i

Mechanisms and markers of CHK1 inhibitors sensitivity in melanoma

Zay Yar Oo

Bachelor of Medicine, Bachelor of Surgery; Master of Science

A thesis submitted for the degree of Doctor of Philosophy at

The University of Queensland in 2017

The Faculty of Medicine

ii

Abstract

Landscape for the treatment of cancer patients has recently been changed with the arrival of

targeted therapies in different malignancies with the drugs targeting specific mutations and genetic

alterations such as, EGF-receptor blockers and BRAF inhibitors, and the drugs such as PARP

inhibitors which exploit defects in the cancers and cause synthetic lethality. In this thesis, I

investigated to understand the mechanism of action of a drug that targets the cell cycle checkpoint

regulator, checkpoint kinase 1 (CHK1) and explored its potential therapeutic usage in specific

cancer types. Checkpoint kinase 1 inhibitor (CHK1i) as a single-agent treatment is effective in some

of the cancer types with high levels of replication stress, including melanoma. However, the

mechanism and manner of cell-killings induced by CHK1i single-agent treatment is still poorly

understood. To identify the patient population who can benefit from CHK1i single-agent treatment,

it is important to understand how single-agent CHK1i induced the cell killing. CHK1i has been

investigated in pre-clinical studies and clinical trials, and shown to enhance the efficacy of

chemotherapeutic drugs, particularly those that promote replication stress such as gemcitabine.

Most of those investigations were based on the previous notion that CHK1i abrogates the G2/M

phase checkpoint to cause mitotic catastrophe and results in cancer cell death. However,

considering numerous roles of CHK1 playing in cell cycle and DNA damage response pathway, this

mechanism alone may not represent the entirety of sensitivity to CHK1i. CHK1 has different roles

in S and G2/M phases of cell cycle such as controlling the G2/M checkpoint, stabilising the stalled

replication fork, and inhibiting the apoptosis together with other DNA damage repair and cell cycle

checkpoint regulators. When one of these other regulators/pathways has defects, inhibiting CHK1

can be synthetically lethal to the cancer cells. Another CHK1 function in S phase is regulating

CDC25A; CHK1 triggers the destabilisation of CDC25A upon DNA damage and replication stress.

The normal role of CDC25A in S phase progression is activation of CDK2 which is required for

progression into and through S phase. CDC25A dysregulation and overexpression has been reported

in various cancers and shown to create increased replication stress in the cells. Tumours with high

level of replication stress have been suggested to be selectively susceptible to the inhibition of

CHK1 and its upstream regulator, ATR.

In this thesis, it was firstly shown that S phase cell cycle checkpoint defect is a common feature of

CHK1i-hypersensitive melanoma cell lines, and this defect is often associated with failure to

degrade CDC25A in response to replication stress. Furthermore, CDC25A over-expression and/or

dysregulation contributes to CHK1i sensitivity in hypersensitive melanoma cell lines. Secondly, it

was demonstrated that CHK1i induced high level of replication stress via RPA hyper-

phosphorylation and subsequently RPA depletion occurred in hypersensitive cell lines resulting in

iii

replication catastrophe, while only low level of replication stress was observed in CHK1i-

insensitive cell lines. Thirdly, adding low level replication stress by the addition of low-dose (0.2

mM) hydroxyurea (HU) significantly sensitised not only CHK1i-insensitive melanoma cell lines but

also the lung cancer cell lines to CHK1 inhibition. Finally, I have developed a three-dimensional

(3D) tumoursphere drug-testing platform. Using CHK1i single-agent treatment as a test system, I

have demonstrated this system to be more predictive of in vivo CHK1i sensitivity than the

traditional 2D model. Taken together, these data suggest that CHK1i single-agent treatment has

potential use in cancer with high levels of endogenous replication stress which can be identified by

the presence of defective S phase checkpoint, overexpression/impaired degradation of replication

stress proteins such as CDC25A or presence of hyper-phosphorylation of RPA. The tumours which

are insensitive to CHK1i single-agent can also be sensitised to the drug by inducing replication

stress with low-dose HU.

iv

Declaration by author

This thesis is composed of my original work, and contains no material previously published or

written by another person except where due reference has been made in the text. I have clearly

stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical

assistance, survey design, data analysis, significant technical procedures, professional editorial

advice, and any other original research work used or reported in my thesis. The content of my thesis

is the result of work I have carried out since the commencement of my research higher degree

candidature and does not include a substantial part of work that has been submitted to qualify for

the award of any other degree or diploma in any university or other tertiary institution. I have

clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and,

subject to the policy and procedures of The University of Queensland, the thesis be made available

for research and study in accordance with the Copyright Act 1968 unless a period of embargo has

been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright

holder(s) of that material. Where appropriate I have obtained copyright permission from the

copyright holder to reproduce material in this thesis.

v

Publications during candidature

Gabrielli, B., Bokhari, F., Ranall, M. V., Oo, Z. Y., Stevenson, A. J., Wang, W., McMillan, N. A.

(2015). Aurora A Is Critical for Survival in HPV-Transformed Cervical Cancer. Mol Cancer Ther,

14(12), 2753-2761. doi: 10.1158/1535-7163.MCT-15-0506

Spoerri, L., Oo, Z. Y., Larsen, J. E., Haass, N. K., Gabrielli, B., & Pavey, S. (2015). Cell Cycle

Checkpoint and DNA Damage Response Defects as Anticancer Targets: From Molecular

Mechanisms to Therapeutic Opportunities. In G. T. Wondrak (Ed.), Stress Response Pathways in

Cancer: From Molecular Targets to Novel Therapeutics (pp. 29-49). Dordrecht: Springer

Netherlands.

Publications included in this thesis

No publications included.

vi

Contributions by others to the thesis

Figure 3.1 – Dose-response graph in Brooks et al.,2013 was further extended by previous Honours

students (Sheena Daignault and James Chen) from Gabrielli Laboratory.

Figure 3.13 & 6.2-6.6 – Technical assistance from Alex Stevenson (ARVEC facility UQDI) for the

overexpression screening and tumourspheres dose-response screening.

Figure 5.5 – Technical assistance from Max Ranall (ARVEC facility UQDI) for the tumourspheres

dose-response screening.

Figure 6.5 – Technical assistance from Catherine Lanagan (Research Assistant, Gabrielli

Laboratory).

Statement of parts of the thesis submitted to qualify for the award of another degree

None.

vii

Acknowledgements

None of this would have been possible without the scholarships I received from The University of

Queensland and all the support and opportunities provided by The University of Queensland

Diamantina Institute, Mater Research Institute and Translational Research Institute.

I would like to express my heartfelt thank you to my supervisor, Professor Brian Gabrielli, for

giving me the opportunities and guiding me throughout these years. Thank you for mentoring,

challenging and giving all the support. Your valuable advice and feedback have been really helpful

throughout this journey. Thank you also for encouraging me through those hard times and your

enthusiasm and passion in science were really infectious.

I also would like to thank my associate supervisors, Professor Nikolas Haass and Dr. Loredana

Spoerri. Thank you so much for reading my reports and providing me with all those valuable and

detailed feedbacks and all the advice and encouragement you gave during these years. To all the

past and present members of Gab lab and ARVEC, you guys were really great to be with and thank

you so much for creating an enjoyable and friendly place to work and all the help, support and

encouragement I received. Also thank you to all the members of Haass’s lab and Duijf’s lab for all

the enjoyable time together and all the support you gave me. I also deeply appreciated the help I got

from all the staffs in the TRI facilities.

Last but not least, I am indebted to all the unwavering support and encouragement provided by my

parents and my sister. I love you all very much and appreciate everything you have done for me.

Without your help and support, I never could have come so far.

viii

Keywords

checkpoint kinase 1, chk1, cell cycle checkpoint, melanoma, tumoursphere, 3d cell culture, drug

testing

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 111201 Cancer Cell Biology 20%

ANZSRC code: 111204 Cancer Therapy 50%

ANZSRC code: 111207 Molecular Targets 30%

Fields of Research (FoR) Classification

FoR code: 1112 Oncology and Carcinogenesis 60%

FoR code: 0604 Genetics 20%

FoR code: 0601 Biochemistry and Cell Biology 20%

ix

Table of Contents

List of Tables ................................................................................................................................... xiii

List of Figures ................................................................................................................................... xiv

List of Commonly Used Abbreviation .............................................................................................xvii

1 Introduction .................................................................................................................................. 1

1.1 Melanoma............................................................................................................................. 1

1.1.1 Current Diagnosis ............................................................................................................ 2

1.1.2 Genetics of melanoma ...................................................................................................... 5

1.1.3 Melanoma treatment ........................................................................................................ 9

1.2 DNA damage and response and repair mechanisms .......................................................... 14

1.2.1 Nucleotide Excision Repair (NER) ................................................................................ 15

1.2.2 Base Excision Repair (BER) .......................................................................................... 15

1.2.3 Single-strand break repair (SSBR) ................................................................................. 15

1.2.4 Double strand break repair (DBSR) ............................................................................... 15

1.2.5 Mismatch repair (MMR) ................................................................................................ 16

1.2.6 Targeting cell cycle checkpoint defects and DNA damage response in cancer treatment

16

1.3 Cell cycle and cell cycle checkpoints ................................................................................ 18

1.3.1 Cell cycle........................................................................................................................ 18

1.3.2 Cell cycle checkpoints ................................................................................................... 20

1.3.3 Cell cycle checkpoint signalling pathways .................................................................... 20

1.3.4 Melanoma and cell cycle checkpoints defects ............................................................... 23

1.4 Checkpoint kinase 1 (CHK1) ............................................................................................. 24

1.4.1 CHK1 function in S phase ............................................................................................. 24

1.4.2 CHK1 function in G2/M phase ...................................................................................... 25

1.4.3 CHK1 function in homologous recombination .............................................................. 26

1.4.4 CHK1 inhibition ............................................................................................................. 26

1.4.5 Combination of CHK1 inhibitors with chemotherapy ................................................... 27

1.5 Hypothesis .......................................................................................................................... 28

1.6 Aim..................................................................................................................................... 29

2 Material and Methods ................................................................................................................ 30

x

2.1 Materials............................................................................................................................. 30

2.1.1 Commonly used buffers and solutions ........................................................................... 30

2.1.2 Inhibitors and drugs ....................................................................................................... 30

2.1.3 Cell culture reagents (2D and 3D) ................................................................................. 31

2.1.4 Antibodies ...................................................................................................................... 32

2.1.5 siRNA and cell viability assays reagents and materials ................................................. 33

2.2 General cell culture methods .............................................................................................. 33

2.2.1 Cell lines and culturing conditions ................................................................................. 33

2.2.2 Cell Viability assay ........................................................................................................ 34

2.3 Melanoma tumourspheres culture methods and assays ..................................................... 34

2.3.1 Preparing non-adherent flasks for tumourspheres culture ............................................. 34

2.3.2 Melanoma tumourspheres culture .................................................................................. 35

2.3.3 Tumourspheres propagation and cryogenic preservation .............................................. 35

2.3.4 JC-10 tumourspheres viability assay.............................................................................. 35

2.3.5 CellTiter-Glo 3D cell viability assay ............................................................................. 36

2.4 Immunoblotting .................................................................................................................. 36

2.4.1 Immunofluorescence ...................................................................................................... 37

2.4.2 Micronuclei formation ................................................................................................... 37

2.4.3 Nucleotide incorporation assay (5-ethynyl-2’-deoxyuridine or EdU Assay) ................ 38

2.5 Immunohistochemistry....................................................................................................... 38

2.6 Transfection ....................................................................................................................... 39

2.6.1 Transfection optimisation .............................................................................................. 39

2.6.2 Transfection for CDC25A knockdown .......................................................................... 39

2.7 Transduction and screening of C013 cells ......................................................................... 39

2.8 Flow cytometry .................................................................................................................. 40

2.8.1 Cell cycle analysis with propidium iodide (PI) .............................................................. 40

2.8.2 Cleaved caspase-3 assay ................................................................................................ 40

2.9 Quantitative real-time PCR (qRT-PCR) confirmation of knockdown............................... 41

2.9.1 RNA extraction .............................................................................................................. 41

2.9.2 cDNA synthesis.............................................................................................................. 41

2.9.3 Quantitative Real-Time PCR (qRT-PCR)...................................................................... 41

2.10 Xenograft mice models ...................................................................................................... 42

2.11 Statistical analysis .............................................................................................................. 42

3 Melanoma cells with defective S phase checkpoint are selectively sensitive to CHK1 inhibition

43

xi

3.1 Introduction ........................................................................................................................ 43

3.2 Results ................................................................................................................................ 45

3.2.1 Classification of Melanoma cell lines on the basis of CHK1i sensitivity. ..................... 45

3.2.2 CHK1i-hypersensitive melanoma cell lines do not commonly have defective Fanconi

Anemia pathway responses ........................................................................................................ 46

3.2.3 CHK1i-hypersensitive melanoma cells have defect in S phase checkpoint .................. 47

3.2.4 Defect in communication between checkpoint activation and CDC25A degradation in

CHK1i-hypersensitive cell lines ................................................................................................ 51

3.2.5 Stabilised CDC25A is in part responsible for sensitivity to CHK1i .............................. 55

3.2.6 Inhibiting WEE1 increases sensitivity to CHK1 inhibitor ............................................. 59

3.2.7 CDC25A together with other replicative stress genes are commonly altered in

melanoma ................................................................................................................................... 61

3.2.8 Overexpression of CDC25A, Cyclin E and Cyclin A sensitises CHK1i-insensitive cells

to CHK1i .................................................................................................................................... 63

3.2.9 Hypersensitive cell lines develop high level of genomic instability after long-term low-

dose HU treatment ..................................................................................................................... 65

3.2.10 CHK1i, GNE323, shows single-agent activity in melanoma cell line in vivo ........... 66

3.3 Discussion .......................................................................................................................... 69

4 DNA-PK induced RPA2 hyper-phosphorylation renders melanoma cell hypersensitive to

CHK1 inhibition ................................................................................................................................. 73

4.1 Introduction ........................................................................................................................ 73

4.2 Results ................................................................................................................................ 74

4.2.1 Effect of pan-Caspase inhibitor and Caspase-2 inhibitor on CHK1i sensitivity in

CHK1i-hypersensitive cell lines ................................................................................................ 74

4.2.2 CHK1i promotes RPA2 hyper-phosphorylation in CHK1i-hypersenstive melanomas . 77

4.2.3 Reduction of E2F1 in CHK1i-hypersensitive cell lines but not in CHK1i-insensitive

cell lines after CHK1 inhibition ................................................................................................. 80

4.2.4 Inhibition of DNA-PK reduces CHK1i-induced RPA2 hyper-phosphorylation and

CHK1i-induced cell killing in hypersensitive melanoma cell lines ........................................... 82

4.2.5 DNA-PK and RPA levels are commonly altered in melanoma ..................................... 85

4.3 Discussion .......................................................................................................................... 86

5 Development of three-dimensional HTS-compatible in vitro drug testing melanoma model

with improved in vivo predictability .................................................................................................. 89

5.1 Introduction ........................................................................................................................ 89

xii

5.2 Results ................................................................................................................................ 90

5.2.1 Establishment of tumourspheres from different melanoma cell lines ............................ 90

5.2.3 Development of HTS-compatible tumourspheres assay ................................................ 95

5.2.4 CHK1 inhibitor sensitivity profile in tumourspheres model determined by JC-10

imaging assay ............................................................................................................................. 98

5.2.5 CHK1 inhibitor sensitivity profile in CellTiter Glo 3D cell viability assay .................. 99

5.2.6 Comparison between 2D and 3D ................................................................................. 103

5.2.7 Tumourspheres model predicts in vivo CHK1i sensitivity in melanoma cell lines ..... 106

5.3 Discussion ........................................................................................................................ 108

6 Concurrent addition of low level replication stress sensitised CHK1i-insensitive cancer cells to

CHK1 inhibition ............................................................................................................................... 111

6.1 Introduction ...................................................................................................................... 111

6.2 Results .............................................................................................................................. 113

6.2.1 Sensitisation of monolayer CHK1i-insensitive cell lines with CHK1i + low-dose

gemcitabine combination ......................................................................................................... 113

6.2.2 Sensitivity profile of CHK1i + low-dose HU combination in 3D melanoma

tumourspheres .......................................................................................................................... 115

6.2.3 Sensitivity profile of CHK1i + low-dose HU combination in normal cell lines ......... 119

6.2.4 Sensitivity profile of CHK1i + low-dose HU combination in lung cancer cell lines .. 121

6.3 Discussion ........................................................................................................................ 124

7 General Discussion .................................................................................................................. 127

7.1 The potential of CHK1 inhibitor as single-agent treatment ............................................. 130

7.2 CHK1 inhibitor in combination treatment ....................................................................... 131

7.3 Future direction ................................................................................................................ 132

7.4 Limitations ....................................................................................................................... 133

8 References ................................................................................................................................ 134

9 Appendices ............................................................................................................................... 155

xiii

List of Tables

Table 1-1: Tumor-Node-Metastasis (TNM) staging categories for cutaneous melanoma. ................. 4

Table 1-2: Anatomic stage groupings for cutaneous melanoma. ......................................................... 5

Table 2-1: Conditions for TaqMan Gene Expression qRT-PCR assays. ........................................... 41

Table 2-2: Sequence for CDC25A primers. ....................................................................................... 42

Table 5-1: The panel of melanoma tumourspheres cell lines developed with their morphology and

mutation status. .................................................................................................................................. 92

Table 5-2: Comparison of IC50 values from two different tumourspheres assays. ......................... 103

Table 6-1: IC50 values for a panel of melanoma cell lines treated with CHK1i HU. .................. 118

Table 6-2: IC50 values for a panel of lung cancer cell lines treated with CHK1i HU. ................ 123

xiv

List of Figures

Figure 1-1: A representation of normal skin anatomy. ........................................................................ 1

Figure 1-2: ABCDEs of melanoma skin cancer. .................................................................................. 3

Figure 1-3: Progression of melanocytes from metastatic melanoma. .................................................. 9

Figure 1-4: Resistance mechanism to RAF inhibitors. ...................................................................... 11

Figure 1-5: Immunotherapy for melanoma.. ...................................................................................... 13

Figure 1-6: The progression of cell cycle and Cyclins and CDKs. .................................................... 20

Figure 1-7: The cell cycle checkpoints. ............................................................................................. 21

Figure 1-8: Cell cycle checkpoints activated by different kinds of DNA damage. ........................... 23

Figure 3-1: IC50 values and the surviving fraction for a panel of melanoma cell lines, neonatal

foreskin fibroblasts (NFF), adult melanocytes (HEMa) and melanoblasts with the CHK1 inhibitor,

GNE323. ............................................................................................................................................ 46

Figure 3-2: FANCD2 and RPA foci formation in melanoma cell lines............................................. 47

Figure 3-3: CHK1i-hypersensitive cell lines have defect in S phase checkpoint. ............................. 49

Figure 3-4: S phase checkpoint defect is commonly present in CHK1i-hypersensitive and –sensitive

cell lines. ............................................................................................................................................ 50

Figure 3-5: CDC25A antibodies optimisation with siCDC25A in Hela cells. .................................. 52

Figure 3-6: CHK1i-hypersensitive melanoma cell lines are commonly defective in degradation of

CDC25A after replication stress imposed by HU. ............................................................................. 54

Figure 3-7: Optimisation of transfection efficiency for D20, D25 and MM329 with different

transfection reagents. ......................................................................................................................... 57

Figure 3-8: Assessment of CDC25A level in D20 cell line transfected with siCDC25A by

immunoblotting and real-time quantitative RT-PCR. ........................................................................ 58

Figure 3-9: CDC25A level regulates CHK1 inhibitor sensitivity. ..................................................... 59

Figure 3-10: Flow cytometry analysis of D28 and C013 cells for combination of CHK1 and WEE1

inhibitors. ........................................................................................................................................... 60

Figure 3-11: Western Blotting analysis of D28 and C013 cells for combination of CHK1 and WEE1

inhibitors. ........................................................................................................................................... 61

Figure 3-12: CDC25A together with other replicative stress genes are commonly dysregulated in

melanoma and other cancer types. ..................................................................................................... 62

Figure 3-13: Overexpression of CDC25A, Cyclin E1/2, Cyclin A and USP17L2. ........................... 64

Figure 3-14: CHK1i-hypersensitive cell lines displayed increased genomic instability after long-

term low-dose HU treatment. ............................................................................................................. 66

Figure 3-15: In vivo efficacy of CHK1 inhibitor, GNE323. .............................................................. 68

xv

Figure 4-1: Inhibition of Caspase-2 did not rescue CHK1i cytotoxicity. .......................................... 75

Figure 4-2: Pan-Caspases inhibition has effect in protecting cells from CHK1i-hypersensitivity. ... 76

Figure 4-3: Immunoblotting analysis of Bcl-2 and Bcl-XL in melanoma cell lines. ......................... 77

Figure 4-4: CHK1i treatment induced RPA2 hyper-phosphorylation in CHK1i-hypersensitive cell

lines. ................................................................................................................................................... 79

Figure 4-5: Cell viability was not relatively compromised with HU treatment compared to CHK1i

treatment in majority of CHK1i-hypersensitive cell lines. ................................................................ 80

Figure 4-6: CHK1i degraded E2F1 in CHK1i-hypersensitive cell lines. .......................................... 82

Figure 4-7: Inhibition of DNA-PK reduced RPA hyper-phosphorylation. ........................................ 83

Figure 4-8: DNA-PK inhibition protected cells from CHK1i hypersensitivity. ................................ 84

Figure 4-9: The expression level of all the subunits of RPA and PRKDC are commonly altered in

melanoma tumour samples from TCGA. ........................................................................................... 85

Figure 5-1: Images of the panel of melanoma tumourspheres in pHEMA-coated flasks. ................. 93

Figure 5-2: High-throughput drug screening workflow for melanoma tumourspheres model. ......... 95

Figure 5-3: MM329 tumourspheres were treated with control (DMSO) or 1 M CHK1 inhibitor for

72 hours and stained with JC-10 (10 g/ml). ..................................................................................... 96

Figure 5-4: Development of JC-10 imaging assay for tumourspheres. ............................................. 97

Figure 5-5: Dose-response curves of CHK1i-treated tumoursphere determined by JC-10 imaging

assay. .................................................................................................................................................. 99

Figure 5-6: The efficacy of two CHK1 inhibitors, GNE-323 and GDC-0575. ............................... 101

Figure 5-7: Dose-response curves of CHK1i-treated tumoursphere determined by CellTiter Glo 3D

cell viability assay. ........................................................................................................................... 102

Figure 5-8: Comparison of IC50 values from two different tumourspheres assays. ....................... 103

Figure 5-9: Changes in IC50 values from 2D melanoma cell lines to melanoma tumourspheres. .. 105

Figure 5-10: Assessment of the intrinsic DNA damage and CHK1 phosphorylation in 2D and 3D

melanoma models. ........................................................................................................................... 106

Figure 5-11: 3D melanoma model displayed improved predictability for the in vivo drug response.

.......................................................................................................................................................... 107

Figure 6-1: Low-dose gemcitabine sensitised CHK1i-insensitive cell lines. .................................. 114

Figure 6-2: Low-dose HU sensitised melanoma tumourspheres to CHK1i. .................................... 117

Figure 6-3: Surviving fractions for a panel of melanoma cell lines treated with CHK1i HU. ..... 119

Figure 6-4: Low-dose HU activity on primary human melanocytes adult (HEMa), neonatal foreskin

fibroblasts (NFF) and melanoblast cell lines. .................................................................................. 120

Figure 6-5: Low-dose HU sensitised lung cancer cell lines to CHK1i. ........................................... 122

Figure 6-6: surviving fractions for a panel of lung cancer cell lines treated with CHK1i HU. .... 123

xvi

Figure 7-1: Proposed model of CHK1 inhibitor hypersensitivity. ................................................... 130

xvii

List of Commonly Used Abbreviation

ATM ataxia telangiectasia mutated

ATR ATM and Rad3 Related

ATRIP ATR interacting protein

BrdU 5-bromo-2’-deoxyuridine

BRCA1 breast cancer associated 1

BRCA2 breast cancer associated 2

BCL-2 B-cell CLL/Lymphoma 2

BCL-XL B-cell lymphoma-extra large

BER base excision repair

BRAF v-raf murine sarcoma viral oncogene homolog B1

BSA bovine serum albumin

CDC25 cell division cycle 25

CDC25A cell division cycle 25 homolog A

CDC25B cell division cycle 25 homolog B

CDC25C cell division cycle 25 homolog C

CDK cyclin dependent kinase

CDKN2A cyclin-dependent kinase inhibitor 2A

cDNA complimentary DNA

CHK1 checkpoint kinase 1

CHK2 checkpoint kinase 2

CO2 carbon dioxide

DAPI 4’,6-diamidino-2-phenylindole

DDR DNA damage response

DTIC Dacarbazine

xviii

DMSO dimethyl sulfoxide

DMEM Dulbecco’s modified Eagle’s medium

DNA deoxyribonucleic acid

DSB double-stranded breaks

dsDNA double strand DNA

ssDNA single strand DNA

ECL enhanced chemiluminescence

Em emission

Ex excitation

FA Fanconi Anemia

FACS Fluorescent activated cell sorting

FANCD2 Fanconi anemia complementation group D2

G0 gap 0

G1 gap 1

G2 gap 2

GFP green fluorescent protein

H2AX histone 2A family member X

HR homologous recombination

HRP horse radish pyroxidase

HU hydroxyurea

IF immunofluorescence

kDa kilodaltons

M mitosis

MITF microphthalmia-associated transcription factor

mRNA messenger ribonucleic acid

mTOR mammalian target of rapamycin

xix

NaF sodium fluoride

NFF neonatal foreskin fibroblasts

NRAS neuroblastoma RAS viral (v-ras) oncogene homolog

NT non-targeting

OTP on target plus

p16 P16INK4A

PARP poly (ADP-ribose) polymerase

PBS phosphate buffered saline

PCNA proliferating cell nuclear antigen

PI propidium iodide

PLK1 Polo-like kinase 1

PMSF phenylmethylsulfonyl fluoride

POLR2A polymerase (RNA) II (DNA directed) polypeptide A

PTEN phosphatase and tensin homolog

qPCR quantitative polymerase chain reaction

RPA replication protein A

S synthesis

SDS-PAGE sodium dodecyl sulfate polyacrylamide gel electrophoresis

Ser serine

siRNA small interfering ribonucleic acid

TBS tris buffered saline

Thr threonine

Tyr tyrosine

UV ultra violet

WB western blot

Chapter 1

1

1 Introduction

1.1 Melanoma

Australia has one of the highest melanoma rates in the world with Queensland having the incidence

rate of 71 cases per 100,000 people. Melanoma is responsible for 10% of all cancer cases and the

fourth most common cancer in Australia. It is accountable for 75% of skin cancer deaths even

though only 2% of all skin cancers is diagnosed with melanoma (Australian Institute of Health and

Welfare, 2016, Melanoma Institute Australia, 2016). Melanomas are derived from melanocytes,

pigment-producing cells, which have neural crest origin. Melanocytes are derived as

undifferentiated precursors, melanoblast, from the neural crest and migrate to their final destination,

the epidermis and hair follicles (Figure 1-1), where they become mature melanocytes and

synthesize melanin in melanosomes.

Melanocytes also reside in the other places apart from cutaneous region such as the eye (e.g.,

conjunctiva, retina, and uveal tract) and mucosae (e.g., anal, buccal, and nasal). Melanomas

typically have dark colour owing to the presence of melanin. However, some melanomas do not

contain obviously visible melanin and are termed amelanotic. Benign neoplastic melanocytic

lesions are commonly termed naevi and melanoma can develop from pre-existing naevi in about 20-

40% of cases (Elder, 2006), with the remaining 60-80% of cases thought to occur de novo.

Figure 1-1: A representation of normal skin anatomy.

Chapter 1

2

The picture depicts the epidermis and dermis. The pull-out shows a close-up of the squamous cell

and basal cell layers of the epidermis. A melanocyte is shown in the layer of basal cells at the

deepest part of the epidermis. Source: (NCI Visuals Online Skin Cancer Foundation)

1.1.1 Current Diagnosis

Early identification of melanoma involves identifying the features of superficial spreading

melanomas such as with the ABCDE rules of melanoma recognition.

The ABCDEs of melanoma skin cancer are:

Asymmetry: One half doesn't match the appearance of the other half.

Border irregularity: The edges are ragged, notched, or blurred.

Colour: the colour (pigmentation) is not uniform. Shades of tan, brown, and black are present.

Dashes of red, white, and blue add to a mottled appearance.

Diameter: The size of the naevus is greater than 1/4 inch (6 mm), about the size of a pencil eraser.

Any growth of a naevus should be evaluated.

Evolution: There is a change in the size, shape, symptoms (such as itching or tenderness), surface

(especially bleeding), or colour of a naevus.

Chapter 1

3

Figure 1-2: ABCDEs of melanoma skin cancer.

Source: (NCI Visuals Online Skin Cancer Foundation)

Total-body photography can be utilised for patients with numerous atypical naevi. Handheld

devices such as Dermoscopy or epiluminescence microscopy offer fast and enlarged observation of

skin lesions. Relationships between Dermoscopy and histology have been proven and diagnosis are

based on colour, aspect and pattern of pigments and on the skin vessels. The TNM (tumour-node-

metastases) classification is used for staging melanoma. The American Joint Committee on Cancer

(AJCC) and Union for International Cancer Control (UICC) staging system is based on the

evaluation of primary tumour (T), with Breslow’s thickness and ulceration as the major prognostic

factors; the presence or absence of regional lymphatic metastases (N), with number of involved

lymph nodes as secondary factors of major prognostic significance; and distant metastases (M),

with metastatic site and lactate dehydrogenase (LDH) concentrations as factors of major prognostic

significance (Balch et al., 2009). The increased serum LDH was discovered to be able to forecast

the survival rate of patients with stage IV disease: whereas patients with normal serum LDH

displayed 65% 1-year survival rate and 40% 2-year survival rates, only 32% 1-year survival rates

and 18% 2-year survival rates for patients with increased level of LDH. Thus, patients with stage IV

disease are assigned as M1c regardless of their distant metastases if serum LDH level is elevated.

Chapter 1

4

Table 1-1: Tumor-Node-Metastasis (TNM) staging categories for cutaneous melanoma.

Source: (Balch et al., 2009)

Chapter 1

5

Table 1-2: Anatomic stage groupings for cutaneous melanoma.

Source: (Balch et al., 2009)

According to the AJCC staging system, stages I and II are primary melanomas without nodal or

distant metastases. Nodal involvement renders stage III, and nodal and distant metastases are

categorised as stage IV. The 5-year survival rate is over 98% in patients with a T1a (i.e., thin)

primary melanoma whereas the 5-year survival for patients with four or more clinically involved

lymph nodes decreases to 25%. Overall, patients with distant metastatic melanoma (i.e., stage IV)

have a poor prognosis. Patients in the AJCC M1a category have a 1-year survival rate of 62%.

Patients in the AJCC category M1b have lung metastases with a 1-year survival rate of 53%. This

decreases to a 33% 1-year survival rate in patients with distant metastases and an elevated serum

LDH (M1c).

1.1.2 Genetics of melanoma

Familial or hereditary melanoma can offer a useful insight into genetic factors contributing to the

abnormal growth and neoplastic transformation of a melanocyte even though they are accountable

for only a small proportion of melanomas (Olsen et al., 2010). One high-risk gene related with

melanoma is the cyclin-dependent kinase inhibitor 2A (CDKN2A) locus on chromosome 9, with

Chapter 1

6

approximately 25-50% of familial melanoma patients carrying mutation at this locus (Nelson et al.,

2009). p16INK4a

and p14ARF

are two important tumour suppressor proteins in cell cycle regulation

and are encoded by the CDKN2A locus. The genetic mutations at CDKN2A locus can alter

signalling of the retinoblastoma (Rb) and p53 pathways. p16INK4A

sustains the hypo-phosphorylated

state of retinoblastoma protein (Rb) to keeps it anti-proliferative and, as a result, causes replicative

senescence. Loss of p16INK4a

results in hyper-phosphorylating Rb protein, initiating transcription of

genes for S phase progression and promotes cell cycle progression (Harbour et al., 2000, Ezhevsky

et al., 1997). That will cause melanocytes to exist G1 arrest and enter G1/S transition.

The Melanocortin 1 receptor (MC1R), which is expressed on the cell surface of epidermal

melanocytes, is a crucial player in controlling skin colour and promoting eumelanin (brown/black

melanin) production when stimulated by α-melanocyte stimulating hormone (α-MSH). MC1R

variants are reported to have higher risk of melanoma development (Kennedy et al., 2001, Palmer et

al., 2000, Valverde et al., 1995): they are usually associated with red hair and fair skin phenotype

and have 5-15 fold increased risk in having BRAF-mutant melanomas depending 1 or 2 variants

carrier irrespective of chronic sun damage (Fargnoli et al., 2008, Landi et al., 2006).

Germline mutations in the gene encoding cyclin dependent kinase 4 (CDK4) exist in small

percentage of familial melanoma (Zuo et al., 1996, Soufir et al., 1998). This mutation renders the

protein resistant to p16INK4a

control while maintaining the interaction between CDK4 and cyclin D1

leading to activation of the complex and abnormal proliferation through retinoblastoma protein

inactivation and E2F activation. In approximately 90% of the melanomas, the culprit for the

advancement of the disease is the genetic lesions acquired after birth rather than inherited.

Numerous somatic mutations related to the disruption of major intracellular signalling pathways

responsible for proliferation and apoptosis have been identified.

BRAF, a serine/threonine kinase, is a component of the RAS-RAF-MAPK (mitogen-activated

protein kinase) signalling cascade. Many external stimuli such as growth-factor binding to receptor

tyrosine kinases and G-protein-coupled receptors activate this cascade and result in MAPK pathway

activation which in turn phosphorylates and regulates the activities of various transcription factors,

cytoskeletal components and other kinases involved in the regulation of cell survival, proliferation,

differentiation and motility (Kwong et al., 2012). The activating V600 mutation in the BRAF

serine/threonine protein kinase causes 1.3 to 700-fold over-activation of BRAF kinase activity and

consequently promotes the oncogenic activation of proliferative and survival signalling pathways in

melanoma cells (Garnett et al., 2004, Thomas, 2006, Gray-Schopfer et al., 2005).

Chapter 1

7

The mutation in the BRAF serine/threonine protein kinase was one of the first somatic mutations

occurs predominantly in melanoma (Davies et al., 2002). 52-66% of superficial spreading

melanoma and 43-55% of nodular melanoma harbour the BRAF mutations while they are less

common in both lentigo melanoma (14-20%) and acral lentiginous tumours (13-17%) (Platz et al.,

2008, Omholt et al., 2011, Greaves et al., 2013, Zebary et al., 2013). The melanomas on non-UV

exposed mucosal membranes carry the lowest BRAF mutation frequencies (0-9%) (Omholt et al.,

2011, Platz et al., 2008, Greaves et al., 2013). BRAF mutations usually harbour a single base pair

change leading to a glutamate for valine substitution at codon 600 in the kinase domain. This

mutation leads to activation of BRAF kinase and results in abnormal stimulation of the mitogen-

activated protein kinase/extracellular signal-regulated kinase (MEK) and extracellular signal-

regulated kinase (ERK) pathway leading to increased activity in several critical pathways involved

in proliferation and survival. Despite of its prevalence in melanoma, the exact time point for the

occurrence of BRAF mutation is elusive, with some suggesting its early occurrence, it is present in

>89% of benign naevi (Pollock et al., 2003), while others suggesting that it happens during

transition from RGP (radial-growth phase) to VGP (vertical-growth phase) (Greene et al., 2009).

BRAF mutation usually works together with other mutations such as loss-of-function mutations in

p53 (Curtin et al., 2006).

Phosphatase and tensin homolog (PTEN) which is located on chromosome 10q23-24, a region that

is frequently lost at an early stage of melanoma, is a phosphatidylinositol phosphate phosphatase

and is frequently inactivated in melanoma (Fountain et al., 1990). PTEN acts as a tumour

suppressor protein and promote apoptosis by blocking the anti-apoptotic activity of the AKT

signalling pathway by modulating the levels of phosphatidylinositol phosphate (PIP3) which

recruits PDK1 and triggers the activation of the serine/threonine kinase AKT. Even though PTEN

allelic loss or altered expression are responsible for 20% and 40% of melanoma, nevi (both benign

and dysplastic) retain PTEN expression (Tsao et al., 2003). PTEN and BRAF mutations are

frequent concurrent events in melanoma, with this dual mutation participating in disrupting both the

mitogen-activated protein kinase (MAPK) and AKT signalling pathways (Haluska et al., 2006), and

causes melanoma progression (Figure 1-3).

Micropthalmia-associated transcription factor (MITF) is regarded as the key regulator of

melanocyte development, differentiation and pigmentation. MITF belongs to the MYC supergene

family of basic helix loop helix transcription factors. Recent data indicated that MITF also play a

role in the control of proliferation, survival and the pathogenesis of melanoma (Bertolotto et al.,

2013). Despite its genomic amplification in 10% of primary and 20% of metastatic melanomas and

correlates with decreased 5 year overall patient survival, there is no strong increased MITF

Chapter 1

8

expression at the protein level (Rees et al., 2000, Strum et al., 2002). This paradoxical function of

MITF can be explained by a variation in its level of expression, its different cofactors and its post-

translational modification.

c-KIT is a type III tyrosine kinase receptor and plays an important role in melanocyte migration

from neural crest to the dermis during development (Masson et al., 2009). Binding of its ligand,

stem cell factor (SCF), leads to dimerization of receptors, auto-phosphorylation, and activation of

the several signalling pathways. Activated receptors trigger numerous downstream events and the

exact role of the receptor in malignant transformation and melanoma progression is complex. It

was reported previously that there was a loss of c-KIT expression during progression from benign

nevi to metastatic melanoma (Montone et al., 1997). Recently, there is evidence that c-KIT is an

important oncogene and activated in only 2-6% of cutaneous melanomas though higher proportion

of KIT mutations have been reported in melanomas of acral (11%), mucosal (21%) and chronic

sun-damaged/lentiginous (17%) types (Curtin et al., 2006). Interestingly, it was also shown that

K642E and L576P, two most common c-KIT mutations in melanoma could transform melanocytes

only when they are under hypoxic conditions or introduced exogenous hypoxia inducible factor 1

(HIF-1) (Monsel et al., 2010).

The transcription factor p53 which is encoded by TP53 gene is activated by various stresses such as

DNA damage, hypoxia or expression of aberrant oncogene. p53 regulates many genes involved in

cell cycle regulation (CDKN1A), induction of autophagy, senescence and apoptosis (NOXA,

PUMA and BAX), as well as genes involved in the DNA repair or cellular metabolism. The

importance of p53 is highlighted by the fact that TP53 knockout mice spontaneously develop

tumour (Donehower et al., 1992). Despite the prevalence of TP53 mutations in human cancers

(approximately 50%), the rate is lower in melanoma. Only 19% of melanoma cases were found to

harbour p53 mutation (Hodis et al., 2012). However, upstream regulators and downstream effectors

of p53 signalling cascade are deregulated. Benign melanocytic hyperplasia that resemble nevi

appear in mouse and zebrafish model with oncogenic BRAF or RAS while melanomas develop in

p53-deficient background (Masson et al., 2009, Razumovskaya et al., 2009, Montone et al., 1997,

Curtin et al., 2006). MDM2, the E3 ubiquitin ligase responsible for p53 ubiquitylation and

degradation via the proteasome, is frequently overexpressed in melanoma cell lines (Muthusamy et

al., 2006). Recently, it has been reported that MDM4 is upregulated in about 65% of stage I-IV

human melanomas (Gembarska et al., 2012). P53 also prevents melanoma invasion and

development of more aggressive tumours by negatively controlling hypoxia-inducible factor 1α

(Ibrahim et al., 2009, Donehower et al., 1992). Furthermore, p53 suppress EMT (Epithelial-

Mesenchymal Transition) by repressing expression of ZEB1, ZEB2 and SNAIL through regulation

Chapter 1

9

of microRNA including members of the miR-200 family and miR34 (Kim et al., 2011a, Kim et al.,

2011b).

Figure 1-3: Progression of melanocytes from metastatic melanoma.

Source: (Vultur et al., 2013)

1.1.3 Melanoma treatment

Metastatic melanoma which has disseminated to distant sites and organs is almost always incurable

with a median survival rate of only 6-9 months, 25% 1 year survival rate and 3 year survival of 15%

(Balch et al., 2009, Atkins et al., 2000). Interferon-a2b (IFN-a2b) and interleukin-2 (IL-2) are two

FDA-approved biological response modifiers for metastatic melanoma though their efficacies are

quite limited. Large, randomized, observation-controlled trials have demonstrated a 10-20%

improvement in relapse-free survival, but no clear effect on melanoma-related mortality (Tarhini et

al., 2006, Agarwala et al., 1996). High-dose interlecukin-2 (IL-2) has been approved by the FDA

for stage IV melanoma because of its ability to bring durable responses in small percentage of

patients (Atkins et al., 2000, Atkins et al., 1999). There are severe toxicities associated with IL-2

therapy and the responses are short-lived, durable responses only account for 10-20% of patients.

The alkylating agent Dacarbazine (DTIC) is the only FDA-approved cytotoxic alkylating agent,

though there are only low and transient responses (10-20 % of patients) and other

chemotherapeutics such as carmustine (BCNU), taxanes and platinum-analogs also have similar

efficacy profiles in the metastatic setting (Tarhini et al., 2006, Wagner et al., 2000, Mays et al.,

Chapter 1

10

1999, Chapman et al., 1999, Middleton et al., 2000). All patients with distant metastasis are

considered appropriate candidates for clinical trials as none of the existing conventional therapies

alter the natural history of the disease for the population as a whole. However, this grim landscape

was recently changed dramatically with the emergence of two distinct therapeutic approaches,

immunotherapy and targeting mutations in tumour cells, which have shown survival benefits for

patients with metastatic melanoma.

1.1.3.i Treatment for BRAF-mutant melanoma

Vemurafenib, a potent inhibitor of the kinase activity of mutant BRAF, decreased cell proliferation

and viability by reducing levels of phosphorylated ERK and cyclin D1 (Tsai et al., 2008).

Vemurafenib showed single-agent clinical activity with tumour shrinkage in 26 of 32 (81%)

patients with BRAF mutant melanoma in multicentre phase I clinical trial (Flaherty et al., 2010). In

Phase II trial, Vemurafenib displayed a confirmed overall response in 53% out of 132 BRAF

mutant melanoma patients with median progression-free survival of 6.8 months (Sosman et al.,

2012). In a Phase III randomized trial, Vemurafenib also showed significantly improved overall and

progression-free survival over Dacarbazine: The media overall survival of 13.2 months with

Vemurafenib compared to 9.9 months with Dacarbazine. The most common adverse effect of

Vemurafenib was the development of cutaneous squamous-cell carcinomas and it occurred in about

26% of patient while many of these tumours harbour HRAS mutations (Su et al., 2012). That side-

effect was related to the paradoxical transactivation of CRAF in keratinocytes as a result of BRAF

inhibition (Oberholzer et al., 2012). However, it can be removed surgically and did not result in

discontinuation of BRAF inhibitor therapy.

Dabrafenib, a reversible ATP-competitive inhibitor, selectively inhibits BRAF V600E and is similar

to Vemurafenib apart from having a shorter half-life (Falchook et al., 2012). Dabrafenib displayed

response in 59% of total 76 patients with median progression free survival of 6.3 months. In Phase

III clinical trial, Dabrafenib showed 5.1 months median progression free survival compared to 2.7

months with Dacarbazine in patients with stage IV or unresectable stage III BRAF mutant

melanoma. Both Vemurafenib and Dabrafenib have comparable results and anti-tumour activities.

Some adverse events related to hyperkeratosis, papillomas, pyrexia, fatigue and arthralgia caused

dose reduction in 28% of the patients while 6% of the patients experienced Squamous-cell

carcinomas or keratoacanthomas.

Trametinib is the orally bioavailable inhibitor of MEK 1 and MEK 2 resulting in an inhibition of

growth factor-mediated cell signalling and cellular proliferation. In Phase III trial, Trametinib

Chapter 1

11

showed improved median progression-free survival of 4.8 months compared to 1.5 months in

chemotherapy group in patients with metastaic melanoma with BRAF mutation (Flaherty et al.,

2012). The safety profile of Trametinib was different from that of Vemurafenib and Dabrafenib.

Even though side-effects of Trametinib associate with diarrhoea and peripheral oedema frequently,

incidents of photosensitivity, arthralgia and pyrexia were less often than with BRAF inhibitors and

interestingly there were no cutaneous squamous cell carcinomas reported with Trametinib. 7% of

patients developed decreased ejection fraction or ventricular dysfuntion and resulted in permanent

discontinuation in 1% of the patients. Another adverse effect was blurred vision which occurred in

9% of patients.

Figure 1-4: Resistance mechanism to RAF inhibitors.

(modified from (Vultur et al., 2013))

Chapter 1

12

1.1.3.ii Mechanism of resistance to BRAF inhibitors

Multiple resistance mechanisms have been identified, including those that lead to reactivation of the

MAPK pathway and other pathways, such as the PI3K-AKT-mTOR and VEGF pathways. In spite

of the flicker of hope offered by striking responses and anti-tumour effects of BRAF inhibitors in

melanoma, therapeutic potential of this approach has been hindered by intrinsic and acquired

resistance. The clinical indication of tumour resistance starts at a median of 5-7 months after initial

tumour regression. Generally, resistance can be developed via MAPK-dependent and MAPK-

independent pathways. The secondary mutations in NRAS and MEK were found to be present in

the samples from the patients who became resistant to BRAF inhibitor (Nazarian et al., 2010,

Wagle et al., 2011). In some cases, resistance develop by activating another pathway such as

overexpression of PDGFR- or IGF1R to promote oncogenic signalling through PI3K-Akt-mTOR

pathway activation (Shi et al., 2011, Villanueva et al., 2010). It was also reported that restoration of

MAPK signalling is associated with increased VEGF production (Sharma et al., 2005).

1.1.3.iii Immunotherapy

Despite being a highly immunogenic tumour, metastatic melanoma cells develop mechanism to

evade the detection from immune system in order to survive. The fact that melanoma incidence

were prevalent in immunosuppressive patients indicates the protection immune system offers

against melanoma. It was also shown that some melanomas infiltrated by T-lymphocytes showed

spontaneous regression (Oble et al., 2009). Melanoma has been a major focus of study of cancer

immunotherapies due to the occurrence of spontaneous regression in primary tumours, the

association with tumour-infiltrating lymphocytes and the detection of antigen-specific cytotoxic T

cells and antibodies in patients with melanoma (Clark et al., 1989, Ferradini et al., 1993).

Chapter 1

13

Figure 1-5: Immunotherapy for melanoma.

Ipilimumab is a humanized antibody against CTLA-4. CTLA-4 is a key receptor in

immunosuppression and blocking CTLA-4 in melanoma patients can stimulate the immune system.

Acting in a similar way, the anti-PD-1 antibody has shown favorable and durable responses.

Source: (Vultur et al., 2013)

Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) is necessary for regulatory T cells to reach

their developed stage. CTLA-4 is a member of the immunoglobulin receptor superfamily and is

responsible for producing an inhibitory signal reducing immune responsiveness after being

activated by ligand expressed on antigen-presenting cells. Binding of CTLA-4 to B7.1 and B7.2, the

ligands on T-cells, resulted in inhibiting activity of T cells (Iida et al., 2000). CTLA-4 restricts

antitumour immunity by extenuating the activation and proliferation of T cell. Human monoclonal

antibodies to CTLA4 (Ipilimumab and Tremelimumab) have been investigated in all the phases of

the clinical trials and Tremelimumab failed to show its superiority over standard chemotherapy in

Phase III clinical trial with advanced stage melanoma patients and as a result was discontinued.

However, Ipilimumab was shown to improve patient survival in Phase III clinical trial with

previously treated metastatic melanoma (Hodi et al., 2010). An additional Phase III study also

showed that combination of CTLA4 blockade and DNA-damaging chemotherapeutic drug

Dacarbazine also improved survival with a similar adverse event profile to those previous studies

(Robert et al., 2011a).

Chapter 1

14

The programmed death 1 (PD-1) receptor is an inhibitory receptor expressed at the surface of

activated T cells. T-cell’s ability to target the tumour cells is inhibited by binding of PD-1 to

programmed death ligand-1 (PDL-1) expressed on cancer cells. The major role of PD-1 is to limit

the activity of T cells in peripheral tissues at the time of an inflammatory response to infection and

to limit autoimmunity and this turns into a major immune resistance mechanism within the tumour

microenvironment. When monoclonal antibody lambrolizumab (MK-3475) was assessed in

metastatic or unresectable melanomas, an objective response was achieved in 38% of patients and

sustained in the majority of patients (Hamid et al., 2013). Phase I trial for combination of

nivolumab (BMS-936558), another PD-1 blocking antibody, and ipilimumab showed that combo

provided long lasting tumour responses in patients with advanced melanoma (Wolchok et al.,

2013).

There are a number of major challenges when it comes to melanoma treatment. Even though five-

year relative survival rates for patients with localised disease is over 90%, advanced stage

melanoma is still one of the most deadly solid cancers (Jemal et al., 2010). Over the past 40 years,

there has been little improvement in the survival for these patients and metastatic melanoma

remains a significant clinical problem. Recent targeted therapies have increased response rates and

durations (Belden et al., 2012, Chapman et al., 2011, Atkins et al., 1999), even though rapidly

developed resistance through molecularly diverse mechanisms is a major roadblock. Similarly,

immunotherapies which are producing durable responses are effective in only a fraction of patients,

and resistance to these therapies is now being found (Ribas et al., 2016, Zaretsky et al., 2016).

Therefore, identification of new selective targeted therapies for melanoma, increasing the efficacy

of current therapies and understanding the molecular basis of resistance are critical steps toward

improving outcomes for the large proportion of patients where no effective treatments are currently

available.

1.2 DNA damage and response and repair mechanisms

Various exogenous sources such as radiation (ultraviolet and ionising), chemical agents such as

alkylating agents, targeted drugs such as topoisomerase I or II inhibitors, environmental pollution

and tobacco smokes can affect the integrity of DNA. At the same time, DNA is also prone to

endogenous damage arise from different factors such as reactive oxygen species (ROS),

spontaneous hydrolysis and alkylation and DNA mismatches, insertions and deletions resulted from

DNA repair processes themselves (McCulloch et al., 2008).

Chapter 1

15

1.2.1 Nucleotide Excision Repair (NER)

NER is important repair mechanism to displace bulky DNA adducts produced by ultraviolet (UV)

radiation, chemicals or ROS. NER consists of two sub-pathways: global genome NER (GG-NER)

which handles the damage present in the entire genome, and transcription-coupled repair (TC-NER)

which targets the damage in transcriptionally active genes (Naegeli, 1995). Defects in NER are

mostly hereditary and can result in UV sensitivity and skin cancer development (Andressoo et al.,

2005).

1.2.2 Base Excision Repair (BER)

BER is responsible for expunging various endogenous and exogenous DNA damage, mostly

correcting lesions caused by oxidation, deamination and alkylation. DNA glycosylase detects and

dislodges the damaged base and the process is further conducted by sub-pathways such as short-

patch repair and long-patch repair (Krokan et al., 2013). BER occurs in both nuclear and

mitochondrial DNA, playing an important role in preventing cancer, aging and neurodegeneration

(Dianov et al., 2013).

1.2.3 Single-strand break repair (SSBR)

Single-strand breaks (SSBs) is one of the most frequent types of DNA damages. SSBs in DNA are

usually caused by endogenous reactive oxygen species (ROS), the process of BER for damaged or

altered bases, external causes such as IR or UV radiation or chemical agents such as

chemotherapeutics drugs. SSBs usually result in stalled replication forks in S phase and lead to

double-strand breaks (DSBs) and replication fork collapse if not repaired. SSBs are promptly

recognised by poly (ADP-ribose) polymerase protein together with GG-NER pathway to discover

and repair the SSBs through all the phases of the cell cycle.

1.2.4 Double strand break repair (DBSR)

DNA DSBs can be produced by various external factors such as IR, ROS and chemotherapeutic

drugs and topoisomerase inhibitors. DSB can also occur naturally at chromosome ends due to

shortening of telomeres during senescence. They present a serious threat to the integrity of DNA

and, if not repaired, can lead to mutations and chromosomal breaks (Jackson et al., 2009). DSBR

involves two major pathways: homologous recombination (HR) and non-homologous end-joining

(NHEJ).

Chapter 1

16

Homologous recombination is an intricate repair mechanism which occurs during late S to M phase

when there is a sister chromatid to provide the template required for this high-fidelity repair

process. Repair involves a large number of components including RAD51, MRE11, NBS1 and

BRCA1. This pathway requires homologous template which serves to provide accurate resynthesis

of the damage DNA (Li et al., 2008). HR is one of the most important repair pathways dealing with

stalled replication forks, single-ended DSB and DNA interstrand cross-links (ICLs) because of its

high-fidelity repair even though it handles only small portion of DSBs. HR is commonly defective

breast and ovarian cancer (King et al., 2003) but has not been associate with melanoma.

NHEJ repairs DSBs and is active in all stages of cell cycle without requiring the undamaged

template. Ku heterodimer consisting of Ku70 and Ku80, with DNA dependent protein kinase

catalytic subunit (DNA-PKcs) and DNA ligase IV are involved in NHEJ process. NHEJ promotes

the direct bridging and ligation of broken DNA ends with minimal end processing. Although NHEJ

is commonly utilised DSB repair pathway, it can be error-prone since deletions or insertions can be

occurred at the sites of repair (Lieber et al., 2010).

1.2.5 Mismatch repair (MMR)

MMR pathway corrects the errors originated from normal DNA replications such as incorporation

of wrong nucleotides, nucleotides deletions or insertions errors and problems that missed replicative

polymerases. Thus, MMR helps maintain genomic integrity by protecting dividing cells from

carrying over permanent mutation. MMR process involved detecting the mismatch pair, excision of

the 5’ and/or 3’ of the mismatch and removal of the error base and resynthesis to fill the gap and

ligation (Stojic et al., 2004). Defects in MMR cause cancer susceptibility and also perturb metabolic

and damage respond signalling pathways in DNA. MMR mutations are related to hereditary non-

polyposis colon cancer (HNPCC) caused by MMR defects failing to fix errors from DNA

polymerase slippage in the genome (Cleaver et al., 2009).

1.2.6 Targeting cell cycle checkpoint defects and DNA damage response in cancer

treatment

Defects in the cell cycle checkpoints or problems in DNA damage repair mechanisms will increase

endogenous damage levels in cancer cells and can drive genomic instability and add more

susceptibility to cancer. Cancer cells need to keep this increased level in endogenous stress to the

tolerable level to survive by relying further on alternative stress response mechanisms or developing

novel mechanisms or an adapting to accommodate the stress. These changes in the behaviour of

Chapter 1

17

cancer cells can significantly affect the cancer treatment outcomes: cancer cells with upregulation

of DDR pathways can become resistant to chemotherapy and radiotherapy while downregulation

can cause dependence on an alternative pathway. This increased dependence on a compensatory

pathway can be exploited by selectively targeting the cancer cells while unimpaired checkpoints

and repair pathways protect normal cells.

Increase in genomic instability can result from dysregulation of CDK activity caused by the defects

in DNA damage and mitotic checkpoints. Various CDK inhibitors have been investigated in clinical

trials mostly with solid tumours and leukaemia (Cicenas et al., 2011). CDK inhibitors were

demonstrated to be cytotoxic in combination with chemotherapy and overcome the drug resistance

in cancer cells despite their modest single-agent activities (Lapenna et al., 2009). First generation

CDK inhibitors, Flavopiridol and Roscovitine, were evaluated in clinical trials: Flavopiridol in

combination with Fludarabine and Rituximab, demonstrated 80% response rate in patients with

mantle-cell lymphoma (Lin et al., 2010). However, Flavopiridol only displayed low clinical activity

in another Phase II trial with several solid tumour types (Asghar et al., 2015). Together with

Flavopiridol, Roscovitine was also the one of the first agents to be investigated in the clinical trials.

Partial response was observed in only one patients out of the 56 patients treated in Phase I trial.

Second-generation of CDK inhibitors with increased selectivity are currently under development

and, among those, Dianciclib (MK-7965) has been extensively studied in patients. In phase I

clinical trials, Dianciclib demonstrated promising results: stable disease in different cancer types

with tolerable toxicity profiles. However, the results from randomised Phase II trials with advanced

breast cancer and non-small cell lung cancers were disappointing (Mita et al., 2014, Stephenson et

al., 2014). Recently new generation of more selective CDK4/6 inhibitors, such as Abemaciclib,

Palbociclib and Ribociclib, showed promising antitumour acitivity with manageable toxicity and

currently in phase III trials. Among them, Palbociclib was recently approved by FDA to treat

hormone receptor (HR)-positive, advanced-stage breast cancer (Cristofanilli et al., 2016, Finn et al.,

2015).

BRCA1 and BRCA2 are important players in homologous recombination repair, and mutations in

these genes significantly increase the risk of susceptibility to breast cancer and other cancers such

as ovarian, prostate, pancreatic and male breast cancer (Wooster et al., 2003). BER compensates the

loss of BRCA-mediated HR repair and PARP inhibitors exploit this by blocking BER activity and

this cause selective lethality to BRCA-mutant cells with only minimal effect on wild type cells

(Ashworth, 2008).

Chapter 1

18

1.3 Cell cycle and cell cycle checkpoints

Deregulation of cell cycle regulatory mechanisms and the cell cycle checkpoints is a common

feature of all cancers and has been well described in melanoma (Brooks et al., 2014, Pavey et al.,

2013, Kaufmann et al., 2008). Cell cycle checkpoints are the mechanisms that respond to

endogenous and exogenous stresses, and their loss provides an obvious growth advantage for the

cancer over normal tissue. However, these checkpoints are also protective mechanisms, and thus

targeting cells with defective checkpoints should provide a means of selectively destroying the

cancer cells. An example of this is in the cancer prone syndrome Ataxia telangiectasia, where

mutation of the gene responsible Ataxia telangiectasia mutated (ATM), which is responsible for

signalling cell cycle checkpoint responses to DNA double strand breaks (DSBs), results in cells

with this defect being hypersensitive to ionising radiation that produces DSBs (Lavin et al., 2006,

Lavin et al., 1997).

1.3.1 Cell cycle

The cell cycle consists of a series of process governing cell division. There are four phases of cell

cycle comprising two gap phases G1 and G2 where the cells grow, S phase where DNA is

replicated and M-phase (mitosis and cytokinesis) where cell divides into two identical daughter

cells. During M-phase, condensation of chromosomes (prophase) occurs and then attach to the

spindle microtubules (prometaphase) and are subsequently aligned at the equator of the mitotic

spindle (metaphase) and are segregated into two sister chromatids (anaphase). Cytokinesis occurs as

the final step of M-phase and the cell is then divided into two daughter cells. The serial process of

the cell cycle phases is governed by the activities of two key classes of proteins, cyclin-dependent

kinase proteins (CDKs) and cyclins in phase specific manner.

Activation of Cyclin D/CDK4/6 and Cyclin E/CDK2 is required for the cells to progress into S

phase from G1. There is also certain degree of overlapped functions between these two complexes

since Cyclin E/CDK2 is able to perform both functions in cells devoid of Cyclin D/CDK4 activity

(Gray-Bablin et al., 1996). The Cyclin D and E/CDK complexes participate in phosphorylating the

retinoblastoma susceptibility protein (Rb) and then enable activation of Rb-bound E2F which is

required to express the genes necessary for S phase progression (Giacinti et al., 2006). In addition,

Cyclin D and E/CDK complexes also play important roles in DNA replication as well (Ohtsubo et

al., 1995, Pagano et al., 1992, Strausfeld et al., 1996). Progression through G2 phase into mitosis is

controlled through Cyclin B/CDK1 activation which is influenced positively by CDC25 family of

dual specificity phosphatases and negatively by Wee1. Cyclin B/CDK1 complex consists of the

Chapter 1

19

catalytic subunit CDK1 and its positive regulatory subunit Cyclin B. Binding of stably expressed

CDK1 subunit by Cyclin B subunit during S and G2 phases makes the complex active. The Cyclin

B/CDK1 complex is mediated by CDK activating kinase (CAK) via activating phosphorylation at

Thr161 (Jeffrey et al., 1995). The two inhibitory sites, Thr14 and Tyr15, are regulated by WEE1

and Myt1 in keeping the complex inactive (Welburn et al., 2007). CDC25 protein phosphatases

dephosphorylate Thr14 and Tyr15, activate CDK1-cyclin B complex and drive entry into mitosis

Figure 1-6. At the end of the mitosis, the anaphase promoting complex/cyclosome (APC/C)

degrades Cyclin B subunit and let the cell exit from mitosis. The recombinant human CDC25C was

reported to drive cells’ entry into mitosis in Xenopus egg extracts demonstrating the control of

CDC25A in mitotic entry (Gabrielli et al., 1992). In similar fashion, CDK4/Cyclin D is regulated in

G1 phase via phosphorylation of Thr17 residue and later dephosphorylated by CDC25A before

entry into S phase (Iavarone et al., 1997, Terada et al., 1995). Both CDK2/Cyclin E, which

regulates S phase progression, and CDK2/ Cyclin, which has functions in both S phase and G1

phase, were handled in similar manner to CDK1/Cyclin B complex.

There are three CDC25 paralogues such as CDC25A, CDC25B and CDC25C in higher eukaryotes

which have a conserved C-terminal catalytic domain but greatly different N-terminal regions. The

one of the major functions of CDC25A is to facilitate the cellular entry into S phase though it

remains to persist throughout the remaining cell cycle. It activates CDK2/Cyclin E and A

complexes and promotes progression into S phase (Hoffmann et al., 1994, Jinno et al., 1994).

CDC25B is active from late S phase to mitotic exit and needed to enter into mitosis (Gabrielli et al.,

1996, Goldstone et al., 2001, Karlsson et al., 1999). Even though CDC25C is stably expressed in all

phases of cell cycle, it becomes only active in mitosis (Gabrielli et al., 1997, Hoffmann et al., 1993)

and there is also suggestion that CDC25C has a role in S phase and DNA replication (Turowski et

al., 2003).

Chapter 1

20

Figure 1-6: The progression of cell cycle and Cyclins and CDKs.

(modified from (Spoerri et al., 2015)).

1.3.2 Cell cycle checkpoints

Cell cycle checkpoints ensure that correct genomic integrity is passed down to next cell generation.

When checkpoints are triggered, the CDKs that regulate that particular cell cycle phase are

generally inhibited, although in the case of the mitotic checkpoint, CDK activity is maintained and

the inhibition of CDK activity required for exit from mitosis is blocked. This results in cell cycle

arrest, blocks cells from progressing into the next cell cycle phase, and provides time for DNA

repair to occur. If the damage is extensive and beyond repair, cells either progress into senescence

or apoptosis which can occur as the direct consequence of extended checkpoint activation.

Different types of DNA damages were countered by four main cell cycle checkpoints (Figure 1-7).

The G1 phase checkpoint blocks cells progression into S phase by deterring the replication of

impaired genomic material. The S phase checkpoint stops and slows down DNA replication via

responding to stalled and abnormal replication forks. The G2 checkpoint blocks cells with damaged

DNA to enter into mitosis while the spindle assembly checkpoint permits cells to progress into

mitosis only after proper connection of chromosomes to the mitotic spindle.

1.3.3 Cell cycle checkpoint signalling pathways

Two major players in checkpoint signalling are the ataxia telangiectasia mutated (ATM) and ataxia

telangiectasia and Rad3-related (ATR) proteins which are critical for the maintenance of genome

integrity and play a central role in the evolution of cancer. ATM is mainly triggered by double

Chapter 1

21

strand DNA breaks (DSBs), while ATR primarily responds to the presence of single-stranded DNA

(ssDNA). When activated, ATR and ATM phosphorylate and activate checkpoint kinase 1 (CHK1)

and checkpoint kinase 2 (CHK2) proteins respectively, which in turn target various proteins such as

CDK/Cyclins and CDC25 phosphatases in deterring cell cycle progression (Figure 1-8). ATM/ATR

and CHK2/CHK1 can also arrest the cell cycle by targeting p53 protein to increase the transcription

of factors such as p21CDKN1A

, GADD45 and 14-3-3 proteins (Elledge, 1996, Elledge et al., 2000,

Ciocca et al., 2000, Huang et al., 2000, Schulman et al., 2000, Tibbetts et al., 2000, Wang et al.,

2000, Zhou et al., 2000). ATM/ATR signalling pathway also regulate PLK1 activity which is also

positively regulated by Aurora A protein kinase and Bora and involve in another mitotic regulatory

pathway (Smits et al., 2000, van Vugt et al., 2001).

Figure 1-7: The cell cycle checkpoints.

(Source: (Pavey et al., 2013)

1.3.3.i The ATM/CHK2 pathway

ATM, a large serine/threonine kinase belongs to the family of Phosphatidylinositol 3-kinase-related

kinases (PIKKs), mainly deals with double-stranded DNA breaks cuased by radiation and

genotoxins by regulating serine/threonine kinase CHK2 to create cell cycle checkpoint arrest.

CHK2 is phosphorylated by ATM on threonine residue 68 (Ahn et al., 2000) and acts on various

Chapter 1

22

proteins such as p53, MDMX which regulates p53, CDC25 phosphatases, BRCA1 and transcription

factors such as FOXM1 and E2F1 (Lukas et al., 2003).

1.3.3.ii The ATR/CHK1 pathway

While DNA double-strand breaks (DSB) were dealt by ATM-CHK2 pathway, ATR-CHK1 pathway

mainly interacts with single stranded DNA (ssDNA) which can be produced by various stresses

such as UV radiation, DNA replication inhibition, virus infection, inter-strand DNA cross-link and

DSB end resection. ATR which is also another member of PIKK family like ATM regulates

multiple protein substrates among which CHK1 is one of the key proteins. CHK1, a

serine/threonine kinase, is phosphorylated by ATR at Serine 317 and 345 and lesser extent at Serine

366 (Zhao et al., 2001, Walker et al., 2009, Liu et al., 2000).

DNA damage response (DDR) responds to DNA lesions by orchestrating a range of events to

maintain genomic stability and cell survival. DNA damage repair is a tightly regulated event and

involved various sensor proteins such as MRN (MRE11-RAD50-NBS) sensor complex and

Replication Protein A (RPA) which detect the presence of DSB and ssDNA breaks respectively.

The MRN complex attracts ATM and also participates in various places of DNA damage response

such as DSB recognition and processing (Stracker et al., 2011) while RPA attracts ATR kinase via

its partner, ATR-interacting partner (ATRIP).

ATR phosphorylating CHK1 is critical event for DDR response and checkpoint activation and this

is proved by the studies reporting that mutations in the Ser345 and Ser317 to Ala produced

checkpoint defects and made the cells more sensitive to replication stress (Capasso et al., 2002).

Phosphorylation at Ser317 of CHK1 is needed for Ser345 phosphorylation to occur but it alone is

not enough to create maximal phosphorylation at Ser345 which needs another optimal conformation

at distal C-terminus for its maximal activation (Wang et al., 2012, Kosoy et al., 2008). While

mutation of Ser317 to Ala in somatic cells only caused the G2/M phase checkpoint abrogation,

Ser345 mutation to Ala produced loss of cell viability in addition to the loss of both checkpoint

(Wilsker et al., 2008).

Chapter 1

23

Figure 1-8: Cell cycle checkpoints activated by different kinds of DNA damage.

1.3.4 Melanoma and cell cycle checkpoints defects

1.3.4.i G1 checkpoint defects

Defective G1 phase checkpoint signalling was reported to be a common feature of melanoma cell.

Nearly 70% of melanomas were reported to be harbouring a defective G1 checkpoint arrest when

they were screened with normal melanocytes for checkpoint functionality (Kaufmann et al., 2008).

Abnormal protein level and problems in post-translational modification with the components of G1

checkpoint pathway were detected especially related to p21 and p53 in some G1 arrest-impaired

cell lines. A gene expression profile, including CDKN1A, DDB2, CDC7 and GEMININ, was

associated with the defective G1 phase checkpoint, demonstrating that this was due to defective p53

function despite the relatively low level (19%) of p53 mutation in melanoma (Hodis et al., 2012).

Furthermore, it showed that p53 was involved in protective mechanism against metastatic spread of

melanoma.

1.3.4.ii G2 phase checkpoint defects

A p16-dependent G2 phase checkpoint triggered by suberythemal dose of UV radiation has been

described as being defective in four out of six (67%) examined melanoma cell lines (Milligan et al.,

1998). Sequencing analysis of the CDKN2A gene, which codes for the tumour suppressor protein

p16, revealed that it was either mutated or deleted in all the four defective cell lines. Moreover, the

inability to cell cycle arrest correlated with an increase in aberrant nuclear structures such as

Double-strandedDNAbreaks

Single-strandedDNAbreaks

ATM ATR

CHK2 CHK1

CDC25

CDK/Cyclin

PCDC25

CDK/Cyclin

P Cellcycleprogression

DegradationorSequestration

PP

Chapter 1

24

multinuclei and micronuclei. Recently, the effect of suberythemal doses of UV radiation on cell

cycle progression was investigated in an enlarged sample of 17 melanoma cell lines reported that

88% showed impairment of an ATR-CHK1-dependent G2 phase checkpoint that was triggered by

UV radiation damage incurred during the previous G1 phase (Wigan et al., 2012). This checkpoint

is part of a post-replication repair mechanism that utilises the replication fork machinery to identify

UV-induced lesions not repaired prior to S phase. The G2 phase checkpoint triggered by ionizing

radiation was shown to be defective in a minority (19%) of melanoma cell lines (Kaufmann et al.,

2008). Sequencing analysis of the two frequently mutated oncogenes in melanoma NRAS and

BRAF, revealed an association between BRAF mutations and the defective G2 phase arrest.

Functional impairments have also been described for G2 checkpoints responding to aberrant DNA

modulation caused by sources different than radiation. In the investigation of the response of a

panel of tumour and non-tumour cell lines to azelaic bishydroxamic acid, a histone deacetylase

inhibitor, four out of five (80%) screened melanoma cell lines showed a defective G2 phase

checkpoint (Qiu et al., 2000). The ATM-dependent G2 phase decatenation checkpoint triggered in

response to unresolved DNA strand catenations that can occur during replication (Deming et al.,

2001), was shown to be defective in at least one-third of melanoma cell lines (Brooks et al., 2014).

This was the result of cells being incapable of maintaining the ATM-dependent arrest due to over-

activation of PLK1.

1.4 Checkpoint kinase 1 (CHK1)

The majority of conventional chemotherapeutic drugs target rapidly proliferating cell population by

creating high levels of DNA damage and causing cell cycle arrest and cell death. In response to

DNA damage and replication stress, cell cycle checkpoints are activated and cell cycle progression

is halted to promote DNA repair and induce cell death with irreparable DNA lesion (Langerak et

al., 2011).

1.4.1 CHK1 function in S phase

CHK1 also plays an important role in normal unperturbed cell cycle and division. The vital role of

CHK1 in normal situation was reported in the embryonic lethality of mice deficient in CHK1 (Liu

et al., 2000). TIG-3-tert telomerised human normal fetal lung fibroblasts’ growth was inhibited after

CHK1 was depleted (Liu et al., 2000). CHK1 depletion also sensitised avian DT40 lymphoma cells

to ionising radiation and caused slower cell growth and promoted cell death (Zachos et al., 2003).

Chapter 1

25

The replication of DNA started at the places termed replication origins. The firing of replication

from these origins is controlled by the DBF4/DRF1-dependent CDC7 kinase and cyclin E/CDK2

complexes (Labib, 2010, Tanaka et al., 2010, Jones et al., 2012). Before firing, these origins need to

be licensed in early G1 phase and this process is done by loading of proteins such as MCM2-7,

CDT1 and CDC6 together to prepare a pre-replication complex. Pre-replication complex needs to

be phosphorylated by DDK and CDK2 to facilitate the loading of other essential replication

cofactors such as CDC45 and GINS complex for fork progression. Approximately 10% of licensed

origins are only utilised in normal cell cycle while the others remain dormant. When faced with

replication stress, these dormant origins are initiated to rescue and complete the DNA synthesis

(McIntosh et al., 2012).

In unperturbed cell condition, ATM and ATR pathways control origin firing through regulation of

CDC25A/CDK2 and CDC7. CDC25A regulation was through CHK1 by ATR and CHK1 controls

CDC25A stability in normal cell cycle. In the face of replication and genotoxic stresses, CHK1

phosphorylates CDC25A on its serine 76, 124, 178, 279 and 293 to produce a phosphodegron that

is recognised by E3 ligase TrCP that targets it to the proteasome for degradation (Busino et al.,

2003, Sorensen et al., 2003, Goloudina et al., 2003, Hassepass et al., 2003, Hoffmann et al., 1994).

Increased CDC25A level causes increased activity of CDK2/cyclin E and thus increasing

inappropriate replication origin firing. Unscheduled dormant origins firing can be detrimental since

necessary resources for cellular replication such as dNTPs may not be ready to accommodate a

huge increase in the active replication forks. As a result, that will lead to stalled replication fork and

DNA damage (Beck et al., 2012). Additionally, limitation in other factors participating in

replication such as Replication protein A (RPA) also contributes to the fork collapse in the event of

unscheduled origins firing (Toledo et al., 2013). Thus, inhibiting CHK1 not only induces G2

checkpoint abrogation but also results in DNA damage in S phase, which can play an important role

in cytotoxicity of CHK1 inhibitors (Sorensen et al., 2012, Toledo et al., 2011).

1.4.2 CHK1 function in G2/M phase

When there is DNA damage in G2 phase, CHK1 phosphorylates CDC25B at Ser323 and bound by

14-3-3 hindering its catalytic activity (Forrest et al., 2001). CHK1 also phosphorylates CDC25C at

Ser216 and CDC25C is necessary to dephosphorylate CDK1 at Tyr15 for its activation. This

phosphorylation of CDC25C is also needed for its association with 14-3-3 which leads to CDC25C

nuclear exportation (Graves et al., 2001). CHK1 activates Wee1 which phosphorylates and inhibits

Chapter 1

26

CDK1 at Tyr15 residue (O'Connell et al., 1997, Rhind et al., 1997). This act prevents CDC25

phosphatases interaction with CDK1/Cyclin B complex in the nucleus. In the absence of DNA

damage, CHK1 is usually inactive in normal G2/M phase transition for an orderly progression of

G2/M transition. This is attained via phosphorylation of CHK1 at Ser280/301 by CDK1/Cyclin B in

late G2 phase and limits phosphorylation of ATR, preventing its activation.

1.4.3 CHK1 function in homologous recombination

Another role of CHK1 is its involvement in homologous recombination (HR) repair, an important

pathway for DNA double strand breaks repair. Inhibiting CHK1 function in HR repair is considered

to be important for sensitise tumour cells to ionising radiation (Morgan et al., 2010). CHK1

phosphorylates RAD51 which is required to attract RAD51 to ssDNA which is usually present in

the sequences flanking the double-strand break. CHk1 also phosphorylates C-terminal domain of

BRCA2 and facilitates binding of RAD51 to BRCA2 (Bahassi et al., 2008, Sorensen et al., 2005).

1.4.4 CHK1 inhibition

CHK1 is a critical kinase involved in halting the cell cycle in response to DNA damage. A

conventional idea is that when CHK1 is inhibited, cancer cells lose their ability to respond and

repair DNA damage, enhancing the cell killing effect of chemotherapy or radiotherapy. Thus,

addition of CHK1 inhibitor to chemotherapy enhances the lethality of the chemotherapeutic drug,

essentially acting as chemo-sensitiser. CHK1is have been shown to be effective in sensitising

tumours to a range of chemotherapeutics such as gemcitabine, SN-38, campothecin, cytarabine,

cisplatin, hydroxyurea and topotecan. The most dramatic sensitisation was in combination with

antimetabolites that promote replication stress, particularly with gemcitabine and hydroxyurea

while significantly less sensitisation was reported with cisplatin, fluorouracil and thioguanine

(Montano et al., 2012, Venkatesha et al., 2012, Xiao et al., 2013). When CHK1 is depleted by

siRNA, there were significantly enhanced killing effect by chemotherapy or radiotherapy in

ovarian, triple negative breast and brain cancers (Arora et al., 2010, Cole et al., 2011, Bennett et al.,

2012). Furthermore, CHK1 inhibition were reported to be effective against p53-defecient cancer

cells compared to p53-proficient cancer cells since a large portion of tumours lost the p53-

dependent G1 checkpoint thereby relying more on the CHK1-dependent S and G2/M checkpoints

for survival (Zhou et al., 2004, Bucher et al., 2008). In melanoma, nearly 70% of a large panel of

Chapter 1

27

melanoma cell lines have defective G1 checkpoint arrest despite relatively low level (20%) of p53

mutation (Carson et al., 2012, Kaufmann et al., 2008, Hodis et al., 2012) ,thus laying the ground for

CHK1 inhibitors treatment in melanoma.

Numerous attempts have been made by various pharmaceutical companies to identify specific

CHK1 inhibitors to enhance the effect of chemotherapy. However, the off-target effects and toxicity

associated with CHK1 inhibitors hinder their translation into the clinic. The first CHK1 inhibitor to

enter clinical trials, 7-hydroxystaurosporine (UCN-01), had disappointing results because of its

strong binding to alpha-1 acid glycoprotein in plasma with associated bioavailability problems, and

also in part due to its relative lack of selectivity, inhibiting a large number of other kinases, e.g.

Protein kinase C (Fuse et al., 1998). Development of many of the first generation CHK1 inhibitors

such as AZD7762, PF00477736 and LY2603618 were discontinued after Phase I and II clinical

trials, and were similarly relatively less selective for CHK1. Recently more specific CHK1

inhibitors such as MK-8776, GDC0425 and GDC0575 are currently going through Phase 1 clinical

trials as chemosensitiser in combination with gemcitabine (Daud et al., 2015, Infante et al., 2016).

CHK1 inhibitors were originally developed as chemo-sensitizing agents, its role as a single agent

was not recognised until recently and there has been little investigation into their potential use in

cancer treatment as single agents. The emergence of BRAF inhibitors in the treatment of

BRAFV600E melanoma shed some light on the power of targeted therapy although rapid onset of

resistance has been developed in the case of BRAF inhibitors. Thus, there is an urgent need to

develop new treatment options for the melanoma patients and CHK1 inhibitor has a potential to fill

in this gap.

1.4.5 Combination of CHK1 inhibitors with chemotherapy

Early combination studies with UCN-01 displayed increasing cisplatin cytotoxicity up to 60-fold in

CHO cells (Bunch et al., 1996) and similar effects were also observed with cisplatin in other more

selective CHK1 inhibitors such as Gö6976, PF0477736 and SB218078 (Thompson et al., 2012,

Blasina et al., 2008, Zenvirt et al., 2010). However, there were also some reports demonstrating no

sensitisation with cisplatin with other CHK1 inhibitors such as AZD7762 and MK-8776 (Wagner et

al., 2009, Montano et al., 2012). Whereas AZD7762 and CHIR-124 were reported to increase the

efficacy of irinotecan in xenograft models, MK-8776 was shown to be not effective in sensitising

SN-38, the active metabolites of irinotecan (Tse et al., 2007, Zabludoff et al., 2008, Ma et al.,

2012). Despite the contrasting reports among these various observations related to sensitisation of

CHK1 inhibitors, the more consistent sensitisation effects have been observed when CHK1

Chapter 1

28

inhibitor are combined with certain anti-metabolites (Xiao et al., 2013). CHK1 inhibitor MK8776

caused 100-fold decrease in IC50 for hydroxyurea treatment (Montano et al., 2012). Sensitization to

gemcitabine has also been reported in other CHK1 inhibitors such as PF00477736, AZD7762,

SAR020106 and XL844 (Blasina et al., 2008, Zabludoff et al., 2008, Walton et al., 2010, Matthews

et al., 2007) . Similar results were also observed with xenograft models for combination treatment

with gemcitabine and clinical trials are undergoing (Garrett et al., 2011).

Hydroxyurea and gemcitabine both inhibit ribonucleotide reductase and thus depletes pools of

dexoyribonucleotides for cells and create stalled replication fork. Potent sensitization by CHK1

inhibition has also been observed with cytarabine which stalled replication forks through chain

termination (Montano et al., 2012). However, CHK1 inhibitors do not sensitise all antimetabolites:

5-Fluorouracil (5-FU) that inhibiting thymidylate synthase and stalls replication fork was reported

to be not sensitised by CHK1 inhibitor (Montano et al., 2012).

1.5 Hypothesis

Our group has shown that novel CHK1 inhibitor GNE323 has single-agent activity in a panel of

melanoma cell lines with IC50s starting from low nanomolar range and sensitivity of CHK1

inhibitors seems to correlate with level of endogenous damage and replicative stress (Brooks, Oakes

et al. 2013). With the p53 pathway being defective in a high proportion of melanomas, we

hypothesise that the sensitivity to CHK1 inhibitor treatment may be attributed to the fact that

melanoma cells become less sensitive to DNA damage, adapt to replicative stress, and rely more on

CHK1 for replication and survival. As a result, their viability is significantly compromised when

CHK1 is inhibited. Moreover, the high levels of endogenous DNA damage and replication stress in

the melanoma cells probably mimic an externally applied replicative stress while normal tissue is

protected from the cytotoxicity of CHK1 inhibitors by the absence of replicative stress. Thus,

inhibition CHK1 holds the potential to serves as an example of synthetic lethal targeting of a

tumour-cell-specific dependency. The results from our previous experiments suggest that CHK1

inhibitors may be very useful in melanoma treatment since melanomas usually bear an extremely

high degree of replicative stress. This appears to be a common feature of melanomas indicated by

the large proportion of metastatic melanomas containing high levels of H2AX. According to the

sensitivity shown by CHK1 inhibitor in our study, CHK1 is a critical component of an adaptation to

replicative stress in these melanoma cells and markers of this sensitivity is needed in order to

identify the melanoma and potentially other tumour types that are more likely to be sensitive to

CHK1 inhibitors as single-agents.

Chapter 1

29

1.6 Aim

To investigate the mechanism of CHK1 inhibitor single-agent hypersensitivity in the

melanoma cells.

To investigate how CHK1 inhibition promotes cell death in the most sensitive cell lines.

To develop the more predictive in vitro model for cancer drug development by using CHK1

inhibitor as a model drug.

To explore the potential alternative treatment for the cell lines which are not sensitive to

CHK1 inhibitor single-agent treatment.

Chapter 2

30

2 Material and Methods

2.1 Materials

2.1.1 Commonly used buffers and solutions

Solution Composition

NETN (pH 7.4) 100 mM NaCl, 2 mM EDTA, 20 mM Tris, 0.5% Nonidet P-40

TBS (pH 7.4) 50 mM Tris, 150 mM NaCl

Transfer Buffer 25 mM Tris, 192 mM Glycine, 0.1% SDS, 20% methanol

Running Buffer 25 mM Tris, 192 mM glycine, 0.1% SDS

SDS-PAGE Sample Buffer (5X) 62.5 mM Tris, 30% glycerol, 2% SDS, 0.04% bromophenol

blue, 1% β-mercaptoethanol

Washing Buffer PBST: PBS solution containing 0.05% Tween 20 or TBST: TBS

solution containing 0.1% Tween 20

Ponceau S 1% Ponceau S, 1% glacial acetic acid

Stripping Buffer 100 mM β-mercaptoethanol, 2% SDS, 62.5 mM Tris (pH6.7)

Antibody blocking solution

(Westernblot) TBST containing 5% skim milk (or) 5% BSA

Antibody blocking solution

(Immunofluorescence) 3% BSA, 0.1% Tween-20, PBS

Western lightning Plus-ECL

(PerkinElmer) As indicated

2.1.2 Inhibitors and drugs

Inhibitors/Reagents(Supplier) Mode of Action Concentration

GNE-323 (Genetech) CHK1 inhibitor 10 nM – 10 M

GDC-0575 (Genetech) CHK1 inhibitor 10 nM – 10 M

MK-1775 (Selleck Chemicals) Wee1 inhibitor 0.5 M

Hydroxyurea (Sigma-Aldrich)

Ribonucleotide reductase

inhibitor 3 nM-2mM

Gemcitabine (Sigma-Aldrich)

Ribonucleotide reductase

inhibitor 3 nM-2mM

Z-VAD-FMK (R&D systems) Pan-caspase Inhibitor 100 M

Chapter 2

31

Z-VDVAD-FMK (R&D systems) Caspase-2 Inhibitor 100 M

Protease inhibitor cocktail (Sigma

Aldrich) Protease inhibitor 1:500

2.1.3 Cell culture reagents (2D and 3D)

Solution Composition/Supplier

PBS (pH 7.4) 137 mM NaCl, 2.7 mM KCl, 4.3 mM Na2HPO4, 1.47 mM

KH2PO4

Trypsin/Versene/PBS 0.025% Trypsin, 1% Versene, 1x PBS

FBS (Fetal bovine serum) Gibco 10099141

HEPES Sigma-Aldrich H0887

Sodium pyruvate Sigma-Aldrich S8636

L-glutamine Sigma-Aldrich G7513

RPMI 1640 media Sigma-Aldrich R0883

DMEM/F-12 GlutaMAX Gibco 10565042

Stem cell factor human recombinant Prospecbio CYT255

Cholera toxin Sigma-Aldrich C8052

Chelex 100 sodium form Sigma-Aldrich C7901

Human EDN3 Prospecbio Hor309

MCDB-153 Media Powder Sigma-Aldrich M7403

Penicillin-Streptomycin ThermoFisher 15140122

Medium 254 ThermoFisher M254500

Human melanocyte growth

supplement-2 ThermoFisher S0165

Keratinocyte-SFM ThermoFisher 17005042

KnockOut serum replacement (KSR) Gibco 10828028

Recombinant human FGF-basic Peprotech

Heparan sulphate (HS) Sigma-Aldrich

Tumourspheres media DMEM/F12 media containing 20% KSR

pHEMA (poly-

Hydroxyethylmethacrylate) Sigma-Aldrich P3932

Chapter 2

32

2.1.4 Antibodies

Antibody Species / Supplier Dilution/Concentr

ation

γH2AX(Ser139)

Rabbit/Cell Signaling

(cst2577) 1:1000

PCNA Mouse/DAKO (M0879) 1:5000

α-Tubulin Mouse/Sigma (T6199) 1:5000

pCHK1(Ser317)

Rabbit/Cell Signaling

(cst2344) 1:1000

Cyclin E Mouse/Santa Cruz (sc-247) 1:1000

CDC25A Rabbit/Abcam (ab92892) 1:1000

CDC25A Rabbit/Abcam (ab137353) 1:1000

pRPA32 (Ser4/Ser8)

Rabbit/Bethyl

(BETHA300-245A) 1:2000

pRPA32(Ser33)

Rabbit/Bethyl

(BETHA300-246A) 1:1000

RPA32

Rabbit/Santa Cruz (sc-

28709)

WB 1:1000,

IF 1:200

FANCD2

Mouse/Santa Cruz (sc-

20022) IF 1:200

Cleaved caspase-3(Asp175) Rabbit/Cell Signaling FACS 1:1000

JC-10

Enzo Life Sciences (ENZ-

52305) 10 g/ml

DAPI Sigma -Aldrich (D8417) 600 nM

anti-Rabbit igG (H+L) Secondary antibody,

HRP (G21234) Goat/Invitrogen 1:2000

anti-Mouse igG (H+L) Secondary antibody,

HRP (626520) Goat/Invitrogen 1:2000

Chapter 2

33

2.1.5 siRNA and cell viability assays reagents and materials

Reagent (Supplier) Application

Concentr

ation

Resazurin sodium salt (Sigma-Aldric) Cell viability assay 44 M

JC-10 (ENZ-52305) Tumourspheres assay 10 g/ml

CellTiter-Glo 3D cell viability assay (Promega) Tumourspheres assay

As

indicated

96-well black-wall clear bottom TC-treated

plates (Corning CLS3603)

Cell viability assay and

Immunofluorescence assay

384-well ultra-low attachment coated plate

(Corning CLS3827) Tumourspheres assay

384-well low-volume flat bottom black-wall

non-treated tissue culture plate (CoringTM3540) Tumourspheres assay

Lipofectamine 2000 (Invitrogen) Transfection reagent

As

indicated

Dharmafect1 (Dharmacon) Transfection reagent

As

indicated

Dharmafect2 (Dharmacon) Transfection reagent

As

indicated

Dharmafect3 (Dharmacon) Transfection reagent

As

indicated

2.2 General cell culture methods

2.2.1 Cell lines and culturing conditions

The human melanoma cell lines used in this thesis were A15, A2058, BL, C002, C013, C025,

C045, C052, C054, D04, D20, D25, D28, HT144, MM127, MM329, MM370, MM415, MM603,

MM648, MM96L, SKMEL13 and SKMEL28. The lung cancer cell lines used in thesis were Calu-

1, H1299, H1650, H1792, H1975, H2052, H2887, H322, H358, H82, HCC2429, HCC4017 and

HCC515. The immortalised normal human bronchial epithelial cell lines (HBEC3-KT and

HBEC30-KT), the human cervical cancer cell line (HeLa), primary neonatal foreskin fibroblasts

(NFF), Primary Human melanocytes adult (HEMa) and melanoblast cell lines (QF1597, QF1610,

QF1618 and QF1619) were also cultured.

Chapter 2

34

All melanoma cell lines and lung cancer cell lines were cultured in RPMI 1640 media (Sigma-

Aldrich, R0883) containing 10% FBS (Fetal bovine serum), 2.5 mM HEPES, 1 mM sodium

pyruvate and 2 mM L-glutamine. NFF cells were also cultured under the same conditions. HeLa

cells were cultured in DMEM media containing 10% FBS, 2.5 mM HEPES, 1 mM sodium pyruvate

and 2 mM L-glutamine. Human melanoblast cells were cultured with MCDB medium

supplemented with 10% chelated FBS, 2% (non-chelated) FBS, 2 mM L-Glutamine, 50 μg/ml

Penicillin/Streptomycin, 1.66 μg/l cholera toxin and added growth factors such as basic fibroblast

growth factor 2.5 ng/ml, stem cell factor 2 ng/ml and endothelin 3 100 nM (Biocore). Primary

human melanocytes adult (HEMa) were cultured with Medium 254 supplemented with HMGS-2.

Immotalised normal human bronchial epithelial cell lines were cultured in Keratinocyte-SFM media

containing 50 μg/ml bovine pituitary extract (BPE) and 5 ng/ml epidermal growth factor (EGF). All

cell lines were confirmed to be mycoplasma free. When cells reached about 70% confluency, cells

were washed with 1x PBS and detached with 0.25% trypsin-EDTA and split into desired ratio into

new tissue culture flasks.

2.2.2 Cell Viability assay

Cells were seeded at 1500-2000 cells/well in 96-well flat bottom black-wall tissue culture plates or

at 500-600 cells/well in 384-well flat bottom black-wall tissue culture plates and plates were

incubated at 37C with 5% CO2. Next day, different concentrations of the drugs were added to the

plates and incubated for 24-72 hours depending on the experiments. After drug treatment, resazurin

(or alamar blue) was added to the culture medium in the wells to obtain a final concentration of 44

M and plates were incubated at 37C with 5% CO2 for 30-90 mins depending on the cell lines. The

resazurin signal was measured on the BioTek Synergy MX microplate reader with Gen5 software

(Millenium Science) with the fluorescence excitation of 544 nm and an emission of 590 nm. Media

only blank wells were used to subtract background fluorescence of resazurin.

2.3 Melanoma tumourspheres culture methods and assays

2.3.1 Preparing non-adherent flasks for tumourspheres culture

pHEMA (poly-Hydroxyethylmethacrylate) stock solution (120 mg/ml) was prepared by dissolving

pHEMA powder into 95% ethanol and stirred overnight at room temperature. Then, stock solution

was diluted next day with 95% ethanol to prepare the final concentration of 5 mg/ml before adding

it to the flasks. Flasks were dried in the biosafety hood with lid on and washed with 1x PBS before

cell seeding.

Chapter 2

35

2.3.2 Melanoma tumourspheres culture

Transition media was prepared from DMEM/F-12 GlutaMAX containing 10% FBS, 1% NEAA

(non-essential amino acid) and 1% HEPES. Tumourspheres media was made from DMEM/F-12

GlutaMAX containing 20% Knock-out serum replacement, 1% NEAA and 1% HEPES. Before

using for cell culture, final concentration of 5 ng/ml of bFGF (basic Fibroblast Growth Factor) and

100 ng/ml heparan sulphate were added into tumourspheres media.

When melanoma cell lines grown in 2D monolayer reached about 70-80% confluency, cells were

detached as described in section 2.2.1 and re-seeded in normal tissue-culture treated flasks with

transition media. 24 hours after being cultured in transition media, cells were detached, counted

using EVE™ automatic cell counter and 1.5-1.8 x 105 cells were seeded into T25 flasks coated with

pHEMA as described in section 2.3.1 in complete tumourspheres media with supplements.

2.3.3 Tumourspheres propagation and cryogenic preservation

For subculturing, tumourspheres were dissociated into individual cells using 0.25% trypsin-EDTA,

counted and reseeded as single cells into new pHEMA-coated T25 flasks according to the cell

numbers mentioned in section 2.3.2. For freezing, tumourspheres were dissociated into single cells

as described above, counted and 1x106 cells were frozen in the tumourspheres freezing media which

contained knock-out serum replacement with 2.5% HEPES, 5% DMSO and 10% ethylene glycol.

2.3.4 JC-10 tumourspheres viability assay

Cells were seeded at 400-500 cells /well, depending on the cell lines, into 384-well low-volume flat

bottom black-wall non-treated tissue culture plate (CoringTM

3540) using Wellmate (Thermo Fisher

Scientific) and cultured at 37C with 5% CO2. The next day different concentrations of the drugs

were added to the cells using the SciClone ALH3000 (Caliper Life Sciences) and plates were

incubated at 37C with 5% CO2 for 24-72 hours depending on the experiments. After drug

treatment, JC-10 (ENZ-52305) was added into each well at a final concentration of 10 g/ml.

Whole well imaging of 384-well low-volume plates were was performed using a 2.5x objective of

Cellomics ArrayScan VTi HCS Reader (Thermo Scientific, Rockford, IL, USA). Total areas of the

objects were quantified using Cellomics quantitative cell analysis software. The data was exported

and further processed in R system for computation and graphics (http://www.r-project.org/).

Chapter 2

36

2.3.5 CellTiter-Glo 3D cell viability assay

Cells were seeded at 1600-2000 cells/well, depending on the cell line, into 384-well ultra-low

attachment coated plate (CorningTM

CLS3827) and cultured at 37C with 5% CO2 using Wellmate

(Thermo Fisher Scientific). Different concentrations of the drugs were added to the plates next day

using the SciClone ALH3000 (Caliper Life Sciences) and plates were incubated for 72 hours at

37C with 5% CO2. After the drug treatment, CellTiter-Glo 3D cell viability assay (Promega) was

performed according to the manufacturer’s protocol. Briefly, after drug treatment, equal volume of

CellTiter-Glo 3D reagent was added to the cell culture medium in the well. The plate was sealed

and placed on a plate shaker at 800 rpm for 5 min at room temperature to induce cell lysis. The

plate was allowed to rest at room temperature for 15 min and luminescence signal was measured on

the BioTek Synergy MX microplate reader with Gen5 software (Millenium Science). The data

was exported and further processed in R system for computation and graphics (http://www.r-

project.org/).

2.4 Immunoblotting

Cells were harvested from culture dishes/flasks with a cell scraper, washed with 1x ice-cold PBS,

snap-frozen with dry ice and stored at -80C until further processing. Cell pellets were lysed in

NETN lysis buffer containing 200 mM NaCl, 2 mM EDTA, 20 mM Tris-HCl pH 7.4, 0.5% NP40

and 0.1% SDS. The following final concentrations of protease and phosphatase inhibitors were also

added into the lysis buffer before adding to the cell pellet. The tubes were put on a rotating mixer at

4C for 1 hour and spin down for 15 min at 14,000 rpm after. Supernatant were removed into a

fresh tube and pellet was discarded.

Protease Inhibitors

Protease Cocktail Inhibitors (1/500)

PMSF 1 mM

Phosphatase Inhibitors

Sodium orthovanadate 0.1 mM

Sodium Fluoride 10 mM

Β-glycerophosphate 25 mM

Chapter 2

37

Protein estimation of the lysis supernatant was performed by using Bio-rad bradford protein assay

using various concentration of Bovine serum albumin (BSA) to generate a standard curve. Sample

buffer (Cell Signalling Technology) containing 150 mM DTT (Dithiothreitol) (Sigma Aldrich) was

added to equalised protein concentration lysates and samples boiled at 100C for 5min . Equal

amount of samples was resolved in 10% or 12.5% SDS-PAGE (SDS-polyacrylamide) gels. Protein

transfer was done on PVDF (polyvinyl difluoride) (IPVH00010 & IPFL00010) membrane using

Trans-Blot semi-dry transfer cell apparatus (Bio-Rad). Proteins of interest were probed with

respective primary antibody overnight at 4C and respective secondary HRP (horseradish

peroxidase) antibody for 1 hour next day as mentioned in the material section. Western lightning

Plus-ECL, Enhanced Chemiluminescence kit (PerkinElmer) was used with ChemiDoc (Vilber

Lourmat) using ChemiCapt Software (Vilber Lourmat) to visualise the bands. Quantification was

performed by using Image Studio Lite software (LI-COR).

2.4.1 Immunofluorescence

Cells were grown on poly-L-lysine coated glass coverslips or 96-well flat bottom black-wall tissue

culture plates depending on the experiments and cultured with control or drug treatment. At the end

of the treatment, cells were fixed with 3.7% (v/v) formaldehyde in 1x PBS for 20 min, washed with

1x PBS, permeabilised with 0.1% (v/v) Triton X-100, washed with 1x PBS, and then blocked with

blocking buffer containing 1.5% (w/v) BSA in PBS with 0.1% (v/v) Tween 20 (PBST-BSA) for 1

hour at room temperature. Cells were then incubated with diluted primary antibodies for overnight

at 4 C. Next day, cells were washed with 1x PBS and incubated with corresponding fluorescence-

conjugated secondary antibodies diluted in blocking buffer and DAPI for 1 hour in the dark. Cells

were washed with 1x PBS again and stored in the dark at 4C for 96-well plates. For glass

coverslips, they were stored in the dark at 4C after being air-dried and mounted onto glass slides

using prolong gold mounting medium (Invitrogen).

2.4.2 Micronuclei formation

Melanoma cell lines were cultured in complete RPMI media described in section 2.2.1 either with

or without 0.1 mM hydroxyurea (HU) for three months and media was changed regularly every

other day. After 3 months, cells were then detached as mentioned in section 2.2.1, seeded and

allowed to attach in 96-well flat bottom black-wall tissue culture plates overnight. Fixation and

permeabilisation of the cells were performed as described in section 2.4.1. Cell nuclei were stained

with 600 nM DAPI (4’,6-diamidino-2-phenylindole) for 1 hour at room temperature and presence

of micronuclei in each cell line was assessed visually by Olympus fluorescence microscope. About

Chapter 2

38

100 cells were manually counted for each replicate and presence of micro/multi-nuclei was

calculated as the percentage of total cell number for each replicate.

2.4.3 Nucleotide incorporation assay (5-ethynyl-2’-deoxyuridine or EdU Assay)

Cells were seeded into 96-well flat bottom black-wall tissue culture plates the day before the drug

treatment and plates were incubated at 37C with 5% CO2. Cells were treated with hydroxyurea

(HU) for 24 hours. Two hours before the end of the treatment, final concentration of 10 M EdU

was introduced into the cells and incubated for 2 hours at 37C with 5% CO2. Cells were then fixed

with 3.7% (v/v) formaldehyde in 1x PBS, washed with 1x PBS, permeabilised with 0.1% (v/v)

Triton X-100, washed with 1x PBS, and then blocked with 1.5% (w/v) BSA in PBS with 0.1% (v/v)

Tween 20 (PBST-BSA) for 45 min at room temperature. Plates were then washed with 1x PBS

before the click reaction took place. Equal volumes of four main components (16 mM CuSO4, 10

μM Cy5 azide, 400 mM TRIS HCl pH 8.5, and 275 mM ascorbate acid) were mixed together before

adding into the cells and incubated for 15-20 mins at room temperature. Following the click

reaction, plates were washed with 1x PBS and blocked again with 1.5% (w/v) BSA in PBS with

0.1% (v/v) Tween 20 (PBST-BSA) for 30 min at room temperature. Samples were stained with 600

nM DAPI for 1 hour at room temperature after. Images were scanned from each well using

ArrayScan VTI HCS Reader and data acquisition and analysis were done with Cellomics Target

Activation (v.3) application software. Cell nuclei were identified by DAPI and assayed for the

fluorescence intensity of DAPI and Cy5 fluorescence. This data was exported and further processed

in R system for computation and graphics (http://www.r-project.org/). Cy5-EdU position thresholds

were set according to the distribution of cells lacking Cy5 fluorescence.

2.5 Immunohistochemistry

The xenograft tumours harvested from mice were fixed for 24 hours in neutral buffered formalin

10% (Australian Biostain) at room temperature and stored in 100% ethanol at 4C. The fixed

tumours were sent to TRI histology facility for paraffin embedding, sectioning, mounting and

haematoxylin and eosin staining. For immunohistochemistry staining, tissue sections were dewaxed

with xylene and rehydrated with decreasing concentration (100%, 100%, 90%, and 70%) of

ethanol: 1 min for each concentration. The sections were then briefly washed in distilled water.

Antigen retrieval was done by using 0.01 M citric acid buffer pH 6 for 5 min at 125 C using de-

cloaking chamber. The sections were allowed to cool on bench for 20 min after and then washed

with 1x TBS (Tris-buffered saline). The sections were blocked with 10% normal donkey serum/ 1%

bovine serum albumin in TBS for 30 min at room temperature. The sections were incubated with

Chapter 2

39

H2AX (Ser139) rabbit antibody for overnight at 4 C. The sections were washed with 1x TBS

including 0.05% Tween-20 next day and incubated with 1% H2O2 in TBS for 10 min at room

temperature. The sections were then washed again with 1x TBS and incubated with Vector Impress

anti-rabbit HRP secondary antibody for 45 min at room temperature followed by 1x TBS washes.

ImmPACT NovaRED working solution was used to incubate the sections for 5 min and then

washed with distilled water for 5-10 min to remove excess chromagen. After this, sections were

lightly counterstained using Mayer’s haematoxylin.

2.6 Transfection

2.6.1 Transfection optimisation

Transfection conditions were optimised for D20, D25 and MM329 cell lines. Three different

concentrations (0.3-0.7%) of different lipids, namely Lipofectamine 2000 (ThermoFisher

scientific), Dharmafect1 and Dharmafect2 (Dharmacon, GE healthcare) were used. NT siRNA

(Dharmacon siGENOME® NT duplex#3) and POLR2A siRNA (Dharmacon siGENOME®

SMARTpool®) were used at 20 nM. 20 l of lipid/siRNA mixture was added into the each well of

96-well black flat bottom tissue culture plate and cells solution was added into the mixture at 2500

cells/well. Transfection efficacy was determined by resazurin assay as described in section 2.2.2.

2.6.2 Transfection for CDC25A knockdown

NT siRNA (Dharmacon siGENOME® NT duplex#3), POLR2A siRNA (Dharmacon siGENOME®

SMARTpool®) or CDC25A siRNA (ON-TARGETplus SMARTpool, Dharmacon, GE healthcare)

was used at 20 nM. D20 cells were transfected using 0.3% Lipofectamine 2000 (ThermoFisher

scientific). 20 l of lipid/siRNA mixture was added into the each well of 96-well black flat bottom

tissue culture plate and cells solution was added into the mixture at 2500 cells/well. 24 hours after

transfection, NT and CDC25A siRNA transfected cells were treated with 100 nM CHK1 inhibitor

(GNE-323) and plates were incubated at 37 C with 5% CO2 for 48 hours and then cell viability

was assessed by resazurin assay as described in section 2.2.2.

2.7 Transduction and screening of C013 cells

Letiviral ORF expression library containing viral supernantants for respective genes arrayed in 96-

well plates was obtained from and screened at ARVEC facilitiy at UQ Diamantina Institute.

Lentiviral expression constructs were generated in pLV411G (synonym pLVEIG, accession

KF486506.1), a Gateway destination vector that allows EF1a promoter-driven coexpression of an

Chapter 2

40

upstream ORF and downstream green fluorescent protein (GFP), separated by an intervening IRES

sequence. Supernatant derived from pLV411G (empty expression plasmid no ORF and no ccdB

gene and mock wells containing viral particles without the expression plasmids were used as

controls. C013 cells were seeded into 96-well black-wall clear bottom TC-treated plates at 1500

cells per well and plate was incubated overnight at 37C with 5% CO2. Media was removed next

day leaving 20 l per well. Viral supernatant and polybrene were added into the plate at 30 l per

well. 120 l media was topped up 2 hours after and plate was incubated overnight at 37C with 5%

CO2. Media was aspirated leaving 20 l per well and 100 l of fresh media was added next day.

After 2 days incubation, plate was treated with different drugs. After the drug treatments, plate was

fixed and stained for EdU and DAPI as described in section 2.4.3

2.8 Flow cytometry

2.8.1 Cell cycle analysis with propidium iodide (PI)

For DNA content analysis, cells were harvested from the culture vessels by scraping with cell

scraper (Greiner), washed once in 1x PBS and fixed in -20C with 70% ethanol. Cells were

resuspended in staining solution containing 2 g/ml PI (propidium iodide) and 500 g/ml RNase A

in PBS at room temperature. Cells were filtered using 37 micon gauze and analysed by using

LSRFortessa X-20 (BD Biosciences). Data were analysed using FlowJo software.

2.8.2 Cleaved caspase-3 assay

Cleaved caspase-3 assay was performed with activated cell sorting (FACS). Cells were harvested

from the culture vessels with respective treatments by scraping with cell scraper (Greiner), washed

once in 1x PBS and fixed in -20C with 70% ethanol. After fixation, cells were washed with 1x

PBS and incubated with cleaved caspase-3 (Asp175) antibody mentioned in the material section

diluted in blocking solution containing 1% BSA and 0.5% Tween 20 in PBS for 1 hour at room

temperature. Cells were washed with 1x PBS after primary antibody and incubated with

fluorescence-conjugated secondary antibody diluted in blocking solution for 1 hour at room

temperature in the dark. Cells were washed with 1x PBS and then resuspended in DNA staining

solution mentioned in section 2.8.1. Cells were filtered and analysed by using LSRFortessa X-20

(BD Biosciences). Data were analysed using FlowJo software.

Chapter 2

41

2.9 Quantitative real-time PCR (qRT-PCR) confirmation of knockdown

2.9.1 RNA extraction

D20 cells were treated with siRNAs for NT and CDC25A as described in section 2.6.2 and

harvested. RNA was extracted by using Bioline Isolate II RNA mini kit (BIO-52072) as per

manufacturer’s instructions. Briefly, cell pellet was lysed by provided lysis buffer and lysates

transferred through different spin columns to collect and purify RNA. Removal of genomic DNA

contamination was done by on-column DNase I digestion during the preparation. Purified total

RNA was eluted in RNase-free water and purity was measured by using NanoDrop

spectrophotometer and A260/280 ratio was confirmed to be between 1.9-2.1.

2.9.2 cDNA synthesis

cDNA synthesis was performed by using Tetro cDNA synthesis kit from Bioline (BIO-65042) for

RT-PCR according to manufacturer’s instructions. In short, 1 g of RNA was added with the

mixture of random hexamers and dNTPs and cDNA synthesis was carried out in the presence of

RNase inhibitor and Tetro reverse transcriptase. Synthesised cDNA was stored at -20C.

2.9.3 Quantitative Real-Time PCR (qRT-PCR)

Previously synthesised cDNA was assessed by qRT-PCR to confirm knockdown of CDC25A

compared to NT siRNA treatment. TaqMan® Gene Expression Assays were used and 18S was

served as controls. TaqMan® Universal Master Mix II (4440047) was used according to

manufacturer’s instructions as outlined in Table 2-1. qRT-PCR was performed on Applied

Biosystem ViiA 7 real-time PCR system (ThermoFisher Scientific) using as follow: 95C for 10

min followed by 95C for 15 seconds and 60C for 60 seconds for 40 cycles. 18S gene was used as

housekeeping gene to normalise the gene expression values.

Reagents 1x reaction

TaqMan® Universal Master Mix II (2✕) 5 l

TaqMan® Probe 0.5 l

cDNA 4.5 l

Total volume 10 l

Table 2-1: Conditions for TaqMan Gene Expression qRT-PCR assays.

Chapter 2

42

Primer Name Sequence 5'-3' Ref.

CDC25A Forward AGTAAGACCTGTATCTCGTGGCTG (Albert et al., 2011)

CDC25A Reverse CAGAGTTCTGCCTCTGTGTGAAGA (Albert et al., 2011)

Table 2-2: Sequence for CDC25A primers.

2.10 Xenograft mice models

Animal experiments were performed according to The University of Queensland Ethics Guidelines.

4-6 weeks old athymic BALB/c Nude mice were used. 3 million cells were resuspended in 50%

Matrigel (Corning

Matrigel

basement membrane matrix, LDEV-free, 356234) and inoculated

into the flank of each mice with 23-gauge needle. Tumour progression and mice health were

monitored regularly and mice were sacrificed when tumours volume reached 1000 mm3. Tumour

volume was measured by using callipers and calculated as (length x width2)/2. When tumours

reached 50-100 mm3, mice were randomly divided into two groups and treated with either vehicle

control or CHK1 inhibitor via oral gavage at 50 mg/kg for 3 days followed by 4 rest days for one

complete cycle. Mice were treated for 3 cycles and tumours from 1-2 mice were collected 24 hours

after the last treatment for immunohistochemistry analysis.

2.11 Statistical analysis

Immunoblots were assessed for the relative intensity of each lane using Image Studio (LI-COR)

and normalised to a loading control. Statistical analysis of relative intensity was calculated by

student’s two-tailed t-test performed using GraphPad Prism (GraphPad Software). GraphPad Prism

or Microsoft Excel was used to generate all graphs and statistical analyses. Means were compared

by student’s two-tailed t-test. Significance was assigned for the following p-values: < 0.05 (*), <

0.01 (**) and < 0.001(***).

Chapter 3

43

3 Melanoma cells with defective S phase checkpoint are selectively

sensitive to CHK1 inhibition

3.1 Introduction

CHK1 has received a lot of attention as a therapeutic target, especially the ability of CHK1 inhibitor

(CHK1i) to enhance the chemotherapeutic drugs potency. Different CHK1 inhibitors are currently

being investigated in various clinical trials to enhance the efficacy of chemotherapeutic drugs,

primarily gemcitabine (Sausville et al., 2014, Daud et al., 2015). Most of those investigations are

based on the previous notion that CHK1i abrogates the G2/M phase checkpoint and causes mitotic

catastrophe, resulting in cancer cell death. However, considering numerous roles of CHK1 playing

in cell cycle and DDR pathways, this mechanism alone may not represent the entirety of sensitivity

to CHK1 inhibitors.

CHK1 plays various important roles in protecting cells from DNA damage and stalled replication

forks, such as regulating cell cycle progression to prevent premature entry into mitosis in response

to G2 phase DNA damage checkpoint activation, stabilising stalled replication forks, controlling the

origin firing, promoting the homologous recombination repair and suppressing the apoptosis

(Maya-Mendoza et al., 2007, Petermann et al., 2010, McNeely et al., 2010, Bahassi et al., 2008).

CHK1 is also functioning together with other repair or cell cycle checkpoint proteins such as Rad51

and FANCD2 (Guervilly et al., 2008, Sorensen et al., 2005) in DNA damage response and repair

pathways. When one of these other proteins/pathway has defects, inhibiting CHK1 can be lethal to

the cells as shown in (Chen et al., 2009) where the cell lines which had Fanconi anemia (FA)

pathway defect are hypersensitive to CHK1 inhibition alone. Another CHK1 function in S phase is

regulating the stability of CDC25A: CHK1 phosphorylates CDC25A on Serine 76, 124,178, 279

and 293 during S phase and G1 phase (Falck et al., 2001, Sorensen et al., 2003, Goloudina et al.,

2003, Hassepass et al., 2003), causes its turnover mediated by TRCP complex which is

responsible for ubiquitin-mediated degradation of CDC25A in response to DNA damage (Busino et

al., 2003), and Dub3 which detaches ubiquitin chains and prevents CDC25A proteasomal

degradation (Pereg et al., 2010). CDC25A dephosphorylates and activates CDK2/ Cyclin E and A

to drive progression through S phase (Hoffmann et al., 1994). CDC25A dysregulation and

overexpression has been reported in various cancers and shown to create increase replication stress

in the cells (Boutros et al., 2007, Kristjansdottir et al., 2004). Tumours with high level of replication

stress has been suggested to be selectively susceptible to the inhibition of protein kinases like

CHK1 and its upstream regulator, ATR (Murga et al., 2011, Zeman et al., 2014, Brooks et al.,

2013). Previous work from our lab and others has indicated that CHK1 inhibitor sensitivity was

Chapter 3

44

strongly linked to replication stress and replication catastrophe (Brooks et al., 2013; King et al.,

2015; Koh et al., 2015). Considering all these previous results, we hypothesised that CHK1

inhibitor’s cytotoxic effect in hypersensitive cell lines is related to S phase of the cell cycle rather

than the mitotic catastrophe. The endogenous DNA damage usually present in melanoma combined

with the replication stress from possible replication, repair or cell cycle checkpoint defect(s) in S

phase of hypersensitive cells probably make the cells rely more on the CHK1 for their survival and

consequently causes the cell death when CHK1 is inhibited.

In this chapter, the presence of the defect in the S phase checkpoint was investigated in melanoma

with different sensitivities to CHK1 inhibitor single-agent treatment. In addition to this, the possible

mechanisms for the S phase defect and CHK1 inhibitor hypersensitivity in melanoma were also

investigated. Moreover, the efficacy of CHK1 inhibitor as single-agent treatment in animal model

was also further explored here.

Chapter 3

45

3.2 Results

3.2.1 Classification of Melanoma cell lines on the basis of CHK1i sensitivity.

We have previously reported that a subset of melanoma cell lines are highly sensitive to killing by

CHK1 inhibitor, GNE323 (Brooks et al., 2013). Previous Honours students from the laboratory

extended this data to a larger panel of 45 cell lines including three normal melanoblast lines (QF

series) and normal adult melanocytes (HEMa). The cell lines were treated with GNE323 for 72

hours and viability was determined by measuring mitochondrial activity by resazurin assay and the

surviving fraction was determined as viable fraction of the cells after 72 hours treatment with 10

M drug. GNE323 reduced the viability of the melanoma cell lines with IC50 values varying from

20 nM to >5 M (Figure 3-1). In the majority of the most sensitive cell lines (IC50 ≤100 nM),

GNE323 was cytotoxic with <20% viability at 10 M drug with the exceptions of A02 and A04:

both had doubling times >72 h which may have reduced the cytotoxicity of the drug but did not

affect their sensitivity to the anti-proliferative effects of the drug. In those cell lines with IC50 >500

nM, which included normal cell lines such as melanocytes, melanoblasts and fibroblasts, the drug

had only modest effects even with 10 M drug concentration, indicated by the over 40% surviving

fraction after 72 hours treatment (Figure 3-1). The most sensitive lines (IC50s 100nM) will be

referred from here on as hypersensitive, those with IC50s from 100- 500 nM as sensitive, and those

> 500 nM as insensitive. I have used this large panel of characterised cell lines, both genomically

and functionally in terms of their CHK1i sensitivity, to investigate the molecular basis of sensitivity

to CHK1i as single-agents.

Chapter 3

46

Figure 3-1: IC50 values and the surviving fraction for a panel of melanoma cell lines, neonatal

foreskin fibroblasts (NFF), adult melanocytes (HEMa) and melanoblasts with the CHK1 inhibitor,

GNE323.

IC50 values were calculated for each of the cell lines tested. Surviving fraction was calculated as

viable fraction of cells after 72 hours treatment with 10 M drug. Cell viability was assessed by

resazurin assay. The asterisk indicates hypersensitive cell lines with doubling time >72 hours.

3.2.2 CHK1i-hypersensitive melanoma cell lines do not commonly have defective

Fanconi Anemia pathway responses

Defective Fanconi Anaemia (FA) pathway has previously been shown to be synthetically lethal

with inhibition of CHK1 in tumour cells (Chen et al., 2009). This suggested that defective FA

pathway signalling may be involved in the CHK1i hypersensitivity observed. FA pathway has roles

in response to DNA cross-links but also has roles in normal S phase checkpoint responses to

various defects that promote replication stress (Howlett et al., 2005). During replication or response

to DNA damage, the FA complex (FANCA, B, C, E, F, G, L and M) assembles in a nuclear

complex required for the activation of FANCD2 via monoubiquitination at Lys561 and result in

FANCD2 foci formation (Wang et al., 2004, Howlett et al., 2005, Garcia-Higuera et al., 2001). Any

defect in the FA proteins of the core complex will result in defective FANCD2 monoubiquitination

and no foci formation. Thus, to assess FANCD2 foci formation in hypersensitive cell lines,

replication stress was induced by 2 mM hydroxyurea (HU). HU reversibly inhibits DNA replication

Chapter 3

47

by restricting ribonucleotide reductase and depletes dNTP pools, stalls the replication fork, stops S

phase progression, and engages the cell cycle checkpoint to prevent the passage through cell cycle

and thus creates replication stress. Replication protein A (RPA) foci were formed at the single-

stranded DNA (ssDNA) sites produced by HU treatment indicating the induced replication stress in

those cells. In all of the tested melanoma cell lines (D20, C045, C002, C013, C054, A15, C025,

D25) except in C002, there was strong formation of FANCD2 foci corresponding to RPA foci

detected using antibodies to RPA2 subunit, demonstrating that FA pathway was activated in

response to replication stress Figure 3-2.

Figure 3-2: FANCD2 and RPA foci formation in melanoma cell lines.

CHK1i-hypersensitive (D25) and –insensitive cell lines (C013) were seeded on the coverslips

overnight and treated with 2 mm HU for 24 hours. Cells were later stained with antibodies for

FANCD2 and RPA. DAPI was used to identify the nucleus.

3.2.3 CHK1i-hypersensitive melanoma cells have defect in S phase checkpoint

The lack of common defects in the FA pathway indicated that other CHK1 dependent process might

be compromised in the CHK1i-hypersensitive melanoma lines. Another CHK1-dependent process

is the S phase checkpoint response to high levels of replication stress. In order to induce replication

stress, 2 mM HU was used. To assess the S phase replication checkpoint functionality, a small panel

of melanoma cell lines representing hypersensitive and insensitive lines were treated with 2 mM

DAPI RPA FANCD2

C013

Contr

ol

HU

D25

Contr

ol

HU

Chapter 3

48

HU for 24 hours, and S phase progression was monitored using EdU (5-ethynyl-2-deoxyuridine)

incorporation. Replication stress was monitored by detecting ssDNA formation with RPA

(replication protein A) foci formation. RPA foci were attracted to the site of ssDNA regions where

the replication fork stalled and replication was stopped in CHK1i-insensitive cell lines in response

to HU-induced replication stress (Figure 3-3). There was only minimal EdU signal above

background levels detected in CHK1i-insensitive cell lines (C025 and D28), indicating that those

cell lines have a functional S phase checkpoint, effectively blocking DNA replication and S phase

progression. Surprisingly, in CHK1i-hypersensitive cell lines (D25 and MM96L), EdU stained

nuclei were readily observed with the formation of RPA foci in HU treatment, indicating the

presence of ssDNA and stalled replication forks (Figure 3-3). This suggested that the S phase cell

cycle checkpoint was defective in those hypersensitive cell lines, and did not respond to the

presence of ssDNA by inhibiting DNA synthesis.

This EdU incorporation analysis was extended to a larger panel of melanoma cell lines with

different CHK1 inhibitor sensitivities (hypersensitive, sensitive and insensitive). Cells were treated

in similar manner and effect of HU was assessed using RPA and EdU staining as above.

Incorporated EdU intensities were measured using the Cellomics ArrayScan high-content imaging

system and data were analysed using R studio. Cell numbers were determined by counts of DAPI

stained nuclei. EdU intensities within the nuclear masks from negative control cells which

underwent the same treatment without EdU incorporation served as the threshold to determine

percentage of EdU stained cells of the different cell lines. The number of EdU positive cells above

the threshold were measured and calculated as a percentage of the total DAPI stained nuclei.

HU treatment resulted in the expected reduction in the proportion of actively replicating CHK1i-

insensitive cells, demonstrated by the reduced percentage (0-4%) of EdU+ cells (Figure 3-4). This

demonstrates that HU triggered the S phase cell cycle checkpoint to block DNA replication in

response to replication stress in those cells. By contrast, CHK1i-hypersensitive and -sensitive cell

lines continued replicating after HU challenge, indicated by the increased proportion of EdU+ cells

(35-50%) (Figure 3-4). Here, the increased percentage of the EdU+ cells reflects the effect of HU in

delaying cells in S phase while the reduced mean fluorescence intensity simply reflects the

depletion of dNTP pools resulting from HU treatment: EdU, a thymidine analogue, being

incorporated as it is an only available nucleoside. The presence of RPA foci demonstrated that HU

treatment was producing replication stress in the CHK1i-hypersenitive cells and should trigger an

ATR-CHK1-dependent S phase checkpoint arrest. However, the continued incorporation of EdU

indicates that there is a defect in checkpoint function in the CHK1i-hypersensitive cells, and this

defect is a common feature of these cell lines.

Chapter 3

49

Figure 3-3: CHK1i-hypersensitive cell lines have defect in S phase checkpoint.

CHK1i-hypersensitive (MM96L and D25) and CHK1i-insensitive (D28 and C025) cells were

seeded on the coverslips overnight and then treated with control (DMSO), or 2 mM HU to induce

replication stress for 24 hours. Cells were pulsed with 5-ethynyl deoxyuridine (EdU) two hours

immediately before fixation and stained for replication protein A (RPA), 5-ethynyl-2'-deoxyuridine

(EdU) and DAPI. Scale bar = 10 m

Chapter 3

50

Figure 3-4: S phase checkpoint defect is commonly present in CHK1i-hypersensitive and –sensitive

cell lines.

Chapter 3

51

Indicated melanoma cell lines (red – CHK1i-hypersensitive and –sensitive & green – CHK1i-

insensitive) with different sensitivities to CHK1i were treated with control (DMSO) or 2 mM HU for

24 hours, then pulsed with EdU for 2 hours before fixing. (A) EdU intensities of individual cells

were plotted against DAPI intensities and (B) percentages of the EdU positive cells were

calculated. Mean and sd of triplicate determinations were plotted.

3.2.4 Defect in communication between checkpoint activation and CDC25A

degradation in CHK1i-hypersensitive cell lines

CDC25A is phosphorylated by CHK1 and rapidly degraded following DNA damage, stalled

replication fork or replication stress, and its degradation is crucial for the S phase cell cycle

checkpoint arrest. CHK1 phosphorylates CDC25A on its serine 76, 124, 178, 279 and 293 to

produce a phosphodegron that is recognised by E3 ligase TrCP that targets it to the proteasome for

degradation (Busino et al., 2003, Sorensen et al., 2003, Goloudina et al., 2003, Hassepass et al.,

2003, Hoffmann et al., 1994). The continued replication in the HU-treated CHK1i-hypersensitve

melanoma cell lines suggested there is a defect in the S phase checkpoint response, although the

presence of RPA foci suggested that ATR was recruited to the stalled replication forks created by

HU treatment. Thus, I investigated whether replication stress caused by HU was triggering CHK1

activation and degradation of CDC25A.

The effect of HU and CHK1i on level of CHK1 activation and CDC25A protein was assessed by

immunoblotting with an antibody that detected total CDC25A protein. It was essential to validate

the bands detected by the antibody, as I assessed two CDC25A antibodies (Ab92892 and

Ab137353) which detected different bands in same samples. The validation of those antibodies was

performed on HeLa cells transfected with CDC25A siRNA SMARTpool. The Abcam antibody

(Ab92892) detected single band between 50-75 kDa. However, that detected band did not change in

HeLa cells transfected with siRNA CDC25A (Figure 3-5). Another Abcam antibody (Ab137353)

detected multiple bands between 50-85 kDa and the band just above 50 kDa (indicated by arrow in

Figure 3-5) was demonstrated to be specific to CDC25A by its accumulation in the CHK1i treated

sample and specific siRNA depletion. Thus, Ab137353 antibody was used to assess CDC25A level

in subsequent immunoblots.

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52

Figure 3-5: CDC25A antibodies optimisation with siCDC25A in Hela cells.

Hela cells were transfected with CDC25A siRNA SMARTpool (siCDC25A) or non-targeting siRNA

as control for 24 hours and then continued to be cultured in the presence or absence of 100 nM

CHK1 inhibitor for 24 hours before lysates were collected and immunoblotted for Abcam

antibodies, Ab92892 and Ab137353.

CHK1 was robustly phosphorylated on the activating Ser317 site in all the melanoma cell lines after

2 mM HU treatment indicating that CHK1 is activated as expected in the checkpoint response

(Figure 3-6). Addition of 1 M CHK1i also resulted in the accumulation of CHK1 phosphorylation

at Ser317, a marker of CHK1 inhibition (Leung-Pineda et al., 2006). However, the effect of CHK1

activation on CDC25A levels was compromised in many melanoma cell lines. The basal level of

CDC25A in untreated cells varied from cell line to cell line, but there was no obvious

differentiation between the CHK1i-hypersensitive and -insensitive lines. The reduction in CDC25A

level as a consequence of CHK1-dependent destabilisation of CDC25A in HU treatment was

observed in the CHK1i-insensitive D28 and MM648, to a lesser extent in C025, and not at all in

C013 cells (Figure 3-6). In the CHK1i-hypersensitve cell lines, HU treatment produced either no

effect on CDC25A levels (D20, HT144, MM96L) or an increase (D25, MM370). The expected

reduction in CDC25A was observed only in BL and MM329 lines.

CHK1i addition produced the expected accumulation of CDC25A, a direct consequence of

inhibiting CHK1-dependent destabilisation of CDC25A protein, in all cell lines except BL, D28 and

MM648 (Figure 3-6). The fact that CDC25A levels increased due to CHK1 inhibition in the

CHK1i-hypersensitive cell lines, which failed to degrade CDC25A, suggested that the defect is not

Chapter 3

53

from CHK-dependent destabilisation of CDC25A mechanism. Thus, the continued stability of

CDC25A was caused by as yet undefined mechanisms.

The level of Cyclin E is a marker of Cyclin E/CDK2 activity, with strongly elevated levels

indicating inhibition of CDK2 as Cyclin E is degraded by the active Cyclin E/CDK2 complex

(Sakurikar et al., 2016). There is a strong accumulation of Cyclin E in HU-treated CHK1i-

insensitive lines (D28, CO13 and CO25), and a more modest accumulation in CHK1i-

hypersensitive cell lines (HT144 and BL). Some of the accumulation will reflect the accumulation

of cells in S phase compared to the untreated controls, although the very strong accumulation in the

insensitive cell lines indicates inhibition of Cyclin E/CDK2, which reflects the loss of replication

and functional S phase checkpoint arrest in these cell lines. These data indicate that loss of the S

phase checkpoint arrest through uncoupling of CHK1 activation and destabilisation of CDC25A is a

common feature in CHK1i-hypersensitve cells.

Chapter 3

54

Figure 3-6: CHK1i-hypersensitive melanoma cell lines are commonly defective in degradation of

CDC25A after replication stress imposed by HU.

The indicated melanoma cell lines (hypersensitive (A&B) and insensitive (C)) were treated with

control (DMSO), 2 mM HU or 1 M CHK1i for 24 hours and analysed by Western blotting for

CDC25A, pCHK1 (Ser317) and cyclin E1. Alpha tubulin was used as loading control. (D) CDC25A

protein levels for indicated melanoma cell lines treated with HU were quantified and expressed

relatively to the DMSO control (red dotted line). These experiments were repeated three times and

Chapter 3

55

immunoblotting data from one representative experiment are shown. Graph shows mean sem

(n=3).

3.2.5 Stabilised CDC25A is in part responsible for sensitivity to CHK1i

The defect in the S phase checkpoint appeared to be a common feature of CHK1i-hypersensitive

melanoma cell lines, suggesting that the defect may be responsible for sensitivity to CHK1i

cytotoxicity. As the stabilisation of CDC25A was a common feature of the checkpoint defect, the

effect of CDC25A depletion on CHK1i sensitivity was investigated using siRNAs. Initially,

transfection conditions were optimised for three CHK1i-hypersensitive cell lines (D20, MM329 and

D25) to attain maximal transfection efficacy with minimal toxicity. Different concentrations of

three transfection reagents (Lipofectamine 2000, DharmaFECT 1 and DharmaFECT 2) were used

with non-targeting siRNA (NT), and cell death triggering siRNA (POLR2A) to assess the

transfection rate on the basis of percentage cell killing. Cell viability was measured using resazurin

assay. Out of three cell lines, only D20 achieved sufficient knockdown determined by reduced cell

viability for POLR2A-transfected samples with minimal toxicity in NT-transfected samples (Figure

3-7). All three transfection reagents rendered sufficient knockdown in D20 but Lipofectamine 2000

(L2K) produced sufficient knockdown with less toxicity at 0.3% compared to DharmaFECT 1 and

2.

Chapter 3

56

%Viability

%Viability

%Viability

Chapter 3

57

Figure 3-7: Optimisation of transfection efficiency for D20, D25 and MM329 with different

transfection reagents.

CHK1i-hypersensitive cell lines (D20, D25 and MM329) were transfected for 24 hours with

different concentrations of three transfection reagents, (A) Lipofetamine 2000, (B) DharmaFECT 1

and (C) DharmaFECT 2, using non-targeting siRNA (NT) and cell death triggering siRNA

(POLR2A). Cell viability was determined by resazurin assay, and results were expressed as the

mean sd of triplicate determinations.

Using the transfection condition for D20 cells defined above, partial knockdown of CDC25A using

CDC25A siRNA SMARTpool to a level which did not interfere with the cell proliferation was

performed. We have previously reported that transit through S phase is critical for the cytotoxic

effect of CHK1i (Brooks et al., 2013), thus a more complete depletion of CDC25A that blocked S

phase entry would reduce CHK1i sensitivity, but would not address the question of whether the

stabilised CDC25A was involved in the cytotoxicity of CHK1i. Partial knockdown of CDC25A

was achieved using these conditions, which reduced CDC25A mRNA and protein levels (Figure

3-8 A&B), but had no effect on proliferation (Figure 3-9; Control). Even though there is no

apparent effect on cell proliferation, this partial knockdown of CDC25A in D20, CHK1i-

hypersensitive cell line, resulted in approximately 2-fold reduction in sensitivity to CHK1 inhibitor.

This data suggest that CDC25A plays a role in CHK1 inhibitor hypersensitivity.

Chapter 3

58

Figure 3-8: Assessment of CDC25A level in D20 cell line transfected with siCDC25A by

immunoblotting and real-time quantitative RT-PCR.

CHK1i-hypersensitive D20 cells were transfected with CDC25A siRNA SMARTpool (siCDC25A) or

control non-targeting siRNA (siCON) for 24 hours and then assessed by (A) immunoblotting for

CDC25A and (B) real-time quantitative RT-PCR using CDC25A primers and TaqMan probes.

Mean and sd of triplicate determinations were presented.

Chapter 3

59

Figure 3-9: CDC25A level regulates CHK1 inhibitor sensitivity.

CHK1i-hypersensitive cells (D20) were transfected with CDC25A siRNA SMARTpool (siCDC25A)

or control non-targeting siRNA (siCON) for 24 hours and then continued cultured in the presence

of 100 nM CHK1 inhibitor for 24 hours. Cell viability was determined by resazurin assay and the

%viability was calculated as the percentage of control. The results were the mean and sd of

triplicate determinations. ***p<0.001.

3.2.6 Inhibiting WEE1 increases sensitivity to CHK1 inhibitor

The observation that partial depletion of CDC25A decreased CHK1 inhibitor hypersensitivity

suggests that CDC25A is playing an important role in CHK1 inhibitor sensitivity. CDC25A targets

Cyclin/CDK2 complex in S phase by dephosphorylating the inhibitory Tyr15 on CDK2 and

activating the kinase complex. WEE1 is the kinase responsible for the inhibitory CDK2 Tyr15

phosphorylation. To investigate whether increasing Cyclin/CDK2 activity is sufficient to increase

sensitivity to CHK1 inhibitor, the effect of inhibiting WEE1, which is clinically more tractable than

increasing CDC25A levels, was assessed in CHK1i-insensitive melanoma cell lines. The effect of

combining WEE1 inhibitor (MK-1775) with CHK1 inhibitor on the level of apoptosis was assessed

by flow cytometry using cleaved Caspase-3 as an apoptotic marker. Individually, WEE1 inhibitor

and CHK1 inhibitor caused minimal apoptosis in the insensitive cell lines (D28 and C013).

However, combination of two agents resulted in increased apoptosis (27.2% in D28 and 11.7% in

C013) (Figure 3-10). Additionally, immunoblotting analysis of cleaved PARP, a marker of

siCON

siCDC25

A

0

20

40

60

80

100Control

CHK1i

% V

iab

ilit

y***

Chapter 3

60

apoptosis, also demonstrated increased apoptosis in samples treated with WEE1 inhibitor + CHK1

inhibitor (Figure 3-11). This data indicates that increased activity of the CDC25A-Cyclin/CDK2

pathway increases CHK1 inhibitor sensitivity, and that combination with a WEE1 inhibitor may be

a useful means of sensitisation to CHK1 inhibitor treatment.

Figure 3-10: Flow cytometry analysis of D28 and C013 cells for combination of CHK1 and WEE1

inhibitors.

CHK1i-insensitive cell lines, D28 and C013, were treated with control (DMSO), 0.5 M WEE1

inhibitor (MK-1775), 1 M CHK1 inhibitor (GNE-323) or combination of WEE1 inhibitor and

CHK1 inhibitor for 24 hours and stained for cleaved Caspase-3 and DNA content (PI) then

analysed by flow cytometry. The apoptotic cells are indicated by the region.

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61

Figure 3-11: Western Blotting analysis of D28 and C013 cells for combination of CHK1 and WEE1

inhibitors.

D28 and C013 cells were treated in similar manners as described in Figure 3-10 and cells were

harvested and immunoblotted for cleaved PARP as a marker of apoptosis, and PCNA as a loading

control.

3.2.7 CDC25A together with other replicative stress genes are commonly altered in

melanoma

Since CHK1 inhibitor sensitivity is related to the dysregulation of the CDC25A-Cyclin/CDK2-

WEE1 axis and a defect in the S phase checkpoint, I interrogated the TCGA melanoma dataset

using the cBioPortal platform (http://www.cbioportal.org/) to assess whether CDC25A was

commonly over-expressed in melanoma. CDC25A expression was found to be elevated in range of

cancer types such as ovarian cancer, triple-negative breast cancer (TNBC), leukaemia, head and

neck cancer, lung squamous cell cancers and melanoma (Figure 3-12 A), which have all previously

been reported to be sensitive to CHK1 inhibitor single-agent or combination treatments (Dai et al.,

2013, Gadhikar et al., 2013).

The expression of CDC25A, the immediate regulators of CDC25A stability, the E3 ligase TrCP

and antagonizing deubiquitinase USP17L2 (DUB3) (Busino et al., 2003), downstream drivers of S

phase progression, Cyclin A and E/CDK2, and PLK1 which can drive exit from the S phase

checkpoint (Peschiaroli et al., 2006), were also examined in melanoma from TCGA provisional

dataset to assess whether this pathway was a common target for dysregulation. This pathway was

dysregulated in >30% of tumours using a z-score cut-off of 2. All the genes except TrCP were up-

regulated as would be expected. TrCP was most commonly down-regulated, which would increase

CDC25A stability and levels, thereby increasing CDC25A activity (Figure 3-12 B).

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62

Figure 3-12: CDC25A together with other replicative stress genes are commonly dysregulated in

melanoma and other cancer types.

B

C

Chapter 3

63

(A) CDC25A pathway analysed in C. (B) CDC25A expression level in various cancer types from

TCGA provisional dataset by using cBioportal platform. (C) Analysis of altered expression of the

indicated transcripts representing the CDC25A pathway shown in A. The TCGA dataset for

cutaneous melanoma was analysed using cBioportal platform, and the percentage of tumours with

upregulation and downregulation of mRNA with a z score of +/- 2 (>2 sd from the mean).

3.2.8 Overexpression of CDC25A, Cyclin E and Cyclin A sensitises CHK1i-insensitive

cells to CHK1i

The dysregulated expression of individual component parts of CDC25A regulatory network shown

in Figure 3-12, suggested that increase expression of the individual parts might be sufficient to

bypass the S phase checkpoint and increase sensitivity to CHK1 inhibitor. Thus, CDC25A, the

immediate regulators of CDC25A stability, USP17L2 (DUB3), and the downstream drivers of S

phase progression, Cyclin A and E, and CDK2 were over-expressed in CHK1i-insensitive cell line

(C013). Cells were transduced with either empty vector, CDC25A, Cyclin A1, Cyclin E1 or E2, or

USP17L2 using lentivirus pLV411 transduction. The genes of interested were expressed off the

human EF1promoter and the transduced cells were identified by their GFP fluorescence using

high-content imaging (Skalamera et al., 2012). Cells were transduced and allowed to over-express

the proteins of interest for 5 days then treated either 48 hours with 1 M CHK1i and the loss of

transduced cells was assessed, or 24 hours with 2 mM HU then labelled with EdU for 2 hours

before fixing and assaying for GFP positive cell numbers and EdU staining (Skalamera et al.,

2011), to assess S phase checkpoint function. Over-expression of Cyclins A1 and Cyclin E1/2, and

USP17L2 caused a modest increase in sensitivity to CHK1i treatment compared to empty vector or

HU-treated cells, demonstrated by the reduction in GFP positive cells, whereas CDC25A produced

a stronger effect, reducing to 20% of the control level (Figure 3-13A). However, when the effect of

over-expression on EdU incorporation in the presence of 2 mM HU was assessed as a marker of S

phase checkpoint activity, none of the over-expressed genes was sufficient to overcome the HU

effect (Figure 3-13B). This suggests that mechanisms that increase S phase CDK2/Cyclin activity

increased sensitivity to CHK1i, but loss of S phase checkpoint response is not required for CHK1i

hypersensitivity.

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64

Figure 3-13: Overexpression of CDC25A, Cyclin E1/2, Cyclin A and USP17L2.

CHK1i-insensitive cell line, C013, was transduced with lentivirus expressing the indicated genes

(pLV411 as the empty vector control) as an IRES-GFP containing transcript and the transduced

cells were identified on the basis of GFP fluorescence. 2-3 days after transduction, cells were

treated with either 1 M CHK1 inhibitor alone or 2 mM HU for 48 hours and 24 hours

respectively. Cells were fixed, stained and analysed by high-content image analysis. (A) The

percentages of GFP expressing cells compared to the vehicle-treated control. (B) Cells were pulsed

with EdU for 2 hours prior to fixing and percentage of GFP-expressing cells stained for EdU are

shown. Mean sd for quadruplicate experiments are presented here.

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65

3.2.9 Hypersensitive cell lines develop high level of genomic instability after long-term

low-dose HU treatment

Loss of cell cycle checkpoint function should provide growth, survival or adaption advantage to the

tumour. I hypothesised that the S phase checkpoint defect in the CHK1i-hypersensitive cells was

likely to be a driver of increased genomic instability when cells with this defect are exposed to low

level of replicative stress for an extended period of time, a situation which resembles the stress in

the tumour microenvironment in vivo. Failing to induce cell cycle arrest results in less time to repair

the DNA damage and cells enter mitosis without fully repairing the damage incurred resulting in

loss of chromosome fragments from unrepaired double strand breaks, or failed mitosis and

cytokinesis due to incorrect repair. These small DNA fragments lead to the formation of

micronuclei, when chromosomes or chromosome fragments segregate improperly during mitosis

and become separated from the main chromatin mass. Micronuclei formation is related to

replication and repair errors, and is regarded as an indicator of genomic instability (Zhang et al.,

2015, Hatch et al., 2013). To mimic low level replicative stress that might be expected in a tumour

environment, cells were grown in media containing 0.1 mM HU for 3 months. This low level of HU

does not affect cell proliferation significantly but is sufficient to induce low level replicative stress

without causing a level of damage to cells that would reduce the proliferation of even S phase

checkpoint functional cell line (Wilhelm et al., 2014). CHK1i-hypersensitive, S phase checkpoint

defective cells would be expected to have higher levels of genomic instability since these cells lose

the ability to stop replication when they encounter the stress compared to CHK1i- insensitive cell

lines which have competent S phase checkpoint.

Four CHK1i-hypersensitive cell lines (BL, D25, MM329, D20) and three CHK1i-insenstitive cell

lines (MM648, D28, C025) were grown in the culture media with either DMSO (control) or 0.1 mM

HU for three months. The percentage of the cells with micronuclei was assessed by

immunofluorescent staining and calculated as percentage change based on vehicle control samples.

Although there were different basal levels of micronuclei in each of cell lines tested, there was a 3-5

fold increase in the proportion of cells with micronuclei in all of the HU-treated checkpoint

defective CHK1i-hypersensitive cell lines, whereas none of the checkpoint functional CHK1i-

insensitive cell lines had any change (Figure 3-14). Even though checkpoint functional cell line,

C025, had high basal level of micronuclei, there was no increase in the percentage of micronuclei

by the presence of HU suggesting that C025 cells must have other defect not related to S phase

checkpoint, possibly in the mitotic spindle assembly checkpoint responsible for ensuring the fidelity

Chapter 3

66

of genome partitioning in mitosis. This also demonstrates that the effect of the low-dose HU was

specific for the S phase checkpoint defect.

Figure 3-14: CHK1i-hypersensitive cell lines displayed increased genomic instability after long-

term low-dose HU treatment.

Indicated CHK1i-hypersensitive and –insensitive melanoma cell lines were cultured in the presence

or absence of 0.1 mM HU for three months. Cells were stained with DAPI and number of the cells

with micronuclei were calculated as the percentage of the total cell numbers. At least 100 cells

were counted for each replicate, and the mean and sd were calculated from triplicate

determinations. * indicate p <0.05, ** p<0.01 t-test.

3.2.10 CHK1i, GNE323, shows single-agent activity in melanoma cell line in vivo

To investigate the in vivo efficacy of CHK1 inhibitor (GNE323), first I established xenograft mice

models from CHK1i-hypersensitive S phase cell cycle checkpoint defective melanoma cell line,

D20. The cells were inoculated into the BALB/c nude mice subcutaneously with 50% matrigel and

tumours were allowed to grow until they reached approximately 50-100 mm3 before the treatment

0

5

10

15

20

25

BL D25 MM329 D20 MM648 D28 C025

Control HU

Hypersensitive Insensitive

%ofmicronuclei

*

**

Chapter 3

67

started. Mice were randomly assigned to two groups and later treated with CHK1 inhibitor,

GNE323, or vehicle treatment orally at 50 mg/kg/day for 3 days per week for 3 weeks. CHK1

inhibitor single-agent treatment significantly inhibited the tumour growth compared to the control

growth in mice xenograft model in D20 cell line (Figure 3-15). Two mice from each treatment

cohort were culled 24 hours after the final drug treatment, and tumours were harvested for

assessment of CHK1i activity. Phosphorylation of H2AX at Ser139 (H2AX) was investigated in

xenograft tumours to assess the CHK1i activity in vivo. Increased levels of H2AX were observed

in the tumours treated with CHK1i compared to the control treatment indicating that there is

accumulation of DNA damage in CHK1i-treated tumours, and demonstrating that H2AX is a

useful marker of in vivo CHK1i action.

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68

Figure 3-15: In vivo efficacy of CHK1 inhibitor, GNE323.

(A) BALB/c nude mice bearing D20, CHK1i-hypersensitive cell line, were given either vehicle or

CHK1 inhibitor (GNE323) orally at 50 mg/kg once daily for 3 days, followed by 4 days of rest (OD

x 3 days, rest 4 days) for three cycles. Tumours were allowed to grow at least 50-100 mm3 before

treatment was started and values are indicated as mean ± sd for 6 animals per point. Tumour

response was determined by tumour volume measurement performed 2-3 times a week during the

course of study. * indicate p <0.05, ** p<0.01 t-test (B) Immunohistochemistry staining for H2AX

antibody. Xenograft tumours from control and treatment groups were collected 24 hours after the

final drug treatment and stained with antibody for H2AX.

***

****

**

**

**

**

Chapter 3

69

3.3 Discussion

It was originally thought that the primary mechanism of action of CHK1i is that it enhances

chemotherapy by its ability to abrogate the ATR-CHK1-dependent G2/M phase checkpoint (Blasina

et al., 2008, Tse et al., 2007, Zabludoff et al., 2008). CHK1 has multiple roles of in cell cycle

regulation and repair, and the ability of CHK1i to abrogate the G2/M phase checkpoint is not

sufficient to explain the manner in which CHK1 inhibitor as single-agent inflicts damage upon the

cancer cells in the absence of exogenous DNA damage, or the CHK1 inhibitor single-agent activity

in cancer cells. Moreover, the potent ability of CHK1i to synergise with agents that selectively

promote replication stress such as gemcitabine, suggests that CHK1i sensitises by targeting the

ATR-CHK1-dependent S phase checkpoint (Koh et al., 2015, Montano et al., 2012, Xiao et al.,

2013).

There have been several reports demonstrating CHK1 inhibitor single-agent sensitivity in cancer

cell lines (Bryant et al., 2014, Davies et al., 2011b), although the underlying mechanism and

markers of sensitivity for the single-agent activity were not explored. Our lab had previously

shown that CHK1 inhibitor single-agent treatment has cytotoxic effect on a significant proportion of

melanoma cells (Brooks et al., 2013). Previous studies from the lab have also shown that, in

hypersensitive cell lines, the timing of apoptosis occurred without cells undergoing mitosis,

whereas less sensitive cell lines appeared to be more dependent on cells progressing into and

through mitosis to trigger apoptosis (Oo et al., in preparation). The aim of this chapter was to

investigate the mechanism by which these hypersensitive melanoma cell lines are being killed by

CHK1 inhibition and, from this insight into the mechanism, to identify potential makers to select

the patient tumours that are likely to be sensitive to CHK1i as single agents for clinical use.

Defective cell cycle checkpoints or defective/mutated DNA repair mechanisms often make cancer

cells reliant on alternative compensatory mechanisms to cope with the additional stress. This

reliance of cancer cells can be exploited by effective new therapeutics as demonstrated in the case

of PARP inhibitors in BRCA mutant and more generally HR defective cancers (Bryant et al., 2005,

Fong et al., 2009, Rottenberg et al., 2008). CHK1 inhibitor was reported to be particularly effective

in targeting cells with a defective FA pathway (Chen et al., 2009). FA defect is a high risk factor for

leukaemia and solid cancers especially with head and neck cancers but not melanoma (Alter et al.,

2003). In fact, FA genes were reported to be upregulated in melanoma (Kao et al., 2011). In this

study, I found that FA pathway defects are not common in melanoma, present in only 1 out of 8 cell

Chapter 3

70

lines tested here. However, the synthetic combination of CHK1 inhibition with FA pathway defect

suggested that other defects affecting S phase DNA damage responses could be present and

contribute to CHK1 inhibitor sensitivity by making those cells rely more on CHK1 for their

survival.

Loss of the checkpoints can contribute to genomic instability and replication stress to the cells

(Bartkova et al., 1996, Brooks et al., 2014, Kaufmann et al., 2008). CDC25A has been reported to

be dysregulated in many types of cancer, and its dysregulation also leads to genomic stability

(Boutros et al., 2007, Ray et al., 2007). Overexpression of CDC25A partially abrogates the S phase

arrest (Joerges et al., 2003) and interferes with the replication checkpoint in response to

hydroxyurea (Molinari et al., 2000). I found that the majority of CHK1i-hypersensitive melanoma

cell lines have defective S phase checkpoint and CDC25A is often found to be dysregulated in those

checkpoint defective cell lines. Loss of the checkpoint arrest would mean that cells with high levels

endogenous replication stress can continue to proliferate with an amount of damage that would

trigger the checkpoint in cells with an intact checkpoint response. This would consequently further

increase the level of intrinsic damages and thus would increase sensitivity to CHK1 inhibitor.

Even though various cell cycle checkpoint defects were reported before in melanomas (Kaufmann

et al., 2008, Wigan et al., 2012, Brooks et al., 2014, Pavey et al., 2013), S phase checkpoint defects

were not commonly reported: only one report showing 11 out of 14 melanoma cell lines failed to

repair UV-induced DNA photoproducts during S phase (Belanger et al., 2014). The previous

failure to detect the S phase checkpoint defect is probably because CHK1 activation is usually

interpreted as an indicator of S phase checkpoint activation. In this chapter, I also found that CHK1

was robustly activated after hydroxyurea treatment in all the cell lines. Despite CHK1 activation,

when measured with functional read-out assay such as EdU incorporation, those CHK1i-

hypersensitive melanoma cell lines were found to be incorporating EdU indicating that they were

still capable of replicating their DNA (although the lack of dNTPs inhibits this process) and S phase

checkpoint response is defective.

There is also increased stability of CDC25A in many CHK1i-hypersensitive cell lines even though

CHK1-dependent destabilisation of CDC25A mechanism was functioning normally. One possibility

behind the increased stability of CDC25A could be related to post-translational modifications of

CDC25A (Boutros et al., 2007): TRCP complex which is responsible for ubiquitin-mediated

degradation of CDC25A in response to DNA damage (Busino et al., 2003) and Dub3 which

removes the ubiquitin chains and prevents CDC25A proteasomal degradation (Pereg et al., 2010).

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71

There have been reports of mutations causing reduced expression of TRCP, although not in

melanoma (He et al., 2005, Gerstein et al., 2002, Kim et al., 2007).

Since S phase checkpoint defect is a common feature of CHK1i-hypersensitive melanoma cell lines,

and CDC25A increased stability was observed only in some hypersensitive cell lines, there could

also be other mechanism(s) or defect(s) contributing to this defective S phase checkpoint. It is

possible that dysregulation of downstream drivers of S phase progression such as Cyclin A, Cyclin

E or CDK2 could also be contributing to this defect in S phase cell cycle checkpoint in the cell lines

that lack increased stability of CDC25A. The expression levels of these downstream drivers of S

phase progression were also found to be increased in some of melanoma cells in TCGA dataset.

However, over-expression of those genes was not found to be over-riding the S phase checkpoint in

our experiments even though over-expression makes the cells more sensitive to CHK1 inhibitor

(Figure 3-13). One possible reason is that the cells over-expressing those genes may not have

sufficient time to adapt to the altered gene expression and develop the defective checkpoint, due to

the short-term nature of the experiment.

Even though it has long been known that CHK1 directly phosphorylates CDC25A and causes its

degradation, it was only recently found that CDC25A has direct effect on CHK1 inhibitor and ATR

inhibitor sensitivity. Ruiz and colleagues (Ruiz et al., 2016) showed that CDC25A depletion

reduces CHK1 and ATR inhibitors sensitivity in embryonic stem cells. Sakurikar and colleagues

(Sakurikar et al., 2016) also showed that failure to activate CDK2, which is dephosphorylated and

activated by CDC25A, increase resistance to CHK1 inhibitor. However, Brooks et al. had shown

that blocking progression into S phase was sufficient to block sensitivity to CHK1i (Brooks et al.,

2013), and neither of the aforementioned studies assessed the effect of CDC25A deletion or CDK2

inhibition on S phase progression, although previous studies have shown that inhibition of either of

these is sufficient to block entry into and through S phase (Sakurikar et al., 2016, Ruiz et al., 2016).

Here I have demonstrated that partial depletion of CDC25A reduced CHK1 inhibitor sensitivity in

melanoma cell lines, without affecting proliferation. Likewise, over-expression of CDC25A

potently sensitised insensitive cells to CHK1i. CDK2 is dephosphorylated and activated by

CDC25A and WEE1 is the kinase responsible for the inhibitory CDK2 Tyr15 phosphorylation.

Unregulated CDK2 activity causes increased genomic instability and DNA damage in replication

stress (Hughes et al., 2013). The data presented in this chapter indicated that inhibiting WEE1 can

enhance the sensitivity to CHK1 inhibitor. This has also been reported by other groups who has

shown that the combination of CHK1 and WEE1 inhibitors can synergise in some of the cell lines

(Davies et al., 2011a, Carrassa et al., 2012, Guertin et al., 2012). WEE1 inhibitors are also currently

being investigated in the various clinical trials (Leijen et al., 2016a, Leijen et al., 2016b, Do et al.,

Chapter 3

72

2015) and inhibition of WEE1 promotes increased CDK activity and increased firing of replication

origin (Beck et al., 2012). CHK1 and WEE1 have multiple roles in regulating cell cycle progression

and genomic stability during DNA replication. CHK1 and WEE1 have been found to have distinct

functions in controlling replication-induced damage. Co-depletion of both proteins causes apoptosis

compared to individual knockdown (Dominguez-Kelly et al., 2011). Thus, these non-redundant

roles of CHK1 and WEE1 could be useful as novel synthetic lethal combination treatment for

CHK1i-insensitive cell lines using similar approach with CHK1 inhibitor in combination with its

upstream regulator, ATR inhibitor (Sanjiv et al., 2016). Considering the fact that both CHK1 and

WEE1 inhibitors are currently undergoing clinical trials, a significant number of patients can be

benefited to this potential combination.

In summary, this chapter demonstrates the insight into mechanism of CHK1i hypersensitivity in

melanomas and identify that the melanoma cell lines with defective S phase cell cycle checkpoint

are hypersensitive to CHK1 inhibitor single-agent treatment and has also led to the identification of

CDC25A role as a contributor and potential marker to CHK1i hypersensitivity.

Chapter 4

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4 DNA-PK induced RPA2 hyper-phosphorylation renders melanoma

cell hypersensitive to CHK1 inhibition

4.1 Introduction

CHK1 inhibitor (CHK1i) single-agent treatment has been found to be effective in some of the

cancer types (King et al., 2015, Brooks et al., 2013, Walton et al., 2012). However, the manner of

cell killings induced by CHK1i is still ambiguous. To identify the patient population who can

benefit from CHK1i single-agent treatment, it was important to understand how single-agent

CHK1i treatment induces cell death. Despite CHK1 deletion being embryonically lethal (Takai et

al., 2000), somatic cell types cope with partial or complete lack of CHK1 function, although they

have slower proliferation rate (Montano et al., 2012). Moreover, normal cell types are not killed

with siRNA –mediated CHK1 depletion at concentrations that inhibited CHK1 functions (Brooks et

al., 2013, Cho et al., 2005). Nevertheless, CHK1 inhibition is effective in sensitising cells to the

agents which promotes DNA damage and replication stress, and CHK1 inhibition alone caused cell

death in cancer cells with defective DNA repair pathways and high level of replication stress

(Brooks et al., 2013, Chen et al., 2009, Buisson et al., 2015).

Cancer cells are constantly under stress by various pathological drivers such as oncogenic

transformation, defective cell cycle checkpoints or DNA repair defects, and these stresses, if not

controlled, will increase genomic instability. The cells require a means of tolerating this increased

stress which, in normal cells, would trigger the cell cycle arrest and repair response. Cells are prone

to DNA damage during S phase which can be caused by replication fork stalling and formation of

ssDNA resulting from uncoupling of polymerase and helicase complexes. Soon after its formation,

ssDNA is coated by Replication Protein A (RPA) complex to form RPA-ssDNA complex. This in

turn recruits ATR and ATRIP complex which activates CHK1. RPA is a heterotrimeric complex

composed of RPA1, RPA2 and RPA3 subunits. The 32 kDa RPA2 subunit is the target of

phosphorylation during normal G1/S transition (Zernik-Kobak et al., 1997, Niu et al., 1997). When

there is DNA damage or replication stress, RPA2 may be phosphorylated by other kinases such as

DNA-PK, ATM and ATR to produce a hyper-phosphorylated state. The hyper-phosphorylated

RPA2 has been linked to the S phase checkpoint, and recovery from replication stress (Liu et al.,

2012, Ashley et al., 2014, Murphy et al., 2014). Phosphorylation of RPA2 at Ser4/Ser8 by DNA-PK

appears to be required for checkpoint activation and replication fork restart after stress. RPA2

hyper-phosphorylation has been linked to CHK1 inhibitor induced apoptosis in cells with high

levels of replication stress (Zuazua-Villar et al., 2015). There are also reports that CHK1 inhibitor

can promote apoptosis through Caspase-2 and Caspase-3 dependent mechanism (Myers et al., 2009,

Chapter 4

74

Sidi et al., 2008, Brooks et al., 2013). In this chapter, I have investigated the potential roles for

Caspase-2 and Caspase-3 in the cell death induced by CHK1 inhibitor single-agent treatment in

hypersensitive cell lines, and whether the hyper-phosphorylation status of RPA2 in melanoma cells

under replication stress induced by CHK1i and hydroxyurea (HU) is involved in the observed cell

death. I have also explored how the changes in the phosphorylation level of RPA2 are influenced

by CHK1 inhibition in melanoma cell lines.

4.2 Results

4.2.1 Effect of pan-Caspase inhibitor and Caspase-2 inhibitor on CHK1i sensitivity in

CHK1i-hypersensitive cell lines

I investigated how blocking Caspase-dependent pathways would affect CHK1i single-agent

sensitivity in hypersensitive melanoma cell lines. I used the Caspase-2 inhibitor (Z-VDVAD-FMK)

and CHK1i-hypersensitive cell lines (A15, HT144 & MM96L), and the CHK1i-insensitive cell line

(MM648) to assess the contribution of Caspase-2 to CHK1i-induced cell death. Cells were treated

with DMSO (control) and either CHK1 inhibitor alone or with 100M Caspase-2 inhibitor and

assayed for viability using the resazurin assay after 3 days. The concentration of 1 M CHK1

inhibitor was used since that concentration was sufficient to kill the hypersensitive cell lines in our

previous dose response experiments (Figure 3-1), and 100 M Caspase-2 inhibitor concentration

was reported to be sufficient in preventing apoptosis in melanoma and Jurkat cell lines

(Chakravarthi et al., 2013, Rudolf et al., 2011). There was only minimal change in the cell viability

in CHK1i-hypersensitive cell lines and no change in CHK1i-insensitive cell lines between the

CHK1i with or without 100 M Caspase-2 inhibitor, indicating that the cytotoxic effect of CHK1i

single-agent treatment in hypersensitive cell lines cannot be rescued by inhibiting Caspase-2

pathway (Figure 4-1).

Similarly, 100 M concentration of pan-Caspase inhibitor Z-VAD-FMK was used to assess the

involvement of Caspase-3 in CHK1i-induced toxicity in the CHK1i-hypersensitive MM329 cell

line. This concentration of Z-VAD-FMK was reported to be sufficient in inhibiting apoptosis in

cancer cell lines (Garimella et al., 2012, Sun et al., 2013). There was a small but significant

decrease in CHK1i-induced loss of viability in MM329 treated with CHK1i and Z-VAD-FMK

compared to those treated with CHK1i alone (from 45% to 59% cell viability) (Figure 4-2).

Interestingly, CHK1i still induced cell-killings even after blocking with pan-Caspase inhibitor,

Chapter 4

75

suggesting that Caspase-independent mechanisms may also be contributing to the cell death

induced by CHK1i.

Since overexpression of Bcl-2 and related anti-apoptotic proteins has been reported to inhibit cell

death imposed by various anti-cancer drugs (Debatin et al., 2002, Reed, 2008, Yip et al., 2008), I

also investigated the possibility of dysregulation of anti-apoptotic factors of the Bcl-2 family such

as Bcl-2 and Bcl-XL playing a role in CHK1i insensitivity in the melanoma cell lines. The levels of

Bcl-2 in all of CHK1i-hypersensitive and CHK1i-insensitive cell lines were similar with the

exception of C013, which had notably higher level of Bcl-2 (Figure 4-3). D25 and C025 had higher

level of Bcl-XL while D20, MM648 and C013 have very low level compared with the majority of

the cell lines. Additionally, there was also no obvious change between the samples treated with 1

M CHK1i or 2 mM HU compared to the control treatment.

Figure 4-1: Inhibition of Caspase-2 did not rescue CHK1i cytotoxicity.

CHK1i-hypersensitive (A15, HT144 and MM96L) and –insensitive (MM648) cell lines were treated

with control (DMSO), 1 M CHK1i either alone or with 100 M Caspase-2 inhibitor for 72 hours.

Cell viability was determined by resazurin assay. % viability was calculated as the percentage of

the control (DMSO) treatment. Mean and sd of triplicate determinations were presented here.

A15

HT14

4

MM

96L

MM

648

0

50

100

150CHK1i

CHK1i+Caspase-2i

%V

iab

ilit

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Chapter 4

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Figure 4-2: Pan-Caspases inhibition has effect in protecting cells from CHK1i-hypersensitivity.

MM329, CHK1i-hypersensitive cell line, was treated with control (DMSO), 300 nM CHK1i either

alone or with 100 M pan-Caspase inhibitor (Z-VAD) for 48 hours. Cell viability was determined

by resazurin assay. % viability was calculated as the percentage of the control (DMSO) treatment.

Mean and sd of triplicate determinations were presented here. *p 0.05.

CHK1i

CHK1i

+Z-V

AD

0

20

40

60

80%

Via

bilit

y*

Chapter 4

77

Figure 4-3: Immunoblotting analysis of Bcl-2 and Bcl-XL in melanoma cell lines.

The indicated melanoma cell lines, CHK1i-hypersensitive (A&B) and CHK1i-insensitive (C), were

treated with DMSO control, 2 mM HU or 1 M CHK1i for 24 hours and analysed by Western

blotting for Bcl-2 and Bcl-XL. Alpha tubulin was used as a loading control.

4.2.2 CHK1i promotes RPA2 hyper-phosphorylation in CHK1i-hypersenstive

melanomas

Replication stress can drive RPA exhaustion, where the amount of ssDNA exceeds the level of RPA

resulting in unmasked ssDNA, and is caused by the excessive replication origin firing (Toledo et

al., 2013). The normal role of the ATR-CHK1 pathway in replication stress is to protect cells from

Chapter 4

78

this state. The presence of the hyper-phosphorylated form of RPA2 (denoted by band migrating

with apparent molecular size of 36kDa; Figure 4-4) is normally only found in cells after the

induction of high levels of replication stress imposed with drugs such as HU with a checkpoint

inhibitor such as CHK1 or ATR inhibitors to overcome the S phase checkpoint, and is the product

of ATR and DNA-PK-dependent phosphorylation (Toledo et al., 2013, Zuazua-Villar et al., 2015,

Vassin et al., 2009). I examined the state of RPA2 phosphorylation in both untreated and cells

treated with 2 mM HU or 1 M CHK1i for 24 hours. A slower migrating form of RPA2, which

migrated with the same mobility as the hyper-phosphorylated form of RPA2 was found in all the

hypersensitive cell lines without treatment, and was more abundant in the most sensitive lines

(Figure 4-4). It accumulated after treatment with either 2 mM HU, or CHK1i alone, and was

associated with reduced level of the faster migrating non-phosphorylated RPA2. The accumulation

was less prominent in untreated, HU or CHK1i treated in CHK1i-insensitive cell lines. Increased

phosphorylation of the ATR/DNA-PK-dependent Ser33 site was observed with both HU and

CHK1i treatment in all cell lines.

Another major RPA2 hyper-phosphorylation sites, Serine 4/8 phosphorylated by DNA-PK (Liu et

al., 2012, Ashley et al., 2014), was also found to be markedly elevated after CHK1i treatment in

hypersensitive cell lines while there was only minimal increase in insensitive cell lines (Figure 4-4).

Interestingly, the slower migrating RPA2 observed in the untreated hypersensitive lines contained a

relatively low level of Ser4/8 and Ser33 phosphorylation, suggesting that the slower migration may

not be solely due to the phosphorylation state of the protein. Although the majority of the cell lines

behaved as described above, there were notable exceptions. In the hypersensitive lines, D25 cells

contained a high level of the slower migrating form of RPA2, but there was little apparent change in

either the faster or slower migrating bands with HU or CHK1i treatment. In HT144 cells, the

slower migrating form was obscured by a strong band detected by the RPA2 antibody running just

ahead of the 32 kDa RPA2 band. In the insensitive lines, CO13 in particular had high levels of the

slower migrating form of RPA2 and the faster migrating, non-phosphorylated form was strongly

reduced with CHK1i treatment.

Histone H2AX phosphorylation at Ser139 (H2AX) was found to be initiated with the formation of

DSBs after the cells were treated with DNA-targeting agents or ionising radiation (Rogakou et al.,

1998). H2AX was also found to be accumulated in the cells treated with CHK1 inhibitor and

chemotherapeutics drugs and its formation was also reported to be correlated with CHK1 inhibitor

sensitivity. Increase in H2AX levels was found with both HU and CHK1i treatments in the

CHK1i-hypersensitive lines. The absolute level varied from cell line to cell line, and was not

Chapter 4

79

proportional to CHK1i sensitivity (Figure 4-4). The insensitive lines had little H2AX but D28 was

the exception. Apart from D20 where there was loss of viability to the same extent as CHK1i, HU

treatment of the other CHK1i hypersensitive lines produced only a minor reduction in viability

reflecting the grow arrest imposed by HU (Figure 4-5). This demonstrates that increased H2AX is

not necessarily a trigger for cell death. Taken together, this data suggested that the major difference

between the CHK1i-hypersensitive and insensitive lines was the relative lack of change in faster

migrating hypo-phosphorylated form of RPA2 and the reduced level of RPA2 hyper-

phosphorylation in the insensitive lines with either HU or CHK1i treatment.

Figure 4-4: CHK1i treatment induced RPA2 hyper-phosphorylation in CHK1i-hypersensitive cell

lines.

Chapter 4

80

The indicated melanoma cell lines, CHK1i-hypersensitive (A&B) and CHK1i-insensitive (C), were

treated with control (DMSO), 2 mM HU or 1 M CHK1i for 24 hours and analysed by Western

blotting for RPA2, pRPA2 (Ser33), pRPA2 (Ser4/Ser8) and H2AX. Alpha tubulin was used as a

loading control. The hyper-phosphorylated form of RPA2 is indicated by the open arrowhead, the

hypo-phosphorylated form by the filled arrowhead. This data is representative of three replicate

experiments.

Figure 4-5: Cell viability was not relatively compromised with HU treatment compared to CHK1i

treatment in majority of CHK1i-hypersensitive cell lines.

CHK1i-hypersensitive cell lines were treated with control (DMSO), 1 M CHK1i or 2 mM HU for

48 hours. Cell viability was determined by resazurin assay. % Viability was calculated as the

percentage of the control (DMSO) treatment. Mean and sd of triplicate determinations were

presented here. ***p 0.001

4.2.3 Reduction of E2F1 in CHK1i-hypersensitive cell lines but not in CHK1i-

insensitive cell lines after CHK1 inhibition

It was intriguing that magnitude of cell-killings between HU-treated and CHK1i-treated samples

were so different (Figure 4-5) even though they have similar level of RPA2 hyper-phosphorylation

and RPA2 exhaustion indicated by the loss of faster migrating hypo-phosphorylated form of RPA2

as described in the previous section. This suggested that there might be other mechanisms

D20 B

L

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96L

MM

329

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HU

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***

Chapter 4

81

preventing replication fork collapse. ATR and CHK1 were found to participate in stabilising E2F1

which is the transcription activator of RRM2, a critical factor for dNTP synthesis. I found that the

E2F1 level was reduced after CHK1 inhibition in four (D20, BL, MM96L and MM329) out of

seven hypersensitive cell lines, whereas none of the insensitive cell lines had decreased E2F1 in the

CHK1i treatment compared to the control treatment (Figure 1.6).

In addition, in HU-treated CHK1i-hypersensitive and CHK1i-insensitive cell lines, E2F1 levels

were found to be increased in all lines except in MM329 and MM370. This is likely to be part of a

mechanism of coping with the dNTP depletion through RRM2 accumulation. The reduction in

E2F1 levels in the CHK1i-treated CHK1i-hypersensitive cell lines are likely to result in reduced

RRM2 levels as reported previously (Buisson et al., 2015), and reduced dNTP levels, thus

mimicking the effect of HU in reducing cellular dNTP levels. The D20, BL, MM96L and MM329

cell lines were also the ones with the greatest reduction in the non-phosphorylated RPA2 band

(Figure 4-4). It is not clear why this effect is not observed in CHK1i-insensitive cell lines, but the

increased RRM2 levels in the HU-treated cells is likely to be associated with the checkpoint

inhibition stabilising RRM2 levels.

Chapter 4

82

Figure 4-6: CHK1i degraded E2F1 in CHK1i-hypersensitive cell lines.

The indicated melanoma cell lines, CHK1i-hypersensitive (A&B) and CHK1i-insensitive (C), were

treated with control (DMSO), 2 mM HU or 1 M CHK1i for 24 hours and analysed by

immunoblotting for E2F1. Alpha tubulin was used as a loading control.

4.2.4 Inhibition of DNA-PK reduces CHK1i-induced RPA2 hyper-phosphorylation

and CHK1i-induced cell killing in hypersensitive melanoma cell lines

I have found that the level of hyper-phosphorylated RPA2 was increased in CHK1i-hypersensitive

cell lines after CHK1 inhibition, but this was not generally evident in CHK1i-insensitive cell lines.

The hyper-phosphorylated RPA2 has previously been reported to contribute to the apoptosis in S

phase checkpoint arrested cells where CHK1 has been depleted (Zuazua-Villar et al., 2015). The

phosphorylation of RPA2 is catalysed by a number of kinases, with DNA-PK responsible for the

phosphorylation at Ser4/8 which is responsible for the slower migrating form of RPA2 found with

ATR-CHK1 inhibition of cells with high levels of replication stress and S phase checkpoint arrest

(Ashley et al., 2014, Zernik-Kobak et al., 1997, Liu et al., 1993, Stephan et al., 2009, Olson et al.,

2006). I investigated the effect of inhibition of DNA-PK on the CHK1i-induced RPA2 Ser4/8

phosphorylation in CHK1i-hpersensitive cell lines. In D20 and BL, inhibiting DNA-PK using

NU7441, a selective DNA-PK inhibitor (Leahy et al., 2004, DeLoughery et al., 2015), markedly

reduced hyper-phosphorylated form of RPA2 including the site Ser4/8 compared to CHK1i alone

treatment. This reduction in hyper-phosphorylated RPA2 (Ser4/8) was also associated with

increased level of non-phosphorylated form of RPA2: especially with BL cell line suggesting the

replenished RPA pool due to reduced RPA2 hyper-phosphorylation (Figure 4-7).

Chapter 4

83

Figure 4-7: Inhibition of DNA-PK reduced RPA hyper-phosphorylation.

D20 and BL, CHK1i-hypersensitive cell lines, were treated with control (DMSO), 1M CHK1

inhibitor (CHK1i) alone, 5M DNA-PK inhibitor (DNA-PKi) alone or combination of CHK1i +

DNA-PKi for 24 hours and analysed by Western blotting for pRPA2(Ser4/Ser8) and RPA2. Alpha

tubulin was used as a loading control.

Since inhibiting DNA-PK reduced the hyper-phosphorylated form of RPA2, I further investigated if

there was a change in the viability of the CHK1i-treated hypersensitivity cell lines when DNA-PK

is inhibited. Four CHK1i-hypersensitive cell lines (D20, BL, MM329 and MM96L) were treated

with 1 M CHK1i +/- 5 M DNA-PKi (NU7441) for 48 hours and cell viability was assessed by

resazurin assay. Inhibiting DNA-PK reduced the loss of viability seen with CHK1i treatment in

some of CHK1i-hypersensitive cell lines, and the magnitude of the rescue effect of NU7441 varied

from cell line to cell line: moderate effect seen in BL and MM329 while only minimal effect

observed in MM96L and no effect in D20 (Figure 4-8 A). A lot of floating dead cells and rounded

Chapter 4

84

apoptotic cells were found in CHK1i alone treatment in BL and MM329 under phase-contrast

microscope while cells treated with CHK1i + DNA-PKi had a reduction in the floating dead cells

(Figure 4-8 B), indicating that inhibition of DNA-PK partially protected cells from CHK1 inhibitor

cytotoxicity.

Figure 4-8: DNA-PK inhibition protected cells from CHK1i hypersensitivity.

(A) CHK1i-hypersensitive cell lines were treated with control (DMSO), 1 M CHK1 inhibitor

(CHK1i) alone, 5 M DNA-PK inhibitor (DNA-PKi) alone or combination of CHK1i + DNA-PKi

for 48 hours. Cell viability was determined by resazurin assay. % Viability was calculated as the

percentage of the control (DMSO) treatment. Mean and sd of triplicate determinations were

Chapter 4

85

presented here. (B) Phase-contrast images of BL and MM29 cell lines treated with CHK1i alone or

CHK1i+DNA-PKi for 48 hours. *p 0.05.

4.2.5 DNA-PK and RPA levels are commonly altered in melanoma

Since RPA2 phosphorylation and DNA-PK were closely related to CHK1 inhibitor hypersensitivity

as described in previous sections, I further investigated if there were any prevalent changes in the

expression levels of all the subunits of RPA and DNA-PK in the skin cutaneous melanoma samples

available in TCGA dataset using cBioportal platform (http://www.cbioportal.org).

The expression of RPA 1, 2 & 3, genes that encode the subunits of RPA, were found to be altered in

about 32% of the melanoma tumours: mostly upregulation with downregulation in some samples.

PRKDC, the gene expressing DNA-PK, which involves in hyper-phosphorylating RPA2, was also

altered in 13% of melanoma samples, mostly with mRNA upregulation. RPA levels have been

reported to be important in preventing replication fork collapse and particularly hyper-

phosphorylation of RPA2 has been reported to mediate S phase cell cycle checkpoints and recovery

from replication stress. This data suggests that melanoma cells with high level of endogenous

replication stress resulted from increased mutation burden and various cell cycle checkpoint defects

may be adapting to contain the increased stress level below the tolerable threshold for their survival

by increasing signalling pathways related to RPA and DNA-PK.

Figure 4-9: The expression level of all the subunits of RPA and PRKDC are commonly altered in

melanoma tumour samples from TCGA.

TCGA dataset for cutaneous melanoma was analysed using the cBioportal platform, and the

percentage of tumours with upregulation and downregulation of mRNA with a z score of 2 (>2

standard deviations from the mean) were displayed here.

Chapter 4

86

4.3 Discussion

When there is disruption to DNA replication and replication forks are stalled, proteins required to

stabilise the stalled forks, repair the blockage and restart the forks are recruited to the stalled forks.

ATR-CHK1 signalling pathway was found to involve as an important player in all of these events

(Ciccia et al., 2010). During replication stress, CHK1 is involved in coordinating replication fork

stabilising, controlling dormant origins, regulating replication initiation and preventing fork

collapse (Ge et al., 2010, Maya-Mendoza et al., 2007, Petermann et al., 2010, Syljuasen et al., 2005,

Speroni et al., 2012, Yang et al., 2008). Another important protein complex involved with

stabilising stalled DNA replication fork is RPA, and the RPA2 subunit of this complex was found

to be phosphorylated in response to replication stress (Ashley et al., 2014, Zernik-Kobak et al.,

1997, Liu et al., 1993, Stephan et al., 2009, Olson et al., 2006).

RPA2 was found to be extensively hyper-phosphorylated in tumour cells when treated with DNA

synthesis and CHK1 inhibitors (Zuazua-Villar et al., 2015). Recently, it was also reported that ATR

inhibition together with DNA replication inhibitor such as HU caused high level of replication

stress by triggering extensive dormant origin firing and thereby depleting the RPA pool due to the

excessive formation of ssDNA, resulting in replication catastrophe (Toledo et al., 2013). Here, I

have shown that in CHK1i-hypersensitive melanoma cell lines, CHK1i alone treatment was also

able to cause increased RPA2 hyper-phosphorylation in the absence of another DNA replication

inhibitor. ATR-CHK1 pathway promotes RRM2 accumulation by stabilising E2F1, the

transcription activator of RRM2 gene (Buisson et al., 2015). E2F1 level was found to be reduced

together with RRM2 mRNA level in ATR and CHK1 inhibited cells (Zhang et al., 2009, DeGregori

et al., 1995, Buisson et al., 2015). In our experiments, E2F1 was found to be reduced in some of the

CHK1i-hypersensitive cell lines after CHK1 inhibition. CHK1 inhibition can also increase CDK

activity and thereby reduce RRM2 level via CDK/SCFcyclinF

ubiquitinated proteolysis (D'Angiolella

et al., 2012). Over-expression of mutant form of RRM2 that was not a Cyclin F substrate blocked

the cytotoxicity of CHK1i and Wee1i, suggesting that reduced E2F1-dependent expression and

stability both contribute to the reduction in RRM2 levels with CHK1i (Pfister et al., 2015). Thus, it

is possible that through those two mechanisms CHK1i alone treatment reduces RRM2 level when

dNTPs are required, and thus cause RPA2 hyper-phosphorylation in the absence of other DNA

replication inhibitors. Interestingly, E2F1 level was not reduced with CHK1i treatment alone in

CHK1i-insensitive cell lines, and this was associated with little RPA2 hyper-phosphorylation. Why

CHK1i reduced E2F1 level in some of the CHK1i-hypersensitive cell lines and not in CHK1i-

insensitive cell lines is still unclear.

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87

RPA2 hyper-phosphorylation and depletion of the hypo-phosphorylated form was equally strong in

CHK1i-hypersensitive cell line with HU and CHK1i treatment, but loss of viability was much lower

with HU than CHK1i treatment. The accumulation of hyper-phosphorylated RPA2 with HU

treatment alone in these cell lines is further evidence of the defective S phase cell cycle checkpoint

in CHK1i-hypersensitive cell lines described in Chapter 3 of this thesis. Due to the lack of S phase

cell cycle checkpoint arrest, CHK1i-hypersensitive cell lines were not able to respond to the

depletion of dNTP pools induced by HU and, thus, continued progressing into the S phase and

created high level of replication stress and, as a result, RPA2 was hyper-phosphorylated. This is

identical to the effect of CHK1i alone in these cell lines, and is further evidence that the effect of

CHK1i in these cells is to effectively reduce dNTP pools though the mechanisms discussed above.

Inhibiting RPA2 hyper-phosphorylation using a DNA-PK inhibitor reduced the toxicity of CHK1i

treatment. The effect was significant although relatively modest, indicating that RPA2 hyper-

phosphorylation was not sufficient for the loss of viability of observed.

H2AX phosphorylation at serine 139 (H2AX) is an indicator of DNA damage and DNA

replication stress. Increased H2AX level was also linked to decreased viability by previous studies

(Rogakou et al., 2000, King et al., 2015). However, in this chapter, I found that increased H2AX

does not always translate into decreased viability. All of the hypersensitive lines, with the exception

of D20, had similarly increased levels of H2AX with HU and CHK1i treatment, yet only CHK1i

decreased viability. It has previously been reported that some of the cancer cell lines failed to

commit to apoptosis despite the persistent appearance of H2AX foci (Gagou et al., 2010). A

possible reason for the discrepancy between this data and other reports could be the way H2AX

activity was measured: H2AX level measured by FACS and immunofluorescence microscopy was

claimed to be more accurate in determining apoptosis compared to Western blotting, the detection

method used in this chapter (Solier et al., 2014, Bonner et al., 2008). However, the very strong

accumulation of H2AX with both treatment, but loss of viability with only CHK1i does suggest

that the DNA damage is by itself an insufficient signal to promote the cell death observed.

The data presented in this chapter demonstrated that CHK1i single-agent treatment caused RPA2

hyper-phosphorylation together with RPA pool exhaustion in CHK1i-hypersensitive melanoma cell

lines, while this was much less prominent in insensitive cell lines. Moreover, a modest reduction in

CHK1i–induced killing was observed in CHK1i-hypersensitive cell lines when RPA2 hyper-

phosphorylation was blocked by co-inhibition of DNA-PK. The data suggest a model where in

hypersensitive lines have already high levels of replication stress and are likely to have a higher

requirement for dNTPs. This is ensured by increased CHK1-E2F1-dependent expression of RRM2

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to maintain RNR levels. Inhibition of CHK1 thus deprives cells of dNTPs. However, this is also

likely to be case in the CHK1i insensitive cell lines, but as these cell lines retain a functional S

phase checkpoint they are able to block replication through CHK1-indpendent mechanisms.

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5 Development of three-dimensional HTS-compatible in vitro drug

testing melanoma model with improved in vivo predictability

5.1 Introduction

Traditional monolayer adherent cell culture (2D) models are widely used in cancer research because

they are convenient to culture, represent certain aspects of different cancer types, and are useful to

investigate cellular mechanisms. Even though 2D culture offers insights into many basic molecular

principles in cell and cancer biology, the cultural conditions for 2D models cannot recapitulate the

actual physiological conditions in the body. 2D models lack various physiological features such as

cell-cell/matrix interactions, proper tissue architecture and mechanical features. Furthermore, 2D

cancer models lack in vivo tumour heterogeneity and limitations in the availability of

oxygen/nutrients for the cells in the inner core of the tumours, both of which can affect cellular

response to drugs. Owing to their adaptation to monolayer culture conditions, cancer cell lines

grown in 2D cell cultures also displayed different genetic profiles from primary samples from the

patients (Ghosh et al., 2005, Baharvand et al., 2006, Benya et al., 1982).

Three-dimensional cell culture (3D) models resemble the actual physiological conditions such as

the limitations in the exchange of nutrients and oxygen, and have gene expression profiles closer to

the tumours in vivo. Various 3D melanoma models have been developed to replicate the actual

tumour conditions in vitro. There are 3D spheroid melanoma models that physically promote cell

growth as compact spheroids using liquid overlay methods, and these have been used incorporating

fluorescent ubiquitination-based cell cycle indicator (FUCCI) system for real-time monitoring of

melanoma cells (Haass et al., 2014, Smalley et al., 2006). Some models attempt to promote the

tumour-initiating stem cell-like population of the melanoma cells, the so-called tumourspheres

(Fang et al., 2005), and there is also 3D skin reconstruct model which resembles human skin

histologically and can be used for testing targeted drug therapies (Bell et al., 1981).

Despite abundant evidence that 3D models offer more optimal platform for drug testing than 2D

models, most of the preclinical research still rely on monolayer culture cells. 3D cultures are not as

widely used as they should be, mostly due to several limitations. Firstly, 3D models maintenance

cost is higher and labour-intensive compared to 2D and secondly, there is an issue of compatibility

with high-throughput screening (HTS) facilities since a large number of preclinical testing are done

in HTS facilities and most the 3D models are not readily compatible with existing HTS platform.

Thirdly, there is a lack of cost effective, robust and HTS-compatible assays for 3D models.

Therefore, future 3D models need to provide an expedient method for controllable culturing and

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real-time measurements of tumour sphere/spheroid populations and those measurements and

models should also go well with multi-well plate format and HTS system routinely used in the

industry.

Tumoursphere cultures can be developed by different methods such as using non-adherent surfaces

which prevents cells from interacting with the substrate and consequently promoting cells to cells

interaction to form spheres or culturing cells in bioreactors such as spinner flasks and rotary cell

vessels to produce spheres via continuous movement of cell suspension. These methods are useful

for mass production and long-term culture but limited by non-uniform sphere formation which can

greatly influence cells functions and drug transport. With the rise of micro- and nano-technologies,

various spheres-on-a-chip devices emerged as devices that can improve the duration and amount for

tumoursphere cultures. However, hydrophobic materials like PDMS and chip format mostly used in

these technologies are not usually appropriate for the already existing HTS equipment in drug

testing facilities. Therefore, there is also another need to develop read-outs and analysis to be

compatible with these devices.

In the present work, I developed not only 3D melanoma model by using non-adherent surface but

also the 3D viability assay compatible with HTS system. The system developed can produce

compact tumourspheres for drug treatment in different melanoma cell lines and is able to easily

incorporated into the existing HTS workflow, and provides the opportunity to monitor tumour cells

responses to the drug treatment in real-time. The validity of the 3D assay was tested against the

commercially available 3D ATP assay and the ability of this 3D model to predict drug response in

vivo was validated in mouse xenograft models.

5.2 Results

5.2.1 Establishment of tumourspheres from different melanoma cell lines

We have previously reported about the tumourspheres cell lines cultured in suspension culture flask

using DMEM/F12 media supplemented with Knockout Serum Replacement containing basic

fibroblast growth factor (bFGF), Heparan Sulphate and β-Mercaptoethanol (Brooks et al., 2014).

However, the presence of reducing agent, β-Mercaptoethanol, interfered with the activity of several

drugs tested, and removing β-Mercaptoethanol from the media severely affected the tumourspheres

formation in suspension culture flasks. Due to those drawbacks, I investigated the possibility of

utilising alternative condition for tumoursphere culture.

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As reducing the cell ability to attach to the surface has been shown to encourage cell-cell adhesion

and promote the extracellular matrix formation, non-adherent surface for tumourspheres formation

was generated by coating tissue culture flasks with pHEMA (poly-Hydroxyethylmethacrylate) with

the protocol adapted from (Folkman et al., 1978). pHEMA is a biocompatible hydrogel that creates

non-adherent surface and thus promotes cells to cells adhesion and spheres formation. Different

melanoma cell lines which were previously maintained as monolayer in 10% FBS containing RPMI

media were subcultured and seeded into transition media containing 10% serum in DMEM/F12

media for 24 hours as monolayer culture. Afterwards, the cells were dissociated into single cells and

reseeded into the pHEMA-coated flasks without any centrifuging to encourage tumourspheres

formation from individual cells. To develop and maintain tumourspheres, the media composition

adapted from (Fang et al., 2005) was used: 20% knockout serum replacement media with basic

fibroblast growth factors and heparin sulphate in DMEM/F12 media. Tumourspheres started to

develop from all of the melanoma cell lines tested within 24 hours and >90% of cell lines continued

proliferating gradually. Morphological differences were observed among the different melanoma

tumoursphere lines, and they can be divided into three categories: tight tumourspheres with round

shape, tight tumourspheres with irregular shape, or loose aggregates. Those observed differences

among the tumourspheres were not found to be correlated with any common mutation present in

melanoma such as BRAF, NRAS, PTEN and p53 (Table 5-1). 90% (20/22) of cell lines tested

developed and propagated into tumourspheres while the remaining 2 cell lines formed

tumourspheres at the beginning but failed to proliferate. 75% (15 out of 20) of the tumourspheres

cell lines developed tight tumourspheres with either round or irregular shape while remaining 25%

developed as cells aggregates (Table 5-1, Figure 5-1).

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Table 5-1: The panel of melanoma tumourspheres cell lines developed with their morphology and

mutation status.

Y=yes, N=no, N.A=information not available.

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Figure 5-1: Images of the panel of melanoma tumourspheres in pHEMA-coated flasks.

A15

A2058

BL

C013

C052

D04

D20

D25

D28

HT144

MM96L

MM329

MM370

MM415

MM603

SKMEL13

SKMEL28

C002

C045

C054

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5.2.2 Development of compatible tumourspheres drug screening model for high-throughput

screening platform

Although there are a number of methods for culturing tumourspheres for drug screening purposes,

many of these are not compatible with already existing high-throughput platforms, and this is one of

the main reasons 3D models are not widely adopted by drug testing facilities (Astashkina et al.,

2012, Kimlin et al., 2013, LaBarbera et al., 2012). With this in mind, and with our lab experience in

high-throughput screening methodologies, my tumoursphere model was developed to be compatible

with automated liquid handlers and imaging instruments commonly used in high-throughput

facilities.

Tumourspheres culture grown in non-adherent surface culture vessels usually results in spheres

with different sizes and consequently produces varying results due to different drug penetration and

distribution profiles (Mehta et al., 2012). In order to maintain a relatively similar distribution of

tumoursphere sizes, tumourspheres were first dissociated, then seeded as single cells (1500-2500

cells depending on the cell line) into each well of low-volume 384-well plates using Matrix

WellMate liquid dispenser (Thermo Fisher Scientific) and allowed to form tumourspheres again for

24 hours in the incubator before drug treatment was commenced. To assess the utility of this

tumoursphere culture system in high-throughput screening, a panel of tumourspheres were assessed

for their CHK1 inhibitor (GNE323) sensitivity. CHK1 inhibitor was serially diluted and increasing

concentrations of drugs (10 nM to 10 M) and vehicle control (DMSO) were added into the

tumourspheres plates by using Sciclone (PerkinElmer) liquid handling equipment and cultured for

72 hours in the presence of the drug. At the end of the drug treatment, two types of tumourspheres

assays, JC-10 imaging assay that measures the loss of mitochondrial membrane potential, and

CellTitre-Glo 3D cell viability assay measuring ATP levels, were performed to measure the drug

response (Figure 5-2).

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Figure 5-2: High-throughput drug screening workflow for melanoma tumourspheres model.

5.2.3 Development of HTS-compatible tumourspheres assay

Lack of cheap, robust and reliable assays which can be incorporated into a HTS platform

seamlessly is another hurdle for utilising tumourspheres/spheroids models in routine drug testing.

To develop an assay for rapid imaging and analysis of tumourspheres, I developed a JC-10

fluorescence assay detected and quantified by automated high-content imaging. JC-10 measures

mitochondrial membrane potential by accumulating and forming J-aggregates which have emission

profile of orange fluorescence 590 nm in normal cells. In unhealthy cells with low mitochondrial

membrane potential, JC-10 remains in monomeric forms which display green fluorescence 525 nm.

These signals can be monitored by using different applications such as flow cytometry,

fluorescence microscopy or plate reader. To be compatible with high-throughput platform, I mainly

focused on developing JC-10 assay on using fluorescence microscopy and plate reader methods.

However, plate reader assay with JC-10 only offered limited dynamic range of sensitivities between

the positive control (cells lysed with Triton X-100) and negative control (DMSO) samples (data not

shown here). Thus, I proceed to developed JC-10 assay using fluorescence microscopy method for

HTS compatibility.

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Tumourspheres were stained with JC-10 (10 g/ml) after drug treatment: the orange fluorescence

590 nM produced by J-aggregates were captured by using TRITC filter to image the viable

tumourspheres. The full images of the individual well of 384-well plate were taken using the

Cellomics ArrayScan VTi using 2.5x objective which captured the whole well of the low-volume

384-well plate to image all the tumourspheres present in the well. Total area of viable

tumourspheres were quantified using Cellomics quantitative cell analysis software and small objects

less than 20 m were rejected to exclude the floating individual cells (Figure 5-3).

Under the fluorescence microscope, viable tumourspheres were observed in the wells treated with

vehicle control while an obvious disruption of the architectural structure of tumourspheres were

observed in the wells treated with 1 M CHK1 inhibitor in tumourspheres cell lines such as D04

and HT144 which were hypersensitive to the drug. This disruption in structure was also

demonstrated in the changes in the total area of viable tumourspheres as well (Figure 5-4B). On the

other hand, only minimal changes were observed in the other cell lines which were insensitive to

the drug and again this fact was also reflected in the total area of viable tumourspheres (Figure 5-4).

Figure 5-3: MM329 tumourspheres were treated with control (DMSO) or 1 M CHK1 inhibitor for

72 hours and stained with JC-10 (10 g/ml).

The whole well images of 384-well plate were taken by ArrayScan high-content system. Threshold

for selecting the tumourspheres was set and smaller objects about <20 m were rejected to exclude

individual floating cells. Scale bars in white represent a length of 200 m.

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Figure 5-4: Development of JC-10 imaging assay for tumourspheres.

Different melanoma tumourspheres cell lines were treated with control (DMSO) or 1M CHK1

inhibitor for 72 hours and stained with JC-10 (10 g/ml). (A) Whole well images of 384-well plate

were taken by Cellomics ArrayScan high-content imager. (B) Total object area of viable

tumourspheres were calculated and plotted. Mean and sd of quadruplicate determinations were

plotted in the graph. Scale bars in white represent a length of 200 m.

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5.2.4 CHK1 inhibitor sensitivity profile in tumourspheres model determined by

JC-10 imaging assay

CHK1 inhibitor single-agent treatment has been founded to be cytotoxic to the large proportion of

melanoma cell lines maintained in monolayer culture in our laboratory previously with different

sensitivity to the drug (Brooks et al., 2013). In this experiment, I investigated whether CHK1

inhibitor single-agent treatment efficacy in melanoma tumourspheres model was different from

monolayer culture. 12 tumourspheres lines which were hypersensitive (IC50<100 nM) and sensitive

(100 nM<IC50<1000 nM) to CHK1 inhibitor as 2D cell lines were used in this experiment. High-

throughput workflow and tumourspheres assay determination by JC-10 imaging were performed as

described in the above sections in this chapter. Tumourspheres were treated with increasing

concentrations of CHK1 inhibitor (10 nM to 10 M) or vehicle control (DMSO) for 72 hours.

Images were scanned from each well using ArrayScan VTI HCS Reader and total area of viable

tumourspheres were quantified by Cellomics CellHealthProfiling.v3 algorithm. Results were

displayed as the percentage of the control samples treated with vehicle control.

The four hypersensitive cell lines (MM96L, HT144, D04, D20,) remained hypersensitive to CHK1

inhibitor in tumourspheres culture (Figure 5-5 A) while other the three hypersensitive cell lines

(MM329, BL, D25) in 2D cultures (IC50 < 100 nM) became totally insensitive to the drug in

tumourspheres cultures (Figure 5-5 B). There was no obvious difference in total area of the

tumourspheres in the control treatment groups between the hypersensitive cell lines which became

insensitive and the ones remained hypersensitive (Figure 5-4). Thus, it was less likely that the

changes in sensitivity was due to the drug distribution and penetration due to difference in size of

tumourspheres.

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Figure 5-5: Dose-response curves of CHK1i-treated tumoursphere determined by JC-10 imaging

assay.

Different melanoma tumourspheres cell lines with various sensitivity to CHK1i, (A) hypersensitive

to CHK1i in both 2D and 3D (B) hypersensitive in 2D but became insensitive in 3D and (C)

sensitive in 2D but became insensitive in 3D, were treated with increasing concentration of CHK1

inhibitor (10 nM -10 M) for 72 hours and stained with JC-10. Images were taken and analysed by

Cellomics ArrayScan high-content imager. Total area of viable tumourspheres were measured and

results were calculated as percentage of the control treatment. Mean and sd of the three biological

replicates were presented here.

5.2.5 CHK1 inhibitor sensitivity profile in CellTiter Glo 3D cell viability assay

In order to compare and validate the results from JC-10 imaging assay developed here,

commercially available CellTiter Glo 3D cell viability assay was used to determine CHK1 inhibitor

sensitivity in same tumourspheres cell lines. CellTiter Glo 3D cell viability assay measures the ATP

present in the culture as a marker for the presence of metabolically active cells, and is developed for

automated high-throughput screens. The assay is formulated with more effective lytic capacity and

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is designed to use with 3D cell culture models. Same tumourspheres cell lines were cultured and

prepared for the drug treatment in the same manner as described in the above section and treated

with another CHK1 inhibitor, GDC-0575, (10 nM to 10 M) or vehicle control (DMSO) for 72

hours. The CHK1 inhibitor, GNE-323 described in Chapter 3 and 4 of this thesis, and GDC-0575

were found to have very similar drug activities in melanoma cell lines (Figure 5-6).

Tumourspheres cell lines such as D04, D20, HT144 and MM96L remained hypersensitive to CHK1

inhibitor in both types of tumourspheres assays: they have higher IC50 values in CellTiter Glo 3D

assay except in D20 which has nearly the same IC50 in both (Table 5-2). BL, MM329 and D25

tumourspheres cell lines changes from being hypersensitive in 2D to insensitive in 3D in both

assays: while CellTither Glo assay displayed lower IC50s values compared to JC-10 assays for BL

and MM329 (about 4 M compared to 10 M in JC-10), the fact that those cell lines became

insensitive to CHK1i did not essentially change between two different assays.

With sensitive cell lines such as A2058, SKMEL13 and MM415, all of the cell lines increased their

IC50s and became resistant to the drug in tumourspheres culture in both assays with slightly

different IC50 values. A2058 and SKMEL13 had 1-1.5 M IC50 values in CellTither Glo assay

while IC50 for A2058 is ~3.5 M and no IC50 for SKMEL13 in JC-10 assay. MM415 had ~4 M

IC50s in CellTither Glo and no IC50 in JC-10 assay. These data indicated that even though there

were slight variations in IC50s between two different assays, sensitive cell lines became more

resistant to the drug in tumourspheres culture in both assays. Overall, despite some difference

between the two assays due to their different nature of determining the cell viability, the dose-

response results from JC-10 imaging assay is similar and comparable with commercially available

CellTiter Glo 3D assay.

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Figure 5-6: The efficacy of two CHK1 inhibitors, GNE-323 and GDC-0575.

(A) D20, CHK1i-hypersensitive cell line, and (B) MM648, CHK1i-insensitive cell line, were treated

with increasing concentration (3 nM -3 M) of two CHK1 inhibitors, GNE-323 and GDC-0575, for

72 hours and cell viability was assessed by resazurin assay. Mean and sd of triplicate

determinations were presented here.

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Figure 5-7: Dose-response curves of CHK1i-treated tumoursphere determined by CellTiter Glo 3D

cell viability assay.

Different melanoma tumourspheres cell lines were treated with increasing concentration of CHK1

inhibitor (10 nM -10 M) for 72 hours and cell viability was determined by measuring the presence

of ATP by CellTiter Glo 3D cell viability assay. Mean and sd of the three technical replicates were

presented here.

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Figure 5-8: Comparison of IC50 values from two different tumourspheres assays.

Cells were seeded and allowed to form tumourspheres (3D) for 24 hours before the CHK1 inhibitor

treatment for 72 hours. Cell viability values were determined by either JC-10 imaging assay or

CellTiter Glo 3D cell viability assay. IC50 values were calculated for each of the cell lines tested.

Cell lines CellTiter Glo JC-10

D20 94.19 107.27

MM96L 216.27 132.88

D04 226.46 106.09

HT144 277.97 107.95

A2058 1180.32 3487.69

SKMEL13 1438.80 10000.00

MM415 3819.44 10000.00

BL 3767.04 10000.00

MM329 3837.07 10000.00

D25 10000.00 10000.00

Table 5-2: Comparison of IC50 values from two different tumourspheres assays.

5.2.6 Comparison between 2D and 3D

The cancer therapeutics responses in 2D and 3D models have been found to be different in previous

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reports (Friedrich et al., 2007, Kimlin et al., 2013, LaBarbera et al., 2012, Mehta et al., 2012). Cells

were usually found to be more resistant to the drugs in 3D models although some reports

demonstrated cells being more sensitive to the drug in a 3D environment (Herrmann et al., 2008,

Pickl et al., 2009, Frankel et al., 2000). In our tumoursphere model, drug sensitivity was essentially

a binary response, either sensitive with IC50 <300 nM, or insensitive (Figure 5-9). Interestingly,

many of the hypersensitive 2D lines remained sensitive to the drug in 3D, although their IC50

values increased. Hypersensitive cell lines in 2D (D20, D04, HT144, and MM96L) remained

hypersensitive: their IC50s <100nM in 2D and changed to IC50s between 90 nM to 280 nM. On the

other hand, other hypersensitive cell lines in 2D (BL, D25, MM329) became insensitive: their

IC50s increased to 4 M -10 M. Previously sensitive cell lines (IC50s 100-300 nM) in 2D culture

such as A2058, MM603 and SKMEL13, reduced their sensitivity to IC50 values of 1-2 M after

culturing in tumourspheres. There were 2-3 fold changes in IC50s for the hypersensitive cell lines

which remained hypersensitive. But the drastic change in IC50s occurred in hypersensitive cell

lines such as BL, D25, MM329 and MM370 where their IC50s changed to essentially insensitive.

Since CHK1 inhibitor sensitivity has been related to intrinsic damage in the cells indicated by

phosphorylation of H2AX at Ser139 (H2AX) levels and the activity of CHK1 itself (Rawlinson et

al., 2014, Brooks et al., 2013), I investigated if there is any change in basal levels of these proteins

in the cells growing in monolayer and tumourspheres cultures. In the cell lines which maintained

their hypersensitivity (highlighted in blue Figure 5-10), D20 and D04 have low basal level of

H2AX while HT144 demonstrated high levels, but there was no significant changes in these levels

in tumourspheres culture. There was only minimal basal CHK1 activation which was shown by

phosphorylation of CHK1 at serine 317 both in monolayer and tumourspheres in that three cell

lines. In the cell lines (BL, D25, MM329) that drastically changes their sensitivities in

tumourspheres culture, only low basal level of H2AX was observed in monolayer culture and no

significant changes were present in tumourspheres culture as well except for MM329 which has

decreased H2AX level but the observed change was nowhere near its degree of change in

sensitivity (Figure 5-10). In sensitive cell lines, SKMEL13 also showed slight decrease in the level

of H2AX and A2058 has no change in tumourspheres compared to monolayer. There was only low

level of pCHK1 (Ser317) activity in all the monolayer cell lines while pCHK1 level for all the cell

lines remained the same except in A2058 and SKMEL13 which had increased pCHK1 activity in

tumourspheres.

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Figure 5-9: Changes in IC50 values from 2D melanoma cell lines to melanoma tumourspheres.

Cells were seeded and allowed to attach (2D) or form tumourspheres (3D) for 24 hours before the

CHK1 inhibitor treatment for 72 hours. Cell viability values for 2D model were determined by

resazurin cell viability assay (fluorescence) and cell viability values for tumourspheres were

determined by CellTiter Glo 3D cell viability assay. IC50 values were calculated for each of the cell

lines were graphed in (A) and tabulated in (B) for comparison.

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Figure 5-10: Assessment of the intrinsic DNA damage and CHK1 phosphorylation in 2D and 3D

melanoma models.

Different melanoma cells were grown in 2D and 3D. The cell lines highlighted in blue are those

remained hypersensitive to CHK1 inhibitor in both 2D and 3D cultures. Cell lysates were extracted

and immunoblotted for H2AX and phosphorylated CHK1 (Ser317). Alpha tubulin was used as a

loading control.

5.2.7 Tumourspheres model predicts in vivo CHK1i sensitivity in melanoma cell lines

Many cancer therapeutic drugs which demonstrated significant anti-cancer activity in vitro

experiments fail to progress into clinical trials as their in vivo efficacies do not match their in vitro

activities (Unger et al., 2014, Zanoni et al., 2016, Tanner et al., 2015). This is in part a limitation of

current drug testing models which are usually based on two dimensional cultures. Most of these

inconsistencies were caused by limitation of the monolayer cell culture model compared to the

actual physiological condition of tumours in patients. Three dimensional models are considered to

be more physiologically relevant and better model to predict the drug sensitivity in vivo. Therefore,

I used CHK1 inhibitor as a model drug and tested our tumourspheres model to assess its

predictability of drug response in xenograft model.

MM96L and MM370 cell lines were previously hypersensitive to CHK1 inhibitor in monolayer cell

culture (IC50 < 100 nM) (Figure 3-1), but their sensitivities changed in tumourspheres model: D20

and MM96L remained hypersensitive (IC50 < 100 nM), whereas MM370 became essentially

insensitive (IC50>1.5 M) to the drug (Figure 5-11 A). The cells were inoculated into the BALB/c

nude mice subcutaneously with 50% matrigel and tumours were allowed to grow until they reached

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approximately 50-100 mm3 before the treatment was started. Mice were randomly assigned to two

groups and later treated with CHK1 inhibitor, GNE323, or vehicle treatment orally at 50 mg/kg/day

for 3 days per week for 3 weeks. CHK1 inhibitor single-agent treatment significantly inhibited the

tumour growth compared to the control growth in mice xenograft model in MM96L. However,

CHK1 inhibitor single-agent treatment did not have any significant inhibitory effect in MM370

xenograft and treatment group was growing about the same rate as the control group (Figure 5-11

B). H2AX which is an indicator of DNA damage was found to be increased in the tumours

harvested 24 hours after last drug treatment in MM96L xenograft compared to the control treatment

while there was only minimal increase in MM370 tumour indicating that CHK1i treatment was

more effective in MM96L xenograft. This data suggested that tumourspheres model displayed

better predictability than 2D model for in vivo drug response.

Figure 5-11: 3D melanoma model displayed improved predictability for the in vivo drug response.

*

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(A) 3D dose-response curve of MM96L and MM370, CHK1i-hypersensitive cell lines in 2D

(IC50<100nM). Cells were seeded and allowed to form tumourspheres (3D) for 24 hours before the

CHK1 inhibitor treatment for 72 hours. Cell viability values were determined by CellTiter Glo 3D

cell viability assay. Each point is the mean and sd of triplicate determinations. (B) BALB/c nude

mice bearing MM96L and MM370 were given either vehicle or CHK1 inhibitor orally at 50 mg/kg

once daily for 3 days, followed by 4 days of rest (OD x 3 days, rest 4 days) for three cycles. The

treatment days are indicated by the bars below the X axis. Tumours were allowed to grow at least

50-100 mm3 before treatment was started and values are indicated as mean ± SD for 5 animals per

point. Tumour response was determined by tumour volume measurement performed 2-3 times a

week during the course of study. (C) Immunohistochemistry staining for H2AX antibody. MM96L

and MM370 xenograft tumours from control and treatment groups were collected 24 hours after the

final drug treatment and stained with antibody for H2AX. *p<0.05

5.3 Discussion

There is a need to develop the cancer drug testing models which can represent the actual tissue

architecture of tumours more accurately and are physiologically closer to the in vivo state than

traditional 2D models. At the same time, the model should be less time-consuming and expensive

than the animal models, and can readily incorporate into the existing high-throughput drug

screening facilities. Here, I have reported the development of a HTS-compatible 3D model which

spontaneously forms tumourspheres resulting in compact tumourspheres with improved

predictability of in vivo drug response to CHK1 inhibitor.

The reasons 3D models are not commonly used in mainstream drug screening applications are the

lack of simple and controlled culture conditions and robust standardised assay for cellular response,

and poor incorporation into HTS system. Various culture conditions were developed to establish

tumourspheres/spheroids but most of them cannot produce rapid and standardised tumourspheres

which can be easily incorporated and lacks simple, reliable and real-time measurement for cellular

response in HTS system (LaBarbera et al., 2012, Astashkina et al., 2012). Liquid overlay method

and spinner flask method can produce large quantities of spheroid needed for drug testing.

However, former method needs long culturing time and difficult for media change while the latter

produces heterogeneous sizes.

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Various melanoma 3D models have been reported in literature. Melanospheres models was first

reported by using mouse embryonic fibroblast conditioned human embryonic stem cell medium to

encourage spontaneous formation of melanospheres (Fang et al., 2005). One of the limitations for

this model was that it took longer to form spheres for drug testing (~14 days) and its melanospheres

formation rate is ~20%. Another type of 3D melanoma model reported was melanoma spheroid

developed by using liquid overlay method and real-time cell cycle imaging by utilising the

fluorescent ubiquitination-based cell cycle indicator (FUCCI) demonstrated to recapitulate tumour

heterogeneity and oxygen gradient similar to in vivo conditions (Haass et al., 2014, Smalley et al.,

2006). Thurber et al. (2011) also reported about difference in BRN2 and MITF expression between

melanospheres and melanoma spheroid models, suggesting that the melanospheres were more

similar to in vivo tumours than the spheroids models on the basis of similarity of expression patterns

of these two influential transcription factors. Among the various advantages offered by those

models, one of the common limitations was its inability to incorporate into the high-throughput

drug testing platform readily. By modifying the media condition described in Fang et al. and

combining with non-adherent surface modification, I have managed to developed tumourspheres

across different melanoma cell lines within a relatively short time frame with simple workflow

compatible with HTS.

Many anti-cancer drug developments have been stalled or failed due to their reduced effectiveness

in vivo and it shows that anti-cancer drug activities in 2D model drug screening platform is not

optimal and 3D models have been showed to be more accurate in simulating drug responses in vivo

(Mehta et al., 2012, Astashkina et al., 2012, Kimlin et al., 2013, Hirschhaeuser et al., 2010, Rimann

et al., 2012). Previously it was showed that 2D cultures overestimated drug responses compared to

3D cultures (Kimlin et al., 2013, Loessner et al., 2010, Karlsson et al., 2012, Edmondson et al.,

2014). In agreement with these studies, I found that there is reduced sensitivity to CHK1 inhibitor

used here as model drug in melanoma cell lines grown as tumourspheres compared to the same

lines cells grown as monolayer cultures. I also demonstrated that tumourspheres culture is more

predictive than 2D cultures by showing that MM96L, which was hypersensitive to CHK1i in both

2D and tumourspheres culture also remained sensitive to the drug in xenograft mice model. On the

other hand, MM370, which switched from being hypersensitive in 2D to insensitive in

tumourspheres culture, displayed resistance to the drug in xenograft (Figure 5-11). All of our data

demonstrated that tumourspheres model probably can avoid the overestimation of anti-cancer drug

efficacy usually experienced with 2D cell culture models and offer better predictability.

It is currently not clear as to the reasons behind the sometimes drastic change in sensitivity between

2D and 3D. There was no difference in the level between CHK1 phosphorylation status and H2AX

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which indicates the intrinsic DNA damage in the cells was unchanged. Various reasons for the

difference between 2D and 3D have been reported even though a lot of these variations could also

be attributed to the different ways of culturing 3D system. One of the possible reasons for the

difference in drug sensitivity could be due to hypoxia: Hypoxia in the tumour is known to

contribute drug resistance through several mechanisms (Brown et al., 1998, Olcina et al., 2010).

Even though the size (40-150 m) of most of the tumourspheres that were used in drug screening

were not as large as the ones reported in the previously studies, there might still be difference in the

level of oxygenation compared to the 2D monolayer cells. Other possible reasons could be

overexpression of anti-apoptotic Bcl-2 family proteins well-known for their roles in blocking cell

death and promoting tumour cells survival. Bcl-2 family proteins were reported to be overexpressed

in some of the cancer stem cell like pool of various tumours (Delbridge et al., 2015, Soengas et al.,

2003).

At present, there are various available 3D model systems and none of them can be considered as a

standard method. It is also not well established that which method would provide the best

predictability for the actual results in the clinics. The limitation of our current tumourspheres model

is that it is not the accurate representation of the actual physiological tumour microenvironment in

vivo, lacking a number of features such as interaction with other cell types such as stromal cells,

blood perfusion and immune components. Although co-culturing with other relevant cell types

would potentially solve this problem, monitoring the changes in the viability and activity of

individual cell type in real-time 3D will be challenging. In summary, despite not being able to fully

represent the actual physiological conditions of the tumour yet, the 3D model system presented in

this chapter offers seamless incorporation into the existing HTS drug screening facility, robust and

economical determination of 3D viability assessment and improved predictability of in vivo drug

response.

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6 Concurrent addition of low level replication stress sensitised

CHK1i-insensitive cancer cells to CHK1 inhibition

6.1 Introduction

The landscape of treatment for metastatic melanoma patients has changed recently with the

emergence of new targeted therapies such as BRAF inhibitors and immunotherapies such as anti-

CTLA4 and anti-PD1 treatments (Chapman et al., 2011, Hauschild et al., 2012, Hodi et al., 2010,

Robert et al., 2011b, Topalian et al., 2012). However, resistance quickly develops after BRAF

inhibitor treatment (Chapman et al., 2011, Hauschild et al., 2012), limiting its effectiveness. With

immunotherapy, it is still unclear with how to identify the patient population who will respond to

the treatment, and there is also evidence of development of resistance (Ribas et al., 2016, Zaretsky

et al., 2016). Thus, there is a significant portion of melanoma patients who are still in need of

effective treatments. CHK1 inhibitor (CHK1i) single-agent treatment was found to be cytotoxic to

various melanoma cell lines and other cancer cell lines as reported in previous chapters in this thesis

and other reports (King et al., 2015, Brooks et al., 2013, Davies et al., 2011b, Walton et al., 2012). I

have found that CHK1i hypersensitivity is associated with defective S phase checkpoint arrest in

melanoma cells, and the majority of the CHK1i-hypersensitive cell lines have an uncoupling of

checkpoint activation and CDC25A degradation that is largely responsible for the S phase arrest, as

discussed in Chapter 3 of this thesis. CHK1i single-agent treatment is effective in less than one-

third of melanoma cell lines, indicating there is still scope to improve the response to this treatment,

or to better identify those patient tumours more likely to be sensitive to CHK1i as a single-agent.

CHK1 inhibitors have been shown to be effective in sensitising tumours to a range of

chemotherapeutics such as gemcitabine, SN-38, campothecin, cytarabine, cisplatin, hydroxyurea

and topotecan. The most dramatic sensitisation was in combination with antimetabolites that

promote replication stress, particularly with gemcitabine and hydroxyurea while significantly less

sensitisation was reported with cisplatin, fluorouracil and thioguanine (Montano et al., 2012,

Venkatesha et al., 2012, Xiao et al., 2013). Gemcitabine and hydroxyurea (HU) both inhibit

ribonucleotide reductase and deplete deoxyribonucleoside triphosphate (dNTPs) pool and thereby

stall replication forks. CHK1i sensitisation with HU was found to give more profound effect than

with gemcitabine (Montano et al., 2012). HU has been used for the treatment of chronic myelocytic

leukaemia, head and neck cancer, metastatic melanoma, sickle cell disease and other solid tumours

but its efficacy has been modest at best (Schrell et al., 1996, Schrell et al., 1997). The safety of the

drug has long been established, and it is still recommended for the treatment of paediatric patients

suffering from sickle cell anaemia (Thornburg et al., 2012). However, most of the clinical trials

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112

with CHK1 inhibitors as chemo-sensitizing agents are in combinations with gemcitabine (Montano

et al., 2012, Xiao et al., 2013, Ma et al., 2012, Garrett et al., 2011), which is the standard-of-care for

treatment of many cancer types. The effect of CHK1i in combination with HU is relatively

unexplored.

Most of the CHK1i clinical trials also focused on CHK1i with gemcitabine. These studies found

CHK1i monotherapy treatment to have well-tolerated toxicity (Daud et al., 2015, Infante et al.,

2016), while CHK1i combination with gemcitabine treatment group had more adverse side-effects

possibly due to the combining adverse effects of CHK1i inhibition with the standard dosage of

gemcitabine (800-1000 mg/m2) (Calvo et al., 2016, Sausville et al., 2014, Doi et al., 2015). Since

CHK1i single-agent treatment was found to be extremely effective in a subset of melanoma cell

lines with high level of replication stress, and most tumours already have some level of intrinsic

replication stress, although below the threshold that sensitises them to the CHK1i alone, I have

investigated whether a small increase in the level of replication stress that does not significantly

affect cell viability alone, is sufficient to sensitise to CHK1i treatment. Previous work has shown

that even doses of HU as low as 0.2 mM were sufficient to sensitise one CHK1i insensitive cell line

(Brooks et al., 2013).

Replication stress is also found to be a common feature in lung cancer cells, and lung cancers have

second highest number of mutation burden following melanoma (Alexandrov et al., 2013, Syljuasen

et al., 2015). Lung cancer is the most common cause of cancer-related death in men and women in

Australia (Australian Institute of Health and Welfare (AIHW), 2014) and the world, where it

accounts for more than one million deaths per year (Siegel et al., 2016). Non-small cell lung cancer

(NSCLC) accounts for >80% of lung cancer cases. In spite of the recent developments in targeted

therapy and immunotherapy, there is still unmet need for new treatments in advanced or recurrent

lung cancer patients (Yang et al., 2016, Saito et al., 2016). Thus, the efficacy of this combination in

lung cancer cell lines was also further investigated.

In this chapter, in an attempt to reduce the adverse side-effects possibly arising from the

combination of CHK1i inhibition with standard dosage of gemcitabine, I explored the possibility of

using low-dose HU and gemcitabine to sensitise CHK1i in melanoma cell lines. CHK1i with low-

dose HU combination was further explored in more clinically relevant 3D melanoma tumourspheres

model which has higher in vivo predictability as discussed in Chapter 5 of this thesis.

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113

6.2 Results

6.2.1 Sensitisation of monolayer CHK1i-insensitive cell lines with CHK1i + low-dose

gemcitabine combination

In an effort to reduce the possible adverse effect resulting from combining standard dosage of

gemcitabine with CHK1i, I initially investigated if utilising low-dose gemcitabine would sensitise

melanoma cells to CHK1i. The effect of a range of doses of gemcitabine (3 nM-3 M) on the

viability of melanoma cell lines were investigated, and 3 nM dosage that had minimal effect on cell

viability was chosen for investigation with CHK1i (Figure 6-1A). MM127 and SKMEL28

melanoma cell lines are not sensitive to CHK1i single-agent treatment alone (Figure 3-1). To

determine the ability of gemcitabine to sensitise the cells to CHK1i, cells were treated with control

(DMSO), 3 nM gemcitabine, increasing concentration of CHK1i or increasing concentration of

CHK1i together with 3 nM gemcitabine for 72 hours. Gemcitabine treatment only caused minimal

effect in cell viability compared to control-treated cells, but sensitised CHK1i in MM127 and

SKMEL28 cell lines, with IC50 values that were similar to those of CHK1i-hypersensitive lines (78

nM in MM127 and 100 nM in SKMEL28; Figure 6-1 B&C). These findings suggested that adding

low-dose gemcitabine significantly sensitised melanoma cells to CHK1i.

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114

Figure 6-1: Low-dose gemcitabine sensitised CHK1i-insensitive cell lines.

(A) Effect of gemcitabine alone treatment on SKMEL28 cell line. CHK1i-insensitive cell line,

SKMEL28, was incubated with increasing concentrations of gemcitabine (3 nM-3 M) for 72 hours.

Chapter 6

115

(B&C) CHK1i-insensitive cell lines, MM127 and SKMEL28, were incubated with control (DMSO),

3 nM gemcitabine or increasing concentrations of CHK1i (3 nM-3 M) either alone or in

combination with 3 nM gemcitabine for 72 hours. At the end of the treatment, cell viability was

determined by resazurin cell viability assay. %Viability was calculated as the percentage of the

control treatment. The grey line on the left side of the graph indicates %viability from 3 nM

gemcitabine treatment alone. Mean and sd of triplicate determinations were plotted here.

6.2.2 Sensitivity profile of CHK1i + low-dose HU combination in 3D melanoma

tumourspheres

Although combination treatment with gemcitabine sensitised CHK1i-insensitive cells to CHK1i,

combination of CHK1i with HU has been shown to produce a greater degree of sensitisation

(Montano et al., 2012, Thompson et al., 2013). As HU is approved as a chemotherapeutic drug but

not a standard-of-care drug in many cancer types, it would be more clinically acceptable to use

lower concentration of HU to sensitise CHK1i. One of major clinical issues facing the combination

with gemcitabine where its use is generally at the clinically prescribed maximum tolerated dose.

Using low-dose HU would also be expected to cause less toxicity to normal tissues as well.

Therefore, I investigated whether adding low-dose HU (0.2 mM) which was previously shown to

have little effect on viability and proliferation (Brooks et al., 2013, Montano et al., 2012) would

sensitise the melanoma cells grown as 3D tumourspheres, which better predict in vivo sensitivity

than the 2D monolayer culture with CHK1i (Chapter 5).

I investigated the efficacy of CHK1i combination with low-dose HU in 20 tumourspheres cell lines.

Those tumourspheres cell lines were treated according to the methods developed in Chapter 5. In

short, cells were seeded as individuals into ultra-low attachment plates, allowed to form

tumourspheres for 24 hours, then treated with control (DMSO), 0.2 mM HU, and increasing

concentration of CHK1i either alone or with 0.2 mM HU for 72 hours. After the treatment,

tumourspheres viability was assessed by using CellTitre Glo 3D viability assay. From this

experiment onwards, I used another CHK1i (GDC-0575) which is currently in the clinical trial in

combination with gemcitabine (NCT01564251) and was found to have an almost identical efficacy

profile to the CHK1i GNE-323 used in the previous chapters (Figure 5-6).

All melanoma tumourspheres were found to be sensitised by HU to CHK1i regardless of their

sensitivities to single-agent CHK1i. IC50 values for CHK1i varied from 1 nM to 150 nM, with

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116

only D25 and BL having higher IC50 values of 194 nM and 240 nM, respectively. In melanoma

tumourspheres which displayed similar hypersensitivity in both 2D and 3D culture systems to

single-agent CHK1i (D20, C045, C002, A15, MM96L, D04 and HT144), the combination treatment

moderately reduced the CHK1i IC50s from <300 nM in single-agent treatment to 3-70 nM in

combination. The combination had little effect on the surviving fraction (viable fraction of cells

after 72 h treatment with 10 M drug) between single-agent and combination groups (Figure 6-3).

On the other hand, for tumourspheres cell lines which were insensitive to the single-agent CHK1i in

either 2D or 3D (A2058, C054, SKMEL13, D28, MM415, C013 and SKMEL28), there was

dramatic sensitisation with combination treatment and those cell lines became hypersensitive to

CHK1i (IC50s from 1-152 nM). For instance, in cell lines such as C013 and SKMEL28 that only

attained IC50s at 10 M in CHK1i alone treatment, combination treatment was able to reduce the

IC50s to 150-200 nM. CHK1i-insensitive cell lines that were sensitised by low-dose HU had two

outcomes, combination treatment resulted in a surviving fraction less than 20% in cell lines such as

A2058, SKMEL13, MM603, MM415 and C013 indicating that combination treatment killed most

of the tumour cells population, and surviving fractions 20% in C054, MM370, D28, BL, MM329,

D25 and SKMEL28, indicating there was still a subset of the tumours cells that could still survive

the combination treatment.

HU treatment alone caused minimal reduction in cell viability in most of the melanoma

tumourspheres cell lines and most of the cell lines had >70% viability after 72 hours HU treatment,

the exception being MM415, which was sensitive to HU and had <50% viability. This data

suggested that adding low-dose (0.2mM) HU was extremely effective in sensitising melanoma

tumourspheres to CHK1i inhibition regardless of their prior sensitivity to single-agent CHK1i.

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117

Figure 6-2: Low-dose HU sensitised melanoma tumourspheres to CHK1i.

20 melanoma tumourspheres were treated with control(DMSO) or 0.2 mM HU or increasing

concentration of CHK1i (10 nM -10 M) alone or with 0.2 mM HU for 72 hours and cell viability

was determined by measuring the presence of ATP by CellTiter Glo 3D cell viability assay.

%Viability was calculated as the percentage of control (DMSO) treatment. Mean and sd of the

quadruplicate determinations were presented here. Red dots on the right side of each graphs

indicate %viability from 0.2 mM HU treatment alone for each cell line.

Chapter 6

118

Table 6-1: IC50 values for a panel of melanoma cell lines treated with CHK1i HU.

IC50 values of CHK1i alone and CHK1i with 0.2 mM HU were calculated for each cell line from

viability data in Figure 6-2.

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119

Figure 6-3: Surviving fractions for a panel of melanoma cell lines treated with CHK1i HU.

Surviving fraction was calculated as viable fraction of cells after 72 h treatment with 10 M drug

as described in Figure 6-2. Mean and sd of the quadruplicate determinations were presented here.

6.2.3 Sensitivity profile of CHK1i + low-dose HU combination in normal cell lines

One of the problems faced by CHK1i sensitization treatment with chemotherapy in clinical trials is

normal tissue toxicity. I therefore assessed the effect of the combination of CHK1i and low-dose

HU combination would cause cytotoxicity in normal cell lines. Primary Human melanocytes adult

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120

(HEMa), neonatal foreskin fibroblasts (NFF) and four melanoblast cell lines were treated with

control (DMSO), 0.2 mM HU or increasing concentration of CHK1i either alone or with 0.2 mM

HU for 72 hours and resazurin assay was performed to assess cell viability. HEMa and

melanoblasts cell lines were not sensitive to CHK1i alone treatment and the addition of low-dose

HU to CHK1i also did not cause cytotoxic effect (Figure 6-4). Even though cell viability of HEMa

and melanoblast fell below 50% compared to the non-treatment control samples, the fact that after

reaching IC50, the treatment did not further reduce the viability and %viability remained the same

with the increasing dosage of the treatment indicated that treatment was rather cytostatic than

cytotoxic.

Figure 6-4: Low-dose HU activity on primary human melanocytes adult (HEMa), neonatal foreskin

fibroblasts (NFF) and melanoblast cell lines.

Primary human melanocytes adult (HEMa), neonatal foreskin fibroblasts (NFF) and four

melanoblast cell lines (QF series) were treated with control (DMSO) or 0.2 mM HU or increasing

concentration of CHK1i (10 nM -10 M) alone or with 0.2 mM HU for 72 hours and cell viability

was determined by resazurin assay. %Viability was calculated as the percentage of control

(DMSO) treatment. Mean and sd of the quadruplicate determinations were presented here. Blue

and green dots on the left and right side of the graphs indicate %viability of control (DMSO) and

0.2 mM HU treatment for each cell line respectively.

HEMa NFF

QF1597 QF1610

QF1618 QF1619

0

40

80

120

0

40

80

120

0

40

80

120

0 2 4 0 2 4

Log (nM Treatment Concentration)

% o

f D

MS

O C

ontr

ol DrugTreatment

DMSO_DMSO

GDC0575_DMSO

GDC0575_HU

HU_DMSO

CHEK1iN and HU Combination Experiment Dose Response Curves - Resazurin Data

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121

6.2.4 Sensitivity profile of CHK1i + low-dose HU combination in lung cancer cell lines

I also investigated single-agent CHK1i and combination of CHK1i and low-dose HU for

cytotoxicity in 13 lung cancer cell lines and 2 immortalised human bronchial epithelial cell lines

(HBEC3-KT and HBEC30-KT) as well. All cell lines were treated with control (DMSO), 0.2 mM

HU, and increasing concentration of CHK1i either alone or with 0.2 mM HU for 72 hours. After the

treatment, cell viability was assessed by resazurin assay. CHK1i treatment had single-agent efficacy

in 10 out of 15 lung cancer cell lines with varying sensitivities, while three cell lines (H1650,

H2887 and HCC515) were completely insensitive. None of the lung cancer cell lines tested

displayed the CHK1i hypersensitivity observed in melanoma cell lines as described in Chapter 3.

Most of IC50s for single-agent CHK1i varied from 109 nM to 830 nM. CHK1i single-agent was

also found to have some effect on the viability of immortalised human bronchial epithelial cell lines

(HBEC3-KT and HBEC30-KT) with their IC50s being about ~250 and ~700 nM respectively. This

was different from the results with normal melanoblast and melanocytes which were insensitive to

the CHK1i single-agent treatment (Figure 6-4). Interestingly, unlike melanoma cells, even though

most of the lung cancer cell lines were not hypersensitive to CHK1i single-agent, their surviving

fractions in CHK1i single-agent treatment were <20% in many cell lines (7/15). Addition of 0.2

mM HU dramatically sensitised all the cell lines to CHK1i and made most of the lung cancer cell

lines hypersensitive to CHK1i (IC50s100nM) including the immortalised human bronchial

epithelial cell lines. Only two lung cancer cell lines, H1650 and HCC515, were insensitive to the

combination treatment: HCC515 has >72 hours doubling time. In many cell lines (10/15), surviving

fractions were below <20% with combination treatment (Figure 6-6). IC50 values were even below

the lowest concentration which is 10 nM in CHK1i + HU treatment in a lot of the cell lines

including HBEC3-KT, immortalised human bronchial epithelial cell lines (Figure 6-5). Unlike

melanoma cells, lung cancer cell lines were found to be more sensitive to low-dose 0.2 mM HU

alone treatment as well: 7 out of 15 cell lines (46%) of lung cancer cell lines were 50% viability

after 0.2 mM HU treatment for 72 hours while only 1 out of 20 cell lines in melanoma

tumourspheres had 50% after cultured with HU alone (Figure 6-2). This data suggested that most

of the lung cancer cell lines and immortalised human bronchial epithelial cell lines were

hypersensitive to CHK1i + low-dose HU treatment and low-dose HU alone treatment also affected

viability of those cell lines more than it did in melanoma.

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122

Figure 6-5: Low-dose HU sensitised lung cancer cell lines to CHK1i.

13 indicated lung cancer cell lines and 2 immortalised human bronchial epithelial cell lines

(HBEC3-KT and HBEC30-KT) were treated with control (DMSO) or 0.2 mM HU or increasing

concentration of CHK1i (10 nM -10 M) alone or with 0.2 mM HU for 72 hours and cell viability

was determined by resazurin assay. %viability was calculated as the percentage of control (DMSO)

treatment. Mean and sd of the quadruplicate determinations were presented here. All the cell lines

were growing at the similar rate except HCC515 and H2887 which had 72 hours doubling time.

Cell lines CHK1i (nM) CHK1i+HU (nM)

HCC4017 109.6 10.0

H1299 211.8 10.0

HBEC3-KT 248.3 10.0

HCC2429 274.2 16.5

H2052 314.8 10.0

Calu-1 319.2 10.0

H1792 352.4 10.0

H358 477.5 10.0

H82 523.6 44.1

H322 594.3 69.5

30KT 3KT Calu-1 H1299 H1650

H1792 H1975 H2052 H2887 H322

H358 H82 HCC2429 HCC4017 HCC515

0

50

100

150

0

50

100

150

0

50

100

150

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Log (nM Treatment Concentration)

% o

f D

MS

O C

ontr

ol

DrugCombination

GNE575_DMSO

GNE575_HU

HU_DMSO

Lung Cancer Cell Lines CHEK1i Dose Response CurvesN.B. 3KT & 30KT are HUBEC's

HBEC3-KTHBEC30-KT

Chapter 6

123

HBEC30-KT 719.4 14.0

H1975 831.8 32.1

H1650 4236.4 343.6

H2887 7194.5 33.2

HCC515 10000 10000.0

Table 6-2: IC50 values for a panel of lung cancer cell lines treated with CHK1i HU.

IC50 values of CHK1i alone and CHK1i with 0.2 mM HU were calculated for each cell line from

viability data in Figure 6-5.

Figure 6-6: surviving fractions for a panel of lung cancer cell lines treated with CHK1i HU.

Surviving fraction was calculated as viable fraction of cells after 72 hours treatment with 10 M

drug as described in Figure 6-5. Mean and sd of the quadruplicate determinations were presented

here. Immortalised human bronchial epithelial cell lines (HBEC3-KT and HBEC30-KT) were

indicated by blue arrows.

0

10

20

30

40

50

60

70

80

90

100

% V

iab

ilit

y

CHK1i CHK1i+HU

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124

6.3 Discussion

Various CHK1 inhibitors have been investigated as combination treatment with chemotherapeutic

agents since the introduction of UCN-01 (Thompson et al., 2012, Blasina et al., 2008, Zenvirt et al.,

2010, Tse et al., 2007, Zabludoff et al., 2008, Ma et al., 2012). In spite of the inconsistent reports of

the ability of CHK1i to sensitise a range of chemotherapeutic agents, CHK1 inhibitors were mostly

reported to have a potent sensitisation effect with agents which promote replication stress such as

gemcitabine and hydroxyurea. As a result, many CHK1 inhibitors are being investigated at various

preclinical and clinical stages as chemo-sensitising agent mostly with gemcitabine since it is

approved as standard-of-care treatment in various cancer types (Daud et al., 2015, Seto et al., 2013,

Sausville et al., 2014). By comparison, CHK1i sensitisation with hydroxyurea is relatively

unexplored. It was reported previously that hydroxyurea in combination with CHK1 inhibitors had

much higher degree of sensitisation compared to gemcitabine (Montano et al., 2012). However,

prior studies about CHK1i sensitization of HU treatment were tested in 2D monolayer cell cultures.

Our own data indicated that 2D model is a poor predictor of in vivo sensitivity as described in

Chapter 5 of this thesis, and, to our knowledge, it was not clear if the combination of effect of

CHK1i with hydroxyurea treatment is translatable in vivo. As shown in previous chapters of this

thesis and also in previous reports of CHK1i (King et al., 2015, Brooks et al., 2013, Davies et al.,

2011b, Walton et al., 2012), only a subset of tumour cell lines is sensitive to CHK1i single-agent

treatment and there is significant proportion of tumour cell lines which are not sensitive to single-

agent treatment. Here, low-dose hydroxyurea (0.2 mM) was added concurrently with CHK1i to

induce replication stress in the melanoma cells and sensitise them to CHK1i inhibition. I

demonstrated that adding low-dose HU sensitised all the melanoma cell lines to CHK1i regardless

of their prior sensitivity to CHK1i single-agent treatment.

It was previously reported that adding antimetabolites drugs such as gemcitabine and hydroxyurea

18-24 hours prior to the addition of CHK1i enhanced the killing of cancer cells. Most of the

previous studies focused on the notion that accumulation of the cells at G2/M checkpoint by

genotoxic drugs before CHK1 inhibition would enhance the sensitisation effect (Wang et al., 1996,

Blasina et al., 2008). However, it was showed here that adding low-dose HU concurrently with

CHK1i also had the similar degree of sensitising tumour cells to the combination treatment:

addition of low-dose HU to CHK1i was able to reduce CHK1i’s IC50 values up to 180 folds in

some insensitive cell lines. In all of the tumourspheres cell lines tested, IC50s values of CHK1i +

low-dose HU combination are below 250 nM. Combination treatment was found to be equally

effective in all the melanoma tumourspheres regardless of their sensitivities to single-agent CHK1i.

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125

Addition of CHK1i with low-dose HU concurrently would probably make the actual treatment

regimen in the clinics more convenient to the patients and result in less normal tissue toxicity at the

same time. Moreover, low-dose HU also differs from high-dose of HU and other replication

inhibitors in that it only has minor effect on dNTP pools and allows continuation of the replication

forks even though at slower rates and, as a result, causes the dormant origins to fire to maintain the

DNA replication rates (Ge et al., 2007). One of the CHK1 functions is to maintain the normal

replication fork progression and suppress the late firing origins and maintain replication fork

integrity (Petermann et al., 2010, Feijoo et al., 2001). Thus, when CHK1 is inhibited in the presence

of low-dose HU, it probably would create massive origins firing followed by RPA exhaustion and

then DNA strand breaks (Toledo et al., 2013, Zuazua-Villar et al., 2015). Due to time constraints, I

was unable to directly assess this.

Most of the CHK1i clinical trials have been in combination with gemcitabine, the standard-of-care

drug in many cancer types, which is usually used in maximum tolerated dose (MTD) when

combined with CHK1i and potentially resulted in enhanced normal tissue toxicity as reported in

previous clinical trials (Calvo et al., 2016, Infante et al., 2016, Sausville et al., 2014, Doi et al.,

2015). Utilising low-dose HU would be more clinically feasible since HU is not standard-of-care

but approved drug in many cancer types and would probably result in less toxicity to normal tissue.

I have also shown here that CHK1i combination with low-dose HU was well-tolerated in different

normal cell lines such as melanoblasts, melanocytes and fibroblasts even at the higher dosage of

CHK1i. Since both hydroxycarbamide (hydroxyurea) and CHK1 inhibitors investigated in this

thesis are orally active and demonstrated to be effective in concurrent treatment, it will create more

convenience for the treatment arrangement of the cancer patients.

Concerned with lung cancer treatments, surgery treatment is the standard for early stage lung cancer

and radiation treatment for non-operable patients. Nearly 75% of the patients diagnosed with lung

cancer have stage 4 disease and only available option is the palliative treatment with chemotherapy

and/or radiotherapy (Bonanno et al., 2014). Platinum-based adjuvant chemotherapy has improved

long-term survival in patients with stage II and IIIa NSCLC (non-small cell lung cancer) following

surgical resection, and platinum-based chemotherapy in combination with radiation has been shown

to improve survival in patients with metastatic NSCLC (Reck et al., 2013). More than 65% of

NSCLC cases present with locally advanced or metastatic disease, and 40% with distant metastases

(Goldstraw et al., 2016). New diagnosis and treatment paradigms are sorely needed as from 1982-

1978 to 2007-2011 the 5-year relative survival rate in NSCLC increased only marginally, from 8-

10% to 11-15%. 10-15% of NSCLCs have EGFR mutation and usually targeted by using tyrosine

kinase inhibitors and 2-6% of the adenocarcinomas have translocation involving the ALK gene and

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126

can be targeted by targeted treatment such as crizotinib and ceritinib (Chia et al., 2014). However,

resistance develops sooner or later and new treatments are currently needed. CHK1 inhibitor with

gemcitabine combination treatment was reported to be effective in lung cancer xenografts and

spheres. The data presented in this chapter also showed that CHK1i with low-dose HU is cytotoxic

to various lung cancer cell lines indicating that this combination strategy has potential to benefit the

lung cancer patients.

Hydroxyurea has been used as chemotherapeutic drugs in clinical settings for a long time but its use

is mainly in haematological malignancies and it is also approved in sensitising head and neck

cancer to radiation. The regularly administered doses of hydroxyurea are well-tolerated with

manageable side-effects (Kinney et al., 1999). As CHK1 inhibitors have been investigated in

clinical trials and HU has been in clinical practices, the data presented here that combination of low

concentrations of both CHK1i and HU enhances killings of melanoma and lung cancer cells could

have significant impact in treatment of melanoma and lung cancer patients.

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127

7 General Discussion

Recently developed targeted therapies such as BRAF inhibitors and immune checkpoint therapies

such as anti-CTLA4 and anti-PD1 have changed the treatment scenario for metastatic melanoma.

However, soon after its deployment in the clinic, clinicians using BRAF inhibitors were grappling

with the issue of rapidly developed resistance, limiting its efficacy. With immunotherapy, scientists

and clinicians are still trying to identify the patient cohort who will respond to the treatment, and

there is also evidence indicating the development of resistance with these drugs. Thus, new

treatments are currently needed for melanomas that are not effectively controlled with these drugs.

Owing to the important roles it plays in DDR (DNA damage Response pathway) and cell cycle,

CHK1 has attracted a lot of attention as a potential target for enhancing the effectiveness of

standard DNA-damaging chemotherapies and ionising radiation therapy. CHK1 inhibitors have

been investigated in preclinical studies and clinical trials in combination with chemotherapeutic

agents. Despite the initial hope it offered, CHK1 inhibitor in combination with chemotherapeutic

drugs have shown varying efficacies in patients (Seto et al., 2013, Sausville et al., 2014, Calvo et

al., 2016, Doi et al., 2015). More specific CHK1 inhibitors were developed along the way and their

efficacy to enhance chemotherapy were also being evaluated and reported to be more successful

than the previous attempts. Recently, GDC-0425 and MK-8776 in combination with gemcitabine

was reported to have promising results and display early evidence of clinical efficacy in Phase I

trial (Daud et al., 2015, Infante et al., 2016). Even though the adverse side-effects in those studies

with CHK1 inhibitor combination with gemcitabine were better tolerated than the earlier generation

of CHK1 inhibitors, there is still considerable proportion of patients suffering from adverse side-

effects possibly from combining adverse effect of CHK1 inhibition with the standard dosage of

gemcitabine (800-1000 mg/m2). At the same time, it was also observed that CHK1 inhibitor

monotherapy group had fewer and better tolerated adverse effects.

Despite CHK1 being essential in mouse embryonic development, it is only recently that the

potential of CHK1 inhibitor as single-agent treatment was appreciated. CHK1 inhibitor single-agent

treatment was demonstrated to be extremely effective in some cancer types including melanoma as

reported in this thesis and others (Bryant et al., 2014, Davies et al., 2011b, Brooks et al., 2013).

Despite its potential to be used as stand-alone anti-cancer drug, CHK1 inhibitor monotherapy was

hindered by the limited understanding of its single-agent hypersensitivity in cancer cells and lack of

appropriate maker(s) of sensitivity to select the suitable patient population. In order to fully exploit

the potential of CHK1 inhibitor single-agent treatment, there needs to be more understanding of the

mechanism of CHK1 inhibitor hypersensitivity as single-agent, and identify potential marker(s) of

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sensitivity to identify the potential patient populations. Phosphorylation of CHK1 itself and H2AX

have been suggested as potential markers to predict CHK1 inhibitor sensitivity (Rawlinson et al.,

2014, Brooks et al., 2013). However, the correlation between those two markers and CHK1

inhibitor sensitivity had been weak.

As reported in Chapter 3 of this thesis, we have screened 45 cell lines including three normal

melanocyte/melanoblast lines and found that about 30% of cell lines are hypersensitive to CHK1

inhibitor single-agent treatment and most of those hypersensitive cells died prior to mitosis

suggesting that this event is different from CHK1 function at the G2/M checkpoint. Works from our

lab and the other recent reports (Koh et al., 2015, Brooks et al., 2013, Sakurikar et al., 2016) point

the roles of CHK1 in S phase playing an important part in hypersensitivity to CHK1i. In this study,

by using functional read-outs such as EdU incorporation, the S phase checkpoint defect was found

to be present in majority of melanoma cell lines which are hypersensitive to CHK1 inhibitor. This

loss of S phase checkpoint would probably allow those melanoma cells with high level of

endogenous replication stress to keep proliferating and consequently further increase their intrinsic

stresses making them more sensitive to CHK1 inhibition alone. In support of this idea, in many of

those cell lines with defective S phase checkpoint, defective CDC25A degradation in response to

replication stress was found and changes in CDC25A levels were found to be influencing CHK1

inhibitor sensitivity. Reduction in CDC25A level via siRNA dampens CHK1 inhibitor sensitivity

and overexpression of CDC25A increases CHK1 inhibitor sensitivity of an insensitive cell line.

Moreover, overexpression of other proteins participating in CDC25A-Cyclin/CDK2-WEE1 axis

were also found to be increasing CHK1 inhibitor sensitivity to some extent. In CHK1i-

hypersensitive cell lines, the presence of defective S phase checkpoint and increased CDC25A

activity lead to continued CDK2/Cyclins activity which promotes inappropriate origin firing. In

order to maintain the increased numbers of origins firing and prevent the replication fork collapse,

these cells have become more reliance on CHK1. However, the fact that the changes in the levels of

CDC25A and other proteins regulating CDC25A-Cyclin/CDK2-WEE1 axis only partly contributed

CHK1 inhibitor sensitivity suggested that there might also be other mechanisms contributing to

CHK1 inhibitor hypersensitivity.

RPA2 hyper-phosphorylation is present in the cells with high level of replication stress. For

instance, it presents in the cells treated with CHK1 inhibitor and DNA damaging agent (Zuazua-

Villar et al., 2015, Ashley et al., 2014). In chapter 4 of this thesis, it was demonstrated that CHK1i

single-agent treatment caused RPA2 hyper-phosphorylation and depletion of hypo-phosphorylated

form of RPA2 in CHK1i-hypersensitive cell lines, indicating that these cell lines already have

increased level of intrinsic stress. By contrast, CHK1i-insensitive cell lines only displayed relatively

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low levels of RPA2 hyper-phosphorylation when treated with CHK1i alone, indicating they have

reduced level of intrinsic stress compared to CHK1i-hypersensitive cell lines. This data in Chapter 4

suggest that it might be possible to assess the level of intrinsic stress level by measuring RPA2

hyper-phosphorylation in patient tumour to determine CHK1i single-agent efficacy.

All the data presented in this thesis suggested that more than one mechanism may be responsible for

CHK1i-hypersensitivity in melanoma. The model presented in Figure 7-1 is proposed as a possible

mechanism for CHK1i hypersensitivity. The increased level of replication stress present in CHK1i-

hypersensitive cell lines probably results from dysregulation of the CDC25A-Cyclin/CDK2 axis

which promote S phase progression of the cells as described in Chapter 3. The cells may adapt to

the changes in the increased level of stress by developing ways such as defects in S phase

checkpoint to overcome this replication stress and keep on growing and surviving. This situation

will create increased origin firings due to the increased activity of CDC25A and CDK2 in those

cells and, in order to regulate those increased firing origins and prevent the fork collapse, those cells

may need to rely more on CHK1. CHK1 regulates origin firings (Gonzalez Besteiro et al., 2015)

and promotes the repair of stalled fork by involving with HR proteins such as RAD51 and BRCA2

(Sorensen et al., 2005, Bahassi et al., 2008). Moreover, it also increases dNTP pools by promoting

RRM2 activity via regulation E2F1 (Zhang et al., 2009). E2F transcription is also found to be

involved in promoting the factors participating in protecting the stalled replication forks such as

RAD51, FANCD2, PCNA and CDC7 (Bertoli et al., 2016).

The mechanism by which CHK1 regulates E2F1 is not totally understood at this moment. It was

reported that CHK1 phosphorylated E2F6, a repressor of E2F-dependent G1/S transcription, leading

its dissociation from promoters and causes increased E2F-dependent transcription (Bertoli et al.,

2013). It was shown that the cells experiencing oncogene-induced replication stress became

addicted to E2F activity to cope with high levels of replication stress (Bertoli et al., 2016). It was

tempting to assume that CHK1i-hypersensitive cell lines rely more on E2F activity through CHK1

regulation of E2F6 to avoid from increased DNA damage and cell death. In CHK1i-insensitive cell

lines, as these cell lines retain a functional S phase checkpoint they need to rely less on E2F and

CHK1 activities to survive. Whether CHK1 phosphorylation on E2F6 is different between CHK1i-

hypersensitive and CHK1i-insensitive cell lines is still remained to be explored.

E2F1 levels were increased with HU treatment in Chapter 4 of this thesis. E2F transcription has

been shown to be involved in protecting the stalled forks in replication stress and also reducing

DNA damage levels in cells treated with HU and CHK1 depletion. This protective effect of E2F

transcription is shown to be different from its action on RRM2, another target of E2F1 (Bertoli et

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130

al., 2016). E2F transcription is also found to be involved in attracting the factors involved in

protecting the stalled replication forks such as RAD51, FANCD2, PCNA and CDC7. This probably

explained why HU did not induce as much cell killing as CHK1 inhibitor even though HU

treatment also causes reduction in dNTP pool and RPA hyper-phosphorylation in CHK1i-

hypersensitive cell lines due to the presence of defective S phase checkpoint.

Figure 7-1: Proposed model of CHK1 inhibitor hypersensitivity.

7.1 The potential of CHK1 inhibitor as single-agent treatment

Since CHK1i single-agent treatment is only likely to be effective in 10-30% of the patient

population, marker(s) of sensitivity to select the potential subset of patients is essential. Even

though the data present in this thesis would contribute into the pool of potential markers for single-

agent hypersensitivity, the identification of clinically applicable definitive marker for CHK1i-

hypersensitivity is still elusive. S phase defective checkpoint was found to be correlated with

CHK1i hypersensitivity in this thesis although it would be challenging to assess the presence of

defective checkpoint in patient samples in the clinical setting as S phase defective checkpoint can

only be assessed after induction with compounds such as hydroxyurea. In theory, the cells from

patient’s biopsy can be isolated and then challenged with hydroxyurea to assess the functionality of

S phase checkpoint using fluorescence microscopy to predict the individual who can benefit from

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the drug treatment. However, in practical setting, this process would be time-consuming, labour-

intensive and impractical. While alterations in the level of proteins involved in S phase progression

such as CDC25A together with other proteins such as Cyclin A and E changes CHK1i sensitivity to

some extent as described in Chapter 3, the degree of the changes does not represent the entirety of

CHK1i hypersensitivity, indicating that they would not be optimal markers to identify CHK1i-

hypersensitive population.

Another alternative marker would be to assess the high level of intrinsic replication stress already

present in the cells. This intrinsic level of high replication stress would render the cells sensitive to

CHK1i single-agent and can be measured by RPA2 hyper-phosphorylation as described in Chapter

4. Detection can be performed by immunohistochemistry using antibodies such as phosphorylate

RPA2 at Ser4/8. Nevertheless, at the moment, it is less likely to identify single marker to identify

the patient who will be hypersensitive to CHK1i and it is more likely to be a panel of markers

which would assess the status of intrinsic replication stress and damage level in the cells to predict

CHK1i single-agent sensitivity. Moreover, according to the in vivo data presented in Chapter 3 and

5, CHK1 single-agent treatment is at best only able to inhibit the tumour growth and not able to

reduce the tumour size. Thus, single-agent treatment probably would only have moderate activity in

the clinics and the immediate way forward would still be the combination treatment by increasing

the efficacy of the drug and reducing the adverse effects experienced in previous clinical trials.

7.2 CHK1 inhibitor in combination treatment

As CHK1i combination treatment with HU was shown to be effective in most of the cancer cell

lines as described in this thesis and other reports (Montano et al., 2012), the marker(s) to predict the

sensitivity may not be essential even though having a marker or a panel of markers similar to

CHK1i single-agent treatment would be certainly beneficial and helpful in increasing the response

rate and reducing the undesired adverse effects. The current problem facing the combination

treatment with CHK1 inhibitor and other chemotherapeutics drugs mostly used in the clinical trials

is the adverse side-effects despite some therapeutics benefits offered by the treatments (Seto et al.,

2013, Sausville et al., 2014, Calvo et al., 2016, Doi et al., 2015). Most of these chemotherapeutics

drugs are used with dosage according to standard-of-care treatment and combining them with

CHK1i may probably result in more adverse effects. As demonstrated in the Chapter 6 of this

thesis, by using low-dose of low-cost and already established drug, hydroxyurea, in combination

with CHK1i, it might be possible to reduce the adverse effect. In fact, combination of CHK1i with

HU has been shown to produce a greater degree of sensitisation (Montano et al., 2012, Thompson et

al., 2013) compared to Gemcitabine which is currently used in most of the clinical trials.

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Hydroxyurea is an approved chemotherapeutic agent and is currently used for the treatment of

myeloproliferative neoplasms. Since HU is an approved drug but not standard-of-care in many

cancer treatments, it would be more clinically feasible to use it in low dose. Combination was found

to be effective in most of the melanoma and lung cancer cells while it was well-tolerated in normal

cell lines such as melanoblasts, melanocytes and fibroblasts as described in Chapter 6.

The other option for combining CHK1 inhibitor would be with another targeted treatment, Wee1

inhibitor as shown in Chapter 3. Since both WEE1 and CHK1 inhibitors are currently in the clinical

trials, this idea would be really attractive. However, there also is some concerns relating to their

efficacy. Even though I found that CHK1 and WEE1 inhibitors have combination effect in

increasing apoptosis in CHK1i-insensitive cell lines, it did not make the cells hypersensitive to

CHK1 inhibitor. Thus, this combination needs to be further explored in more resistant cell lines

with different types of cytotoxic assay before drawing the conclusion on their combination efficacy.

The work presented in this thesis has shed some light into the mechanism of CHK1i single-agent

hypersensitivity by demonstrating that CHK1i-hypersensitive cell lines have defects in the S phase

checkpoint and identifying the possible markers related to this defect to target the patient population

for CHK1 inhibitor single-agent treatment. CHK1 inhibitor single-agent treatment was found to be

effective in deterring tumour growth in xenograft mice model as well. In addition, the potential and

efficacy of low-dose HU in combination with CHK1 inhibitor was also demonstrated and this

combination will be useful in reducing the adverse effects currently faced by CHK1 inhibitor

combination with traditional chemotherapies.

7.3 Future direction

While the work in this thesis has provided the potential of using CHK1 inhibitor treatment in

melanoma and lung cancers, there are still more work to be done to bring the treatment into the

actual clinics. Concerning with the CHK1 inhibitor combination with low-dose HU, the efficacy

still needs to be investigated in in vivo model for melanoma to determine whether the combination

can totally inhibit the tumour growth. Moreover, it is also necessary to determine what would be the

optimal dosage of CHK1 inhibitor to be used in combination with HU since low-dose HU was

found to sensitise CHK1 inhibitor significantly. Next, what dosage of HU in mice would reflect the

low-dose HU (0.2 mM) in cell culture used in this thesis need to be determined since HU was found

to have faster clearance in mice compared to human. Relating to the lung cancer cell lines, all the

lung cancer cell lines tested with CHK1i+HU combination in this thesis are traditional monolayer

culture. Thus, it also needs to be determined what would be the efficacy of combination in lung

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tumourspheres culture and xenograft model. With CHK1i single-agent hypersensitivity, still more

work need to be done to identify the elusive marker of sensitivity. In order to do that, screening of

the CHK1i-hypersensitive and –insensitive cell line to identify the presence of increased level of

intrinsic replication stress by using antibodies such as phosphor RPA2 ser4/8 to detect RPA2 hyper-

phosphorylation or BrdU to detect the presence of ssDNA. The regulation of CHK1 on E2F1 (for

example, CHK1 phosphorylation on E2F6) is also worthy of further investigation since CHK1

seems to be more involved with E2F1 regulation in CHK1i-hypersensitive than in CHK1i-

insensitive cell lines. Furthermore, whether CHK1i single-agent hypersensitivity has direct

relationship with dNTP pool depletion should also be further explored. With tumourspheres model

presented in this thesis, it was demonstrated this tumourspheres cell culture model has better

predictability in term of CHK1 inhibitor. Thus, more studies with different targeted treatments and

traditional chemotherapeutics drugs need to be tested in this culture system to validate its

predictability. It also would be interesting to investigate the possible reasons for the difference in

CHK1 inhibitor hypersensitivity in 2D and 3D culture models in some of the melanoma cell lines

by using the functional read-out assay like EdU incorporation to detect the presence of S phase cell

cycle checkpoint in 3D tumourspheres of the cell lines which changed sensitivity. Next would be to

assess the gene expression difference between the tumourspheres and 2D cell culture models to

assess possible expression changes behind this drastic changes in sensitivity by using Microarray or

RNA-seq methods.

7.4 Limitations

There are also limitations worth mentioning for this study. Most of the CHK1 inhibitor single-agent

work was done in melanoma and melanoma cells are known to be heterogeneous and harbour

highest mutation rate. Therefore, it would be interesting to find out if the S phase checkpoint defect

is also a common feature in CHK1i-hypersensitive cell lines in other cancer types. Since all the in

vivo experiments were carried out in immunocompromised mice, the involvement of immune

system in response to the activity of CHK1 inhibitor was lacking. The tumourspheres system

developed in this thesis was based only on the melanoma cell lines and lacks interaction with other

different cell types as in actual physiological conditions and that can also greatly influence the drug

response.

134

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9 Appendices

Appendix 1: The percentage of cells that die via apoptosis before, during or after the first mitosis

after treatment with either 1 M GNE323, 9 M CDK1i or combination of the two inhibitors. A15

and D20 are hypersensitive, D24 and D28 are sensitive cell lines. (Oo et al., manuscript in

preparation)

Appendix 2: The mutation data for melanoma cell lines.

Cell Lines BRAF Nras PTEN p53

A15 V599K wt wt wt

A2058 V599E wt L112Q, L186M wt

BL V599E wt Q298Stop I195T

D20 V599E wt HD R248Q

D25 V599E wt wt D281N

D28 V599K wt wt wt

HT144 V599E wt HD wt

MM96 V599E wt wt wt

MM329 wt wt wt wt

MM370 V599E wt wt wt

MM415 wt Q61L wt wt

MM603 V599E wt wt wt

SKMEL13 V599E wt wt R248W

SKMEL28 V599E wt T167A L145R

C002 wt Q61K wt wt

C013 wt Q61L 380G>A_ Gly127Glu Pro278Serine (rs17849781)- 832C>T

C045 V600E wt wt wt

C054 wt Q61K wt wt

D04 wt Q61L wt wt

MM648 V599E wt wt wt

156

Appendix 3: The subtypes of lung cancer cell lines.

Appendix 4: The format of the cell plate and drug plate for high-throughput drug screening.

Cellline Cancersubtype

HBEC3-KT Humanbrochialepitheliacells

HBEC30-KT Humanbrochialepitheliacells

H1650 Adenocarcinoma

H1792 Adenocarcinoma

H82 Smallcelllungcarcinoma

H1975 Adenocarcinoma

H358 Adenocarcinoma

Calu-1 NSCLC

HCC4017 Largecellcarcinoma

H1299 Largecellneuroendocrinecarcinoma

HCC515 Adenocarcinoma

HCC2429 NSCLC

H2887 NSCLC

H2052 Mesothelioma

H322 Adenocarcinoma

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

A

B

C

D Blank DMSO Drug DMSO Drug DMSO Drug DMSO Drug DMSO Drug Blank

E

F

G

H

I

J

K

L

M

N

O

P

MediaMedia Cellline Cellline Cellline Cellline Cellline

DMSO Drug DMSO Drug DMSO Drug DMSO Drug DMSO Drug DMSO Drug

A NoCells(Blankmedia)

B LowestconcentrationDrugs

C

D 7x3-folddilutions

E

F

G

H HighestconcentrationDrugs