anticancer genes with FOXK2? A further question of clinical relevance is whether using adenovirus carrying shRNA targeting FOXK2 can cause tumour regression in vivo? Is there a positive correlation of expression between these 3 putative anticancer genes,

FOXK2 and c-Myc in patient tumour samples? Addressing these questions will advance our understanding of the biology of these 3 putative anticancer genes, FOXK2 and their function as a synthetic lethal target against c-Myc overexpression, as well as the novel function and pathways associated with these three FOXK2 specific genes in relation with FOXK2 and c-

Myc.

In summary, this work is an attempt to identify novel anticancer genes and to understand the functions of these genes against genetic changes associated with cell transformation and cancer. Further work has been conducted on c-Myc in an attempt to identify the molecular targets of these 16 c-Myc specifc candidate anticancer genes. These genes have shown potent effects in inducing cell death in transformed cells as tumour suppressors. However, further validation is needed to define their exact modes of action and to confirm that they do not show cytotoxic effects in normal cells to meet the strict criteria of being an “anticancer gene”. The findings described here are not only a significant step towards the understanding the novel tumour suppressive functions of these anticancer genes but also provide a potential therapeutic application of these genes for the treatment of cancer.

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Appendices

Generation of constructs containing recombinant E1A, E1B55k

Recombinant E1A was cloned into expression vector pcDNA3.0 containing neomycin resistance gene and E1B55K was cloned into the expression vector pLenti7.3Puro containing puromycin resistance gene. Both PCR products were cloned into expression vectors in between BamHI and NotI sites. With E1B55k, SURE (Stop unwanted rearrangement events) competent cells (Stragtagene) was used. All PCR and cloning procedures and transformation of plasmids were performed by following the manufacturer‟s protocols.

The DNA templates for recombinant E1A (rE1A) and E1B55K are two plasmids obtained from colleague Professor Dobbelstein Matthias in Germany as a gift. Phusion DNA polymerase was used in this PCR reaction. The PCR protocol was adapted and followed from manufactuer‟s protocol. Primer information for generating recombinant E1A and E1B55k is shown below:

rE1A forward primer: 5‟- ACTGTGGATCCGCAGACATGAGACATATTATC-3‟ rE1A reverse primer: 5‟- TGACA GCGGCCGTTATGGCCTGG -3‟

E1B55k forward primer: 5‟- ACTGT GGATCC GCAGAC ATGGAGCGAAGAAAC -3‟

E1B55k reverse primer: 5‟-TGACA GCGGCCG TCAATCTGTATCTTCATCGC -3‟

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