doctor of philosophy dna repair deficincies as a biomarker

DOCTOR OF PHILOSOPHY

DNA repair deficiencies as a biomarker for treatment response in acute myeloid leukaemia

Crean, Clare

Award date:2020

Awarding institution:Queen's University Belfast

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Download date: 03. Jun. 2022

https://pure.qub.ac.uk/en/studentTheses/216c9d8f-9f93-4fc1-9780-ee86f317f62b

DNA Repair Deficiencies as a Biomarker for Treatment Response

in Acute Myeloid Leukaemia

Clare Crean

MScR in Genetics and Molecular Medicine

BA (Hons) in Human Genetics

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

School of Medicine, Dentistry and Biomedical Sciences

Queen’s University Belfast September 2019

Abstract Acute Myeloid Leukemia is most commonly seen in people over the age of 65 and has

a median age of 63. Globally, there is an increasingly elderly population so the rate of

incidence of AML is set to increase. The therapy landscape for AML has changed little

over the past four decades. Cytarabine, first approved in 1969, is still the standard of

care induction therapy for AML. There has been only modest improvements in

survival rates during this time and there is currently no method of determining which

patients will or will not respond to Cytarabine treatment.

An assay, developed in 2014, used microarray data to determine which breast cancer

patients had a DDRD and therefore would be more susceptible to DNA damaging

agents. This project has assessed the potential of using the DDRD assay for AML

patients. The assay was applied to publicly available microarray data of >600 AML

patients (TCGA AML data & GSE6891), who were classed as DDRD negative or DDRD

positive. The assay was also applied to microarray data of a panel of myeloid cell lines

to be used as in vitro models. Clonogenic assays, flow cytometry and

immunofluorescence was used to assess the repair efficiency of the in vitro models.

A pooled CRSIPR screen and single cell sequencing ascertained which mutations may

be driving the DDRD phenotype in patients.

Kaplan Meier analysis showed the DDRD positive patients survived significantly

(p=0.00047) worse than the DDRD negative cohort. Whole exome sequencing

identified mutations which were common in DDRD positive patients. CRISPR

screening detected multiple genes, such as BRCA2 and RAD51, which may be driving

DDRD positivity.

Immunofluorescent staining of DNA repair genes showed a clear repair deficiency in

the DDRD positive cell lines, as the assay would predict. Responses of the model cell

lines to a range of chemotherapeutic agents in clonogenic assays can also be

segregated based on DDRD score.

These results together validate the accuracy of the DDRD score at assessing DNA

damage repair deficiencies. Furthermore, they demonstrate the benefit of using the

DDRD score to predict the response to a chemotherapeutic agent prior to patient

administration.

Declaration

I hereby declare that this thesis is a result of my own work and all experiments

described herein were carried out at the School of Medicine, Dentistry and

Biomedical Sciences, Queen’s University Belfast under the supervision of Professor

Ken Mills and Professor Kevin Prise. Work other than my own is clearly referenced to

the appropriate author or their publication. No material contained herein has

previously been submitted for a degree at this or any other university. The copyright

of this thesis rests solely with author. No quotation of it should be published in any

format, including electronic/digital, or the internet, without the author’s prior

consent. All information derived from this thesis must be acknowledged correctly and

in full.

Clare Crean

____________________________

Date

____________________________

Acknowledgements Firstly, I would like to thank my supervisors Ken Mills, Chris Scott and Kevin Prise for

all the help they have given me during my project and Queen’s University for the

opportunity to complete my degree.

Secondly, I would like to thank all my friends and family for their endless help and

encouragement throughout my PhD. I would like to especially thank my fellow PhD

students for all their guidance and support. If it was not for them, my PhD experience

would have been entirely different and infinitely worse.

Lastly, I would like to thank my Mammy and Daddy for everything they have done for

me over the years. They have always instilled in me the belief that there was nothing

I could not achieve if I put my mind to it. I know that without them, I would not be

where I am today.

Conferences, Courses, Publications and Awards

Conferences

ESMO Symposium on Signalling Pathways in Caner – DNA Damage Response in Oncology

Barcelona, March 2017

Annual Conference of the Haematology Association of Ireland Belfast, September 2017 Oral Presentation – Presidential Symposium

4th International Conference on AML – Advancements in Biology and Treatment European School of Haematology

Estoril, October 2017 Poster Presentation

1st European Alliance for Personalised Medicine Belfast, November 2017 Poster Presentation

14th Annual UK Genome Stability Network Meeting Cambridge, January 2018

60th Annual Meeting and Exposition of the American Society of Haematology

San Diego, December 2018 Poster Presentation

Courses

Partek Genomic Suite Training Course, March 2017

Partek Flow and Genomics Bioinformatic Analysis Workshop, June 2018

10x Sequencing Training Course, October 2018

Publications

Published: CM Crean, KI Mills, KI Savage. The potential of targeting DNA Repair deficiency in

acute myeloid leukemia. JCT.2017.88060

Manuscripts in preparation: 1. J. Smith, S. Craig, S. McDade, C.Crean K. Savage, K. Mills. Chronic loss of STAG2

leads to altered chromatin structure contributing to de-regulated

transcription in AML

2. C.Crean, I. Lobb, K. Mills. The effects of a pooled CRISPR knockout screen on

AML cell lines

Abstracts: C. Crean, K.I. Savage, K.I. Mills. The role of DNA Repair Mechanisms in Acute Myeloid

Leukaemia. EHA Library, Jun 14, 2018; 216220; PB1686

C.Crean, K.I. Savage, K.I. Mills. The Potential of Using DNA Damage Repair Deficiency

As a Biomarker for Cytarabine Response in AML Patients. Blood 2018 132:2812

Awards Highest Scoring Abstract in the Scientific Section at the Annual Conference of the

Haematology Association of Ireland 2017

Abbreviations 53BP1 p53 binding protein

ABL Abelson murine leukaemia

AML Acute Myeloid Leukaemia

APL Acute Promyelocytic

Leukaemia

AraC Cytosine arabinoside

ASH American Society of

Haematology

ATM ataxia telangiectasia mutated

ATO Arsenic trioxide

ATR ATM-Rad3 related protein

ATRA All-Trans Retinoic Acid

ATRIP ATR-interacting protein

BCA Bicinchoninic Acid

BCR breakpoint cluster region

BER Base Excision Repair

BRCA1 breast cancer susceptibility

gene

BRCA2 breast susceptibility 2

BSA Bovine serum albumin

CAR Chimeric antigen receptor

CARTs CAR transduced T cells

Cas9 CRISPR associated protein 9

CDK Cyclin-dependent kinases

cDNA complementary DNA

CEBP CCAAT/enhancer binding

protein alpha

CHIP Clonal haematopoiesis of

indeterminate significance

Chk1 checkpoint kinase 1

CML Chronic Myeloid Leukaemia

CN-AML cytogenetically normal AML

CRISPR clustered regularly

interspaced short palindromic repeats

CtBP C-terminal binding protein

CTG CellTitre Glo

CtIP CtBP interacting protein

D-loop displacement loop

DAPI 4′,6-diamidino-2-phenylindole DCK deoxycytidine kinase

DDR DNA damage repair

DDRD DDR deficiencies

DMEM dulbecco’s modified eagle

medium

DMSO dimethylsulfoxide

DNA deoxy-ribonucleic acid

DNA-PKcs DNA dependent protein

kinase

DNMT3A DNA Methyltransferase 3

alpha

dNTPs deoxyribonucleotides

triphosphate

DLB DROPseq Lysis Buffer

DSBs double stranded breaks

ECL enhanced

chemoluminescence

EDTA ethylenediaminetetraacetic

acid

EJ End-joining

ELN European LeukaemiaNet

ETO eight twenty one

EtOH Ethanol

FA Fanconi anaemia

FAB French-American-British

FACS Flow-activated cell sorting

FDA Food and Drug

Administration

FISH fluorescent in situ

hybridisation

RT-PCR reverse transcriptase

polymerase chain reaction

FLT3 Fms-Like Tyrosine Kinase

3

G-CSF Granulocyte-colony

stimulating factor

GFP Green fluorescent protein

GO Gemtuzumab ozogamicin

gRNAs Guide RNAs

Gy Gray

H-RT Human – reverse

transcriptase

H2AX Histone 2 variant X

HCL Hydrochloric acid

HR Homologous

Recombination

HRP horseradish peroxidase

HRR homologous

recombination repair

HSCs Hematopoietic stem cells

IDH1&2 isocitrate dehydrogenase

isozymes

IDT Iodonitrotetrazolium

chloride

IR radiation

ITD internal tandem

duplications

JM juxta-membrane

LB lithium borate

LT long-term

MACS magnetic-activated cell

sorting

MCM Mini-chromosome

maintenance protein complex

MDS myelodysplastic syndrome

MLL Mixed lineage leukaemia

MMP multipotent progenitors

MMR Mismatch Repair

MOI multiplicity of infection

Mrc1 mediator of the replication

checkpoint

MRN MRE11-RAD50-NBS1

mRNA Messenger ribonucleic acid

NaCl sodium chloride

NER Nucleotide Excision Repair

NGS Next-Generation

Sequencing

NHEJ Non-Homologous End-

Joining

NPM1 Nucleophosmin 1

OS Overall Survival

P.I. Propidium Iodide

PAGE Polyacrylamide Gel

Electrophoresis

PAM protospacer adjacent motif

PARP poly-ADP ribose polymerase

PBS phosphate-buffered saline

PFA Paraformaldehyde

PML promyelocytic leukaemia

PMSF phenylmethylsulphonyl

fluoride

PolyA poly-adenylated

QC quality control

QUB Queen’s University Belfast

RARA retinoic acid receptor alpha

RBCs Red blood cells

RFC Replication Factor C

RIPA Radioimmunoprecipitation assay

RMI1 & RMI2 RecQ-mediated

genome instability 1&2

ROS reactive oxygen species

RPA replication protein A RPA

RPMI Roswell Park Memorial

Institute FBS Foetal Bovine

Serum

RR ribonucleotide reductase

SDS Sodium Dodecyl Sulphate

SDSA synthesis-dependent strand

annealing

SEM standard error from the

mean

sgRNAs single guide RNAs

SSBs single-stranded breaks

SSC saline sodium citrate

ST short-term

STAR Spliced transcripts

alignment to a reference

STR Sgs1-Top3-RMi1

t-AML therapy related AML

TALENs transcription activator-

like effector nucleases

TBS-T Tris-based saline-tween

TLS Translesion Synthesis

U.V ultraviolet

UMI Unique Modifier Identifier

WBCs White blood cells

WHO World Health

Organisation

XLF XRCC4-like factor

XRCC3 x-ray cross

complementing 3

XRCC4 x-ray cross

complementing 4

ZFNs zinc finger nuclease

Table of Contents

Chapter 1 Introduction ……………………………………………………….………….………………… 1

1.1 Haematopoiesis ………………………………………………….……….……………….……….…. 1

1.1.1 Haematopoietic Stem Cells …………………..…….…………………………….….….. 1

1.2 Malignant Haematology and Diseases ………………………………….…………………….. 3

1.2.1 Acute Myeloid Leukaemia ………………………..……….………….……….……………. 4

1.2.2 AML Classifications …………………………….....……………………….………..….…….. 5

1.2.3 AML Mutations ……………………………………...……………….…….……………………. 7

1.2.4 Therapy Related AML ……………………………...……………….…….……….….………. 9

1.3 Current and potential therapies for AML ……...……………………….……….………….10

1.4 DNA Repair Mechanisms ………………………………...………………..…….………..……… 12

1.4.1 Double Stranded Break Repair ……………...………………………….….………..…. 12

1.4.2 Non-Homologous End Joining ………………..…………………………...…..….…….. 13

1.4.3 Homologous Recombination ………………………...….……..…….……..….………. 16

1.4.4 DSB Repair Pathway Choice ……………………………………………..........……….. 18

1.4.5 DNA Damage Foci ………………………...…………….……………………..………..…… 19

1.4.5.1 Advantages and disadvantages of foci analysis …………..........…….… 20

1.4.5.2 Uses of Foci staining analysis …………………………………….…….…………. 20

1.5 The Cell Cycle and Cell Cycle Checkpoints ………………………………..……….……….. 21

1.5.1 Cell Cycle Checkpoints …………………………………………………..………….………. 22

1.5.2 Cell Cycle Phases and their Checkpoints ……………………..…………………….… 23

1.5.3 Cell Cycle Recovery ……………………..………………………….…………….………..… 25

1.5.4 Synthetic Lethality ……………………..………………………….…….……….……….… 27

1.6 DNA Damage Repair in AML ……………………………………………………..….……..…….. 28

1.7 DNA Damage Repair Deficiency Assay ……..………………………………….………..…… 30

1.8 DNA Damaging Agents – Mechanisms of Action …………………..……….……...…… 32

1.8.1 Nucleoside Analogues ………………………………………………..…………….….…… 32

1.8.2 Checkpoint Pathway and DNA Repair Protein Inhibitors ………………..….. 33

1.9 Genome Editing ……………………………………………………..…………………..……………. 34

1.9.1 CRISPR Cas9 Technology …………………………………………….……….………….... 34

1.9.2 Pooled CRISPR Screens …………………………..……………..………………..……….. 36

1.9.3 Single Cell RNA Sequencing …………..………………………….………………….…….. 37

1.9.4 CROPseq CRISPR Screens …………..……….……….…………...…………………….... 40

1.10 Hypothesis …………..…………………………………………………………….………………..… 42

1.11 Aims …………..……….………………………………..……………………….………..…………….. 42

Chapter 2: Materials and Methods ………………..………………………...…….………………… 43

2.1 Tissue Culture ………………..…………….……………………………….….……….……………… 43

2.1.1 Cell Lines Used ……..…………..…..……………………………….….…………..…….…… 42

2.1.2 Thawing Frozen Cells ……………….…….……….……………….……….………..……… 44

2.1.3 Freezing Cells ………………………...………………………….…….…………….………..… 44

2.1.4 Counting Cells ……………….……..…………………………………..….………………….. 44

2.1.5 Cell Maintenance …..………….…….……..………………..…………...………………… 44

2.2 In vitro Drug Treatments …..………….…….……..…..………………….……..……………… 45

2.2.1 Clonogenic Assays …..………….…….……..………..…………...……..………………… 45

2.2.2 Flow Cytometry …..………….…….………………………………..….……..………..…… 46

2.2.3 Growth Curves …..………….…….………………………………………..…………...……… 46

2.2.4 CellTitre Glo Assay ………..………………………………………....………..…………….. 46

2.3 Protein Analysis ……….…………………………………………………….………….…………….. 47

2.3.1 Protein Extraction ….….……………………………………………………………………….. 47

2.3.2 Protein Quantification ….………………………………………….………...………….... 47

2.3.3.Sodium Dodecyl Sulphate (SDS) Polyacrylamide Gel Electrophoresis

(PAGE)

…………………………………………………………………………………………..………….………….. 47

2.3.4 Transferring Proteins onto a Nitrocellulose Membrane ………….…………. 48

2.3.5 Protein Immunoblotting ………….……………………………………….…….………... 48

2.3.5.1 Protein Analysis Buffers …………………………….……………………..…………49

2.4 DNA Damage Repair Deficiency Signature Calculations …………….………...….…. 50

2.4.1 Score Calculation for Publicly Available Patient Data Sets …………………. 50

2.4.2 Calculating Survival Analysis for Publicly Available Patient Data Sets ..… 51

2.4.3 Analysing genetic mutations associated with the DDRD Scores …………… 51

2.4.4 Pathway analysis of DDRD positive associated genes …………………...…… 52

2.5 Immunofluorescent detection of cellular proteins ……………………………..…..… 52

2.5.1 Cytospining cells onto glass slides/coverslips ………………………….…...…… 52

2.5.2 Irradiating Cells …………….……………………………………..……….……………..…… 52

2.5.3 Staining cells for the 53BP1 protein ……………………………………………….….. 52

2.5.4 Staining cells for the RAD51 protein ……………..…..…………………………..….. 53

2.5.5 Foci Counting ……………..……………………………………..………..……………….….. 54

2.6 CROPseq CRISPR Screen …..……………………………………………………………….....….. 54

2.6.1 Blasticidin and Puromycin Kill Curves ……………………………….……………….. 54

2.6.2 Creation of a stable CAS9 expressing cell line ………………….…...……….….. 54

2.6.3 Testing the stable CAS9 cell line ……………………………………..………………….. 55

2.6.4. Guide Design …………………………………….…………….………………………………... 55

2.6.5 Plasmid Preparation and Restriction Digestion …….…………………….……… 56

2.6.5.1 Electrophoresis Buffers and LB Buffers …………………………………………….. 57

2.6.6 Assembly of gRNA-encoding ssDNA oligonucleotides into the vector

backbone …………………..…………….………………….………………………………..…..….….. 58

2.6.7 Next-generation sequencing of gRNA library ………………………….…………. 58

2.6.8 Lentivirus production and Titration ……………………………………………..……. 58

2.6.9 Transduction of the stable CAS9 cells for the CROP-seq screen …….…... 59

2.6.10 Preparation of Cells for CROP-seq analysis …………………………..………..… 59

2.6.11 Single-cell RNA-seq based Drop-seq ………………………………....…….……… 59

2.6.11.1. Preparation of beads …………………………….………………………………… 59

2.6.11.2 Droplet Generation ………………………….…….………….…………..………… 60

2.6.11.3 Droplet Breakage …………………………….………….……………………...…… 60

2.6.11.4 Reverse Transcription and Exonuclease I Treatment …………..….… 60

2.6.12 Preparation of Drop-Seq cDNA Library for Sequencing and Sequencing

your sample ……………………………………….……………………………..……..………………... 61

2.6.13 Read Alignment and Generation of Digital Expression Data ………..…… 61

2.7 Materials ………………………………………….…………………………………………………...…. 62

2.7.1 Primary Antibodies ………………………….………………………………………….……... 62

2.7.2 Secondary Antibodies ……..………………………………………..…………………….…. 62

2.7.3 Drugs …………………………….………………………………….…………..……………….…. 62

2.7.4 Restriction Enzymes ………………………………………….………………..………….…. 62

2.7.5 Cell Media Used ………………………………..……….…………………………..………... 63

Chapter 3: Assessing the Potential of using the DNA Damage Repair Deficiency

Assay in Acute Myeloid Leukaemia ……………………………………………………….……… 64

3.1 Introduction ……………………………………………………………………………………….…….. 64

3.2 Aims and Objectives ……………………………………………………………………………..…… 66

3.3 Results …………………………………………………………………...………………………………… 66

3.3.1. Validating the DDRD score Calculation ………………...…………………..……..… 66

3.3.2 Applying the DDRD score assay to publicly available AML patient

datasets…………………………………………………………………….……..…………………….….. 67

3.3.3 Survival Analysis of the AML patient datasets ……………………….………..…. 70

3.3.4 Analysis of the mutations associated with the DDRD positive patients .. 70

3.3.5 Pathway analysis of DDRD positive mutations ……………………………....….. 75

3.3.6 DDRD Analysis of Myeloid Leukaemic Cell Lines ………..…………………..….. 77

3.4 Discussion ……………………………………………………………………………...…………….….. 77

3.4.1 Validating the DDRD score Calculation ………………………………….……….….. 77

3.4.2 Applying the DDRD score assay to publicly available AML patient

datasets.. ………………….…………………………………………………………………..…………… 78

3.4.3 Survival Analysis of the AML patient datasets ……………….…………...……… 78

3.4.4 Analysis of the mutations associated with the DDRD positive patients .. 79

3.4.5 Pathway analysis of DDRD positive mutations ..………………….…………….. 81

3.4.6 DDRD Analysis of Myeloid Leukaemic Cell Lines …………………………........ 81

3.5 Summary ……………………………………………………………………………………..…………… 82

Chapter 4: Analysis of DNA Damage Repair Foci in DDRD Positive and DDRD

Negative Cell Lines ……………………………………………………………………………………….. 83

4.1 Introduction ……………………………………………………………………………………………... 83

4.2 Aims and Objectives ……………………………………………………………….………………... 84

4.3 Results ……………………………………………..……………….…………………….……………….. 84

4.3.1 Analysis of 53BP1 foci following 2Gy radiation treatment ………….……….. 84

4.3.2: Analysis of RAD51 foci following 2Gy radiation treatment ……....……….. 94

4.3.3 Analysis of RAD51 foci following 1M cytarabine treatment …………….. 103

4.3.4 Comparisons in repair effectiveness following 2Gy radiation vs. cytarabine

treatment …………….……………………..…………………….…………………………………….. 108

4.4 Discussion ……………………………………..…………………….…………………….……….….. 116

4.4.1 Analysis of 53BP1 foci following 2Gy radiation treatment ………….…….….. 116

4.4.2: Analysis of RAD51 foci following 2Gy radiation treatment …………….……. 117

4.4.3 Analysis of RAD51 foci following 1M cytarabine treatment ………...….…. 118

4.4.4 Comparisons in repair effectiveness following 2Gy radiation vs. cytarabine

treatment ………………………………..…………..……………….……………..………………….….. 119

4.5 Chapter Summary ………………..…………..……………….…………………………….…….. 121

Chapter 5: Candidate Drug Treatments of DDRD Positive and DDRD Negative

Patients ………………..…………..…………………….…………………………….………….………….. 122

5.1: Introduction …………….…………………..…………………….…………………….….…….….. 122

5.2 Chapter Aim ……….…….…………..……..……………….…………………….………….….…… 115

5.3 Results …..…………………………….…………………….……………………………………….…… 123

5.3.1 Evaluating the effects of standard of care therapies on DDRD Positive and

DDRD Negative cell lines ……………....………………………………………………..………… 123

5.3.2 Evaluating the effects of nucleoside analogues on DDRD Positive and

DDRD Negative cell lines …………….…………………………………………………...………. 127

5.3.3 Evaluating the effects of checkpoint inhibitors on DDRD Positive and

DDRD Negative cell lines …………….……………………………………………….……..……. 133

5.3.4 Evaluating the effects of DNA Repair Protein inhibitors on DDRD Positive

and DDRD Negative cell lines …………………………………………………………………... 141

5.4 Discussion ……………….…………………………………….………………….……………………. 151

5.4.1 Evaluating the effects of standard of care therapies on DDRD Positive and

DDRD Negative cell lines ………………….…..……………….…………………………….……. 151

5.4.2 Evaluating the effects of nucleoside analogues on DDRD Positive and

DDRD Negative cell lines ………………….……………………….…….………..………………. 152

5.4.3 Evaluating the effects of checkpoint inhibitors on DDRD Positive and

DDRD Negative cell lines ………………………………………….…….……………..…………. 154

5.4.4 Evaluating the effects of DNA Repair Protein inhibitors on DDRD Positive

and DDRD Negative cell lines …………………………………………………….……………… 157

5.5 Chapter Summary ……………………………………………….…………………….….………… 159

Chapter 6: Large pooled knock-out CRISPR screen analysis of a DDRD Negative

Cell Line ……………………………………………………………………………………………………… 161

6.1 Introduction ………………………………………………………………………………….………… 161

6.2 Aims and Objectives ……………………………………………………………………….………. 162

6.3 Results ………..……………………………………………………………………………………...….. 162

6.3.1 Blasticidin and Puromycin Kill Curves ……………………………………………….. 162

6.3.2 Testing of the stable CAS9 cell line ……………………………………..…………… 163

6.3.3 Plasmid Digestion ………………………………..……………………..……………..…… 163

6.3.4 Library Assembly and sequencing …………………..……………………..…..…… 165

6.3.5 Lentiviral MOI ………………………………..…………………………………………..…… 170

6.3.6 RNA-Seq Analysis and DDRD Score Mapping …………………..………….…… 171

6.3.7 Pathway Analysis and Hierarchal Clustering ………………..……………….…… 178

6.4 Discussion ……………….…….……….………………………………………….…………..………. 181

6.4.1 Blasticidin and Puromycin Kill Curves ………..……………….………………..…. 181

6.4.2 Testing of the stable CAS9 cell line ………..…………….………….………………. 181

6.4.3 Plasmid Digestion ………..…………………..……….…………..……….………………. 181

6.4.4 Library Assembly and sequencing ………………………………..….………………. 181

6.4.5 Lentiviral MOI ………..………….……………..……….……………………………………. 182

6.4.6 Single Cell RNA-Seq Analysis and DDRD Score Mapping ……….……………. 183

6.4.7 Pathway Analysis and Hierarchal Clustering ………………..…………..….……. 185

6.5 Chapter Summary …………..………………….…….………….………………….…….……….. 187

Chapter 7: Summary ………….……………………….……….…….………………….…….……….. 188

7.1 General Summary ………….………………….……..………….………………….…….……….. 188

7.2 Conclusions ………….……………………….……..…….……………………….….……………… 191

7.3 Future Works ………….………………….……..………….…………………….….……………… 193

Chapter 8: Appendices ………….………………..………….…………………….……..…………… 195

Chapter 9: References ………….………………..………….…………….……….…………………… 228

Table of Figures

Figure 1.1: Illustration of the Haematopoietic stem cell tree …………………………………. 2

Figure 1.2 Relative Survival by Year by Age at Diagnosis ………………………………………… 5

Figure 1.3 Graph of most common driver mutation genes in AML …………………………. 7

Figure 1.4: Representation of chromosomal translocations occurring due to poor

DNA repair ………………………………………………………………………………………………….………….

10

Figure 1.5 Depiction of the Non-Homologous End Joining Repair Pathway …….……. 15

Figure 1.6 Depiction of the Homologous Recombination Pathway ………………….……. 18

Figure 1.7 Depiction of the cell cycle phases ………………………………………………..………. 22

Figure 1.8: Graphical depiction of the pathway components of the Chk1 and Chk2

checkpoint pathways ………………………………………………..…………………………………………. 26

Figure 1.9: Portrayal of the synthetic lethal phenotype ………………………………………… 27

Figure 1.10 The CRISPR Cas9 Genome Editing System …………………………………………… 36

Figure 1.11 Flow diagram of the different aspects of the DROPseq Single Cell

Sequencing method ………………………………………………..…………………………………………… 39

Figure 1.12 Depiction of the re-engineered CROPseq-Guide-Puro plasmid ………..…. 41

Figure 2.1: Depiction of the binding order of Immunofluorescence …………………..…. 54

Figure 3.1: XY scatter plot of the published breast cancer DDRD scores and the DDRD

scores calculated in-house ………………………………………………..…………………………………. 67

Figure 3.2: Waterfall plot of all the DDRD scores calculated …………………………………. 68

Figure 3.3 Kaplan Meier Survival Analysis of DDRD Positive and DDRD Negative

Patients up to 5 years …………………………………………………………………………………..………. 70

Figure 3.4 DDRD Scores of Myeloid Leukaemic Cell Lines ……………………………..………. 77

Figure 4.1 Graph of Average No. of Foci per Cell ……………………………..…………………… 85

Figure 4.2 Graph of % Damage Positive Cells …………………………………...…………………… 86

Figure 4.3 Foci Distribution Graph …………………………………...………………..………………… 87

Figure 4.4 Foci Distribution Graph …………………………………...………………..………………… 89

Figure 4.5 Fluorescent Microscope Images of 2Gy irradiated HL60 cells ……………. 91

Figure 4.6 Fluorescent Microscope Images of 2Gy irradiated NB4 cells …………….. 92

Figure 4.7 Fluorescent Microscope Images of 2Gy irradiated SKM1 cells …………... 93

Figure 4.8 Growth Curves of HL60, NB4 and SKM1 cell lines ……………..………………… 94

Figure 4.9 Graph of Average No. of Foci per Cell ……………..…………………………………… 95

Figure 4.10 Graph of % Damage Positive Cells ……………..……………………………………… 96

Figure 4.11 Foci Distribution Graph 4hrs after 2Gy radiation …………………….………… 97

Figure 4.12 Foci Distribution Graph 24hrs after 2Gy radiation ……………….………….… 98

Figure 4.13 Foci Distribution Graph 48hrs after 2Gy radiation ……….……….……………. 99

Figure 4.14 Fluorescent Microscope Images of 2Gy irradiated HL60 cells ……….… 100

Figure 4.15 Fluorescent Microscope Images of 2Gy irradiated NB4 cells …………… 101

Figure 4.16 Fluorescent Microscope Images of 2Gy irradiated SKM1 cells ….….…. 102

Figure 4.17 Graph of Average No. of Foci per Cell ……….………………………….…………. 103

Figure 4.18 Graph of % Damage Positive Cells ……….……………………….………………… 104

Figure 4.19 Foci Distribution Graph 4hrs after 1M Cytarabine treatment ………… 105



Figure 4.22 Comparison of damage positive cytarabine and 2Gy radiation treated

cells …………………………………………………………………………………………………………………… 108

Figure 4.23 Comparison of cytarabine and 2Gy radiation treated HL60 cells

distribution graphs …………………………………………………………………………………………….. 109

Figure 4.24 Comparison of cytarabine and 2Gy radiation treated NB4 cells


Figure 4.25 Comparison of cytarabine and 2Gy radiation treated SKM1 cells


Figure 5.1: Percentage colony growth of DDRD Negative and DDRD Positive cell lines

following treatment with Cytarabine ……………………………………………..………………….. 124

Figure 5.2: Cell cycle analysis of DDRD positive and DDRD negative cell lines following

Cytarabine treatment at 1M ………………………………………………………..………………….. 126


following treatment with gemcitabine at varying concentrations ..……………………. 128


Gemcitabine treatment at 5M ……………………………………………………..…………………. 129


following treatment with Sapacitabine at varying concentrations ..……………………. 130


Sapacitabine treatment at 5M ……………………………………………………..……….…………. 132


following treatment with WEE1i MK-1775 at varying concentrations …….…………. 134


WEE1i MK-1775 treatment at 5M ………………………………………………..……….…………. 135

Figure 5.9: Growth Curves of DDRD Positive and DDRD Negative Cell Lines both

treated and untreated with WEE1i MK-1775 …………………………………..…………………. 136

Figure 5.10 CHEK1 Gene Expression Value from microarray data of the NB4 and SKM1

Cell Lines ……………………………………………………………………………….………..…………………. 137


following treatment with CHK1i Rabusertib at varying concentrations ………….……. 138

Figure 5.12: Cell cycle analysis of DDRD positive and DDRD negative cell lines

following CHK1i rabusertib treatment at 5M ……………………….………..…………………. 139

Figure 5.13: CHK1 Gene Expression Analysis from 795 patients combined from

publicly available data sets; TCGA, GSE6891, GSE49642, GSE62190 and GSE66917

…………………………………………………………………………………………………………………………...140


following treatment with PARP1i talazoparib at varying concentrations ……….…….142


following treatment with ATMi KU55933 at varying concentrations …………………… 143


following ATRi KU55933 treatment at 5M ………………………………………………………… 145

Figure 5.17: ATM Gene Expression Analysis from 795 patients in publicly available

data sets; TCGA, GSE6891, GSE49642, GSE62190 and GSE66917 ………………………… 146


following treatment with ATRi AZD6738 at varying concentrations ……………….…… 147


following ATMi AZD6738 treatment at 500nM …………………………………………………… 148

Figure 5.20: ATR Gene Expression Analysis from 795 patients in publicly available data

sets; TCGA, GSE6891, GSE49642, GSE62190 and GSE66917 ……….………………….…… 149

Figure 5.21 ATR Gene Expression Value from microarray data of the HL60, NB4 and

SKM1 Cell Lines ……….………………………………………………………………………….……………… 150

Figure 5.22: Visual representation of the interactions between the repair and

checkpoint genes ….………………..………………………………………………………….……………… 160

Figure 6.1: Depiction of the beads and bead attachments used in the DROPseq single

cell sequencing ………………………………………………………….……………………….……………… 162

Figure 6.2: Dose response Curve of SKM1 cells treated with varying doses of

Blasticidin and Puromycin ………………………………………………………….……….……………… 163

Figure 6.3: Western blot analysis of test guide CRISPR knock-downs .………….……… 164

Figure 6.4: Agarose gel electrophoresis of the plasmid digestion products …………. 165

Figure 6.5A: The read frequency distribution of the forward primers from library one

………………………………………………………..………………………………….……………………………… 168

Figure 6.5B: The read frequency distribution of the reverse primers from library one

………………………………………………………..………………………………….……….…………….……… 168

Figure 6.5C: The read frequency distribution of the forward primers from library two

……………………………………………………..………………………………….……….……………..……….. 169

Figure 6.5D: The read frequency distribution of the reverse primers from library two

……………………………………………………..………………………………….……….…………………….… 169

Figure 6.6: Percentage Viability graph of the CAS9 cell line following transduction

with the guide RNAs lentivirus ……..………………………………….……….…………………….… 170

Figure 6.7: Frequency of error-containing guides compared to total and correct

guides ……..……………………………………………………………………….……….…………………….… 171

Figure 6.8: Frequency of all guides present in the CROPseq library compared with

frequency of guides in cells with only one guide ……………….……….…………………….… 172

Figure 6.9: Graph of average DDRD score per gene ………….……….…………………….… 173

Figure 6.10 Hierarchical clustering of the 200 top and bottom DDRD scoring

cells……………………………………………………………………………………………………………….. 180

Figure 7.1 Graphical Abstract ……………………………………………………………………………… 192

Table of Tables

Table 1.1: Table of common DNA repair genes in human cancers ………………………… 28

Table 3.1: Table containing the p-values calculated from Kaplan Meier Survival

Graphs …………………………………………………………………………………………….…………………… 69

Table 3.2: Table containing the p-values relating to the correspondence between

prognosis and DDRD score ………………………………………………………..…….…………………… 71

Table 3.3: List of genes more commonly mutated in DDRD positive patients ……….. 72

Table 3.4: Co-occurrence mapping of DDRD positive mutations …………………………… 74

Table 3.5: List of the top 20 most dysregulated pathways in DDRD positive patients

……………………………………………………………………………………………………………………..……… 76

Table 3.6: Table of genes most commonly mutated in HR defective cells compiled by

J. Murai ……………………………………………………………………………………………………..………… 79

Table 4.1: Table of Significance Values from the Foci Distribution Graph ..………….… 88

Table 4.2: Table of Significance Values from the Foci Distribution Graph ……………… 90

Table 4.3: Table of Significance Values from the 4hr Foci Distribution Graph …..…… 97

Table 4.4: Table of Significance Values from the 24hr Foci Distribution Graph ……… 98

Table 4.5: Table of Significance Values from the 48hr Foci Distribution Graph ……… 99

Table 4.6: Table of Significance Values from the 4hr Foci Distribution Graph ……..…

105

Table 4.7: Table of Significance Values from the 24hr Foci Distribution Graph ….... 106

Table 4.8: Table of Significance Values from the 48hr Foci Distribution Graph ….... 107

Table 4.9: Significance values for the HL60, NB4 and SKM1 cell line comparison

distribution graphs …………………………………………………………………………………..………… 112

Table 5.1: Table of significance values from the cytarabine treated clonogenic assays

………………………………………………………………………………………………………………...………… 124

Table 5.2: Table of significance values from the gemcitabine treated clonogenic

assays ……………………………………………………………………………………………………...………… 128

Table 5.3: Table of significance values from the sapacitabine treated clonogenic

assays ……………………………………………………………………………………………………...………… 130

Table 5.4: Table of significance values from the WEE1i MK-1775 treated clonogenic

assays ……………………………………………………………………………………………………...………… 134

Table 5.5: Table of significance values from the CHK1i Rabusertib treated clonogenic

assays ……………………………………………………………………………………………………...………… 138

Table 5.6: Table of significance values from the PARP1i talazoparib treated

clonogenic assays ……………………………………………………………………………………………… 141

Table 5.7: Table of significance values from the ATMi KU55933 treated clonogenic

assays ……………………………………………………………………………………………………...………… 144

Table 5.8: Table of significance values from the ATRi AZD6738 treated clonogenic

assays ……………………………………………………………………………………………………...………… 147

Table 6.1: List of genes targeted in the CROPseq CRISPR Screen …………...……….…… 166

Table 6.2: Tables of total reads and percentages mapped from the guide libraries

…………………………………………………………………………………………………………………………… 167

Table 6.3: Table of the number of reads which fell outside the optimum range … 167

Table 6.4: Table of DDRD Scores from the control guides …………...……………..….…… 174

Table 6.4 A&B: Table of the mean DDRD score from the control guides and the SEM

of all the DDRD scores ………………………………………………………………………………………… 174

Table 6.5: Genes at least 1 SEM under the average DDRD score of the control guides

…………………………………………………………………………………………………………………………… 175

Table 6.6: Genes at least 1 SEM above the average DDRD score of the control guides

…………………………………………………………………………………………………………………………… 176

Table 6.7: Genes not significantly different from the control DDRD Score …………… 177

Table 6.8: Pathways deregulated by mutations in genes that increase a DDRD score

…………………………………………………………………………………………………………………………… 178

Table 6.9: Pathways deregulated by mutations in genes that decrease a DDRD score

…………………………………………………………………………………………………………………………… 179

Table 8.1 WHO AML Classification ……………………………………………………………………… 196

Table 8.2: ELN risk categories and abnormalities ………………………………………………… 197

Table 8.3: Table of guide sequences for CRISPR CROPseq screen ……………………… 298

Table 8.4: Table of DDRD scores for all cells containing only one guide from. library

one of the CROPseq experiment ……………………….………………………………………………… 207

1

Chapter 1 Introduction

1.1 Haematopoiesis

Haematopoiesis is the formation of all the blood cellular components in the body

(1)(2). It includes the formation and maturation of these blood components into their

differentiated lineages.(3) Blood cell components can be split into three main groups;

red blood cells (RBCs), white blood cells (WBCs) and platelets. Most of these

components are produced in the bone marrow with the exceptions being two types

of WBCs. T and B cell lymphocytes are produced in the lymph nodes and spleen, some

T cells can be produced in the thymus gland. (4) (5) The lifespan of these blood cells

varies drastically between cell types. RBCs can last for up to 120 days where the

majority of WBCs last from between a few hours to a few days, although some can

last for several years. (6)(7)

1.1.1 Haematopoietic Stem Cells

Haematopoietic stem cells (HSCs) are the precursors for blood cells. (3) It is therefore

necessary for HSCs to maintain self-renewal potential throughout their lifetime. The

new idea of a haematopoietic tree is a long-term (LT) HSCs head which can

differentiate into short-term (ST) HSCs and then into multipotent progenitors

(MMP)(8) (3). The ability to self-renew decreases as you move further down the tree.

From the MPP cells, the tree branches into the myeloid and lymphoid lineages. Cells

in the myeloid lineage include monocytes, macrophages, neutrophils, erythrocytes

and platelets. Lymphoid cells include T cells, B cells and natural killer cells. (9)(10)

2

Figure 1.1: Illustration of the Haematopoietic stem cell tree

This illustration shows the hierarchy of the HSCs tree. The LT-HSCs sits at the tope and can

differentiate into the ST-HSCs and then to MPPs. From here a specific lineage is decided

and the tree divides into either the myeloid branch or the lymphoid branch (11)

Hematopoietic Stem Cell (HSC), Multi-Potent Progenitor (MPP), Common Lymphoid

Progenitor (CLP), Common Myeloid Progenitor (CMP), Granulocyte/Macrophage Progenitor

(GMP), Megakaryocyte/Erythrocyte Progenitor (MEP).

New research has indicated that not all HSCs are identical; it is believed that there

are different fractions of HSCs that give rise to different subpopulations. (3) The now

out-dated theory of haematopoiesis stated that HSCs were in a ‘poised’ state which

were not committed to any one lineage.(1,11) Based on endogenous signals from its

surroundings, the HSC could be differentiated into a specific cell state.(9) It was

believed that HSCs had low levels of transcription factors of cells from differentiated

3

lineages but not enough to fully commit to a specific lineage. For example,

CCAAT/enhancer binding protein alpha (CEBP) is a transcription factor necessary for

the differentiation of neutrophils and PU.1 is needed for monocyte differentiation. It

was thought that both of these factors could be present in low levels in HSCs. Upon

signalling from factors such as cytokine Granulocyte-colony stimulating factor (G-

CSF), an imbalance in factors can occur which initiates differentiation in a lineage

specific manner. (12)

New technologies have allowed for a deeper analysis of the journey of HSCs. The use

of fluorophores to trace, in single cells, the expression of transcription factors

revealed that cells do not co-express transcription factors of different lineages. (8)

They demonstrated that progenitor cells express only the transcription factors of the

cell lineage into which they will differentiate. It is possible that this system was not

able to detect minute levels of other transcription factors, but this data is a definite

challenge to the previous theory.(8) Single cell messenger Ribonucleic Acid (mRNA)

sequencing of myeloid progenitor cells aided in the classification of transcription

factors in multiple subsets of cells. When further single-cell analysis on HSCs was

completed, it was found that cells did not express transcription factor signatures from

multiple lineages. (11)(8)

1.2 Malignant Haematology and Diseases

While HSCs are normally quiescent, they can be forced to replicate in response to cell

stresses in order to repair or excise any damage that may have accumulated. (12)(13)

These cells are predominantly dependent on the homologous recombination repair

(HRR) process. This can be seen through experiments that demonstrate that a loss of

breast cancer susceptibility 1 (BRCA1) in embryonic haematopoietic cells completely

depletes HSCs in adult mice. Loss of Fanconi anaemia (FA) repair components causes

a higher rate of bone marrow failure and cell death in comparison to healthy

counterparts. (2) (14)

Clonal haematopoiesis of indeterminate significance (CHIP) describes the scenario in

which somatic mutations are found in bone marrow cells yet the criteria to define a

haematological malignancy is not met. (15) (16) CHIP can be described as a

premalignant state, although not all people with these mutations go on to develop a

4

haematological malignancy. (15) Often a second or third mutation is required to

‘push’ the cell from a premalignant to malignant state. These are known as driver

mutations as the drive oncogenesis. (17)

HSCs can acquire mutations, which can lead to changes in cell proliferation or can

give rise to differentiated cells that carry the same mutations. A higher rate of

proliferation can lead to a higher percentage of a certain blood cell type which

disrupts the balance and can crowd out other cell types. These cell types are generally

immature and incapable of carrying out the necessary functions of blood cells. (18)

1.2.1 Acute Myeloid Leukaemia

Acute Myeloid Leukaemia (AML) is a developing disease of the myeloid lineage of

haematopoietic cells. AML presents as an uncontrolled growth of immature white

blood cells. (19) AML can develop as de novo cases, as a result of inherited conditions,

following treatment with deoxy-ribonucleic acid (DNA) damaging agents or as a

progression from another myelodysplastic or myeloproliferative disorder (20). The

different methods of AML development show different mutational landscapes. De

novo development of AML is the more common than the later methods. Inherited

conditions such as Fanconi Anaemia, Bloom syndrome and Down’s Syndrome all

confer a higher incidence of AML. (21)

The median age of diagnosis of AML is 65 and is far more common in people over the

age of 60. Cases can be seen in younger patients although it is rare in the <50 age

bracket. (22) The survival rate of AML has improved over recent years as the

previously uncurable disease now has a 30-40% chance of survival for patients under

the age of 60. For those older patients, survival rates are still dismal (Figure 1.2)

5

Figure 1.2: Relative Survival by Year by Age at Diagnosis

Overall Survival (OS) according to age for AML (non-APL) patients diagnosed in 1997 to

2006, with follow-up in December 2008 (23)

1.2.2 AML Classifications

While AML is not a difficult disease to diagnose, it can be hard to classify due to its

genetic and molecular diversity. (24) AML diagnosis is multi-step process. If a patient

presents with symptoms or a combination of symptoms such as tiredness, paleness,

loss of weight, unusual bleeding and frequent infections, the GP will likely order a

blood test. From the blood tests, a high number of abnormal white blood cells or a

very low blood cell count could be an indicator of leukaemia. (25) From here a bone

marrow biopsy can be used to confirm a diagnosis. This would involve isolating a

small sample of bone marrow from the hip bone. Genetic testing will then be carried

out on the blood samples and bone marrow samples to confirm the AML diagnosis

and to determine which type of AML the patient has. (26) These tests currently

include fluorescent in-situ hybridisation (FISH) testing or real-time polymerase chain

reaction (RT-PCR). (25,26) The NHS is currently in the process of phasing out these

techniques and replacing these tests with Next-Generation Sequencing (NGS). This

eliminates the need for multiple tests to identify any mutations or translocations

6

which may be present. (27) Cell morphology is also examined and cell shape, count

and size are taken into consideration.

The French-American-British (FAB) system was originally used to classify AML

patients into eight potential subgroups based on morphology. This method has

become largely outdated now with the advent of molecular diagnostics.(26) (28) The

World Health Organisation (WHO) has developed different classification of AML

patients. After several amendments, the classification now uses different groupings

which include groups related to specific chromosomal translocations, AML with

myelodysplasia-relate changes, AML not otherwise stated and AML relating to Downs

Syndrome. A substantial fraction of AML cases (52%) have chromosomal

abnormalities such as deletions or translocations. (28) Full WHO classification table

can be found in Chapter 8 Appendices Table 8.1. The remaining patients are

cytogenetically normal AML (CN-AML). The mutational landscape of CN-AML has

been well characterised over the past few years due to a number of whole genome

sequencing experiments. (29) Approximately 50 mutations have been described in

AML. However, on average, CN-AML patients have 13 mutations.(30) Only five of

these mutations are likely ‘drivers’ however with the remaining 8 being random

passenger mutations with little significance. The driver mutations are those

mutations which drive the oncogenic phenotype in these cells. (17) (30) The

identification and analysis of these mutations can lead to more effective treatment

options for these patients. Some of the most commonly mutated genes in CN-AML

include NPM1, FLT3, DNMT3A, NRAS and IDH1& 2 mutations. (29)

The European LeukaemiaNet (ELN) is an alternative system for classifying AML risk

groups. (25) The full ELN risk grouping table can be found in Chapter 8 Appendices

Table 8.2.

7

Figure 1.3: Graph of most common driver mutation genes in AML

1540 patients were analysed. Each gene has its own bar on the x-axis with the y-axis

displaying the number of patients. The colours relate to the prognosis risk according to the

European LeukemiaNet (ELN)

1.2.3 AML Mutations

Nucleophosmin 1 (NPM1) is the one of the most commonly mutated gene in AML,

occurring in approx. 25-30% of all AML cases.(31) Disrupted expression of the NPM1

protein can stimulate myeloid proliferation and leukemic development.(21) (29) Part

of the NPM1s protein main function is to chaperone the protein product of the

tumour suppressor gene p14Arf. Loss of its chaperone protein often leads to a

decrease in the function of p14Arf, negating its anti-tumour effects.(32) (33) NPM1 is

a class II oncogene as a mutation in this gene blocks the maturation of

haematopoietic blast cells.(34) Patients with NPM1 mutations are generally

considered to have a favourable prognosis. This prognosis is mitigated if the NPM1

mutation co-occurs with a FLT3 mutation. (32)

The Fms-Like Tyrosine Kinase 3 (FLT3) gene is involved in the cell cycle and

proliferation of HSCs.(35) FLT3 mutations are detected in around 20% of all AML

cases, and in up to 40% of cytogenetic normal (CN)-AML patients. (29) Mutations can

affect two parts of the FLT3 gene: either in tyrosine kinase domain (TKD) or internal

8

tandem duplications (ITD) can occur in the juxta-membrane (JM) domain. (35,36) FLT-

ITD mutations are common not only in AML but also in other myeloid blood cancers

such as myelodysplastic syndrome (MDS) and in Chronic Myeloid Leukaemia (CML).

(37) These mutations cause the FLT3 receptor to remain constantly active which

initiates the rapid growth seen in the undifferentiated cells in AML.(38) (30) FLT3

mutations are associated with a poor prognosis. This is primarily due to the high

relapse rate associated with these mutations. If only one allele of the FLT3 gene is

mutated or if there is a co-occurring NPM1 gene, the prognosis improves slightly.

(37)(22)

Chromosomal translocations are commonplace in AML. (28) Breakages for known

translocations in AML tend to occur in genes that have important cellular functions

such as PML, RARA, AML1, ETO, BCR and ABL. (20) The effects and prognoses of these

translocations vary greatly and are governed by the genes involved. Some of these

translocations create irregular proteins which can have a severe impact on cellular

processes. Others may combine an active promoter with a oncogene which is

normally supressed. (39)(40)

The PML-RARA translocation is one of the most distinguished fusions in AML. (41)

This translocation is so common and cytogenetically different to other translocations,

it has its own subtype, acute promyelocytic Leukaemia (APL). (42) The identification

of this translocation prior to treatment induction is crucial as APL has its own

treatment regime. Current methods of identification include FISH testing or RT-PCR.

The fusion protein of these genes blocks the maturation of myeloid progenitor cells

leading to a severe decrease in the amount of circulating functional myeloid cells.

(43) This fusion protein has been implicated in the suppression of genes involved in

the DNA damage repair pathways. It can target breast susceptibility 2 (BRCA2) and

RAD51 with are crucial players in homologous recombination. (44) (18)

9

1.2.4 Therapy Related AML

As the rate of cancer increases, so too will the number of people who have received

a chemotherapeutic treatment or radiation therapy for cancer. These people are at

risk for developing therapy related AML (t-AML). (45) T-AML is categorised as any

AML that has developed following treatment with DNA damaging chemotherapeutic

agents or radiotherapy that has been directed at active bone marrow sites. (46) The

activity of these treatments can cause genomic instability and mutations.

Topoisomerase II inhibitors, which are widely used anti-cancer drugs, can severely

damage the genetic landscape of cells. There is an 8bp region on the PML gene that

is susceptible to cleavage by mitoxantrone, a topoisomerase II inhibitor. This can lead

to translocations involving this PML gene which is well known to lead to leukaemia

inducing translocations. Etoposide has been linked to translocations involving the

AML-1 and mixed lineage leukaemia (MLL) genes. (47,48) The prognosis of t-AML is

often very poor due to the presence of adverse karyotypes and a lack of treatment

options. (49) Once a patient has received a chemotherapeutic treatment for a cancer,

it is unwise to reuse this therapy in further treatments due to the high likelihood of

resistance. (50) Allogenic stem cell transplants are often the best therapeutic option.

(51)

10

Figure 1.4: Representation of chromosomal translocations occurring due to poor DNA repair

Certain chemotherapies cause rifts in the DNA which can lead to double stranded breaks

(DSBs). If left unrepaired these DSBs can result in chromosomal translocations

1.3 Current and potential therapies for AML

The current induction therapy for AML is cytarabine and anthracycline treatment.

This is called the 7 + 3 regimen as cytarabine is administered for 7 days continuously

combined with a short infusion of an anthracycline such as daunorubicin or

doxorubicin for the first 3 days. (52) (53) Younger and fitter patients receive higher

doses of these drugs whereas some elderly patients may not tolerate this harsh

treatment. Depending on certain circumstances however, not all patients will receive

this therapy. Patients deemed too old or unfit may receive a demethylating agent

such as azacytidine or decitabine. (54) What can often occur in incidences of therapy

related AML is that the patient has reached the maximum cumulative lifetime dose

of cytarabine treatment and so will have to explore alternative therapies. (31) APL

patients have more beneficial treatment options available to them. All-Trans Retinoic

Acid (ATRA) is the induction therapy in these cases. (43) ATRA functions by disrupting

the interactions between the PML-RARA protein and the nuclear co-repressor

domain. (43,46) This allows the RARA protein to resume its normal function. Arsenic

11

trioxide (ATO) is the second-generation treatment option for APL. (55) This was

originally a Chinese therapy over 2000 years ago, yet it was not fully clear how this

therapy was operating until recently. (56) The toxicity of ATO causes cancer cells to

undergo apoptosis but as it can also be quite damaging to healthy cells it is used only

if patients have relapsed following ATRA treatment. (41) Allogenic stem cell

transplants are a favourable option in patients which can tolerate this treatment. As

this treatment involves high doses of myeloablative conditioning, the toxicities are

too high for most patients over the age of 50 or patients which are suffering from

other co-morbidities. There is as well the issue of graft versus host disease which can

lead serious complications for the patient. (31) (57)

As the rate of survival in AML patients, especially those over the age of 60, remains

poor, now therapies are desperately needed. As these patients are elderly and are

less able to tolerate harsh treatment regimes, the new therapies should be targeted

to increase chances of survival and to minimise off-target effects. (58)

As previously mentioned, patients that have a mutation in the FLT3 gene also have a

high rate of relapse. (59) Targeted therapies have been developed to counteract the

effects of this mutation on event-free survival. These inhibitors have had mixed

results, however. (60)

Tyrosine kinase inhibitors have been developed for FLT3 mutated AML treatment

such as midostaurin, sunitinib and sorafenib. (61) These compounds as single agents

have shown little efficacy in clinical trials. Even some combinations with these

compounds have not had significant difference in patient outcome. (62) (38) Sunitinib

with consolidation therapy has shown a modest increase in survival rates, as has the

combination of midostaurin and induction therapy. (36,60)

Some second-generation therapies have had promising results. Quizartinib (AC220)

did have a positive effect on blast clearance yet was susceptible to resistance. (63,64)

Novel inhibitors G-749 and ASP2215 have also had encouraging results in initial trials

yet further research is needed to see if these results can be sustained. (65)

Immune therapies are the new frontier of blood cancer therapies. Monoclonal

antibodies, which target cancer cell specific surface antigens, have been developed.

12

Gemtuzumab ozogamicin (GO) targets the CD33 antigen. GO has shown efficiency in

APL and de novo AML cases. (66)

Chimeric antigen receptor (CAR)-transduced T cells (CARTs) are an exciting new

therapeutic strategy. CARTs are T cells engineered to express a specific antigen

receptor target designed against a specific cell-surface antigen. (67) (68) The results

of early stage trials have been extremely varied. CARTs have the ability to induce a

complete remission yet also cause early death of AML patients. It is currently unclear

as to why some patients respond well whereas others do not. Further study is greatly

needed to improve this promising treatment option. (69)

1.4 DNA Repair Mechanisms Every cell in our body suffers approximately one billion DNA damaging events every

day.(70) These events may be endogenous, such as reactive oxygen species (ROS) or

exogenous, such as irradiation or chemical agents. The body has adapted many

different mechanisms to protect against these sources of damage. The mechanisms

are known as DNA repair mechanisms. As there are multiple types of DNA damage,

there are multiple different DNA repair mechanisms, each with their own affinity for

a certain type of damage. (71)(72)

There are six main repair mechanisms for DNA repair although they all have

deviations and sub-mechanisms. These include Nucleotide Excision Repair (NER),

Base Excision Repair (BER), Homologous Recombination (HR), Non-Homologous End-

Joining (NHEJ), Mismatch Repair (MMR) and Translesion Synthesis (TLS). (21) (73)

1.4.1 Double Stranded Break Repair

Both the HR pathway and the NHEJ pathway are involved in the repair of DSBs. (74)

(75) Double stranded breaks are extremely harmful to the cell as they can cause

massive damage, such as chromosomal translocations and even cell death, if left

unrepaired. Therefore, the systems that repair these defects must be accurate and

effective to avoid further mutations and genomic instability. (76) (77) The more

accurate of these two methods is HR. (78)

13

Double stranded breaks can be caused by a number of different factors. Very few

DNA damaging events are capable of causing DSBs immediately. Ionizing radiation

such as -rays or x-rays can cause straight DSBs where both chromosomes are cut

simultaneously. (79) Most DSBs however are formed from single-stranded breaks

(SSBs) which have led to a blockage in fork replication, fork collapse and then DSBs

ensue. (80) There are two different types DNA DSBs; clean and dirty. Clean ends occur

where the DNA has been cleaved with no alterations or additions to the ends of the

DNA. Dirty ends can contain phosphoglycolates and terminal nucleotides which

cannot be ligated. (77)

It is necessary in both the HR and NHEJ pathway to have relaxed chromatin prior to

repair to allow access of the repair machinery. The chromatin remodelling is carried

out through the reversible actions of acetylation, phosphorylation and methylation.

(74)

The cell-cycle plays a major role in the cell’s decision of which repair mechanism will

be used. HR is only available to the cell during S and G2/M phase. As sister chromatid

is needed as a template in HR and so this method is not available in for the majority

of the cell cycle. (70) (81)

1.4.2 Non-Homologous End Joining

The NHEJ repair pathway has multiple sub-pathways and alternative processes which

come into use depending on the break type, place in the cell cycle, and the proteins

available in the cell at the time.(82) NHEJ is most common in G1 as HR is not available.

As the name suggests, homology is not necessary for NHEJ. (76) End-joining (EJ) repair

consists of joining two DSB ends directly by ligation. As there is no homology or guide

repair, NHEJ is highly error-prone. (76) The canonical NHEJ pathway is the most well

defined and more commonly used EJ repair pathway.(83) This pathway is dependent

on the Ku proteins and DNA ligase IV. Ku, DNA dependent protein kinase (DNA-PKcs)

and XRCC4-like factor (XLF) take only seconds to be recruited to DSBs. X-ray cross

complementing 4 (XRCC4) stabilizes the binding of XLF. (82)(84)

During the NHEJ repair, the Ku70/80 heterodimer first binds the DNA ends. The Ku

proteins then interacts with the DNA-PKcs to join the two DNA ends which need to

14

be repaired. (74) To fuse the two ends, the DNA ligase IV/XRCC4 complex are

recruited. XRCC4 stabilizes the DNA ligase IV and promotes the ligase activity. (85)

(76) DNA-PKcs coordinates with the nuclease Artemis to cleave any damaged DNA

bases at the end of the DNA overhang. Members of the polymerase X family, such as

Pol μ or Pol λ interacts with the Ku proteins and fills the gaps in the DNA which may

have been cleaved.(86,87)

There are many alternate steps which can occur in EJ repair, these fall under the

umbrella name of alt-NHEJ. (88,89)

15

Figure 1.5: Depiction of the Non-Homologous End Joining Repair Pathway

The KU complex stabilizes the DNA ends and recruits DNA-PKcs to join the ends. Artemis

cleans the DNA ends and XRCC4 stabilizes Lig IV and the polymerases fills the gaps between

the DNA.

Ku Complex

DNA-PKcs

Artemis

DNA Pol

XRCC4

Lig IV

16

1.4.3 Homologous Recombination

To begin HR, the DNA ends need to be resected at the 3’ end to create single-stranded

DNA (ssDNA) overhangs. (90) The MRE11 endonuclease makes the initial SS cut to

help prime the ssDNA for HR repair. (91) Phosphorylation of the C-terminal binding

protein (CtBP)-interacting protein (CtIP) by Cyclin-dependent kinases (CDK)

stimulates HR during S-phase.(51,92) CtIP, in-conjunction with the MRE11-RAD50-

NBS1 (MRN) complex, is essential for the processing of the DNA ends. (83) The MRN

complex recruits the ataxia telangiectasia mutated (ATM) protein kinase to the site

of the DSB. (90,93) MRE11 is composed of Mn2+/Mg2+ dependent phosphoesterase

domain and two binding domains. The function of MRE11 is to synapse the two DNA

ends. (94) RAD50 is a protein which has a structural homology to the maintenance of

the higher order structure of chromatin (SMC) family proteins. NBS1 recruits DNA

repair proteins, including ATM, to sites of damage. (95) The MRN complex members

can localise within seconds following identification of DSBs. ATM recruitment binds

to the C-terminus of NBS1. (96) ATM is involved in signalling to checkpoint regulators

to pause cell cycling until the damage has been repaired. (95,97)

The replication protein A (RPA) binds to the resected 3’ overhang and activates ATM-

Rad3 related protein (ATR) through interactions with the ATR-interacting protein

(ATRIP) to also enlist a checkpoint response. (92,98) Activated ATM phosphorylates

the histone 2 variant H2AX. The phosphorylated version of this protein is known as

H2AX and facilitates the accretion of DNA repair and checkpoint proteins.

Phosphorylation of H2AX amplifies the signal of DNA damage and aids the

recruitment of other effectors of DNA repair. (97,99) RAD52 encourages the

replacement of RPA and then recruits the RAD51 protein with the aid of BRCA2.

RAD51 and BRCA2 bind the conserved DNA repeats known as the BRC repeats. These

repeats activate RAD51 filament formation on ssDNA. (76,100)

RAD51 is responsible for the stimulation of the strand exchange process. This process

involves the ssDNA invades the homologous duplex DNA to form a displacement loop

(D-loop). From here, two separate methods of repair can occur; synthesis-dependent

strand annealing (SDSA) or the double Holliday junction model. (74,91)

17

The SDSA pathway is a non-crossover method of DSB repair. In this method the 3’

end in the D-loop is elongated by repair synthesis and the newly synthesised DNA

strand dissociates to bind to the other DNA end to complete the reaction. The

invading strand is displaced and binds to the resected DSB end. (101) (102)

In the double Holliday junction method, the second DNA end is detained in the D-

loop. This forms an intermediate that contains two Holliday junctions. These can be

resolved in either a crossover or non-crossover resolution depending on the number

of strands.(103,104) In non-crossover resolutions, the D-loop is dissolved by the

Sgs1-Top3-RMi1 (STR) complex whereas dissolution of D-loops by the MUS81-EME1

or GEN1 proteins can produce either crossover or non-crossover products depending

on the cell cycle. (102,104) Dissolution by BLM-mediated branch migration alongside

TOPOIII and the RecQ-mediated genome instability 1&2 (RMI1 & RMI2) proteins

create solely non-crossover products. Members of the RAD family and XRCC3 play a

role in homology search and strand invasion. (78)

18

Figure 1.6: Depiction of the Homologous Recombination Pathway

CtIP and the MRN complex prepare the DNA ends and resect the end to make a ssDNA

overhang. RPA coats the overhang. BRCA2 and RAD52 assist in the replacement of RPA with

RAD51. XRCC3 and members of the RAD family aid in strand invasion and homology search.

1.4.4 DSB Repair Pathway Choice

The choice of which repair pathway to be used depends heavily on the cell cycle.

NHEJ can be used in the G1, S and G2 phases of the cell cycle. As HR repair needs

sister chromatid templates, it is available only during S and G2 phase. (105)

MRN

CtIP

RPA

RAD51 BRCA1

BRCA2

XRCC3

RAD54 RAD51C

19

One-ended DSBs in S phase primarily use HR repair as they have overhangs available

for RPA binding. The one-ended breaks also do not facilitate NHEJ as they do not have

paired DNA ends. (106,107) One ended DNA breaks are common after fork stalling

which causes SSBs to become DSBs. HR repairs only 30% of DSBs in G2 phase with the

remaining breaks repaired by NHEJ. The HR pathway is activated by CDKs and so is

restricted to just the S and G2 phase. (74)(106)

Chromatin structure and DNA end intricacies are important factors in pathway

choice. As the Ku complex, a complex in the NHEJ pathway, can bind to cleaved DNA

ends in S and G2, which is thought to play a major role in pathway determination.

(84) Even after the Ku complex has bound, HR repair is still an option for the cell. Ku

can be removed via the combined processes of neddylation, proteasome-mediated

degradation, phosphorylation and digestion of the DNA ends. This allows for the

loading of RAD51 onto the DNA ends and initiate HR. Neddylation is the process by

which the ubiquitin-like protein NEDD8 is conjugated to its target proteins. It is

currently unclear as to what determines whether the Ku complex is removed or not.

(84,108)

The balance of BRCA1 and p53 binding protein (53BP1) on the DNA break sites can

also be a determinant in pathway choice. BRCA1 is involved in the HR pathway and

can tilt the pathway choice towards HR. Similarly, 53BP1 is involved in the NHEJ

pathway and so an excess of this protein can result in NHEJ pathway activation.

(74,86)

1.4.5 DNA Damage Foci

Foci are accumulations of nuclear proteins, often in response to a stimulus such as

induced DNA damage.(109) As the components of DNA repair pathways converge for

repair, it is possible to visualise them microscopically using fluorescent markers. The

H2AX protein was one of the first proteins to be analysed in this manner. (109) The

H2AX protein is the phosphorylated, and activated, form of the H2AX protein and is

only found surrounding DSBs. H2AX is used as an anchor for other proteins such as

53BP1, which can also be analysed in this manner. (110)

20

1.4.5.1 Advantages and disadvantages of foci analysis

Foci analysis can be very useful in a number of ways. You can easily identify in which

area of the cell the protein is being expressed. This is an easier and less-time

consuming method than cellular fractionation and western blotting. It can also

identify which proteins cluster together at break sites which again is a less taxing

method than the co-immunoprecipitate assays.

There are disadvantages to using foci analysis to study DNA damage repair. There

have been inconsistencies with the foci formed from some DDR proteins and DSB

repair. This can occur occasionally when foci form and disband too quickly to allow

time for visualization. (111) Conversely the foci may form slowly and so the use of

early timepoints can lead to foci formation being missed. Most foci are formed as

secondary events when unrepaired DNA causes stalled fork progression or fork

collapse. As fork stalling and fork collapse can also be signs of cellular stress and

aging, foci are not always representative of DSBs. (112)

1.4.5.2 Uses of Foci staining analysis

Assessing the exact location of where the damage is being repaired can assist in the

understanding of these repair mechanisms. Legube and colleagues have recently

identified the phenomenon in which DSBs can cluster following induced damaged

before being repaired. (113) Without foci staining, certain advancements in cellular

research would not be made.

Foci staining can assess patients following radiation (IR) treatment. As the effects of

IR are well characterised and the foci formed are easily distinguishable, peripheral

blood can be examined and the number of foci calculated. Mathematical modelling

can determine the level of exposure to radiation in other parts of the body. (114)

Comparably, it can be used to map the long-term effects of the mutagenicity of

certain carcinogens such as cigarette smoke. As chemotherapies are becoming more

and more targeted, it is necessary to identify which patients will or will not respond

to a certain chemotherapeutic agent.(110) New methods have evolved into directly

analysing patient samples in vitro for responses to different therapies. Part of this

21

examination often includes foci analysis to see if the therapies can induce damage

which the cell cannot repair.

Despite the challenges described here, the use of foci to analyse of DSBs is a well-

used and widely accepted method. It is a good baseline for analysing DNA repair

deficiencies and can give promising results, which can be investigated further in

follow-up experiments.

1.5 The Cell Cycle and Cell Cycle Checkpoints

The cell cycle is a sequence of events in which the cell prepares itself to divide. Before

division, the cell needs to duplicate its entire content to ensure the daughter cells

have enough material to survive.(115) This includes creating copies of all the cells

organelles and doubling its genetic content. The cell cycle can be divided into four

main stages; G1, S, G2 and M. The G1, S and G2 phases are collectively known as

interphase. The M phase, or mitotic phase, can be further split into four sections

known as prophase, metaphase, anaphase and telophase. (116,117) The G1 and G2

phases are described as gap phases as no significant events take place in these

phases, these phases give the cells time to grow. In S-phase the cells synthesize the

nascent DNA while in M phase mitosis occurs. (116) The cell cycle is an important

process which needs to be highly regulated to avoid mistakes which could be

catastrophic to a cell’s survival.

22

Figure 1.7: Depiction of the cell cycle phases

The cell cycle has four main phases; G1, S, G2 and M. In M phase mitosis occurs which is

divided into four events; prophase, metaphase, anaphase and telophase. During the course

of the cell cycle, the cell duplicates all its organelles and genetic information and divides into

an identical sister cell.

1.5.1 Cell Cycle Checkpoints

The cell has adapted a number of checkpoints to ensure the cell cycle does not

progress if mistakes are present. These checkpoints try to identify errors in cell size,

genetic material and nutrient distribution. (118) As these events occur in different

parts of the cell cycle, there are different checkpoints that act at these different

points. DNA damage repair and cell cycle checkpoints are heavily linked. During

interphase, the cell has time to repair any DNA damage that has accumulated. (119)

The cell cycle is regulated primarily by CDKs and cyclins. CDKs are serine/threonine

protein kinases, cyclins are a protein family involved in the activation of CDKs. Certain

DNA repair proteins have the dual role of deactivating CDKs to prevent the cell cycle

from progressing before the damaged DNA could be repaired. (115,120)

23

1.5.2 Cell Cycle Phases and their Checkpoints

In G1, ATM initially phosphorylates H2AX to enhance recruitment of other necessary

repair proteins. (90,97) Following identification of stalled replication, ATM

phosphorylates the mediator of the replication checkpoint (Mrc1) factor which then

autophosphorylates, ensuring checkpoint activation. (121) ATM then activates CHK2

and stabilizes p53 which induces numerous downstream pathway components such

as p21. Accumulated p21 protein inhibits cyclin cdk complexes. (122) ATM enhances

CHK2 activation by phosphorylating threonine 68. Similar to CHK1 activation, this is

followed by numerous autophosphorylation events. ATM and CHK2 prevent S-phase

entry by phosphorylating CDC25A, inhibiting the function of CDK2 which normally

allows the cells to pass through G1. (123)

In S phase, CDK2 is involved in the activation of DNA replication. The intra-S phase

checkpoint is important in maintaining a stable replisome following a block in

replication such a SSB. (121,124) The stability is needed to keep the two sister

chromatid together to facilitate an easy restart of replication following repair.(125)

DSBs disrupt this phase in a number of ways. Initiation of HR causes the activation of

HR pathway components which introduces signalling from ATR and CHK1. ATR and

CHK1 can also be activated as a result of SSB formation following stalled replication

forks. (126) As was described in the previous section, following damage, the resected

ssDNA strand becomes coated in RPA. This catalyses the recruitment of ATR with

ATRIP and the 9-1-1 complex (Rad9-Rad1-Hus1) which is loaded with the help of a

member of the Replication Factor C (RFC) complex. These factors enlist BRCT-domain

proteins which in turn recruit CHK1. (78,79) CHK1 is then phosphorylated, and

thereby activated, by ATR. CHK1 is activated through phosphorylation on serine 317

or serine 245. The CHK1 kinase can then autophosphorylate serine 296 to stabilize its

structure and to create binding sites for CDC25A/B/C. (127) Once activated, CHK1 is

released from ATR so that it may phosphorylate CDK2. CHK1 negatively regulates

CDC25A. Inhibiting CDC25A causes a delay in the cells entry into S-phase. (122,127)

CDC25A is heavily involved in the intra S-phase checkpoint. It functions by

24

dephosphorylating CDK2, which lifts its inhibition and allows it to form the

CDK2/Cyclin E/Cyclin A complex which is necessary for entry into S-phase. (121)

CHK1 also phosphorylates the WEE1 protein kinase and the CDC25C phosphatase

which work in tandem to preserve the inactivation of CDK2.(128) CDK2 also

phosphorylates other key members of the HR repair pathway discussed above. This

helps keep HR repair regulated in S and G2/M phase. Once the damage has been

repaired, CHK1 can be dephosphorylated by type 1 phosphatases and restart the cell

cycle. (129)(106)

WEE1 also plays an important role in G2 checkpoint control. WEE1 inhibits CDK1,

preventing entrance into mitosis. (130) Checkpoint arrest in G2 does not absolutely

depend on p53, p21 or ATM. ATR and CHK1 are crucial for checkpoint activity in G2.

This is in contrary to G1 where ATR and CHK1 are not necessary for checkpoint

stoppage. (95) PALB2 and BRCA2 mediate strand invasion in HR. In their absence, the

maintenance of the G2 pathway fails. This implies that the components in HR have

the dual role of maintaining S and G2 checkpoints activation, likely to ensure time for

an accurate repair of the damaged DNA.(94)(121) CHK1 and CHK2 negatively regulate

CDC25C by phosphorylating serine 216 and activating the G2/M checkpoint. (95,127)

CHK2 phosphorylates and activates several other substrates including the mini-

chromosome maintenance protein complex (MCM) helicase to stabilize the

replisome. The MCM helicase unwinds the double stranded DNA into single stranded

DNA to assist repair. These factors will stay in place until the damage has been

repaired and replication can resume. (125) If the damage is not repaired in a timely

fashion or in an ineffective manner, the replisome may disassociate, and the stalled

replication fork may collapse.

During normal cell division, polo kinase 1 (PLK1) phosphorylates WEE1, leading to its

degradation. WEE1 can then no longer inhibit CDK1 and mitosis ensues. (131) After

activation of the G2/M checkpoint, co-operation of CHK1, CHK2 and WEE1 is needed

to negatively regulate CDK1. (127) ATM and ATR phosphorylate PLK1 after damage,

which inhibits the protein and releases its effects on WEE1. The CDK1 cyclin B

complex inhibition is essential for the progression through the G2/M phase. (94)

25

DDR is not common during mitosis. Although ATM is still activated after DNA damage,

its downstream substrates, such as CHK2, are not. (94)

1.5.3 Cell Cycle Recovery

An abundance of protein modifications are used in checkpoint activations and cell

cycle arrest. The removal of these changes to the proteins are necessary for normal

cell cycle to resume. Yet, as the modifications were introduced in a specific manner,

at a specific time and for a specific reason, their removal should follow the same

specificity. (115,118) As phosphorylation is one of the main activators in checkpoint

arrest, dephosphorylation is important for cell cycle continuation. PP1, PP2A, Wip1

and PP6 are all regulated through DDR mediators. These phosphatases are involved

in the dephosphorylation of inhibitors of cell cycle. Many of these phosphatases can

target the same phosphorylation site, likely to ensure there is always a phosphatase

present which is capable of dephosphorylation, regardless of the stage of the cell

cycle.(121) For example ATM and CHK2 can all be targeted for dephosphorylation by

Wip1, PP1 and PP2A.(121,123)

Cell cycle recovery after checkpoint activation is similar to that of an undisturbed cell

yet has some key differences. (94,116) DDR blocks the activity or degrades many

proteins which promote mitosis. As a result, the cell must restructure its pathways

and develop new entry methods into mitosis. In post-checkpoint activated cells, a

dependency on PLK1 is seen. PLK1 is not usually necessary for entry into mitosis, it

just creates a delay, yet it is crucial in repaired cells. PLK1, in-conjunction with Aurora

A and Bora, activates cyclin-B1-Cdk1 which in turn initiates cell cycle progression and

mitotic entry. (115,118)

26

Figure 1.8: Graphical depiction of the pathway components of the CHK1 and CHK2 checkpoint pathways

The repair proteins and checkpoint proteins are heavily linked. Well established interactions

activate checkpoint regulators following the identification of DNA damage to stall the cell

cycle, giving the cell time to repair.

As the regulation of the cell cycle is so important, any defects in this system can have

catastrophic results on the cell. These can range from genomic instability and

chromosomal rearrangements to cell death. (115,123) The CHK1 pathway is often

upregulated in cancers, in most instances due to the disruption of p53. This makes

CHK1 and its substrates are an obvious target for therapeutic regimens. As many

cancers have defects in either their ability to repair damaged DNA or in their cell cycle

checkpoint pathways, targeting the remaining functioning pathways can induce a

synthetic lethal phenotype. (127,131)

NBS1

MRE11

RAD50ATM

CDK2

G1

CDC25AP

CHK2

P

RAD17

RAD1

RAD9

HUS1 ATR

CHK1

P

CDK1

CDC25CP

WEE1

S G2 M

27

1.5.4 Synthetic Lethality

Synthetic lethality is the phenomenon in which a combination of mutations or defects

lead to the death of a cell.(132) Researchers have exploited this process in the past

few years as they have tried to develop treatments which will have serious effects on

mutated cancerous cells yet leave healthy cells virtually untouched. One of the first

examples of synthetic lethality in use was the development of poly-ADP ribose

polymerase (PARP-1) inhibitors for the treatment of HR deficient cancers. PARP-1

plays a role in the repair of SSBs through the BER pathway and the repair of DSBs

through the NHEJ pathways. When PARP-1 is inhibited, the unrepaired SSBs are

converted to DSBs during replication.(18,132) As the cells now have a defective NHEJ

pathway they are more dependent on HR. Cells which have an inherent deficiency in

this pathway have almost completely lost their ability to repair damaged DNA and so

are pushed into a state of such disarray that the only option available to them is

apoptosis. (133,134)

Figure 1.9: Portrayal of the synthetic lethal phenotype

When both Gene A and Gene B are fully functioning, the cell can function normally and

remains alive. The knockout of either Gene A or Gene B separately is not enough to cause

cell death as the cell can still rely on the remaining gene. When both genes are depleted

however, the cell can no longer function, and cell death ensues.

Gene A Gene A Gene A Gene B Gene B

Gene B Gene B Gene A

Alive Alive Alive Dead

28

1.6 DNA Damage Repair in AML

DNA damage repair (DDR) genes are not commonly mutated in AML. The in-depth

review by Eli Papaemmanuil of the most common mutations in AML did not include

any of the commonly mutated DNA repair genes in cancer as listed by the cBioportal

website https://www.cbioportal.org .(135)

CHEK1 RAD51

CHEK2 ATM

BRCA1 ATR

BRCA2 MDC1

MLH1 PARP1

MSH2 FANCF

Table 1.1: Table of common DNA repair genes in human cancers

This may be the reason behind the lack of research into the role of DNA repair

mechanisms in AML or in most blood cancers. In the TCGA AML dataset, there are no

mutations in any of these genes in the 183 patients of which there is whole exome

available. While no ATM mutations have been found to date in AML, the Cancer Atlas

Research Network analysis of AML show 3 out of 191 patients have ATM copy number

gains. No mutations or copy number variants in ATR, CHK1, CHK2 or any of the other

DNA repair genes have been found in AML. (99)(136)

The lack of mutation in DDR genes does not necessarily mean that there is no DDR

deficiencies (DDRD) in these cells. One of the only known instances of DNA damage

repair genes found mutated in an AML patient are BRCA1/2 mutations from breast

cancers patients which have developed t-AML. (45,49) Most cancers have a basal

level of DDRD as they have failed to repair the mutations that have driven the

oncogenesis. Blood cancers have a high rate of translocations. Translocations are

inherently a sign of poor DNA repair. When DSBs are not repaired or repaired

inefficiently, severe genome instability and chromosomal rearrangements can ensue.

(40) (137)

https://www.cbioportal.org/

29

The aberrant proteins formed as a result of chromosomal translocations can

potentially disrupt the regulation of DDR genes. Studies have demonstrated the

repressive effects the AML1-ETO fusion protein has on DDR genes such as ATM which

is highly important in HR repair. In a similar fashion, the PML-RARA oncofusion

protein can supress a wide variety of DDR genes which affect a multitude of

pathways. (22,50) RPA1, BRCA1 and RAD51C, all of which play a key role in HR repair,

are known to be repressed. (99) DDR genes are not necessarily mutated in AML yet

can be epigenetically silenced. Chromatin regulating genes and epigenetic genes are

commonly mutated in AML and affect the transcription of DDR genes.(29,47)

Recent studies have shown that single nucleotide polymorphisms (SNPs) in DDR

genes are connected to AML pathogenesis. RAD51 mutations have been linked to an

increased risk of developing t-AML. (49) This risk can be increased when combined

with a mutation in the XRCC3 gene. Loss of 5q, which is a common deletion found in

AML, results in the reduced expression of two vital genes of DSB repair, RAD50 and

XRCC4. (18,99)

FLT3 mutations shows a correlation with increased ROS which in turn correlates with

an increased level of DSBs. These high levels of DSBs were also associated with a

lower efficiency of NHEJ and a higher rate of repair errors. Oncogenes such as

FLT3/ITD and MYC have been reported to alter the cells response to DNA damage.

(99) MYC mutated cells maintain a high proliferative state by upregulating genes

involved in the ATM/ATR/CHK1/CHK2 pathways. The hyperactivation of the

ATR/CHK1 pathway is fundamental in protecting from fork collapse. (99)

Although mutations in well-known DDR genes are uncommon in AML, this does not

mean that DNA repair mechanisms are not negatively affected in AML. As these

pathways are complex and multifaceted, it can be difficult to determine how and if

they are affected. A method which could determine if DDR pathways are defective

without relying solely on identifying mutations in key genes would be invaluable.

30

1.7 DNA Damage Repair Deficiency Assay

Until 2014, there was no clinical assay to determine whether a cancerous cell is DDR

deficient. To combat this, Prof. Kennedy’s group from Almac Diagnostics developed

an assay to determine which breast cancer patients would or would not respond to

DNA damaging anthracycline and cyclophosphamide chemotherapy. (138) This

treatment plan is currently in use for many types of breast cancers with highly varied

responses. To assess which patients would or would not benefit from this treatment,

they looked to identify which patients had a defective DDR response. To do this, they

first looked at the repair pathways often disrupted in breast cancers. The FA/BRCA

pathway is defective in approx. 25% of breast cancer patients. As there are many

different components and mechanisms in play in this pathway, it can be difficult to

identify if and how this pathway is disrupted. The DDRD assay aimed to identify

deficiencies in this pathway. The assay is a mathematical equation which gives a

DDRD score. Based on a patients DDRD score, they could either be DDRD negative,

which would predict they do not have a DNA repair deficiency, or they could be

classed as DDRD positive and have a predicted DNA repair deficiency. To create the

assay, gene expression values of patients with FA and gene expression values of

BRCA1 or BRCA2 mutated patients were compared to the gene expression values of

a healthy cohort. The healthy cohort was chosen to include people of the same age,

gender and race to ensure the most accurate comparison. Other factors such as

Estrogen receptor (ER) status was investigated in the BRCA1/2 mutated patients.

After advanced computational analysis, including hierarchical clustering, the 44 most

differential expressed genes were chosen. Based on the effect of the gene on the

assay, each gene was assigned a weight and bias. The bias could be a positive or

negative figure based on the contribution of the gene to the assay score. To create

the equation of the assay the weight and bias of these genes along with the average

expression value of the gene and the median gene expression of the entire data set

are combined with a constant value added at the end.

31

The DDRD score equation is:

∑ w x (ge – b – amedian) + k

To validate the DDRD score the assay was applied to an independent dataset of breast

cancer patients. To determine the cut-off score to differentiate between DDRD

negative and DDRD positive patients, the calculated score from this independent data

set were plotted in a waterfall plot. The scores from the part of the graph which

showed the biggest change in the curve were taken forward as potential cut-off

scores. The prospective scores were used to create multiple Kaplan Meier graphs.

The score from the graph which had the most significant p-value was taken forward

as the cut-off score. The validation showed the score could distinguish between two

groups of breast cancer patients which had a significance difference (p=0.03) in

survival. When treated with anthracycline and cyclophosphamide chemotherapies

the DDRD positive patients with the predicted DNA repair deficiencies had the better

5-year survival. This confirmed the assay had the ability to identify patients with a

deficiency in DNA repair and therefore would benefit from DNA damaging agents.

Whilst not fully elucidated in the original paper, the score is more accurate at

predicting deficiencies in the homologous recombination pathway. The assay was

based of differences between FA and BRCA1/2 mutated patients and these genes are

heavily involved in the HR pathway. (138)

Although this assay was developed for use in breast cancer, its principle should be

transferable to any cancer type. The developers of this assay have already started to

apply this assay to oesophageal cancer. (139)

w = weight

ge = average probe expression

b = bias

amedian

= media expression value

k – constant (0.3738)

32

1.8 DNA Damaging Agents – Mechanisms of Action

1.8.1 Nucleoside Analogues

Nucleoside analogues have been in use clinically for over 50 years, both for the

treatment of cancers and for viral infections. Nucleoside and nucleotide analogues

are the synthetically developed replicas of the endogenous complexes which are

involved in DNA and RNA synthesis among other important mechanisms.(52) These

replicas can disrupt the synthesis and replication of DNA and RNA by incorporating

themselves into the genetic material. The first nucleoside analogue approved for

clinical use was cytarabine. This was approved by the US Food and Drug

Administration (FDA) in 1969 for the treatment of AML. (140) Since then a number

of nucleoside analogues have been developed. While all nucleoside analogues are of

similar design, they do have distinct differences which explains why some compounds

are more effective in different tumour types. (53)

Almost all nucleoside analogues use the same mechanism of active transport by

membrane transporters.(141) They are all phosphorylated by deoxycytidine kinase

(DCK), to the active 5’-triphosphate derivatives. They can also inhibit the enzyme

ribonucleotide reductase (RR) which leads to a reduction in the pools of

deoxyribonucleotides triphosphate (dNTPs). This aids in the incorporation of the

active 5’-triphosphate derivatives into the DNA. (53,142)

Cytosine arabinoside (cytarabine) is a pyrimidine analogue. The cytotoxic effects of

cytarabine are largely due to the inhibition of DNA polymerase and its’ incorporation

into the DNA. Its incorporation causes chain termination which blocks DNA synthesis.

(140,143) Cytarabine is used in the treatment of multiple blood cancers. (142)

Gemcitabine is a pyrimidine analogue that has been used in the treatment of

pancreatic, lung and breast cancer. (144) Gemcitabine has a similar chemical

structure to cytarabine yet has the addition of two fluorine molecules replacing the

hydrogen atoms on the deoxyribose sugar. Gemcitabine’s mechanism of action

involves incorporation into the growing DNA strand and becoming masked by the

33

addition of an endogenous nucleoside. This masks gemcitabine’s presence and

prevents it from being excised by BER. This leads to a termination of the DNA chain.

(144,145) Gemcitabine also reduces the pools of dNTPs. (141) As it is BRCA2 and

RAD51 that inhibits replication fork progression which leads to DSBs and then cell

death, it is a more effective treatment in HR competent cells. (146)

Sapacitabine is a prodrug of the nucleoside analogue CNDAC. (80) Sapacitabine is

converted into its active form by amidases. The mechanism of action of sapacitabine

is significantly different to the mechanism of the previously mentioned nucleoside

analogues cytarabine and gemcitabine. It induces SSBs after incorporation into the

DNA and causing DNA chain termination. These SSBs are converted into DSBs during

S-phase which is predominantly repaired through the HR pathway. (80,147) HR

repairs the damage caused by sapacitabine, therefore, cells which are deficient in this

pathway would be more susceptible to sapacitabine treatment. (141)

1.8.2 Checkpoint Pathway and DNA Repair Protein Inhibitors

Upon activation of CHK1 by ATR, intra-S phase and G2/M checkpoint activity is

initiated. CDC25A is a downstream substrate of CHK1. CHK2 initiates arrests in S-

phase following phosphorylation by ATM. (131) WEE1 regulates the G2/M checkpoint

by maintaining the inactivation of CDK2 following phosphorylation by CHK1. WEE1

can also stabilize the DNA replication fork and is indirectly involved in homologous

recombination.

WEE1 inhibition has shown to be effective in solid tumours both as a monotherapy

or in combination with DNA damaging agents. WEE1i causes a severe decrease in

cellular growth.(148) (118)

CHK1 inhibitor have shown efficacy in ex vivo lung cancer and ovarian cancer studies.

(126) It has also been used in combination with gemcitabine for the treatment of

pancreatic cancer which inhibited tumour growth. (149) It has been demonstrated

that ATM inhibitors sensitize cells to topoisomerase II inhibitors and to radiomimetic

drugs such as bleomycin. (93,150) ATR inhibitors can increase the effects of platinum

agents such as cisplatin and radiation. (126,151) Both ATM and ATR inhibitors are

34

currently in multiple clinical trials both as monotherapies and in combination with

other chemotherapies. (44,150)

All of these substrates have inhibitors which have been used in trial phases. The idea

of synthetic lethality comes into play with inhibiting DNA repair or checkpoint

proteins. (100,133) As the cell can scarcely manage with the loss of one of these

important pathways, impeding another pathway will have detrimental effects. As

these pathways are complex and interact with a wide variety of other substrates, it

is hard to predict the outcome of their inhibition. Nevertheless, as they are all

undoubtably important in the regulation of the cell cycle, their inhibition can only

lead to further problems in a cancerous cell. A great deal of further research is

needed to fully elucidate the effects of disrupting cell cycle checkpoints. (152)

1.9 Genome Editing

Genome editing is a useful tool for investigating cellular mechanisms and disease

processes. While targeted genome editing mechanisms such as zinc finger nucleases

(ZFNs) and transcription activator-like effector nucleases (TALENs) have been used

over the past few decades, other systems have surpassed these slightly out-dated

mechanism.(153,154) The clustered regularly interspaced short palindromic repeats

(CRISPR) CAS9 (CRISPR associated protein 9) system has overtaken these previous

mechanisms as the go-to system for complex genome editing. (155,156) There are

numerous reasons as to why this is the case. The CRISPR CAS 9 method has pros in

both the design and efficiency. Guide RNAs (gRNAs) can be designed to target almost

any region of the genome both quickly and cost-effectively. (155) This system is also

more time-efficient. Multiple genes can be targeted concurrently by introducing

multiple gRNAs into the cell. (157)

1.9.1 CRISPR Cas9 Technology

The CRISPR CAS 9 technology has been adapted from a bacterial mechanism in which

they can seize DNA particles from viruses which have invaded the bacterial cell and

create DNA arrays known as CRISPR segments. These segments can identify the

35

virus’s DNA if it invades again and can use the CAS9 enzyme to chop the DNA into

pieces, deactivating the virus. (158) This process has been cleverly isolated and

adjusted for use in both in vitro and in vivo experiments. The CRISPR guides are

designed so they can recognise areas of the genome which the CAS9 enzyme is then

instructed to cut. (159,160) The CRISPR CAS9 system has two main elements; a guide

containing CRISPR plasmid which can pair with the DNA sequence of which the guide

is targeting and the CAS9 enzyme which can cut the region of interest. (161) The cut

ends can then be ligated through NHEJ or through HR repair depending on what is

available to be used in the cell at the time. (154) A protospacer adjacent motif (PAM)

is present next to the target sequence which allows for initial DNA binding and is

crucial for the target recognition by the Cas9 enzyme. (161,162)

There are however potential off-target effects of using CRISPR CAS9. Mutations can

be introduced in regions of the genome which have a similar sequences to that of the

gRNA designed.(155,163) This can be a difficult problem to overcome when starting

to design the gRNAs. Fortunately, multiple computer programs have been developed

which have scanned the genome and have several guides designed most known

genes. These guide designs have been investigated and the percentage of homology

to other regions of the genome have been deduced and their CG content has been

calculated. (163)

36

Figure 1.10: The CRISPR CAS9 Genome Editing System

The CRISPR CAS9 system consists of CRISPR plasmid containing guide RNA to direct the CAS9

enzyme to a site to cut. The guide RNA can be designed to target almost any area of the

genome.

1.9.2 Pooled CRISPR Screens

Advancements in CRISPR experiments have included the use of pooled CRISPR

screens which target several genes at once, pooling the guides together and

transducing the cells in a single application. (157) This method comes with many

benefits and disadvantages.

There are two types of CRISPR screens; pooled and arrayed. Arrayed CRISPR screens

involve inducing alterations into individual cells and analysing the effects of each

CRISPR guide independently. (164) This allows for multiple different types of analyses

to be done in conjunction with in depth transcriptional analysis such as RNA

sequencing. The individual responses to cellular pressures such as drug responses can

be identified. If carried out on a small scale, targeting only a small number of genes,

this method can be highly beneficial. (165) The downside however is if you want to

produce mutations in a large number of different genes, in individual cells, this

method becomes laborious, time consuming and costly. To counteract this problem,

pooled screens were introduced. Pooled screens involve introducing a large number

of mutations into one cell population at the same time while ensuring only one guide

Target DNA

5’

5’

3’

3’

Guide RNA

5’

3’ Cas9

PAM

37

is inserted per cell. This eliminates the need for multiple transductions and the

separate storage and passaging of cells with different guides. (164,166)

The readout of most pooled screens however lacks enough depth to discern specific

phenotypic attributes from the specific guides. They are limited to identifying

markers such as drug resistance or cell proliferation. Pooled screens analysis can

involve ascertaining any changes which may have occurred in the number of gRNAs

in the pooled sample. (166) Most pooled CRISPR screening methods are not

compatible with single cell RNA sequencing and so a transcriptomic readout is not an

option. In many cases, pooled screens are used as a first line experiment with the

results being validated down the line using more comprehensive methods of analysis.

(167) To combat this, a number of groups have developed methods to combine the

benefits of pooled CRISPR screening and single cell sequencing. New pooled CRISPR

screening techniques have been developed to allow for single cell RNA sequencing to

be performed on the cells transduced with pooled guide library. The addition of poly

adenylated (PolyA) tails into the CRISPR plasmid allows for the detection of the guide

RNAs in single cell sequencing methods. (168,169)

1.9.3 Single Cell RNA Sequencing

Advances in single cell sequencing were also needed in order to keep abreast with

advancements in CRISPR pooling. Although almost all the cells in our body contain

identical genetic material, the transcriptome of these cells can be vastly different. As

there are many different cell types in the body, combined population sequencing only

reflects the average expression data for the pool of cells.(169) It cannot identify

expression levels on a single cell basis and so some cell types or cells may have a high

expression level while others may have a low or non-existent expression level and

yet the expression data would only show the average of these two extremes. Gene

expression can be varied, even within the same cell type. Individual single cell

sequencing can offer a deeper level of understanding into what is happening

transcriptionally within a cell population. (170,171)

Single cell RNA-seq has led to the identification of rare populations of cells that had

not been detected using conventional RNA-seq methods. Single cell RNA-seq can aid

38

in the discovery of cells, and genes, which may be conferring resistance to therapies

in cancers. These methods have also assisted in the validation of HSCs fate and cell

lineage mapping. (8)

Some recent methods allow for mRNA-seq analysis of individual cells, yet they are

limited to profiling a few hundred cells at a time. These cells would first need to be

seperated by a flow cell sorter and then separately amplifying the transcriptome of

each cell. This is a slow and painstaking method and is therefore unable to cope with

the large cell volumes needed for pooled CRISPR screens. (172) The low throughput

methods of single cell sorting which have been in use throughout the past few

decades, include single-cell plating at diluted cell volumes. This is a painstaking

method however and not often used. Fluorescent-activated cell sorting (FACS), based

on the presence of fluorophores integrated into the cell, has been a more accurate

method used in the past few years. (171,173)

Sequestering single cells in microfluidic bubbles has become a popular isolation

method in the recent years. This method has gained popularity due to a number of

reasons. Firstly, it requires a low volume of samples and therefore has a lower cost

involved. It also removes the need for antibodies or fluorophore integration. It is a

highly accurate and regulated method of isolating just a single cell. (174,175)

The Drop-Seq method is a process of isolating single cells for RNA-seq. (176) This

process can probe mRNA expression in thousands of cells by first enclosing single

cells in tiny droplets, which are composed of water and oil. To ensure the identity of

the cell from which the mRNA was isolated can be determined, a barcoding system

was applied. Each cell was paired with a barcoded particle prior to droplet

encapsulation. (176,177)

Oligonucleotide primers were manufactured directly onto the beads with each

oligonucleotide comprising of four parts: a constant sequence to be used as a PCR

priming site; a cell barcode which is different to cell barcodes on other beads; a

Unique Modifier Identifier (UMI) which is different on all primers to help eliminate

PCR duplicates; and an oligo-dT sequence for capturing polyadenylated mRNAs and

priming reverse transcriptase. (176) Currently there are 16,777,216 different

barcodes available to be used simultaneously. These were designed by adding a

39

different DNA base to the oligonucleotides 12 times. Between each of these 12

cycles, the oligonucleotides were split and re-pooled so that 412 possibilities were

created. (168,176)

In Drop-Seq, beads in a lysis buffer are combined with a flow of isolated single cells

suspension in a microfluidic chip that creates an emulsion droplet containing the

bead/cell combo. While still in the droplets the cells are lysed with their mRNA bound

to the oligo-dT carrying beads.(175) After the droplets were ruptured,

complementary DNA (cDNA) and library generation were performed in tandem with

all the other cells. PCR amplifies the libraries and RNA-seq can then be performed.

During analysis the barcodes can help map back the RNA-seq readout to a specific

cell. The ratio of beads to cell is approx. 20:1 to ensure no bead had more than one

cell. As a result, each cDNA molecule has a bead specific (and cell specific) barcode

and UMI. In this method a large quantity of libraries can be generated at a low cost.

It costs just $0.06 per cell to perform Drop-seq run. (176)

Figure 1.11: Flow diagram of the different aspects of the DROPseq Single Cell Sequencing method

Single cells and the beads containing UMI are combined in an emulsification of water and oil.

In the droplet, the cells are lysed, and their mRNA remained bound to the UMI. Library

generation and RNA-seq can be carried collectively. During analysis the barcodes can help

map back the RNA-seq readout to a specific cell. (176)

40

While single cell RNA-seq has many benefits, the analysis of the data obtained can

pose a challenge. It is necessary to first pair the sequence read to the specific cell

before any quality control (QC) or analysis is carried out. In most single cell RNA-seq

methods this is performed by identifying the barcodes present. (169,178)

1.9.4 CROPseq CRISPR Screens

Systems such as PERTURB-seq and CRISP-seq link specific single guide RNAs (sgRNAs)

to known, expressed guide barcodes. This allows for identification of guides following

sequencing (165,168). The CROP-seq method goes a step further as it is not reliant

on the pre-generated libraries that have linked guides and barcodes. CROP-Seq

integrates several different aspects of CRISPR design and single cell RNA sequencing

necessities.(157) The CROP-seq method re-designed the commonly used CRISPR

plasmid, LentiGuide-Puro, so that the gRNA would be incorporated into a (PolyA)

mRNA transcript. A hU6-gRNA cassette was placed in the 3’ long terminal repeat so

that it would it would be in the puromycin-resistance mRNA region that is transcribed

by the RNA polymerase II. It is therefore combatable with poly-A enrichment RNA-

seq methods. This system can then be combined with single-cell RNA-seq methods

such as Drop-seq to allow for a single cell transcriptome readout of a pooled CRISPR

screen. (167) As it is a relatively new technique there are few published studies

highlighting the advantages of this system, yet I believe in the years to come we will

see a rapid increase in the use of this method. We will also see an increase in the

quantity and quality of RNA-seq data.

41

Figure 1.12: Depiction of the re-engineered CROPseq-Guide-Puro plasmid

The hU6-gRNA cassette was added into the region of the 3’ long-terminal repeat. In this

position the gRNA is part of the puro-resistance mRNA transcribed by RNA pol II and is

therefore detectable by scRNA-seq protocols that use poly-A enrichment

To date there are no treatments which target the DNA repair pathways of a cell in

AML. This is likely due to the lack of research completed on the potential of using

these types of therapies on AML patients. It is clear however that DNA repair

mechanisms are likely deficient in AML patients and therefore offer a target which

can be exploited.

5’ LTR hU6 sgRNA EF-1a Puro WPRE 3’LTR

400bp

5’ LTR EF-1a Puro WPRE hU6 sgRNA

hU6 sgRNA

EF-1a Puro WPRE hU6 sgRNA

LentiGuide-Puro

CROPseq-Guide-Puro

gRNA cassette copied during lentiviral

integration Pol II CROPseq mRNA

Genome Editing Genome Editing

Pol III Pol III

Genome Integration

42

1.10 Hypothesis

Detecting DNA repair deficiencies in acute myeloid leukaemia can identify area which

could potentially be targeted therapeutically. Characterising the genes or mutations

which cause the repair deficiency can also assist in the development of new

therapies.

1.11 Aims

1 To use the Almac DDRD Assay to ascertain if there is a subset of AML patients

and AML cell lines which have DDRD

2 To investigate if the DDRD positive cells lines have repair deficiencies using foci

analysis

3 To identify new potential therapies for DDRD positive and DDRD negative

patients based by performing in vitro drug treatments

4 To distinguish which mutations may be causing the DDRD positive phenotype by

using the CROPseq pooled CRISPR screen method

43

Chapter 2: Materials and Methods

2.1 Tissue Culture To ensure sterile conditions, all tissue culture was carried out in a Thermo Electron

Class II biosafety laminar flow hood.

2.1.1 Cell Lines Used:

SKM1: The SKM1 cell line was derived from a peripheral blood sample of a 76- year- old

Japanese man. The patient had presented with Acute Monoblastic Leukaemia (AML

M5) following an earlier diagnosis of Myelodysplastic Syndromes (MDS). The cells

were grown in Roswell Park Memorial Institute (RPMI) 1640 L-glutamine media

supplemented with 10% v/v Foetal Bovine Serum (FBS) and 1% v/v Penicillin-

Streptomycin.

Mutations include Ras point mutations 17p deletions and null p53 status.

HL60: The HL60 cell line was established from the peripheral blood sample of a 36- year-

old female patient with Acute Promyelocytic Leukaemia. This cell line was maintained

in RPM1 1640 L-glutamine media supplemented with 10% v/v FBS and 1% v/v

Penicillin-Streptomycin.

Mutations include an amplified c-myc gene, an NRAS point mutations and a CDKN2A

point mutation.

NB4: The NB4 cell line was derived from a peripheral blood sample of a 23- year-old female

diagnosed with relapsed Acute Promyelocytic Leukaemia. These cells were also

maintained in RPM1 1640 L-glutamine media supplemented with 10% FBS v/v and

1% v/v Penicillin-Streptomycin.

Mutations include KRAS point mutations and the t(15;17) PML-RARA fusion gene.

44

2.1.2 Thawing Frozen Cells A vial of cells was taken from -80°C storage or liquid nitrogen and placed immediately

into a water bath at 37°C to thaw. Once thawed the cells were moved to a 15ml

Falcon Tube containing 9 mL of pre-warmed complete media. The cells were then

centrifuged at 100 x g for 10 minutes and the supernatant was discarded. The pellet

was carefully resuspended in 1 mL of pre-warmed complete media. The resuspended

cells were then transferred to a T25 filtered cap flask containing 4 mL of pre-warmed

complete media. The flask was then left standing in the incubator overnight.

2.1.3 Freezing Cells 1 x 107 cells were placed into a 15 mL Falcon Tube and were centrifuged at 200 x g

for 5 minutes. The supernatant was removed and the cell pellet was resuspended in

1ml of freezing media. The 1 mL was then transferred to a cryotube and carefully

labelled. The tubes were then placed into a freezing chamber and placed in the -80°C

freezer for at least 24 hours. Following this time, the cells were moved to personal

cell storage boxes in either the -80C freezer or the liquid nitrogen storage unit.

Freezing media contains 90% FCS with 10% dimethylsulfoxide (DMSO).

2.1.4 Counting Cells Flasks containing cells were gently shaken to disperse cells that had settled. 50 µL of

cells was added to 10 mL of Casyton Solution in a Casy cup and gently shaken. This

Casy cup was then placed under the probe of the Casy Counter and the specific cell

line function was selected to count the cells. The Casy counter provides information

on both cell quantity and cell viability.

2.1.5 Cell Maintenance Cells were kept in filter capped flasks. The flasks were maintained at 37°C in an

incubator kept at 5% CO2. All cell lines were grown in RPM1 1640 L-glutamine media

supplemented with 10% FBS and 1% Penicillin-Streptomycin (100 µg/ 100 µL

45

Penicillin; 100 µg/ 100 µL Streptomycin) unless otherwise stated. Cell lines were

passaged at least three times a week to maintain them at a concentration of 1 x 106.

2.2 In vitro Drug Treatments:

The cells were counted prior to all drug treatments and known volumes of cells were

used to ensure consistency and reproducibility. To calculate the volume of cells

needed for each experiment the following equation was used:

All drugs used were prepared per the manufacturers recommendations. Drugs were

primarily dissolved in ddH2O where soluble, otherwise were dissolved in anhydrous-

DMSO. Aliquots at small volumes were kept at -80°C and freeze-thaw cycles were

avoided unless stated specifically by the manufacturer that it was safe to do so.

2.2.1 Clonogenic Assays For the clonogenic assays, all cell lines were plated in the same fashion despite not

all cell lines using the same methylcellulose media. A master mix of each cell

line/dose combination was first made. 5000 cells in 50 µL of media were added to

950 µl of methylcellulose media. From this mix 400 µL was added to one of the 8

centre wells of a 24 well plate. The remaining 16 outer-wells were filled with 1 mL of

sterile phosphate-buffered saline (PBS) to prevent the methylcellulose from drying

out.

All clonongenic assay plates were left for 10 days in a sterile incubator at 37°C with

5% CO2. After 10 days, the wells were stained with (Iodonitrotetrazolium chloride)

IDT solution at 8mg/mL and left at least 16 hours before imaging. Colonies were

counted using GelCounter technology. To ensure consistency the same parameters

were used on all counts.

Required Cell Density

Current Cell Density x Volume

46

2.2.2 Flow Cytometry

Samples from cells treated with chemotherapeutic drugs were taken 24hrs, 48hrs

and 72hrs post treatment. A control sample was taken from untreated cells. The

samples were harvested by centrifuging approx. 8 x 105 cells at 200 g for 5 minutes

and resuspending the pellet in 1 mL of cold 70% ethanol and stored at 4C for at least

2 days. The samples were then washed with PBS and resuspended in 500 L of

Propidium Iodide (P.I.) staining solution. P.I. staining solution contains 20 g/mL of

Propidium Iodide; 10 g/mL of RNAse topped up to 500 L with PBS. Samples were

analysed using the LSRII flow cytometer and the BD (Flow-activated cell sorting)

FACSDiva software. Total number of events for each sample was determined and the

percentage of cells in each fraction calculated.

Propidium Iodide Stain

20 g Propidium Iodide

10 g RNAse

To 1 mL with PBS

2.2.3 Growth Curves

8 x 105 cells of each cell line was plated in duplicate. On day one, one set of cells was

treated with the chemotherapeutic drugs of interest and the other set left untreated.

Cell counts were taken at the same time each day for four consecutive days using the

casy counter.

2.2.4 CellTitre Glo Assay

35 L of the CellTitre Glo Reagent (CTG), sourced from Promega U.K., was added to

35 L of cells in a white, flat-bottomed 96 well plate. The cells and reagent were

incubated for 10 minutes at room temperature in the dark while shaking.

Fluorescence was read using the Tecan GENios Microplate Reader. All samples were

normalised to an untreated control.

47

2.3 Protein Analysis

2.3.1 Protein Extraction

Total protein was isolated by lysing cell pellets in RIPA buffer on ice for 30 minutes.

The lysed cells were then centrifuged for 20 minutes at 4°C at 13,200 g. The

supernatant was transferred to a fresh labelled tube and either used immediately or

placed at -20°C for no more than a week.

2.3.2 Protein Quantification Protein lysate was quantified using the Pierce Bicinchoninic Acid (BCA) assay (Thermo

Scientific, Waltham, MA). Reagent A and Reagent B were added together at a ratio

of 50:1. 200 µL of this mixture was added to a Nunc flat bottom 96 well plate

(ThermoFisher Scientific, Denmark). 5 µL of each protein sample was added to a

mixture containing well. The plate was placed at 37°C for 30 minutes. 570nm

wavelength absorbance was measured using a plate reader and software. Protein

concentrations were calculated by plotting the absorbance of samples of known

concentrations.

To prepare the protein lysate, 20-40 µg of protein was added to 3 µL of 10x loading

dye and topped up to 30 µL with ddH2O. The samples were heated to 95°C for 5

minutes to denature the proteins.

2.3.3.Sodium Dodecyl Sulphate (SDS) Polyacrylamide Gel Electrophoresis (PAGE)

The protein samples were all, unless otherwise stated run on a 12% SDS

polyacrylamide gel. These gels were hand-cast using 1.0mm thickness glass plates. A

resolving gel was first poured in to approximately 4/5 of the way up. This was left to

set with a layer of isopropanol on top to ensure a level surface without bubbles. Once

the resolving gel was set, the stacking gel was poured on a comb was inserted to form

lanes.

The gels were placed in sealable gasket in an electrophoresis tank and 1 x running

buffer was poured in to fill the gasket and to cover the wire in the tank to ensure the

48

current could flow. The gels were run for 1 hour at 170 V and 400 mA per gel with

the volts kept constant. The samples will have run evenly to the bottom of the gel.

2.3.4 Transferring Proteins onto a Nitrocellulose Membrane

The proteins were then transferred from the gel to a nitrocellulose membrane. The

gel was placed in a transfer sandwich along with sponges, filter paper and the

nitrocellulose membrane, all of which had been previously soaked in 1 x Transfer

buffer and placed in a specific order. From the black side of the sandwich a soaked

sponge was place followed by a piece of filter paper, the gel, the nitrocellulose

membrane, filter paper and then another sponge. The sandwich was placed in the

transfer tank with the black side of the sandwich facing the black side of the tank. An

ice pack was placed in the tank to prevent the buffer from over-heating and the tank

was filled to the fill-line with 1x transfer buffer. The gel was transferred at 100 V and

400 mA for one hour with the volts kept constant. The nitrocellulose membrane was

placed in Ponceau S Stain briefly to check if the protein had been successfully

transferred. The stain was then washed off in Tris-based saline-tween (TBS-T) with

gentle shaking. The membranes were blocked in either 5% milk or 3% BSA dissolved

in TBS-T for >1 hour at room temperature shaking gently depending on the antibody.

After the membranes were blocked they were placed in the primary antibody of the

protein being investigated and left rolling overnight at 4°C. The antibodies were made

up in either 5% milk or 3% bovine serum albumin (BSA). A full list of antibodies and

their dilutions is shown in a table below.

2.3.5 Protein Immunoblotting

The membrane was washed of excess antibody in TBS-T shaking gently for 5 minutes.

This step was repeated three times. Following the washes membrane was placed in

a secondary antibody for 1 hour at room temperature. The secondary antibody was

chosen based on the species in which the primary antibody was raised. The

membrane was again washed of excess antibody in TBST shaking gently for 5 minutes.

This step was repeated three times. The blots were visualised using Pierce

49

enhanced chemoluminescence (ECL) Western Blotting Substrate and the Syngene

G:Box fluorescent imager and the GeneSys application for analysis.

2.3.5.1 Protein Analysis Buffers

Running Buffer (10x) 30g Tris Base

144g Glycine

10g SDS

Adjust pH to 8.3 using (Hydrochloric acid) HCl

To 1 L with ddH2O

Transfer Buffer (10x) 30g Tris Base

144g Glycine

To 1 L with ddH2O

Tris-buffered saline (TBS) (20x) 48.4g Tris Base

160g sodium chloride (NaCl)

Adjust pH to 7.6 with HCl

To 1 L with ddH2O

TBS-Tween 2 mL Tween

98ml of 20x TBS

1.5M Tris pH 8.8 181.65g Tris base


To 1L with ddH2O

50

1M Tris pH 6.8 121g Tris Base


To 1 L with ddH2O

Loading buffer (non-reducing) (2x) 5 mL 1M Tris pH 7

25 mL 20% SDS

20 mL glycerol

2 mg bromophenol blue

To 100 mL with ddH2O

Loading Buffer (2x)

950 L 2x non-reducing sample loading buffer

50 L -mercaptoethanol

RIPA Buffer 10 mM Tris-Cl (pH8.0)

1 mM EDTA

1% Triton x-100

0.1% sodium deoxycholate

0.1% SDS

140mM NaCl

1mM phenylmethylsulphonyl fluoride (PMSF)

2.4 DNA Damage Repair Deficiency Signature Calculations

2.4.1 Score Calculation for Publicly Available Patient Data Sets

The DDRD scores for >1200 patients were calculated using both microarray and RNA

sequencing data. The reads had all been normalised and converted to Log2. The data

sets were downloaded as text files from the Geo Accession website,

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi, and uploaded onto the Partek

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi

51

Genomic Suite 6.6 program. Before filtering the data set to the required 44 genes,

the median of the entire set was worked out to be used later in the equation. The

data set was then filtered to the 44 genes described in the assay. List of the 44 genes

is in Table 3.1. For the genes which had more than one probe available, the averages

of the probes were determined.

Once the over-all median and the average probe expression had been calculated,

they were entered into the DDRD equation:

∑ w x (ge – b – amedian) + k

Patients were characterised as either DDRD positive or DDRD negative depending on

their DDRD score. A cut-off was determined by plotting the scores of the patients in

a waterfall plot (section 3.3)

2.4.2 Calculating Survival Analysis for Publicly Available Patient Data Sets

Using the Partek Genomic Suite version 6.6, a Kaplan Meier survival curve was

created with death as the event status. p-values were calculated using the log-rank

chi-squared model.

2.4.3 Analysing genetic mutations associated with the DDRD Scores

Pathogenic mutations were determined using the genomic websites ClinVar and

cBioportal. https://www.ncbi.nlm.nih.gov/clinvar/ and https://www.cbioportal.org.

Non-pathogenic or unknown mutations were unused. Any gene that had more than

two mutations and which greater than 50% of those mutations were in DDRD positive

patients were classed as DDRD positive mutations. Significance values were

calculated using the chi-squared significance testing.

w = weight

ge = average probe expression

b = bias

amedian

= media expression value

k – constant (0.3738)

https://www.ncbi.nlm.nih.gov/clinvar/


52

2.4.4 Pathway analysis of DDRD positive associated genes

The mutated genes were put through the Toppgene https://toppgene.cchmc.org

functional enrichment analyser, which created a list of the top most deregulated

pathways associated with mutations in these genes.

2.5 Immunofluorescent detection of cellular proteins

2.5.1 Cytospining cells onto glass slides/coverslips

Approx. 1 x105 cells were spun onto glass slides or coverslips using the Shandon

Cytospin 3 centrifuge spun at 400g for 5 minutes.

2.5.2 Irradiating Cells

Cells were irradiated using the X-RAD iR-225 Series Biological Irradiator (PXi Precision

x-ray, U.K.) according to the machines specific guidelines. All experiments used 2 Gray

(Gy) radiation unless specifically stated otherwise. The cells were irradiated for 3

minutes and 24 seconds at 50cm from the radiation source.

2.5.3 Staining cells for the 53BP1 protein

A wax ring was drawn around the cells spun onto a glass slide. The cells were fixed by

adding 400 µL of fresh 4% PFA to the cells and leaving them on ice for 30 mins. This

was followed by 3x5 minute washes in PBS with very gentle shaking. 400 µL of 0.8%

Triton was added to the cells to permeabilize the cell membrane and they were left

at room temperature for 30 minutes. This was followed by 3x5 minute washes in PBS

with very gentle shaking. The cells were blocked in 400 µL of 3% BSA for 1hr at room

temperature. The primary antibody was added at 1 in 5000 in 3% BSA and left for 1

hour at room temperature. The cells were then washed in PBS for 3x5 minutes,

shaking gently. The secondary antibody conjugated to Alexa Fluor 488 was added to

the cells at a ratio of 1 in1000 and left for 1hr at room temperature. The cells were

washed again in PBS for 3x5 minutes shaking gently. A drop of Dapi Fluoroshield

https://toppgene.cchmc.org/

53

(Sigma-Aldrich, U.K.) was added to the cells a cover slip was gently lowered on top.

The slide was left to set overnight.

All steps after the addition of the secondary antibody was added were carried out in

the dark.

2.5.4 Staining cells for the RAD51 protein

The cells were spun onto small circular cover slips. These coverslips were placed into

wells of a 24-well plate. Ice-cold methanol was added to each well for 20 minutes to

fix the cells. The methanol was removed and ice-cold acetone was added for 1 minute

to permeabilise the cells. The acetone was removed and the cells were washed 3x10

minutes with PBS + 1% FCS. The cells were blocked with PBS +1% FCS for 1 hr. The

primary RAD51 antibody was added at 1 in 15000 made up in PBS +1% FCS. This was

left overnight at 4C. The primary antibody was washed off with PBS + 1% FCS in 3x10

minute washes. All steps after this point were done in the dark.

The secondary antibody containing Alexa Fluor 488 was added to the cells at a ratio

of 1 in1000 and left for 1hr at room temperature. This was washed off with PBS + 1%

FCS in 3x10 minute washes. A drop of Dapi Fluoroshield was placed on a glass slide.

The cover slip containing the cells were carefully placed on top of the Dapi drop, cells

facing down. The cover slip / slide was left to set overnight.

54

Figure 2.1: Depiction of the binding order of Immunofluorescence

2.5.5 Foci Counting

The total number of foci per cell in 100 cells were counted for each cell line and each

time point. The cells were imaged and examined using the Nikon Fluorescent

Microscope.

2.6 CROPSeq CRISPR Screen

2.6.1 Blasticidin and Puromycin Kill Curves

1x106 cells of each cell was plated in 5ml of media in a 6-well plate. Blasticidin or

puromycin doses ranging from 5mg/mL to 0.3125mg/mL were added to the cells.

After 72 hours a CTG assay was performed to determine the lowest dose which

caused all the cells to die.

2.6.2 Creation of a stable CAS9 expressing cell line

Transduction protocols were tested in the SKM1 cell line by using a CAS9- Green

fluorescent protein (GFP) construct. The lentivirus containing the CAS9-GFP protein

was thawed on ice. 1ml of virus was added to 5x105 cells in 1 mL of media in a 24-well

plate. 8mg/mL of polybrene was added to the cells. The plate was gently rocked

forwards and backwards and side-to-side to ensure an even mixture of cells and virus.

Fluorophores Secondary Antibody

Primary Antibody

Protein of Interest

55

16 hours after transfection the viral media was removed and replaced with fresh

RPMI media. 24 hours later a sample was taken to be analysed by flow cytometry.

To create the stable CAS9 expressing cell line the CAS9-GFP transduction protocol

was replicated using the CRISPR-CAS9 lenti-virus. Following the removal of the viral

media, the cells were left for a week to recover before being treated with 5g/mL of

Blasticidin. The cells were treated every second day for a week until all the un-

transduced cells had died.

2.6.3 Testing the stable CAS9 cell line

The stable CAS9 cell line was transduced with CRISPR guides that are known to work

as they have been used previously in the lab. The guides targeted the UBS gene. The

virus was added to 5x105 cells in 1.5 mL of media in a 24-well plate with 8mg/mL of

Polybrene. The viral media was removed after 16hrs and replaced with fresh RPM1.

After one week the cells were treated with 1g/mL of Puromycin every second day

for one week. Protein was extracted from the cells and run on a western blot. The

blots were probed for the protein the guides were targeting.

2.6.4. Guide Design

The guide library was designed by taking appropriate guide sequences form the

Moffat Lab TKOv3library (https://www.ncbi.nlm.nih.gov/pubmed/28655737), which

has been evaluated to be one of the top guide libraries. If the gene wasn't present in

this library, then the Yusa library

(https://www.ncbi.nlm.nih.gov/pubmed/27760321), was used. If the guides were

not present in either of these libraries, the Broad Institute sgRNA design tool was

used. (https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design.

The guides were designed for the genes identified in Chapter 3 as being more

commonly mutated in DDRD positive patients. Multiple control guides were also

designed. Some guides were designed to target known DNA repair genes, others

targeted regions that are not present in the human genome while some just

contained empty vectors.

https://qmail.qub.ac.uk/owa/redir.aspx?C=AttYjzfxR52vZmJdKMVHYV8oX1I4DWexRF3PkpZ04t8eSSfqIzHXCA..&URL=https%3a%2f%2fwww.ncbi.nlm.nih.gov%2fpubmed%2f28655737

https://qmail.qub.ac.uk/owa/redir.aspx?C=S2LjIL1RySr9_VWnCFN7BVb6cu5iiD00AVAsVICBHHAeSSfqIzHXCA..&URL=https%3a%2f%2fwww.ncbi.nlm.nih.gov%2fpubmed%2f27760321

https://qmail.qub.ac.uk/owa/redir.aspx?C=W7UOIQuFw8rWlc-ZQfGoqxlfxzLNcOMtHvYB5H2E2S4eSSfqIzHXCA..&URL=https%3a%2f%2fportals.broadinstitute.org%2fgpp%2fpublic%2fanalysis-tools%2fsgrna-design

56

2.6.5 Plasmid Preparation and Restriction Digest

The CROPseq-Guide-Puro plasmid by Block et al arrived from Addgene as a bacterial

slab. From here a micropipette tip was used to take a scraping of plasmid and was

then placed in sterile LB broth containing 10mg/mL of ampicillin. This was left

overnight shaking at 30C. The following day the LB broth was prepared according to

manufacturer’s instructions using the Qiagen Plasmid Purification Kit to isolate DNA.

Restriction enzyme digestion was used to analyse the resulting product.

The restriction digests were carried on DNA isolated from the plasmid maxi prep

experiment. All enzymes used were FastDigest restriction enzymes from Thermo

Scientific, USA. The enzymes were added to the isolated DNA, FastDigest Green

buffer and nuclease free water at the quantities set forth in the manufacturer’s

instructions. The mixture was incubated at 37C for 1 hour. The resulting product was

run on an agarose gel for visualisation.

The CROPseq-Guide-Puro plasmid was digested using the BsmBI restriction enzyme

to isolate the 8,333 bp backbone fragment. The digested plasmid was run on a 0.8%

gel, loaded with Crystal Violet Loading Dye. The 8,333 fraction was extracted from

the gel without ultraviolet (U.V.) light and purified using the Qiagen Gel Extraction Kit

as per the manufacturer’s instructions.

A 1% agarose gel was used in all other experiments in this thesis. To make a 1%

agarose gel, 1g of agarose was dissolved in 100 mL of TAE by heating gently in a

microwave. For the CROPseq-Guide Puro digestion 100mg/mL of Crystal violet was

added to the dissolved mixture. This mixture was then poured in an electrophoresis

gel mould and a comb was added to create lanes for the DNA. It was then left for an

hour at room temperature to set. Once set the gel was added to an electrophoresis

gel tank and the tank was filled with 1 x TAE with 100mg/mL crystal violet. All DNA

products were mixed with suitable volumes of 6X loading dye and were then added

to the wells. A 10kb GeneRuler DNA ladder Thermo Scientific, was run alongside the

samples. The gel was run at 100V for 30 minutes, or longer if the DNA was not well

seperated.

57

For the isolated plasmid digestion tests, 10 L of SyberSafe DNA Gel Stain Thermo

Scientific, was added to the dissolved mixture. This mixture was then poured in an

electrophoresis gel mould and a comb was added to create lanes for the DNA. It was

then left for an hour at room temperature to set. Once set the gel was added to an

electrophoresis gel tank and the tank was filled with 1 x TAE. All DNA products were

mixed with suitable volumes of 6X loading dye and were then added to the wells. A

10kb GeneRuler DNA ladder Thermo Scientific, was run alongside the samples. The

gel was run at 100V for 30 minutes, or longer if the DNA was not well seperated.

2.6.5.1 Electrophoresis Buffers and LB Buffers:

TAE (50x) 242.2g Tris base

600 mL of ddH2O

57.1 mL glacial acetic acid, added slowly

100 mL 0.5M EDTA, pH 8

To 1 L with ddH2O

DNA loading buffer (6x) 60g glycerol

12 mL 0.5M EDTA pH8

10mg bromophenol blue

To 100 mL with ddH2O

Lithium Borate (LB) Media 10g Tryptone

5g yeast extract

10g NaCl

Adjust pH to 7.4

To 1L with ddH2O

LB-Agar 1 L of LB media

15g of agar

58

2.6.6 Assembly of gRNA-encoding ssDNA oligonucleotides into the vector backbone To assemble the guide-RNAs, the method published in the supplementary info of the

paper by Bock et al was followed. This included the assembly of the guides using the

Gibson assembly method, the electroporation of electro-competent cells and the

plasmid mega-preparation using the Qiagen Plasmid Mega Kit.

2.6.7 Next-generation sequencing of gRNA library

The guide library was amplified in a single PCR reaction with specifically designed

primers. This step adds the sequences required for compatibility with Illumina

Sequencers. The next-generation prep was carried out in accordance with the

methods published in the supplementary info of the paper by Bock et al. The Next-

generation sequencing was carried out by Genewiz who performed Illumina NGS

using their AmpliconEZ service. (https://www.genewiz.com/en-

GB/Public/Services/Next-Generation-Sequencing/Amplicon-Sequencing-

Services/Amplicon-EZ/). The library was sequenced twice to ensure enough reads to

instil confidence in the data. Genewiz guaranteed 50,000 reads per sample which

would only equate to 25 reads per guide so when duplicated this would double to 50

reads.

2.6.8 Lentivirus production and Titration.

The production of the lentivirus was carried out in accordance with the methods

published in the supplementary info of the paper by Bock et al. To test the multiplicity

if infection (MOI) of the virus created, 1x106 cells of the stable CAS9 cell line were

added to wells in a 6 well plate. Different volumes of the virus ranging from 100 L

to 1ml were added to the cells in the 6-well plate. The wells were topped up to a

volume of 3ml with RPMI media. Polybrene was added at 8mg/ml.

1x106 cells of the stable CAS9 cell line were placed in a 15ml falcon tube. Different

volumes of the virus ranging from 100 L to 1 mL were added to the cells in the Falcon

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59

Tubes. One lot of cells were left without virus as a control. The tubes were topped up

to a volume of 3ml with RPMI media. Polybrene was added at 8mg/mL. The cells were

spinoculated at 680g for 45 minutes at 30C. Following this the cells were carefully

moved from the Falcon Tubes to a 6-well plate. 24 hours later the each well was split

into 2 lots and placed in 12-well plates. One lot of cells received Puromycin at

5g/mL, the other set were left untreated. The puromycin-treated cells received

5g/mL of puromycin every 2 days for one week. The MOI was determined when all

the cells in the control well had died. MOI is the number of virions that are added per

cell in an infection. CTG assay was used to assess the viability.

2.6.9 Transduction of the stable CAS9 cells for the CROP-seq screen

The transduction of the stable CAS9 cell line was carried out in the same method has

the MOI determination (section 2.6.8). The virus volume used was that had a 20-40%

transduction rate from the virus titre.

2.6.10 Preparation of Cells for CROP-seq analysis

The cells were prepped for CROP-seq analysis by first cleaning the cell samples using

Miltenyi Biotec’s Dead Cell Removal Kit and the magnetic activated cell sorter (MACS)

cell sorter. The clean-up was carried out according to the manufacturer’s

instructions. Following the dead cell removal, the cells were filtered through cell

mesh to remove cell clumps. Approx. 12,000 cells were resuspended in 0.004% BSA.

2.6.11 Single-cell RNA-seq based Drop-seq

All the Drop-seq preparation and single-cell RNA-seq was carried out by the QUB

Genomic Core Technology Unit.

2.6.11.1. Preparation of beads

To prep the beads they were first washed twice with EtOH and with DROPseq Lysis

Buffer (DLB).The cells were then resuspended in DLB with DTT to give a bead

concentration 120 beads/L.

60

2.6.11.2 Droplet Generation

To generate the droplets the three solutions – the single cell suspension, the bead

suspension and the droplet generation oil – were loaded into separate plastic

syringes. The three syringes were connected to a flow device at different rates to

result in an emulsion drop with a volume of 1nL in a 125M. Droplets were collected

in 1ml aliquots in a 50 mL falcon tube. A magnetic stirrer was used to keep the beads

in suspension.

2.6.11.3 Droplet Breakage

The excess oil was removed from the bead aliquots and 30ml of 6x SSC was added.

600l of perfluoro-1-octanol was added and the tube was inverted vigorously 30

times to break the droplets. The tube was centrifuged for 1 min at 1000 x g. The

samples were kept on ice for the remainder of the protocol. The majority of the

supernatant was removed to just above the oil-aqueous interface and the beads were

again washed in 30 mL of 6x saline sodium citrate SSC. The aqueous layer was

transferred to a new tube and centrifuged at 1000 x g for 1 min. The supernatant was

removed and the bead pellet was transferred to a fresh Eppendorf tube. The pellet

was washed twice in 6x SSC and once in 5x Maxima Human – reverse transcriptase

(H-RT) buffer.

2.6.11.4 Reverse Transcription and Exonuclease I Treatment

Add 200 L of RT mix to the beads and incubate at room temperature for 30 minutes

and then at 42C for 90 minutes. Wash the beads once with 1 mL of TE and SDS, twice

with 1 mL of TE/TW and then one wash in 10 mM Tris pH 7.5. Resuspend the pellet

in 200 L of exonuclease I mix and incubated at 37 oC for 45 minutes. Wash the beads

once with 1 mL of TE and SDS, twice with 1 mL of TE/TW and then once with 1 mL

ddH2O. Resuspend the beads in ddH2O. Amplify 1000 beads at a time by PCR in of 50

L of 1x Hifi HotStart Readymix and 0.8 M Template_Switch_PCR primer.

61

2.6.12 Preparation of Drop-Seq cDNA Library for Sequencing and Sequencing your sample

Vortex Agencourt AMPure XP beads to mix. Add the beads to the PCR amplified

samples in a 0.6 beads to sample ratio. Purify the beads according to manufactures

and elute into 10 L of H20. Run samples on a BioAnalyzer High Sensitivity Chip

according to manfactures instructions. To run on the Illumina MiSeq sequencer, a

library pool in 10 L at 3nM was created for the denaturation. A final dilution of 400

L of sample with 600 L of the HT1 buffer was made.

2.6.13 Read Alignment and Generation of Digital Expression Data

The transcriptome Read2 was tagged with the cell barcode and the UMI gathered

from Read1. These were trimmed for sequencing adaptors and PolyA sequences.

Alignment was carried out using the Spliced transcripts alignment to a reference

(STAR) v2.4.0 to the human reference genome, Ensemble GRCh38. The

DetectBeadSynthesisErrors tool was used to correct for bead synthesis error. Reads

aligning to exons were tagged with their gene name and UMI deduplicated reads per

gene within each cell were used to construct a digital gene expression matrix. This

matrix was converted to transcripts per million and then log2 transformed for further

analysis. To assign gRNAs to cells, the overlap of UMI deduplicated reads to the

specific gRNA sequence within the CROPseq-Guide-Puro plasmid chromosomes were

quantified and cells were assigned with their most abundant gRNA. Abundance was

classified as the most prominent gRNA having at least 3 times more expression than

the sum of the other guides. If the abundance was less than 3 times that of the sum

of the other guides, they were classified as unassigned and excluded from the study.

The normalised and aligned data that had been seperated by the individual guides

was analysed using the DDRD score signature on both Partek Genomic Suite 6.6 and

Microsoft Excel.

62

2.7 Materials

2.7.1 Primary Antibodies:

Antibody Supplier

Rabbit polyclonal anti-53BP1 antibody NovusBio

Rabbit polyclonal anti-RAD51 antibody abcam

Mouse monoclonal anti-GAPDH antibody abcam

2.7.2 Secondary Antibodies:

Antibody Supplier

Rabbit anti-mouse HRP* antibody abcam

Mouse anti-rabbit HRP antibody abcam

Goat anti-rabbit (Alexa Fluor 488) antibody abcam

* horseradish peroxidase

2.7.3 Drugs:

Drug Supplier

Cytarabine Selleckchem U.K.

Gemcitabine Selleckchem U.K.

Sapacitabine Sigma-Aldrich U.K.

Rabusertib (LY2603618) Selleckchem U.K.

MK-1775 (Adavosertib) Selleckchem U.K.

Talazoparib (BN673) Selleckchem U.K.

AZD6738 (ATRi) Selleckchem U.K.

KU55933 (ATMi) Selleckchem U.K.

2.7.4 Restriction Enzymes:

Enzyme Supplier

FastDigest SacII ThermoFisher Scientific U.K.

FastDigest Xhol ThermoFisher Scientific U.K.

FastDigest Ndel ThermoFisher Scientific U.K.

FastDigest EcoRI ThermoFisher Scientific U.K.

63

2.7.5 Cell Media Used:

Media Supplier

RPMI 1640 ThermoFisher Scientific U.K.

DMEM Dulbecco’s Modified ThermoFisher Scientific U.K.

Foetal Bovine Serum (FBS) ThermoFisher Scientific U.K.

MethoCult H4230 STEMCELL Technologies U.K.

MethoCult H4434 Classic STEMCELL Technologies U.K.

Penicillin-streptomycin ThermoFisher Scientific U.K.

* dulbecco’s modified eagle medium

64

Chapter 3:

Assessing the Potential of using the DNA Damage Repair Deficiency Assay in Acute Myeloid Leukaemia

3.1 Introduction:

AML is a progressive disease of the myeloid blood line of hematopoietic cells. A rapid

growth of undifferentiated white blood cells is seen in AML, which causes a reduction

in the number of mature and functioning red blood cells, white blood cells and

platelets. (23,42) AML is most commonly seen in people over the age of 65, but it can

be seen in younger patients. As we have an increasing elderly population the rate of

incidence of AML is set to increase. (42) The therapy landscape for AML has changed

little over the past four decades. Cytarabine is still a standard of care treatment

option for AML. (140) This is despite the fact that there has been little improvement

in survival rates during this time. An alternative therapy regime is needed for patients

who do not respond to Cytarabine treatment. There is also no current method of

determining which patients will or will not respond to Cytarabine. (54)

In 2014, a gene signature was developed by Almac Diagnostic, Mulligan et al, 2014 to

determine which breast cancer patients had a DNA damage repair deficiency (DDRD)

and therefore would be more susceptible to DNA damaging chemotherapeutic

agents such as anthracyclines and cyclophosphamide. Gene expression analysis of

Fanconi Anaemia patients and patients which harboured a BRCA1/2 mutation were

compared to a healthy cohort of people. There were 44 genes which were

differentially expressed between the two groups, these genes were assigned a bias

and a weight. This bias and weight along with median gene expression values and a

constant (k= 0.3738) were adapted into an equation:

∑ w x (ge – b –amedian) + k

w = Weight ge = Average gene expression of probes b = bias amedian = median gene expression value from data set k = constant (0.3738)

65

When the signature is applied to microarray data sets a DDRD score for each

individual patient can be determined. Based on this score, a patient is classified as

DDRD Positive or DDRD Negative. DDRD positive patients are predicted to have a DNA

damage repair deficiency whereas the DDRD negative patients are predicted not to

have a DNA damage repair deficiency. (138)

This gene signature has effectively been used in breast cancer and has subsequently

been trialled in other solid cancers such as oesophageal and ovarian. To date, there

has been no work published on the use of this signature in any type of blood cancer.

Targeting DNA repair pathways has been shown to be an effective therapeutic

strategy in other cancer types yet little to no work has been carried out on targeting

DNA repair pathways in AML. (139)

Publicly available data sets with both clinical and transcriptional information were

used to determine the DDRD score of >600 AML patients. From here further analysis

was carried out including survival curves and mutational analysis.

The DDRD signature was also applied to microarray data of several myeloid blood cell

lines to calculate their score in order to identify a suitable in vitro model.

The aim of this chapter was to determine whether this DDRD signature could be

adapted for use in blood cancers, specifically AML.

66

3.2 Aims and Objectives

To assess the potential use of the DNA Damage Repair Deficiency score in

AML

To use Kaplan Meier analysis to determine if there is a survival difference

between DDRD positive and DDRD negative patients.

To analyse mutations which more commonly occur in the DDRD positive

patients

To investigate the possible pathways involved in the resistance of the DDRD

positive patients to Cytarabine

3.3 Results

3.3.1. Validating the DDRD score Calculation

To ensure the correct use of the DDRD score assay, the signature was first applied to

the breast cancer dataset used in the original DDRD score paper. The score for each

breast cancer patient was re-calculated and then compared to the scores which had

been previously published. (138)

67

Figure 3.1: XY scatter plot of the published breast cancer DDRD scores and the DDRD scores calculated in-house

The in-house calculated DDRD scores were plotted against the Almac published DDRD scores

in an XY graph. R2 value calculated using Prism GraphPad linear equation.

The two calculated scores show a high level of correspondence, R2 = 0.99063.

3.3.2 Applying the DDRD score assay to publicly available AML patient datasets

To test the DDRD score in an AML patient population, publicly available datasets

GSE6891 and TCGA AML were downloaded from the Geo Accession website and the

TCGA website. Aligned and normalised data was converted to Log2 before the DDRD

score could be calculated.

Once the DDRD scores were calculated a cut-off needed to be determined to

segregate the patients into DDRD positive and DDRD negative. To determine a cut-

off, a waterfall plot of all the DDRD scores were graphed.

68

Figure 3.2: Waterfall plot of all the DDRD scores calculated

617 DDRD scores were plotted in a waterfall XY graph ranging from lowest to highest scores.

A separation on the graph could be seen between the 0.2 & 0.3 DDRD scores and the

0.7 & 0.8 DDRD scores. Using the 10 different figures in between these two numbers

as potential cut-offs, several Kaplan Meier survival graphs were calculated. The score

which created the most significant difference between the DDRD positive and DDRD

negative group was taken forward as the cut-off.

0 200 400 6000.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

AML Patients

DD

RD

Sco

re

Waterfall Plot

69

Score p-value

0.2 0.0162

0.22 0.0132

0.24 0.0051

0.26 0.023

0.28 0.00047

0.3 0.0047

0.37 0.0567

0.7 0.178

0.75 0.164

0.8 0.1338

Table 3.2: Table containing the p-values calculated from Kaplan Meier Survival Graphs

The score 0.28 had the most significant difference in survival between the DDRD

negative and DDRD positive groups.

70

3.3.3 Survival Analysis of the AML patient datasets

A Kaplan Meier graph was created using the data from the patients which had survival

information available. 617 AML patients from the GSE6891 and TCGA AML dataset.

Figure 3.3 Kaplan Meier Survival Analysis of DDRD Positive and DDRD Negative Patients up to 5 years

The Kaplan Meier graph was created by plotting the survival in days of the 617 DDRD positive

and DDRD negative patients separately. Graph was cut at the 5 year point. Significance

calculated using log-rank chi-squared analysis on the Partek Genomic Suite version 6.6.

There was a significant difference in survival between the two groups, p=0.00047.

3.3.4 Analysis of the mutations associated with the DDRD positive patients

As the DDRD positive patients had the worst survival, this group of patients were

more heavily focused on for further analysis. The available clinical parameters within

each data sets were assessed for their correlation with DDRD score. Clinical

DDRD Positive DDRD Negative

p-value = 0.00047

71

parameters such as prognostic groups did not seem to be related to the survival

outcome of these patients but cytogenetic risk factors did.

Prognosis p-value(0 vs. 1)

Favourable 0.0661467

Poor 0.0747971

Intermediate 0.451946

Table 3.2: Table containing the p-values relating to the correspondence between prognosis and DDRD score

Significance calculated using log-rank chi-squared analysis on the Partek Genomic Suite version 6.6.

Therefore, it is probable that specific mutations may be contributing to the difference

in outcome of the two patient groups. Only the TCGA dataset had whole exome

sequencing data available and so it was only possible to analyse these 183 patients.

However, the TCGA data set has identified mutations in over 2000 genes. These were

filtered to remove SNPs and large chromosomal deletions so that only 168 known

pathogenic mutations were examined for their segregation between DDRD positive

and negative patient groups. Pathogenic mutations were identified by using the

Clinvar and cBioportal websites, https://www.ncbi.nlm.nih.gov/clinvar/ and

https://www.cbioportal.org. The number of times that each mutation was presented

in the DDRD sub-groups was identified and only those with more than 2 mutations

were considered. There were 28 genes which had more than 2 mutations.

https://www.ncbi.nlm.nih.gov/clinvar/


72

Genes No. of Mutations in

DDRD Positive

No. of Mutations in

Total

% p-Value

PHF6 3 3 100 0.319

ASXL1 4 4 100 0.252

EZH2 3 3 100 0.322

CBL 3 3 100 0.322

TP53 15 16 93.75 0.078

RUNX1 11 12 92 0.182

CEBPA 9 10 90 0.278

U2AF1 7 8 87.5 0.426

NRAS 12 14 85.714 0.363

PTPN11 5 6 83.333 0.657

KIT 5 6 83.333 0.657

RAD21 4 5 80 0.820

SMC3 4 5 80 0.820

STAG2 4 5 80 0.820

DNMT3A 29 37 78 0.669

IDH2 14 18 78 0.828

BZRAP1 3 4 75 0.974

PKD1L2 3 4 75 0.974

FLT3 37 51 72.549 0.537

TET2 12 17 70.588 0.606

TTN 4 6 66.667 0.600

SMC1A 4 6 66.667 0.600

SUZ12 2 3 66.667 0.713

KRAS 4 6 66.667 0.600

TCEAL3 2 3 66.667 0.713

NPM1 31 47 65.957 0.071

WT1 5 9 55.556 0.149

Table 3.3: List of genes more commonly mutated in DDRD positive patients

Table 3.4 shows the genes which are more commonly mutated in DDRD positive patients, the

number of times this gene is mutated, the percentage of these mutations in DDRD positive

patients and the significance values. Significance was calculated using the chi-squared

difference between two squares test.

73

To determine if it was these genes as single mutations were causing the DDRD

positive phenotype or a combination of mutations, a co-occurrence table was

created. A gene on the list which commonly co-occurs with another gene may not

actually be contributing to the DDRD positive phenotype, it may just be a passenger

mutation.

74

PHF6 PHF6

ASXL1 0 ASXL1

TP53 0 0 TP53

CBL 0 0 0 CBL

EZH2 0 0 0 0 EZH2

RUNX1 3 4 0 1 1 RUNX1

CEBPA 0 0 1 0 0 0 CEBPA

U2AF1 1 1 0 0 0 2 0 U2AF1

NRAS 0 0 2 1 1 1 3 1 NRAS

PTPN11 1 0 1 1 0 0 1 0 1 PTPN11

KIT 0 0 0 0 0 0 0 1 0 0 KIT

RAD21 0 0 0 0 0 0 0 0 1 0 0 RAD21

SMC3 1 0 1 0 0 1 1 1 1 1 0 0 SMC3

STAG2 0 0 0 1 0 2 2 0 1 2 0 0 0 STAG2

DNMT3A 1 1 2 0 1 1 3 3 5 3 1 3 6 0 DNMT3A

IDH2 1 4 0 0 0 7 1 1 0 0 0 0 0 1 5 IDH2

PKD1L2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 PKD1L2

FLT3 1 0 0 0 0 1 2 0 2 2 1 2 3 2 21 2 2 FLT3

TET2 0 0 1 1 1 2 1 1 2 0 1 0 1 2 6 0 1 5 TET2

TTN 1 2 0 0 0 4 0 0 0 0 0 0 1 0 2 2 0 2 0 TTN

SMC1A 0 0 0 0 0 0 1 0 0 1 0 0 0 0 3 0 0 4 1 0 SMC1A

SUZ12 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 SUZ12

KRAS 0 1 0 0 1 1 0 1 0 0 0 0 0 0 4 3 0 0 1 0 0 0 KRAS

TCEAL3 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 TCEAL3

NPM1 1 0 0 1 0 0 2 1 5 6 1 3 3 3 28 4 0 27 2 2 4 0 3 0 NPM1

WT1 2 0 0 1 0 2 2 0 1 1 1 0 1 1 2 1 0 5 0 1 1 1 0 0 5

Table 3.4: Co-occurrence mapping of DDRD positive mutations

Co-occurrence maps every gene to all other genes in the table. The graph is colour coded,

the more times the mutations co-occur, the darker the square.

Very few genes co-occur in this graph. Only the well-known co-occurrences of

DNMT3A and FLT3, DNMT3A and NPM1 and FLT3 and NMP1 have a high rate of co-

mutational status.

75

3.3.5 Pathway analysis of DDRD positive mutations

To see the effects the mutations in these genes may be having on the cellular

processes of the DDRD positive patients, the list of the most commonly mutated

genes in the DDRD positive patients was put through pathway analysis software using

the ToppGene website, https://toppgene.cchmc.org .

https://toppgene.cchmc.org/

76

Pathway No. of Genes Mutated

in that Pathway

Negative Regulation of Nucleobase-Containing Compound

Metabolic Process

16

Negative Regulation of Nitrogen Compound Metabolic Process 16

Negative Regulation of Cellular Biosynthetic Process 15

Cell Cycle 15

Regulation of Transcription by RNA Polymerase II 15

Chromosome Organization 14

Negative Regulation of Cellular Macromolecule Biosynthetic

Process

14

Negative Regulation of Macromolecule Biosynthetic Process 14

Negative Regulation of Developmental Process 13

Peptidyl-amino Acid Modification 13

Regulation of Protein Phosphorylation 13

Regulation of Phosphorylation 13

Positive Regulation of Transcription 13

Negative Regulation of Multicellular Organismal Process 12

Negative Regulation of RNA Metabolic Process 12

Protein Modification by Small Protein Conjugation 11

Negative Regulation of Cell Proliferation 9

Nuclear Chromosome Segregation 6

Sister Chromatid Cohesion 5

Hematopoietic Progenitor Cell Differentiation 5

Myeloid Progenitor Cell Differentiation 4

Table 3.5: List of the top 20 most dysregulated pathways in DDRD positive patients

The top 20 most deregulated pathways are shown in Table 3.6. Pathways which are

commonly dysregulated in leukaemogenesis were amongst the top 20 dysregulated

pathways. The most dysregulated pathway however was the pathway “Negative

Regulation of Nucleobase-Containing Compound Metabolic Process”.

77

3.4.6 DDRD Analysis of Myeloid Leukaemic Cell Lines

To have a model to take forward for in vitro analysis of DDRD positivity and DDRD

negativity, the DDRD gene signature was applied to microarray data of several blood

cancer cell lines.

Figure 3.4: DDRD Scores of Myeloid Leukaemic Cell Lines

Using the same cut-off (0.28) as the patient samples, cell lines were classed as either DDRD

positive or DDRD negative.

3.4 Discussion

The DDRD assay has shown to be an effective way of determining DNA damage repair

deficiencies in solid cancers. The results presented in this chapter has suggested that

it also has potential in blood cancers.

3.4.1. Validating the DDRD score Calculation

To ensure the DDRD signature could be calculated correctly, it was first applied to a

dataset of which the scores were already known. As we can see in (Figure 3.1), there

is an extremely close correlation between the in-house calculated scores and the

published scores, R2 = 0.99063. The slight discrepancies between the two sets of

scores is likely due to the three missing probes from the data collected in-house.

KASUMI-1

NALM6

NB4

SKM-1

OCI-A

ML3

U937UT7

K562

HL60

MDS-L

REH

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Cell Lines

DD

RD

Sco

re Cut-Off = 0.28

DDRD Negative DDRD Positive

78

Forty-one of the probes needed for the DDRD signature were available to download

from the supplementary data associated with the original paper, the remaining three

were not possible to download. These three probes are Almac specific probes which

were not present in any datasets assessed in this thesis.

3.4.2 Applying the DDRD score assay to publicly available AML patient datasets

Once the signature could be accurately applied to microarray data, it was used to

analyse publicly available AML datasets representing a combined 617 patients. A cut-

off score was needed to determine which patients would be classed as DDRD positive

and which patients would be classed as DDRD negative. From the waterfall plot in

(Figure 3.2), we can see changes in the curve between scores 0.2 & 0.3 and between

0.7 & 0.8. A variety of values between these figures were taken and used to calculate

Kaplan Meier graphs. The score which produced the most significant Kaplan Meier

graph was taken forward to be used as the cut-off; this figure was 0.28. The score

used in the breast cancer research was 0.37 and so it was the first potential score

used. As can be seen in (Table 3.2) however it was not significant.

3.4.3 Survival Analysis of the AML patient datasets

From the Kaplan Meier graph in (Figure 3.3) there is a significant difference in survival

between the two subgroups. In contrast to what was seen in the paper by Kennedy

et al, 2014 (138) it is the AML DDRD positive patients that have the worse survival,

despite being treated with DNA damaging agents. While at first this was confusing,

further analysis has shed some light on the reasoning behind this difference. Firstly,

the DNA damaging agents used in induction therapy in AML are different to those

that are used to treat breast cancer. As becomes clear in future chapters different

DNA damaging agents function in different ways and are not always more effective

in DNA deficient cells. Secondly, the pathway analysis shown in (Table 3.6)

highlighted that pathways in the DDRD positive patients that may be responsible to

the lack of response to cytarabine. The pathway which is involved in the metabolism

of cytarabine is commonly deregulated in DDRD positive patients. Cytarabine needs

79

to be metabolised into its active form to become cytotoxic to the cell. Nonactivated

forms of cytarabine would not cause much damage to a cell. Furthermore, a paper

published on the response of DNA repair deficient cells to gemcitabine put forward

some interesting facts that may play a role in response of these cell lines to

cytarabine. (146) This will be discussed further in future chapters. I believe that a

combination of these factors has caused the shift in order in the outcome between

DDRD positive and negative patients between the AML graph and the graph from the

DDRD paper.

3.4.4 Analysis of the mutations associated with the DDRD positive patients

The next stage was to identify which gene mutations may be causing the DDRD

positive phenotype a list of the common mutations in DDRD positive patients was

compiled. As can be seen in (Table 3.4), from the 183 patients of which there was

whole exome sequencing available, 27 gene mutations occur more than three times,

with more than 50% of these mutations are in the DDRD positive patients. A number

of genes had just 2 mutations. While 100% of these mutations were in the DDRD

positive patients, their low frequency lessened their significance.

Many of these genes are commonly mutated in AML such as FLT3, NPM1 and

DNMT3A. Interestingly, none of the commonly mutated genes are directly actively

involved in DNA repair processes. A review by J. Murai in 2017 compiled a list of the

most commonly mutated genes in HR deficient cells.(180) None of these genes were

in our list of mutated genes with the exception of the commonly mutated p53 gene.

80

RAD51 DNA2

BRCA1 PALB2

BRCA2 RAD52

RAD51B RAD54B

RPA1 RAD54L

RPA2 FBH1

RAD51C RN

MRE11 BLM

RAD50 BRIP1

NBS1 PCNA

CtIP RAD18

EXO1 p53

Table 3.6: Table of genes most commonly mutated in HR defective cells compiled by J. Murai

DNA repair genes are not commonly mutated in AML. For example, BRCA1/2

mutations occur very infrequently and are often only associated with therapy related

AML following treatment for breast cancer. (45)

There was only one gene, WAC, which had more than 3 mutations and which greater

than 50% of these mutations were in the DDRD negative patients. As a result of this

there was no pathway analysis or co-occurrence mapping carried out on the DDRD

negative patients. Analysis of these DDRD negative patients showed they had a lower

mutation rate than the DDRD positive patients yet had a higher rate of chromosomal

abnormalities such as t(15;17) and 5/7q deletions. DDRD positive patients had a total

of 109 mutations whereas the DDRD negative patients had a total of just 36. The

greater mutational burden in the DDRD positive patients could also be adding to the

poorer survival of these patients.

Co-occurrence mapping showed that very few of the genes from (Table 3.5)

frequently occur together. The most common co-occurrences include DNMT3A &

FLT3, DNMT3A & NPM1 and NPM1 & FLT3. These are all genes are repeatedly found

mutated together in AML patients. The seminal review by Papaemmanuil et al 2017

depicted the common mutations and co-occurrences in AML. (135) The genes which

were found mutated together in our data matches that in the Papaemmanuil which

validates the results we found. The fact that the rest of the mutations are not

frequently found together indicates that they are likely independent causes of the

81

DDRD phenotype in that particular patient. This will be further investigated in

Chapter 6.

3.4.5 Pathway analysis of DDRD positive mutations

Pathway analysis of the mutations found in the DDRD positive patients showed some

highly interesting results. Two of the top most deregulated pathways, shown in

(Table 3.6) were the “Hematopoietic Progenitor Cell Differentiation” and the

“Myeloid Progenitor Cell Differentiation” pathways. These pathways are involved in

the differentiation of myeloid progenitor and hematopoietic progenitor cell

differentiation. Disruptions in these pathways are likely causes of the

leukaemogenesis in these patients. Other pathways such as “Cell Cycle” and

“Negative Regulation of Cell Proliferation” are commonly disrupted in all cancer types

as uncontrolled growth is a hallmark of all cancers. One the most noteworthy

pathways however, was the top most deregulated pathway, “Negative Regulation of

Nucleobase-Containing Compound Metabolic Process”. This pathway is involved in

the control of the metabolism of nucleobase-containing compounds. Cytarabine is a

nucleobase containing compound, therefore disruption of this pathway could be the

reason the DDRD positive patients do not respond as well to cytarabine treatment as

the DDRD negative patients. Many pathways which are highly important in cellular

processes are deregulated by the mutations present in the DDRD positive patients, a

combination of all these disruptions is likely causing the worse prognosis of the DDRD

positive patients.

3.4.6 DDRD Analysis of Myeloid Leukaemic Cell Lines

The DDRD signature was used on the microarray data of the cell lines as it was

important to have an in vitro model going forward. The graph in (Figure 3.4) shows

the DDRD scores of all the cells lines analysed. The cells lines HL60, SKM1 and NB4

were chosen to be used in the in-vitro studies for multiple reasons. Firstly, they are

commonly used, well characterised cell models of AML which were readily available

82

to be used within the lab. Secondly, they are representational of a DDRD positive and

two DDRD negative phenotypes respectively.

3.5 Summary

The DDRD assay has shown to be an effective way of determining DNA damage repair

deficiencies in solid cancers. The results presented in this chapter has suggested that

it may also have a potential in blood cancers, specifically in AML. As a high proportion

of AML patients either do not respond to cytarabine treatments or fail to maintain

remission, it is necessary to have an alternative treatment option for these patients.

AML is a rapidly progressing disease and can cause death within weeks or even days

if left untreated or if the wrong treatment option is used. AML patients therefore do

not have the luxury of undergoing trial-and-error treatments. A biomarker is needed

to determine which patients will and will not respond to cytarabine prior to

treatment initiation. The DDRD assay has effectively segregated AML patients into

two groups with significantly different survival rates. The mutational landscape of the

DDRD positive patient’s highlights molecular areas that could be targeted in future

treatments.

83

Chapter 4

Analysis of DNA Damage Repair Foci in DDRD Positive and DDRD Negative Cell Lines

4.1 Introduction

Foci are accumulations of nuclear proteins, often in response to a stimulus such as

induced DNA damage. (181) As the components of DNA repair pathways converge

for repair, it is possible to microscopically visualize the repair foci using fluorescent

markers.(111) Foci analysis can be used to determine DSB repair efficiency. Repair

machinery aggregates around breaks and so it can be identified as a focus or foci if

stained with a fluorescent marker. (110) These proteins only accumulate around

break sites so the foci only appear when there is unrepaired damage. By tracking the

presence of these foci, the quantity of damage and the speed of repair can be

assessed. (111)

The 53BP1 protein is a marker of double stranded break repair. Although it is a key

effector in the NHEJ pathway, it binds to DSB ends prior to repair pathway choice.

(86,128) Presence of the 53BP1 protein is therefore a reliable indicator of DSBs and

DSB repair.

The RAD51 protein is only in effect in the HR pathway. It is involved in strand

exchange and is only present in an aggregated form in the cell when the cell has

decided on the HR pathway for repair. As such, it is an excellent marker of HR repair.

(181)

Fluorophores can be ligated to antibodies to allow for the detection of the antibody

substrates using methods such as flow cytometry or fluorescence microscope

analysis. (182) Fluorophores can absorb and emit light of a variety of wavelengths

within the absorption and emission spectra. Depending on the fluorophore used

different colours of light can be emitted. The use of multiple fluorophores can map

the presence and quantity of different proteins simultaneously. (183)

84


The aim of this chapter is to identify cell lines which have deficient repair mechanisms

by analysing foci formation through fluorescent microscopy. This aim will be carried

out by inducing DNA damage through irradiation or cytarabine treatment and

staining for markers of DSBs.

4.3 Results

4.3.1 Analysis of 53BP1 foci following 2Gy radiation treatment

To analyse the repair efficiency of DSBs in these cell lines, they were each treated

with 2Gy radiation. 2Gy radiation is a standard level of damage to induce when

assessing DNA breaks. Radiation causes immediate DSBs as both strands are cut

simultaneously. This damaged can be induced during any stage of the cell cycle. (181)

(114)

53BP1 is a marker of DSBs as it binds to DSB ends prior to pathway repair choice. As

such, it is a good marker for the presence of all unrepaired DSBs. The HL60, NB4 and

SKM1 cell lines were treated with 2Gy radiation to induce DSBs. The number of foci

were counted before radiation treatment and 24 hours after 2Gy radiation. The foci

numbers were analysed in a variety of methods to show the different aspects of foci

numbers and represent a more accurate depiction of the differences between the

cell lines.

To account for differences in foci levels, which are present in the cells prior to

treatment, a cell line specific DNA damage cut-off was determined. Each cut-off was

chosen based on the foci counts in the untreated cells. The foci number that was

present in only the top 10% of the cells was used as the DNA damage cut off.

To correct for basal damage, the cut-off value was subtracted from each foci count.

For the HL60 cell line the cut-off was 2 foci per cell, the NB4 cut-off was 4 foci per cell

and the SKM1 cut-off was 5 foci per cell. Therefore, 2 was subtracted from every foci

count from the 100 cells counted per replicate in the HL60 cells. For example, if the

initial foci count of HL60 cell was 4, following correction for basal DNA damage, the

85

foci count would be 2. It was from these corrected values that the average number

of foci per cell was calculated.

Figure 4.1: Graph of Average No. of Foci per Cell

The average number of foci per cell was calculated for each replicate and each cell line 24hrs

following 2Gy radiation treatment. Error bar represent mean +/- SEM, n= 3 biological

replicates. Students t-test statistical analysis; * (p<0.05), ** (p < 0.01), *** (p <0.001), ****

(p <0.0001).

The average foci number gives a good overview of the foci quantity yet it does take

into account any outliers that may be present. For example, a cell line which has quite

a low number of foci per cell yet has one or two cells with a high foci count, can have

an erroneous final average

Each cell line will have a different level of DNA damage and DNA damage repair prior

to inducing damage. This needs to be taken into account. The damage positive cells

were any cells which had more foci than the cut-off value.

Control 2hr 24hr0

5

10

15

No

. o

f F

oci p

er

Cell

Averages

HL60

NB4

SKM1

86

Figure 4.2: Graph of % Damage Positive Cells

The percentage of DNA damage positive cells was calculated by counting the number of cells

which had a greater number of foci than the DNA damage foci cut-off. This was approx. 10%

of the untreated cells. The percentage of damage positive cells was calculated for each

replicate and each cell line 24hrs following 2Gy radiation treatment. Error bar represent

mean +/- SEM, n= 3 biological replicates. Students t-test statistical analysis; * (p<0.05), ** (p

< 0.01), *** (p <0.001), **** (p <0.0001).

Another way of representing the foci counts is in a distribution graph. These graphs

can help identifying outliers which may be shifting the average counts. They show a

more in-depth analysis of the foci dispersal.

Contr

ol2h

r

24hr

0

20

40

60

80

100%

of

Dam

ag

e P

osit

ive C

ells

Damage Positive

HL60

NB4

SKM1

**

****

87

Figure 4.3: Analysis of Foci Dispersal

The number of foci per cell of 100 cells per replicate were counted and plotted on a

distribution graph. The points are plotted with the number of cells on the y-axis which

contain the quantity of foci on the x-axis. n=3 biological repeats. Error bar represent mean

+/- SEM, n= 3 biological replicates.

0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9 >10>1

2>1

5>2

00

20

40

60

80

100

No. Foci per Cell

No

. o

f C

ells

2hr

HL60

NB4

SKM1

88

HL60 v NB4 HL60 v SKM1 NB4 v SKM1

0 n/s n/s n/s

>0 n/s n/s n/s

>1 n/s n/s n/s

>2 n/s n/s n/s

>3 n/s n/s n/s

>4 n/s n/s n/s

>5 n/s n/s n/s

>6 n/s n/s n/s

>7 n/s n/s n/s

>8 n/s n/s n/s

>9 n/s n/s n/s

>10 n/s n/s n/s

>12 n/s n/s n/s

>15 n/s n/s n/s

>20 n/s n/s n/s

Table 4.1: Table of Significance Values from the Foci Distribution Graph

Significance was calculated using multiple students t-test in the Prism GraphPad software.

* (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001), n/s = not significant.

89

Figure 4.4: Analysis of Foci Dispersal





0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10 >12 >1

5>2

00

20

40

60

80

100

24hr

No. Foci per Cell

No

. o

f C

ells

HL60

NB4

SKM1

90

HL60 v NB4 HL60 v SKM1 NB4 v SKM1

0 n/s n/s n/s

>0 n/s n/s n/s

>1 n/s n/s n/s

>2 n/s ** n/s

>3 n/s * n/s

>4 n/s * n/s

>5 n/s *** n/s

>6 n/s *** n/s

>7 n/s *** n/s

>8 n/s *** n/s

>9 n/s *** n/s

>10 n/s ** n/s

>12 n/s ** n/s

>15 n/s ** n/s

>20 n/s ** n/s

Table 4.2: Table of Significance Values from the Foci Distribution Graph



To ensure that the HL60 cell line was not cycling more slowly that the other two cell

lines and therefore having a slower repair time, growth curves were calculated.

91

Figure 4.5 Fluorescent Microscope Images of 2Gy irradiated HL60 cells

The HL60 cells were treated with 2Gy radiation and samples were taken 2hrs and 24hrs

following irradiation. These samples were fixed blocked and stained with ant-53BP1 antibody

and suitable secondary antibody with a conjugated fluorophore. Images were taken on a

Nikon fluorescent microscope using the Dapi and FITC light channels.

HL60 Control 53BP1

IR

HL60 2hr

53BP1

IR

HL60 24hr

53BP1

IR

Merged FITC Dapi

92

Figure 4.6 Fluorescent Microscope Images of 2Gy irradiated NB4 cells

The NB4 cells were treated with 2Gy radiation and samples were taken 2hrs and 24hrs




NB4 2hr

53BP1

IR

NB4 24hr

53BP1

IR

NB4 Control 53BP1

IR

Merged FITC Dapi

93

Figure 4.7 Fluorescent Microscope Images of 2Gy irradiated SKM1 cells

The SKM1 cells were treated with 2Gy radiation and samples were taken 2hrs and 24hrs




SKM1 2hr

53BP1

IR

SKM1 24hr

53BP1

IR

SKM1 Control 53BP1

IR

Merged FITC Dapi

94

Figure 4.8: Growth Curves of HL60, NB4 and SKM1 cell lines

The 8 x 105 cells from each cell line were plated on Day 0. Cell counts were taken at the same

time for the next 4 days. Actual cell count values were plotted. Error bars represent mean +/-

SEM, n= 4 biological replicates.

4.3.2: Analysis of RAD51 foci following 2Gy radiation treatment

The 53BP1 protein is a pan marker for DNA DSBs whereas RAD51 is only present once

the cell has committed to using HR to repair the breaks. It is therefore commonly

used to assess the speed and efficiency of HR repair in cell lines. As the DDRD score

predicts for deficiencies in the HR pathway, the effectiveness of HR repair in the

DDRD cell lines was investigated.

The counts at 4hrs, 24hrs and 48hrs were used. At 4hrs, a vast amount of damage will

have been induced. At 24hrs and 48hrs the damage should be mostly repaired if the

cell is capable of repair.

Day

0

Day

1

Day

2

Day

3

Day

40

10

20

30

40

50C

ells (

1x10

-5)

Growth Curves

HL60

NB4

SKM1

95


The average number of foci per cell was calculated for each replicate and each cell line, 4hrs,

24hrs and 48hrs following 2Gy radiation treatment. Error bar represent mean +/- SEM, n= 3

biological replicates. Students t-test statistical analysis; * (p<0.05), ** (p < 0.01), *** (p

<0.001), **** (p <0.0001).

The damage positive cells were calculated in the same fashion as the 53BP1 cells, by

calculating a cut-off using the untreated cells.

Control 4hr 24hr 48hr0

2

4

6

8

Averages

No

. o

f F

oci p

er

Cell

HL60

NB4

SKM1

**

96





replicate and each cell line 4hrs, 24hrs and 48hrs following 2Gy radiation treatment. Error

bars represent mean +/- SEM, n= 3 biological replicates. Students t-test statistical analysis; *

(p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

To identify any outliers and to give a more an in-depth analysis of foci dispersal,

distribution graphs were plotted.

4hr

24hr

48hr

0

20

40

60

80%

of

Dam

ag

e P

osit

ive C

ells

Damage Positive

HL60

NB4

SKM1

**

**

*

*

97

Figure 4.11: Analysis of Foci Dispersal 4hrs after 2Gy radiation





HL60 v NB4 HL60 v SKM1 SKM1 v NB4

0 ns ns ns

>0 ns ns ns

>1 ns ns ns

>2 ns ns ns

>3 ns ns ns

>4 ns ns ns

>5 ns ns ns

>6 ns ns ns

>7 ns ns ns

>8 ns ns ns

>9 ns ns ns

>10 ns ns ns

>12 ns ns ns

>15 ns ns ns

>20 ns ns ns

Table 4.3: Table of Significance Values from the 4hr Foci Distribution Graph



0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10>1

2>15>20

-20

0

20

40

60

80

100

4hr

No. Foci per Cell

No

. o

f C

ells

HL60

NB4

SKM1

98







0 n/s n/s n/s

>0 n/s n/s n/s

>1 n/s n/s n/s

>2 n/s n/s n/s

>3 n/s n/s n/s

>4 n/s n/s n/s

>5 n/s n/s n/s

>6 n/s n/s n/s

>7 n/s n/s n/s

>8 n/s n/s n/s

>9 n/s n/s n/s

>10 n/s n/s n/s

>12 n/s n/s n/s

>15 n/s n/s n/s

>20 n/s n/s n/s


Significance was calculated using multiple students’ t-test in the Prism GraphPad software.


0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10 >12 >15 >20

0

20

40

60

80

No. Foci per Cell

No

. o

f C

ells

24hr

HL60

NB4

SKM1

99







0 n/s n/s n/s

>0 n/s n/s n/s

>1 ** n/s n/s

>2 n/s n/s n/s

>3 n/s n/s n/s

>4 n/s n/s n/s

>5 n/s n/s n/s

>6 n/s n/s n/s

>7 n/s n/s n/s

>8 n/s n/s n/s

>9 n/s n/s n/s

>10 n/s n/s n/s

>12 n/s n/s n/s

>15 n/s n/s n/s

>20 n/s n/s n/s




0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10 >12 >15 >20

0

20

40

60

80

No. Foci per Cell

No

. o

f C

ells

48hr

HL60

NB4

SKM1

100

Figure 4.14 Fluorescent Microscope Images of 2Gy irradiated HL60 cells

The HL60 cells were treated with 2Gy radiation and samples were taken 2hrs and 24hrs

following irradiation. These samples were fixed blocked and stained with anti-RAD51

antibody and suitable secondary antibody with a conjugated fluorophore. Images were taken

on a Nikon fluorescent microscope using the Dapi and FITC light channels.

HL60 Control RAD51

IR

HL60 4hr

RAD51

IR

HL60 24hr

RAD51

IR

HL60 48hr

RAD51

IR

Merged FITC Dapi

101

Figure 4.15 Fluorescent Microscope Images of 2Gy irradiated NB4 cells

The NB4 cells were treated with 2Gy radiation and samples were taken 2hrs and 24hrs




NB4 Control RAD51

IR

NB4 4hr

RAD51

IR

NB4 24hr

RAD51

IR

NB4 48hr

RAD51

IR

Merged FITC Dapi

102

Figure 4.16 Fluorescent Microscope Images of 2Gy irradiated SKM1 cells

The SKM1 cells were treated with 2Gy radiation and samples were taken 2hrs and 24hrs




SKM1 Control RAD51

IR

SKM1 4hr

RAD51

IR

SKM1 24hr

RAD51

IR

SKM1 48hr

RAD51

IR

Merged FITC Dapi

103

4.3.3 Analysis of RAD51 foci following 1M cytarabine treatment

Cytarabine induces double strand breaks but it is not its primary mechanism of

action. Cytarabine causes stalled replication forks which can be converted to double

stranded breaks if left unrepaired. These stalled forks are created in S-phase and so

have the potential of using RAD51 mediated HR. (53)

The counts at 4hrs, 24hrs and 48hrs were used. At 4hrs, a vast amount of damage

should be induced. At 24hrs and 48hrs the damage should be mostly repaired if the

cell is capable of repair.

The cells in this section were treated with 1M of cytarabine for 1hr. The cells were

then washed with PBS and resuspended in fresh media. The timepoints were taken

4hrs, 24hrs and 48hrs after the cells had been washed. When left unwashed, the

cytarabine continues to cause damage and the cells do not get a chance to repair the

damage.


The average number of foci per cell was calculated for each replicate and each cell line, 4hrs,

24hrs and 48hrs following 1M cytarabine treatment. Error bars represent mean +/- SEM, n=

3 biological replicates. Students t-test statistical analysis; * (p<0.05), ** (p < 0.01), *** (p

<0.001), **** (p <0.0001).

Control 4hr 24hr 48hr0

2

4

6

Averages

No

. o

f F

oci p

er

Cell

HL60

NB4

SKM1

*

**

*

104

To counteract the innate damage levels in the cells, the percentage of damage

positive cells were plotted. The damage positive cells were calculated in the same

fashion as the 53BP1 cells, by calculating a cut-off using the untreated cells.





replicate and each cell line 4hrs, 24hrs and 48hrs following 1M cytarabine treatment. Error

bars represent mean +/- SEM, n= 3 biological replicates. Students t-test statistical analysis; *

(p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

To identify any outliers and to give a more an in-depth analysis of foci dispersal,

distribution graphs were plotted.

4hr

24hr

48hr

0

20

40

60

80

% o

f D

am

ag

e P

osit

ive C

ells

Damage Positive

HL60

NB4

SKM1

105

Figure 4.19: Analysis of Foci Dispersal 4hrs after 1M Cytarabine treatment






0 n/s n/s n/s

>0 n/s n/s n/s

>1 n/s n/s n/s

>2 n/s n/s n/s

>3 n/s n/s n/s

>4 n/s * n/s

>5 n/s * n/s

>6 n/s ** n/s

>7 n/s * n/s

>8 n/s ** n/s

>9 n/s * n/s

>10 n/s * n/s

>12 n/s * n/s

>15 n/s n/s n/s

>20 n/s n/s n/s




0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10>12>15>20

-20

0

20

40

60

80

100

4hr

No. Foci per Cell

No

. o

f C

ells

HL60

NB4

SKM1

106







0 n/s n/s n/s

>0 n/s n/s n/s

>1 n/s n/s n/s

>2 n/s n/s n/s

>3 n/s n/s n/s

>4 n/s n/s n/s

>5 n/s n/s n/s

>6 n/s n/s n/s

>7 n/s n/s n/s

>8 n/s n/s n/s

>9 n/s n/s n/s

>10 n/s n/s n/s

>12 n/s n/s n/s

>15 n/s n/s n/s

>20 n/s n/s n/s




0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>1

0>1

2>15>2

0-20

0

20

40

60

80

100

24hr

No. Foci per Cell

No

. o

f C

ells

HL60

NB4

SKM1

107







0 n/s n/s n/s

>0 n/s n/s n/s

>1 n/s n/s n/s

>2 n/s n/s n/s

>3 n/s n/s n/s

>4 n/s n/s n/s

>5 n/s n/s n/s

>6 n/s n/s n/s

>7 n/s n/s n/s

>8 n/s n/s n/s

>9 n/s n/s n/s

>10 n/s n/s n/s

>12 n/s n/s n/s

>15 n/s n/s n/s

>20 n/s n/s n/s




0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>1

0>1

2>15>2

0-20

0

20

40

60

80

100

48hr

No. Foci per Cell

No

. o

f C

ells

HL60

NB4

SKM1

108

4.3.4 Comparisons in repair effectiveness following 2Gy radiation vs. cytarabine treatment

Figure 4.22: Comparison of damage positive cytarabine and 2Gy radiation treated cells




replicate and each cell line 4hrs, 24hrs and 48hrs following 1M cytarabine or 2Gy radiation

treatment. Error bars represent mean +/- SEM, n= 3 biological replicates. Students t-test

statistical analysis; * (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

4hr24hr

48hr0

20

40

60

80

% o

f Dam

age

Posi

tive

Cel

ls

HL60

Cytarabine

IR**

*

4hr24hr

48hr0

20

40

60

80

% o

f Dam

age

Posi

tive

Cel

ls

NB4

Cytarabine

IR

*

4hr24hr

48hr0

20

40

60

80

% o

f Dam

age

Posi

tive

Cel

ls

SKM1

Cytarabine

IR

109

Figure 4.23: Comparison of cytarabine and 2Gy radiation treated HL60 cells distribution graphs





0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10

>12>15>200

20

40

60

80

100

HL60 4hr

No. Foci per Cell

No

. o

f C

ells

IR

Cytarabine

0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10

>12>15>200

20

40

60

80

100

HL60 24hr

No. Foci per Cell

No

. o

f C

ells

IR

Cytarabine

0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10>12>15>20

0

20

40

60

80

No. Foci per Cell

No

. o

f C

ells

HL60 48hr

IR

Cytarabine

110

Figure 4.24: Comparison of cytarabine and 2Gy radiation treated NB4 cells distribution graphs





0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10>12>15>20

-20

0

20

40

60

80

100

NB4 4hr

No. Foci per Cell

No

. o

f C

ells

IR

Cytarabine

0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10>12>15>20

0

20

40

60

80

No. Foci per Cell

No

. o

f C

ells

NB4 24hr

IR

Cytarabine

0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10>12>15>20

0

20

40

60

80

100

NB4 48hr

No. Foci per Cell

No

. o

f C

ells

IR

Cytarabine

111

Figure 4.25: Comparison of cytarabine and 2Gy radiation treated SKM1 cells distribution graphs





0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10>12>15>20

-20

0

20

40

60

80

100

SKM1 4hr

No. Foci per Cell

No

. o

f C

ells

IR

Cytarabine

0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10>12>15>20

-20

0

20

40

60

80

100

SKM1 24hr

No. Foci per Cell

No

. o

f C

ells

IR

Cytarabine

0 >0 >1 >2 >3 >4 >5 >6 >7 >8 >9>10>12>15>20

0

20

40

60

80

100

SKM1 48hr

No. Foci per Cell

No

. o

f C

ells

IR

Cytarabine

112

HL60 NB4 SKM1

4hr 24hr 48hr 4hr 24hr 48hr 4hr 24hr 48hr

0 n/s **** n/s n/s * n/s n/s n/s n/s

>0 n/s **** n/s n/s * n/s n/s n/s n/s

>1 n/s **** n/s n/s * n/s n/s n/s n/s

>2 * ** n/s n/s * n/s n/s n/s n/s

>3 * ** n/s n/s n/s n/s n/s n/s n/s

>4 n/s ** n/s n/s n/s n/s n/s n/s n/s

>5 n/s n/s n/s n/s n/s n/s n/s n/s n/s







>15 n/s n/s n/s * n/s n/s n/s n/s n/s


Table 4.9: Significance values for the HL60, NB4 and SKM1 cell line comparison distribution graphs



113

Figure 26 Fluorescent Microscope Images of 1M Cytarabine HL60 cells

The HL60 cells were treated with 1M Cytarabine and samples were taken 2hrs and 24hrs




HL60 Control RAD51

Cytarabine

HL60 4hr RAD51

Cytarabine

HL60 48hr RAD51

Cytarabine

HL60 24hr RAD51

Cytarabine

114

Figure 27 Fluorescent Microscope Images of 1M Cytarabine NB4 cells

The NB4 cells were treated with 1M Cytarabine and samples were taken 2hrs and 24hrs




NB4 24hr

RAD51

Cytarabine

NB4 24hr

RAD51

Cytarabine

NB4 48hr

RAD51

Cytarabine

NB4 Control RAD51

Cytarabine

Merged FITC Dapi

115

Figure 28 Fluorescent Microscope Images of 1M Cytarabine SKM1 cells

The SKM1 cells were treated with 1M Cytarabine and samples were taken 2hrs and 24hrs




SKM1 Control RAD51

Cytarabine

SKM1 4hr

RAD51

Cytarabine

SKM1 24hr

RAD51

Cytarabine

SKM1 48hr

RAD51

Cytarabine

Merged FITC Dapi

116

4.4 Discussion

4.4.1 Analysis of 53BP1 foci following 2Gy radiation treatment

The graph in (Figure 4.1) displays the average foci number per cell. The average value

gives a good indication of the repair efficiency of the cell line although it does have

its flaws. Average counts do not take into consideration any foci counts that may fall

well outside the range. These cells, with either extremely high or extremely low foci

counts can alter the average so that it no longer effectively represents what may be

happening in the cell group. In (Figure 4.1( we can see that the DDRD positive HL60

cell line has higher average number of foci per cell than the two DDRD negative cell

lines, NB4 and SKM1 although this has not reached significance. This was the

expected result as the DDRD positive cell line has a predicted repair deficiency. A

higher number of foci, 24hrs after treatment, depicts a repair deficiency. During this

time the cell line should have repaired the defects and the foci number should have

returned to a basal level.(114) In both the DDRD negative cell lines, the average

number of foci has dropped to almost that of the control level.

To correct for different levels of foci which are found in the untreated cells, the DNA

damage positive cells were assessed. The DNA damage cut-off was different for each

cell line as it was calculated based on the foci per cell in the untreated cells. The graph

in (Figure 4.2) exhibits a similar pattern to the averages graph in (Figure 4.1). The

HL60 cell line has a significantly higher level of damage than the two DDRD negative

cell lines, NB4 and SKM1. Even when basal levels are taken into consideration, the

DDRD positive cell line still shows a repair deficiency, as predicted by the DDRD assay.

To account for any outliers and to see the distribution of foci per cell, distribution

graphs were plotted in (Figure 4.3) and (Figure 4.4). In (Figure 4.3) we can see an

approximately equal amount of damage was induced in the cells. From the graph in

Figure 4.4 we can see that at the higher foci quantity, the HL60 cell line has a greater

number of cells with that foci count. As the damage cut-off was not applied to this

data it does not takes into consideration the different innate foci levels.

117

To ensure cell cycle values were not the reason for the slower repair rate in the HL60

cells, growth curves were calculated. From the curves in (Figure 4.8), it is clear that

the HL60 cells do not replicate more slowly than the other cell lines. On the contrary,

it is the fastest cycling cell line. This could be due to a potential checkpoint defect.

Due to this defect, the cells may be allowed to quickly cycle without pausing to repair

damage.

These three graphs all indicate that the HL60 cell line has a DSB repair deficiency. The

average number of foci per cell, the percentage of damage positive cells and the foci

distribution graphs all show a significant difference between the DDRD positive and

DDRD negative cell line. These results confirm what the DDRD assay had predicted.

4.4.2: Analysis of RAD51 foci following 2Gy radiation treatment

The RAD51 analysis show a better picture of HR repair efficiency or deficiency. The

differences between the cell lines is likely to not be as distinct in this assay as HR can

only be used in S and G/2 phase and so this roughly halves the number of cells which

will contain RAD51 foci, even directly after induced damage.(101) As a result, the

average graphs for this experiment does not give as much detail as it did for the

53BP1 experiment. Despite this, the graphs still show a suspected repair defect in the

HL60 cell line.

The graph of foci averages in (Figure 4.9) again show a repair deficiency in the HL60

cell line. At 4hrs there is no significant difference between the three cell lines. This is

not unexpected as the damage has just been induced and the cells have not had

sufficient time to repair the damage. Even at 24hrs and 48hrs there is no significant

difference between the three cell lines. This is likely due to the differences in basal

damage within the cells. In the control column we can see that there are more foci

per cell in the NB4 cell line and significantly more foci per cell in the SKM1 cells.

Damage positive graphs give a better idea of actual foci counts.

The DNA damage positive graph in (Figure 4.11) again suggests that the HL60 cells

have DNA repair deficiency. At 24hrs and 48hrs post induced damage, the percentage

of DNA damage positive cells remains high. It lies between 30% and 40% in the HL60

118

cells both 24hrs and 48hrs post-treatment. At both 24hrs and 48hrs post-treatment,

the percentage of damage positive cells in the NB4 and SKM1 cell lines reverts back

to approx. 10%. This shows how the DDRD negative cell lines have efficiently repaired

their DNA damage.

In the distribution graphs from (Figure 4.11) to (Figure 4.13) we can see there is no

difference in the number of foci per cell between the HL60 DDRD positive cell line

and the DDRD negative cell lines, NB4 and SKM1. At 4hrs there is no significant

difference between the three cell lines. Again, this is not surprising as an equal

amount of damage has been induced in the cells and they have not had time to repair

the damage. At 24hrs and 48hrs, the cells should have had time to repair the majority

of the damage. The number of foci per cell has decreased significantly in all three cell

lines and there is little difference between the cell lines. The lack of difference

between the DDRD negative and DDRD positive cell lines is likely due to the basal

level of damage not being taken into consideration.

4.4.3 Analysis of RAD51 foci following 1M cytarabine treatment

The cytarabine treated cells did not show the same pattern of damage response as

the irradiated cells. This can initially be seen in the average graph (Figure 4.17). There

are significantly less HL60 foci at almost all timepoints that the SKM1 cells. The NB4

cells also have visibly more foci at all timepoints. The average graphs do not show as

accurate a depiction as they do in the 53BP1 section however. As RAD51 can only be

present in S-phase, there will be a substantial volume of cells which will have zero

foci present as they are not in S or G2 phase. Therefore the foci average can change

dramatically based on the cell cycle.

The damage positive graphs in (Figure 4.18) corroborates the results in the averages

graph. At all timepoints there is no significance disparity between the three cell lines.

Furthermore, at the 48hr timepoint the percentage of damage positive cells in the

HL60 cell line has fallen below the level seen in the NB4 and SKM1 cell lines, although

not significantly so. The percentage damage positive values have remained high in all

three cell lines however. At 48hrs the three cell lines have damage positive

119

percentages between 20% and 40%. This is highly unusual in NB4 and SKM1 cell lines

as they usually show competent repair.

The 4hr and 24hr distribution graph does not show a varied dispersal of foci.

The 48hr distribution graph mirrors the pattern of average and percentage positive

graphs with the quantity of foci in the HL60 cells falling significantly below the level

of the SKM1 and NB4 cells. This is quite different to the irradiated cell experiments

where the HL60 cells consistently had a higher level of foci.

4.4.4 Comparisons in repair effectiveness following 2Gy radiation vs. cytarabine treatment

(Figure 4.22) compares the percentage damage positive cells from the radiation

experiment and the cytarabine experiment. In the HL60 cell line, there are approx.

20% more damage positive cells after 4hrs in the IR treated fraction. This is not

surprising as cytarabine creates DSBs much slower than the radiation treatment and

so the repair machinery is not recruited for several hours. (53) This pattern shifts after

24hrs with the IR treated cells containing almost 30% less damage positive cells. They

even out at 48hrs with both comprising of roughly 30% damage positive cells.

In the NB4 section we can again see a decreased damage positive percentage in the

cytarabine treated cells at 4hrs although this is not significant. In a similar fashion to

the HL60 cells, this shifts after 24hrs so that the cytarabine treated cells have almost

20% more damage positive cells than the IR cells. The same is true after 48hrs post

treatment.

The SKM1 cell line do not show as great a difference as the other cell lines. The

cytarabine treated cells consistently have a higher level of damage positive cells yet

these values never reach significance.

(Figure 4.23) compares the distribution graphs of the irradiated treated and

cytarabine treated cells in the HL60 cells. These graphs are comparable to the

damage positive cell graphs. All three timepoints follow the same pattern of the

damage positive graphs with foci levels starting higher in the irradiated cells initially,

then dropping below the cytarabine treated cells at 24hrs before levelling out at

48hrs with both lots of cells containing approx. equal foci numbers. The same is true

120

in the NB4 cell line. At 4hrs there is very little difference in foci levels while at 24hrs

and 48hrs the cytarabine treated cells have more foci per cell

The SKM1 graphs also follow this pattern with very little difference at the 4hr

timepoint and a slight increase in the cytarabine treated cells after 24hrs and 48hrs.

The difference at 24hrs shows a higher foci level in all the cell lines in the cytarabine

treated cells. This is likely due to slower induced damage and a failure to repair. At

48hrs in the HL60 cell line, the percentage of damage positive cells drops dramatically

from approx. 60% at 24hrs to 30%. This is not the case in NB4 and SKM1 cell lines.

Here, the damage percentage remains consistent at 30-40% damage positive.

BRCA2 and RAD51 inhibit replication fork progression following cytarabine treatment

which leads to DSBs. (146) Therefore the RAD51 foci seen may be due to stalled

replication forks rather than DSB repair. As the HL60 cell line is predicted to have a

HR defect, high levels of RAD51 may have been initially recruited to compensate for

a lack of other functional repair components. These cells may have then failed to

convert the damage into DSBs as they do not have a functional HR pathway and so

the level of RAD51 begins to decrease considerably as there are no DSB to repair.

BRCA2 is not necessary for S-phase RAD51 formation and so the HL60 cells may be

deficient in BRCA2 and therefore fail to convert stalled replication forks into DSBs.

(79) Yet, they retain the ability to form RAD51 foci independently and colocalise with

BRCA1 and RPA. (101)Therefore RAD51 foci may still form following radiation

treatment in BRCA2 deficient cells. The DDRD negative cell lines, NB4 and SKM1, have

a functional repair complex and so BRCA2 and RAD51 can function together to create

the DSBs. This may be the reason the RAD51 levels are initially lower in these cell

lines at 24hrs. Once the DSBs are created, the cells require RAD51 to try and repair

the damage and so the levels of RAD51 remain constant. This constant level indicates

that the NB4 and SKM1 cell line are failing to repair the damage induced by

cytarabine.

These results have together implied that the DDRD negative cell lines do not have a

DNA repair defect whereas the DDRD positive cell lines do. From the 2Gy radiation

experiments, which induce classical DSBs, the HL60 cells show diminished repair

121

capabilities. In all of the damage positive graphs from Section 4.4.1 and Section 4.4.2,

the HL60 cell line has consistently more foci 24hrs and 4hrs post treatment which is

indicative of DNA repair failure. In contrast, when allowed time to repair, the NB4

and SKM1 cell lines have reduced their foci numbers to similar levels of the control

cells. This is in stark contrast to the results in Section 4.4.3 when the cells are treated

with cytarabine. In this section, the NB4 and SKM1 cells seem to have failed in their

attempt to repair the damage induced by cytarabine. The levels of damage positive

cells remains high even 48hrs following treatment. Cytarabine clearly has a greater

effect in these cells than the radiation. The HL60 cells have reduced their foci

numbers after 48hrs yet they still remain quite high. As cytarabine requires a

functional repair mechanism to induce damage, its reduced efficiency in the HL60 cell

line is still indicative of a repair defect.

4.5 Chapter Summary

It is clear from these results that further research is needed to elucidate in greater

detail the mechanism in which cytarabine induces DSBs and how these breaks are

repaired. The mammalian mediators of RAD51 are less well understood than their

yeast counter parts. BRCA2 function has been comprehensively studied at the cellular

level but less is known about how it functions in vivo.

A greater understanding of this mechanism may highlight methods of resistance of

sensitivity in AML patients.

122

Chapter 5:

Candidate Drug Treatments of DDRD Positive and DDRD Negative Patients

5.1: Introduction: The therapeutic landscape for AML has remained largely unchanged over the past forty to

fifty years. Cytarabine, which was first approved by the FDA in 1969, is still the first line

induction therapy for AML patients in combination with an anthracycline. (42) However,

survival rates have remained worrying low during this time. Although there has been an

upward trend in the rate of new emerging therapies, there is still a need for more targeted

approach. As many chemotherapies in use today are DNA damaging agents, stratifying

treatments based on the patients’ ability to repair DNA could be a beneficial strategy.

Nucleoside analogues such as cytarabine have been used in leukaemia and lymphoma

treatments for some time now yet there is a wide variety of nucleoside analogues that have

yet to be used in the treatment of these diseases. (52)

Checkpoint inhibitors and DNA repair protein inhibitors have become common place in

recent cancer treatments. Their ability to manipulate the cell cycle and either stall or bypass

repair pathways allows for a build-up of damage which will eventually force a cell into

apoptosis. A number of these agents are currently in use in other cancer types however

none have been used to date in the treatment of AML. (126,149)

Clonongenic assays are a reliable method of analysing cell growth and inhibition of growth.

Compared to colorimetric assays which measure metabolic processes in cells as an

alternative to measuring viability, clonogenic assays give a more realistic picture of the

effects drugs have on cell growth and viability. They do have some of the same trappings

as these viability assays however. Clonogenic assays fail to differentiate between cell death

and inhibition of growth. To combat this, cell cycle analysis on the flow cytometer was

performed. Propidium Iodide stain is a fluorescent intercalating dye which binds to DNA.

(184) The cells in S-phase have more DNA than in G1 and the cells in G2/M will have twice

as much DNA as cells in G1. Cells with more DNA take up more PI and therefore will

fluoresce more brightly. This cell cycle analysis can help distinguish between cell cycle

arrest and apoptosis. (184,185)

123

5.2 Chapter Aim:

The goal of this chapter was to identify new therapeutic targets for AML patients based on

their DDRD characterisation.

5.3 Results

5.3.1 Evaluating the effects of standard of care therapies on DDRD Positive and DDRD Negative cell lines.

As previously stated, cytarabine is the standard of care induction therapy for the vast

majority of AML patients despite the high rate of relapse and low rates of overall survival.

To test the potential of new therapeutic agents, the response of these cell lines to standard

of care treatment was needed as a control. To examine the growth rate of cells following

treatment, the DDRD Negative cell lines, NB4 and SKM1, and the DDRD Positive cell line,

HL60, were treated with varying doses of cytarabine before being plated in Methylcellulose

media for 10 days. The results were normalised to an untreated control well.

124

Figure 5.1: Percentage colony growth of DDRD Negative and DDRD Positive cell lines following treatment with cytarabine.

HL60, NB4 and SKM1 cells were treated with 7 doses of cytarabine in doses ranging from

1nM to 1M. The percentage colony growth was calculated by normalizing the results to an

untreated control. N=2 technical repeats; N=4 biological repeats. Error bar represent mean

+/- SEM, n= 3 biological replicates. * standard error from the mean.

Table 5.1: Table of significance values from the cytarabine treated clonogenic assays

Significance was determined using an unpaired students t-test. Students t-test statistical

analysis; * (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

0 1 2 3 40

50

100

150

200

Log Concentration (nM)

% C

olo

ny G

row

th

Cytarabine

HL60

NB4

SKM1

Conc. M NB4 V HL60 SKM1 v HL60 SKM1 v NB4

1 ns ns ns

5 ns ns ns

10 ns ns ns

50 ns ** ns

100 ns ns ns

500 ns ns ns

1000 ns ns ns

125

To examine the cell cycle profile following treatment, the same cell lines were treated with

1M of cytarabine and samples were taken after 24hrs, 48hrs and 72hrs. A control sample

was also taken. The samples were stained with P.I. and analysed on the flow cytometer.

126

Figure 5.2: Cell cycle analysis of DDRD positive and DDRD negative cell lines following cytarabine

treatment at 1M.

Samples were taken from HL60, NB4 and SKM1 cells 24, 48 and 72 hours after 1M cytarabine

treatment. Percentage of events in each phase is displayed. Significance values in the table to the

left calculated using Tukey’s multiple comparisons test on Prism GraphPad. * (p<0.05), ** (p < 0.01),

*** (p <0.001), **** (p <0.0001).

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

HL60 Cytarabine

G1

S

G2/M

Sub-G1

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

NB4 Cytarabine

G1

S

G2/M

Sub-G1

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

SKM1 Cytarabine

G1

S

G2/M

Sub-G1

Cytarabine

HL60 NB4 SKM1

G1 Significance Significance Significance

Control vs. 24hr *** **** **

Control vs. 48hr ns ns ns


24hr vs. 48hr * **** ns

24hr vs. 72hr ** **** ns

48hr vs. 72hr ns ns ns

S

Control vs. 24hr ** *** ns



24hr vs. 48hr ** **** ns

24hr vs. 72hr *** **** ns


G2/M

Control vs. 24hr ns * ns

Control vs. 48hr ns * *





Sub-G1

Control vs. 24hr ns *** ns

Control vs. 48hr ** ** ns





127

5.3.2 Evaluating the effects of nucleoside analogues on DDRD Positive and DDRD Negative cell lines.

Nucleoside analogues are commonly used in the treatment of haematological

malignancies. As previously described, cytarabine is used in the treatment of AML as well

as other acute leukaemias and lymphomas. Most cytotoxic nucleoside analogues work by

integrating into new forming DNA strands during synthesis leading to stalled replication

forks, fork collapse and cell death. (53) Sapacitabine however has a different mechanism

of action which increases its toxicity in DNA repair deficient cells. Sapacitabine causes single

strand breaks which get converted to double-stranded breaks in S-phase when the cells

begin to replicate. Cells which have a decreased ability to repair or cells which have an S-

phase checkpoint defect are more susceptible to sapacitabine treatment. (80,147)

Gemcitabine is a nucleoside analogue which has a similar mechanism of action to.

cytarabine. Slight differences in uptake and metabolism however increase the cytotoxicity

of gemcitabine.(144) It has proven to be an effective therapeutic agent in the treatment of

solid cancers such as pancreatic and lung carcinomas. Due to the specific mechanism of

action, cells which have homologous repair defects do not respond to gemcitabine

treatment. A functioning HR pathway is necessary to form the stalled fork intermediates

which signal the cell to initiate apoptosis. Without these intermediates, the cells do not

undergo cell death but rather continue to cycle and accumulate damage.(145,146) As cells

with an effective HR pathway respond well to this potent cytotoxic agent, it was considered

as a potential new therapy for DDRD negative patients.

To examine the growth rate of cells following treatment, the DDRD Negative cell lines, NB4

and SKM1, and the DDRD Positive cell line, HL60, were treated with varying doses of

gemcitabine before being plated in Methylcellulose media for 10 days. The results were

normalised to an untreated control well.

128

Figure 5.3: Percentage colony growth of DDRD Negative and DDRD Positive cell lines following treatment with gemcitabine at varying concentrations.

HL60, NB4 and SKM1 cells were treated with 7 doses of gemcitabine in doses ranging from

0.5nM to 10nM. The percentage colony growth was calculated by normalizing the results to

an untreated control. N=2 technical repeats; N=4 biological repeats. Error bar represent

mean +/- SEM, n= 3 biological replicates.

Table 5.2: Table of significance values from the gemcitabine treated clonogenic assays


analysis; * (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

To examine the cell cycle profile following treatment, the same cell lines were also treated

with 5M of gemcitabine and samples were taken after 24hrs, 48hrs and 72hrs. A control

sample was also taken. The samples were stained with P.I. and analysed on the flow

cytometer.

-0.5 0.0 0.5 1.0 1.50

50

100

150


% C

olo

ny G

row

th

Gemcitabine

HL60

NB4

SKM1

Conc. nM NB4 V HL60 SKM1 v HL60 SKM1 v NB4

0.5 ns * ns

0.75 ** * ns

1 ns ** ns

2.5 * ns ns

5 * ** ns

10 *** **** ns

129

Figure 5.4: Cell cycle analysis of DDRD positive and DDRD negative cell lines following gemcitabine

treatment at 5M.

Samples were taken from HL60, NB4 and SKM1 cells 24, 48 and 72 hours after 1M gemcitabine



*** (p <0.001), **** (p <0.0001).

Contr

ol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

HL60 Gemcitabine

G1

S

G2/M

Sub-G1

Contr

ol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

NB4 Gemcitabine

G1

S

G2/M

Sub-G1

Contr

ol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

SKM1 Gemcitabine

G1

S

G2/M

G0

Gemcitabine

HL60 NB4 SKM1


Control vs. 24hr *** ns ***

Control vs. 48hr ** ns ****

Control vs. 72hr ** ns ****




S







G2/M


Control vs. 48hr ns ns *





Sub-G1

Control vs. 24hr **** * ****

Control vs. 48hr **** ** ****

Control vs. 72hr **** ** ****




130



sapacitabine before being plated in Methylcellulose media for 10 days. The results were


Figure 5.5: Percentage colony growth of DDRD Negative and DDRD Positive cell lines following treatment with sapacitabine at varying concentrations.

HL60, NB4 and SKM1 cells were treated with 7 doses of sapacitabine in doses ranging from

0.625nM to 20M. The percentage colony growth was calculated by normalizing the results to

an untreated control. N=2 technical repeats; N=4 biological repeats. Error bar represent


Table 5.3: Table of significance values from the sapacitabine treated clonogenic assays


analysis; * (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

-0.5 0.0 0.5 1.0 1.50

50

100

150

200

250

Log Concentration (uM)

% C

olo

ny G

row

th

Sapacitabine

HL60

NB4

SKM1


0.625 ns ns ns

1.25 ** ns **

2.5 ** ns ns

5 * ns ns

7.5 ** ns *

10 * *** ns

20 ** ns ns

131

To examine the cell cycle profile following treatment, the same cell lines were also

treated with 5M of sapacitabine and samples were taken after 24hrs, 48hrs and

72hrs. A control sample was also taken. The samples were stained with P.I. and

analysed on the flow cytometer.

132


sapacitabine treatment at 5M.

Samples were taken from HL60, NB4 and SKM1 cells 24, 48 and 72 hours after 5M sapacitabine



*** (p <0.001), **** (p <0.0001).

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

HL60 Sapacitabine

G1

S

G2/M

Sub-G1

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

NB4 Sapacitabine

G1

S

G2/M

Sub-G1

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

SKM1 Sapacitabine

G1

S

G2/M

Sub-G1

Sapacitabine

HL60 NB4 SKM1


Control vs. 24hr ** ns ns

Control vs. 48hr ** ns *

Control vs. 72hr ** ns *

24hr vs. 48hr ns ns *



S







G2/M

Control vs. 24hr *** ns ns



24hr vs. 48hr **** ns ns



Sub-G1


Control vs. 48hr **** ns ns

Control vs. 72hr **** ns *


24hr vs. 72hr **** ns *


133

5.3.3 Evaluating the effects of checkpoint inhibitors on DDRD Positive and DDRD Negative cell lines.

DNA repair pathways are heavily linked to checkpoint inhibitors and regulators. Cell cycle

checkpoints can be silenced or activated by signals sent from DNA repair genes.(123) Cells

with DNA repair defects often have altered cell cycles and erroneous checkpoint silencing.

Checkpoint inhibitors have also been in use, clinically, in patients with repair defects. By

impeding checkpoint activation, cells can build up such a level of DNA damage that the cell

is eventually forced into apoptosis. (97,122)

Rabusertib is a CHK1 inhibitor and has been in use in clinical trials for the treatment of solid

tumours including non-small cell lung carcinomas and pancreatic cancers. CHK1 is a serine-

threonine kinase involved in cell cycle activation, DNA repair and apoptosis. (131,150)

WEE1 is also a serine-threonine kinase which plays an important role in cell cycle

progression. WEE1 can mediate entry into mitosis by exerting its effect on CDK1. Inhibiting

CDK1 allows cells to pass through the G2/M checkpoint without pausing for repair. MK-

1775 is an effective WEE1 inhibitor. (152,186)


and SKM1, and the DDRD Positive cell line, HL60, were treated with varying doses of MK-

1775 before being plated in Methylcellulose media for 10 days. The results were


134

Figure 5.7: Percentage colony growth of DDRD Negative and DDRD Positive cell lines following treatment with WEE1i MK-1775 at varying concentrations.

HL60, NB4 and SKM1 cells were treated with 7 doses of WEE1i MK-1775 in doses ranging

from 0.5M to 10M. The percentage colony growth was calculated by normalizing the results to an untreated control. N=2 technical repeats; N=4 biological repeats. Error bar represent mean +/- SEM, n= 3 biological replicates.

Table 5.4: Table of significance values from the WEE1i MK-1775 treated clonogenic assays

Significance was determined using an unpaired students t-test. Students t-test statistical analysis;

* (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).


with 5M of MK-1775 and samples were taken after 24hrs, 48hrs and 72hrs. A control


cytometer.

-0.5 0.0 0.5 1.0 1.50

50

100

150


% C

olo

ny G

row

th

MK-1775

HL60

NB4

SKM1


0.5 ** * ***

1 **** ns ***

2 **** ns ***

4 **** ns ****

6 **** ns ***

8 **** ns ***

10 *** ns **

135

Figure 5.8: Cell cycle analysis of DDRD positive and DDRD negative cell lines following WEE1i MK-

1775 treatment at 5M.

Samples were taken from HL60, NB4 and SKM1 cells 24, 48 and 72 hours after 5M WEE1i MK-1775



*** (p <0.001), **** (p <0.0001).

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

HL60 WEE1i

G1

S

G2/M

Sub-G1

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

NB4 WEE1i

G1

S

G2/M

Sub-G1

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

SKM1 WEE1i

G1

S

G2/M

Sub-G1

WEE1i

HL60 NB4 SKM1








S







G2/M







Sub-G1

Control vs. 24hr ns *** *

Control vs. 48hr ns ** ****

Control vs. 72hr ns ** ****




136

The DDRD Negative cell lines, NB4 and SKM1, and the DDRD Positive cell line, HL60, were

treated with 5M of the WEE1i MK-1775.

Figure 5.9: Growth Curves of DDRD Positive and DDRD Negative Cell Lines both treated and untreated with WEE1i MK-1775

Two lots of 8 x 105 cells of each cell line were plated on day one, one lot was treated with

5M of MK-1775 and the other lot was left untreated. Counts were taken once daily for four

consecutive days. Actual cell counts were plotted.

Day 1

Day 2

Day 3

Day

40

10

20

30

40

Time

No

. o

f C

ells (

10

-5)

HL60 WEE1i

HL60 +

HL60 -

Day

1

Day

2

Day 3

Day

40

10

20

30

40

50

Time

No

. o

f C

ells (

10

-5)

NB4 WEE1i

NB4 +

NB4 -

Day 1

Day 2

Day

3

Day 4

0

10

20

30

40

50

Time

No

. o

f C

ells (

10

-5)

SKM1 WEE1I

SKM1 +

SKM1 -

137

Figure 5.10: CHK1 Gene Expression Value from microarray data of the NB4 and SKM1 Cell Lines

The median gene expression value of each probe was calculated. The gene expression values

of each probe was then normalised to the median of that probe by dividing the expression

value by the median. Normalised gene expression values were plotted. Significance was

determined using an unpaired students t-test. Error bar represent mean +/- SEM. * (p<0.05),

** (p < 0.01), *** (p <0.001), **** (p <0.0001).



rabusertib before being plated in Methylcellulose media for 10 days. The results were


NB4 SKM10.0

0.5

1.0

1.5

Gen

e E

xp

ressio

n V

alu

es

CHEK1

**

138

Figure 5.11: Percentage colony growth of DDRD Negative and DDRD Positive cell lines following treatment with CHK1i rabusertib at varying concentrations.

HL60, NB4 and SKM1 cells were treated with 7 doses of CHK1i rabusertib in doses ranging

from 1nM to 1M. The percentage colony growth was calculated by normalizing the results

to an untreated control. N=2 technical repeats; N=4 biological repeats. Error bar represent


Table 4.2: Table of significance values from the gemcitabine treated clonogenic assay

Table 5.5: Table of significance values from the CHK1i Rabusertib treated clonogenic assays


analysis; * (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).


with 5M of rabusertib and samples were taken after 24hrs, 48hrs and 72hrs. A control


cytometer.

0 1 2 3 40

50

100

150

200


% C

olo

ny G

row

th

Rabusertib

HL60

NB4

SKM1


1 ns ns ns

5 ns * ns

10 ns ** ns

50 ns * ns

100 ns **** ns

500 ns * ns

1000 ns ns ns

139

Figure 5.12: Cell cycle analysis of DDRD positive and DDRD negative cell lines following CHK1i

rabusertib treatment at 5M.

Samples were taken from HL60, NB4 and SKM1 cells 24, 48 and 72 hours after 1M CHK1i

rabusertib treatment. Percentage of events in each phase is displayed. Significance values in

the table to the left calculated using Tukey’s multiple comparisons test on Prism GraphPad.

* (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

Contr

ol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

HL60 Rabusertib

G1

S

G2/M

Sub-G1

Contr

ol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

NB4 Rabusertib

G1

S

G2/M

Sub-G1

Contr

ol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

SKM1 Rabusertib

G1

S

G2/M

G0

HL60 NB4 SKM1




Control vs. 72hr * ns ns




S







G2/M







Sub-G1







140

To examine any potential differences in expression levels of CHK1 in AML patients,

the gene expression values from publicly available datasets were analysed.

Figure 5.13: CHK1 Gene Expression Analysis from 795 patients combined from publicly available data sets; TCGA, GSE6891, GSE49642, GSE62190 and GSE66917.

The median gene expression value of each probe was calculated. The gene expression values of

each probe was then normalised to the median of that probe by dividing the expression value by

the median. Normalised gene expression values were plotted. Error bar represent mean +/- SEM. *

(p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

The gene expression values from the 795 patients analysed showed that CHK1 expression

is decreased in DDRD positive patients.

Neg

ativ

e

Posi

tive

0.0

0.5

1.0

1.5

2.0

Gen

e E

xp

ressio

n V

alu

es

CHEK1

****

141

5.3.4 Evaluating the effects of DNA Repair Protein inhibitors on DDRD Positive and DDRD Negative cell lines.

Exploiting DNA repair pathway defects has shown to be an effective strategy in treating

other cancers but this tactic is not yet in use to treat blood cancers. (150,187) Synthetic

lethality occurs when inhibiting two genes creates a lethal situation for the cell whereas

the inhibition of one gene on its own does not. Cells which have a defect in one of their

repair pathways rely heavily on the remaining avenues of repair. Blocking further repair

channels in these cells causes a build-up of damage which the cells cannot overcome. To

try and induce a synthetic lethal phenotype the cell lines were treated with inhibitors of

DNA repair proteins. (44,188)

Talazoparib is a first-line PARP-1 inhibitor currently in use for gBRCAm HER2-negative

locally advanced or metastatic breast cancer patients. It blocks PARP-related DNA repair in

cells which leads to an accretion of damage eventually resulting in cell death. (18,134)



talazoparib before being plated in Methylcellulose media for 10 days. The results were


142

Figure 5.14: Percentage colony growth of DDRD Negative and DDRD Positive cell lines following treatment with PARP1i talazoparib at varying concentrations.

HL60, NB4 and SKM1 cells were treated with 7 doses of PARP-1i talazoparib in doses ranging

from 1nM to 1M. The percentage colony growth was calculated by normalizing the results



Table 5.6: Table of significance values from the PARP1i talazoparib treated clonogenic assays

Significance was determined using a paired students t-test. Students t-test statistical analysis;

* (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

0 1 2 3 40

50

100

150

200


% C

olo

ny G

ro

wth

Talazoparib

HL60

NB4

SKM1


1 ns ns ns

5 ns ns ns

10 ns ns ns

50 ns ns ns

100 ** ns ns

500 ns ns ns

1000 ** ** ns

143

ATM and ATR are two of the uppermost kinases involved in the DNA repair pathway.

These large serine/threonine kinases respond to DNA damage and signal a cascade of

downstream events which lead to the repair of damaged DNA. (151,189) Similar to the

checkpoint inhibitors, blocking the activity of repair proteins in cells increases the

sensitivity of these cells to DNA damage. ATM inhibitors have been used clinically to

sensitise patients with head and neck squamous cell carcinoma to radiation therapy. ATR

inhibitors are also in use clinically either as single agents or in combinations with DNA

damaging chemotherapies. (99,188)

KU55933 is a potent inhibitor of ATM. AZD6738, developed by AstraZeneca, targets ATR.



KU55933 before being plated in Methylcellulose media for 10 days. The results were


Figure 5.15: Percentage colony growth of DDRD Negative and DDRD Positive cell lines following treatment with ATMi KU55933 at varying concentrations.

HL60, NB4 and SKM1 cells were treated with 7 doses of ATMi KU55933 in doses ranging from

0.156M to 10M. The percentage colony growth was calculated by normalizing the results



-1.0 -0.5 0.0 0.5 1.0 1.50

50

100

150


% C

olo

ny G

row

th

KU55933

HL60

NB4

SKM1

144

Table 5.7: Table of significance values from the ATMi KU55933 treated clonogenic assays


* (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).


with 5M of KU55933 and samples were taken after 24hrs, 48hrs and 72hrs. A control


cytometer.


0.156 ns ns ns

0.3125 ns ns ns

0.625 ns ns ns

1.25 ns ns ns

2.5 ns ns ns

5 ns ns ns

10 ns ns ns

145

Figure 5.16: Cell cycle analysis of DDRD positive and DDRD negative cell lines following ATRi

KU55933 treatment at 5M.

Samples were taken from HL60, NB4 and SKM1 cells 24, 48 and 72 hours after 5M ATRi KU55933



*** (p <0.001), **** (p <0.0001).

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

HL60 KU55933

G1

S

G2/M

Sub-G1

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

NB4 KU55933

G1

S

G2/M

Sub-G1

Con

trol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

SKM1 KU55933

G1

S

G2/M

Sub-G1

KU55933

HL60 NB4 SKM1








S







G2/M







Sub-G1







146

To examine any potential differences in expression levels of ATM in AML patients,

the gene expression values from publicly available datasets were analysed.

Figure 5.17: ATM Gene Expression Analysis from 795 patients in publicly available data sets; TCGA, GSE6891, GSE49642, GSE62190 and GSE66917.



the median. Normalised gene expression values were plotted. Significance was determined using a

paired students t-test. Error bar represent mean +/- SEM. * (p<0.05), ** (p < 0.01), *** (p <0.001),

**** (p <0.0001).



AZD6738 before being plated in Methylcellulose media for 10 days. The results were


Neg

ativ

e

Posi

tive

0.0

0.5

1.0

1.5

Gen

e E

xp

ressio

n V

alu

es

ATM

****

147

Figure 5.18: Percentage colony growth of DDRD Negative and DDRD Positive cell lines following treatment with ATRi AZD6738 at varying concentrations.

HL60, NB4 and SKM1 cells were treated with 7 doses of ATRi AZD6738 in doses ranging from

0.156M to 10M. The percentage colony growth was calculated by normalizing the results



Table 5.8: Table of significance values from the ATRi AZD6738 treated clonogenic assays


* (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).


with 500nM of KU55933 and samples were taken after 24hrs, 48hrs and 72hrs. A control


cytometer.

-1.0 -0.5 0.0 0.5 1.0 1.50

50

100

150

200

250


% C

olo

ny G

row

th

AZD6738

HL60

NB4

SKM1


0.156 ns ns ns

0.3125 ns ** ns

0.625 ns ** ns

1.25 ns * ns

2.5 ns ns ns

5 ns ns ns

10 ns ns ns

148

Figure 5.19: Cell cycle analysis of DDRD positive and DDRD negative cell lines following ATMi

AZD6738 treatment at 500nM.

Samples were taken from HL60, NB4 and SKM1 cells 24, 48 and 72 hours after 5M ATMi

AZD6738 treatment. Percentage of events in each phase is displayed. Significance values in

the table to the left calculated using Tukey’s multiple comparisons test on Prism GraphPad.

* (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

Contr

ol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

HL60 AZD6738

G1

S

G2/M

Sub-G1

Contr

ol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

NB4 AZD6738

G1

S

G2/M

Sub-G1

Contr

ol

24hr

48hr

72hr

0

50

100

150

Time

% o

f C

ells

SKM1 AZD6738

G1

S

G2/M

Sub-G1

AZD6738

HL60 NB4 SKM1








S







G2/M







Sub-G1







149

To examine any potential differences in expression levels of ATR in AML patients, the

gene expression values from publicly available datasets were analysed.

Figure 5.20: ATR Gene Expression Analysis from 795 patients in publicly available data sets; TCGA, GSE6891, GSE49642, GSE62190 and GSE66917.

The median gene expression value of each probe was calculated. The gene expression values

of each probe was then normalised to the median of that probe by dividing the expression

value by the median. Normalised gene expression values were plotted. Error bar represent

mean +/- SEM. * (p<0.05), ** (p < 0.01), *** (p <0.001), **** (p <0.0001).

To examine any potential differences in expression levels of ATR in AML cell lines, the

gene expression values from publicly available datasets were analysed.

Neg

ativ

e

Positiv

e 0.0

0.5

1.0

1.5

Gen

e E

xp

ressio

n V

alu

es

ATR

150

Figure 5.21 ATR Gene Expression Value from microarray data of the HL60, NB4 and SKM1 Cell Lines



the median. Normalised gene expression values were plotted. Significance was determined using a

paired students t-test. Error bar represent mean +/- SEM. * (p<0.05), ** (p < 0.01), *** (p <0.001),

**** (p <0.0001).

NB4

SKM

1

HL-

600.0

0.5

1.0

1.5

Gen

e E

xp

ressio

n V

alu

es

ATR

151

5.4 Discussion

AML patients are in desperate need of new treatment options, especially those over the

age of 65. Cytarabine has been proven over the years to be an ineffective treatment

option for many as can be seen by the high relapse and low survival rate. (140) This

chapter aimed to identify alternative treatment options for AML patients based on their

DDRD score.

5.4.1 Evaluating the effects of standard of care therapies on DDRD Positive and DDRD Negative cell lines.

To carry out this aim I first looked at the response of DDRD Positive and DDRD Negative cell

lines to Cytarabine. As we can see in (Figure 5.1), there is little to no difference in the

response of the cell lines to cytarabine. The flow cytometer analysis in (Figure 5.2) shows a

similar picture. There is a large increase in S-phase after 24hrs in all three cell lines. This

indicates that the cells are attempting to repair the damage caused by the cytarabine

treatment, which generally accumulates in S-phase. There is a moderate increase in cells

undergoing cell death in the HL60 and NB4 cell lines with little to no death in the SKM1 cell

line. The lack of death mirrors what we see in the clonogenics, reinforcing the conclusion

that cytarabine is not an effective therapy in these cell lines.

From the patient data in (Figure 3.3), it was expected that the DDRD negative cell lines

would have a better response to cytarabine however this is not the case. There are

several reasons as to why the result is not as expected. Firstly, a study carried out by Ms.

Rabe from the University of Heidelberg, presented at the 2018 American Society of

Haematology (ASH) annual meeting, showed that cytarabine is one of the top compounds

which shows a differential response when treated in vitro and not co-cultured with bone

marrow stroma cells. When cells are co-treated with stroma cells, they have a more

similar environment to their environment in vivo and therefore would give a more

accurate response. Due to time-constraints it was not possible to replicate this

experiment or any of the other drug treatments in cells which have been co-cultured with

bone marrow stroma cells.

APL cell lines such as the NB4 cells and also the HT93 cell line are less sensitive to

cytarabine treatment than cell lines from other leukaemic lineages. (190) Although the

152

NB4 cell line has the DDRD negative signature which would have predicted a response to

cytarabine, its underlying cytogenetics confers a resistance that the signature cannot

envisage.

5.4.2 Evaluating the effects of nucleoside analogues on DDRD Positive and DDRD Negative cell lines.

Nucleoside analogues are a staple in the treatment of haematological malignancies. Their

efficiency however, varies from patient to patient. From the results shown here in (Figures

5.3 to 5.6) we can see the dramatically different response cell lines can exhibit after

treatment with different antimetabolites. From the gemcitabine graph in (Figure 5.3) we

can see that cell lines with competent repair pathways are more susceptible to treatment

than those cell lines with a predicted deficiency. Gemcitabine works in a similar fashion to

cytarabine yet as we can see from the IC50 values it is far more potent.

The flow cytometer analysis, (Figure 5.4) underpins the clonongenic assay results in the

SKM1 and NB4 cell lines. The large increase in cell death and the almost total reduction of

the other fractions highlights the potency of this drug in these cell lines. There is a large

increase in the sub-G1 fraction in the HL60 cell line also, this is the anthesis of what we see

in the clonogenic assays. It is unclear as to why this large increase is observed in the flow

data and not in the clonogenic assays. The dose used in the flow cytometer experiments

was a higher dose than the IC50 value in the HL60 cell line in the clonogenic assay. This is

quite a large dose and could be the reason so much death is the cell cycle profile.

Gemcitabine has been used in clinics for the treatment of solid tumours for approximately

15 years yet has never been prescribed for the treatment of a haematological malignancy.

In recent years Gemcitabine has been used as a first line therapy for advanced pancreatic

cancer. It has also been shown in pancreatic cancer treatment that DNA repair mechanisms

play an important role in gemcitabine response. (191) This is mirrored in the treatment of

ovarian cancer. (180) Inhibiting replication leads to double-stranded breaks as a result of

stalled fork processing. Petermann et al, 2014 discuss in their paper the phenomenon of

HR mutations protecting the cells from gemcitabine induced cell death. As HR complexes

are needed to convert stalled replication forks into DSBs, it is necessary for cells to have a

functional HR complex in order for gemcitabine to exert its full potential. Without these

153

complexes, the stalled forks remain until new origins are fired or the gemcitabine has been

removed from the cells and fork progression restarts. The formation of DSBs however are

extremely damaging to a cell and often lead to apoptosis. (146)

The results of the drug treatments shown in (Figures 5.3. to 5.4) correspond well to the

results carried out by multiple researchers in different cancer types. I believe the results

here show that Gemcitabine may be a valid treatment option for DDRD Negative AML

patients either as a second-line treatment option or even potentially as an induction

therapy. As its effects are specifically in cells which have HR repair capabilities, it would be

necessary to segregate patients prior to treatment.

Before carrying out this experiment it was believed that sapacitabine would have a greater

effect in the DDRD positive cell line as they have the suspected repair defect. From the

colony growth graph in (Figure 5.5) it is clear that the DDRD positive cell line is significantly

more susceptible to sapacitabine than the DDRD negative cell line. Again, from the cell cycle

analysis, (Figure 5.6) we can see the HL60 cell line responds well to the sapacitabine

treatment. Treatment with Sapacitabine causes double-strand breaks in S-phase. (192)

After 24hrs there is a huge increase in the G2/M fraction of the HL60 cells, this is indicative

of an S-phase checkpoint defect. The cells have cycled through S-phase without stopping

to be repaired. After 48hr and 72hr the G2/M fraction has dramatically decreased. These

cells have accumulated too much damage and have moved into the sub-G1 fraction of

apoptotic cells. The cell cycle profile of the NB4 and SKM1 cells after treatment is still quite

similar to that of the control profiles. There is a moderate increase in the sub-G1 fraction

after 72hr which is to be expected as the dose used was the approx. IC50 of these cells.

They also have a slightly increased s-phase fraction as they are pausing to repair the

damage induced before continuing through the cell cycle.

Studies have shown that deficiencies in HR lead to a heighten sensitivity to sapacitabine.

Sapacitabine incorporation into the DNA does not immediately lead to chain termination.

It interrupts the following round of replication however turning ssDNA breaks into DSBs.

(193) As these double stranded breaks are formed during S-phase, HR plays a huge role in

their repair. (192) Sapacitabine is currently in phase 2/3 clinical trials but the results of

these trials have not been published to date. The trials were also designed on a random

154

basis with no patient segregation based on repair capabilities. (194) If the patients had

been assigned treatment based on these parameters, I believe the outcome of the trials

may be different. Other investigations have shown mixed reviews about Sapacitabine

treatment. Czemerska et al showed there was no significant difference between

monotherapy sapacitabine treatment and cytarabine treatment or sapacitabine in

combination with decitabine. (193) Other papers however have identified markers of

sensitivity to sapacitabine. Pommier et al have suggested that cells with DNA damage

repair gene mutations such as BRCA2 mutations increase sensitivity to sapacitabine

treatment. (195) Reviewing these papers have highlighted the need to segregate patients

prior to sapacitabine treatment.

5.4.3 Evaluating the effects of checkpoint inhibitors on DDRD Positive and DDRD Negative cell lines.

MK-1775 inhibits WEE1 in a dose dependent manner. The downstream effects of blocking

WEE1 include lifting the G2/M checkpoint which allows the cells to cycle without pausing

for repair. (130,186) (131) The clonogenic assays shown in (Figure 5.7) show a marked

difference between the HL60 and SKM1 cell line and the NB4 cell line. The WEE1i exerts

little to no effect on the NB4 cell line with a decrease in colony formation only being seen

at the highest dose. The HL60 and SKM1 cell line however shows a vast decrease in colony

growth formation at all doses. While it was first believed that the inhibitor was causing cell

death in the HL60 cell line, the flow analysis contradicted this idea. From the cell cycle

analysis we can see that there is only a moderate increase in death. To investigate further,

growth curves were calculated. In (Figure 5.9) the growth of the cells with the drug remains

constant, whereas the untreated cells grow exponentially. This suggests that the drug is

causing a stall in growth in these cells rather than initiating cell death. The SKM1 cell line

shows an increase in cell death which is again mirrored in the clonogenic assays. The NB4

cells show a small increase in cell death in the flow analysis. While this death isn’t as clear

in the clonogenic assay, the dose used in the flow analysis is the same dose that caused the

decrease in colony formation in the clonogenic assays.

It is unclear as to why the two DDRD negative cell line responded differently to this

inhibitor. After extensive analysis of the mutational landscape of both cell lines, there was

155

still no obvious mutation or cytogenetic event that could be causing the extreme sensitivity

or resistance to the WEE1i. The NB4 cell line and the HL60 cell line responded in the way

the literature had suggested they would. The SKM1 cell line has no known mutations that

would cause it’s heightened sensitivity to WEE1i. Recent papers suggested that inhibition

of CHK1 can sensitize cells to WEE1i treatment. (131) (152) Examination of the expression

levels of CHK1 in these two cell lines may have shed some light on the difference in drug

responses. The SKM1 cell line had a significantly lower (p=0.0048) expression of CHK1 than

the NB4 cell line, (Figure 5.10.) This is potentially the reason behind the difference in

response in the two DDRD negative cell lines.

Although there is little information available on the cellular aspects which confer sensitivity

to WEE1 inhibitors, there is one common characteristic which seems to appear in most

papers about WEE1 inhibitors; losses in the HR pathway. (93,130,196)

Although the SKM1 cell line did not respond in the way the literature would have

suggested, I still believe that WEE1 inhibitors could be a beneficial therapeutic option for

AML patients. Again, I believe it would be necessary to segregate patients based either on

their repair capabilities and their levels of CHK1. The data put forward in this chapter would

suggest that only the DDRD positive patients would benefit from WEE1 inhibition yet,

research has indicated that inhibiting CHK1 in conjunction with inhibiting WEE1 could be

advantageous in the DDRD negative patients.

Rabusertib is an effective CHK1 inhibitor. Blocking CHK1 activity leads to losses in regulation

of the S and G2/M checkpoints. The SKM1 cells show a marked sensitivity to rabusertib in

the clonogenic assay results in (Figure 5.11). The DDRD negative signature shows a

decreased CHK1 gene expression as can be seen in (Figure 5.13). When hit with an inhibitor,

expression levels would further decrease, lifting the regulation at the S and G2/M

checkpoints. This sensitivity is again shown in the cell cycle data, (Figure 5.11). After 24hrs,

the percentage of cells in the S-phase and G2/M fraction has dramatically decreased. The

lack of checkpoint regulation has allowed the cells to pass through these stages of the cell

cycle without pausing to repair accumulated damage. The increased G0 fraction shows the

death resulting from this lack of repair.

156

We can see a similar pattern in the NB4 cells yet to a lesser extent. The cell cycle analysis

again shows a decrease in the S-phase and G2/M fractions and a moderate increase in the

G0 fraction. The clonogenic assay shows corresponding results with a steady dose

dependent decrease in colony growth and an intermediate IC50 value. The NB4 cell line

has a higher expression level of CHK1 compared to the SKM1 cell line and so when the

inhibitor is added it does not have as much of an effect as it does in the SKM1 cells.

From the clonogenic assays in (Figure 5.11) we can see that the HL60 cells do not have a

strong response to rabusertib treatment with a decrease in colony formation only being

seen at the highest doses. As the HL60 cells have a predicted S-phase defect, the effects of

inhibiting this checkpoint further may not cause as much cellular disorder as it would in a

cell with a functioning S-phase checkpoint. In the cell cycle analysis however (Figure 5.12),

there is a defined increase in the G0 fraction denoting an increase in apoptotic cells.

Although this is not seen as clearly, in the clonogenic assays, the dose used for the flow was

the highest dose used in the clonogenic assay which did result in cell death.

Although there has been a significant increase in the research carried out on the effects of

inhibiting CHK1, the data is still conflicting and undefined. As CHK1 has so many functions,

inhibiting it can have many different effects. When these effects are combined with a cells

own mutational landscape, it can be very hard to predict the outcome. Few papers have

suggested that DNA repair deficiencies can be a marker for CHK1 sensitivity. (70,149) This

would have led us to believe the DDRD positive cell line would be more susceptible to CHK1

treatment. As we can see this is not the case. The HL60 cell line has the predicted HR defect,

this also affects the S-phase checkpoint. From the results in (Figures 5.11 and 5.12) we can

see this is not exactly the case. It was hypothesised that the deficiencies already present in

the HL60 cells render the inhibitor ineffective as the cells are already defective. The NB4

and SKM1 cell lines both have predicted efficient repair mechanisms and so CHK1 inhibition

should cause a greater effect. This effect is more pronounced in the SKM1 cell line as it

already has a decreased CHK1 gene expression in comparison to the NB4 cell line.

Cytarabine induced DNA damage is repaired through CHK1 mediation. (197) It stabilizes the

stalled forks created and induces S-phase arrest. A high expression of CHK1 or ATR lessens

the effects of Cytarabine as forks are stabilized and DNA repair is activated. (198) This

157

expression data counter-acts the survival data gathered from the patient set. The DDRD

positive patients have a worse response to cytarabine yet a lower expression level of CHK1

(Figure 5.13). This could be due to a number of factors. The DDRD positive patients may be

responding to the Cytarabine treatment more that the DDRD negative patients, but,

considering the DDRD patients have a higher number of mutations on average and these

mutations are often markers of poor prognosis, this could be the cause of the poor

outcome in these patients and not the lack of response to Cytarabine.

5.4.4 Evaluating the effects of DNA Repair Protein inhibitors on DDRD Positive and DDRD Negative cell lines.

PARP inhibitors haven a proven track record as being a successful treatment option for

patients with known repair defects such as BRCA mutations. (18,134) As the DDRD Positive

cell line is predicted to have a DNA repair defect it was theorised that it would be more

sensitive to talazoparib than the DDRD negative cell line. As we can see in (Figure 5.14) the

IC50 values of both the DDRD Positive and the DDRD Negative cell lines are approximately

the same. The PARP inhibitor didn’t show preferential targeting of the DDRD Positive cell

line. Talazoparib is an extremely potent PARP inhibitor and therefore could cause

significant damage even in a cell line without any known repair defects. All cancerous cells

have a base line level of repair defectiveness as they have accumulated the damage

necessary to cause them to be cancerous. This baseline defectiveness could be enough for

the PARP inhibitors to exploit.

DNA damage mediators such as ATM and ATR are heavily associated with cell cycle

checkpoint activation. KU55933 is an inhibitor of the DNA damage response regulator

ATM. (99,115,189)

As the expression levels of ATM differs so greatly between the DDRD positive and DDRD

negative patients (Figure 5.17) it was believed that a great difference would be seen when

the DDRD cell lines were treated with an ATM inhibitor. As we can see in the clonogenic

assays in (Figure 5.15) however, this is not the case. The three cell lines all respond in a

similar fashion cell death is only seen at the highest dose used. The cell cycle analysis in

(Figure 5.16) emulates the clonogenic assay results. No cell death is seen and a small

increase in the S-phase fraction indicates the cells are pausing to repair the damage. ATM

inhibition impedes HR signalling as can be seen by a decrease in RAD51 and H2AX foci

158

following induced damaged. With a functioning ATR pathway however the cells have a

backup mechanism of dealing with the damage induced or the intrinsic damage within the

cell. This is likely the reason known of the cell lines had a great response to the ATM

inhibitor. The ATR gene expression levels, (Figure 5.21), are very similar in all three cell lines

and there is no discernible difference in the response of the ATMi in these cell lines.

The expression levels of ATR do not vary between the DDRD positive and DDRD negative

patients, (Figure 5.20), yet the cells show a significant difference in their responses to ATR

inhibitors. A paper by Stankovic et al in 2015 demonstrated that deficiencies in ATM,

sensitises cells to ATR inhibitors such as AZD6738. (199) This correlates with the expression

data we have seen in the patients (Figure 5.17). ATM functions by sensing DNA damage

and phosphorylating CHK2 which initiates downstream events leading to cell-cycle

checkpoint activation. When ATM expression is deceased, this checkpoint is not as active

leading to cells cycling without being repaired. When ATM expression is deficient and ATR

is inhibited, the cell loses two of its most important DNA repair genes. This induces a

synthetic lethal phenotype in the cells and causes cell death. This can be seen in (Figure

5.18) in the clonogenic assays as the NB4 and SKM1 cell lines show a greater response to

the ATM inhibitor.

From the flow cell cycle analysis however in (Figure 5.19) we can see that there is very little

death in these cell lines. In the NB4 cell line there is an increase in G2/M indicating that

these cells are likely stalled at this checkpoint and have ceased growing. The SKM1 cell line

shows only a slight increase in the sub-G1 fraction implying the drug is having little effect.

The doses however may have been too low to cause death, especially after only 72hrs.

Gene expression values from the patient data, (Figure 5.17), show that DDRD positive

patients have significantly higher expression of ATM than the DDRD negative patients. ATM

can mediate CHK1 activity by activating ATR which in turn phosphorylates CHK1. (127) (200)

Although the DDRD patients have a lower expression level of CHK1, they may in fact have

a higher level of CHK1 activation. CHK1 expression levels often do not correlate with CHK1

phosphorylation levels and while there may be an abundance of CHK1 in the cells, its

activity levels may not be increased. This could again be a reason as to why the DDRD

positive patients do not respond to cytarabine treatment.

159

There are over 450 potential targets of the DNA repair pathways and mechanisms that exist

inside the cell. (201) While some of these are more well characterised than others, such as

WEE1, ATM and ATR, there is still a great deal information to be uncovered. The same is

true of checkpoint components. While there is no doubt that targeting the DNA repair

pathways offers an attractive target for cancer therapeutics, the lack of concrete

information concerning the pathways and downstream effects of these targets makes it

extremely difficult to selectively choose which patients will or will not respond to

treatment.

5.5 Chapter Summary

For the most part the DDRD negative cell lines follow similar patterns of response to each

other which differ to that of the DDRD positive cell line. The results of these drug

treatments support the hypothesis that the DDRD positive cell line has a DNA repair defect

while the DDRD negative cells do not. The differential results highlight the potential of using

the DDRD signature to assess which patients will or will not respond to a certain treatment

type.

I believe that many of the drugs tested in this chapter could potentially be used as new

therapies in the treatment of AML. While some are already in use in other cancer types

such as gemcitabine and the WEE1i MK-1775, a great benefit could be gained by

introducing these chemotherapies into the treatment regimen in AML.

Although the assay was initially designed to predict DNA repair deficiencies, the drug

responses indicate the assay also has the potential to predict checkpoint defects. Repair

pathways and checkpoint regulation are two cellular aspects that are heavily linked. The

phenotypic consequences of these defects can be portrayed in a similar fashion. As the

assay was designed based on the gene signature of patients that had known repair defects,

it was presumed to be capable of predicting other patients or cells that had repair defects.

As the gene signature of a patient or cell with a checkpoint defect would be similar, this

assay is likely also able to highlight these patients.

Whatever defect this assay is predicting, it does seem capable of segregating patients and

cell types and predicting response to many different chemotherapeutic drugs. Moving

160

forward I believe this assay can effectively be used to stratify patients to allow clinicians to

tailor the best treatment options.

Figure 5.22: Visual representation of the interactions between the repair and checkpoint genes

NBS1

MRE11

RAD50ATM

CDK2

G1

CDC25AP

CHK2

P

RAD17

RAD1

RAD9

HUS1 ATR

CHK1

P

CDK1

CDC25CP

WEE1

S G2 M

161

Chapter 6:

Large pooled knock-out CRISPR screen analysis of a DDRD Negative Cell Line

6.1 Introduction

Advancements in CRISPR experiments have included the use of pooled CRISPR

screens which target several genes at once. New pooled CRISPR screening techniques

have been developed to allow for single cell RNA sequencing to be performed on the

cells transduced with a pooled guide library. (164) The addition of PolyA tails into the

CRISPR plasmid allows for the detection of the guide RNAs in single cell sequencing

methods. (167)

In the CROPseq method the LentiGuide-Puro plasmid was redesigned to incorporate

a Poly A tail so that it would be compatible with single-cell sequencing techniques.

This system can then be combined with single-cell RNA-seq methods such as Drop-

seq to allow for a single cell transcriptome readout of a pooled CRISPR screen.

(167,176)

The SKM1 cell line was chosen to be the model for the CRISPR screen for a number

of reasons. Firstly it is a DDRD negative cell line. As the goal of this chapter was to

identify which of these genes may be causing the DDRD positive phenotype, it was

necessary to use a DDRD negative model to see the change in signature. The SKM1

cell line has also already been used by other laboratory members in CRISPR

experiments and so the transduction protocol was already optimised.

When choosing the library of genes to knock-down, lists of genes with different

attributes were compiled. The first list included the genes which were more

commonly mutated in the DDRD positive patients, Table 3.3. The second list

contained the genes which were only mutated twice in the 183 patients analysed but

with both of those mutations being found in the DDRD positive patients. The third list

was composed of well-known genes involved in DNA repair. Approx. 60 genes were

chosen from these lists to be used in the CRISPR screen.

162


The aim of this chapter is to identify which genes may be contributing to a DDRD

positive score. To achieve this aim, a number of genes enriched in the DDRD positive

patients were knocked-out in a DDRD negative cell line using a pooled CRISPR screen

method.

6.3 Results:


As puromycin and blasticidin would be used as selection markers in later experiments

in this chapter, kill doses needed to be determined for the cell line being used. The

cell line, SKM1, was treated with a variety of doses of puromycin or blasticidin and

left for 72hrs.

Figure 6.1: Dose response Curve of SKM1 cells treated with varying doses of blasticidin and puromycin

The SKM1 cell line was treated with 6 doses of blasticidin or puromycin ranging from

0.3125M to 10M. Percentage viability was determined using CTG 72hrs after treatment.

All results were normalised to an untreated control.

Kill dose was determined as the lowest dose that killed all the cells. For both

antibiotics, the dose 5M was taken forward as the kill dose.

0 2 4 6 8 10 120

20

40

60

80

100

120

Dose (uM)

% V

iab

ilit

y

Kill Curve

Puromycin

Blasticidin

163

6.3.2 Testing of the stable CAS9 cell line

To ensure the stable CAS9 cell line was functioning and could be used for the CRISPR

screen, the cell line was transduced with CRISPR guides that had been used previously

in the lab and were known to be effective. The cell line was transduced with the virus

containing the test guides as was described in section 2.6.3 in Chapter 2.

Figure 6.2: Western blot analysis of test guide CRISPR knock-downs

Test guide RNAs targeting the UBS gene were transduced into the stable CAS9 cell line. The

cells were treated with puromycin for 7 days and then a protein sample was taken from the

transduced cells and a sample from cells which had not been transduced with the guide

RNAs. The blot was probed for the UBS protein using the antibody UBS primary antibody.

It was deduced from this blot that the CAS9 cells were functioning.

6.3.3 Plasmid Digestion

To confirm the plasmid prep had not been contaminated, the plasmid was digested

with multiple digestions enzymes known to cut the plasmid and the digestion

products were run on an agarose gel.

S N

S

#1

S #2

120kDa UBS

36kDa GAPDH

S = SKM1 Cas9 Cells

#1 = Guide 1

#2 = Guide 2 Targets UBS

164

Figure 6.3: Agarose gel electrophoresis of the plasmid digestion products

Plasmid samples were incubated with the one of the restriction enzymes listed as per the manufacturer’s instructions. The products of the restriction digest were run for 30 mins at 100V of an 1% agarose TAE gel. Imaged using the UV G:Box analyser.

The restriction enzymes cut the plasmid a different number of times and so the

number of fragments and the fragment sizes can be used to ensure the plasmid is

pure.

10000

20000

1500

500

2000

SacII – 3 cuts

Xhol – 2 cuts Ndel – 1 cut

EcoRI – No cut L – Ladder

L Undigested SacII Xhol Ndel EcoRI

165

Figure 6.4: Restriction site map of the CROPseq-Guide-Puro plasmid by Bock et al

6.3.4 Library Assembly and sequencing

As previously discussed, the genes were chosen from the list of commonly mutated

genes in the DDRD positive patients along with some common DDR genes.

166

ARHGEF4 PCDHA13

ASXL1 PHF6

ATM PKD1L2

ATR PTPN11

BCOR RAD21

BRCA1 RAD51

BRCA2 RUNX1

CBFB RUNX1T1

CBL SF3B1

CDKN2A SMC1A

CEBPA SMC3

CHEK1 SPEG

CHEK2 SRSF1

CLEC18B STAG2

CSMD1 SUZ12

DDX11 TCEAL3

DNMT3A TET2

DRD2 TP53

EZH2 TP53BP1

FANCF TTN

FLT3 U2AF1

HNRNPK WT1

IDH1 DIS3

IDH2 LacZ

KIT luciferase

KRAS EGFP

LRP1B chrX_safe

MDC1 chr13_safe

MLH1 chr12_safe

MSH2 chr11_safe

MUC16 chr10_safe

MYC chr17_safe

NOTCH2NL chr16_safe

NPM1 chr15_safe

NRAS chr14_safe

PARP1 chr19_safe

Table 6.1: List of genes targeted in the CROPseq CRISPR Screen

The list of genes chosen from previous experiments were augmented with genes which are

either not present in human cells or ‘safe’ genes that target regions of the genome that does

not create a difference in the cell

167

Table 6.2: Tables of total reads and percentages mapped from the guide libraries

The number of reads from the forward and reverse primers from both libraries were totalled

and the percentage of reads that mapped back to the libraries were calculated. The number

of guides identified were also calculated.

When the sequencing results returned from Genewiz, the r files were aligned against

the CRISPR guide library. The total reads per sample, how many reads mapped back

to the library and the associated percentages were calculated. From this table we can

see that only approximately 60% of the reads could be mapped back to the library of

guides. This was initially worrying but, contact with the authors from the original

paper lessened worries.

The frequency of the guides in the library was also analysed. The optimum frequency

was between the 10th and 90th percentile. The quantity of guides which fell outside

this range are shown in the table below.

Table 6.3: Table of the number of reads which fell outside the optimum range

File Reads Mapped Percentage Total sgRNAs

IL01-CROP1-

1_R1_001.fastq.gz

231579 141909 0.6128 196

IL01-CROP1-

1_R2_001.fastq.gz

231579 138508 0.5981 196

IL02-CROP1-

2_R1_001.fastq.gz

234596 143812 0.613 196

IL02-CROP1-

2_R2_001.fastq.gz

234596 140463 0.5987 196

Reads Percentiles

1113.25 90th percentile

348.25 10th percentile

168

The optimum guide frequency should be between the 10th and 90th percentile of frequency

distribution and so reads that fell outside this range were identified.

The read count frequency of each guide from the forward and reverse primers were graphed.

Figure 6.5A: The read frequency distribution of the forward primers from library one

4CC_I

DH2_

3

4CC_A

TM_2

4CC_F

ANCF_2

4CC_F

LT3_

1

4CC_E

GFP_1

4CC_C

SM

D1_

2

4CC_P

HF6_

2

4CC_D

NM

T3A_3

4CC_T

P53

_3

4CC_U

2AF1_

1

4CC_T

ET2_

2

4CC_R

AD21

_3

4CC_D

NM

T3A_1

4CC_I

DH2_

1

4CC_A

SXL1_

3

4CC_N

RAS_1

4CC_W

T1_1

4CC_D

IS3_

3

4CC_C

HEK

2_3

4CC_F

ANCF_3

4CC_C

SM

D1_

1

4CC_L

RP1B

_1

4CC_L

acZ_2

4CC_l

ucife

rase

_3

4CC_S

MC3_

1

4CC_R

UNX1_

2

4CC_M

YC_1

4CC_S

UZ12

_2

4CC_A

TM_1

4CC_C

DKN2A

_2

4CC_C

EBPA

_2

4CC_T

ET2_

3

4CC_S

MC3_

2

4CC_M

YC_3

4CC_S

PEG

_1

4CC_B

RCA2_

3

4CC_A

SXL1_

2

4CC_M

YC_2

4CC_C

BFB

_3

4CC_B

RCA2_

1

4CC_B

RCA1_

1

4CC_E

ZH2_

3

4CC_C

HEK

1_3

4CC_M

DC1_

1

4CC_A

RHGEF4_

2

4CC_N

OTC

H2N

L_3

4CC_P

CDHA13

_1

4CC_P

KD1L

2_1

4CC_T

TN_3

0

1000

2000

3000

Library 1 Forward

Guide

Read

Fre

qu

en

cy

169

Figure 6.5B: The read frequency distribution of the reverse primers from library one

Figure 6.5C: The read frequency distribution of the forward primers from library two

4CC_I

DH2_

3

4CC_A

TM_2

4CC_F

ANCF_2

4CC_F

LT3_

1

4CC_E

GFP

_1

4CC_C

SM

D1_

2

4CC_P

HF6_

2

4CC_D

NM

T3A_3

4CC_T

P53

_3

4CC_U

2AF1_

1

4CC_T

ET2_

2

4CC_R

AD21

_3

4CC_D

NM

T3A_1

4CC_I

DH2_

1

4CC_A

SXL1_

3

4CC_N

RAS_1

4CC_W

T1_1

4CC_D

IS3_

3

4CC_C

HEK2_

3

4CC_F

ANCF_3

4CC_C

SM

D1_

1

4CC_L

RP1B

_1

4CC_L

acZ_2

4CC_l

ucife

rase

_3

4CC_S

MC3_

1

4CC_R

UNX1_

2

4CC_M

YC_1

4CC_S

UZ12

_2

4CC_A

TM_1

4CC_C

DKN2A

_2

4CC_C

EBPA

_2

4CC_T

ET2_

3

4CC_S

MC3_

2

4CC_M

YC_3

4CC_S

PEG_1

4CC_B

RCA2_

3

4CC_A

SXL1_

2

4CC_M

YC_2

4CC_C

BFB

_3

4CC_B

RCA2_

1

4CC_B

RCA1_

1

4CC_E

ZH2_

3

4CC_C

HEK1_

3

4CC_M

DC1_

1

4CC_A

RHGEF4_

2

4CC_N

OTC

H2N

L_3

4CC_P

CDHA13

_1

4CC_P

KD1L

2_1

4CC_T

TN_3

0

1000

2000

3000

Library 1 Reverse

Guide

Read

Fre

qu

en

cy

4CC_I

DH2_

3

4CC_A

TM_2

4CC_F

ANCF_2

4CC_F

LT3_

1

4CC_E

GFP_1

4CC_C

SMD1_

2

4CC_P

HF6_

2

4CC_D

NM

T3A_3

4CC_T

P53_3

4CC_U

2AF1_

1

4CC_T

ET2_2

4CC_R

AD21

_3

4CC_D

NM

T3A_1

4CC_I

DH2_

1

4CC_A

SXL1_3

4CC_N

RAS_1

4CC_W

T1_1

4CC_D

IS3_

3

4CC_C

HEK

2_3

4CC_F

ANCF_3

4CC_C

SMD1_

1

4CC_L

RP1B

_1

4CC_L

acZ_2

4CC_l

ucife

rase

_3

4CC_S

MC3_

1

4CC_R

UNX1_

2

4CC_M

YC_1

4CC_S

UZ12

_2

4CC_A

TM_1

4CC_C

DKN2A

_2

4CC_C

EBPA

_2

4CC_T

ET2_3

4CC_S

MC3_

2

4CC_M

YC_3

4CC_S

PEG_1

4CC_B

RCA2_

3

4CC_A

SXL1_2

4CC_M

YC_2

4CC_C

BFB

_3

4CC_B

RCA2_

1

4CC_B

RCA1_

1

4CC_E

ZH2_

3

4CC_C

HEK

1_3

4CC_M

DC1_

1

4CC_A

RHG

EF4_2

4CC_N

OTC

H2N

L_3

4CC_P

CDHA13

_1

4CC_P

KD1L

2_1

4CC_T

TN_3

0

1000

2000

3000

Library 2 Forward

Guide

Read

Fre

qu

en

cy

170

Figure 6.5D: The read frequency distribution of the reverse primers from library two

Figures 6.5 A-D show the frequency distribution graphs of the reverse and forward primers

of both libraries. The 196 guides present are graphed in order of frequency.

The frequency of the guides were analysed to ensure all the guides were present. It

was also necessary to confirm the frequency of the guides were relatively equal.

6.3.5 Lentiviral MOI

An MOI titre test was carried out to identify the volume of virus which would produce

a transduction rate of between 20-40%. This would ensure that only one virus particle

would enter a cell. A variety of volumes of the virus were added to the cell line and

the cells were treated with puromycin for 7 days before using CTG to analyse viability.

4CC_I

DH2_

3

4CC_ATM

_2

4CC_F

ANCF_2

4CC_FLT

3_1

4CC_EG

FP_1

4CC_C

SMD1_2

4CC_PH

F6_2

4CC_D

NM

T3A_3

4CC_TP53_

3

4CC_U

2AF1_

1

4CC_TET2_2

4CC_R

AD21_3

4CC_D

NM

T3A_1

4CC_ID

H2_1

4CC_A

SXL1_3

4CC_N

RAS_1

4CC_W

T1_1

4CC_D

IS3_3

4CC_C

HEK

2_3

4CC_F

ANCF_3

4CC_C

SMD1_

1

4CC_L

RP1B

_1

4CC_LacZ

_2

4CC_lu

cifera

se_3

4CC_SM

C3_1

4CC_R

UNX1_2

4CC_M

YC_1

4CC_SU

Z12_2

4CC_ATM

_1

4CC_C

DKN2A

_2

4CC_C

EBPA

_2

4CC_TET2_3

4CC_SM

C3_

2

4CC_M

YC_3

4CC_SPEG

_1

4CC_B

RCA2_

3

4CC_A

SXL1_2

4CC_M

YC_2

4CC_C

BFB

_3

4CC_B

RCA2_1

4CC_B

RCA1_1

4CC_EZH

2_3

4CC_C

HEK

1_3

4CC_M

DC1_1

4CC_A

RHG

EF4_2

4CC_N

OTC

H2N

L_3

4CC_PC

DHA13_1

4CC_P

KD1L2_

1

4CC_TTN

_30

1000

2000

3000

Library 2 Reverse

Guide

Read

Fre

qu

en

cy

171

Figure 6.6: Percentage Viability graph of the CAS9 cell line following transduction with the guide RNAs lentivirus

Six volumes of the lentiviral soup, ranging from 100l to 1ml, were added to 1 x 106 cell of

the SKM1 cells. The viability was calculated after one week using Cell-Titre Glo analysis,

normalised to an untreated control.

The volumes of virus which fell in the 20-40% transduction rate were 500l, 750l

and 1000l.

6.3.6 Single Cell RNA-Seq Analysis and DDRD Score Mapping The raw sequencing data was normalised and sorted based on guide presence, as

described in Section 2.6. As shown in Table 6.2, only 60% of the reads mapped back

to the guides, this was due to the presence of erroneously assembled guides. Upon

analysis of the data, the number of error-containing guides and correct guides were

graphed. This result is shown in the figure below, Figure 6.7.

0 400 800 12000

10

20

30

40

Viral Volume

% V

iab

ilit

y

MOI Titre Test

172

Figure 6.7: Frequency of error-containing guides compared to total and correct guides

The number of error-containing guides and correct guides were compared to the total

number of guides sequenced.

The transcriptome data was analysed to calculate a DDRD score for every cell. The

cells which either did not contain a guide or contained more than one guide were

excluded. Out of the 4419 cells sequenced, this left 1704 cells that had a single guide.

4CC_A

RHGEF4_

1

4CC_A

SXL1_

2

4CC_A

TM_3

4CC_B

COR_1

4CC_B

RCA1_

2

4CC_B

RCA2_

3

4CC_C

BL_1

4CC_C

DKN2A

_2

4CC_C

EBPA

_3

4CC_C

HEK2_

1

4CC_C

LEC18

B_2

4CC_C

SM

D1_

3

4CC_D

NM

T3A_1

4CC_D

RD2_

2

4CC_E

ZH2_

3

4CC_F

LT3_

1

4CC_H

NRNPK_2

4CC_I

DH1_

3

4CC_K

IT_1

4CC_K

RAS_2

4CC_L

RP1B

_3

4CC_M

LH1_

1

4CC_M

SH2_

2

4CC_M

UC16

_3

4CC_N

OTC

H2N

L_1

4CC_N

PM

1_2

4CC_N

RAS_3

4CC_P

CDHA13

_1

4CC_P

HF6_

2

4CC_P

KD1L

2_3

4CC_R

AD21

_1

4CC_R

AD51

_2

4CC_R

UNX1_

3

4CC_S

F3B1_

1

4CC_S

MC1A

_2

4CC_S

MC3_

3

4CC_S

RSF1_

1

4CC_S

TAG2_

2

4CC_S

UZ12

_3

4CC_T

ET2_

1

4CC_T

P53

_2

4CC_T

P53

BP1_

3

4CC_U

2AF1_

1

4CC_W

T1_2

4CC_D

IS3_

3

4CC_l

ucife

rase

_1

4CC_E

GFP

_2

4CC_c

hr13_

safe

4CC_c

hr17_

safe

4CC_c

hr19_

safe

0

50

100

150

200

Guides

Fre

qu

en

cy o

f re

ad

s Total

Error

Correct

4CC_A

RHGEF4_

1

4CC_A

SXL1_

2

4CC_A

TM_3

4CC_B

COR_1

4CC_B

RCA1_

2

4CC_B

RCA2_

3

4CC_C

BL_1

4CC_C

DKN2A

_2

4CC_C

EBPA

_3

4CC_C

HEK2_

1

4CC_C

LEC18

B_2

4CC_C

SMD1_

3

4CC_D

NM

T3A_1

4CC_D

RD2_

2

4CC_E

ZH2_

3

4CC_F

LT3_

1

4CC_H

NRNPK

_2

4CC_I

DH1_

3

4CC_K

IT_1

4CC_K

RAS_2

4CC_L

RP1B

_3

4CC_M

LH1_

1

4CC_M

SH2_

2

4CC_M

UC16

_3

4CC_N

OTC

H2N

L_1

4CC_N

PM1_

2

4CC_N

RAS_3

4CC_P

CDHA13

_1

4CC_P

HF6_

2

4CC_P

KD1L

2_3

4CC_R

AD21

_1

4CC_R

AD51

_2

4CC_R

UNX1_

3

4CC_S

F3B1_

1

4CC_S

MC1A

_2

4CC_S

MC3_

3

4CC_S

RSF1_

1

4CC_S

TAG2_

2

4CC_S

UZ12

_3

4CC_T

ET2_1

4CC_T

P53

_2

4CC_T

P53BP1_

3

4CC_U

2AF1_

1

4CC_W

T1_2

4CC_D

IS3_

3

4CC_l

ucife

rase

_1

4CC_E

GFP

_2

4CC_c

hr13_

safe

0

50

100

150

200

Guide Frequency

Guide

Gu

ide F

req

uen

cy

All Guides Present

Single Guide Cells

173

Figure 6.8: Frequency of all guides present in the CROPseq library compared with frequency of guides in cells with only one guide

The guide distribution of the 1704 cells which contained only one guide compared to the

4419 cells which contained either multiple guides or no guides.

A new DDRD score was needed to be calculated for the SKM1 cell line from the

control guides. This was so a shift in scores could be normalised to a control within

the same experiment.

To determine a new score, the mean of the DDRD scores for all the control guides

was calculated.

Table of all calculated DDRD scores can be found in Chapter 8 Appendices.

To calculate the DDRD score for a cell, all cells were segregated based on their guides.

Most of the guides were present in multiple cells. The averages of the guides were

calculated. Any outliers were determined using Prism GraphPad’s ROUT method and

removed. The average of the gene was determined by averaging the mean of each

guide for that cell.

174

Figure 6.9: Graph of average DDRD score per gene

The average DDRD score per gene was calculated by calculating the average DDRD score of

each guide. All genes had approx. 3 guides. Any outliers were calculated using PRISM

GraphPad ROUT method. The outliers were removed and the averages of the guides was

calculated and used as the average DDRD score for that gene.

chr1

5 CH

K1 IDH

2 DR

D2 E

ZH2

RU

NX

1T1

U2A

F1 PH

F6 chr1

6S

TAG

2 chr1

2D

DX

11 chr1

3S

MC

1A TET2 chrX

CB

LM

SH

10 PAR

P1

TCE

AL3 chr1

0P

TPN

11

CLE

C18

B

luci

fera

se BC

OR

CE

BPA EG

FP TTNR

AD

21ATM

HN

RN

PK TP53 B

RC

A2 S

PE

G KR

AS

FAN

CF C

BFB

CS

MD

1 IDH

1B

RC

A1 ch

r14

RA

D51 FLT

3 chr1

9 WT1 C

HK

2M

UC

16ATR M

YC

NO

TCH

2NL D

IS3 S

MC

3 KIT N

RA

S MLH

1P

KD

1L2 N

PM

1

AR

HG

EF4

PC

DH

A13 S

RS

F1

DN

MT3A RU

NX

1A

SX

L1LR

P1B S

UZ12

CD

KN

2A MD

C1 LacZ

SF3B

1

TP53

BP

1 chr1

7 chr1

1

0.1

0

0.1

5

0.2

0

0.2

5

0.3

0

Gen

es

Average

DD

RD

Sco

re

175

Genes Guides DDRD Score

LacZ guide_179 0.223862967

LacZ guide_180 0.272741218

luciferase guide_181 0.183298806



EGFP guide_184 0.174488198

EGFP guide_185 0.174111747

EGFP guide_186 0.221427854

EGFP guide_187 0.217886437

chrX guide_188 0.186937549

chr13 guide_189 0.184851239

chr12 guide_190 0.183612346

chr11 guide_191 0.254001072

chr10 guide_192 0.191353729

chr17 guide_193 0.2417743

chr16 guide_194 0.181266946

chr15 guide_195 0.146130058

chr14 guide_196 0.202971511

chr19 guide_197 0.204439663

Table 6.4: Table of DDRD Scores from the control guides

The DDRD scores from the control guides were calculated and displayed in Table 6.4. The

mean of these scores was used as the re-calculated DDRD score for the SKM1 cell line.

Table 6.4 A&B: Table of the mean DDRD score from the control guides and the SEM of all the DDRD scores

The SEM was calculated using the entire list of DDRD scores. To assess changes in DDRD

scores, only genes which had a difference of at least 1 SEM were classed having an altered

DDRD score.

To assess whether the knocking down of a gene alters the DDRD score the SEM of all

the DDRD scores was calculated. It was determined that a gene changed the DDRD

score of the cell line if it was at least 1 SEM above or below the control DDRD score.

Below Above

SEM 1 0.19938912 0.20979832

SEM 2 0.19738912 0.21179832

Mean DDRD Score

0.20779832

176

The genes which had not greatly altered the score were classed as non-changing

genes.

Genes Average DDRD Score Per Gene

DRD2 0.102149827

CHK1 0.15969509

EZH2 0.168447419

U2AF1 0.172126603

IDH2 0.174752273

RUNX1T1 0.176505331

PHF6 0.178300097

DDX11 0.178786645

CLEC18B 0.181159028

STAG2 0.182747932

SMC1A 0.183971644

MSH2 0.186702556

CBL 0.189215749

TTN 0.190441121

RAD21 0.190534514

TET2 0.190689987

BRCA1 0.191171746

PTPN11 0.191532747

HNRNPK 0.194208046

NRAS 0.195194388

TCEAL3 0.195439142

ATM 0.197062556

BCOR 0.197432317

NOTCH2NL 0.198625925

CSMD1 0.198737919

Table 6. 5: Genes at least 1 SEM under the average DDRD score of the control guides

The genes in this table were at least 1 SEM below the control DDRD score. The bottom three

genes were only 1 SEM different form the control whereas the remaining genes were at least

2 SEMs from the control score.

177


KIT 0.212321502

PCDHA13 0.212485246

SMC3 0.212924137

FLT3 0.214358139

ARHGEF4 0.214877654

NPM1 0.214988362

CDKN2A 0.217154411

MLH1 0.219022594

SUZ12 0.220704971

DNMT3A 0.22073403

PKD1L2 0.221210422

LRP1B 0.221761875

RUNX1 0.221827286

RAD51 0.223341636

SRSF1 0.227018971

DIS3 0.228836585

SF3B1 0.231174292

ASXL1 0.232851436

MDC1 0.234153033

BRCA2 0.244011069

Table 6.6: Genes at least 1 SEM above the average DDRD score of the control guides

The genes in this table were at least 1 SEM above the control DDRD score. The top two genes

were only 1 SEM different form the control whereas the remaining genes were at least 2

SEMs from the control score.

178


TP53 0.200473333

SPEG 0.201336119

CEBPA 0.201672929

PARP1 0.202649264

IDH1 0.20392908

CHK2 0.203995295

CBFB 0.204663462

MYC 0.205423839

WT1 0.206151424

ATR 0.207001646

KRAS 0.207665268

TP53BP1 0.207804817

MUC16 0.208119719

Table 6.7: Genes not significantly different from the control DDRD Score

The genes in this table were not classed as different from the control DDRD score as they

were within the SEM of the control score.

179

6.3.7 Pathway Analysis and Hierarchal Clustering

RNA Polymerase II Transcription

Generic Transcription Pathway

Oxidative Stress Induced Senescence

Oncogene Induced Senescence

Transcriptional regulation by RUNX3

Cellular Senescence

TP53 Regulates Transcription of DNA Repair Genes

SUMO E3 ligases SUMOylate target proteins

SUMOylation

RUNX3 regulates p14-ARF

Diseases of Cellular Senescence

RUNX3 regulates RUNX1-mediated transcription

Meiosis

Transcriptional Regulation by TP53

Cell Cycle

Transcriptional Regulation by E2F6

Reproduction

Transcriptional regulation by the AP-2 (TFAP2) family of transcription factors

Cellular responses to stress

Table 6.8: Pathways deregulated by mutations in genes that increase a DDRD score

The genes from Table 6.6 were put through pathway analysis software using the Reactome

website https://reactome.org/PathwayBrowser.

https://reactome.org/PathwayBrowser

180

TP53 Regulates Transcription of DNA Repair Genes

Transcriptional Regulation by TP53

Cohesin Loading onto Chromatin

Meiosis

Establishment of Sister Chromatid Cohesion

Transcriptional Regulation by E2F6

Mitotic Telophase/Cytokinesis

Signaling by SCF-KIT

Reproduction

Meiotic synapsis

Gene expression (Transcription)

SUMOylation of DNA damage response and repair proteins

RNA Polymerase II Transcription

SUMO E3 ligases SUMOylate target proteins

SUMOylation

Generic Transcription Pathway

Presynaptic phase of homologous DNA pairing and strand exchange

Homologous DNA Pairing and Strand Exchange

Signalling by FGFR4

Signalling by FGFR3

Table 6.9: Pathways deregulated by mutations in genes that decrease a DDRD score

The genes from Table 6.5 were put through pathway analysis software using the Reactome

website https://reactome.org/PathwayBrowser.

https://reactome.org/PathwayBrowser

181

Figure 6.10 Hierarchical clustering of the 200 top and bottom DDRD scoring cells

The top 200 scoring DDRD genes and the bottom 200 DDRD scoring cells were submitted to

hierarchical clustering using Partek Genomic Suite 7.

DDRD Genes

DDRD Genes

Top 200

Bottom 200

182

6.4 Discussion:


The kill curve in (Figure 6.1) indicated that the minimum dose that killed all the cells

was approx. 5M for both puromycin and blasticidin. This dose was used in further

experiments when blasticidin or puromycin resistance was needed as a selective

marker.

6.4.2 Testing of the stable CAS9 cell line

The CAS9 cell line needed to be tested prior to use in the CROPseq experiment as this

experiment could only be carried out once. The cell line was tested using guides

which target the UBS gene. These guides have been used in multiple previous

experiments and are known to be highly functioning. From the western blot in (Figure

6.2) we can see that the guides have successfully knocked down the UBS gene

demonstrating the functionality of the stable CAS9 cell line.

6.4.3 Plasmid Digestion

The CROPseq-Guide-Puro plasmid was digested following maxi-prep to ensure that

there was no contamination of the plasmid during prep and that the plasmid received

was the correct plasmid. Multiple enzymes were used which had restriction sites

within the plasmid. From the plasmid digestion in (Figure 6.3) we can see the plasmid

is pure. The enzymes cut in the correct places, indicated by the size of bands on the

electrophoresis gel. The plasmid map specifies the sizes the cut bands should be and

restriction gel mirrors this exactly.

6.4.4 Library Assembly and sequencing

When the sequencing results returned from Genewiz, the read depth was greater

than expected. The minimum guaranteed depth was 50,000 reads whereas the

output was actually approx. 500,000. The files were aligned against the CRISPR guide

library. The total reads per sample, how many reads mapped back to the library and

the associated percentages were calculated.

183

A relatively low percentage, approx. 60%, of the total reads mapped back mapped

back to the library, exact percentages ca be seen in (Table 6.2). This was initially quite

worrying but upon deeper analysis it was shown to not be as serious an error as first

thought. The 40% of reads which did not map back to the guide library contained

either 1 of 2 errors. The most common error was the addition of a G immediately

prior to the sgRNA. The other error was the loss of a T immediately after the sgRNA.

It was reasoned that the loss of T was so infrequent that it would not unduly affect

the experiment. Also, the addition of the G would not alter the binding of the sgRNA

and therefore, despite the high abundance, would likely not affect the experiment.

Our reasoning was supported by the authors of the original CROPseq paper who were

contacted for advice. They had confirmed the unlikeliness of these errors causing a

serious effect.

The sequencing showed that 196 out the 197 guides were present in the library

(Figure 6.5). The guide which was not present was the guide labelled 4CC_LACZ_1

which targeted the LACZ gene. This result was not too concerning as two other guides

were still present which target this gene. The rest of the guides were present in the

library. The vast majority of the reads for the rest of the cells were within the

optimum range i.e. between the 10th and 90th percentile.

(Figure 6.5 A-D) shows the frequency distribution graphs of the forward and reverse

primers. From these graphs we can see that library one is almost identical to library

two. This is a positive result as the it shows the sequencing was accurate. As both

libraries were comprised of the same cells the sequencing results should be the same.

6.4.5 Lentiviral MOI

An MOI titre test was used to calculate the volume of virus needed to guarantee only

one virus particle entered a cell. A 20% - 40% transduction rate was needed to

mathematically ensure only one virus particle entered a cell. This cannot be

guaranteed, however. Transduction rates were calculated by assessing viability post

transduction and puromycin treatment. The percentage of viable cells following

transduction and puromycin treatment should equate to the percentage of cells

effectively transduced. In the graph in (Figure 6.6) we can see the transduction rates

184

from the volumes 500l, 750l and 1000l fell within this range. It was decided to

use the volume 750l was within the optimum transduction range. It also meant that

there was sufficient virus remaining in case the experiment needed to be repeated.

6.4.6 Single Cell RNA-Seq Analysis and DDRD Score Mapping

The single cell sequencing data was normalised as described in (Section 2.6). This

normalisation was carried out by the Genomics Core Technology Unit in the QUB. The

data was returned in the format of normalised, log2 transcriptome reads with each

cell aligned to a specific guide. Unfortunately, although two libraries of the same cells

were sequenced, only on library was analysed at the time of submission. The

bioinformatician was unable to amalgamate the two libraries during analysis.

When the single cell sequencing results were analysed, it was clear that the error

containing guides were not integrated into the cells, (Figure 6.7). This did not affect

our experiment as the number of cells containing guides and the read depth was still

more than adequate. Also, the frequency distribution of the guides was within an

acceptable range.

There were 4419 cells which had been sequenced with enough depth not be removed

during QC. Of these cells, only 1704 had one guide present. The remaining 2715

contained either multiple guides or no guides. The distribution of guides found in the

remaining 1704 compared to all the distribution of all the guides found following

sequencing of the CROPseq library is shown in (Figure 6.8). This graph shows the

limited number of guides we were able to analyse compared to the genes present.

When library 2 is analysed and the data merged, hopefully we will get a greater guide

coverage.

DDRD score were calculated for the 1704 cells which contained only one guide. The

full list of DDRD scores can be found in (Table 8.4). (Figure 6.9) shows the graph of

average DDRD score per gene. These averages were calculated by first segregating

the cells based on their guides. Most of the guides were present in multiple cells. The

averages of the guides were calculated. Any outliers were determined using Prism

185

GraphPad’s ROUT method and removed. The average of the gene was determined by

averaging the mean of each guide for that cell.

A new DDRD score was needed for the SKM1 cell line to take into consideration any

effects the CRISPR plasmid may be having on the cell and the differences in

sequencing methods between single cell sequencing and pooled RNA sequencing.

The old DDRD score of the SKM1 cell line calculated in Chapter 3 was not used in this

chapter. To determine the new DDRD score, the mean of the control guides used was

calculated. This new score was 0.208, far below that of the original DDRD score. This

is likely due to the decreased read depth of the single cell experiment. A gene is also

missing from the 41 genes which are used to calculate the DDRD score. The FYB gene

was not present in the sequencing data. While this is likely not sufficient enough to

cause such a change, it may be a contributing factor.

To assess which genes altered the DDRD score, the SEM of all the DDRD scores was

evaluated. A gene was classed as altering the DDRD score only if it was at least 1 SEM

above or below the new SKM1 DDRD score. This was to remove background noise

and eliminate any genes whose DDRD scores had not greatly changed.

(Table 6.5) shows the list of genes which had a DDRD score at least 1 SEM below the

mean. There are 25 genes in this list which have change the score. While the initial

aim of this experiment was to assess if any genes increased the DDRD, any significant

change in score, up or down, is still of interest. From this list we can see that 4 of the

genes are from the common DNA damage genes list on cBioportal, CHK1, MSH2,

BRCA1 and ATM. While it may seem unusual that the knock down of key DNA damage

repair genes, I believe this stands to the validity of the DDRD score. This score was

created as it not always possible to determine repair deficiency based on gene

mutations, it takes into consideration the expression values of 44 genes not just a

mutation in one. Therefore, while it may seem unusual that knocking down a DDR

gene does not increase the score, it may be because the many other components of

the pathways are negating the effects of the knockdown. Some of the other genes on

this list include members of the cohesin complex, STAG2, RAD21 and SMC1A. The

cohesin complex has recently been implicated in repair mechanisms. (202,203) While

most of the genes on this list were found to be commonly mutated in DDRD positive

patients, the mutation types may have been different in the patients. Alterations of

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genes can have many varied effects depending on the area of the gene targeted and

the remaining functionality of the gene. Depending on where the guide cut, a gene

may retain almost full functionality following CRISPR. Or alternatively, it may be fully

depleted. Therefore, the mutations induced using the CRISPR CAS9 method in this

chapter may not be representative of the mutations found in the DDRD positive

patients.

(Table 6.6) contains the list of 20 genes at least 1 SEM above the control DDRD score.

There are also 4 genes on this list which come from the common DNA damage genes

list on cBioportal, MLH1, RAD51, MDC1 and BRCA2. Conversely, another member of

the cohesin complex, SMC3 was found on the increased DDRD score list. Again,

supporting the idea that it is not always possible to assess repair deficiency from just

one mutation, many factors need to be taken into consideration. The genes in this

list are likely to be at the least partially responsible for the DDRD positive phenotype

that is seen in patients. The majority of the genes in this list were chosen due to their

mutational status in the DDRD positive patients.

The genes in (Table 6.7) contain the list of genes which do not have an altered DDRD

score. Again, on this list there were genes from the cBioportal common DDR genes,

PARP1, CHK2 and ATR. This once more, highlights the fact that knocking down or

altering one gene in a pathway may not cause the DDRD score to increase.

6.4.7 Pathway Analysis and Hierarchal Clustering

Of the 19 pathways found to be deregulated by genes which increased the DDRD

score (Table 6.9), 7 of these were also found in the 20 pathways deregulated by genes

which decreased DDRD score (Table 6.8). This overlap included pathways such as the

“TP53 regulated transcription of DNA repair genes”. Interestingly, the analysis from

the genes which decreased the score contained more pathways pertaining to DNA

repair. “The Establishment of Sister Chromatid Cohesin”, “SUMOylation of DNA

damage response and repair proteins”, “SUMOylation of DNA Damage Response and

Repair Proteins”, “Presynaptic Phase of Homologous DNA Pairing and Strand

Exchange” and “Homologous DNA Pairing and Strand Exchange” pathways are all

heavily involved in ensuring efficient DNA repair. It is unusual that these pathways

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would be associated with genes which decrease a DDRD score but again this

reverberates the DDRD score philosophy. These pathways are heavily linked and

contain multiple genes. Many genes and pathways can be rescued by similar acting

pathways and so a mutation in one may not cause a repair deficiency.

While there were also pathways involved in DNA repair present in the analysis of the

genes which increased the score, they may not be the reason behind the shift in the

score. The pathways in this list are varied and disrupt many different cellular

mechanisms. Any one of these pathways or any combination, may be shifting the

score. A lot of further research is needed to narrow down and isolate contributors to

DDRD positivity.

From the hierarchical clustering maps in (Figure 6.10) we can see a vast difference

between the top 200 and bottom 200 DDRD scoring cells. As the clustering was

carried out on the 44 DDRD genes, the variations in the clusters are directly linked to

the alternating DDRD scores.

The top 200 cells contained guides representing 51 genes and the bottom 200 cells

contained guides representing 52 genes. These genes show a significant amount of

overlap. Due to the overlap, it was not possible to perform differential pathway

analysis on these two groups.

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6.5 Chapter Summary

The CROPseq system has shown to be an effective method of combining pooled

CRISPR screening with single-cell RNA seq analysis. The use of this method allowed

for the analysis of multiple gene knockouts concurrently, greatly reducing the time

and resources that would have been needed for individual gene knock downs. Single

cell sequencing permitted in depth examination of the effects of these knock downs,

individually rather than by a pooled cellular response screen. Although there were

many technical issues which had to be overcome, this experiment yielded some

extremely interesting results.

Firstly, it was shown that single mutations can significantly alter the DDRD score of a

cell line. The score can shift higher or lower depending on the mutation. It also

emphasised the fact that mutations in known DNA damage repair genes does not

necessarily equate to a repair deficiency. This was the ideology behind the

development of the DDRD score. As DNA repair mechanisms are highly linked

multigene complexes, it is difficult to determine which mutations may be causing a

repair defect even if the gene has an important repair function. The results in this

chapter validate the use of DDRD score to determine DNA repair deficiencies.

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Chapter 7: Summary

7.1 General Summary

The survival rate of AML remains worrying low, especially for the patients over the

age of 65. While research into new therapies has been ongoing, to date Cytarabine is

still the induction therapy for almost all new AML patients, despite having low

survival rates.

The Almac DDRD Assay in an effective method of determining which patients have a

DNA repair deficiency or potentially, a checkpoint deficiency. The results shown in

Chapter 3 highlighted the efficacy of the score at segregating AML patients into two

groups which have a significantly different survival rate. It was the DDRD positive

patients which showed the worse survival rate following treatment with cytarabine.

From chapters 4 and 5 we see that the DDRD positive cell line confirmed the

ineffectiveness of cytarabine in these cells. In the clonogenic assays, flow cytometry

assays and foci analysis we can see that the cytarabine fails to illicit a strong response

from the DDRD positive cells. Little research has been carried out on the mechanism

of action of cytarabine, beyond looking at conversion of cytarabine from its pro-drug

state to its active constitution. While defects in this mechanism have been

investigated, no clinical outcomes have come from this research. It has yet to be fully

investigated how the method in which cytarabine causes damage within the cell

could be causing the response witnessed in cells. This is not the case in the similar

nucleoside analogue, gemcitabine. This drug has been investigated at a DNA damage

level by research groups who focus on DNA repair mechanisms. As gemcitabine and

cytarabine have similar chemical structures and function similarly in the cell, it is

logical to assume that they garner the same response in cells. The gemcitabine

research has stressed the importance of functional repair mechanisms in inducing

significant damage in the cell following gemcitabine treatment. If this mechanism is

mirrored in cytarabine treated cells, it is likely the reason behind the lack of response

in the DDRD positive AML patients and their earlier death. Although a positive

response was not seen in the DDRD negative cell lines in the clonogenic assays and

flow cytometry assays, the foci data does reveal a decrease in ability of the NB4 and

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SKM1 cells to repair the cytarabine induced damage in comparison to IR induced

damage. The NB4 cell contains the t15;17 translocation. Patients which contained

this translocation were excluded from our Kaplan Meier graphs as they receive

different treatment regime’s and so are not directly comparable to patients receiving

the standard AML induction therapy.

The assay has also shown the ability to predict repair response in the DDRD positive

and DDRD negative cell lines. This has been seen through the foci analysis and the

cell lines response to DNA damaging agents and repair protein inhibitors. The foci

data has revealed that the DDRD positive cell line has a compromised ability to repair

damage. This has been mirrored in the candidate drug treatment chapter. A vast

amount of research has been completed on targeting repair pathway complexes in

cancer therapeutics. Our research closely tallies with the research undertaken by

other groups. Many repair inhibitors function by creating a synthetically lethal

phenotype. They disrupt one of the backup pathways that the cell has become

dependent on following the loss of a major repair pathway, likely during oncogenesis.

The inhibition of ATM by the ATMi AZD6738, is highly effective in the DDRD positive

cell line HL60. This is the cell line which has the predicted repair defect and so

interrupting an important repair complex create too much disruption in the cell and

cell death ensues. The nucleoside analogues closely follow the predictions with the

exception of the DDRD negative cell lines and cytarabine which has previously been

described.

It has been postulated that based in these results the DDRD assay has the ability to

predict a checkpoint defect. As has been discussed previously, the assay was based

on the differences in gene expression levels between FA or BRCA1/2 mutated

patients and a healthy cohort. As the phenotype of DNA repair defective cells and

checkpoint defective cells are similar, it may be that the gene signature the assay

describes may be very similar to the gene signature of a checkpoint defect. This area

is also currently being researched by the Almac scientists who developed the assay.

Their follow-up paper described the immune response of a cell which follows DNA

damage and the defective immune checkpoints in DDRD positive cells. This group

have also seen the potential of using this signature to identify patients which could

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respond to checkpoint based therapies. As the repair pathway components and

checkpoint machinery are highly linked, it is likely that this assay can predict for

defects in both. This has both advantages and disadvantages. A benefit of this is that

it widens the predictive nature of the assay and so it can be used to predict response

not only to DNA damaging agents but also theoretically to checkpoint inhibitors. This

also has downfalls, however. A less specific assay has a higher rate of outliers which

will not comply with the predictions set forth. This has been seen in the WEE1

inhibitor clonogenic assays as the SKM1 cell line. As a DDRD negative cell line, it was

predicted not to have a strong response to the inhibitor yet as we can see from Figure

4.7, the WEE1i was extremely potent in this cell line. Upon further research it was

revealed that CHK1 expression levels play a major role in WEE1i sensitivity, an aspect

the assay could not predict. That being said, it is clear from the experiments and

results put forward in this thesis and the research completed by other groups, that

this assay can accurately segregate patients based on their predicted response to

DNA damaging agents and checkpoint inhibitors. This can be hugely beneficial in

choosing treatment options for patients. This can be especially advantageous in AML

treatments as patients require an immediate effective treatment as the disease is so

progressive and fast-acting.

The CROPseq method has proven to be a highly effective technique of knocking down

multiple genes simultaneously. Although there were procedural issues which had to

be overcome, mastering this technique will lead to further experiments being carried

out in this manner and therefore much more information will be garnered. The

results of this experiment has emphasised the effects a mutation may have on the

DDRD score of a cell. It was supported the idea that a mutation in a known DDR gene

does not necessarily mean a repair deficiency. This score eliminates the need to

assess the effects of one mutation in a multigene pathway and can assess the

transcriptome on a whole.

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7.2 Conclusions

The results in this thesis have demonstrated the effects of benefits of using the DDRD

assay to predict DNA repair deficiency. This score has the ability to segregate patients

into two distinct groups, DDRD positive and DDRD negative, which have significantly

different survival rates. While not all mutations found in the DDRD positive patients

are necessarily the cause of the positive DDRD score, we have isolated some

mutations which are likely associated with DDRD positivity. The DDRD score also has

the ability to predict the repair proficiency of cell lines in in vitro assays. These assays,

flow cytometry, clonogenic assays and immunofluorescence all demonstrated a

repair deficiency in the DDRD positive cell lines. Moreover. From the clonogenic

assays confirmed the potential of using the DDRD assay to determine which AML

patients will or will not respond to specific treatments. These treatments need not

only be DNA damaging agents as was first believed but any agent that responds

differently in cells which have defective DNA repair or checkpoint pathways. With

further analysis, I believe this score could be used clinically to aid in the treatment

planning for AML patients.

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Figure 7.1 Graphical Abstract

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7.3 Future Works

During the course of this PhD there were many experiments which I would have liked

to perform but due to time constraints I did not manage to complete.

An issue I have perceived in this project is the lack of in vitro models for DDRD positive

and DDRD negative analysis. From the list of blood cancer cell lines sequenced, only

one of the DDRD positive cell lines was an AML cell line, the HL60 cell line. Therefore,

this was the only model used to examine DDRD positivity. Experiments involving

sequencing AML patient samples and assigning them a DDRD score were postulated.

These samples would then be used in foci analysis experiments and clonogenic assays

to corroborate the results seen in previous chapters. Great difficulties were faced

however when this experiment was started. AML patient samples are notoriously

difficult to work with. They show minimal growth and are only viable in-vitro for a

maximum of 2-3 weeks. It was therefore impossible to get enough cells from one

sample from which RNA had to be extracted for sequencing and cells could be plated

for clonogenic assays and flow cytometry analysis. Future experiments could involve

sequencing a larger quantity of cell lines to try and identify other AML cell lines which

were DDRD positive. This would add a greater depth to the analysis.

Further investigation into potential checkpoint defects would have been completed

if not due to time constraints. It was only towards the end of this project, when all

the data had been gathered, that the idea of the DDRD positive cell line containing a

checkpoint defect was conceived. Further cell cycle analysis and protein analysis of

checkpoint proteins could have given a deeper understanding of these pathway

disruption.

Deeper analysis of the damage cytarabine induces in the cells could be carried out.

As we have noticed in the foci data, a different response is seen in the DDRD positive

and DDRD negative cell lines. While it was hypothesised that the decrease in RAD51

foci in the DDRD positive cell lines was due to their failure to convert the stalled

replication forks into DSB, this area needs a great deal of further research.

The CROPseq analysis unearthed a wealth of information of which I have just

scratched the surface. The aim of this experiment was to identify which genes may

be driving the DDRD phenotype. While this aim was carried out using this method, it

195

also presented masses of information on the effects of knocking down these genes

in an AML cell line. The results of this CROPseq experiment will be analysed in full by

other members of the haematology lab in order to fully elucidate the effects of this

data. Also, at the time of submission, only one library had been normalised as it was

not possible to concatenate the two libraries. When the technical issues with

combining these libraries are resolved, the merged library will be reanalysed. While

it is highly likely that the results will be very similar, the extra reads will give greater

depth and significance.

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Chapter 8: Appendices

Risk category Genetic abnormality

Favourable t(8;21)(q22;q22.1); RUNX1-RUNX1T1

inv(16)(p13.1q22) or t(16;16)(p13.1;q22); CBFB-MYH11

Mutated NPM1 without FLT3-ITD or with FLT3-ITDlow

Biallelic mutated CEBPA

Intermediate Mutated NPM1 and FLT3-ITDhigh†

Wild-type NPM1 without FLT3-ITD or with FLT3-ITDlow (without adverse-risk genetic lesions)

t(9;11)(p21.3;q23.3); MLLT3-KMT2A

Cytogenetic abnormalities not classified as favourable or adverse

Adverse t(6;9)(p23;q34.1); DEK-NUP214

t(v;11q23.3); KMT2A rearranged

t(9;22)(q34.1;q11.2); BCR-ABL1

inv(3)(q21.3q26.2) or t(3;3)(q21.3;q26.2); GATA2,MECOM(EVI1)

−5 or del(5q); −7; −17/abn(17p)

Complex karyotype, monosomal karyotype

Wild-type NPM1 and FLT3-ITDhigh

Mutated RUNX1

Mutated ASXL1

Mutated TP53

Acute myeloid leukemia (AML) and related neoplasms

AML with recurrent genetic abnormalities

AML with t(8;21)(q22;q22.1);RUNX1-RUNX1T1

AML with inv(16)(p13.1q22) or t(16;16)(p13.1;q22);CBFB-MYH11

APL with PML-RARA

AML with t(9;11)(p21.3;q23.3);MLLT3-KMT2A

AML with t(6;9)(p23;q34.1);DEK-NUP214

AML with inv(3)(q21.3q26.2) or t(3;3)(q21.3;q26.2); GATA2, MECOM

AML (megakaryoblastic) with t(1;22)(p13.3;q13.3);RBM15-MKL1

Provisional entity: AML with BCR-ABL1

AML with mutated NPM1

AML with biallelic mutations of CEBPA

Provisional entity: AML with mutated RUNX1

197

AML with myelodysplasia-related changes

Therapy-related myeloid neoplasms

AML, NOS

AML with minimal differentiation

AML without maturation

AML with maturation

Acute myelomonocytic leukemia

Acute monoblastic/monocytic leukemia

Pure erythroid leukemia

Acute megakaryoblastic leukemia

Acute basophilic leukemia

Acute panmyelosis with myelofibrosis

Myeloid sarcoma

Myeloid proliferations related to Down syndrome

Transient abnormal myelopoiesis (TAM)

Myeloid leukemia associated with Down syndrome

Blastic plasmacytoid dendritic cell neoplasm

Acute leukemias of ambiguous lineage

Acute undifferentiated leukemia

Mixed phenotype acute leukemia (MPAL) with t(9;22)(q34.1;q11.2); BCR-ABL1

MPAL with t(v;11q23.3); KMT2A rearranged

MPAL, B/myeloid, NOS

MPAL, T/myeloid, NOS

Table 8.1: WHO AML Classification

2016 Updated WHO AML Classification. Published in Blood 2016

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Risk category Genetic abnormality

Favourable t(8;21)(q22;q22.1); RUNX1-RUNX1T1

inv(16)(p13.1q22) or t(16;16)(p13.1;q22); CBFB-MYH11

Mutated NPM1 without FLT3-ITD or with FLT3-ITDlow

Biallelic mutated CEBPA

Intermediate Mutated NPM1 and FLT3-ITDhigh†

Wild-type NPM1 without FLT3-ITD or with FLT3-ITDlow (without adverse-risk genetic lesions)

t(9;11)(p21.3;q23.3); MLLT3-KMT2A

Cytogenetic abnormalities not classified as favourable or adverse

Adverse t(6;9)(p23;q34.1); DEK-NUP214

t(v;11q23.3); KMT2A rearranged

t(9;22)(q34.1;q11.2); BCR-ABL1

inv(3)(q21.3q26.2) or t(3;3)(q21.3;q26.2); GATA2,MECOM(EVI1)

−5 or del(5q); −7; −17/abn(17p)

Complex karyotype, monosomal karyotype

Wild-type NPM1 and FLT3-ITDhigh

Mutated RUNX1

Mutated ASXL1

Mutated TP53

Table 8.2: ELN risk categories and abnormalities

The updated 2017 ELN risk stratification by genetics. Published in Blood 2017

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Gene Sequence in Plasmid

ARHGEF4 TGGAAAGGACGAAACACCGTCTCTGCAGAGACTGTGCGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ARHGEF4 TGGAAAGGACGAAACACCGGAGATTCAAAGACTCTGGAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ARHGEF4 TGGAAAGGACGAAACACCGTCTCGGCAGCTCATCAGCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ASXL1 TGGAAAGGACGAAACACCGTGTCCGCCTCACCAGGCGCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ASXL1 TGGAAAGGACGAAACACCGATAGCATTGAGGCATGCGAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ASXL1 TGGAAAGGACGAAACACCGTGGATGGCGAGACCACTGCAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ATM TGGAAAGGACGAAACACCGGATGGCAGATATCTGTCACCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ATM TGGAAAGGACGAAACACCGGACACAATGCAACTTCCGTAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ATM TGGAAAGGACGAAACACCGAAAGTCAAACAGCATACTGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ATR TGGAAAGGACGAAACACCGGAAATCAAGCAACATCACGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ATR TGGAAAGGACGAAACACCGCTTGTGTAACAAATGACAGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

ATR TGGAAAGGACGAAACACCGGTGATGGAATATCACCCAAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

BCOR TGGAAAGGACGAAACACCGTGTGAACGTTCCCATACAGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

BCOR TGGAAAGGACGAAACACCGACTGGGCGATACCACAGCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

BCOR TGGAAAGGACGAAACACCGACTGACCTCACAGTAAGCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

BRCA1 TGGAAAGGACGAAACACCGGAACTCTGAGGACAAAGCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

BRCA1 TGGAAAGGACGAAACACCGCAAGGAGCCAACATAACAGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

BRCA1 TGGAAAGGACGAAACACCGTAACCTGATAAAGCTCCAGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

BRCA2 TGGAAAGGACGAAACACCGTTTACAGGAGATTGGTACAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

BRCA2 TGGAAAGGACGAAACACCGAACAAACTCCCACATACCACGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

BRCA2 TGGAAAGGACGAAACACCGGAGCACAGTAGAACTAAGGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CBFB TGGAAAGGACGAAACACCGGAGAAGCAAGTTCGAGAACGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

200

CBFB TGGAAAGGACGAAACACCGAGCGTGCCTGGCGTTCCTCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CBFB TGGAAAGGACGAAACACCGGAGAGACAGATTGGTTCCTGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CBL TGGAAAGGACGAAACACCGGACGGTGGACAAGAAGATGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CBL TGGAAAGGACGAAACACCGATGAGGAGAATTCTCAGCCTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CBL TGGAAAGGACGAAACACCGCCTGTCGAAAGCTCTTCCAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CDKN2A TGGAAAGGACGAAACACCGACGCACCGAATAGTTACGGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CDKN2A TGGAAAGGACGAAACACCGTGACTGGCTGGCCACGGCCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CDKN2A TGGAAAGGACGAAACACCGGTGGCCAGCCAGTCAGCCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CEBPA TGGAAAGGACGAAACACCGTGAAGCCAAGCAGCTGGCGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CEBPA TGGAAAGGACGAAACACCGCCTGCCGTCCAGGTAGCCGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CEBPA TGGAAAGGACGAAACACCGGTCGGCCGACTTCTACGAGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CHEK1 TGGAAAGGACGAAACACCGCTTCCATCAACTCATGGCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CHEK1 TGGAAAGGACGAAACACCGGGAATGGTAATTCTTGCTGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CHEK1 TGGAAAGGACGAAACACCGGGTGTGTCAGAGTCTCCCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CHEK2 TGGAAAGGACGAAACACCGGATTGGCAAATCCATCCTGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CHEK2 TGGAAAGGACGAAACACCGCTTAGACTGCAAACTGGCCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CHEK2 TGGAAAGGACGAAACACCGGCATACATAGAAGATCACAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CLEC18B TGGAAAGGACGAAACACCGGCACCACCTGGGCAGAGGTGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CLEC18B TGGAAAGGACGAAACACCGGTGGAAGGGAAAATGCACCTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CLEC18B TGGAAAGGACGAAACACCGGTGTAGCAGGGCTTACCCGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CSMD1 TGGAAAGGACGAAACACCGGAGGCTCCTCACTGCAGCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CSMD1 TGGAAAGGACGAAACACCGACGATGGACAGCCTCAACAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

CSMD1 TGGAAAGGACGAAACACCGCATGGAACGAGATTCAACATGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

201

DDX11 TGGAAAGGACGAAACACCGAAAGTCACGGAGCCAAGAGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DDX11 TGGAAAGGACGAAACACCGTGTAGGCGGAGCAGGCCAGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DDX11 TGGAAAGGACGAAACACCGCAGCCGAACATCCTTGCCAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DNMT3A TGGAAAGGACGAAACACCGGCTACCACGCCTGAGCCCGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DNMT3A TGGAAAGGACGAAACACCGGAGCAGCTGAAGGCACCCGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DNMT3A TGGAAAGGACGAAACACCGGTACCGCAAAGCCATCTACGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DRD2 TGGAAAGGACGAAACACCGCCTGATCGTCAGCCTCGCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DRD2 TGGAAAGGACGAAACACCGTAGGTGAGTGGAAATTCAGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DRD2 TGGAAAGGACGAAACACCGGAAGGACAGGACCCAGACGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

EZH2 TGGAAAGGACGAAACACCGACTGGGAAGAAATCTGAGAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

EZH2 TGGAAAGGACGAAACACCGACCAAGAATGGAAACAGCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

EZH2 TGGAAAGGACGAAACACCGGATCTGGAGGATCACCGAGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

FANCF TGGAAAGGACGAAACACCGAGATAGACAGGAGACAGCGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

FANCF TGGAAAGGACGAAACACCGTCTGAGGCAAGCGCTCCCACGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

FANCF TGGAAAGGACGAAACACCGAGAGAACCCAAATCTCCAGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

FLT3 TGGAAAGGACGAAACACCGGAAGTCATCATCATATCCCAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

FLT3 TGGAAAGGACGAAACACCGAGTGTACGAAGCTGCCGCTGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

FLT3 TGGAAAGGACGAAACACCGAAAGTAGGTATTCTCCAGCTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

HNRNPK TGGAAAGGACGAAACACCGGTTTCAGTCCCAGACAGCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

HNRNPK TGGAAAGGACGAAACACCGAAATGAGCCTACCTCTTCCAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

HNRNPK TGGAAAGGACGAAACACCGATCCCACTGGGCGTCCGCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

IDH1 TGGAAAGGACGAAACACCGATGTAGATCCAATTCCACGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

IDH1 TGGAAAGGACGAAACACCGACCCATCCACTCACAAGCCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

202

IDH1 TGGAAAGGACGAAACACCGTAGGCTCATCGACGACATGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

IDH2 TGGAAAGGACGAAACACCGAGAGCGAGCGCACGACCCGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

IDH2 TGGAAAGGACGAAACACCGGATCAAGGTGGCGAAGCCCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

IDH2 TGGAAAGGACGAAACACCGACACCCTCGCACCTTCCACAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

KIT TGGAAAGGACGAAACACCGTCAGACTTAATAGTCCGCGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

KIT TGGAAAGGACGAAACACCGGAAAGAAGACAACGACACGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

KIT TGGAAAGGACGAAACACCGGCAAGCTATCTTCTTAGGGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

KRAS TGGAAAGGACGAAACACCGGTAGTTGGAGCTGGTGGCGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

KRAS TGGAAAGGACGAAACACCGGGACCAGTACATGAGGACTGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

KRAS TGGAAAGGACGAAACACCGAAGAGGAGTACAGTGCAATGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

LRP1B TGGAAAGGACGAAACACCGATTGCCAGGGTGCTGACCGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

LRP1B TGGAAAGGACGAAACACCGGACGAAGGAGTACATTGTCAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

LRP1B TGGAAAGGACGAAACACCGGGTGACACATACAGAACCGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MDC1 TGGAAAGGACGAAACACCGGTAACGTGGAGCCAGTAGGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MDC1 TGGAAAGGACGAAACACCGGCAGAGAGACATCCAGGCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MDC1 TGGAAAGGACGAAACACCGGAGGTGGAAGGCTGAAGCTCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MLH1 TGGAAAGGACGAAACACCGAGTGGTGAACCGCATCGCGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MLH1 TGGAAAGGACGAAACACCGCTGGGTGAAGTACATCCTGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MLH1 TGGAAAGGACGAAACACCGTAAGGTCTATGCCCACCAGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MSH2 TGGAAAGGACGAAACACCGGGTCTTGAACACCTCCCGGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MSH2 TGGAAAGGACGAAACACCGGGTATGTGGATTCCATACAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MSH2 TGGAAAGGACGAAACACCGTGAGAGGCTGCTTAATCCACGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MUC16 TGGAAAGGACGAAACACCGAATGGATACCACCTCCACCAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

203

MUC16 TGGAAAGGACGAAACACCGTGCAGGATGAGTGAGCCACGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MUC16 TGGAAAGGACGAAACACCGGAGGAGGACATGCGGCACCCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MYC TGGAAAGGACGAAACACCGGCTGCACCGAGTCGTAGTCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MYC TGGAAAGGACGAAACACCGAGGGCGAGCAGAGCCCGGAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

MYC TGGAAAGGACGAAACACCGGAAGGGTGTGACCGCAACGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

NOTCH2NL TGGAAAGGACGAAACACCGTGAGAAGAGGAACAGAGCTCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

NOTCH2NL TGGAAAGGACGAAACACCGCTGTAAACCCTGAGGCACATGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

NOTCH2NL TGGAAAGGACGAAACACCGGGGATGAGACAGGCAGGCATGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

NPM1 TGGAAAGGACGAAACACCGTCTCCCTTCTAGGTTTCCCTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

NPM1 TGGAAAGGACGAAACACCGCAAAGATTATCACTTTAAGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

NPM1 TGGAAAGGACGAAACACCGTCTTAAGTATATCTGGAAAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

NRAS TGGAAAGGACGAAACACCGACTGGGCCTCACCTCTATGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

NRAS TGGAAAGGACGAAACACCGAATGACTGAGTACAAACTGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

NRAS TGGAAAGGACGAAACACCGCAATACATGAGGACAGGCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PARP1 TGGAAAGGACGAAACACCGAAGAAGACAGCGGAAGCTGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PARP1 TGGAAAGGACGAAACACCGAGCTAGGCATGATTGACCGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PARP1 TGGAAAGGACGAAACACCGCAAGCAGCAAGTGCCTTCTGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PCDHA13 TGGAAAGGACGAAACACCGAGTGGAAATCGAGTTTCCGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PCDHA13 TGGAAAGGACGAAACACCGAAAGAAGTGATGGTAACCTCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PCDHA13 TGGAAAGGACGAAACACCGAAGCGCCACAGGCTTCGTCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PHF6 TGGAAAGGACGAAACACCGATACGAGAGAAACCTTCACAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PHF6 TGGAAAGGACGAAACACCGAGTGACACCAGGCCTAAATGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PHF6 TGGAAAGGACGAAACACCGGTACTTCAGGAGATTAAACGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

204

PKD1L2 TGGAAAGGACGAAACACCGGAATCTGTCGGAGAATATCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PKD1L2 TGGAAAGGACGAAACACCGGGGCTGGCCAGACAATGACAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PKD1L2 TGGAAAGGACGAAACACCGGTCTACACAGGACACCGACGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PTPN11 TGGAAAGGACGAAACACCGGGAGGAACATGACATCGCGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PTPN11 TGGAAAGGACGAAACACCGGTAGGATCTGCACAGTTCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

PTPN11 TGGAAAGGACGAAACACCGCTGACAGCGAATCATAACATGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RAD21 TGGAAAGGACGAAACACCGGGAGAGTATCATCTCACCAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RAD21 TGGAAAGGACGAAACACCGGATCGTGAGATAATGAGAGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RAD21 TGGAAAGGACGAAACACCGTCTGGCAGAAGTTCTAACTCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RAD51 TGGAAAGGACGAAACACCGAGAAGCTGGATTCCATACTGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RAD51 TGGAAAGGACGAAACACCGTTGGTGGAATTCAGTTGCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RAD51 TGGAAAGGACGAAACACCGGCCAGAACGGCTGCTGGCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RUNX1 TGGAAAGGACGAAACACCGGATGAGCGAGGCGTTGCCGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RUNX1 TGGAAAGGACGAAACACCGAACCTGGTTCTTCATGGCTGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RUNX1 TGGAAAGGACGAAACACCGTGGTAGGTGGCGACTTGCGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RUNX1T1 TGGAAAGGACGAAACACCGTACCACTAGTCCCAGAACGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RUNX1T1 TGGAAAGGACGAAACACCGGTTTGGCCAGTCTTGCGCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

RUNX1T1 TGGAAAGGACGAAACACCGGGTGAGGCAGGCCATTGGGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SF3B1 TGGAAAGGACGAAACACCGAAGATCGCCAAGACTCACGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SF3B1 TGGAAAGGACGAAACACCGTAAGGAAGGAGTATGCCCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SF3B1 TGGAAAGGACGAAACACCGACTCCTCGAACAGATCGAGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SMC1A TGGAAAGGACGAAACACCGAGATTATCGGACCATTTCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SMC1A TGGAAAGGACGAAACACCGTTGGCAGCTGGCTTGCCCACGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

205

SMC1A TGGAAAGGACGAAACACCGGCGAAAGAAGGAAATGGTGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SMC3 TGGAAAGGACGAAACACCGGACAAGAGCAGATTAAGCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SMC3 TGGAAAGGACGAAACACCGTAAGTTGGAGCTTAAAGCCAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SMC3 TGGAAAGGACGAAACACCGACAGTCCATAGTGAAAGCACGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SPEG TGGAAAGGACGAAACACCGGTGGCCATGCAGAAAGCCCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SPEG TGGAAAGGACGAAACACCGTGTGCAGGGAACCCAGCGCCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SPEG TGGAAAGGACGAAACACCGAAGGCAAGCACAGGCAACCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SRSF1 TGGAAAGGACGAAACACCGGACCTCAAGAATCGCCGCGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SRSF1 TGGAAAGGACGAAACACCGATCGACCTCAAGAATCGCCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SRSF1 TGGAAAGGACGAAACACCGGACGCGGTGTATGGTCGCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

STAG2 TGGAAAGGACGAAACACCGTCATCACCAACAGAATGGAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

STAG2 TGGAAAGGACGAAACACCGATTTCGACATACAAGCACCCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

STAG2 TGGAAAGGACGAAACACCGGCTGAATGTCATCCTCCCGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SUZ12 TGGAAAGGACGAAACACCGGCTTCGGGCGGCAAATCCGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SUZ12 TGGAAAGGACGAAACACCGAAGGAGAGCAAGAATCTCATGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

SUZ12 TGGAAAGGACGAAACACCGTTACTGGAAACTGCAAGGGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TCEAL3 TGGAAAGGACGAAACACCGAGAATGCGAGGGAAAGAGAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TCEAL3 TGGAAAGGACGAAACACCGCAAAAAGAAAAACGGACAGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TCEAL3 TGGAAAGGACGAAACACCGCTCAGGAGGACTTACAGGAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TET2 TGGAAAGGACGAAACACCGGATGGATTAGGACTCTGGGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TET2 TGGAAAGGACGAAACACCGTTGCCAGAAGCAAGATCCCAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TET2 TGGAAAGGACGAAACACCGGGAAGGCCGTCCATTCTCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TP53 TGGAAAGGACGAAACACCGGATCCACTCACAGTTTCCATGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

206

TP53 TGGAAAGGACGAAACACCGACCAGCAGCTCCTACACCGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TP53 TGGAAAGGACGAAACACCGGCAGTCACAGCACATGACGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TP53BP1 TGGAAAGGACGAAACACCGTGTGCGTCTGGAGATTAGGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TP53BP1 TGGAAAGGACGAAACACCGTGTGCGTCTGGAGATTAGGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TP53BP1 TGGAAAGGACGAAACACCGTCCAAGTTAGAAGAATCCACGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TTN TGGAAAGGACGAAACACCGAGAGTCAGCATACCTTCAGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TTN TGGAAAGGACGAAACACCGGCCCAAGAAACCTGTGCCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

TTN TGGAAAGGACGAAACACCGATTAATGTACCTTTGGGTGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

U2AF1 TGGAAAGGACGAAACACCGAAACAAACCTGGCTAAACGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

U2AF1 TGGAAAGGACGAAACACCGTCGGCTGTCCATTAAACCAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

U2AF1 TGGAAAGGACGAAACACCGGGAATACTCACTTCTTGCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

WT1 TGGAAAGGACGAAACACCGATTCAAGCATGAGGATCCCAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

WT1 TGGAAAGGACGAAACACCGGTAGCTGGGCGTCCCGTCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

WT1 TGGAAAGGACGAAACACCGGGTGTGGCAGCCATAGACCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DIS3 TGGAAAGGACGAAACACCGTGCGGGCAGACGCTGCTCGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DIS3 TGGAAAGGACGAAACACCGTCGGATGCGTTTATATACGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

DIS3 TGGAAAGGACGAAACACCGTCTATATCAGTACATCCTGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

LacZ TGGAAAGGACGAAACACCGGCTGGTGGTCAGATGCGGGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

LacZ TGGAAAGGACGAAACACCGCTGCGATGTCGGTTTCCGCGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

LacZ TGGAAAGGACGAAACACCGCCCGAATCTCTATCGTGCGGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

luciferase TGGAAAGGACGAAACACCGCGCAGGCAGTTCTATGCGGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

luciferase TGGAAAGGACGAAACACCGAGGTAACCCAGTAGATCCAGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

luciferase TGGAAAGGACGAAACACCGGCAGGCAGTTCTATGCGGAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

207

EGFP TGGAAAGGACGAAACACCGTTCAAGTCCGCCATGCCCGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

EGFP TGGAAAGGACGAAACACCGTGGTTGTCGGGCAGCAGCACGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

EGFP TGGAAAGGACGAAACACCGCGGCGGTCACGAACTCCAGCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

EGFP TGGAAAGGACGAAACACCGGGTTGTCGGGCAGCAGCACGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

chrX_safe TGGAAAGGACGAAACACCGGGCAAAATTCCCTCAGTTTAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

chr13_safe TGGAAAGGACGAAACACCGGACCCAGAATCTCAGAATCTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

chr12_safe TGGAAAGGACGAAACACCGGAAGTGTTGCCATTCAATTCGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

chr11_safe TGGAAAGGACGAAACACCGGCCCTTAGTGAGATAGGGAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

chr10_safe TGGAAAGGACGAAACACCGGCCCTACTGTGGTAACTTTGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

chr17_safe TGGAAAGGACGAAACACCGGAATTAAATTCACTTTGAACGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

chr16_safe TGGAAAGGACGAAACACCGGTACTCAAATTAGACACTATGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

chr15_safe TGGAAAGGACGAAACACCGGGCACTCGCTGAGAGGTGCTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

chr14_safe TGGAAAGGACGAAACACCGGCATTGCATTCCTCTTGTAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

chr19_safe TGGAAAGGACGAAACACCGGTCTGTATTTCAGTCTGTGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC

Table 8.3: Table of guide sequences for CRISPR CROPseq screen

Guide sequences include 5’ and 3’ overhangs that are needed for the library assembly and

amplification.

208

Guides Genes DDRD Score

Guides Genes DDRD Score

guide_1 ARHGEF4 0.16657047

guide_6 ASXL1 0.21958115


guide_4 ASXL1 0.20778502


guide_5 ASXL1 0.35916565


guide_5 ASXL1 0.09190252


guide_6 ASXL1 0.17105957


guide_4 ASXL1 0.27585428


guide_6 ASXL1 0.39586678


guide_5 ASXL1 0.22911176


guide_4 ASXL1 0.26875556


guide_4 ASXL1 0.2083626


guide_7 ATM 0.12717568


guide_8 ATM 0.26628048


guide_8 ATM 0.2823604


guide_9 ATM 0.10547493


guide_8 ATM 0.22934826


guide_9 ATM 0.24906125


guide_8 ATM 0.10310758


guide_8 ATM 0.27733552

guide_4 ASXL1 0.23262406

guide_9 ATM 0.17786838

guide_6 ASXL1 0.11384623

guide_8 ATM 0.22368884

guide_6 ASXL1 0.30170613

guide_8 ATM 0.16634588

guide_5 ASXL1 0.29106431

guide_9 ATM 0.27919649

guide_4 ASXL1 0.12790572

guide_9 ATM 0.12757925

guide_4 ASXL1 0.11750197

guide_7 ATM 0.16597547

guide_6 ASXL1 0.16689071

guide_8 ATM 0.17542953

guide_6 ASXL1 0.20865953

guide_7 ATM 0.23436963

guide_6 ASXL1 0.30755058

guide_8 ATM 0.15688613

guide_4 ASXL1 0.26478406

guide_7 ATM 0.23849155

guide_4 ASXL1 0.2469835

guide_8 ATM 0.11306346

guide_4 ASXL1 0.19750141

guide_8 ATM 0.16915177

guide_4 ASXL1 0.27137831

guide_7 ATM 0.27305118

guide_4 ASXL1 0.21787382

guide_7 ATM 0.29966623

guide_6 ASXL1 0.23252564

guide_9 ATM 0.08014153

guide_6 ASXL1 0.31829632

guide_9 ATM 0.15854603

guide_4 ASXL1 0.19796556

guide_8 ATM 0.18241912

guide_4 ASXL1 0.19527123

guide_9 ATM 0.25090357

guide_6 ASXL1 0.2198422

guide_8 ATM 0.20818086

guide_4 ASXL1 0.2433199

guide_9 ATM 0.31105979

guide_6 ASXL1 0.08748501

guide_7 ATM 0.18394043

guide_5 ASXL1 0.34021623

guide_7 ATM 0.12029949

guide_4 ASXL1 0.12166875

guide_9 ATM 0.09264287

209

guide_4 ASXL1 0.13456632

guide_8 ATM 0.16802172

guide_9 ATM 0.25925911

guide_15 BCOR 0.28725561

guide_8 ATM 0.21326394

guide_14 BCOR 0.14421629

guide_10 ATR 0.20119096

guide_13 BCOR 0.1842287

guide_12 ATR 0.20922596

guide_16a BRCA1 0.15076733

guide_12 ATR 0.2767233

guide_18 BRCA1 0.19984986

guide_12 ATR 0.27960235

guide_18 BRCA1 0.15603412

guide_10 ATR 0.12279359

guide_18 BRCA1 0.23601877

guide_12 ATR 0.23829012

guide_17 BRCA1 0.17777058

guide_10 ATR 0.28009788

guide_18 BRCA1 0.30757765

guide_10 ATR 0.2046559

guide_17 BRCA1 0.23710361

guide_10 ATR 0.25513238

guide_18 BRCA1 0.20628002

guide_10 ATR 0.20641373

guide_17 BRCA1 0.19338114

guide_12 ATR 0.16992071

guide_16 BRCA1 0.1656223

guide_12 ATR 0.17742161

guide_18 BRCA1 0.18811014

guide_12 ATR 0.16341474

guide_17 BRCA1 0.18077755

guide_12 ATR 0.15783207

guide_16 BRCA1 0.11048626

guide_12 ATR 0.23266716

guide_17 BRCA1 0.23689996

guide_10 ATR 0.23841563

guide_17 BRCA1 0.17477835

guide_12 ATR 0.14364427

guide_17 BRCA1 0.3083361

guide_10 ATR 0.16433245

guide_20 BRCA2 0.16286303

guide_13 BCOR 0.153664

guide_19 BRCA2 0.07185922

guide_13 BCOR 0.2677774

guide_20 BRCA2 0.16398291

guide_13 BCOR 0.12029949

guide_20 BRCA2 0.15740117

guide_15 BCOR 0.24438952

guide_20 BRCA2 0.10591517

guide_14 BCOR 0.21059016

guide_21 BRCA2 0.38117342

guide_14 BCOR 0.18513324

guide_20 BRCA2 0.23047558

guide_14 BCOR 0.29534843

guide_20 BRCA2 0.15534079

guide_13 BCOR 0.18842035

guide_20 BRCA2 0.20254046

guide_13 BCOR 0.16152249

guide_20 BRCA2 0.31466305

guide_15 BCOR 0.18176936

guide_20 BRCA2 0.16887923

guide_14 BCOR 0.20085128

guide_21 BRCA2 0.22967815

guide_14 BCOR 0.24588563

guide_20 BRCA2 0.14750419

guide_13 BCOR 0.12029949

guide_20 BRCA2 0.2472995

guide_14 BCOR 0.03963402

guide_19 BRCA2 0.1343291

guide_14 BCOR 0.23541345

guide_20 BRCA2 0.27931673

guide_13 BCOR 0.2145021

guide_21 BRCA2 0.33522543

guide_13 BCOR 0.21712583

guide_19 BRCA2 0.13308156

guide_14 BCOR 0.15784935

guide_21 BRCA2 0.27864218

guide_14 BCOR 0.12584812

guide_19 BRCA2 -0.0192622

guide_14 BCOR 0.23955478

guide_21 BRCA2 0.33803738

guide_15 BCOR 0.15881738

guide_20 BRCA2 0.06659679

210

guide_14 BCOR 0.24008994

guide_20 BRCA2 0.14458304

guide_20 BRCA2 0.19420067

guide_27 CBL 0.17270367

guide_20 BRCA2 0.21622355

guide_26 CBL 0.17083458

guide_21 BRCA2 0.2310211

guide_26 CBL 0.24092526

guide_20 BRCA2 0.25622047

guide_26 CBL 0.21461353

guide_22 CBFB 0.26154504

guide_27 CBL 0.11412065

guide_24 CBFB 0.26400635

guide_26 CBL 0.1716704

guide_22 CBFB 0.32709034

guide_25 CBL 0.21356021

guide_23 CBFB 0.17392586

guide_27 CBL 0.22626039

guide_23 CBFB 0.19423998

guide_27 CBL 0.23921586

guide_23 CBFB 0.17054741

guide_26 CBL 0.20209776

guide_23 CBFB 0.15813654

guide_27 CBL 0.09343388

guide_24 CBFB 0.04475795

guide_27 CBL 0.15557111

guide_23 CBFB 0.1873081

guide_30 CDKN2A 0.06931293

guide_22 CBFB 0.30375366

guide_28 CDKN2A 0.17381026

guide_23 CBFB 0.25731861

guide_28 CDKN2A 0.2383619

guide_22 CBFB 0.1527792

guide_29 CDKN2A 0.24029892

guide_23 CBFB 0.24868609

guide_30 CDKN2A 0.30441247

guide_23 CBFB 0.18739746

guide_28 CDKN2A 0.20346987

guide_23 CBFB 0.14917796

guide_29 CDKN2A 0.25542765

guide_23 CBFB 0.21474593

guide_28 CDKN2A 0.38552597

guide_24 CBFB 0.15395845

guide_28 CDKN2A 0.17211991

guide_24 CBFB 0.17944103

guide_29 CDKN2A 0.15677021

guide_24 CBFB 0.15058591

guide_30 CDKN2A 0.21040947

guide_26 CBL 0.12853384

guide_30 CDKN2A 0.20860813

guide_25 CBL 0.12029949

guide_28 CDKN2A 0.25339122

guide_27 CBL 0.24724279

guide_30 CDKN2A 0.15747195

guide_27 CBL 0.27434302

guide_28 CDKN2A 0.27540648

guide_27 CBL 0.15942118

guide_28 CDKN2A 0.24158061

guide_27 CBL 0.23473684

guide_28 CDKN2A 0.1787757

guide_26 CBL 0.1409335

guide_28 CDKN2A 0.22697597

guide_27 CBL 0.23928661

guide_28 CDKN2A 0.33371664

guide_26 CBL 0.17837823

guide_32 CEBPA 0.19277764

guide_26 CBL 0.12060621


guide_26 CBL 0.14559852


guide_25 CBL 0.25620802


guide_27 CBL 0.13997523


guide_27 CBL 0.28647524


guide_26 CBL 0.23523614


guide_27 CBL 0.15621428


guide_26 CBL 0.1443113


guide_27 CBL 0.19129728


211

guide_27 CBL 0.21337773



guide_39 CHK2 0.2502247


guide_39 CHK2 0.19324102


guide_38 CHK2 0.16621764


guide_37 CHK2 0.17508996


guide_38 CHK2 0.25836341


guide_38 CHK2 0.25913863


guide_37 CHK2 0.26571768


guide_37 CHK2 0.17154484


guide_39 CHK2 0.10561328

guide_34 CHK1 0.20863606

guide_37 CHK2 0.26631474

guide_35 CHK1 0.11520584

guide_38 CHK2 0.27494937

guide_36 CHK1 0.15412142

guide_39 CHK2 0.21995144

guide_36 CHK1 0.03059395

guide_39 CHK2 0.28694226

guide_34 CHK1 0.06905691

guide_38 CHK2 0.04211793

guide_36 CHK1 0.21881199

guide_38 CHK2 0.22119509

guide_34 CHK1 0.04243561

guide_39 CHK2 0.28550389

guide_35 CHK1 0.08045115

guide_37 CHK2 0.19250513

guide_35 CHK1 0.17056895

guide_39 CHK2 0.31734989

guide_34 CHK1 0.26525313

guide_38 CHK2 0.05787048

guide_35 CHK1 0.22552246

guide_39 CHK2 0.12029949

guide_36 CHK1 0.19071075

guide_37 CHK2 0.11625157

guide_35 CHK1 0.19362902

guide_38 CHK2 0.12358052

guide_35 CHK1 0.2408785

guide_39 CHK2 0.11180289

guide_35 CHK1 0.1236047

guide_39 CHK2 0.18240875

guide_34 CHK1 0.21918525

guide_38 CHK2 0.30219456

guide_34 CHK1 0.12029949

guide_37 CHK2 0.20699054

guide_35 CHK1 0.13353594

guide_192 chr10 0.18455768

guide_34 CHK1 0.15036718

guide_192 chr10 0.22715313

guide_35 CHK1 0.18243485

guide_192 chr10 0.23590792

guide_34 CHK1 0.22005741

guide_192 chr10 0.3040935

guide_35 CHK1 0.19965021

guide_192 chr10 0.18167203

guide_34 CHK1 0.18050719

guide_192 chr10 0.31530146

guide_38 CHK2 0.13011955

guide_192 chr10 0.1831992

guide_38 CHK2 0.13596601

guide_192 chr10 0.06414055

guide_37 CHK2 0.33488924

guide_192 chr10 0.20696918

guide_38 CHK2 0.31591027

guide_192 chr10 0.12747916

guide_38 CHK2 0.25750456

guide_192 chr10 0.15330695

guide_37 CHK2 0.18369614

guide_192 chr10 0.20587288

guide_37 CHK2 0.24941105

guide_192 chr10 0.18448787

guide_39 CHK2 0.25739801

guide_192 chr10 0.11865222

guide_37 CHK2 0.17857852

guide_192 chr10 0.17751222

212

guide_38 CHK2 0.1938243

guide_191 chr11 0.15486499

guide_191 chr11 0.23102866

guide_196 chr14 0.25220079

guide_191 chr11 0.20912491

guide_196 chr14 0.18199314

guide_191 chr11 0.27474489

guide_196 chr14 0.22607154

guide_191 chr11 0.38709032

guide_196 chr14 0.2735377

guide_191 chr11 0.24198958

guide_196 chr14 0.12029949

guide_191 chr11 0.24641044

guide_196 chr14 0.27404151

guide_191 chr11 0.29112457

guide_196 chr14 0.17449213

guide_191 chr11 0.24963128

guide_195 chr15 0.14440565

guide_190 chr12 0.17465815

guide_195 chr15 0.04368718

guide_190 chr12 0.09280025

guide_195 chr15 0.15863264

guide_190 chr12 0.21733372

guide_195 chr15 0.23779477

guide_190 chr12 0.1838129

guide_194 chr16 0.048711

guide_190 chr12 0.32086597

guide_194 chr16 0.24460962

guide_190 chr12 0.15641809

guide_194 chr16 0.22372796

guide_190 chr12 0.12580409

guide_194 chr16 0.10212641

guide_190 chr12 0.1981216

guide_194 chr16 0.26165293

guide_190 chr12 0.18269636

guide_194 chr16 -0.0157871

guide_189 chr13 0.24207619

guide_194 chr16 0.13307652

guide_189 chr13 0.2187408

guide_194 chr16 0.25677059

guide_189 chr13 0.2086212

guide_194 chr16 0.1719833

guide_189 chr13 0.22491507

guide_194 chr16 0.17503372

guide_189 chr13 0.15509159

guide_194 chr16 0.14923769

guide_189 chr13 0.10302282

guide_194 chr16 0.12029949

guide_189 chr13 0.06521408

guide_194 chr16 0.2499322

guide_189 chr13 0.23618793

guide_194 chr16 0.18647751

guide_189 chr13 0.03834353

guide_194 chr16 0.23359778

guide_189 chr13 0.23291121

guide_194 chr16 0.16970353

guide_189 chr13 0.26173375

guide_194 chr16 0.31172628

guide_189 chr13 0.3855627

guide_194a chr16 0.21968001

guide_189 chr13 0.07272126

guide_194 chr16 0.13991385

guide_189 chr13 0.24145096

guide_194 chr16 0.1533406

guide_189 chr13 0.05185978

guide_194 chr16 0.27079203

guide_189 chr13 0.1947258

guide_193 chr17 0.2717123

guide_189 chr13 0.18390156

guide_193 chr17 0.21847602

guide_189 chr13 0.21024208

guide_193 chr17 0.22680606

guide_196 chr14 0.1766036

guide_193 chr17 0.26695895

guide_196 chr14 0.3397315

guide_193 chr17 0.22491817

guide_196 chr14 0.07691942

guide_197 chr19 0.20973244

guide_196 chr14 0.18821469

guide_197 chr19 0.27090784

guide_196 chr14 0.11414739

guide_197 chr19 0.26185687

guide_196 chr14 0.20043267

guide_197 chr19 0.20985459

213

guide_196 chr14 0.24291557

guide_197 chr19 0.21939363

guide_197 chr19 0.06579725

guide_41 CLEC18B 0.16491641

guide_197 chr19 0.23691372

guide_41 CLEC18B 0.2335884

guide_197 chr19 0.25713647

guide_40 CLEC18B 0.13244651

guide_197 chr19 0.10836416

guide_42 CLEC18B 0.10044515

guide_188 chrX 0.15142658

guide_41 CLEC18B 0.19637315

guide_188 chrX 0.15732362

guide_42 CLEC18B 0.1361547

guide_188 chrX 0.21161893

guide_42 CLEC18B 0.15586443

guide_188 chrX 0.11246047

guide_44 CSMD1 0.12095771

guide_188 chrX 0.17752483

guide_43 CSMD1 0.26071562

guide_188 chrX 0.18789287

guide_45 CSMD1 0.15936502

guide_188 chrX 0.22529365

guide_43 CSMD1 0.31601596

guide_188 chrX 0.19984945

guide_44 CSMD1 0.12029949

guide_188 chrX 0.24015729

guide_43 CSMD1 0.23581749

guide_188 chrX 0.14086942

guide_43 CSMD1 0.1076156

guide_188 chrX 0.17023876

guide_45 CSMD1 0.15879693

guide_188 chrX 0.22263957

guide_45 CSMD1 0.06724798

guide_188 chrX 0.23289271

guide_43 CSMD1 0.17734683

guide_41 CLEC18B 0.27345129

guide_45 CSMD1 0.12430555

guide_41 CLEC18B 0.2119465

guide_45 CSMD1 0.12029949

guide_41 CLEC18B 0.17364437

guide_44 CSMD1 0.22523016

guide_41 CLEC18B 0.17485252

guide_43 CSMD1 0.08206947

guide_41 CLEC18B 0.35224075

guide_44 CSMD1 0.22608417

guide_42 CLEC18B 0.20213981

guide_43 CSMD1 0.19667451

guide_40 CLEC18B 0.28842679

guide_43 CSMD1 0.50881379

guide_41 CLEC18B 0.21373424

guide_45 CSMD1 0.23709858

guide_41 CLEC18B 0.18782692

guide_45 CSMD1 0.20470082

guide_40 CLEC18B 0.21224383

guide_45 CSMD1 0.20763881

guide_41a CLEC18B 0.23480792

guide_43 CSMD1 0.15910308

guide_40 CLEC18B 0.12725508

guide_44 CSMD1 0.1883114

guide_40 CLEC18B 0.08379914

guide_45 CSMD1 0.2152264

guide_41 CLEC18B 0.2354947

guide_44 CSMD1 0.2685563

guide_42 CLEC18B 0.18790106

guide_43 CSMD1 0.25298365

guide_40 CLEC18B 0.09763456

guide_45 CSMD1 0.22787536

guide_41 CLEC18B 0.29547778

guide_43 CSMD1 0.2267479

guide_41 CLEC18B 0.14338172

guide_44 CSMD1 0.16679565

guide_42 CLEC18B 0.1802518

guide_45 CSMD1 0.28997924

guide_42 CLEC18B 0.22931446

guide_43 CSMD1 0.17876672

guide_41 CLEC18B 0.22904277

guide_48 DDX11 0.1892618

guide_41 CLEC18B 0.16577391

guide_46 DDX11 0.12146254

guide_42 CLEC18B 0.23666405

guide_48 DDX11 0.27782685

guide_41 CLEC18B 0.15815216

guide_48 DDX11 0.19108807

214

guide_42 CLEC18B 0.12029949

guide_48 DDX11 0.27347153

guide_48 DDX11 0.2080111

guide_177 DIS3 0.22879119

guide_48 DDX11 0.10006543

guide_177 DIS3 0.20106599

guide_48 DDX11 0.23978078

guide_175 DIS3 0.10555322

guide_47 DDX11 0.19594809

guide_175 DIS3 0.20054915

guide_48 DDX11 0.23810927

guide_175 DIS3 0.15297767

guide_48 DDX11 0.0519528

guide_175 DIS3 0.18876244

guide_48 DDX11 0.20150689

guide_177 DIS3 0.16640353

guide_48 DDX11 0.15077908

guide_175 DIS3 0.15795796

guide_48 DDX11 0.19973008

guide_176 DIS3 0.2720468

guide_48 DDX11 0.19360556

guide_177 DIS3 0.19682295

guide_48 DDX11 0.16880231

guide_175 DIS3 0.22046574

guide_46 DDX11 0.20012304

guide_51 DNMT3A 0.26038109

guide_47 DDX11 0.09219152

guide_49 DNMT3A 0.26563536

guide_46 DDX11 0.23410069

guide_49 DNMT3A 0.10655284

guide_48 DDX11 0.223914

guide_51 DNMT3A 0.23358964

guide_47 DDX11 0.21337954

guide_50 DNMT3A 0.2408748

guide_48 DDX11 0.23775378

guide_49 DNMT3A 0.29407386

guide_47 DDX11 0.15263933

guide_51 DNMT3A 0.18683482

guide_48 DDX11 0.06470264

guide_51 DNMT3A 0.36298334

guide_48 DDX11 0.16021391

guide_51 DNMT3A 0.28606438

guide_48 DDX11 0.20122764

guide_49 DNMT3A 0.27778952

guide_48 DDX11 0.18002768


guide_177 DIS3 0.22414576

guide_51 DNMT3A 0.31810221

guide_177 DIS3 0.28324514

guide_49 DNMT3A 0.18869991

guide_175 DIS3 0.29367131

guide_50 DNMT3A 0.22932295

guide_177 DIS3 0.22685254

guide_49 DNMT3A 0.05911419

guide_175 DIS3 0.26869002

guide_50 DNMT3A 0.13456107

guide_177 DIS3 0.21551374

guide_49 DNMT3A 0.21055371

guide_177 DIS3 0.24556778

guide_49 DNMT3A 0.20743618

guide_175 DIS3 0.21882827

guide_51 DNMT3A 0.19393824

guide_177 DIS3 0.18384421

guide_49 DNMT3A 0.18407473

guide_177 DIS3 0.2405011


guide_177 DIS3 0.1750567

guide_50 DNMT3A 0.16435643

guide_177 DIS3 0.26301138

guide_51 DNMT3A 0.14854614

guide_177 DIS3 0.25071116

guide_51 DNMT3A 0.20533581

guide_177 DIS3 -0.0168794

guide_50 DNMT3A 0.23566156

guide_177 DIS3 0.17362515

guide_49 DNMT3A 0.26887349

guide_175 DIS3 0.27063708

guide_50 DNMT3A 0.32012653

guide_177 DIS3 0.22463954

guide_51 DNMT3A 0.22432899

guide_177 DIS3 0.24307891

guide_51 DNMT3A 0.24566531

guide_177 DIS3 0.21992648

guide_50 DNMT3A 0.18292454

215

guide_177 DIS3 0.1871495

guide_49 DNMT3A 0.10280424

guide_51 DNMT3A 0.23544216

guide_184 EGFP 0.24217261


guide_186 EGFP 0.25235746

guide_51 DNMT3A 0.37553477

guide_187 EGFP 0.18558577

guide_49 DNMT3A 0.06014077

guide_184 EGFP 0.23770449

guide_49 DNMT3A 0.22618298

guide_185 EGFP 0.05590539

guide_50 DNMT3A 0.32294463

guide_184 EGFP 0.24106424

guide_50 DNMT3A 0.33472114

guide_184 EGFP 0.14056883

guide_50 DNMT3A 0.06771621

guide_185 EGFP 0.1699992

guide_52 DRD2 0.06782174

guide_184 EGFP 0.13712969

guide_52 DRD2 0.17488662

guide_186 EGFP 0.28019658

guide_52 DRD2 0.212065

guide_186 EGFP 0.34546301

guide_53 DRD2 0.13627803

guide_186 EGFP 0.18753757

guide_52 DRD2 0.15996639

guide_186 EGFP 0.26682747

guide_52 DRD2 0.26709482

guide_57 EZH2 0.16877338

guide_54 DRD2 0.09115042

guide_55 EZH2 0.23776236

guide_53 DRD2 0.04462557

guide_55 EZH2 0.1753051

guide_52 DRD2 0.23856238

guide_55 EZH2 0.23143447

guide_52 DRD2 0.1732153

guide_55 EZH2 0.14292365

guide_53 DRD2 0.11321112

guide_55 EZH2 0.17788511

guide_52 DRD2 0.17140086

guide_55 EZH2 0.12376824

guide_52 DRD2 0.21604049

guide_56 EZH2 0.12029949

guide_53 DRD2 0.21864682

guide_55 EZH2 0.24573053

guide_53 DRD2 0.0363927

guide_56 EZH2 0.21410145

guide_52 DRD2 0.25149809

guide_57 EZH2 0.04862013

guide_52 DRD2 0.20045463

guide_56 EZH2 0.11242179

guide_52 DRD2 0.23597496

guide_57 EZH2 0.11473049

guide_54 DRD2 0.09778719

guide_57 EZH2 0.23245394

guide_52 DRD2 0.23259864

guide_56 EZH2 0.17873341

guide_52 DRD2 0.19753199

guide_56 EZH2 0.21355607

guide_187 EGFP 0.2501871

guide_56 EZH2 0.17176002

guide_184 EGFP 0.11571298

guide_55 EZH2 0.23094311

guide_184 EGFP 0.12029949

guide_58 FANCF 0.27604229

guide_184 EGFP 0.15326198


guide_185 EGFP 0.16700149


guide_185 EGFP 0.24886147


guide_185 EGFP 0.22879119


guide_186 EGFP 0.17381267


guide_186 EGFP 0.23065581


guide_186 EGFP 0.11287353


guide_186 EGFP 0.14312658


guide_184 EGFP 0.12029949


216

guide_184 EGFP 0.23666819



guide_65 HNRNPK 0.24873568

















guide_62 FLT3 0.18886896


guide_63 FLT3 0.26373711


guide_61 FLT3 0.25785486


guide_61 FLT3 0.16057103


guide_61 FLT3 0.28000773


guide_61 FLT3 0.19976874


guide_61 FLT3 0.26454242


guide_61 FLT3 0.18338924


guide_62 FLT3 0.17750386


guide_61 FLT3 0.15996949


guide_61 FLT3 0.18667749


guide_61 FLT3 0.16124821


guide_63 FLT3 0.27105603


guide_61 FLT3 0.25108339


guide_61 FLT3 0.07259085


guide_63 FLT3 0.09106472


guide_61 FLT3 0.19411421

guide_67 IDH1 0.15578771

guide_62 FLT3 0.29341632

guide_67 IDH1 0.19369196

guide_63 FLT3 0.28734705

guide_67 IDH1 0.23412322

guide_61 FLT3 0.22971648

guide_67 IDH1 0.10329332

guide_63 FLT3 0.14410873

guide_69 IDH1 0.09129223

guide_61 FLT3 0.2055429

guide_67 IDH1 0.17087483

guide_61 FLT3 0.24278121

guide_67 IDH1 0.16877221

guide_61 FLT3 0.1768941

guide_67 IDH1 0.23425757

guide_63 FLT3 0.15536591

guide_67 IDH1 0.20576926

guide_63 FLT3 0.20294112

guide_67 IDH1 0.1258394

guide_61 FLT3 0.21275423

guide_68 IDH1 0.12029949

guide_63 FLT3 0.23913713

guide_68 IDH1 0.15845088

guide_61 FLT3 -0.0023756

guide_67 IDH1 0.23353742

guide_62 FLT3 0.34213052

guide_67 IDH1 0.2643733

guide_62 FLT3 0.22447018

guide_67 IDH1 0.20162919


guide_68 IDH1 0.19635185

217


guide_68 IDH1 0.12913918

guide_69 IDH1 0.36798994

guide_71 IDH2 0.22108242

guide_68 IDH1 0.28195351

guide_71 IDH2 0.23283914

guide_67 IDH1 0.12720379

guide_72 IDH2 0.2101722

guide_69 IDH1 0.18753375

guide_72 IDH2 0.09344829

guide_67 IDH1 0.24210566

guide_72 IDH2 0.1431861

guide_68 IDH1 0.37394781

guide_72 IDH2 0.1231254

guide_67 IDH1 0.21731956

guide_72 IDH2 0.19319457

guide_67 IDH1 0.14488229

guide_72 IDH2 0.12029949

guide_68 IDH1 0.20031791

guide_72 IDH2 0.17056996

guide_67 IDH1 0.26870115

guide_72 IDH2 0.23051272

guide_68 IDH1 0.29007421

guide_70 IDH2 0.17093865

guide_68 IDH1 0.2450891

guide_72 IDH2 0.08732582

guide_69 IDH1 0.14340255

guide_70 IDH2 0.30220635

guide_67 IDH1 0.17277752

guide_72 IDH2 0.2137242

guide_72 IDH2 0.0854914

guide_72 IDH2 0.13232368

guide_70 IDH2 0.13542929

guide_70 IDH2 0.15094469

guide_72 IDH2 0.18228888

guide_72 IDH2 0.22384222

guide_70 IDH2 0.19435789

guide_74 KIT 0.15628801

guide_72 IDH2 0.15932353

guide_74 KIT 0.1707464

guide_70 IDH2 0.20661655

guide_75 KIT 0.19452815

guide_72 IDH2 0.06724902

guide_74 KIT 0.20472508

guide_72 IDH2 0.17703118

guide_75 KIT 0.22235183

guide_72 IDH2 0.14469432

guide_74 KIT 0.06730421

guide_71 IDH2 0.24904336

guide_75 KIT 0.24549059

guide_72 IDH2 -0.0086238

guide_73 KIT 0.23530304

guide_72 IDH2 0.2553194

guide_75 KIT 0.20629161

guide_71 IDH2 -0.0153452

guide_75 KIT 0.1986978

guide_72 IDH2 0.1446143

guide_74 KIT 0.21029538

guide_71 IDH2 0.16135177

guide_73 KIT 0.22273236

guide_72 IDH2 0.15088737

guide_75 KIT 0.25190582

guide_72 IDH2 0.12029949

guide_75 KIT 0.26658688

guide_72 IDH2 0.22388055

guide_75 KIT 0.2615625

guide_72 IDH2 0.15458801

guide_73 KIT 0.24032483

guide_70 IDH2 0.15692223

guide_74 KIT 0.24372764

guide_70 IDH2 0.23855771

guide_74 KIT 0.1959805

guide_72 IDH2 0.09230045

guide_75 KIT 0.18424111

guide_72 IDH2 0.20986406

guide_78 KRAS 0.14666762

guide_72 IDH2 0.1804166

guide_76 KRAS 0.22752002

guide_72 IDH2 0.25765423

guide_77 KRAS 0.17485738

guide_72 IDH2 0.15825298

guide_78 KRAS 0.18439873

guide_72 IDH2 0.22625021

guide_77 KRAS 0.27998485

218

guide_72 IDH2 0.19540046

guide_78 KRAS 0.23504567

guide_78a KRAS 0.26054048

guide_81 LRP1B 0.39586469

guide_77 KRAS 0.1446364

guide_79 LRP1B 0.19533236

guide_77 KRAS 0.13014157

guide_79 LRP1B 0.23174556

guide_78 KRAS 0.27253102

guide_80 LRP1B 0.11467842

guide_77 KRAS 0.15252637

guide_79 LRP1B 0.22680562

guide_77 KRAS 0.12239243

guide_80 LRP1B 0.18362469

guide_77 KRAS 0.12029949

guide_80 LRP1B 0.17269707

guide_78 KRAS 0.21127739

guide_80 LRP1B 0.22200577

guide_77 KRAS 0.24437692

guide_79 LRP1B 0.20106798

guide_78 KRAS 0.27279545

guide_81 LRP1B 0.2813403

guide_77 KRAS 0.22878157

guide_80 LRP1B 0.23177064

guide_77 KRAS 0.22572185

guide_81 LRP1B 0.13740135

guide_78 KRAS 0.12157493

guide_81 LRP1B 0.24209698

guide_179 LacZ 0.24055097

guide_183 luciferase 0.19983149

guide_179 LacZ 0.21631878


guide_179 LacZ 0.23132026


guide_179 LacZ 0.19470712


guide_179 LacZ 0.31074345


guide_180 LacZ 0.27274122


guide_179 LacZ 0.24700399


guide_179 LacZ 0.21171258


guide_179 LacZ 0.18347301


guide_179 LacZ 0.17893654


guide_80 LRP1B 0.21941435


guide_80 LRP1B 0.22833666


guide_79 LRP1B 0.36093353


guide_80 LRP1B 0.38629473


guide_80 LRP1B 0.23721046


guide_81 LRP1B 0.17813499


guide_80 LRP1B 0.32828043


guide_81 LRP1B 0.24864659


guide_79 LRP1B 0.11023753


guide_79 LRP1B 0.39507688


guide_79 LRP1B 0.20424465


guide_80 LRP1B 0.17281648


guide_79 LRP1B 0.08052073


guide_80 LRP1B 0.25167021


guide_80 LRP1B 0.24401595

guide_83 MDC1 0.17657303

guide_79 LRP1B 0.23821553

guide_82 MDC1 0.22082524

guide_81 LRP1B 0.04517391

guide_83 MDC1 0.33517754

guide_79 LRP1B 0.09468518

guide_82 MDC1 0.23155316

219

guide_80 LRP1B 0.28712735

guide_83 MDC1 0.22745164

guide_82 MDC1 0.15861965

guide_90 MSH19 0.18414554

guide_82 MDC1 0.20169837

guide_88 MSH2 0.12253819

guide_84 MDC1 0.32970071

guide_88 MSH2 0.22464837

guide_83 MDC1 0.13611313

guide_88 MSH2 0.14579789

guide_84 MDC1 0.18135123

guide_89 MSH2 0.22904752

guide_83 MDC1 0.24449858

guide_89 MSH2 0.12029949

guide_82 MDC1 0.23189445

guide_89 MSH2 0.22415609

guide_83 MDC1 0.22380018

guide_88 MSH2 0.19061513

guide_83 MDC1 0.29220333

guide_89 MSH2 0.10201541

guide_82 MDC1 0.23487866

guide_88 MSH2 0.08110032

guide_85 MLH1 0.2311509

guide_89 MSH2 0.21101491

guide_87 MLH1 0.15683736

guide_88 MSH2 0.10987511

guide_87 MLH1 0.18500593

guide_88 MSH2 0.24861404

guide_86 MLH1 0.26914495

guide_88 MSH2 0.21380005

guide_87 MLH1 0.16788415

guide_89 MSH2 0.10283183

guide_85 MLH1 0.14957182

guide_88 MSH2 0.1485918

guide_87 MLH1 0.12029949

guide_88 MSH2 0.26434955

guide_86 MLH1 0.26983856

guide_88 MSH2 0.2467569

guide_87 MLH1 0.17660844

guide_88 MSH2 0.23732872

guide_86 MLH1 0.26968729

guide_89 MSH2 0.23376065

guide_87 MLH1 0.16176458

guide_88 MSH2 0.30303797

guide_87 MLH1 0.131052

guide_90 MSH2 0.17620368

guide_85 MLH1 0.24689798

guide_88 MSH2 0.15739795

guide_87 MLH1 0.29986304

guide_90 MSH3 0.09184579

guide_86 MLH1 0.20878301

guide_90 MSH4 0.20279552

guide_87 MLH1 0.27237376

guide_90 MSH5 0.18547714

guide_85 MLH1 0.25746276

guide_90 MSH6 0.11562718

guide_85 MLH1 0.2599345

guide_90 MSH7 0.23444538

guide_86 MLH1 0.17548721

guide_90 MSH8 0.25548311

guide_87 MLH1 0.17290753

guide_90 MSH9 0.22990711

guide_85 MLH1 0.22590658

guide_92 MUC16 0.23609396

guide_87 MLH1 0.14647

guide_93 MUC16 0.2210425

guide_87 MLH1 0.28883958

guide_91 MUC16 0.15751923

guide_90 MSH10 0.2600027

guide_93 MUC16 0.2165211

guide_90 MSH11 0.26750292

guide_92 MUC16 0.16975065

guide_90 MSH12 0.12029949

guide_91 MUC16 0.20856876

guide_90 MSH13 0.22139429

guide_92 MUC16 0.25416579

guide_90 MSH14 0.13609082

guide_91 MUC16 0.21957958

guide_90 MSH15 0.2506916

guide_93 MUC16 0.20784432

guide_90 MSH16 0.12859397

guide_91 MUC16 0.35491259

guide_90 MSH17 0.15788343

guide_91 MUC16 0.3380571

220

guide_90 MSH18 0.25407202

guide_93 MUC16 0.1656135

guide_93 MUC16 0.16234324

guide_98 NOTCH2NL 0.11184421

guide_91 MUC16 0.12029949


guide_92 MUC16 0.36745505


guide_93 MUC16 0.2569845


guide_92 MUC16 0.19877654


guide_91 MUC16 0.16075252


guide_92 MUC16 0.17700177


guide_92 MUC16 0.15751084


guide_91 MUC16 0.09236969


guide_93 MUC16 0.27864905


guide_91 MUC16 0.24480982


guide_91 MUC16 0.24315074


guide_93 MUC16 0.18703623


guide_91 MUC16 0.16852833


guide_93 MUC16 0.24269993


guide_93 MUC16 0.08501748

guide_100 NPM1 0.23111671

guide_91 MUC16 0.25558572

guide_101 NPM1 0.18855523

guide_92 MUC16 0.17565027

guide_101 NPM1 0.09114723

guide_93 MUC16 0.23041416

guide_101 NPM1 0.22707824

guide_93 MUC16 0.24870888

guide_101 NPM1 0.22710162

guide_91 MUC16 0.01942978

guide_100 NPM1 0.15553581

guide_94 MYC 0.17454392

guide_100 NPM1 0.13884463

guide_96 MYC 0.22620474

guide_102 NPM1 0.2158409

guide_96 MYC 0.18170652

guide_101 NPM1 0.33694563

guide_95 MYC 0.30109418

guide_100 NPM1 0.31874203

guide_95 MYC 0.19169961

guide_102 NPM1 0.18120978

guide_96a MYC 0.15081864

guide_100 NPM1 0.23268078

guide_96 MYC 0.22259293

guide_100 NPM1 0.15749282


guide_100 NPM1 0.34345954


guide_100 NPM1 0.23565794


guide_101 NPM1 0.26387299


guide_100 NPM1 0.25978999


guide_100 NPM1 0.22992202


guide_100 NPM1 0.16610616


guide_101 NPM1 0.21205246


guide_100 NPM1 0.25252595


guide_100 NPM1 0.27650188


guide_100 NPM1 0.22474176


guide_101 NPM1 0.23700608


guide_101 NPM1 0.09338302


guide_100 NPM1 0.15638535

221


guide_101 NPM1 0.25963942

guide_101 NPM1 0.21022781

guide_104 NRAS 0.27087492

guide_100 NPM1 0.1919738

guide_104 NRAS 0.22365622

guide_101 NPM1 0.17261605

guide_104 NRAS 0.23331481

guide_100 NPM1 0.12029949

guide_108 PARP1 0.18657966

guide_100 NPM1 0.24572919

guide_108 PARP1 0.12029949

guide_102 NPM1 0.2077043

guide_108 PARP1 0.13575199

guide_100 NPM1 0.1862178

guide_108 PARP1 0.19751395

guide_100 NPM1 0.25146198

guide_108 PARP1 0.23299246

guide_100 NPM1 0.19584414

guide_108 PARP1 0.21177045

guide_101 NPM1 0.23728698

guide_108 PARP1 0.25634384

guide_102 NPM1 0.28704656

guide_108 PARP1 0.19401519

guide_100 NPM1 0.0980635

guide_107 PARP1 0.13623865

guide_101 NPM1 0.27053693

guide_106 PARP1 0.28295506

guide_100 NPM1 0.24531857

guide_108 PARP1 0.20335844

guide_100 NPM1 0.24367481

guide_108 PARP1 0.16948041

guide_101 NPM1 0.20476572

guide_108 PARP1 0.17121545

guide_100 NPM1 0.17173714

guide_108 PARP1 0.15847383

guide_101 NPM1 0.119735

guide_108 PARP1 0.15916126

guide_100 NPM1 0.19563908

guide_108 PARP1 0.18653046

guide_105 NRAS 0.15768078

guide_108 PARP1 0.2331523

guide_103 NRAS 0.22836273

guide_106 PARP1 0.18500316

guide_103 NRAS 0.10956214

guide_108 PARP1 0.11586934

guide_104 NRAS 0.28538092

guide_107 PARP1 0.15478823

guide_103 NRAS 0.1905038

guide_108 PARP1 0.12029949

guide_104 NRAS 0.25436541

guide_108 PARP1 0.15913081

guide_104 NRAS 0.38426536

guide_108 PARP1 0.1773837

guide_103 NRAS 0.26457537

guide_106 PARP1 0.17458561

guide_105 NRAS 0.19943261

guide_108 PARP1 0.18567846

guide_103 NRAS 0.3231154

guide_108 PARP1 0.17691842

guide_103 NRAS 0.07015599

guide_106 PARP1 0.19386496

guide_105 NRAS 0.09968645

guide_108 PARP1 0.09632267

guide_103 NRAS 0.263

guide_106 PARP1 0.22704301

guide_105 NRAS 0.11636282

guide_108 PARP1 0.13863466

guide_104 NRAS 0.33310876

guide_108 PARP1 0.16540858

guide_103 NRAS 0.14545538

guide_106 PARP1 0.25220923

guide_104 NRAS 0.23807108

guide_107 PARP1 0.36545344

guide_103 NRAS 0.25736472

guide_108 PARP1 0.23174453

guide_104 NRAS 0.1651678

guide_108 PARP1 0.24631853

guide_104 NRAS 0.18468249

guide_107 PARP1 0.16419307

guide_104 NRAS 0.11570235

guide_106 PARP1 0.24161287

guide_104 NRAS 0.178377

guide_108 PARP1 0.23806872

222

guide_103 NRAS 0.18169022

guide_11 PCDHA13 0.28308673

guide_111 PCDHA13 0.31061242

guide_112 PHF6 0.2074621

guide_111 PCDHA13 0.19501409

guide_114 PHF6 0.13984565

guide_110 PCDHA13 0.12425942

guide_112 PHF6 0.09765201

guide_111 PCDHA13 0.27105499

guide_112 PHF6 0.14993745

guide_110 PCDHA13 0.15794988

guide_113 PHF6 0.0993747

guide_11 PCDHA13 0.24142565

guide_113 PHF6 0.28697141

guide_11 PCDHA13 0.231351

guide_112 PHF6 0.36745878

guide_111 PCDHA13 0.12029949

guide_112 PHF6 0.21891515

guide_111 PCDHA13 0.21590054

guide_114 PHF6 0.21633834

guide_111 PCDHA13 0.22173292

guide_113 PHF6 0.19168026

guide_111 PCDHA13 0.13999337

guide_113 PHF6 0.13339429

guide_110 PCDHA13 0.21364013

guide_114 PHF6 0.24001989

guide_111 PCDHA13 0.08851334

guide_113 PHF6 0.24890084

guide_110 PCDHA13 0.29884367

guide_114 PHF6 0.25503882

guide_111 PCDHA13 0.23044997

guide_112 PHF6 0.17402552

guide_110 PCDHA13 0.27109928

guide_113 PHF6 0.28301998

guide_11 PCDHA13 0.21513419

guide_112 PHF6 -0.0337591

guide_11 PCDHA13 0.21800998

guide_112 PHF6 0.17143382

guide_111 PCDHA13 0.1898747

guide_114 PHF6 0.21869877

guide_111 PCDHA13 0.1691843

guide_112 PHF6 0.26170649

guide_109 PCDHA13 0.23179174

guide_114 PHF6 0.10678177

guide_111 PCDHA13 0.16761087

guide_113 PHF6 0.22202803

guide_111 PCDHA13 0.15583758

guide_113 PHF6 0.03383815

guide_111 PCDHA13 0.2432351

guide_113 PHF6 0.10408861

guide_111 PCDHA13 0.18015883

guide_114 PHF6 0.12029949

guide_110 PCDHA13 0.3380146

guide_113 PHF6 0.16950368

guide_11 PCDHA13 0.18422016

guide_117 PKD1L2 0.18535565

guide_111a PCDHA13 0.27343158

guide_117 PKD1L2 0.26955314

guide_111 PCDHA13 0.28408119

guide_117 PKD1L2 0.2167602

guide_109 PCDHA13 0.12693826

guide_117 PKD1L2 0.17538021

guide_109 PCDHA13 0.24409809

guide_115 PKD1L2 0.40253802

guide_11 PCDHA13 0.15979984

guide_117 PKD1L2 0.27390794

guide_110 PCDHA13 0.22831981

guide_115 PKD1L2 0.28086753

guide_114 PHF6 0.17409157

guide_117 PKD1L2 0.22432307

guide_112 PHF6 0.23356626

guide_116 PKD1L2 0.20896156

guide_112 PHF6 0.12275137

guide_117 PKD1L2 0.17191323

guide_113 PHF6 0.18007448

guide_116 PKD1L2 0.13370706

guide_112 PHF6 0.21995603

guide_117 PKD1L2 0.24960056

guide_114 PHF6 0.1278686

guide_117 PKD1L2 0.23231507

guide_113 PHF6 0.0386448

guide_116 PKD1L2 0.23473167

guide_113 PHF6 0.23254417

guide_116 PKD1L2 0.17844071

223

guide_114 PHF6 0.21328047

guide_117 PKD1L2 0.2668803

guide_115 PKD1L2 0.26791782

guide_121 RAD21 0.24252933

guide_117 PKD1L2 0.12549248

guide_122 RAD21 0.13840419

guide_117 PKD1L2 0.31423609

guide_121 RAD21 0.23942247

guide_116 PKD1L2 0.22912034

guide_121 RAD21 0.23684209

guide_117 PKD1L2 0.06843736

guide_123 RAD21 0.17893258

guide_117 PKD1L2 0.0480557

guide_121 RAD21 0.29209796

guide_117 PKD1L2 0.23024795

guide_121 RAD21 0.22887111

guide_116 PKD1L2 0.09239545

guide_121 RAD21 0.35682324

guide_115 PKD1L2 0.16423851

guide_121 RAD21 0.12291413

guide_117 PKD1L2 0.23044242

guide_123 RAD21 0.18476063

guide_120 PTPN11 0.21995455

guide_121 RAD21 0.13990875

guide_120 PTPN11 0.22768074

guide_123 RAD21 0.27406941

guide_118 PTPN11 0.21358086

guide_123 RAD21 0.17238961

guide_118 PTPN11 0.09188836

guide_121 RAD21 0.23302516

guide_119 PTPN11 0.29587507

guide_121 RAD21 0.12201677

guide_120 PTPN11 0.11769269

guide_126 RAD51 0.32358676

guide_119 PTPN11 0.15797996

guide_125a RAD51 0.16273838

guide_119 PTPN11 0.20936545

guide_126 RAD51 0.32442161

guide_118 PTPN11 0.22983338

guide_125 RAD51 0.18732808

guide_119 PTPN11 0.24615228

guide_126 RAD51 0.21154013

guide_118 PTPN11 0.27253861

guide_124 RAD51 0.11808628

guide_119 PTPN11 0.20373235

guide_124 RAD51 0.20106885

guide_120 PTPN11 0.22935788

guide_125 RAD51 0.19604359

guide_119 PTPN11 0.18906706

guide_125 RAD51 0.26342987

guide_118 PTPN11 0.09344836

guide_125 RAD51 0.21054797

guide_120 PTPN11 0.12029949

guide_124 RAD51 0.2033844

guide_118 PTPN11 0.17813885

guide_124 RAD51 0.12029949

guide_119 PTPN11 0.14484153

guide_125 RAD51 0.22603054

guide_118 PTPN11 0.23897553

guide_125 RAD51 0.11670032

guide_120 PTPN11 0.25991195

guide_124 RAD51 0.27871085

guide_120 PTPN11 0.08187273

guide_124 RAD51 0.12804196

guide_123 RAD21 0.23707504

guide_124 RAD51 0.26426271

guide_123 RAD21 0.12770397

guide_124 RAD51 0.20265879

guide_121 RAD21 0.20902295

guide_125 RAD51 0.08885363

guide_122 RAD21 0.11101747

guide_124 RAD51 0.22812622

guide_122 RAD21 0.3338415

guide_125 RAD51 0.23646366

guide_121 RAD21 0.12125731

guide_124 RAD51 0.25517363

guide_122 RAD21 0.14388177

guide_125 RAD51 0.08384795

guide_123 RAD21 0.17760379

guide_124 RAD51 0.24535017

guide_123 RAD21 0.15907471

guide_125 RAD51 0.23214161

guide_123 RAD21 0.07859644

guide_124 RAD51 0.17062273

224

guide_121 RAD21 0.22592926

guide_127 RUNX1 0.1194959

guide_129 RUNX1 0.22197357

guide_135 SF3B1 0.18854758

guide_128 RUNX1 0.36240165

guide_134 SF3B1 0.23558439

guide_129 RUNX1 0.25523836

guide_133 SF3B1 0.15805596

guide_128 RUNX1 0.20856625

guide_133 SF3B1 0.25198535

guide_128 RUNX1 0.17781017

guide_134 SF3B1 0.24547779

guide_129 RUNX1 0.2734541

guide_134 SF3B1 0.3082572

guide_128 RUNX1 0.15686652

guide_137 SMC1A 0.09901227

guide_128 RUNX1 0.14473051

guide_136 SMC1A 0.16067341

guide_129 RUNX1 0.2240133

guide_137 SMC1A 0.18398695

guide_128 RUNX1 0.2253687

guide_137 SMC1A 0.24356026

guide_127 RUNX1 0.20243719

guide_137 SMC1A 0.07021713

guide_128 RUNX1 0.25282223

guide_136 SMC1A 0.15047033

guide_128 RUNX1 0.24233065

guide_137 SMC1A 0.24344565

guide_128 RUNX1 0.17254503

guide_136 SMC1A 0.2171726

guide_127 RUNX1 0.17326529

guide_136 SMC1A 0.16147431

guide_128 RUNX1 0.27802861

guide_137 SMC1A 0.27191861

guide_127 RUNX1 0.29438464

guide_136 SMC1A 0.19257208

guide_128 RUNX1 0.2471087

guide_136 SMC1A 0.21379942

guide_131 RUNX1T1 0.21150444

guide_138 SMC1A 0.17642618

guide_130 RUNX1T1 0.14731401

guide_136 SMC1A 0.21881766

guide_130 RUNX1T1 0.107568

guide_137 SMC1A 0.07525037

guide_131 RUNX1T1 0.24047298

guide_137 SMC1A 0.24375917

guide_131 RUNX1T1 0.11890684

guide_137 SMC1A 0.18858314

guide_131 RUNX1T1 0.10516318

guide_137 SMC1A 0.15834581

guide_131 RUNX1T1 0.12216495

guide_136 SMC1A 0.26646675

guide_130 RUNX1T1 0.21879985

guide_141 SMC3 0.15575996

guide_130 RUNX1T1 0.27732408

guide_139 SMC3 0.09794934

guide_131 RUNX1T1 0.23462394

guide_141 SMC3 0.17947552

guide_131 RUNX1T1 0.09522698

guide_139 SMC3 0.15024252

guide_130 RUNX1T1 0.1717131

guide_141 SMC3 0.17178425

guide_130 RUNX1T1 0.22843353

guide_139 SMC3 0.22605585

guide_134 SF3B1 0.25709965

guide_139 SMC3 0.15723643

guide_133 SF3B1 0.25814507

guide_140 SMC3 0.22943987

guide_134 SF3B1 0.21205077

guide_140 SMC3 0.30175557

guide_133 SF3B1 0.21840065

guide_140 SMC3 0.19019219

guide_134 SF3B1 0.17998065

guide_140 SMC3 0.27786565

guide_135 SF3B1 0.21359958

guide_139 SMC3 0.28796575

guide_135 SF3B1 0.32174664

guide_141 SMC3 0.24333145

guide_134 SF3B1 0.36457792

guide_141 SMC3 0.15403646

guide_135 SF3B1 0.22800299

guide_139 SMC3 0.11005302

guide_135 SF3B1 0.17844602

guide_141 SMC3 0.1861955

225

guide_135 SF3B1 0.15546104

guide_141 SMC3 0.38978321

guide_141 SMC3 0.35519032

guide_146 SRSF1 0.23657495

guide_141 SMC3 0.1676453

guide_147 SRSF1 0.26286297

guide_139 SMC3 0.12736234

guide_146 SRSF1 0.15867904

guide_141 SMC3 0.23372432

guide_145 SRSF1 0.12029949

guide_143 SPEG 0.25270417

guide_146 SRSF1 0.25490966

guide_143 SPEG 0.11778535

guide_145 SRSF1 0.31567004

guide_143 SPEG 0.21182818

guide_145 SRSF1 0.26822168

guide_142 SPEG 0.16625749

guide_146 SRSF1 0.18458666

guide_143 SPEG 0.08019921

guide_145 SRSF1 0.16818232

guide_144 SPEG 0.1749089

guide_145 SRSF1 0.2438311

guide_142 SPEG 0.2447999

guide_146 SRSF1 0.34766125

guide_142 SPEG 0.26943661

guide_146 SRSF1 0.12516342

guide_142 SPEG 0.27336308

guide_146 SRSF1 0.20021986

guide_143 SPEG 0.19982832

guide_149 STAG2 0.17009883

guide_142 SPEG 0.14529218

guide_149 STAG2 0.2190263

guide_143 SPEG 0.22449352

guide_149 STAG2 0.20127832

guide_144 SPEG 0.25654712

guide_148 STAG2 0.15621473

guide_143 SPEG 0.23847868

guide_148 STAG2 0.2738272

guide_143 SPEG 0.2847839

guide_149 STAG2 0.17158882

guide_142 SPEG 0.20861331

guide_148 STAG2 0.05690373

guide_143a SPEG 0.05010897

guide_148 STAG2 0.18460194

guide_143 SPEG 0.15541295

guide_148 STAG2 0.15719963

guide_143 SPEG 0.18523997

guide_150 STAG2 0.13686453

guide_143 SPEG 0.19728803

guide_150 STAG2 0.22613514

guide_143 SPEG 0.14805304

guide_148 STAG2 0.103127

guide_143 SPEG 0.2426888

guide_148 STAG2 0.25984465

guide_144 SPEG 0.19248613

guide_150 STAG2 0.29343033

guide_142 SPEG 0.1750024

guide_149 STAG2 0.21767047

guide_144 SPEG 0.2249278

guide_149 STAG2 0.2675426

guide_143 SPEG 0.23713477

guide_148 STAG2 0.13267444

guide_142 SPEG 0.27276684

guide_149 STAG2 0.14646575

guide_142 SPEG 0.29016089

guide_150 STAG2 0.08617899

guide_143 SPEG 0.14755843

guide_150 STAG2 0.17987004

guide_144 SPEG 0.24026545

guide_150 STAG2 0.13443108

guide_143 SPEG 0.24320089

guide_149 STAG2 0.21718277

guide_144 SPEG 0.10534133

guide_149 STAG2 0.2161508

guide_142 SPEG 0.14260601

guide_149 STAG2 0.02998534

guide_143 SPEG 0.12029949

guide_148 STAG2 0.2079059

guide_143 SPEG 0.19879496

guide_149 STAG2 0.17708762

guide_146a SRSF1 0.17065847

guide_149 STAG2 0.20209646

guide_146 SRSF1 0.2180532

guide_149 STAG2 0.19352278

226

guide_145 SRSF1 0.12862137

guide_148 STAG2 0.24239461

guide_150 STAG2 0.21684519

guide_156 TCEAL3 0.08904195

guide_150 STAG2 0.25015143

guide_155 TCEAL3 0.24751141

guide_148 STAG2 0.24202885

guide_158 TET2 0.18633668

guide_150 STAG2 0.21868057

guide_159 TET2 0.20350752

guide_150 STAG2 0.27090016

guide_157 TET2 0.30244383

guide_149 STAG2 0.25096048

guide_158 TET2 0.23437485

guide_148 STAG2 0.1193587

guide_159 TET2 0.17657936

guide_149 STAG2 0.12029949

guide_157 TET2 0.25453049

guide_149 STAG2 0.16067863

guide_158 TET2 0.15985216

guide_148 STAG2 0.22663838

guide_158 TET2 0.12029949

guide_150 STAG2 -0.0181545

guide_158 TET2 0.31831816

guide_151 SUZ12 0.08464866

guide_159 TET2 0.16535249

guide_152 SUZ12 0.13625464

guide_158 TET2 0.03386879

guide_151 SUZ12 0.18540142

guide_158 TET2 0.23016818

guide_151 SUZ12 0.27011049

guide_158 TET2 0.25668622

guide_152 SUZ12 0.26610691

guide_158 TET2 0.07403968

guide_152 SUZ12 0.17766383

guide_157 TET2 0.15951102

guide_151 SUZ12 0.19852214

guide_158 TET2 0.15612667

guide_152 SUZ12 0.2510897

guide_159 TET2 0.19802731

guide_153 SUZ12 0.2034638

guide_157 TET2 0.12029949

guide_153 SUZ12 0.23778885

guide_160 TP53 0.26909405

guide_152 SUZ12 0.30477729

guide_160 TP53 0.06274709

guide_152 SUZ12 0.18565801

guide_161 TP53 0.2552199

guide_153 SUZ12 0.15861547

guide_161 TP53 0.14461732

guide_152 SUZ12 0.22304785

guide_161 TP53 0.27432875

guide_151 SUZ12 0.45127211

guide_161 TP53 0.22002497

guide_152 SUZ12 0.24874507

guide_160 TP53 0.12029949

guide_156 TCEAL3 0.12850414

guide_161 TP53 0.22575985

guide_156 TCEAL3 0.24373593

guide_160 TP53 0.24221181

guide_155 TCEAL3 0.18331113

guide_161 TP53 0.29103408

guide_156 TCEAL3 0.1600978

guide_161 TP53 0.23511952

guide_155 TCEAL3 0.26079333

guide_160 TP53 0.20036092

guide_155 TCEAL3 0.23906313

guide_160 TP53 0.26810804

guide_156 TCEAL3 0.16657735

guide_161 TP53 0.07589544

guide_156 TCEAL3 0.2237607

guide_161 TP53 0.2303702

guide_155 TCEAL3 0.30154704

guide_161 TP53 0.22589577

guide_156 TCEAL3 0.11890164

guide_160 TP53 0.1802134

guide_156 TCEAL3 0.15034123

guide_160 TP53 0.30130436

guide_156 TCEAL3 0.22233928

guide_161 TP53 0.20074908

guide_155 TCEAL3 0.10172197

guide_162 TP53 0.19545527

guide_155 TCEAL3 0.21524327

guide_160 TP53 0.06395995

227

guide_156 TCEAL3 0.19235243

guide_161 TP53 0.22790408

guide_160 TP53 0.21573261

guide_167 TTN 0.24485407

guide_160 TP53 0.15391055

guide_166 TTN 0.24167518

guide_161 TP53 0.16007993

guide_168 TTN 0.0753533

guide_160 TP53 0.05730977

guide_166 TTN 0.16167727

guide_160 TP53 0.19813252

guide_166 TTN 0.32659639

guide_162 TP53 0.21466666

guide_166 TTN 0.22133302

guide_160 TP53 0.17744081

guide_167 TTN 0.15437125

guide_162 TP53 0.21864534

guide_166 TTN 0.15556319

guide_161 TP53 0.15842892

guide_168 TTN 0.18483953

guide_161 TP53 0.20032121

guide_166 TTN 0.20189832

guide_160 TP53 0.23983051

guide_168 TTN 0.24646226

guide_162 TP53 0.20901558

guide_166 TTN 0.10005829

guide_161 TP53 0.1918521

guide_166 TTN 0.30218071

guide_160 TP53 0.17588984

guide_168 TTN 0.20357433

guide_161 TP53 0.19619377

guide_166 TTN 0.21470573

guide_162 TP53 0.24109354

guide_168 TTN 0.17316638

guide_161 TP53 0.15866999

guide_166 TTN 0.08195118

guide_161 TP53 0.14308505

guide_167 TTN 0.17419081

guide_161 TP53 0.15565333

guide_168 TTN 0.09824533

guide_161 TP53 0.23642651

guide_166 TTN 0.17830469

guide_161 TP53 0.25255377

guide_168 TTN 0.24660743

guide_165 TP53BP1 0.25414948

guide_170 U2AF1 0.16357381

guide_165 TP53BP1 0.21574943

guide_170 U2AF1 0.19966052

guide_165 TP53BP1 0.28703696

guide_169 U2AF1 0.16989893

guide_165 TP53BP1 0.18462051

guide_170 U2AF1 0.09696345

guide_165 TP53BP1 0.23575

guide_171 U2AF1 0.2571412

guide_165 TP53BP1 0.15939106

guide_169 U2AF1 0.15506132

guide_165 TP53BP1 0.21406445

guide_170 U2AF1 0.18662033

guide_165 TP53BP1 0.24503369

guide_170 U2AF1 0.21193523

guide_165 TP53BP1 0.40378106

guide_170 U2AF1 0.37884384

guide_163 TP53BP1 0.17121223

guide_169 U2AF1 0.20968685

guide_166 TTN 0.16114292

guide_170 U2AF1 0.16927717

guide_166 TTN 0.22905433

guide_171 U2AF1 0.11069176

guide_167 TTN 0.22819692

guide_171 U2AF1 0.2149769

guide_167 TTN 0.22396011

guide_171 U2AF1 0.10541166

guide_166 TTN 0.22022214

guide_171 U2AF1 0.20548282

guide_168 TTN 0.09924193

guide_171 U2AF1 0.23762738

guide_167 TTN 0.16739905

guide_169 U2AF1 0.0567874

guide_166 TTN 0.18585306

guide_171 U2AF1 0.15258494

guide_166 TTN 0.30105123

guide_170 U2AF1 0.22093669

guide_166 TTN 0.25903055

guide_170 U2AF1 0.15396605

228

guide_170 U2AF1 0.12272267

guide_172 WT1 0.24092056

guide_173 WT1 0.17352566

guide_173 WT1 0.25963876

guide_174 WT1 0.08220328

guide_172 WT1 0.00892003

guide_172 WT1 0.21901623

guide_172 WT1 0.13626085

guide_172 WT1 0.17509551

guide_172 WT1 0.19864657

guide_174 WT1 0.23064385

guide_173 WT1 0.2141106

guide_172 WT1 0.26852504

guide_172 WT1 0.22682086

guide_172 WT1 0.20881219

guide_172 WT1 0.22844627

guide_173 WT1 0.15393858

guide_174 WT1 0.24760095

guide_173 WT1 0.27745169

guide_172 WT1 0.29331537

guide_173 WT1 0.38505909

guide_174 WT1 0.1509508

guide_172 WT1 0.20918372

guide_172 WT1 0.14249316

Table 8.4: Table of DDRD scores for all cells containing only one guide from. library one of the CROPseq experiment

229

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