ROLE of NOTCH and JAK-STAT SIGNALING PATHWAYS in ACUTE MEGAKARYOBLASTIC LEUKEMIA KELLY ONG OOI KEE (B.Sc (Hons), UWE Bristol)

ROLE OF NOTCH AND JAK-STAT SIGNALING PATHWAYS IN

ACUTE MEGAKARYOBLASTIC LEUKEMIA

KELLY ONG OOI KEE

(B.Sc (Hons), UWE Bristol)

A THESIS SUBMITTED

FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

CANCER SCIENCE INSTITUTE OF SINGAPORE

NATIONAL UNIVERSITY OF SINGAPORE

2015

Declaration

I hereby declare that this thesis is my original work and it has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used

in the thesis.

This thesis has also not been submitted for any degree in any university previously.

______

Kelly Ong Ooi Kee

19 January 2015

ACKNOWLEDGEMENTS

I would like to express my heartfelt gratitude to Associate Professor Chng Wee

Joo for his patience, enthusiasm and immense knowledge to make my Ph.D. experience productive and stimulating. His guidance and encouragement has greatly helped me, especially during the tough times in the Ph.D. pursuit.

I am also very grateful to Associate Professor Motomi Osato, my direct mentor, for his excellent mentorship. His patience, flexibility, genuine caring and concern, and faith in me during the dissertation process helped push me through the last chapter and a half. For this, I cannot thank him enough. I am forever grateful.

I would like to extend my gratitude to Associate Professor Allen Yeoh for providing patient leukemia samples, Associate Professor Henry Yang for assistance in the bioinformatics analysis, Professor Ritsuko Shimizu and Professor Masayuki

Yamamato who provided Runx1 Tg and Gata-1.05 mice.

I would like to express my appreciation to members of MO lab, Du Linsen,

Chelsia Wang, Desmond Chin, Koh Cai Ping, Michelle Mok, Tan Huifang and

Takayoshi Matsumura for their technical guidance, support, and input are greatly appreciated.

As a member of CWJ lab, I have been surrounded by wonderful colleagues, Jim

Tay, Nurulhuda Mustafa, Lin Baohong, Cheong Lip Lee, Eric Chan, Chooi Jing Yuan,

Bi Chonglei, and Teoh Phaik Ju who have provided a comfortable environment to study and explore new ideas.

My special thanks also go to Namiko Yamashita for her invaluable help in the retroviral insertional mutagenesis study, Shaw Liu, for teaching me experiments involving mice and Kong Li Ren, for providing me with necessary reagents and advice.

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Other past group members that I have had the pleasure to work with or alongside of are postdocs Senali Abayratna, Angela Ng, Liu Bee Hui, Vikas Madan,

Sharada Ravikumar, Wendy Stewart, Swathi Yada and Avinash. I would also like to acknowledge my past supervisors, Akira Kawasaki and Robert Weiss for teaching me a lot about research.

One of the joys of completion is to look over the journey past and remember all the friends and family who have helped and supported me along this long but fulfilling road. My sincere thanks to Joan Lim for the encouragement and support in applying for the CSI Ph.D. programme. To my dear friends, Varsha Doshi, Carolyn Lee, Yan Lai

Peen, Eileen Yong and others, thank you for your encouragement, support and most of all your humour. You all kept things light and me smiling.

I must acknowledge with tremendous and deep thanks to Wong Meng Kang, for the support and unwavering belief in me that I am able to complete this long dissertation journey. There are no words that can express my gratitude and appreciation for all you have done and been for me. To my beloved Bedtime Bear and Klomty the

Cat, thank you for accompanying me on this adventure.

I dedicate this thesis to my family for their constant support and unconditional love. I am especially grateful for my mother. Her understanding and her love encouraged me to work hard and to continue pursuing a Ph.D. abroad. Her firm and kind-hearted personality has affected me to be steadfast and never bend to difficulty.

She always lets me know that she is proud of me, which motivates me to work harder and do my best. I love you all dearly.

Last but not least, I thank God for answering my prayers, fulfilling my wishes and giving me everything to accomplish this thesis: patience, health, wisdom, and blessing.

TABLE OF CONTENTS

DECLARATION……………………………………………….………………..…ii

ACKNOWLEDGEMENTS………………………………………………………..iii

TABLE OF CONTENTS……………………………………….………………..…v

SUMMARY……………………………………………………………………… ...x

LIST OF TABLES……………………………………………….………………..xii

LIST OF FIGURES……………………………………………………………....xiii

LIST OF SYMBOLS………………………………………………………….…..xv

PUBLICATION LIST……………………………………………………...…....xxv

CHAPTER 1: INTRODUCTION………………………………………………...... 1

1.1 Megakaryopoiesis..………………………………………………………...... 1

1.1.1 Megakaryopoiesis during development in mammals…………...... 2

1.1.1.1 Characteristics of neonatal and adult megakaryopoiesis..…………………………………………5

1.1.2 Megakaryocytic lineage specification in adult..……………..…....6

1.1.2.1 Immunophenotypical identification of megakaryocytes..……………………………………..….....9

1.1.3 Endomitosis and proplatelet formation..………………………….12

1.2 Transcription factors, cytokines and signaling pathways associated with megakaryopoiesis..………………………………….....…………………....14

1.2.1 Transcriptional regulation in normal and malignant megakaryopoiesis…………………………………………...... 16

1.2.1.1 GATA-1..……………………………………………….....18

1.2.1.2 RUNX1..…………………………………………….……..19

1.2.1.3 Other megakaryocyte-selective transcription factors...... ,.....21

1.2.2 Cytokines involved in the regulation of megakaryopoiesis…….….23

1.2.2.1 Thrombopoietin (TPO)……………………………...……...23

1.2.2.2 Other cytokines involved in the regulation of megakaryopoiesis…………………………………….….....24

1.2.3 Signaling pathways influencing megakaryocyte development.……………………………………………..………...26

1.2.3.1 JAK-STAT signaling pathway..…………………………....26

1.2.3.1.1 JAK structure and function...…………...……..27

1.2.3.1.2 STAT structure and function..…………...…....29

1.2.3.1.3 Mechanism of JAK-STAT signaling………….31

1.2.3.1.4 Implications of JAK-STAT signaling in hematological malignancies……....………...…33

1.2.3.2 Notch signaling pathway..………………….………...... 35

1.2.3.2.1 Notch receptors and ligands..……..…………...36

1.2.3.2.2 Regulatory mechanism of Notch signaling…………………………………..…...38

1.2.3.2.3 Notch target genes……………...... ……...... 40

1.2.3.2.4 Implications of Notch signaling in hematological malignancies….……………………………...…41

1.2.3.2.4.1 Notch as an oncogene.……………...... 42

1.2.3.2.4.2 Notch as a tumor suppressor……….…43

1.3 Megakaryoblastic leukemias. …………………………………………..….....44

1.3.1 Transient abnormal myelopoiesis (TAM). ……………………...... …45

1.3.2 Acute megakaryoblastic leukemia (AMKL). ………………..…...…47

1.3.2.1 Down syndrome-associated AMKL (DS-AMKL)...... 49

1.3.2.2 Non-Down syndrome AMKL (non-DS AMKL)…….….....50

1.3.3 Prevalence of AMKL in Singapore and Malaysia. …………..…...... 52

1.4 Mouse models for AMKL. ………………………..……………………..…55

1.5 Retroviral insertional mutagenesis…………………………………….…....56

1.5.1 Uncovering genetic interactions in cancer…………………..…...….58

1.5.2 Mechanisms of deregulation of genes by retroviral insertions…...…60

1.5.3 Methods for the retrieval and analysis of integrations...………….....61

1.5.3.1 Mapping retroviral integration sites………...... ….61

1.5.3.2 BLAST-like alignment tool (BLAT)…...……...... 63

1.5.3.3 Retroviral tagged cancer gene database (RTCGD) and Mutapedia………………………………………………....63

1.6 Next-generati on sequencing…………………………………………….…..64

1.6.1 Sample preparation and technical challenges for NGS applications………………………………………………………....65

1.6.2 NGS studies on AMKL……………….………………………...…...68

1.7 Aims of the project……………………………………………………….....70

CHAPTER 2: MATERIALS AND METHODS………………………….….....71

CHAPTER 3: RESULTS………………………………..…………………...….86

3.1 Runx1 Tg/Gata-1.05 compound mice develop megakaryoblastic leukemia………………………………………………………………….…86

3.2 Cooperation of genetic hits with Gata-1-knockdown and Runx1- overexpressing status in leukemogenesis…………………………..……....89

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3.3 Mutation screening of candidate genes associated with JAK-STAT and Notch pathways in AMKL using targeted deep sequencing...... …..91

3.4 Anti-proliferative effects of JAK inhibitor 1 in AMKL cell lines….………..98

3.5 JAK inhibitor 1 inhibits STAT3 and STAT5 phosphorylation in AMKL cells……………………………………………………………………...…....99

3.6 JAK inhibitor 1 induces suppression of JAK1 and paradoxical hyperphosphorylation of JAK3 in AMKL cells…..……………………...... 100

3.7 Effect of phenetyl isothiocyanate (PEITC) and γ-secretase inhibitor (DAPT) on cell proliferation of AMKL cell lines…………………………………...102

3.8 PEITC induces apoptosis via caspase-3/PARP pathway and upregulates expression of p21 in AMKL cells...... …………………………………..104

3.9 PEITC induces γ-secretase cleavage of NOTCH1 and enhances transcriptional activity of Notch in AMKL cells…...…………...…………106

3.10 Combination of JAK inhibitor 1 and PEITC trigger synergistic AMKL cell death………………………………………………...…………….…...109

CHAPTER 4: DISCUSSION……………………………………………....….….111

4.1 Megakaryoblastic leukemia develops in the Runx1 Tg/Gata-1.05 mouse model.…………………………………………..…………………..……….111

4.2 RIM identifies CIS associated with JAK-STAT and Notch pathways…………………………………………...……………………..…112

4.3 Deep sequencing validates genetic alterations associated with JAK-STAT and Notch pathways in human AMKL samples….…………………………...... 114

4.4 Retroviral insertional mutagenesis still remains powerful………...……….116

4.5 Potential therapeutic efficacy of JAK inhibition and Notch activation in treating AMKL……...………...…………………………………………....117

4.5.1 JAK inhibition suppresses AMKL cell proliferation…………………………………………………..……..117

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4.5.2 JAK inhibition suppresses AMKL cell proliferation…………………………………….…...………….……118

4.5.3 Synergistic cytotoxic effect of JAK inhibitor 1 and PEITC against AMKL…………………...…………………………….....……….…120

4.6 Future work……………………………………..……………………………121

4.7 Conclusions………..…………………………………………………………123

BIBLIOGRAPHY…………………………………………………………….…….124

APPENDICES…..……………………………………………………………....….148

SUMMARY

Children of Down syndrome (DS), the most common congenital disease, are at high risk of developing a specific subtype of acute myeloid leukemia, acute megakaryoblastic leukemia (AMKL). According to Malaysia-Singapore multicenter leukemia studies for childhood acute leukemia, the prevalence of AMKL in Singapore and Malaysia is strikingly high as compared to those in the worldwide populations, and its prognosis is unfavorable. Despite such widespread and local relevance, the mechanistic basis for AMKL remains elusive.

To discover currently unknown genetic alterations associated with AMKL, an

AMKL mouse model was developed employing retroviral insertional mutagenesis

(RIM) on the genetically engineered mouse with alterations in Gata-1 and Runx1 genes, both of which are well documented to be critical for normal megakaryopoiesis and deregulated in AMKL. As RIM enables easy identification of affected genes potentially involved in leukemogenesis, I readily identified potential AMKL genes, which are classified into two signaling pathways, namely JAK-STAT and Notch pathways.

In order to validate whether the genetic alterations detected in the RIM mouse model are also affected in human AMKL samples, I employed targeted deep sequencing strategy in 40 AMKL patient samples and 7 AMKL cell lines. Besides known AMKL-related genetic changes such as GATA-1 point mutation and trisomy 21 causing an extra copy of RUNX1, genetic mutations in Notch and JAK-STAT pathways were frequently identified. Subsequent experimental characterization of mutants and known functions of mutated domains suggest that these mutations lead to the activation of JAK-STAT pathway and inactivation of Notch pathway.

To further validate the oncogenic role of Notch and JAK-STAT pathways in

AMKL, the inhibitory effect on AMKL cell lines by Notch agonist, phenetyl isothiocyanate (PEITC), and JAK inhibitor 1 were examined. Both drugs significantly suppressed leukemia cell proliferation by inducing apoptosis individually as a single agent and synergistically as a combination therapy. This potent inhibition holds a potential of therapeutic application of Notch agonist alone or in combination with JAK-

STAT inhibitor for AMKL.

LIST OF TABLES

Table 1.1: Summary of characteristics of neonatal and adult megakaryopoiesis...... 5

Table 1.2: Summary cell surface phenotypes of hematopoietic progenitors, megakaryocytic precursors and mature cells in the MK lineage in BM...... 11

Table 1.3: Expression of transcription factors involved in normal adult megakaryopoiesis and KO phenotypes...... 17

Table 1.4: Gene targeting studies on Janus kinases (JAKs) family members...... 28

Table 1.5: Gene targeting studies on signal transducer and activator of transcription (STAT) family members...... 31

Table 1.6: Oncogenic and tumor suppressive roles of Notch signaling in human hematological malignancies...... 41

Table 1.7: Mouse models carrying triplicated chromosomal regions related to Down syndrome...... 55

Table 1.8: Mutations detected in previous NGS studies on AMKL...... 69

Table 2.1: Primer sequences used for mutation screening...... 81

Table 3.1: Frequency of different types of cancers in MoMuLV-inoculated mice...... 87

Table 3.2: Classification of RIS identified in the DS-AMKL mouse model...... 90

Table 3.3: Recurrently mutated genes other than GATA1 in childhood AMKL samples in targeted deep sequencing...... 94

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LIST OF FIGURES

Figure 1.1: Megakaryopoiesis...... 2

Figure 1.2: Hematopoietic development in human and mouse...... 4

Figure 1.3: Hematopoiesis differentiation chart in adult mammals...... 7

Figure 1.4: Endomitosis and proplatelet formation...... 14

Figure 1.5: Models for the expression of GATA-1 isoforms...... 19

Figure 1.6: A diagrammatic representation of RUNX1...... 20

Figure 1.7: Domain structure of Janus kinases (JAKs)...... 28

Figure 1.8: Linear representations of the domain structures of the signal transducer and activator of transcription (STAT) protein family...... 30

Figure 1.9: Schematic representation of the JAK-STAT pathway...... 33

Figure 1.10: Domain organization of the human NOTCH receptors and ligands...... 37

Figure 1.11: Schematic diagram showing canonical Notch signaling...... 40

Figure 1.12: Stochastic model of leukemogenesis in Down’s syndrome...... 45

Figure 1.13: Frequency of acute myeloid leukemia subtype in Singapore-Malaysia and other countries...... 53

Figure 1.14: Schematic diagram showing the RIM strategy...... 59

Figure 1.15: Schematic diagram of hybrid selection method...... 66

Figure 3.1: Immunophenotype and morphology of megakaryoblastic leukemic cells from a Runx1 Tg/Gata-1.05 compound mouse #1472...... 88

Figure 3.2: Mutations associated with JAK-STAT and Notch pathways in AMKL...... 95

Figure 3.3: JAK inhibitor 1 inhibits cell proliferation and induced apoptosis in AMKL cell lines...... 98

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Figure 3.4: JAK inhibitor suppresses activation (phosphorylation) of STAT3 and STAT5, but not STAT1 in AMKL cells...... 100

Figure 3.5: JAK inhibitor induces suppression of JAK1 and paradoxical hyperphosphorylation of JAK3 in AMKL cells ...... 101

Figure 3.6: Effect of PEITC and DAPT on cell proliferation of AMKL cells...... 103

Figure 3.7: PEITC induces apoptosis via caspase-3/ PARP pathway and upregulates expression of p21 in AMKL cells...... 105

Figure 3.8: PEITC induces γ-secretase cleavage of NOTCH1 and enhances transcriptional activity of Notch in AMKL cells...... 108

Figure 3.9: Combination of JAK inhibitor 1 and PEITC trigger synergistic AMKL cell death...... 110

Figure 4.1: A proposed model for the role(s) of JAK-STAT and Notch pathways in AMKL leukemogenesis and treatment...... 120

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LIST OF SYMBOLS

+21 trisomy 21

ADP adenosine diphosphate

AGM aorta-gonad-mesonephros

Akt protein kinase B

ALL acute lymphoblastic leukemia

AMKL acute megakaryoblastic leukemia

AML acute myeloid leukemia

APC adenomatous polyposis coli

APH1 anterior pharynx-defective 1

Ara-C cytarabine

BACH1 BTB and CNC homology 1

BM bone marrow

BFU-E burst-forming units-erythroid

BFU-MK burst forming units-megakaryocyte

BLAT BLAST-like alignment tool bp base pair

CBC complete blood count

CBF1 C promoter-binding factor 1

CBFA2T3 Core-binding factor, runt domain, alpha subunit 2; translocated to, 3

CBL Cbl proto-oncogene, E3 ubiquitin protein ligase

CCR continuous complete remission

CFU-MK colony forming units-megakaryocyte

CHD5 chromodomain helicase DNA binding protein 5

CI combination index

CIS common integration site

CLL chronic lymphocytic leukemia

CLP common lymphoid progenitor

CMML chronic myelomonocytic leukemia

CMP common myeloid progenitor cMPL myeloproliferative leukemia virus oncogene

CN cytogenetically normal cnLOH copy-neutral regions of loss of heterogeneity

CNV copy number variant

CSF colony-stimulating factor

CSL CBF1/Suppressor of Hairless/LAG-1

CSMD1 CUB and Sushi multiple domains 1

CtBP1 C-terminal binding protein 1

CTCF CCCTC-binding factor (zinc finger protein)

C-ZF carboxy-terminal zinc-finger domain

DAPT (3.5-Difluorophenylacetyl)-alanyl-phenylglycine-t-butyl-ester

DCAF7 DDB1 and CUL4 associated factor 7

DLEC1 deleted in lung and esophageal cancer 1

DLL Delta-like ligand

DMSO dimethylsulfoxide

DN double-negative (T-cell precursor)

DNA deoxyribonucleic acid

DP double-positive (T-cell precursor)

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dpc days post-coitum

DS Down syndrome

DSL Delta/Serrate/LAG-2

EBS ETS binding sequence

EGF epidermal growth factor

EM electron microscope

EPO erythropoietin

EPOR erythropoietin receptor

ERG Ets-related gene (ERG)

EryP erythroid progenitor

ETS v-ets avian erythroblastosis virus E26 oncogene

EXT1 exostosin glycosyltransferase 1

EZH2 enhancer of zeste homolog 2

FAB French-American-British

FBXW7 F-box and WD repeat domain containing 7

FcγRII/III Fc-gamma receptor II/III

FERM four-point-one, ezrin, radixin, moesin (FERM)

Fli-1 Friend leukemia integration 1 transcription factor

FLT3 fms-related tyrosine kinase 3

FOG-1 Friend of GATA-1

FPD familial platelet disorder

GABP GA-binding protein

GATA-1 GATA binding factor 1

GAPDH glyceraldehyde 3-phosphate dehydrogenase

GC guanine-cytosine

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G-CSF granulocyte- colony-stimulating factor receptor

GFI-1B growth factor independent 1B transcription repressor

GLIS GLI-similar

GMP granulocyte/monocyte progenitor

GM-CSF granulocyte-monocyte colony-stimulating factor

GP1bβ glycoprotein-1bβ

GTP guanosine triphosphate

GWAS genome-wide association studies

HCL hairy-cell leukemia

HD heterodimerization domain

HES Hairy enhancer of split genes

HEY Hairy/enhancer of split related genes with YRPW motif

HLA human leukocyte antigen

HOX homeotic

HRD hematopoietic regulatory domain

HSC hematopoietic stem cell

HSPC hematopoietic stem and progenitor cell

IC50 median inhibitory concentration

IFN interferon

Ifngr2 interferon-γ receptor 2

IL interleukin

IMS invaginated membrane system

Indel small insertions or deletions; iPCR inverse polymerase chain reaction

ITCH itchy E2 ubiquitin protein ligase

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JAK Janus kinase

JH JAK homology

JAG Jagged ligand

KASH Klarsicht/ANC-1/Syne-1 homology

KANSL1 KAT8 regulatory NSL complex subunit 1 kb kilobases

KBF2 kappa-binding factor 2 kDA kilodaltons

KO knockout

KRAS Kirsten rat sarcoma viral oncogene homolog

LAG-1 Longevity-assurance gene-1

LDB1 LIM domain-binding protein 1

LIF leukemia inhibitory factor

LIM LIN-11, ISL-1 and MEC-3

Lin Lineage

LIN negative regulatory LIN repeats

LMO2 LIM only protein 2

LPA lipoprotein (a)

LPS lipopolysaccharide

LT-HSC long-term hematopoietic stem cell

LTR long terminal repeat

MAL megakaryocytic acute leukemia

MAML Mastermind-like protein

MDA multiple displacement amplification

MEM minimum essential medium

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MEP megakaryocyte/ erythroid progenitor

MINT Msx2 interacting nuclear target

MK megakaryocyte

MLK-1 megakaryoblastic leukemia-1

MM multiple myeloma

MoMuLV Moloney murine leukemia virus

MPD myeloproliferative disorder

MPN myeloproliferative neoplasm

MPP multi-potent progenitor

MTG16 myeloid translocation gene 16 mTOR mechanistic target of rapamycin

MYND myeloid, nervy and Deaf-1

N-CoR nuclear receptor co-repressor

NCSTN Nicastrin

NEC Notch extracellular subunit

NEDD4 neural precursor cell expressed

NF1 neurofibromin 1

NF-E2 nuclear factor-erythroid 2 transcription factor

NGS next-generation sequencing

NHR nervy homology regions

NICD Notch intracellular domain

NIPBL Nipped-B homolog

NK natural killer

NLS nuclear localization signal nM nanomolar

NRARP Notch-regulated ankyrin repeat protein

NRAS neuroblastoma RAS viral (v-ras) oncogene homolog

NRR negative regulatory region

NSCLC non-small cell lung carcinoma

NGS NOD/SCID/IL2 receptor gamma chain knockout

NTM Notch transmembrane domain

NUMB protein numb homolog

N-ZF amino-terminal zinc-finger domain

OSM oncostatin M

OTT one twenty-two

PARK2-PACRG Parkin coregulated gene protein

PARP poly (adenosine diphosphate ribose) polymerase

PAX2 Paired box gene 2

PB peripheral blood

PBS phosphate-buffered saline

PCR polymerase chain reaction

PEBP2/CBF Polyomavirus enhancer binding protein 2/Core binding factor

PEITC phenethyl isothiocyanate

PEN-2 presenilin enhancer 2

PEST proline-glutamate-serine-threonine-rich

PIAS protein inhibitors of activated stats

PLAT plasminogen activator, tissue

PPO platelet peroxidase

Pro-E pro-erythroblast

Pro-MK pro-megakaryocyte

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PTP protein tyrosine phosphate

PTPN11 protein tyrosine phosphatase, non-receptor type 11

PSEN presenilin

RBM15 RNA-binding motif protein-15

RBPJ recombination signal binding protein for immunoglobulin J region

RhoA Ras homolog gene family, member A

RILPL1 Rab interacting lysosomal protein-like 1

RIM retroviral insertional mutagenesis

RIS retroviral integration site

Rorc RAR-related orphan receptor C

RTCGD Retrovirus Tagged Cancer Gene Database

RUNX1 Runt-related transcription factor 1

SAP SAF-A/Acinus/PIAS

SCF stem cell factor

SCID severe combined immunodeficiency

SCL stem-cell leukemia factor

SDF-1 stromal cell-derived factor-1

SH2 Src homology 2

SH2B3 SH2B adaptor protein 3

SHARP SMRT/HDAC-associated repressor protein

SIRT1 sirtuin 1

SMC1/3 structural maintenance of chromosomes 1/3

SMRT silencing mediator for retinoid and thyroid hormone receptor

SNP single nucleotide polymorphism

SOCS suppressors of cytokine signaling

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SPEN Split ends

STAG2 stromal antigen 2

STAT signal transducer and activator of transcription

ST-HSC short-term hematopoietic stem cell

Su(H) Suppressor of Hairless

SUZ12 suppressor of zeste 12 homolog

SYNE1 synaptic nuclear envelope protein 1

TACE TNF-α converting enzyme

TAD transactivation domain

TAL1 T-cell acute lymphoblastic leukemia gene 1

T-ALL T cell acute lymphoblastic leukemia

TAM transient abnormal myelopoiesis

TCRβ T cell receptor beta

Tg transgenic

Th T helper

TMD transient myeloproliferative disorder

TP53 tumor protein p53

TPO thrombopoietin

TSG tumor suppressor gene

TNC Tenascin C

TTN titin

UCSC University of California, Santa Cruz

URGCP upregulator of cell proliferation

VAF variant allele frequency

VEGF Vascular endothelial growth factor

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vWF von Willebrand factor

WBC white blood cell

WGA whole genome amplification

WHO World Health Organization

WM Waldenström's macroglobulinemia wpc weeks post-coitum

WT wild-type

YS yolk sac

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PUBLICATION LIST

1. Co-targeting Notch and JAK-STAT signaling pathways in acute megakaryoblastic leukemia. Ong KO, Wang CQ, Matsumura T, Du L, Chin DW, Koh CP, Mok MM, Ng KP, Yanagida M, Yamashita N, Jacob, B, Mori S, Shimizu R, Yamamoto M, Ito Y, Yin Kham SK, Yeoh EJ, Chng WJ, Osato M. Manuscript in preparation

2. The pro-metastasis tyrosine phosphatase, PRL-3 (PTP4A3), is a novel mediator of oncogenic function of BCR-ABL in human chronic myeloid leukemia. Zhou J, Cheong LL, Liu SC, Chong PS, Mahara S, Bi C, Ong KO, Zeng Q, Chng WJ. Mol Cancer. 2012 Sep 21;11:72

3. Dual inactivation of Hus1 and p53 in the mouse mammary gland results in accumulation of damaged cells and impaired tissue regeneration. Yazinski SA, Westcott PM, Ong K, Pinkas J, Peters RM, Weiss RS. Proc Natl Acad Sci U S A. 2009 Dec 15;106(50):21282-7

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CHAPTER 1: INTRODUCTION

1.1 Megakaryopoiesis

Megakaryopoiesis is the process concerned with the unique maturation of megakaryocytes (MKs) that is derived from the pluripotent hematopoietic stem cells

(HSCs) and involves multipotential stem/progenitor cells commitment1, nuclear polyploidization, development of an extensive internal demarcation membrane system, formation of proplatelet processes and finally the controlled release of functional platelets into the vascular sinusoids within the bone marrow which is critical for normal hemostasis2 (Figure 1.1). The hallmark of the MK is its large diameter (50-100 μm) and its single, multilobulated, polyploid nucleus, accounting for less than 1% of marrow elements3. A mature MK can produce up to 104 platelets during its lifespan4. The adult human produces 1011 platelets daily at steady state by the cytoplasmic fragmentation of

MKs and the production level can increase 10- to 20-fold in response to heightened demand and an additional 5- to 10-fold under the stimulation of thrombopoietin (TPO)- mimetic drugs5-7. A supportive bone marrow stroma consisting of osteoblasts, endothelial and other cells, matrix glycosaminoglycans, cytokine signaling [TPO, interleukin-6 (IL-6), interferon-gamma (IFN-γ), stem cell factor (SCF) and stromal cell- derived factor-1 (SDF-1)] and multiple transcription factors (Runx1, Gata-1, Fli1 and c-

Myb) are known to impact megakaryopoiesis2-3.

Figure 1.1: Megakaryopoiesis. Schematic diagram illustrates the process of megakaryopoiesis and platelet production which derived from HSC through successive lineage commitment steps, including proliferation and differentiation of MK progenitors and maturation of MKs into large polyploidy mature MKs that shed platelets. Abbreviations: MK, megakaryocyte; BFU- MK, burst forming units-megakaryocyte; CFU-MK, colony forming units- megakaryocyte. (Reproduced with permission from refs 8-9).

1.1.1 Megakaryopoiesis during development in mammals

Hematopoiesis, which refers to the development and production of the diverse types of blood cells in the organism, occurs in sequential stages that involve 2 distinct waves, namely primitive and definitive hematopoiesis (Figure 1.2). The multiple waves of blood cell development have been best studied in mouse, which has ultimately served as the main mammalian model system, integral to our understanding of hematopoietic biology.

Primitive hematopoiesis first emerges in mammals within the blood islands of the extraembryonic yolk sac (YS) at embryonic day 7.5 (E7.5) and is defined by a relatively rapid commitment of the hemangioblast mesodermal precursor to hematopoietic precursors and endothelial cells10-11. Majority of the hemangioblast

precursors also contains megakaryocytic potential, indicating that megakaryocytic lineage is also an integral component of embryonic hematopoiesis12. The unique bipotential primitive megakaryocyte/ erythroid progenitors (MEPs) transiently appear in the murine YS between embryonic days 7.25 and 9.0 (E7.25-E9.0) along with unipotential primitive erythroid and MK progenitors12.

The second wave of definitive hematopoiesis is multilineage that results in the production of all lineages of blood cells that populate the organism and is characterized by the emergence of multipotent HSCs13. Definitive MEPs arise and expand in the YS with MK progenitors and definitive erythroid progenitors (burst-forming units-erythroid

[BFU-Es]), accompanied temporally by mast cells, granulocyte, macrophage, and multipotential high proliferative potential colony forming cells between embryonic days

8.25 and 10.0 (E8.25-E10.0) prior to the formation of fetal liver14-17. The presence of megakaryocytic cells based on the expression of glycoprotein-1bβ (GP1bβ)-positive cells in the mouse conceptus is first detected exclusively in the yolk sac at embryonic day 9.5 (E9.5)12. No GP1bβ-positive cells are evident in the embryo proper but the

GP1bβ-positive cells are found to be abundant in the surrounding maternal decidual tissues12. Subsequently, the number of MK progenitors begins to decline in the YS and expand in the fetal liver at embryonic day 10.5 (E10.5), which likely reflects a contribution of MK progenitors from aorta-gonad-mesonephros (AGM)-derived HSCs that are evident in the liver beginning of embryonic day 11 (E11)12.

The rapid initiation of platelet production in a process termed thrombopoiesis, is evident by the presence of large and highly reticulated platelets detected in the embryonic vasculature between embryonic day 10.5 to 11.5 (E10.5-E11.5) and these reticulated circulating platelets continued to persist in elevated levels through to embryonic day 15.5 (E15.5) which suggests that active thrombopoiesis is required in

the developing fetus12. Fetal platelets are anucleate, discoid and biconvex in shape, which is similar to adult platelets12. However, fetal platelets are distinguished by large size with a diameter 1.6 fold larger than adult platelets, and other notable features such as massive canalicular system and the presence of small alpha granules12. The average platelet diameter as well as the levels of reticulated platelets decreased during ontogeny to those of adult platelets by birth12.

Figure 1.2: Hematopoietic development in human and mouse. Schematic diagram illustrates the timeline and shifting sites of hematopoietic events during embryonic development in the human and mouse. Arrows above indicate the onset of specific hematopoietic cell generation and/or appearance; arrows below indicate the earliest time of colonization of the secondary hematopoietic territories. Abbreviations: HSC, hematopoietic stem cell; AGM, aorta-gonad-mesonephros; dpc, days post-coitum; wpc, weeks post-coitum. (Modified from refs 18-20).

1.1.1.1 Characteristics of neonatal and adult megakaryopoiesis

The molecular differences between fetal/neonatal and adult megakaryopoiesis (Table

1.1), play a critical role in the pathogenesis of several megakaryocytic disorders with developmental stage-specific features. Several studies have demonstrated that fetal and neonatal MK progenitors have a higher proliferation rate and significantly larger MK colonies generated in colony assays than adult progenitors21-23. Production of MKs by fetal and neonatal MK progenitors are significantly smaller, more cytoplasmically mature, and of lower ploidy than adult MKs24-25. Significantly higher concentration of

TPO is also observed in healthy neonates compared to normal adults22,26,27. In addition, the response to TPO concentrations is more sensitive in healthy neonates than in normal adults22,26,27.

Table 1.1: Summary of characteristics of neonatal and adult megakaryopoiesis.

Abbreviations: TPO, thrombopoietin; MK, megakaryocyte. (Adapted from ref 28).

1.1.2 Megakaryocytic lineage specification in adult

The hematopoietic system is organized and schematically portrayed as the “tree of hematopoiesis”, which depicts the process of hierarchical hematopoiesis conveying the possible developmental choices and is generally conserved throughout vertebrae evolution29 (Figure 1.3). Hematopoiesis in adult vertebrates is a dynamic process that requires constant replenishment of terminally differentiated hematopoietic cells

(leukocytes, erythrocytes and platelets) which have a limited life span. Therefore, homeostasis of the hematopoietic system is considered to occur by the recruitment of

HSCs, which are a rare population of multipotent (the potential to give rise to all blood cell lineages) cells in the bone marrow. The capacity of HSCs to undergo extensive self-renewal (the ability to generate identical daughter cells with the same unrestricted proliferation potential) and commitment to differentiation to sustain mature blood cell production is crucial for the maintenance of life-long multilineage hematopoiesis

(Figure 1.3).

Multilineage hematopoiesis can be reconstituted following transplantation of a single HSC31. The HSC compartment is composed of long-term (LT)-HSCs, which can reconstitute the hematopoietic system for life and prevent premature HSC pool exhaustion by remaining quiescent under steady state, whereas short-term (ST)-HSCs can sustain hematopoiesis temporarily either by limited self-renewal activity or undergo differentiation into committed progenitors cells to repopulate myeloid or lymphoid cells. Differences between LT-HSCs and ST-HSCs have been characterized as having differences in marrow homing32.

Multi-potent progenitor (MPP), which is defined as a cell population that has lost the self-renewal capacity of HSC, gives rise to committed oligopotent progenitors,

namely common myeloid progenitor (CMP) and common lymphoid progenitor (CLP)33.

These highly proliferative CMPs and CLPs generate a variety of lineage-restricted precursors and progressively acquire characteristics of their respective terminally differentiated mature blood cells.

During the course of hematopoiesis, the highly regulated differentiation of quiescent HSC along one lineage involves lineage commitment, which is defined as the initiation of developmental program(s) that lead to a particular cell fate, and is accompanied by the gradual lost of the capacity for multi-lineage differentiation

(lineage maintenance)34. Lineage commitment and maintenance are complementary processes that guide cell fate decisions. Thus, cells committed to a particular lineage have alternative developmental choices until lineage maintenance is complete35.

Figure 1.3: Hematopoiesis differentiation chart in adult mammals. Flow chart illustrates the “classical” model of hematopoiesis, showing a hierarchy with HSCs differentiation into different progenitors. The maturation patterns of myeloid and lymphoid cells into their respective lineages are also shown. Abbreviations: HSC, hematopoietic stem cell; LT-HSC, long-term HSC; ST-HSC, short-term HSC; MPP, multipotent progenitor; CMP, common myeloid progenitor; GMP, granulocyte/monocyte progenitor; MEP, megakaryocyte/erythrocyte progenitor; CLP, common lymphoid progenitor; MK, megakaryocyte; Pro-E, pro-erythroblast; B, B-cell; T, T-cell; DN, double negative T-cell precursors; DP, double positive T-cell precursors. (Adapted from ref 34).

According to the “classical” model of hematopoiesis, CMP and CLP are symmetrically derived from the same MPP, representing the first step of irreversible lineage commitment from HSCs during early hematopoietic ontogeny36.

The CMPs give rise to the megakaryocyte/erythroid progenitors (MEPs) committed to the differentiation and formation of erythrocytes and megakaryocytes/platelets and also granulocyte/macrophage progenitors (GMPs) which are able to differentiate into granulocytes including neutrophils, eosinophils and basophils, as well as monocytes which further differentiate into macrophages. The myeloid lineage is involved in various functions such as innate immunity, adaptive immunity (WBCs), blood clotting (platelets) and oxygen transportation throughout the organism (erythrocytes).

The CLP carries differentiation potential for all types of lymphoid cells including B cells, T cells and natural killer (NK) cells, through their respective precursors. Immediately following the CLP stage, development of B cells and T cells occurs separately, with B cells developing in the bone marrow and T cells in the thymus. In the bone marrow, earliest B lineage precursors, pro-B cells develop through pre-B cells into immature B cells which translocate into the peripheral lymphoid organs to mature through transitional stages into mature B cells. In the thymus, early thymic

+ + progenitors develops through CD4 CD8 double-negative (DN) and CD4 CD8 double- positive (DP) stages into mature T cells. B and T lymphocytes are responsible primarily for the basic functions of antibody production by terminally differentiated plasma cells and cell-mediated immune responses including T helper, T regulatory, and cytotoxic T lymphocyte effector cell functions, respectively.

MK lineage arises from MEP through differentiation of CMP derived from

HSCs. In response to environmental factors, cytokines and chemokines, bipotential

MEP can develop into the highly proliferative, most primitive MK progenitor that is the burst-forming unit-MK (BFU-MK), or the more mature smaller progenitor designated colony-forming unit-MK (CFU-MK)37-38. BFU-MK can develop into large, complex and multifocal colonies that include satellite collections of MKs and contain up to 40 to

500 MKs within 1 week, whereas CFU-MK develop into a small, simple and unifocal colony containing from 3 to 50 mature MKs that vary in their proliferation potential39

(Figure 1.4). The transition from MK progenitors to mature MKs is characterized by a significant level of overlap, which gives rise to a population of cells known a pro- megakaryoblast and the more mature diploid MK progenitors (megakaryoblasts) with increasing ploidy through a unique process known as “endomitosis” and decreasing proliferative potential40-41. This leads to the formation of pro-MKs, which contains a lobulated nucleus, polychromatic cytoplasm and developing granules39. Subsequently,

MK undergoes terminal maturation, which is characterized by 2 morphological forms.

The earlier mature MK is characterized by a low ratio of nuclear to cytoplasmic matter and a loss of basophilic staining of the cytoplasm42. The latter stage involves the formation of proplatelets and circulating platelets from large, granular and polyploid mature MKs by acquisition of the cytoplasmic structural and functional characteristics necessary for platelet action.

1.1.2.1 Immunophenotypical identification of megakaryocytes

MKs constitute a unique system of hematopoietic cell differentiation characterized by polyploidization, maturation and organized fragmentation of the cytoplasm leading to the release of platelets in the blood stream43. Platelets, the enucleated fragments of

MKs, are critical in the maintenance of vascular integrity and also contribute to wound

healing, angiogenesis, and innate immunity. The analysis of MK cellular differentiation markers is useful in studies investigating megakaryopoiesis in normal and disease state.

However, measurement of MK frequency, ploidy distribution, and maturation stage has been complicated by the fragility of MKs as well as inherent tendency to aggregate.

Flow cytometry has been shown to be a sensitive, rapid and useful technique for the analysis of marrow MK cellular and molecular characteristics such as cell size, complexity of cytoplasm architecture, ability to undergo endomitotic cycles and expression of individual molecular markers44. Thus, the introduction of flow cytometry represents a significant technical advance in studies of MKs overcoming many of the technical limitations associated with separation and discrimination between MK maturation subsets.

Evaluation of precursors and mature cells in the MK lineage based on the expression of cell surface markers of MK differentiation and cell surface glycoprotein receptors that are essential for platelet function is outlined in Table 1.2. In humans, MK

+ development commences with myeloid progenitors found within the c-Kit Sca-1 Lin population which can be subfractionated with CD34 and FcγRII/III (FcγR) markers into

45 + + lo CMPs and MEPs . CMPs are identified as c-Kit Sca-1 Lin CD34 FcγR and MEPs as

+ lo c-Kit Sca-1 Lin CD34 FcγR . The early commitment of primitive progenitors toward

MK lineage, MK progenitors are marked by an increase in CD9 (tetraspanin) and CD41

(GPIIb or integrin αIIb) within the MK differentiation pathway as

+ lo + hi 46 c-Kit Sca-1 Lin CD34 FcγR CD9 CD41 immunophenotype . The degree of MK differentiation can be monitored by analyzing the expression phenotype of early

(CD61, GP11b/IIIa complex) and late (CD42b, GPIb) markers of MK differentiation47.

+ lo + hi hi Immature MKs are defined as c-Kit Sca-1 Lin CD34 FcγR CD9 CD41 CD61 , while mature functional MKs are identified as

+ lo + hi hi hi 47,48 c-Kit Sca-1 Lin CD34 FcγR CD9 CD41 CD61 CD42b . Expression of von

Willebrand factor (vWF)49-50 and acetylcholinesterase staining51 is in concordance with

MK maturation, whereas the expression of human leukocyte antigen (HLA)-DR declined with maturation and become negative in platelets49.

It is important to emphasize that CD41/CD61 were thought to be an integrin specific and selective phenotypic markers for the MK/platelet lineage52, these markers should be used with caution in identifying MK cells as there is increasing evidence that

CD41 expression is not restricted to cells with potential to differentiate into MK cells53-56, but also in hematopoietic stem and progenitor cells (HSPCs) with a myeloid and lymphoid potential57.

Table 1.2: Summary cell surface phenotypes of hematopoietic progenitors, megakaryocytic precursors and mature cells in the MK lineage in BM.

Abbreviations: MK, megakaryocyte; HSC, hematopoietic stem cell; Lin, Lineage marker including IL-7R; FcγR, Fc-gamma receptor II/III; CMP, common myeloid progenitor; MEP, megakaryocyte/erythrocyte progenitor; lo, low expression; hi, high expression. (Compiled from refs 45-49).

1.1.3 Endomitosis and proplatelet formation

Endomitosis, also called endoreduplication of megakaryoblasts, is a TPO-driven process in which a series of recurrent cycles of DNA replication occurs without cytokinesis58-59 (Figure 1.4). During differentiation of megakaryocytic cells, the phenomenon of nuclear polyploidization allows megakaryocytes to accumulate DNA content ranging from 8N to 128N in a single poly-lobulated nucleus as well as facilitates the massive protein and lipid synthesis to create the invaginated membrane system (IMS) required for proplatelet formation60. The mechanism of endomitosis is poorly characterized.

The hypothesis that up-regulation of G1-phase components may be important in promoting cycles of endomitotic DNA synthesis to allow for the development of high ploidy MKs has been supported by multiple studies that have identified roles for G1/S- phase regulators such as the cyclins D and E in endomitosis61-63, while studies in MK- transformed cell lines showed that the switch to polyploidization is dependent on degradation of cyclin B and reduced activity of the cyclin-B-dependent Cdc2 kinase64-

66. Subsequent studies with primary MKs revealed that endomitosis occurs because of a defect in late cytokinesis that results in incomplete formation of the cleavage furrow, which is a contractile ring consisting of myosin II and F-actin that generates the mechanical forces necessary for cell separation67-68. Cleavage furrow inhibition in polyploidization may be due to the failure in RhoA (a small GTPase that regulates the actin cytoskeleton) localization and activation at the contractile ring69.

Following nuclear polyploidization, MKs undergo cytoplasmic maturation involving the formation of IMS, which is an extensive complex of cisternae and tubules distributed throughout the MK cytoplasm that is continuous with the plasma membrane and it is the origin of the proplatelet and platelet surface70-71. The exact mechanism of platelet formation from mature MKs has been proposed by 2 major contrasting models; namely cytoplasmic fragmentation model and proplatelet model72.

According to the cytoplasmic fragmentation model, MKs travel from the bone marrow to the lung where platelets are released by a rupture of MK in the microvasculature73. In contrast, the proplatelet model predicts that terminally differentiated MKs in the bone marrow develop multiple branching processes that extend into the marrow sinusoids to release platelets into the circulation72,74. In support of the latter model, studies using live cell microscopy on cultured MKs showed that proplatelets are comprised of platelet-sized swellings in tandem arrays that are connected by thin cytoplasmic bridges75. Proplatelet production has been observed in vivo by imaging proplatelets extending into the sinusoidal blood vessels of bone marrow and in vitro with MKs derived from murine fetal liver stem cells and human cell-derived MKs. However, there is still much to be revealed about what molecular pathways or signals that regulate both platelet formation and size.

Figure 1.4: Endomitosis and proplatelet formation. Flow chart depicts the proliferation and differentiation of MK progenitors, including the MEP, BFU-MK and CFU-MK, into platelet-producing mature MKs. During this process, MKs undergo endomitosis to increase their size and DNA content. In murine cells, the DNA content can increase up to 128N. Associated surface antigens/markers are shown as pink arrows to indicate the approximate stage in MK development at which they are expressed. Abbreviations: MEP, megakaryocyte/erythrocyte progenitor; MK, megakaryocyte; BFU-MK, burst forming units-megakaryocyte; CFU-MK, colony forming units-megakaryocyte. (Modified from ref 3).

1.2 Transcription factors, cytokines and signaling pathways associated

with megakaryopoiesis

Transcription factors are a family of DNA binding proteins that recognize specific DNA sequence motifs such as DNA promoter sequence of a gene proximal to the transcription start site76-77, which interact with sets of transcription factors binding on a distal DNA element known as cis-regulatory elements and recruitment of RNA polymerases, leading to the initiation and modulation of transcriptional activation or repression78. Transcription factors also exert control over spatial and temporal

expression of target genes through combinatorial interactions with other transcription factors, co-factors, and chromatin-remodeling proteins.

Transcription factors are shown to act as key regulators for the normal functioning of the hematopoietic system and hematopoietic lineage fate decision based on gene targeting experiments. However, the molecular roles of these transcription factors can be altered in the event of genetic perturbations, including mutations, chromosomal translocations or viral insertions either in the transcription factor sequence itself or in its cis-regulatory elements79. About 5-10% of genetic disorders results from germline point mutations in transcription factors of identified gene and although many of these mutations are rare, majority of them affect embryonic development which provided evidence of the importance of these transcription factors in early development80. Somatic mutations in transcription factors are also observed in

38% and 44% of the genes involved in chromosomal abnormalities associated with hematopoietic and solid tumors respectively81.

The consequences of mutations in transcription factors are (i) gene dosage effect resulting in haploinsufficiency of the transcription factor function, (ii) the mutant can act as a dominant negative and interfere with the wild type transcription factor, or (iii) generation of gain-of-function mutants, especially if the mutation is in an inhibitory domain of the protein.

Cytokines are a large family of specific extracellular ligands produced by diverse cell types, which can act systematically via the bloodstream or lymphatic vessels or locally within the microenvironment of HSPCs. These cytokines play key roles in mediating hematopoietic differentiation during steady-state and its adaptation upon inflammation or injury. Cytokines of the hematopoietic system include interleukins (ILs), colony-stimulating factors (CSFs), interferons, erythropoietin (EPO)

and thrombopoietin (TPO). Cytokines signal by binding to, and activating, a family of structurally and functionally conserved cytokine receptors, which can be composed of dimers of a single receptor [granulocyte-CSF receptor (G-CSFR), EPO receptor

(EPOR), TPO receptor (c-MPL)] or can be heterodimeric with a common signaling subunit and a unique ligand-binding chain.

In addition to transcription factors, there is accumulating evidence that some, but not all cytokine receptors can transduce a genuine lineage-determining signal at some points in hematopoiesis, especially at the point of divergence of the myeloid and lymphoid lineages82.

Understanding of the molecular basis of megakaryopoiesis has progressed substantially since the discovery of the production of platelets by MKs. Regulation of megakaryopoiesis is tightly controlled by an intricate regulation of a myriad of signaling pathways whereby a complex network of hematopoietic growth factors are involved in either a TPO-dependent signaling or a TPO-independent signaling83.

Despite great advances, the precise cell-signaling events underlying the regulation of megakaryopoiesis are as yet not well understood.

1.2.1 Transcriptional regulation in normal and malignant megakaryopoiesis

Combination of transcriptional regulatory proteins have been shown to play an essential role in MK development as binding of a single transcription factor most likely insufficient to regulate megakaryopoiesis, which requires corresponding activation of

MK-specific gene and effective inhibition of erythroid differentiation by lineage- specific transcription factors such as GATA-1, RUNX1/AML1, SCL, ETS family members, GFI-1B and NF-E2.

Generation of gene-targeted mice involving critical factors such as Runx1 and

Scl results in embryonic lethality, which hinders the understanding of the role of these factors in adult megakaryopoiesis. More recently, conditional knockout (KO) mice provide experimental systems to overcome such technical problems. Table 1.3 summarizes the results obtained from studies of selected transcription factors which are involved in normal adult megakaryopoiesis.

Table 1.3: Expression of transcription factors involved in normal adult megakaryopoiesis and KO phenotypes.

Abbreviations: KO, knockout; HSC, hematopoietic stem cell; LT-HSC, long-term HSC; ST-HSC, short-term HSC; MPP, multipotent progenitor; MEP, megakaryocyte/erythrocyte progenitor; MKs, megakaryocytes; HSPC, hematopoietic stem/progenitor cell; TPO, thrombopoietin. (Modified from refs 3,84-86).

1.2.1.1 GATA-1

The X-linked gene GATA-1 is a transcription factor containing two phylogenetically conserved zinc fingers (known as the N-finger and the C-finger), whereby the C-finger recognizes and binds to the consensus “WGATAR” sequence in the cis-regulatory elements of many lineage-restricted genes87-89, and the N-finger increases the affinity of

GATA-1 binding to complex, palindromic sites and recruits co-factors such as Friend of

GATA-1 (FOG-1). FOG-1 is a large multifinger protein that interacts with GATA-1 to mediate the terminal maturation of MKs as well as proplatelet formation, and FOG-1 deficient mice lack MKs, supporting a critical role in early MK development90,91.

Tissue-specific expression of GATA-1 gene employs two alternative promoters, namely the distal (IT) promoter for hematopoietic cells92,93 and the proximal (IE) promoter for testis94-95.

GATA-1 has been shown to be critical for the development of erythroid and megakaryocytic lineages, suppressing the proliferation of megakaryocytic and erythroid precursors while promoting their differentiation85. Several studies have confirmed the findings that acquired mutations within exon 2 (or the intron immediately downstream) of GATA-1 are detected in both transient myeloproliferative disorder (TAM) and Down syndrome-associated acute megakaryoblastic leukemia (DS-AMKL), which results in an alternative 40 kDa translation product (referred to as GATA-1s) that lacks the N- terminal transactivation domain and initiates translation at methionine at amino acid 84 in exon 3, thereby preventing the synthesis of the 50 kDa full- length GATA-196-100

(Figure 1.5). GATA-1s still promoted terminal differentiation of MKs although fetal liver megakaryopoiesis was hyper-proliferated101. Mutagenesis of GATA-1 is an early event in DS leukemogenesis but it has been shown to be insufficient for overt leukemia.

Figure 1.5: Models for the expression of GATA-1 isoforms. Schematic diagram illustrates the translation of GATA-1 protein from the GATA-1 mRNA only, whereas the translation of GATA-1s protein either from the GATA-1 mRNA or from the alternative spliced GATA-1s mRNA lacking exon 2 due to short nucleotide insertions, deletions or missense or nonsense mutations that lead to the introduction of a premature stop codon. Abbreviations: N-ZF, amino-terminal zinc- finger domain; C-ZF, carboxy-terminal zinc-finger domain; NLS, nuclear localization signal; aa, amino acid. (Modified from refs 99,102,103).

1.2.1.2 RUNX1

The RUNX1 (Runt-related transcription factor 1) gene encodes the DNA-binding α- subunit of the heterodimeric Polyomavirus enhancer binding protein 2/Core binding factor (PEBP2/CBF) complex, known to play a critical role in hematopoietic stem cell and MK development104-106. RUNX1 has been shown to be the most frequently mutated gene in human leukemia, in which it has been implicated in over 30 different translocations, and was first identified in chromosomal rearrangements observed in patients with AML and hence RUNX1 is also called AML1107. Interestingly, RUNX1 is

located within the critical region of chromosome 21 that is responsible for clinical manifestations of DS108. The RUNX genes have an evolutionarily conserved 128-amino acid Runt domain required for DNA-binding at consensus DNA-binding sites

(Pu/TACCPuCA) and heterodimerization with the β subunit104 (Figure 1.6).

Figure 1.6: A diagrammatic representation of RUNX1. Normal RUNX gene expressed from two promoters (P1 or distal, and P2 or proximal), The Runt domain is the most conserved feature of the proteins, as well as a nuclear- matrix-targeting signal (NMTS) and the carboxy-terminal VWRPY motif. Abbreviations: aa, amino acid. (Modified from ref 109).

Haploinsufficiency of RUNX1 causes familial platelet disorder (FPD) with a predisposition to AML in human, exhibiting mild thrombocytopenia and platelet abnormalities including diminished dense core granules and decreased cellular adenosine diphosphate (ADP) levels110. Adequate expression of RUNX1 is essential for normal platelet homeostasis, as Runx1-heterozygous animals display mid thrombocytopenia and a decrease in long-term repopulating hematopoietic stem cells.

Diminished RUNX1 function leads to a significant decrease in polyploidization and terminal maturation of MKs as well as platelet formation in vivo111.

Gene targeting of Runx1 in mice also demonstrates that MK differentiation is sensitive to Runx1 dosage. RUNX1 expression is elevated in human fetal livers with trisomy 21112,113. Moreover, transgenic mice which overexpress RUNX1 exhibited a transient increase of MK surface marker CD61+ myeloid cells in neonates114. Therefore,

the increased RUNX1 expression at fetal stage may be one of the mechanisms accounting for the abnormal megakaryopoiesis in DS patients.

1.2.1.3 Other megakaryocyte-selective transcription factors

The role of basic helix-loop-helix protein stem cell leukemia (SCL)/ T-cell acute lymphoblastic leukemia gene 1 (TAL1) in early hematopoietic progenitors for specification of the megakaryocytic lineage115 is well established with loss-of-function studies showing a requirement for SCL in the development of fetal liver megakaryopoiesis116-117. Analysis of conditional Scl-knockout mice in hematopoietic cells revealed perturbation of megakaryopoiesis and erythropoiesis with a loss of early progenitor cells in both lineages. Long-term defects in Scl-deleted bone marrow cells in adult mice also revealed a late defect in MK differentiation under stress conditions leading to defective platelet production118. SCL exerts its activity as part of a large pentameric complex, along with E47, LMO2, LDB1, and GATA-1 at all activating

GATA-1 elements in MKs119. However, the function and transcriptional activity of Scl in megakaryopoiesis remained to be fully characterized.

ETS family of a transcription factors is defined by a conserved winged helix- loop-helix DNA binding (ETS) domain that allows recognition of purine-rich DNA sequences with a core GGA(A/T) consensus, designated EBS (ETS binding sequence)120. Several members of the ETS gene family of transcription factors such as

GA-binding protein α (GABPα), ETS1, ETS2, Ets-related gene (ERG) and Friend leukemia integration 1 (Fli-1), play critical roles in megakaryopoiesis, which can function either as activators or repressors depending upon the specific promoter and cellular context3. The heterodimeric GA-binding protein (GABP) complex consist of 2

components whereby GABPα mediates DNA-binding and GABPβ required for transcriptional activation as well as nuclear localization of GABPα121-123. Notably,

GABPα has been shown to function as a regulator of early MK-specific gene expression. Another member of the ETS family, Fli-1 is also necessary for the normal maturation of MKs and controls the expression of multiple MK-specific genes124,125. Moreover, the ratio of GABPα/Fli-1 expression declines throughout MK maturation, indicating that Fli-1 binds to promoters of both early and late MK-specific genes126. Expression of ETS1 was shown to increase during MK maturation and overexpression of ETS1 leads to enhanced differentiation as well as upregulation of

GATA-2 and MK-specific genes127. ETS2 and ERG are encoded within the DS critical region on human chromosome 21 and are able to promote megakaryopoiesis independent of GATA-1128. In addition, specific collaboration of ETS2 and ERG with alterations in GATA-1 revealed to promote immortalization of fetal liver progenitors.

The zinc finger transcriptional repressor, growth factor independence-1B (GFI-

1B) is essential for terminal MK maturation and Gfi-1b deficiency in fetal liver progenitors causes a reduction in proliferation concomitantly with an inhibition of differentiation of immature progenitors129. The basic region-leucine zipper DNA transcription factor of nuclear factor-erythroid 2 (NF-E2) is primarily essential for platelet biosynthesis by promoting the expression of mid-late stage MK genes including

β1-tubulin as homozygous null NF-E2 mice revealed to have a defect in platelet production resulting in absolute thrombocytopenia130,131.

Aberrant expression of these transcription factors are emphasized in abnormal megakaryopoiesis although further studies are necessary to define the contribution of these factors to AMKL.

1.2.2 Cytokines involved in the regulation of megakaryopoiesis

Numerous pleiotropic cytokines are known to influence megakaryocyte development at different stages of the process such as TPO, IL-6 and IFN-γ. Although it is widely accepted that TPO is the primary regulator of megakaryopoiesis, the other megakaryocyte-active cytokines undoubtedly contribute to the regulation of this process.

1.2.2.1 Thrombopoietin (TPO)

While a number of cytokines have been associated with the process of megakaryopoiesis, the primary growth factor that has been isolated and identified to be responsible for the regulation of megakaryopoiesis and thrombopoiesis (which is a the process of formation of thrombocytes/platelets) is the hematopoietic cytokine, thrombopoietin (TPO). TPO exerts its function through binding and activation of the

TPO receptor (c-Mpl), which is a type I transmembrane receptor predominantly expressed in hematopoietic tissues, hemangioblasts, MKs and platelets132,133.

Human TPO, the main physiological regulator of megakaryopoiesis and thrombopoiesis, is produced constitutively by the liver6,134. TPO is encoded by a single human gene, located on chromosome 3q26.3-3q27, a region of the long arm of chromosome 3, which produces a 353 amino acid precursor protein and an acidic and heavily glycosylated 332 amino acid mature protein6. TPO avidly binds and activates the c-Mpl receptors on MKs and selectively initiates the proliferation and maturation of

MK progenitor cells as well as stimulate the expression of characteristic cell surface proteins including CD61/41 (GpIIb/IIIa) and CD42 (Gp1b)135 in addition to inducing

endomitosis by creating the IMS required for proplatelet formation and cytoplasmic delivery of platelets into the circulation136,137. Regulation of megakaryopoiesis by plasma levels of unbound TPO reflects the balance between constitutive production and rate of destruction that is generally dictated by the overall platelet production6. Mice deficient in TPO or its ligand c-Mpl genes were shown to have severe thrombocytopenia with 10-20% residual platelet counts and reduction in progenitors committed to the MK lineage as well as functionally normal MKs138. Although these studies have underscored the importance of TPO in promoting the differentiation of

HSCs into mature MKs, they also demonstrated that TPO is central but not essential for megakaryopoiesis and platelet release.

1.2.2.2 Other cytokines involved in the regulation of megakaryopoiesis

Interleukin-6 (IL-6) is a 26 kDa pleiotropic cytokine that has been shown to have various effects on MK development in vitro and in vivo. IL-6 primarily affects the later stages of megakaryopoiesis. IL-6 alone does not affect MK colony formation but synergizes with IL-3 or GM-CSF to increase the number, size, and DNA content of developing MKs139. Additionally, IL-6 acts as a differentiation-inducing factor required for full MK maturation140,141. Furthermore, administration of recombinant human IL-6 to mice augmented the platelet counts accompanied by an increase in the size of their

MKs142. Clinical studies of IL-6 have shown its stimulatory effects on thrombopoiesis in patients with myelodysplastic syndrome and that IL-6 increased the frequency of higher ploidy MKs but did not significantly increase the number of MK progenitor cells143. In another study, human soluble IL-6 receptor dose-dependently enhanced the generation of MK (CD41+ CD61+ cells) from human CD34+ cells in serum-free

suspension culture supplemented with IL-6 and SCF through glycoprotein (gp)130 signaling144.

Interferon-γ (IFN-γ) is a dimerized soluble cytokine of the type II class of interferons produced by activated T-cells and natural killer cells in response to viral infection or other stimuli145. IFN-γ has been shown to support megakaryopoiesis by promoting the development of MK progenitor, CFU-MK, under some conditions in vitro146 as well as in cultures of murine bone marrow cells supplemented with IFN-γ and SCF147. IFN-γ in combination with TPO and SCF significantly augmented the development of MK colonies and increased the DNA content of cultured MKs145. In addition, the marked increased in MK numbers were recapitulated in vivo and the recovery of platelets was facilitated by the administration of IFN-γ, in conjunction with

IL-3. Moreover, effect of IFN-γ was observed to shortened the duration of thrombocytopenia in animals treated with 5-fluorouracil, whereas IL-3 alone had not effect148. IFN-γ was also found to support TPO-independent proliferation of human MK progenitor cells as the addition of anti-TPO polyclonal antibody almost completely abrogated TPO-dependent MK colony formation but failed to inhibit the generation of

MK colonies induced by IFN-γ145. Therefore, several studies have denoted that IFN-γ plays a unique role as a positive regulator of the MK lineage. Taken together, these results suggest that additional megakaryocyte-active cytokines or other signals are essential for the maximal development of human megakaryocytes.

1.2.3 Signaling pathways influencing megakaryocyte development

Activation of JAK-STAT pathway through interaction of TPO with its receptor exerts significant influence on MK proliferation and differentiation. Also, Notch signaling has been found to inhibit megakaryopoiesis by suppressing the essential transcription factor

GATA-1. I chose to pursue a candidate approach focusing on the JAK-STAT and Notch signaling pathways.

1.2.3.1 JAK-STAT signaling pathway

The evolutionarily conserved Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway is an intracellular signaling pathway that was originally discovered through studies on gene induction by interferons (IFNs) and mediates the effects of members of the cytokine receptor superfamily and growth factors, including those clinically important cytokines such as G-CSF, EPO, TPO, the interferons, and numerous interleukins149-151. JAK-STAT signaling pathway is essential for numerous developmental and homeostatic processes, including hematopoiesis, immune cell development, stem cell maintenance, organismal growth and mammary gland development152.

1.2.3.1.1 JAK structure and function

JAKs, in reference to the two-faced Roman god, is a unique class of tyrosine kinases that contain two symmetrical kinase-like domains; the C-terminal JAK homology 1

(JH1) domain possesses tyrosine kinase function while the adjoining JH2 domain is a catalytically inert, pseudo-kinase domain which may regulate kinase activity of

JAKs153-154. In mammals, there are four members of the JAK family, namely, JAK1,

JAK2, JAK3 and tyrosine kinase 2 (TYK2) and they all contain seven JH domains organized into four regions; kinase (JH1), pseudo-kinase (JH2), FERM (band-4.1, ezrin, radixin, and moesin) which consists of the N-terminal JH7, JH6, JH5 and part of JH4, and SH2 (Src Homology 2)-like which consists of JH3 and part of JH4151,155 (Figure

1.7). Expression of JAK1, JAK2 and Tyk2 is ubiquitous; however expression of JAK3 is restricted to hematopoietic cells156,157. Several specific hematopoietic cytokines activate JAK2 in normal megakaryocyte precursors and megakaryocytic cell lines for megakaryocytic cell proliferation158,159. The critical roles of JAK proteins in hematopoietic ontogeny have been clearly demonstrated via targeted gene disruption studies in mice (Table 1.4). Loss of Jak1, Jak3, or Tyk2 results in impaired lymphopoiesis, whereas Jak2 deficiency results in embryonic lethality as a result of failure of definitive erythropoiesis159.

Figure 1.7: Domain structure of Janus kinases (JAKs). JH1 is the kinase domain containing two tyrosines that can be phosphorylated after ligand stimulation. JH2 is the pseudo-kinase domain. JAKs have an Src homology 2 (SH2) domain and the amino terminus of JAKs (JH5-JH7) has homology to the band four-point-one, ezrin, radixin, moesin (FERM)-domain containing proteins. The JH6 and JH7 domains mediate the binding of JAKs to receptors. (Modified from ref 151).

Table 1.4: Gene targeting studies on Janus kinases (JAKs) family members.

Abbreviations: Jak1-3, Janus kinase 1-3; IL, interleukin; LIF, leukemia inhibitory factor; OSM, oncostatin M; GM-CSF, granulocyte-macrophage colony-stimulating factor; EPO, erythropoietin; TPO, thrombopoietin; IFN, interferon; SCID, severe combined immunodeficiency; LPS, lipopolysaccharide. (Adapted from refs 150,160-169).

1.2.3.1.2 STAT structure and function

Signal transducer and activator of transcription (STAT) proteins are a 7-member mammalian family of latent cytoplasmic transcription factors (STAT1, STAT2, STAT3,

STAT4, STAT5a, STAT5b, and STAT6)170 which is highly homologous in several regions, including a SH2 domain involved in the activation and dimerization of

STATs171,172, a DNA-binding domain173, and a transactivation domain located at the carboxyl terminus174,175 whereas the amino-terminal region of STATs is involved in regulating STAT activity such as the formation of STAT tetramers176 and tyrosine dephosphorylation177 (Figure 1.8). The critical roles of STAT proteins in hematopoietic ontogeny have been clearly demonstrated via targeted gene disruption studies in mice

(Table 1.5). Mice lacking Stat1, Stat2, Stat4, or Stat6 are viable but exhibit specific signaling abnormalities, predominantly in lymphoid cells159. Stat3 deficiency results in early embryonic lethality as a result of severe developmental defects, and simultaneous loss of both Stat5a and Stat5b leads to perinatal lethality with anemia and leukopenia.

Figure 1.8: Linear representations of the domain structures of the signal transducer and activator of transcription (STAT) protein family. The amino-terminal domain mediates the interaction between two STAT dimmers to form a tetramer, whereas the coiled-coil domain is involved in interactions with regulatory proteins and other transcription factors. The DNA-binding domain makes direct contact with STAT-binding sites in gene promoters. Reciprocal interactions between the SH2 domain of one STAT monomer and the phosphotyrosine residue of another mediate dimer formation, which is required for the binding of STATs to DNA. The transactivation domain is involved in the transcriptional activation of target genes through interactions with other proteins. The carboxy-terminal domain contains a site of serine phosphorylation that enhances transcriptional activity in some STATs. The activity of STATs can be regulated by protein modification, including tyrosine and serine phosphorylation, methylation, sumoylation, ISGylation and acetylation. The modification sites of ISGylation and acetylation have not been identified. Abbreviations: SH2, Src homology 2; Y, tyrosine; S, serine. (Adapted from ref 178).

Table 1.5: Gene targeting studies on signal transducer and activator of transcription (STAT) family members.

Abbreviations: IL, interleukin; GM-CSF, granulocyte-macrophage colony-stimulating factor; G-CSF, granulocyte colony-stimulating factor; Th1, T helper. (Adapted from refs 150,151,179-191).

1.2.3.1.3 Mechanism of JAK-STAT signaling

Members of the type I and type II cytokine receptor families often employ JAK family of tyrosine kinases to phosphorylate and activate downstream proteins involved in their signal transduction pathways as these receptors lack intrinsic catalytic kinase activity

(Figure 1.9). Intracellular activation occurs when ligand binding induces the multimerization of receptor subunits. For some ligands, such as EPO and growth hormone, the receptor subunits are bound as homodimers while, for others, such as

IFNs and interleukins, the receptors subunits are heteromultimers192. For signal dissemination through either homodimers or heteromultimers, the cytoplasmic domains of two receptor subunits must be associated with JAK tyrosine kinases. Cytokine

stimulation leading to JAK activation results in the tyrosine phosphorylation of the cytoplasmic domain of the receptor generating docking site(s) for STAT SH2 domains193.

Once recruited to the receptor-JAK complex, STATs are tyrosine phosphorylated at the C-terminus by JAKs, leading to the formation of STAT homodimers or heterodimers via SH2 domain-phosphotyrosine interactions194.

Following dimerization, STATs rapidly translocate into the nucleus by a mechanism that is dependent on importin α-5 (also called nucleoprotein interactor 1) and the Ran nuclear import pathway195. Once in the nucleus, dimerized STATs interact and bind specific regulatory elements to induce target gene transcription that stimulates several cellular events such as cell proliferation, differentiation, cell migration and apoptosis196.

The termination of STAT signaling is due to a number of different factors, including receptor degradation via the lysosome and proteasome, activation of cellular phosphatases and expression and activation of specific regulatory proteins197. There are three major classes of negative regulator: SOCS (suppressors of cytokine signaling) which can either bind to phosphotyrosines on the receptors or bind directly to JAKs and/or receptors or interact with the elongin BC complex SAP (SAF-A/Acinus/PIAS) domain at the N-terminus198; PIAS (protein inhibitors of activated stats) which bind to activated STAT dimmers and prevent them from binding DNA192; PTPs (protein tyrosine phosphates) which reverse the activity of the JAKs199,200.

Figure 1.9: Schematic representation of the JAK-STAT pathway. Ligand-induced receptor oligomerization activates JAKs that subsequently phosphorylate tyrosine residues on the cytoplasmic portion of the receptor. The quiescent STAT monomers are then recruited to the activated receptor complex via the interaction of the SH2 domains with phosphotyrosine docking sites. STATs are phosphorylated by the JAKs on a conserved tyrosine residue in the c-terminal domain to form STAT homodimers or heterodimers. STATs dissociate from the receptor after the dimerization and translocate into the nucleus. In the nucleus, STATs bind to specific response elements and induce gene transcription. (Adapted from ref201).

1.2.3.1.4 Implications of JAK-STAT signaling in hematological malignancies

Genetic alterations affecting members of the JAK family, causing aberrant JAK-STAT signaling and constitutive STAT activation have been discovered in a wide array of cancers and are particularly prominent in hematological malignancies. Mutations in

JAK family members is increasingly recognized with the discovery of the V617F mutation in JAK2 in a substantial proportion of patients with chronic myeloproliferative neoplasms (MPNs), a spectrum of neoplastic disorders characterized by overproduction of terminally differentiated cells of the myelo-erythroid lineage and share a predisposition to the development of AML202-205. Additionally, rare variants in exons 12 to 15 have also been identified in patients with MPNs206.

Recently, point mutations that activate JAK family members have also been revealed to be common in acute leukemias. Acquired lesions involving JAK1, JAK2,

and JAK3 (but not TYK2) as well as several analogous JAK2 fusion proteins have been reported in both AML and ALL. More importantly, several non-recurrent JAK3 mutations were identified in the CMK megakaryoblastic cell line and patients with

AMKL207, while JAK3 mutations are rare in ALL and AML. Several different JAK3 mutations (I87T, P132T, A572T, A573V, M576L, A593T and V722I) have also been reported in primary AMKL samples but are yet to be functionally characterized208-210.

Although only the A572V and V722I mutants are found to be situated within the pseudokinase domain and P132T mutation lies within the receptor binding region of

JAK3, all three mutations were found to constitutively activate the JAK3 protein.

Constitutive activation of STAT3 and STAT5 has been linked with disease outcomes in AML demonstrated by analyses of primary peripheral blood and bone marrow specimens197. Additionally, AML patients with STAT3 polymorphism were found to be associated with unfavorable cytogenetics and failure to achieve complete remission211. Constitutive activation of STAT5 has also been associated poor overall survival and reduced progression-free survival in patients with AML197.

1.2.3.2 Notch signaling pathway

The Notch gene was first described on mutant Notch phenotypes of fruit flies

(Drosophila melanogaster) in 1914, and, rapidly thereafter, identification of mutant alleles with the observation of a Drosophila strain with notched wings212. However, it was not until after the molecular biology revolution that the Notch receptor was cloned and the wing-notching phenotype was attributable to gene haploinsufficiency213,214.

Notch signaling is a highly evolutionary conserved pathway, which plays pleiotropic roles during embryogenesis and is important for the regulation of self- renewing tissues of adult organism215. The physiological functions of this signaling cascade include maintenance of stem cells, determination of cell fates and regulation of cell proliferation, differentiation as well as apoptosis. Notch signaling has also multiple functions during normal lymphoid development, which include regulation of T-cell commitment from a multipotential precursor, regulation of αβ T-cell development, and marginal zone B-cell development216. However, the function of Notch signaling in the development of myeloid cells including megakaryocytes is still not well defined.

Although Notch signaling has mostly been associated with oncogenic and growth-promoting roles, there is growing evidence that components of the same pathway may also function as a tumor suppressor depending on the cellular context217.

1.2.3.2.1 Notch receptors and ligands

The human NOTCH family includes four distinct receptors (NOTCH 1-4) and five canonical ligands which include 3 Delta-like ligands (DLL1, DLL3 and DLL4) and 2

Jagged ligands (JAG 1 and 2)215,218,219. NOTCH receptors are expressed at the cell surface as heterodimers comprising an extracellular subunit (NEC), which linked to a second subunit containing the extracellular heterodimerization domain (HD) followed by a transmembrane domain and the cytoplasmic region of the receptors (NTM) which also contains the intracellular domain of NOTCH (NICD). The extracellular domains of

NOTCH1 and NOTCH2 each have 36 epidermal growth factor (EGF)-like repeats, while NOTCH3 harbours 34 and NOTCH4 contains 29 repeats220 (Figure 1.10). EGF- like repeats is required for efficient binding of receptors to ligands and therefore, differences in the number of EGF-like repeats between NOTCH receptors may be significant because EGF-like repeats are fucosylated on specific serine and threonine residues by O-fucosyltransferase221-223. Modifications of these O-fucose moieties by the addition of N-acetylglucosamine by the Fringe family of 1, 3 N- acetylglucosaminyltransferases223 subsequently regulate the affinity of NOTCH receptors for certain ligands225,226. NEC also contains a juxtamembrane negative regulatory region (NRR) consisting of three cysteine-rich LIN12/NOTCH repeats that prevent ligand-independent signaling and a HD227 which is a C-terminal hydrophobic region mediating heterodimerization between NEC and NTM 228. The NICD consists of seven ankyrin repeats which are essential for the formation of the transcriptional activation complex and the recruitment of a member of the mammalian Mastermind- like proteins (MAML1-3) MAML1229, a transactivation domain (TAD), a nuclear localization signal (NLS), and a proline-glutamate-serine-threonine-rich (PEST) domain

regulating protein stability229-231. Other differences are found within the NICD; specifically, NOTCH1 contains a strong TAD and NOTCH2 a weak TAD, but

NOTCH3 and NOTCH4 lack TAD, influencing promoter selectivity at least in vitro220,232. NOTCH ligands are transmembrane proteins of which the extracellular domain contains a characteristic number of EGF-like repeats and a cysteine rich N- terminal Delta/Serrate/LAG-2 (DSL) domain, responsible for the interaction with

NOTCH receptors233,234.

Figure 1.10: Domain organization of the human NOTCH receptors and ligands. There are four Notch receptors, NOTCH1 to NOTCH4 in mammals and classical NOTCH ligands including JAG1 and 2 as well as Delta-like ligands DLL1, 3 and 4. Abbreviations: EGF-like repeats, epidermal growth factor-like repeats; LIN, negative regulatory LIN repeats; HD, heterodimerization domain; NRR, negative regulatory region; TM, transmembrane domain; RAM, RBPJ associated molecule domain; NLS, nuclear localization signal; ANK, ankyrin repeats; TAD, transactivation domain; PEST, proline-glutamate-serine-threonine-rich domain; CR, cysteine rich domain; DSL, Delta, Serrate and LAG-2 domain. S1, S2 and S3 indicate the crucial proteolytic sites. (Modified from ref 235).

1.2.3.2.2 Regulatory mechanism of Notch signaling

Notch receptors are single pass type I transmembrane receptors translated as a single polypeptide that is cleaved during post-translational modification in trans-Golgi vesicles by a furin-like convertase (S1 cleavage) and exist as a non-covalently linked heterodimer at the cell surface236. Canonical Notch signaling pathway, which is the most extensively characterized signaling pathway237, is initiated by binding of the

Notch transmembrane receptors with their specific ligands between two neighbouring cells, leading to two rapid consecutive proteolytic cleavages215 (Figure 1.11). The first

(S2) cleavage occurs extracellularly in the HD mediated by ADAM (a disintegrin and metalloprotease) family, ADAM10 or TACE (TNF-α converting enzyme, also known as ADAM17)238,239, followed by a mono-ubiquitination process240 and eventually generates the substrate for the S3 cleavage by the γ-secretase complex, which is composed of Presenilin, APH1, PEN2, and Nicastrin (NCSTN)241. Subsequent proteolytic cleavage of S3 liberates the NICD of the receptor which then translocates to the nucleus242,243.

Canonical Notch signaling requires the formation of a complex with a transcription factor CSL (CBF1/Suppressor of Hairless/LAG-1) family DNA-binding protein {C promoter-binding factor 1 (CBF1) is also known as recombination signal binding protein for immunoglobulin J region (RBPJ) or kappa-binding factor 2 (KBF2( in mammals, as Suppressor of Hairless [Su(H)] in flies and Longevity-assurance gene-1

(LAG-1) in C. elegans} and initiates the transcription of Notch target genes244. In the absence of receptor activation and NICD, RBPJ acts as a transcriptional repressor by binding DNA in a sequence-specific manner of several co-repressors including

SMRT/NcoR, SHARP (or MINT), CtBP1 and SIRT1, which recruit histone

deacetylases and the histone demethylase LSD1245-248. Human SHARP (MINT for murine) belongs to the evolutionarily conserved Split ends (SPEN) gene family249.

Upon entering the nucleus, NICD competes and displaces the co-repressor complex, consequently interacts and binds to CSL and mediates their conversion to a transcriptional activator followed by the recruitment of the coactivators protein

MAML1 and histone acetyltransferases such as p300 to create a short-lived transcriptional activation complex244.

Apart from the canonical Notch signaling, there are also other types of non- canonical Notch signaling that can be initiated by a non-canonical ligand, or may not require cleavage of the Notch receptor233. The first one involves Notch ligation and translocation of activation signals independent of CBF1 (NICD-dependent), the second involves activation of Notch target genes that are independent of γ-secretase cleavage

(NICD- and CBF1-indepedent) and the third involves CBF1-dependent gene activation without receptor cleavage and NICD release250. Additional non-canonical functions have been characterized such as regulation of actin cytoskeleton and interactions with

Wnt signaling251.

Termination of Notch signaling can occur at or downstream of the Notch receptor250. The Notch receptor can be degraded through the lysosomes by the ubiquitin ligase Itch/AIP4 or Nedd4, which act together with Numb and Itch/AIP4 to stimulate endocytosis and lysosomal degradation of the Notch receptor. The binding of Notch on

DNA appears to be a quick and vibrant process controlled by the kinase CDK8 and the ubiquitin ligase FBXW7, followed by phosphorylation, ubiquitination, and proteasomal degradation of Notch, which abolished the Notch induced transcriptional activation252.

Figure 1.11: Schematic diagram showing canonical Notch signaling. Notch signaling is initiated by the ligand (DLL 1, 3, 4; JAG 1 and 2) engagement of the Notch (NOTCH1-4) receptor. The intracellular domain of Notch goes into the nucleus to a transcriptional repressor RBPJ which displaces the co-repressor complex and activates the expression of the Notch target genes. S1, S2 and S3 indicate the crucial proteolytic sites. Abbreviations: DLL, Delta-like; RBP-J, recombination signal binding protein for immunoglobulin J region. (Adapted from ref 253).

1.2.3.2.3 Notch target genes

Considering the number of developmental processes regulated by Notch, it was a surprised that only few target genes of Notch have been identified to date. The most extensively studied and best understood Notch target genes are two families of basic helix-loop-helix transcription factors: Hes (Hairy enhancer of split genes) and Hey

(Hairy/enhancer of split related genes with YRPW motif) family254,255. These transcription factors function as transcriptional repressors and play an important role in development. Seven Hes (Hes1-7) and three Hey (Hey1,2 and L) genes have been identified in the mouse genome, but only Hes1, Hes5 and Hes7 as well as all Hey genes are induced by Notch activation.

Other studies have shown that CD25 and the transcription factor GATA3 are direct Notch target genes activated in T-cell development256. Additionally, two other

Notch target genes NRARP (Notch-regulated ankyrin repeat protein) and Deltex1 are shown to be negative regulators of Notch signaling257. Further Notch targets are cyclin

D1, c-myc, p21, p27, Akt, mTOR, VEGF and many more target genes of Notch signaling remained to be determined234,258.

1.2.3.2.4 Implications of Notch signaling in hematological malignancies

Notch signaling pathway has been shown to be genetically altered in various hematological malignancies, which can lead to either activation or repression of the pathway depending on the context and the activation status of other potentially oncogenic pathways. Several cases of hematological malignancies in which Notch has tumor suppressive (myeloid malignancies) or oncogenic (T cell acute lymphoblastic leukemia and chronic lymphocytic leukemia) roles are presented in Table 1.6.

Table 1.6: Oncogenic and tumor suppressive roles of Notch signaling in human hematological malignancies.

Abbreviations: T-ALL, T cell acute lymphoblastic leukemia; CLL, chronic lymphocytic leukemia; CMML, chronic myelomonocytic leukemia. (Adapted from refs 259-263).

1.2.3.2.4.1 Notch as an oncogene

The importance of Notch signaling in lymphocyte development and oncogenic transformation has been well characterized. Notch1 has been shown to play an important role in directing T-cell lineage commitment at the expense of B-cell development as conditional deletion of Notch1 or Rbpj results in blocked T-cell development and the appearance of ectopic B cells in the thymus264,265. The first evidence for the involvement of Notch signaling in human cancer was the identification of a t(7;9)(q34;q34.4) translocation in T-cell acute lymphoblastic leukemia (T-ALL) patients resulting in a rearrangement between the intracellular part of NOTCH1

(NICD1) and the T cell receptor beta (TCRβ) locus that leads to overexpression of truncated, constitutively active NOTCH1 in leukemia266. A crucial finding was the first activating NOTCH1 mutations in human T-ALL, occurring in approximately 56% of all cases, proving that NOTCH1 is a strong oncogenic allele260. Additionally, inactivating mutations or deletions were identified in the ubiquitin ligase FBXW7 and associated with sustained Notch signaling259,267-269. Subsequent animal modeling by other groups suggested that Notch1 mutations (“hot spots” of mutations in HD or PEST) are insufficient to induce disease but can collaborate with other oncogenic lesions such as

KrasG12D to accelerate T-ALL, suggesting that NICD1 dosage is a crucial determinant in Notch oncogenicity270.

Chronic lymphocytic leukemia (CLL) is the most frequent leukemia in adults.

Recently, NGS-based approaches have demonstrated that 10-12% of CLL cases exhibit activating mutations of NOTCH1 predicted to impair FBW7-induced NOTCH1 degradation with the majority of patients harboring the same frameshift mutation

(P2515Rfs*4), highlighting the significance of such mutations as a prognostic

marker261,262,271. In another study, mutational analysis of NOTCH1 was primarily found to occur more significantly in patients with the more clinically aggressive non-mutated

IGV(H) subtype (20.4%) or in the high-risk Richter syndrome (31.0%) and in chemorefractory CLL (20.8%)244. These results including a very recent large-scale clinical analysis of CLL patients confirmed that NOTCH1 mutations are an adverse prognostic parameter in CLL and this appear to define a distinct high-risk clinical subtype for therapeutic intervention.

After the discovery of its involvement in T-ALL, Notch signaling was also implicated in various solid tumors, including breast cancer, medulloblastoma, colorectral cancer, non-small cell lung carcinoma (NSCLC), and melanoma272, therefore it seems likely that the list of neoplasms involving some alteration in Notch signaling will continue to grow.

1.2.3.2.4.2 Notch as a tumor suppressor

In stark contrast to the oncogenic roles in the lymphoid compartment, there is growing evidence that components of the same oncogenic pathway in lymphocytes may have a growth-suppressive function in myeloid cells. Recently, a study has shown that conditional Notch loss-of-function through deletion of an indispensable component of the γ-secretase complex, NCSTN, or compound deletion of Notch1/2 in vivo led to the induction of chronic myelomonocytic leukemia (CMML)263, which is a disease classified as a myelodysplastic/ myeloproliferative overlap syndrome, characterized by increased extramedullary hematopoiesis, monocytosis, myeloproliferation, and frequent progression to acute myeloid leukemia (AML)244. Additionally, whole-transcriptome analysis revealed that Notch signaling regulates an extensive myelomonocytic-specific

gene signature, through the direct suppression of gene transcription by the Notch target gene Hes1262. Finally, novel somatic-inactivating Notch pathway mutations in NCSTN,

MAML1, APH1A and NOTCH2 were identified in around 12% of CMML patients, which were unique as these mutations were not observed in other myeloproliferative disorders (MPD) such as polycythemia vera and myelofibrosis272.

1.3 Megakaryoblastic leukemias

Deregulation of any mechanisms mentioned in the previous sections causes MK-related human diseases such as platelet disorders, or leukemias. Acute leukemia is the most common pediatric malignancy, and the risk of developing leukemia in Down syndrome

(DS) children with constitutional trisomy 21 is approximately 10-20-fold higher than in the general pediatric population274-276. Children with DS have a unique predilection to develop megakaryoblastic leukemias, including transient abnormal myelopoiesis

[TAM, also known as transient myeloproliferative disorder (TMD)]277,278 and primary acute megakaryoblastic leukemia (AMKL)279. AMKL also occurs in adults much less frequently and is more likely to evolve as secondary AMKL from an antecedent idiopathic myelodysplasia or occurring after exposure to chemotherapeutic agents, such as topoisomerase II inhibitors279,280 (Figure 1.12).

Figure 1.12: Stochastic model of leukemogenesis in Down’s syndrome. Flow chart depicts the multi-step leukemogenesis pointing at multiple effects of several trisomic human chromosome 21 (HSA21) genes. The HSA21 genes are shown in purple, measurable effects in red, multiple cell symbols signify hyperproliferation, cast dice symbols represent increased chance of mutagenic event occurring and the warning sign symbolizes an acquired mutation that has occurred. Abbreviations: ES, embryonic stem cells; HSC, hematopoietic stem cell; CMP, common myeloid progenitor; MEP, megakaryocyte/erythrocyte progenitor; CLP, common lymphoid progenitor; TMD, transient myeloproliferative disorder; AMKL, acute megakaryoblastic leukemia. (Adapted from ref 113).

1.3.1 Transient abnormal myelopoiesis (TAM)

Approximately 10% of newborn infants with Down syndrome (DS) as well as phenotypically normal newborn infants with trisomy 21 mosaicism are born with

TAM281, a preleukemic status characterized by the clonal expansion of myeloid cells including immature megakaryoblasts282.

TAM shows a variety of clinical phenotypes, ranging from asymptomatic to causing perinatal death. Additional manifestations of TAM include cutaneous involvement, hyperviscosity, cardio-pulmonary syndrome with pleural and/or pericardial effusions, myelofibrosis, hepatomegaly and splenomegaly283. The majority of the affected infants with TAM are asymptomatic284-286; the probability of failure to

diagnose TAM in these asymptomatic cases raises the possibility that the true incidence of TAM is higher than currently assumed.

Consequently, the diagnosis of TAM is confirmed most frequently by routine complete blood cell (CBC) analysis or from laboratory tests ordered for other reasons287. Peripheral blood characteristics of TAM are variable but typically involved varying percentages of circulating blasts, usually leukocytosis and thrombocytopenia and occasionally, basophilia. The morphology of blasts in TAM are usually large cells with finely stippled chromatin, 1 to 2 prominent nucleoli in amorphous nuclei, surrounded by small amount of basophilic cytoplasm and show foci of cytoplasmic azurophilic granules, appearance of cytoplasmic blebs which is a characteristic of megakaryoblasts, and, in contrast to other acute leukemias, are morphologically heterogenous and capable of infiltrating organs such as liver, heart and skin288.

Flow cytometric immunophenotyping of TAM blasts show evidence of megakaryocyte differentiation and typically express CD45, CD33, CD38 and CD36; usually express CD34; variably express glycophorin A, CD41a (GP IIb/IIIa), CD42b

(GP Ib), CD61, CD11b, CD13, human leukocyte antigen (HLA)-DR, CD7 and CD56; rarely express CD62 (p-selectin)281,288-291.

The median age at diagnosis of TAM is 3-7 days292 and almost all cases presented within 2 months of birth according to data from three prospective studies of more than 200 newborn infants288,293,294. TAM in the majority of affected infants undergoes spontaneous remission within the first 3 months of life although over 10% of cases show hydrops fetalis, a serious condition characterized by severe edema in multiple body areas of a fetus or a newborn infant295,296. Up to 15% of cases with TAM show potentially fatal fulminant hepatic failure due to hepatic fibrosis and blast

infiltration. Moreover, approximately 20-30% of DS infants with TAM subsequently develop full-blown AMKL within 3 years281,287,298-301.

1.3.2 Acute megakaryoblastic leukemia (AMKL)

Acute megakaryoblastic leukemia (AMKL) is a distinct subtype of acute myeloid leukemia (AML-M7) according to the French-American-British (FAB) classification302, diagnosed by the presence of at least 20% leukemic blasts in the bone marrow (BM) and by definition requires confirmation of megakaryocytic lineage in 50% or more of the leukemic blasts according to the World Health Organization (WHO) classification of hematopoietic neoplasms303 by studies of cell morphology and immunophenotyping.

Immunophenotypically AMKL blasts typically co-express stem/progenitor cell markers (CD34, CD33 and CD117), myeloid (CD33), megakaryocytic (CD41 and

CD42b), and erythroid markers (CD36 and Glycophorin A) as well as the T cell marker

(CD7)290,292,304,305.

AMKL accounts for approximately 10% of pediatric AML, resulting in an incidence that is estimated to be 500-fold higher in children with Down syndrome (DS) than in the general pediatric population306. AMKL is divided into two major subgroups:

AMKL arising in patients with Down syndrome (DS-AMKL), and leukemia arising in patients without Down syndrome (non-DS AMKL). DS-AMKL is generally associated with a very favourable prognosis, with an approximately 80% survival in contrast to the non-DS AMKL population, which has a 3 year survival of less than 40% despite high- intensity chemotherapy279,307,308. Leukemic blast cell sensitivity to cytotoxic agents such as cytarabine (Ara-C) has attributes to some extent to the favourable outcome in patients with DS-AMKL309-311.

Combining conventional and molecular cytogenetic analyses such as G-banding, comparative genomic hybridization (CGH), and whole chromosome painting (WCP) defined recurrent clonal chromosomal abnormalities, which will aid in the identification of critical genes that are abnormal in AMKL cells312. Cytogenetic differences between

DS and non-DS-AMKL include the absence of the translocation t(1;22), and instead, the presence of trisomies involving chromosome 1 and 8 as well as monosomy 7313. The most common abnormality detected by CGH that was not evident from conventional cytogenetic analysis was gain of chromosome 19 or arm19q, analyzed in both primary patient samples and megakaryoblastic cell lines312. Similarly, an approximately 20% incidence of chromosome 19 abnormalities with trisomy 19 occuring in 8 (16%) of 50 patients lacking the Philadelphia chromosome was reported in another study280. Later, with analysis of larger database, it was recognized that nonrandom gain of chromosome

19 and 21 noted in particular FAB AML-M7.

Several studies have determined gene expression profiles by microarray analysis for DS or non-DS AMKL, which were typically limited to small group of samples due to the rarity of the AML-M7 subtype of leukemia and the criteria to limit the analysis to pediatric age. Therefore, an integrated quantitative transcriptome maps by systematic meta-analysis from available gene expression profile datasets related to AMKL in pediatric age was made possible by a recent approach using Transcriptome Mapper

(TRAM) tool, providing the identification of genes relevant for the pathophysiology of

AMKL which can potentially be new clinical markers314. Transcriptome map comparison of both DS and non-DS AMKL with normal MK has revealed three potential clinical markers of progression to AMKL: TMEM241 (transmembrane protein

241) located on chromosome 18, which was the most over-expressed single gene; while

APOPC2 (apolipoprotein C-II) and ZNF587B (zinc finger protein 587B), both located

on chromosome 19, appear to be the most discriminant markers of progression, specifically to DS AMKL314.

1.3.2.1 Down syndrome-associated AMKL (DS-AMKL)

The median age of onset of DS-AMKL is 1.8 years308. Clinical and hematological features of DS-AMKL typically reveal anemia, marked thrombocytopenia and dysplastic changes in all myeloid lineages in the bone marrow, often accompanying marrow fibrosis before the development of overt AMKL292. Diagnosis of AMKL is confirmed by immunophenotyping of leukemic blasts with platelet-associated antigens.

Identification of platelet peroxidase (PPO) activity using electron microscope (EM) is recommended by FAB classification315,316; however, it is not employed usually due to limited availability of EM. The morphology of blast cells appears to be less mature in patients with DS-AMKL than in those with non-DS AMKL317.

The primary mechanistic basis for DS-AMKL is thought to involve increased gene dosage due to an additional copy of chromosome 21. Potential contribution of candidate genes located on chromosome 21 involved in DS-AMKL pathogenesis includes RUNX1 (also known as AML1), BACH1, ETS2 and ERG are mentioned in the previous Section 1.2.

Non-chromosome 21 genes were shown to also contribute to DS-AMKL pathogenesis (discussed previously in Section 1.2). In particular, acquired mutations of the GATA-1 gene on chromosome X, a key transcription factor involved in erythroid and MK terminal maturation, were detected early at TAM stage and in almost all cases of DS-AMKL, which lead to a blockage of full-length transcript expression and production of the shorter variant GATA-1 isoform (GATA-1s) that lacks the N-terminal

transactivation domain of 83 amino acids100,318,319. Detection of identical GATA-1 mutations in both TAM and DS-AMKL from sequential longitudinal samples from a single patient indicates that the DS-AMKL evolves from latent TAM clones that presumably persist following its clinical regression97. Interestingly, studies on the role of GATA-1 mutations in the transformation of MKs have shown that DS-AMKL cells express reduced levels of the GATA-1 target gene cytidine deaminase320, and as a consequence, these leukemic cells are hypersensitive to Ara-C chemotherapy resulting in their favourable outcome for a group of AMKL patients, with an overall survival of

70-80% at 5 years310-311.

1.3.2.2 Non-Down syndrome AMKL (non-DS AMKL)

Non-DS AMKL patients appear to be more heterogeneous317 and molecular lesions that underlie this leukemia subtype remains elusive with the exception of the t(1;22)(p13;q13) detected in approximately one third of cases in infant non-DS

AMKL321-322. The t(1;22)(p13;q13) occurs primarily during infancy (median age of 4 months) showed hepatosplenomegaly and pronounced myelofibrosis. The translocation t(1;22)(p13;q13) results in the one twenty-two (OTT) [also known as RNA-binding motif protein-15 (RBM15)] gene located on 1p13 fused to the megakaryocytic acute leukemia (MAL) [also known as megakaryoblastic leukemia-1 (MLK1)] gene on chromosome 22 leading to the OTT-MAL [also known as RBM15-MLK1] fusion gene on the derivative 22. OTT (RBM15) contains several RNA recognition motifs and shows significant homology to the Drosophila protein spen, which regulates HOX homeotic function and neuronal cell fate. MAL (MLK1) contains a SAP (SAF-A/B, acinus, PIAS) DNA-binding domain and a coiled-coil domain, which presumably

mediates protein-protein interactions. The OTT-MAL (RBM15-MLK1) transcript encodes a chimeric protein containing all the functional domains of both partner proteins and thus, the OTT-MAL (RBM15-MLK1) potentially might deregulate RNA processing and possibly HOX signaling. The exact mechanism by which OTT-MAL

(RBM15-MLK1) exerts its oncogenic properties remains unknown.

Recently, a novel recurrent translocation involving CBFA2T3 and GLIS2 were identified in about 30% of children with non-DS AMKL324. The CBFA2T3-GLIS2 fusion protein resulted from a chromosomal inversion of chromosome 16 where both

CBFA2T3 and GLIS2 genes are localized on each arm of the chromosome 16 in proximity to the telomeres, in 16q24.3 and 16p13.3, respectively. CBFA2T3 (also known as MTG16) is a member of the ETO/CBFA2T1/MTG8 family containing three members related to the Drosophila melanogaster nervy gene, initially identified as a fusion partner with RUNX1 in t(16;21)(q24;q22) translocation of therapy-related AML and more recently, implicated in the maintenance of HSC quiescence. GLIS2 (GLI- similar 2) is a member of the GLI-similar (GLIS1-3) subfamily of Krüppel-like zinc finger transcription factors and is closely related to the GLI family of transcription factors mediating the transcriptional response to the Hedgehog pathway activation and is not expressed in megakaryoblastic cells. One of the nervy homology regions (NHRs) of CBFA2T3, which includes a myeloid, nervy and Deaf-1 domain (MYND) class of zinc finger domain reported to interact with the N-CoR repressor complex is lost as a result of the fusion to GLIS2. However, all of the GLIS2 zinc finger domains are present in the fusion protein, resulting in high level expression of the C-terminal portion of the protein including its DNA binding domain. Interestingly, patients with the

CBFA2T3- GLIS2 fusion present a clear expression signature clustering them apart from other non-DS AMKL. Although the CBFA2T3-GLIS2 fusion protein has recently

been described as a distinctive feature of pediatric non-DS AMKL, the fusion protein has also been found in pediatric cytogenetically normal (CN) AML and not restricted to

AMKL.

There is currently limited understanding of the molecular basis for non-DS

AMKL which affects current treatment options. Non-DS AMKL patients have a poor prognosis279,307,308 with a reported survival of 14% to 34% and the majority of patients relapsing within 1 year though the patients who undergo allogeneic stem cell transplantation after entering remission have significantly improved survival287.

1.3.3 Prevalence of AMKL in Singapore and Malaysia

Malaysia-Singapore multicenter leukemia study for childhood acute leukemia

(MaSpore 2006), organized by Associate Professor Allen Yeoh, Department of

Paediatrics, National University of Singapore, demonstrated that the incidence of

AMKL (M7 subtype according to the FAB classification) in Singapore and Malaysia is strikingly high (18 out of 71, 25.4%, p=0.0003, 2x2 χ2–test, yellow bar in Figure 4.2A) compared to those in the worldwide populations (4.1 - 9.8%, green, violet and blue bars in Figure 1.13A). Likewise, another study in Singapore and Malaysia also showed high frequency of AMKL (29 out of 164, 17.7%, p=0.006, 2x2 χ2–test orange bar in Figure

1.13A). Asian average (Avg) is calculated based on 6 distinct studies in Japan, Korea,

Hong Kong and Taiwan (n=555); American avg, two studies (n=969); European avg, 5 studies in Netherlands, Germany, UK and Italy (n=2,814).

In the above mentioned two Singapore related studies, the numbers of DS patients within AMKL are 4 out of 18 (22%) and 10 out of 29 (34%) respectively. None of DS patients are seen in subtypes other than AMKL. This strong correlation between

AMKL and DS suggests that AMKL diagnosis in the two Singapore-related cohorts seems correct.

Because the risk of having a baby with DS rises above 1 in 250 at the 35th birthday for women, it has become the standard care to offer the screen for DS to all mothers 35 years and older. In Singapore, the prenatal screening test for DS is well conducted for almost all mothers 35 years and older, and the risk of having a DS baby is expected to be very low compared to other countries where the screening is not routinely performed. Nevertheless, the frequency of AMKL including DS-AMKL is being reported to be high (Figure 1.13A). This high incidence of AMKL despite the estimated low incidence for DS suggests that Singapore-Malaysia populations carry unknown risk factors for AMKL.

Percentage Others M0(%) M1(%) M2(%) M3(%) M4(%) M5(%) M6(%) M7(%) (%) MASPORE AML 2006 8.5 0.0 22.5 14.1 12.7 9.9 0.0 25.4 7.1 Singapore& Malaysia study 4.9 1.2 21.3 12.2 12.2 9.1 1.2 17.7 20.1 Asian Avg 2.4 13.7 33.2 6.5 16.3 13.9 2.5 9.8 1.6 American Avg 0.9 19.4 27.1 5.4 20.2 15.7 2.2 6.5 2.6 European Avg 3.8 15.6 22.6 7.2 21.7 18.1 1.6 4.1 5.4

0 AML-M0 AML-M1 AML-M2 AML-M3 AML-M4 AML-M5 AML-M6 AML-M7 AML No data on Chinese (%) Malay Total (%) Indian Total (%) Others Total (%) FAB

Figure 1.13: Frequency of acute myeloid leukemia subtype in Singapore-Malaysia and other countries. (A) Incidences of each AML subtype, FAB classification M1 to M7, are shown in percentage. (B) Frequencies of each AML subtype in indicated races, Chinese (n=82), Malaysian (n=54), Indian (n=8) and others (n=20) in ‘Singapore & Malaysia study’ are given.

Prognosis of AMKL depends on cause. One third of cases is associated with a t(1;22)(p13;q13) mutation in children. These cases carry a poor prognosis. Another third of cases is found in Down syndrome. These cases have a reasonably fair prognosis. The last third of cases may be heterogeneous, and carry a poor prognosis.

Therefore, 66-78% of Singapore cases are expected to be poor prognosis. The proposed study regarding the locally relevant disease with poor prognosis holds both scientific and clinical significance, in particular for Singapore citizen’s health and wellness.

Interestingly, the Notch agonist that I have tested is a natural product found abundant in edible cruciferous vegetables such as broccoli. As several studies have shown the cancer preventive activity of dietary botanicals, regular consumption of food naturally high with PEITC such as broccoli may help repress the development of DS-

AMKL in infants who suffered from TAM.

Furthermore, results obtained in my study have shown the promising potential of Notch agonist as an anti-cancer agent, thus, Notch agonist could serve as a lead compound for further drug development in clinical applications.

1.4 Mouse models for AMKL

An earlier study has shown that mice bearing the Gata-1.05 knockdown allele have a significantly shorter life span and displayed marked splenomegaly characterized by the abnormal accumulation of cells of the erythroid and megakaryocytic lineages325. These mice were found to elicit two distinct types of acute leukemia, an early-onset c-Kit- positive nonlymphoid leukemia and a late-onset B-lymphocytic leukemia326.

There are many other strains which recapitulate trisomy 21 such as Tc1, Ts16,

Ts65Dn, and Ts1Cje mouse. Among these trisomy mice listed above, three strains summarized in Table 1.7 below are well characterized. Ts65Dn mice demonstrated myeloproliferative disorder (MPD) with megakaryocyte hyperplasia which is reminiscent of TMD and DS-AMKL. Moreover, Runx1 was shown to contribute to this hematopoietic phenotype. However, Ts65Dn mice which are trisomic for 104 orthologs of human chromosome 21 genes did not develop leukemia327.

Table 1.7: Mouse models carrying triplicated chromosomal regions related to Down syndrome.

Abbreviations: MPD, myeloproliferative disorder; MK, megakaryocyte. (Compiled from refs 327-329).

Previous study has shown that the GATA-1 promoter-driven Runx1 Tg mice developed a transient modest increase of MK marker-positive myeloid cells but no spontaneous leukemia, these mice were then crossed with myeloid-leukemia prone

BXH2 mice whereby the presence of an extra copy of Runx1 in these mice resulted in shortening of the latency of leukemia with increased frequency of megakaryoblastic leukemia114. This suggests that increased Runx1 dosage contributes to leukemia development and plays a role in the development of myelomegakaryoblastic leukemia.

1.5 Retroviral insertional mutagenesis

Retroviral insertional mutagenesis (RIM) is a powerful whole-genome screening technique which has been successfully utilized in transgenic or gene knockout mice to identify causative oncogenes and tumor suppressor genes (TSGs), including those involved in the development of hematopoietic neoplasms330-332. The use of different insertional mutagens has been successfully used to reveal new proto-oncogenes333-335, and more importantly, the method has recently been used to identify cooperative genes for certain oncogenic stimuli331,336.

Integration of the retroviral double-stranded DNA copy of their RNA genome into the genome of the host cell appears to occur randomly, suggesting that retroviruses can act as agents of insertional mutagenesis. Oncogenic retroviruses have been classified in two groups, acute and slow transforming retroviruses. Acute transforming retroviruses induce the formation of polyclonal tumors by highly expressing virus- eoncoded oncogenes with short latency about 2-3 weeks, while slow transforming retroviruses drive the formation of mono-or oligoclonal tumors by inserting into the host genome proximal (approximately 300 kb) upstream or downstream of oncogenes

or TSGs with long latency about 3-12 months333. Acute transforming retroviruses are replication-defective as their viral genes are replaced with oncogene sequences and these viruses require co-infection with a related “helper virus” that provides the viral structural proteins to make infectious particles. However, slow transforming retroviruses do not carry viral oncogenes and have the standard genome organization of retroviruses and their capacity to induce neoplasms is based on the ability of their provirus to integrate in the host DNA, and mutate or transcriptionally activate flanking cellular genes.

Forward insertional mutagenesis genetic screens using slow transforming retroviruses such as Moloney murine leukemia virus (MoMuLV, a γ-retrovirus) is a valuable tool for the identification of novel genes involved in the pathogenesis of human cancers333,337. The rationale of these screens is infecting newborn mice at postnatal day 3 (where the immune system has yet to develop) with replication- competent, lymphotropic MoMuLV338, consequently acquires a life-long latent status within the organism as the mice were unable to mount an effective immune response against the virus. During this latent period, millions of host cells such as hematopoietic cells are infected and numerous proviral random integrations across the genome of host cells. When such insertions result in deregulation of nearby genes either by activation of proto-oncogenes or disruption of TSGs, the targeted cell may acquire a selective growth advantage, promoting their clonal outgrowth, acquiring multiple mutations after successive rounds of mutagenesis, eventually leading to the development of leukemia or lymphoma (Figure 1.14).

In order to perform cancer gene discovery by insertional mutagenesis, the requirements are : (1) the proper oncogenic agent that efficiently integrates in a random or semi-random fashion into the host genome and deregulates the genes surrounding the

insertion site; (2) an animal model that is permissive to tumor formation; (3) efficient technologies to perform retrieval of integration sites; (4) bioinformatics and statistical tools to map integrations onto the host genome and to identify sites that are enriched for insertions, and hence loci containing candidate cancer drivers335.

1.5.1 Uncovering genetic interactions in cancer

Leukemogenesis is a multistep molecular process that requires combinations of specific activated oncogenic mutations, inactivated TSGs, epigenetic alterations of gene expression and structural abnormalities of chromosomes331,339. The discovery of a number of genes involved in leukemogenesis and their functional significance through molecular analysis of human cancers potentially provides important information about the underlying mechanism of synergy between molecular pathways in leukemogenesis.

But more importantly, such information could help to further dissect the cooperative genetic interaction of individual genetic alterations in leukemogenesis as well as to help improve or direct future therapeutic intervention strategies. In other words, a gene can be defined as a putative cooperating gene if it is more frequently targeted in mice with specific genetic background compared to wild-type (WT) under the same retroviral insertional stress. I have applied this RIM method on Gata-1 knockdown; Runx1 transgenic mice model to identify genes cooperating with Gata-1-deficient and Runx1- overexpressing status in leukemogenesis.

Figure 1.14: Schematic diagram showing the RIM strategy. To initiate MoMuLV-mediated insertional mutagenesis, newborn mice are infected with MoMuLV at postnatal day 3 by injection with a supernatant of virus-producing cells. The virus will infect host cells and induce mutations in multiple ways by integration of the retrovirus in the host genome (green box). Proviral insertions can induce overexpression of cellular genes through the enhancers (enhancer insertions) that are present in the viral LTRs or by promoter insertions, creating a viral-host gene fusion transcript. Truncating or inactivating mutations may be induced by viral insertion within a host gene. Cells can be re-infected by the virus and thus accumulate multiple mutations that induce tumorigenesis. Abbreviations: RIM, retroviral insertional mutagenesis; MoMuLV, Moloney murine leukemia virus; LTR, long terminal repeat. (Modified from ref 340).

1.5.2 Mechanisms of deregulation of genes by retroviral insertions

Insertional mutagenesis mediated by DNA-integrating viruses (provirus) or retrotransposons into the host genome and subsequent deregulation of genes in the neighborhood of the insertion site by several mechanisms, including activation of transcription of flanking oncogenes by viral long terminal repeats (LTRs), or, more rarely, inactivation of TSGs, through disruption of coding sequences337,341 (Figure

1.14). The mutagenic effects of the retrovirus are clonally selected because of a resulting pre-malignant growth advantage conferred by the virus330. Usually, a single genomic integration is insufficient to promote tumorigenesis and multiple rounds of proviral insertions within the same genome are required in order for a cancer to develop335.

The most frequent mechanism of gene activation by insertional mutagenesis is transcriptional enhancement that results in the upregulation of endogenous gene expression, which may be explained by the flexibility with respect to the proviral orientation as well as the distance between provirus and target gene333. Although enhancer insertions usually act in an orientation-independent manner, however, classically, enhancer insertions are located upstream of a mutated gene in the antisense orientation, or downstream in the sense orientation, especially for retroviruses. Gene expression can also be affected by enhancer insertions over large distances as regulatory elements may be distal to a gene and the activity of elements may be affected via chromatin loops342, and thus complicating the identification of the affected genes.

The other mechanism of gene activation is by promoter insertion, which occur in such a way that the integration of provirus is in the sense orientation in or close to the proximal promoter region of an endogenous gene, thereby replacing the function of the

normal promoter with elements found within the insertional mutagen which results in transcription driven by the promoter of the insertional mutagen. Frequently, joining of the first vector-coded exon to those of the host gene induced by splice signals from the integrating vector can result in the translation of high levels of chimeric transcripts337,343.

Apart from these modes of action, proviral insertion into a cellular gene can interfere with the splicing of genes into which they integrate using engineered or endogenous polyadenylation signal of the LTR that may elicit premature termination of gene transcription335. Truncation of transcripts due to the premature termination may result in an mRNA encoding an inactive or unstable protein. Alternatively, landing of a provirus within a gene may result in aberrant splicing which abrogates gene function.

Even though inactivation of both copies of a classical TSG may be a rare event as compared to activation of oncogenes in insertional mutagenesis that use retroviruses, identification of candidate TSGs is still possible with increasing the numbers of insertions per screen in order to increase the frequency of detection.

1.5.3 Methods for the retrieval and analysis of integrations

1.5.3.1 Mapping retroviral integration sites

The completion of the mouse genome project in 2002 allows for strikingly enhanced mapping of the retroviral integration sites (RIS) onto the genome. Identification of genes that contribute to neoplastic transformation is made possible by identifying viral insertions in individual tumors that occur at frequencies higher than expected by chance. Often more than 2 insertions inside or near different oncogenes and tumor suppressor genes can be found in one tumor, indicating that the tumors are either

monoclonal with several RIS or polyclonal. In polyclonal tumors, some of the clones share one or more initiating oncogenic insertions but they also have additional insertions that are not shared between clones. Therefore, consecutive insertions of the virus at different stages of the clonal expansion of the infected cell within the same genome are required in order for tumorigenesis to occur. A subset of the clonal insertions found in a single tumor probably mark genes that are responsible for tumor initiation and progression, whereas others are passenger insertions that are co-selected during clonal outgrowth of tumor cells. Most insertions were thought to be random and not causally related to the disease due to cancer cells typically harbor many sites of RIS.

On the other hand, genes in loci that recurrently contain proviral insertions in individual tumors, referred to common integration sites (CIS), were believed to represent pathogenic signatures334,344. A CIS is defined as 2 RIS within 30 kilobases (kb), 3 RIS within 50 kb or 4 or more RIS within 100 kb. Identified candidate cancer genes would require further experimental validation.

Genes influenced by proviral integration and responsible for tumorigenesis can be identified through inverse polymerase chain reaction (iPCR) using proviral DNA as a molecular tag332. This is followed by mapping of the genomic sequences flanking the provirus to the mouse genome using the publicly available UCSC

(University of California, Santa Cruz) Genome Bioinformatics database

(http://genome.ucsc.edu/)114,345,346. Regions in the genome that are found to carry insertions in multiple independent tumors significantly more frequently than expected by chance are called common insertions sites (CISs). The data from many RIS screens are accessible through the Retrovirus Tagged Cancer Gene Database (RTCGD) and

Mutapedia.

1.5.3.2 BLAST-like alignment tool (BLAT)

BLAT is a very effective tool which allows for the identification of the chromosomal position and candidate genes at the loci, thus enabling the identification of multiple RIS in each tumor. The program is designed to rapidly scan for relatively short matches

(hits) of 95% and greater similarity of length 40 bases or more, and extends these into high-scoring pairs. However, it may miss more divergent or shorter sequence alignments346.

1.5.3.3 Retroviral tagged cancer gene database (RTCGD) and Mutapedia

Data of various retroviral insertional mutagenesis screens are compiled in the publicly available RTCGD homepage (http://variation.osu.edu/rtcgd) and Mutapedia homepage

(http://mutapedia.nki.nl) which allows the identification of published CIS genes and corresponding retroviral integration site sequences for any chromosome or mouse genomic region of interest. The programs also enable the comparison of CISs identified in different laboratories using different mouse models and identify the CIS genes that are the most promiscuous347. Finally, these databases make it possible to easily identify

CIS genes that are mutated in the same tumor and hence likely to cooperate to induce disease.

1.6 Next-generation sequencing

The advent of next-generation sequencing (NGS) technologies has facilitated massively parallel, cost efficient sequencing of genomic fragments concurrently with high throughput repertoire sequencing in comparison to the traditional Sanger sequencing by capillary electrophoresis348,349. NGS has many useful applications, ranging from measuring gene expression levels, detecting both rare and common disease-causing variants as well in both coding and non-coding regions using various NGS methods such as whole-genome and exome sequencing, targeted DNA and RNA sequencing.

NGS has now become an unbiased, accurate and comprehensive approach for the robust detection of cancer-related mutations350. NGS technology constitutes various strategies that rely on a combination of template preparation, sequencing, genome alignment and assembly methods351.

In cases where a suspected disease or condition has been identified, targeted deep-sequencing is preferentially employed as a rapid and cost-effective alternative to whole-genome and whole-exome sequencing, which focus on detection of known and novel variants in selected sets of genes or genomic regions with high specificity and sensitivity, improve the depth of coverage, while taking into consideration of critical factor such as maintaining low DNA input especially in human disease research applications352-354. Counting corresponding deep-sequencing reads allows the quantitative assessment of the mutation burden in a given sample. Utility of deep- sequencing is currently reasonable in a diagnostic setting, where multiple genes or mutational hotspot regions of established markers thereof are sequenced in a massively parallel way by incorporating molecular barcodes and thus allows the multiplexing of multiple parameters and patients per sequencing lane. The efficiency of deep

sequencing approach can also be exploited for pre-clinical drug development to query the validity of xenograft model used in cancer research by enabling the comparison of the mutational profile of matched xenograft and primary tumor samples to study potential targets and evaluate therapies in a physiological context353.

Thus far, groundbreaking discoveries made using NSG approaches has led to the identification of novel mutations for many key hematological malignancies such as

AML, myelodysplastic syndrome (MDS), chronic lymphocytic leukemia (CLL), hairy- cell leukemia (HCL), Waldenström's macroglobulinemia (WM) or multiple myeloma

(MM)352. Additionally, targeted NGS mutation screening using amplicon deep- sequencing has already been successfully applied to genetically characterize a variety of myeloid malignancies.

1.6.1 Sample preparation and technical challenges for NGS applications

The various commercially available NGS platforms vary substantially in terms of template preparations, sequencing as well as data analysis. Nevertheless, amplicon preparation is a labor intensive part of the entire NGS process348. I have employed

SureSelectXT2 Target Enrichment Solutions (Agilent Technologies) in pre-capture indexing formats to prepare amplicon libraries for Illumina Multiplexed Sequencing

(Figure 1.15). This capture method is based on probe hybridization to enrich sequencing libraries made from whole genome samples351,352. Hybridization capture approaches generally work well but can suffer from off-target capture and struggle to effectively capture sequences with high levels of repetition or low complexity. Also, the probes are based on reference sequence, and variations that substantially deviate from the

reference, as well as significant insertion or deletion mutations, are not always going to be identified.

Sequencing of short amplicons also makes obtaining entire sequences possible in either single read or using a paired-end read design by adding adapters directly to the ends of the amplicons and sequenced to retain haplotype information351. However, it is often necessary to design longer amplicons for targeted deep-sequencing applications, whereby the PCR products or amplicons need to be fragmented using acoustic shearing, sonication or enzymatic digestion for sequencing. An issue associated with amplicon sequencing is the presence of chimeric amplicons generated during PCR by PCR- mediated recombination which can be exacerbated in low complexity libraries and by overamplification.

Figure 1.15: Schematic diagram of hybrid selection method. The genomic DNA sample (‘pond’) is sheared and hybridized with biotinylated RNA library baits (ultra long 120mer). Magnetic streptavidin beads are used to pull down the complex of capture oligos and genomic DNA fragments. Unbound fragments are removed by washing. Finally the enriched fragment pool is amplified and the enriched sample is ready for sequencing and aligned to the bait sequences. (Adapted from ref352).

Most of the NGS studies thus far have been designed to fulfill specific aims such as discovering mutations, identifying somatic mutational signatures, characterizing clonal evolution and advancing personalized medicine350,356. Determining somatic mutations that are most likely to contribute to the cancer phenotype has been the most common aim of cancer-related NGS studies. Mutations can affect key domains of cancer genes associated with cancer progression and resistance to therapy, and exist in restricted regions of the genome termed mutational hotspots. Somatic mutation calling has several sources of false positives and false negatives, including technical bias, sequencing error, alignment artifacts, normal DNA contaminated with cancer DNA, tumor heterogeneity, copy number variants (CNVs) and copy-neutral regions of loss of heterogeneity (cnLOH)356.

During the standard library construction process, PCR introduces duplicate reads and strand bias along with guanine-cytosine (GC) bias can be compounded351. Also, currently available NGS technologies have higher raw base-calling error rates than

Sanger sequencing357. Therefore, attempts to minimize false-positive calls include further filtering which can ameliorate PCR artifact bias, increasing the depth of coverage to achieve higher consensus accuracy and making variant calling criteria more stringent. Alignment tools could produce artifacts in regions of low mappability such as low copy or simple tandem repeats that may be remediable to some extent by filtering based on mapping quality.

NSG is a very powerful tool in molecular biology and it has a great potential.

However, the major challenge in NSG technologies will be to analyze and manage the massive amount of sequencing data generated, which is bigger by several orders of magnitude in comparison to microarray analysis358. In addition, many researchers face serious problems with the use of increasingly complex mathematical models and

statistical tools as well as complicated programs and databases dedicated to the analysis of biochemical pathways and ontologies as data analysis can be time-consuming and may require special knowledge of bioinformatics to garner accurate information from raw sequence data, consequently depending heavily on the assistance of bioinformatics specialists348. Therefore, the successful use of NGS technologies require not only the development of new robust techniques of data processing and analysis but also the development of new user-friendly bioinformatics tools and statistical methods for handling of the growing number of collected data.

1.6.2 NGS studies on AMKL

At present, the majority of major cancer types have been characterized by exome and transcriptome sequencing. Table 1.8 presents the key findings according to underlying mutations and their impact on prognosis in AMKL. Often, discovery cohorts were used then subsequently the findings were verified in larger series of independent patient cohorts.

For example, the increasing use of NGS technologies applied to study cancer genomes is making a remarkable contribution to our knowledge of the molecular landscape of AMKL. Following the identification of GATA-1 mutations and trisomy

21, this strategy has recently led to the discovery of various genes recurrently mutated in AMKL and revealed driver mutations that potentially trigger progression of

TAM/TMD to AMKL.

Table 1.8: Mutations detected in previous NGS studies on AMKL.

Abbreviations: DS, Down syndrome; AMKL, acute megakaryoblastic leukemia; TMD, transient myeloproliferative disorder; TAM, transient abnormal myelopoiesis. (Adapted from ref 324, 359, 360)

1.7 Aims of the project

The first aim of my project is to comprehensively identify genetic changes associated with AMKL. To this end, an AMKL mouse model was developed employing

RIM on the genetically engineered mouse with alterations in Gata-1 and Runx1 genes.

RIM allows for the easy identification of disease genes by simple PCR-based technique.

The second aim of my project is to validate whether the genetic alterations detected in the RIM mouse model are also affected in human AMKL samples. For this,

I employ targeted deep sequencing strategy in 40 AMKL patient samples and 7 AMKL cell lines.

Based on the information of mutations newly unveiled in the above mentioned mouse and human AMKL studies, the third aim of my project is to develop novel and effective therapies for the treatment of AMKL.

CHAPTER 2: MATERIALS AND METHODS

Mice. Runx1 transgenic (Tg) mouse that over-expresses mouse Runx1 in hematopoietic cells under the control of the hematopoietic regulatory domain (HRD) of

GATA-1 promoter was generated as previously described361 and maintained on the

C57/BL6 background. Gata-1 knockdown mouse, Gata-1.05 with BALB/c background, is rendered genetically defective in the synthesis of mouse Gata-1 mRNAs from the erythroid-specific promoter at approximately 5% of the level present in wild-type embryos362. All mice were kept in specific pathogen free conditions and all animal procedures were performed according to the National University of Singapore

Institutional Animal Care and Use Committee (IACUC).

Genotyping. Genomic DNA was extracted from mouse tails using Direct PCR Lysis

Reagents (Viagen Biotech, Los Angeles, CA), while genomic DNA from other tissues were extracted using the phenol/chloroform method. Genomic DNA samples were then used for genotyping by PCR. Reactions (20 μl) were performed with the following primers (0.1 μM), 0.5 mM dNTPs, 2.5 mM MgCl2, 2.5 U GoFlexi Taq polymerase and

100 μg to 200 μg DNA. DNA was denatured at 95°C for 5 minutes then amplified by

30 cycles at 95°C for 45 seconds, 60°C for 50 seconds, and 72°C for 45 seconds. The amplified PCR products are detected visually after separation by electrophoresis on a

2% GelRed-stained agarose gel. Sequences of the primers used were as follows: endogenous Runx1 transcript (expected size, 253 bp),

5’-AGCATGGTGGAGGTACTAGC-3’ and 5’-GGTCGTTGAATCTCGCTACC-3’; transgene-derived Runx1 transcript, 5’-GGTGGCTGAATCCTCTGCAT-3’ and the reverse primer used for the endogenous Runx1 transcript primer set;

erythroid-specific Gata-1 transcript (expected size, 483 bp),

5’-TAAGGTGGCTGAATCCTCTGCATC-3’ and

5’-CCGTCTTCAAGGTGTCCAAGAACGT-3’; testis-specific Gata-1 (expected size,

529 bp), 5’-CGTGAAGCGAGACCATCGTC-3’ and the reverse primer used for the erythroid-specific Gata-1 transcript primer set; neomycin phosphotransferase

(expected size, 339 bp), 5’-AAGTATCCATCATGGCTGATG-3’ and

5’-TAGCCAACGCTATGTCCTGATA-3’. Neo gene primers were used for detecting the Gata-1.05 allele, because the mutation was generated by inserting a Neo gene cassette into the 58 flanking region of the GATA-1 locus.

Analysis of PB cells. PB was collected by retro-orbital bleeding and CBC were performed using an automated hemocytometer (Reichert Scientific Instruments,

Buffalo, NY, USA). Mice were analyzed for PB morphology, after May-Grunwald-

Giemsa staining of PB smears, upon demonstration of phenotype.

Isolation of BM cells. BM cells were flushed from the long bones (tibias and femurs) with minimum essential medium alpha medium (α-MEM) (Invitrogen Corporation,

Carlsbad, CA, USA) supplemented with 10% heat-inactivated fetal bovine serum

(Biowest, France). Cells were filtered through nylon filter (35 μm) to obtain a single- cell suspension.

Cytospin preparation. BM cells were obtained from mice upon demonstration of phenotype. 5-10 x 104 cells per well were cytospun using a cytocentrifuge (Cytospin4,

Thermo Shandon, USA) on a slide. Morphology of BM was then analyzed after May-

Grunwald-Giemsa staining.

May-Grunwald-Giemsa staining. The slides were first stained with May-Grunwald stain solution [May-Grunwald solution (Merck KGaA, Darmstadt, Germany) : Methyl

Alcohol = 1:3] for 5 minutes. After draining off the solution, the slides were stained with Giemsa stain solution [Giemsa solution (Merck KGaA, Darmstadt, Germany) : phosphate buffer (pH6.4, KH2PO4 6.63 g, Na2HPO4 2.56 g / 1000 ml) = 1:20] for 20 minutes at room temperature. Slides were then washed with running water and air- dried.

Flow cytometric analysis and cell sorting. BM, spleen or liver single-cell suspensions were pre-incubated with normal mouse serum (EMD Millipore Corporation, Billerica,

MA, USA) for 15 minutes on ice before staining to block nonspecific binding. Samples were centrifuged and pellets were resuspended in FACS buffer (PBS, 1% bovine growth serum and 0.01% NaN3) and the fluorochrome-conjugated antibody reaction was carried out for 20 minutes on ice. The antibodies used in this study were FITC- conjugated antibodies (anti-Gr-1, anti-CD41, anti-CD71, anti-CD25 and anti-B220) as well as PE-conjugated antibodies (anti-cKit, anti-CD61, anti-Ter119, anti-CD44 and anti-CD3). All monoclonal antibodies were purchased from BD Pharmingen (San

Diego, CA, USA). After washing, cells were resuspended in FACS buffer and 0.5

μg/ml of Hoechst 33258, pentahydrate (Invitrogen Corporation, Carlsbad, CA, USA) were added to exclude dead cells, flow cytometric analysis was performed using BD

LSRII instrument (BD Biosciences) and analyzed using the FlowJo software (Tree Star

Inc., Ashland, OR, USA).

RIM. Runx1 Tg and Gata-1.05 mice were mated and all F1 progenies were inoculated intraperitoneally with Moloney murine leukemia virus (MoMuLV) 3 days after birth and monitored by examining their health condition and CBC analysis. Mice were carefully observed daily upon observation of symptoms and/or when WBC counts increased beyond 20 x 103/μl. Moribund mice were then sacrificed and subjected to necropsy. Abnormalities in hematopoietic tissues were recorded as follows: enlargement of the spleen, liver and thymus, and swelling of lymph nodes.

Leukemic/lymphoma cells from PB, BM, spleen and thymus were subjected to May-

Grunwald-Giemsa staining. Immunophenotype analyses were carried out by flow cytometric analysis to check for the presence of cell surface markers for immature cells, myeloid cells, T-cells and B-cells in the BM or thymus of leukemic or lymphoma mice, respectively.

Identification of RIS by iPCR. For all viable tumor samples, 5 μg of genomic DNA extracted from mouse leukemic/lymphoma cells from the spleen or thymus was digested by BstYI and self circularized overnight by T4 DNA ligase (New England

Biolabs, Inc., MA, USA) at 16°C. Then 5’ and 3’ integration flanking fragments were amplified individually by iPCR. The first PCRs were performed using AccuPrime Taq

(Invitrogen Life Technologies, Carlsbad, CA, USA) employing an initial preheating step at 95°C for 5 minutes followed by 30 cycles of 95°C for 1 minute, 60°C for 1 minute and 68°C for 3 minutes. The second PCRs were performed by GoFlexi Taq

(Promega, Madison, WI, USA) employing an initial preheating step at 95°C for 5 minutes followed by 35 cycles of 95°C for 1 minute, 55°C for 1 minute and 68°C for 2 minutes. The amplified PCR products were cloned by the TA cloning method using the plasmid pDrive cloning vector (Qiagen GmBH, Hilden, Germany) and subjected to

cycle sequencing (1st BASE, Singapore) using the M13-forward and M13-reverse primer. The position mapping of the RIS on the mouse chromosome was performed by

BLAT searching of the UCSC Genome Bioinformatics database

(http://genome.ucsc.edu). The definition of a CIS was the same in the RTCGD

(http://variation.osu.edu/rtcgd/); each window size is 100, 50, or 30 kb for CIS with 4

(or more), 3, or 2 insertions, respectively, in each model.

Genomic DNA samples. AMKL patient samples used for this project were derived from samples collected specifically for the childhood leukemia tissue bank amongst hospitals in Malaysia and Singapore, which was established by Associate Professor

Allen Yeoh. All samples for this project were taken with written informed consent and all the data were anonymized and only information on gender and ethnicity of the patients were taken. Genomic DNA was isolated from bone marrow using the standard phenol-chloroform extraction method.

Whole genome amplification. Whole genome amplification (WGA) was performed using the commercially available REPLI-g Mini Kit (Qiagen GmBH, Hilden, Germany) following the manufacturer’s protocol. The starting amount of genomic DNA input into the WGA reaction was more than 10 ng in all cases. The REPLI-g method is based on

Multiple Displacement Amplification (MDA) and includes denaturation of DNA and isothermal amplification. DNA was denatured by adding 5 μl of denaturation buffer D1 to 5 μl of template DNA and the sample was incubated at room temperature for 3 minutes. Denaturation was stopped by adding 10 μl of neutralization buffer N1.

Preparation of the REPLI-g master mix were performed on ice by adding 29 μl of

REPLI-g mini reaction buffer and mixed by vortexing and centrifuged briefly before the

addition of 1 μl of REPLI-g mini DNA polymerase. Then the 30 μl of REPLI-g master mix were added to the sample, and amplification was performed 30°C for 16 hours.

REPLI-g mini DNA polymerase was inactivated by heating at 65°C for 3 minutes.

Concentrations of amplified DNA were measured with Qubit dsDNA BR kit, according to the protocol supplied by the manufacturer (Invitrogen Corporation, Carlsbad, CA,

USA).

DNA shearing. The first step in Agilent SureSelectXT2 preparation is DNA fragmentation, which was performed on a Covaris S220 system (Covaris, Inc., Woburn,

MA, USA). Shearing of 1 μg of genomic DNA was carried out in 50 μl of 1x TE buffer using the following instrument parameters: duty cycle 10%, intensity 5, cycles per burst

200, time 6 cycles per 60 seconds. The resulting size distribution and concentration were checked using the Agilent 2100 BioAnalyserDNA1000 assay (Agilent

Technologies, Santa Clara, CA, USA).

Agilent SureSelectXT2 library preparation for pre-capture sample processing.

Libraries were prepared starting from 1 μg of original and REPLI-g amplified DNA according to the sample preparation guidelines of the Agilent SureSelectXT2 Target

Enrichment System for Illumina Multiplexed Sequencing Protocol. The protocol comprises standard steps for Illumina DNA library preparation: end-repair of fragmented DNA, A-tailing, adapter ligation and amplification. Purifications between steps were carried out with Agencourt AMPURE XP beads (Beckman Coulter, Inc.).

The libraries’ yield, estimated with Qubit dsDNA HS kit (Invitrogen Corporation,

Carlsbad, CA, USA) varied between 287 and 301 ng for all samples.

Cell lines and culture conditions. AMKL cell lines (CMK, CMK-11-5, CMK-86,

M-07e, MG-S, and MEG-01) and T-ALL cell lines (KOPT-K1 and Jurkat) were cultured in RPMI 1640 medium, containing 10% fetal bovine serum (Biowest, France), or 20% fetal bovine serum (Biowest, France) for MOLM-16, together with 2 mM L- glutamine, and 100 units/mL penicillin and streptomycin at 37⁰C in a humidified atmosphere with 5% CO2 and AMKL cell line, M-07e, was cultured in addition of human interleukin-3 (IL-3). AMKL cell line, UT-7 was cultured in basic growth medium (BGM) consisting of alpha-minimum essential medium (α-MEM) (Invitrogen

Corporation, Carlsbad, CA, USA) containing 20% fetal bovine serum, 2 mM L- glutamine, and 100 units/mL penicillin and streptomycin at 37⁰C in a humidified atmosphere with 5% CO2.

Measurement of cell viability. AMKL and/or T-ALL cells were seeded at a density of

2 x 105 cells/ml in 100 μl volume per well in a 96-well plate. Leukemic cells were treated with JAK inhibitor 1, γ-secretase inhibitor (DAPT) or phenethyl isothiocyanate

(PEITC), or combination therapy. The proliferation of leukemic cells following 144 hours of treatment was assessed using Colorimetric CellTiter 96 Aqueous One Solution

Cell Proliferation Assay (MTS assay, Promega, Madison, Wisconsin, USA). The absorbance of each well was recorded at 490 nm using Tecan Infinite M200 plate reader

(Tecan, San Jose, California, USA). Each treatment was carried out in triplicate and the percentage of viable cells was reported as the mean of optical density of the treated wells divided by the mean of optical density of dimethylsulfoxide (DMSO) control wells after normalization to the background absorbance from wells without cells. The median inhibitory concentration (IC50) was determined by MTS assay and calculated by

CalcuSyn software (Biosoft, Cambridge, UK).

Assessment of apoptosis of AMKL cells. For flow cytometric evaluation of apoptosis in AMKL cell lines, the cells (CMK, MG-S and MEG-01) were grown in 6- well plates and then treated with γ-secretase inhibitor (DAPT) or PEITC at the desired concentrations. After exposure to drugs for 8 hours, 1 x 106 cells (CMK, MG-S and

MEG-01) were washed with phosphate-buffered saline (PBS) and resuspended in binding buffer (10 mM HEPES (pH7.4), 140 mM NaCl, 2.5 mM CaCl2). Cells were incubated with 1.5 µl of Annexin V-FITC (BD PharMingen) and 1 µl of propidium iodide (PI) for 15 minutes at room temperature in the dark, followed by addition of 400

µl of binding buffer. Ten thousand events were acquired on a BD LSRII flow cytometer

(Franklin Lakes, NJ, USA). Results were expressed as percentage of specific apoptosis, according to the following formula: % specific apoptosis = [(% of apoptotic treated cells - % of apoptotic control cells) x 100] / (100 - % of apoptotic control cells).

Caspase 3/7 activity assay. Following treatments cells were subjected to Caspase 3/7 activities measurement with Caspase-Glo assay kit (Promega, Madison USA). Briefly, the white, opaque 96-well plates containing cells were removed from the incubator and allowed to equilibrate to room temperature for 30 minutes. 100 μl of Caspase-Glo reagent was added to each well, the content of well was gently mixed with a plate shaker at 300–500 rpm for 30 seconds. The plate was then incubated at room temperature for 30 minutes. The luciferase substrate derivative is specifically cleaved by active caspase-3/7 resulting in the conversion of the substrate into a luminescent signal (RLU). The luminescence of each sample was measured in a Tecan Infinite

M200 plate-reading luminometer (Tecan, San Jose, California, USA) with parameters of 1 minute lag time and 0.5 second/well read time. The experiments were performed in triplicate and repeated on two separately-initiated cultures.

Neon transfection. AMKL cells (CMK, MG-S and MEG-01) were plated at 2 x 106 cells per well in 6-well plate and transiently transfected with 1.5 μg of Hes1 promoter firefly luciferase reporter construct or 0.5 μg of pMax-GFP control plasmid and 1ng of pRL-SV40 vector containing Renilla luciferase plasmid (Promega Corp., Madison, WI,

USA) using Neon transfection system (Invitrogen, Life Technologies, Carlsbad, CA,

USA).

Luciferase reporter assay. Luciferase activities were determined 24 hours after transfection using a dual luciferase reporter assay system (Promega Corp., Madison,

WI, USA). All reagents were prepared as described by the manufacturer. The 5× passive lysis buffer was supplied by the manufacturer and used for cell lysis. Briefly, transfected cells were grown in the appropriate medium for 16 hours and treated with either γ-secretase inhibitor (DAPT) and/or PEITC for another 8 hours, and then the cells was removed directly from culture and transferred to 100 μl of ×1 passive lysis buffer.

After allowing lysis for 10 to 15 seconds, a 10-μl aliquot was used for luminescence measurements with a Tecan Infinite M200 plate reader (Tecan, San Jose, California,

USA). The following steps were used for luminescence measurements: 100 μl of the firefly luciferase reagent (LARII) was added to the test sample, with a 10-s equilibration time and measurement of luminescence with a 10-seconds integration time, followed by addition of 100 μl of the Renilla luciferase reagent and firefly quenching (Stop & Glo), 10-seconds equilibration time, and measurement of luminescence with a 10-seconds integration time. Results were represented as the ratio of firefly to Renilla luciferase activity and normalized to vector control.

Quantitative reverse transcription-PCR (qRT-PCR). AMKL cells (CMK, MG-S and

MEG-01) were seeded at a density of 5 x 105 cells/ml in 6-well plates and then treated with γ-secretase inhibitor (DAPT) and/or PEITC at the desired concentrations and cells were then incubated for 8 hours at 37⁰C. RNA samples were isolated using QIAzol

Lysis Reagent and RNeasy Mini kit (Qiagen GmBH, Hilden, Germany). Total RNA was reversed transcribed to cDNA using iScript Reverse Trasncriptase Supermix for

RT-qPCR according to the manufacturer’s protocol (Bio-Rad, Hemel Hempstead, UK).

The resulting cDNA was used as the substrate to measure relative expression levels by qRT-PCR with primers specific for human HEY1 and GAPDH for normalization:

HEY1 forward 5’-AACTGTTGGTGGCCTGAATC-3’ and HEY1 reverse

5’-GCTGGTAAATGCAGGCGTAT-3’; ______GAPDH ______forward

5’-GGACCTGACCTGCCGTCTAGAA-3’ and GAPDH reverse

5’-GGTGTCGCTGTTGAAGTCAGAG-3’. Duplicate reactions were prepared and qPCR was performed using a Rotor-Gene 6000 (Qiagen GmBH, Hilden, Germany) instrument with the following run specifications: Hot start primer activation for 10 minutes at 95⁰C, followed by 45 cycles of duplex melting for 10 seconds at 94⁰C and annealing for 45 seconds at 55⁰C. Threshold-cycle (Ct) values were automatically calculated for each replicate and used to determine the relative expression of the gene of interest relative to reference gene for both treated and control samples by the 2-ΔΔCt method. All amplifications crossed intro-exon boundaries to exclude genomic DNA amplifications.

Table 2.1: Primer sequences used for mutation screening.

Amplified Primer name Primer sequence (5' --> 3') product length (bp) GATA1-Ex2 F AGAGGAGCAGGTGAAAGGATGT 382 GATA1-Ex2 R TGACCTAGCCAAGGATCTCCAT NOTCH1 e22 FW TGGGGTTTTCCCTTCCCCTA 755 NOTCH1 e22 RV GGTTTCGTCAGTGGCCCAAG NOTCH2 e1 FW TTCGTTGCACACCCGAGAAA 658 NOTCH2 e1 RV CGGAACCTGAAGGGCAAAAC NOTCH2 e1 FW2 CTGGAGAAGGATGCGGACG 839 NOTCH2 e1 RV2 AACCTGAAGGGCAAAACTGC NOTCH2 e1 FW3 CTTTGAAGCAGGAGGAGGGG 273 NOTCH2 e1 RV3 CACACAGAGAAGGACCGAGG NOTCH2 e3 FW AGGTGGTCCTTGTAGGTCTGT 583 NOTCH2 e3 RV TCTCTTTCTCTGCCCCCTGA NOTCH2 e4 FW CCACCCACTGAGTCCTTGTG 714 NOTCH2 e4 RV GGGCCCATAAAACTCCTCCC NOTCH2 e7 FW GCTCCACTTTCCTGTCTCCG 713 NOTCH2 e7 RV GCTATTGCAGTTTCTGCCCA NOTCH2 e17 FW AACATGGGTGTGGAGAAGCC 690 NOTCH2 e17 RV CCAATGTCAGGCGTGAAAGG NOTCH2 e17 FW2 GGGTGTGGAGAAGCCAAGAA 684 NOTCH2 e17 RV2 CAATGTCAGGCGTGAAAGGC NOTCH2 e26 FW TGGGCCACAGAAAATGGGTAG 732 NOTCH2 e26 RV GAGGCAACTCTGGCAAAAGG NOTCH2 e29 FW AGAGCGTGGGTGCATCTATT 640 NOTCH2 e29 RV TGACTTGACTACTGCCAGCAT NOTCH2 e34 FW TGTGCGCCTTCTGGATGAAT 720 NOTCH2 e34 RV CGAGGGGTGGTTATGTGCTT NOTCH2 e10 FW AGAGAGAAGGTTTCTGTTGCCT 763 NOTCH2 e10 RV GCTGAGAAGCCCCAGTGAA NOTCH3 e28 FW TTGCTGCTGGTCATTCTCGT 710 NOTCH3 e28 RV CACCTCCAGGAAGAGTGCAG NOTCH3 e30 FW CCCTGTGCCCTAGGAGTAGT 731 NOTCH3 e30 RV CAGAGGAATCAGGAGCACCC

Amplified Primer name Primer sequence (5' --> 3') product length (bp) NOTCH4 e7 FW CTCTTTCTCCTGCCTCTGCC 593 NOTCH4 e7 RV GACTGTCAGGGAAGGTGTGG NOTCH4 e7 FW2 GAGAGCCTCACCCCCTCTTA 378 NOTCH4 e7 RV2 GGGACCAAACTCATGGGGAC NOTCH4 e16 FW CACCTTCTCCTGCCTCTGTG 771 NOTCH4 e16 RV TTTGCTCTCCAGTCAGTGCC NOTCH4 e29 FW TGAGATGCGGTGGAAGTACG 792 NOTCH4 e29 RV CCCAGTGGTTACGTTGGTGA NOTCH4 e29 FW2 CTGGGGTAACAACCTCCCAC 284 NOTCH4 e29 RV2 CCGATATCAGGGAAGGCCAC MAML1 e2 FW GCAAGGCTCCATCTCAAAAC 792 MAML1 e2 RV TGAGGTCTGGCATGAGGTTG MAML1 e3 FW GCCCTGTGGGATGGTTGTTA 507 MAML1 e3 RV ACTCTAACAACAGGCACGCA MAML1 e4 FW CACAGGAGCCCATTTGTGAG 748 MAML1 e4 RV GCCTATGAAGTTCCCTGGCA MAML2 e1 FW TATGAACGAGGTAGGGCCGA 730 MAML2 e1 RV CTGAAAAATGGCTTGGGGGC MAML2 e2 FW TTTGTGCTCTCTCTGTGCCA 525 MAML2 e2 RV CAGGGTCGTCTCCGTACCTA MAML2 e2 FW2 AGCCAGAGGAGCACACCTAG 633 MAML2 e2 RV2 AGGCTTGGTGTTGCCCTGAC MAML2 e2 FW3 CAGATCAAGCGAACCAGCAG 619 MAML2 e2 RV3 TAGATGCCCTCAACGAACCA MAML2 e5 FW GCCACCTACCTTAGGGCCAA 646 MAML2 e5 RV ATCACTGTTTAGGGCAGGGC APH1A-1F GCTGCTGTCTCTGTCCTTCTA 740 APH1A-1R GTCTCTCAGGGAAGGCCAAT NCSTN-1F AAAGAGACCTCCTGCTTCCCAT 499 NCSTN-1R AGAGCTAGGGAGCAGTGGAA NCSTN-2F#2 CAGGGGATTGGGATGGAGAG 419 NCSTN-2R#2 TCAAGCTCTTTGCTGGCGAT

Amplified Primer name Primer sequence (5' --> 3') product length (bp) SPEN-1F CTCACCCAGGGGATCAACAC 774 SPEN-1R ATAGGGGTGCAGTCGGTACT SPEN-2F GCGATCCAAGCATCCCCATA 730 SPEN-2R ACATTCACTGGCCCCGTAAG SPEN-3F CTCCTTACCTGGAACCCCGA 426 SPEN-3R TTCTGCTTGTGCCCTCTGAA SPEN-4F CAGTTGCAAAGACAAAGACA 664 SPEN-4R ACTATGCTGAAACCATTCCCA SPEN-5F GTCCGACATGGTTCCTTCCA 467 SPEN-5R TCTCGCTCACGTAGCTTGTC SPEN-6F TCGCCAAGGTCCCAGAAAAC 404 SPEN-6R ACCACCCCAGATTCCCTCTC SPEN-7F AGCCCCATCAGCACTAGAGA 572 SPEN-7R CGGGCTTCTCCACGATACTC SYNE1-1F AATGCAGTCTTGAGACACAGTC 428 SYNE1-1R ATGTTCTTCACAAAAGAGCCAC SYNE1-2F TTACGCTGCAGAGAACTGGG 437 SYNE1-2R TTGTCGCTTCAGCCTGTGAT SYNE1-3F CTCAGTGGTTCCAGTGGCTG 556 SYNE1-3R TGCCCTAGTATCACCTGTATTTATG SYNE1-4F CCCTTTGGACCCAGCAATGT 507 SYNE1-4R TGGGACCGAACAGCATTTGA SYNE1-5F GGGCCCACCTTTCTGTACTT 510 SYNE1-5R ACAGCACCTAGTCCTCAAAC SYNE1-6F ACCCTGACTTTCTCCTGCCT 479 SYNE1-6R AGGACCTAAGTGGCGTGAAT SYNE1-7F CCTCTGCCATAGACTGTAGTG 720 SYNE1-7R AAGAGACAAGTGGCCGTGAC SYNE1-8F GCACTGTGGTGACCTTCGTA 516 SYNE1-8R ATCTGAAGACAGCATGGCCC

Amplified Primer name Primer sequence (5' --> 3') product length (bp) JAK1 exon 5 FW GGTTTGCCAGGAGCTACAGT 560 JAK1 exon 5 RV AATGTGGCAGAGGAAGGTGA JAK1 exon 14 FW CCCTGTTTGCAGTGTCCTGA 661 JAK1 exon 14 RV GCTGAGTGTGAGGAGCTGTT JAK1 exon 22 FW TAATGAAGGGGCGAAGCTGG 635 JAK1 exon 22 RV ACGTATTGCCGAGAACCCAA JAK2 exon 3 FW TGCTGTTGGAAAAATGGTGCT 774 JAK2 exon 3 RV ACAAAACGCTGCACAAACCA JAK2 exon 9 FW ACCTTTAGGATGGATTTTGCCA 519 JAK2 exon 9 RV TCCTCTAATTCTGCAACCACTGT JAK3 exon 14 FW CAGACAGGGCTCAACACCTT 527 JAK3 exon 14 RV ACACGCCCTGACATCCAAG JAK3 exon 15 FW GGTGTTTGAGAAGGGGAGGG 670 JAK3 exon 15 RV CCCCAACCCAATAGACCCAC JAK3 exon 17 FW GGCATTTTCCTGTGTCTGGC 783 JAK3 exon 17 RV CCTCCAACCTCACCAGACAC JAK3 exon 20 FW GTGTCATTGTTGCGGTTCCC 712 JAK3 exon 20 RV CCACCAGAAAATGGGGCTCT MPL exon 2 FW ACACAGTGGCGGAGAAGATG 644 MPL exon 2 RV CCCATGCCCAACTTCTGTCT MPL exon 4 FW GCCCCAGAGATCCCAAGAAC 618 MPL exon 4 RV GAGTCCTGAGATGAGGCAGC MPL exon 7 FW GGGAAGAGGCCAGACAACAA 600 MPL exon 7 RV TACAGGGATGTCAGCAGGGA MPL exon 10 FW CACGCTTCTTTGCTCAGGTG 729 MPL exon 10 RV GCGGTATAGTGGGCGTGTTA

Amplified Primer name Primer sequence (5' --> 3') product length (bp) PIAS1 SeqEx2F GTGAGCTGGCCTTTATTTGC 313 PIAS1 SeqEx2R CAGGCGTCATGATTTTCTGTG PIAS1 SeqEx14F CAACACCTCCTTGCTTGCCG 333 PIAS1 SeqEx14R TTCATTCGATCTGGGGTGGGG PIAS2 SeqEx2F GGCGGATTTCGAAGAGTTGAGG 436 PIAS2 SeqEx2R CACATCAGGATGGACAGGAGGA PIAS3 SeqEx5F GAGGGCTTCAGGCATCTATGAC 314 PIAS3 SeqEx5R CCTACTATGTGCCACGCACTG PIAS3 SeqEx7F GGAACCATCCAGTGTCCTG 294 PIAS3 SeqEx7R GCCAGGAAGACTTGAATCCAG PIAS3 SeqEx11F CCTGTTCCCCAGCTAAGCT 296 PIAS3 SeqEx11R GCAATCCTGACTTCTTCCCC PIAS4 SeqEx2F CAGATGCTCCTGGGTTTCGTG 405 PIAS4 SeqEx2R CCACTCACCTAATTCGGTGGG

Statistical Analysis. Differences between samples were evaluated by unpaired two- tailed student‘s t-test. All values are expressed as means ± S.E.M. Bioinformatics analysis on raw sequence data from targeted deep sequencing was performed by institutional bioinformatics group with Associate Professor Henry Yang’s assistance.

CHAPTER 3: RESULTS

3.1 Runx1 Tg/Gata-1.05 compound mice develop megakaryoblastic

leukemia

Several lines of evidence have demonstrated that constitutional trisomy 21 and acquired somatic N-terminal truncating GATA-1 mutations together may not be sufficient to cause AMKL in DS and additional genetic alterations are needed to transform

TAM/TMD into overt AMKL. Therefore, an AMKL mouse model employing RIM strategy on the genetically engineered mouse was generated to identify potential genes cooperating with Gata-1-knockdown and Runx1-overexpressing status in leukemogenesis. Runx1 transgenic mouse (Runx1 Tg) which overexpresses Runx1 in hematopoietic cells under the control of hematopoietic cell specific promoter element of

GATA-1 gene was generated previously to, at least in part, recapitulate trisomy 21. In addition, GATA-1 mutation was introduced by employing Gata-1 knockdown mouse

(Gata-1.05). These Runx1 Tg and Gata-1.05 mice were mated to generate wild-type

(WT), Gata-1.05, Runx1 Tg, and compound mice (Runx1 Tg/Gata-1.05). All F1 progenies were injected with MoMuLV and when these mice became moribund, they were sacrificed and necropsy was carried out. Intriguingly, leukemia cells from 6 out of the 16 (37.5%) Runx1 Tg/ Gata-1.05 mice were positive for megakaryoblast markers (c-

Kit+ CD41+ CD61+) (Figure 3.1A), and majority of them exhibited cytoplasmic blebs, one of the typical morphological feature of megakaryoblastic leukemia (Figure 3.1B).

Leukemia cases showed elevated WBC counts and hepatosplenomegaly with normal thymus and lymph nodes, while lymphoma cases showed normal or mildly elevated

WBC counts and enlarged thymus or lymph nodes. Based on the combination of WBC

counts, necropsy, immunophenotype and morphological analyses, the diseased mice were classified into the following 5 groups (Table 3.1): (1) megakaryoblastic leukemia,

(2) myeloid leukemia, (3) biphenotypic (myeloid and T-cell) leukemia, (4) T-cell leukemia, and (5) T-cell lymphoma. AMKL-like leukemias developed in 2 of 9 (22%)

Runx1 Tg mice, but not in any of 11 Gata-1.05 as well as 9 WT mice.

Table 3.1: Frequency of different types of cancers in MoMuLV-inoculated mice.

*Cancer classification was conducted based on combination of leukocyte counts, necropsy, immunophenotype and morphological analyses. The details of these analyses are described elsewhere. Abbreviations: WT, wild-type littermate control; Gata-1.05, Gata-1 knockdown mice; Runx1 Tg, Runx1 transgenic mice; Runx1 Tg/Gata-1.05, compound mice.

Figure 3.1: Immunophenotype and morphology of megakaryoblastic leukemic cells from a Runx1 Tg/Gata-1.05 compound mouse #1472. (A) Representative flow cytometry analysis of the leukemic cells from the compound mouse. Percentages of indicated antigen-positive or –negative cells are shown in the profiles. The top-left profile without any antibodies serves as a negative control. (B) Representative morphology (May-Giemsa staining) of the leukemic cells, some of which show cytoplasmic blebs (arrowhead) characteristic of megakaryoblastic leukemia.

3.2 Cooperation of genetic hits with Gata-1-knockdown and Runx1-

overexpressing status in leukemogenesis

To identify the RIS in the compound mice (Runx1 Tg/Gata-1.05), as well as the Runx1

Tg, Gata-1.05 and WT mice, genomic DNA were extracted from the leukemia/lymphoma cells in tumor containing organs and subjected to the inverse PCR method. An individual tumor (leukemia/lymphoma) induced by MoMuLV generally had 2 to 14 RIS, presumably because multiple genetic changes are required for the induction of a single full-blown leukemia.

The identified RIS were mapped to the mouse genome using the UCSC Genome

Bioinformatics assembly to identify the chromosomal positions and candidate genes at the loci. 19 loci (from a total of 74 leukemic mice) affected more than once by retroviral integrations were referred to as common integration sites (CISs) which were considered to be near candidate leukemogenic genes (Table 3.2). The relative locations of these CIS were compared to the tags from RTCGD and this comparison revealed that

13 of the CIS correspond to previously known loci. In addition, 6 CIS that were detected only by this study, hence designated as Slis (Singapore leukemia integration site), and were classified as novel CIS (Table 3.2).

The criteria for selection of candidate disease genes are defined as genes near

CIS which are affected with high frequency in Runx1 Tg/Gata-1.05 mice but affected with lower frequency in their respective control mice that may be specifically involved in the leukemogenesis of Gata-1-knockdown and Runx1-overexpressing mice. The

CISs, of special interest, are the genetic changes that occurred preferentially in the genetically engineered mice, in particular, Runx1 Tg/Gata-1.05 mice, but not in WT mice.

Table 3.2: Classification of RIS identified in the mouse leukemia model.

CIS indicates common integration site; and RIS, retroviral integration site. a The genomic positions of the RISs were determined according to BLAT searching of the UCSC Genome Bioinformatics database. b Candidate genes in the vicinity of the RIS are shown. The bold rows indicate the particularly interesting CIS that are discussed in the text.

In the RIM studies, Ifngr2, Rorc and Notch1, were identified as candidate disease genes, two of which are interferon (IFN) signaling-related factors and the other a Notch signaling-related factor.

3.3 Mutation screening of candidate genes associated with JAK-STAT and

Notch pathways in AMKL using targeted deep sequencing

Mouse model for AMKL has suggested that JAK-STAT and Notch pathways are the key driver mechanism for AMKL leukemogenesis. In addition, subsequent exome sequencing on a discovery cohort consisting of 6 paired (onset and complete remission times) AMKL patients has indeed identified the mutations in the genes associated with

JAK-STAT and Notch pathways. To further confirm the genetic mutation in the two pathways, targeted deep sequencing for a prevalent cohort consisting of 40 AMKL patient samples (onset time only) of which 9 are paired (onset and complete remission times) AMKL patients who were registered in the Malaysia-Singapore multicenter leukemia study for childhood acute leukemia and 7 AMKL cell lines were performed

(Appendix I-III). Capturing of genomic DNA (gDNA) fragment was done for coding regions of approximately 600 selected genes including JAK-STAT and Notch pathway genes and known leukemia and cancer genes (capture size, 2.9 Mb, SureSelect XT2).

All captured and barcoded gDNA library were sent for HiSeq2000 sequencing with over 300x depth. Subsequent bioinformatics analysis on raw sequence data was performed by institutional bioinformatics group. Individual genetic variation was subsequently validated in confirmatory Sanger sequencing.

Due to time constraints, only mutations in the genes associated with JAK-STAT and Notch signaling pathways were confirmed by Sanger sequencing as shown in

Table 3.3. As germ-line controls were available in a limited number of patients, all the identified mutations could not be completely confirmed as somatic mutations.

Therefore, there is a possibility that part of the identified mutations might be rare single nucleotide polymorphisms (SNPs). The variant allele frequency (VAF) is defined as the

portion of DNA sequence reads covering the variant position containing the variant allele rather than the reference or germ-line allele for single-nucleotide mutations363. In general, the VAF directly determines the cellular fraction; for example, a cluster with

VAF ≥ 0.5 is equivalent to homozygous mutations found in more than 50% of tumor cells, or heterozygous mutations in 100% of tumor cells, this would most likely result from a non-annotated SNPs or a PCR error occurring upstream of sequencing354.

Therefore, alterations of VAF≥ 0.5 were excluded and only mutations that are non - synonymous are included in the mutated gene list.

NOTCH2 mutation was the most frequently identified (13 patients, 32.5%), followed by GATA-1 (8 patients, 20%), SYNE1 (3 patients, 7.5%), MAML2 (3 patients,

7.5%), NOTCH4 (2 patients, 5%) and MPL mutations ((2 patients, 5%) as shown in

Figure 3.2A. Recurrently affected genes are of primary interest in identifying mutations.

GATA-1 mutations detected in most of the DS-AMKL cases were either small insertions or deletions, indicating sufficient sensitivity in the targeted deep sequencing. The distribution of alterations in individual frequently mutated genes is displayed in Figure

3.2C.

NOTCH2 was found to be most abundantly expressed in AMKL cell lines compared to other Notch family genes, and is reported to be mutated in human lymphomas364. In this study, NOTCH2 represents one of the most frequently mutated genes in childhood AMKL, as mutations were identified in 3 of 9 DS-AMKL cases

(33.3%) and in 10 of 31 non-DS-AMKL cases (32.3%) (Figure 3.2B). A representative of the mutation found in NOTCH2 is shown in Figure 3.2D. Unlike mutations in T-ALL where HD and PEST domains are affected, thereby resulting in Notch activation, the mutations found in AMKL are located in the EGF-like repeats region that is required for ligand interaction. Thus, these mutations probably ablate Notch signaling. Another

Notch family member, NOTCH4, which has been shown to be involved in MK differentiation365, was mutated in a single DS-AMKL and non-DS-AMKL cases.

SYNE1 have been implicated in the regulation of nuclear polarity366, a process that operates upstream of NOTCH1 in squamous epithelia and promotes NOTCH1- dependent epidermal differentiation367. Gene expression profile analysis from another study has shown that SYNE1, a spectrin superfamily member, is up-regulated in core- binding factor–related leukemias368. Heterozygous mutations in SYNE1 were found in two DS-AMKL and one non-DS-AMKL cases (Figure 3.2C).

MAML2 is a non-DNA-binding transcriptional co-activator, associating with the

CSL-NICD complex required for Notch-induced transcription of Notch target genes.

Recently, MAML2 was identified as a novel fusion partner of MLL in secondary AML and myelodysplastic syndrome (MDS) with inv(11)(q21q23). MAML2 mutations were identified in one DS-AMKL cases and two non-DS-AMKL cases (Figure 3.2C). One mutation in other MAML family members, MAML1 was observed in the prevalent cohort but no mutation was observed in MAML3.

MPL plays an important role in the development of MKs and platelets as well as the self-renewal of hematopoietic stem cells. However, numerous MPL mutations have been identified in hematopoietic diseases. The MPL-W515L mutation has been found to be as high as 25% in AMKL with myelofibrosis. In this study, MPL mutations were only found in a single DS-AMKL and non-DS-AMKL cases (Figure 3.2C).

A JAK3 somatic mutation was found in one non-DS-AMKL case (Figure 3.2C and 3.2E). Another JAK3 mutation was found in the AMKL cell line, MG-S, as reported in another AMKL study210. Notably, the domains affected by these mutations were different from those previously found in AMKL patients and cell lines. All known

JAK3 mutations uniformly occurred in the pseudokinase domain or FERM domain208.

Despite the relative paucity of somatic mutations, recurrent lesions were identified in JAK kinase genes, MPL and GATA1 as being clear mutational targets, which have been previously shown to play a role in AMKL209,324. Therefore, this study certainly supports such findings and strengthens the importance of such mutations.

Table 3.3: Recurrently mutated genes other than GATA-1 in childhood AMKL samples in targeted deep sequencing.

Figure 3.2: Mutations associated with JAK-STAT and Notch pathways in AMKL. (A) Frequencies of analyzed gene mutations associated with JAK-STAT and Notch pathways. Frequency of each mutated gene is shown. (B) Distribution of identified mutations in 52 samples of 13 DS-AMKL cases, 31 non-DS-AMKL cases and 8 AMKL cell lines. Types of mutations are distinguished by color. (C) Distribution of JAK3, NOTCH2, SYNE1 and MAML2 alterations. Alterations encoded by confirmed somatic mutations are indicated by red arrowheads. The JAK3-R657Q mutation was identified in AMKL cell line, MG-S. (D) Representative mutation of NOTCH2 confirmed by Sanger sequencing found in a DS AMKL patient at onset (top panel) but not at remission time (bottom panel) (E) Representative mutation of JAK3 confirmed by Sanger sequencing found in a non-DS AMKL patient at onset (top panel) but not at remission time (bottom panel) Abbreviations: +21, trisomy 21; Indel, small insertions or deletions; FERM domain, protein 4.1,ezrin, radixin, moesin domain; SH2 domain, Src homology 2 domain; EGF-like repeats, epidermal growth factor-like repeats; LIN, negative regulatory LIN repeats; HD, heterodimerization domain; NRR, negative regulatory region; TM, transmembrane domain; RAM, RBPJ associated molecule domain; NLS, nuclear localization signal; ANK, ankyrin repeats; TAD, transactivation domain; PEST, proline-glutamate-serine-threonine-rich domain; CH domain, calponin-homology domain; KASH domain, Klarsicht/ANC-1/Syne-1 homology domain; aa, amino acids; CCR, continuous complete remission.

3.4 Anti-proliferative effects of JAK inhibitor 1 in AMKL cell lines

To investigate the anti-proliferative effect of JAK inhibitor 1 in AMKL cells, a panel of human AMKL cell lines was treated with JAK inhibitor 1 at different concentrations, and cell viability was determined using the MTS assay. JAK inhibitor 1 induced anti- proliferative effects in a dose-dependent manner (Figure 3.3A). After 72 hours of incubation, the half maximal inhibitory concentration (IC50) values for JAK inhibitor 1 ranged from 100 to 800 nM (Figure 3.3B); M-07e cells were the most sensitive to JAK inhibitor 1 while MEG-01 cells were shown to be resistant to JAK inhibitor 1.

To further investigate the mechanisms of the anti-proliferative activity of JAK inhibitor 1, the inhibitor's effect on apoptosis was determined. Consistent with the MTS data in Figure 3.3A, JAK inhibitor 1 induced poly (adenosine diphosphate ribose) polymerase cleavage (Figure 3.3C). This was observed after 48 hours of incubation with

JAK inhibitor 1 at IC50 values used in three representative cell lines, two of which are

DS-AMKL cell lines (CMK and MG-S) as well as a non-DS AMKL cell line MEG-01.

A B

Figure 3.3: JAK inhibitor 1 inhibits cell proliferation and induced apoptosis in AMKL cell lines. (A) MTS assay of the eight AMKL cell lines treated with JAK inhibitor 1. Cells were incubated with increasing concentrations of JAK inhibitor 1 (0.01 – 8 μM) for 72 h. The value for each cell line is the mean ± S.E.M. of two independent experiments performed in triplicate. (B) IC50 values after AMKL cell lines were incubated with increasing doses of JAK inhibitor 1 for 72 h. (C) Immunoblotting showing cleavage of poly (adenosine disphophate ribose) polymerase (PARP) in CMK, MG-S and MEG-01 cells were treated for 48 h with JAK inhibitor 1 at 606 nM, 833 nM and 1 μM, respectively. Control cells were treated with dimethyl sulfoxide (DMSO). Blots were stripped and re- probed with anti-GAPDH antibody.

3.5 JAK inhibitor 1 inhibits STAT3 and STAT5 phosphorylation in AMKL

cells

Activation status of downstream factors, phosphorylated forms of STATs, was investigated and STAT1, STAT3 as well as STAT5 were found to be activated in the three cell lines in which at least one active phosphorylated member of the JAK family was detected. Potent inhibition of STAT3 and STAT5 but not STAT1 phosphorylation was observed after 48 hours of incubation with JAK inhibitor 1 at IC50 values in the two

DS-AMKL cell lines but not in the non-DS AMKL cell line MEG-01 (Figure 3.4). This result suggests that STAT3 and STAT5, but not STAT1, may play a dominant role for

AMKL cell proliferation.

Figure 3.4: JAK inhibitor suppresses activation (phosphorylation) of STAT3 and STAT5, but not STAT1 in AMKL cells. Immunoblotting showing STAT1 Y701, STAT3 Y705 and STAT5 Y694 phosphorylation in CMK, MG-S and MEG-01 cells were treated for 48 h with JAK inhibitor 1 at 606 nM, 833 nM and 1 μM, respectively. Control cells were treated with dimethyl sulfoxide (DMSO). Blots were stripped and re-probed with anti-GAPDH antibody.

3.6 JAK inhibitor 1 induces suppression of JAK1 and paradoxical

hyperphosphorylation of JAK3 in AMKL cells

Besides the suppression of STAT phosphorylation by JAK inhibitor 1, its effect on the phosphorylation of the JAK family members in AMKL cells was examined. Only the tyrosine-phosphorylated forms of JAKs are known to exhibit kinase activity, and phosphorylation at two tandem tyrosine residues in the activation loop appears to be required for enzymatic activity. Therefore, the effect of JAK inhibitor 1 on the phosphorylation status of the JAK family members at the activation loop was investigated. Treatment of CMK and MG-S cells, but not MEG-01 cells, with JAK inhibitor 1 for 48 hours induced slight but significant inhibition of JAK1 Y1022/1023

(Figure 3.5). Phosphorylated TYK2 (Y1054/1055) status were not changed in the

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presence and absence of JAK inhibitor 1 in all AMKL cell lines. In addition, no inhibition of JAK2 Y1007/1008 was observed in all AMKL cells incubated with JAK inhibitor 1. In contrast, a paradoxical increase in JAK3 Y980/981 phosphorylation was observed after 48 hours of incubation of CMK and MG-S cells, but not MEG-01 cells, in the presence of JAK inhibitor 1. CMK and MG-S cells were shown to bear activating

A572V and R657Q mutations in JAK3, respectively, whereas MEG-01 do not carry

JAK3 mutations.

Figure 3.5: JAK inhibitor induces suppression of JAK1 and paradoxical hyperphosphorylation of JAK3 in AMKL cells. Immunoblotting showing JAK1 Y1022/1023, JAK2 Y1007/1008, JAK3 Y980/981 and TYK2 Y1054/1055 phosphorylation in CMK, MG-S and MEG-01 cells were treated for 48 h with JAK inhibitor 1 at 606 nM, 833 nM and 1 μM, respectively. Control cells were treated with dimethyl sulfoxide (DMSO). Blots were stripped and re-probed with anti-GAPDH antibody.

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3.7 Effect of phenetyl isothiocyanate (PEITC) and γ-secretase inhibitor

(DAPT) on cell proliferation of AMKL cell lines

Activation of Notch is mediated via the γ-secretase activity of the presenilin-containing macromolecular complex. To investigate the anti-proliferative effect of Notch agonist,

PEITC and γ-secretase inhibitor (GSI), DAPT on AMKL cells, cellular viability was measured at 144 hours using standard MTS assay in different AMKL cell lines as GSI, which blocks the release of NICD, has been reported to exert growth inhibitory effects only after days 5 or 7 in NOTCH1-dependent leukemia cells369. As Notch signaling is deregulated in the majority of T-ALL as a result of activating mutations in NOTCH1, human T-ALL cell line KOPT-K1, which bears a mutation in the extracellular heterodimerization domain of Notch1 that results in ligand-independent S2 cleavage260,370 was used as a positive control (GSI-sensitive) for DAPT and Jurkat, which bears a wild type Notch1 and a mutated FBXW7371 as well as a rare juxtamembrane expansion activating NOTCH1 mutations372 was used as a negative control (GSI-resistant) for DAPT. After 144 hours of incubation, the growth of most

AMKL cell lines was suppressed by PEITC in a dose-dependent manner whereas

PEITC had no significant effect on both T-ALL cell lines (Figure 3.6A). In contrast, compared to the GSI-sensitive and GSI-resistant T-ALL cell lines, DAPT had no significant effect on the growth of AMKL cell lines. The IC50 was established for

AMKL cell lines for both PEITC and DAPT (Figure 3.6C). The possible explanation for the low IC50 values calculated for both PEITC and DAPT in MG-S after 6 days of treatment could be due to cells entered a plateau phase whereby the proliferation is greatly reduced or ceased entirely when all growth medium is spent as similar low absorbance values for both negative control and treated cells were obtained.

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Figure 3.6: Effect of PEITC and DAPT on cell proliferation of AMKL cells. Cells were incubated with increasing concentrations of PEITC (0.1 - 12.8 μM) or DAPT (1 – 32 μM) for 144 h. The value for each cell line (A - B) is the mean ± S.E.M. of two independent experiments performed in triplicate. (A) MTS assay of the eight AMKL and two T-ALL cell lines treated with PEITC. (B) MTS assay of the eight AMKL and two T-ALL cell lines treated with DAPT. (C) IC50 values of AMKL and T-ALL cell lines treated with PEITC and DAPT for 144 h. (* = GSI-resistant and Δ = GSI-sensitive in T-ALL cell lines)

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3.8 PEITC induces apoptosis via caspase-3/PARP pathway and upregulates

expression of p21 in AMKL cells

Next, to determine whether the decreased viable cell number of AMKL cells after treatment with PEITC was due to induction of apoptosis, three AMKL cell lines were treated with PEITC and DAPT for 8 hours followed by annexin V-FITC/ propidium iodide staining. Consistent with the MTS data in Figure 3.6A, PEITC induced apoptotic cell death but not DAPT (Figure 3.7A). To examine whether PEITC treatment alters the expression levels of anti-apoptotic proteins such as Bcl-2 and Bcl-XL, Western blot analysis was performed following PEITC treatment. As shown in Figure 3.7B, there was no marked difference in the expression of Bcl-2 and Bcl-XL after treatment with

PEITC for 8 hours. Since activation of caspases is a key hallmark of apoptosis373, the effect of PEITC-induced apoptosis on the activation of caspases and cleavage of PARP was investigated. Caspase-8 and caspase-9 are known to play critical roles as initiator caspases in the extrinsic (death receptor-mediated) and intrinsic (mitochondria- mediated) apoptotic pathways, respectively. Consistent with induction of apoptosis,

Western blot analysis revealed that PEITC induced substantial activation of caspase-3 and caspase-8 in AMKL cells, but to a much lesser extent activation of caspase-9 in only CMK and MG-S, thus leading to enhanced PARP cleavage in AMKL cells (Figure

3.7B). However, no activation of caspase-3 activity (Figure 3.7C) and induction of

PARP cleavage were observed in AMKL cells treated with DAPT. In addition, cell cycle inhibitor, p21 was markedly upregulated by PEITC, which largely mirrored that of caspase-3 cleavage, but not DAPT (Figure 3.7D).

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Figure 3.7: PEITC induces apoptosis via caspase-3/PARP pathway and upregulates expression of p21 in AMKL cells. (A) Specific apoptosis was determined by flow-cytometric detection of Annexin V- FITC-positive/PI-negative cells after 8 h incubation in PEITC or DAPT. Control cells were treated with dimethyl sulfoxide (DMSO). Bars represent the mean ± S.E.M. from three independent experiments. (B) Immunoblotting showing Bcl-2, Bcl-XL, cleavage of caspase-3,-8,-9 and poly (adenosine disphophate ribose) polymerase (PARP), as well as p21 in CMK, MG-S and MEG-01 cells were treated for 8 h with 5 μM PEITC and 30 μM DAPT respectively. Control cells were treated with DMSO. Blots were stripped and re-probed with anti-GAPDH antibody. (C) Caspase-3/7 activities were expressed as the percent change relative to the mean luminescence measured for control cells. Bars represent the mean ± S.E.M. from three independent experiments. (D) Real-time PCR analyses of p21 mRNA levels in AMKL cell lines treated with PEITC or DAPT. Control cells were treated with DMSO. Bars represent the mean ± S.E.M. of triplicate repeats from two independent experiments. For (A) to (C), statistical analysis were performed by two-tailed unpaired Student’s t- test with 95% confidence intervals (*p<0.05; **p<0.001).

3.9 PEITC induces γ-secretase cleavage of NOTCH1 and enhances

transcriptional activity of Notch in AMKL cells

To assess the specificity of PEITC on activation of Notch pathway, cleavage of Notch was examined. Interestingly, PEITC treatment of the three representative AMKL cell lines led to an increase of cleavage of NOTCH1, NICD1, which is the active form of the protein (Figure 3.8A). However, DAPT, which known to prevent Notch processing and transcriptional signaling, significantly decreased the amount of cleaved NOTCH1

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in AMKL cells. Also, NOTCH2 protein is constitutively activated in the three AMKL cell lines regardless of drug treatment, as shown by its cleavage (NICD2) and these

AMKL cell lines were shown to contain NOTCH2 mutation located in the EGF-like repeats region that is required for ligand interaction, similar to the NOTCH2 mutation shown on Figure 3.2D. Further functional studies need to be performed to address whether mutations in NOTCH2 ablate Notch signaling. NOTCH1 is activated by a series of proteolytic cleavage events, mediated by γ-secretase and other enzymes, resulting in the liberation of NICD1. NICD1 then translocates into the nucleus, where it binds with RBPJ/CBF-1 and other proteins to form a DNA-binding complex. This complex activates transcription of target genes such as hairy enhancer of split 1, HES1.

Therefore, to determine whether the NOTCH1 induced by PEITC is functionally active, dual luciferase reporter assay incorporating the HES1 promoter was used to measure the functional activity of NOTCH1. PEITC treatment caused a comparable increase in the HES1 promoter activity (Figure 3.8B) and a significant upregulation of the endogenous Notch target gene HEY1, as determined by real-time RT-PCR

(Figure 3.8C). However, a reduction in both endogenous HEY1 mRNA levels and

HES1-luciferase promoter activity was observed after treatment of AMKL cells with

DAPT.

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Figure 3.8: PEITC induces γ-secretase cleavage of NOTCH1 and enhances transcriptional activity of Notch in AMKL cells. (A) Immunoblot showing γ-secretase cleavage of NOTCH1 (Val 1744) and cleavage of NOTCH2 in AMKL cells were treated for 8 h with 5 μM PEITC and 30 μM DAPT respectively. Control cells were treated with dimethyl sulfoxide (DMSO). Blots were stripped and re-probed with anti-GAPDH antibody. (B) AMKL cells transfected with HES1-luciferase were either treated with DAPT or PEITC, normalized to Renilla determined by dual luciferase reporter assay. Bars represent the mean ± S.E.M. from three independent experiments. Statistical significance: *p<0.05, **p<0.001. (C) Real-time PCR analyses of HEY1 mRNA levels in AMKL cell lines treated with PEITC or DAPT. Control cells were treated with DMSO. Bars represent the mean ± S.E.M. of triplicate repeats from two independent experiments. Statistical significance: *p<0.05, **p<0.001, n.s. = non-significant.

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3.10 Combination of JAK inhibitor 1 and PEITC trigger synergistic AMKL

cell death

As combination therapy can maximize effects and minimize side effects of individual drugs from a clinical perspective, the synergistic effect of JAK/STAT inhibitor and

Notch agonist was tested in in vitro experiments. AMKL cell lines such as CMK, MG-S and MEG-01 which are relatively more resistant to both PEITC and JAK inhibitor 1 were selected for the analysis as combination therapy may be able to re-sensitize cells to drug-induced apoptosis and produce a greater anti-cancer activity in these cell lines compared to the more sensitive cell lines such as M-07e and CMK-86. In this study,

JAK inhibitor 1 and PEITC were used at concentrations lower than their respective IC50 for each cell line. CMK, MG-S and MEG-01 cells were treated for 72 hours with doses of JAK inhibitor 1, PEITC or the combination and analyzed for viability by MTS assay.

A more significant decrease in viability of all cell lines tested was noted in response to treatment with combined doses of JAK inhibitor 1 and PEITC than with either agent alone (p<0.05, n=2; Figure 3.9A-C). Isobologram analysis confirmed that combined

JAK inhibitor 1 plus PEITC triggered synergistic cell death in AMKL cell lines, albeit with differential kinetics. The drug combination analysis was performed using the

Chou-Talalay method374. Drug synergy was statistically defined by combination index

(CI) values less than 1, antagonism by CI >1 and additive effect by CI equal to 1. The combination ratio of agents varied for each cell line to achieve a similar degree of cell death. These in vitro findings suggest that combination therapy with JAK inhibitor 1 and PEITC (Notch agonist) is of potential value for the treatment of AMKL.

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Figure 3.9: Combination of JAK inhibitor 1 and PEITC trigger synergistic AMKL cell death. CMK (A), MG-S (B) and MEG-01 (C) were treated with treated with DMSO, JAK inhibitor 1 (blue bar), PEITC (red bar), or combined JAK inhibitor 1 plus PEITC (yellow bar) and assessed for viability using MTS assays performed at 72 h. The concentration of drugs, either alone or in combination, were as follows: CMK cells: 120 nM JAK inhibitor 1, 5 μM PEITC or JAK inhibitor 1(120 nM) plus PEITC (5 μM); MG-S cells: 400 nM JAK inhibitor 1, 5.5 μM PEITC, or JAK inhibitor 1 (400 nM) plus PEITC (5.5 μM); MEG-01 cells: 8 μM JAK inhibitor 1, 6 μM PEITC, or JAK inhibitor 1 (8 μM) plus PEITC (6 μM). Data presented are means ± S.E.M. (n=2; p <0.05 for all cell lines). A combination index, CI (red dot) of less than 1 indicates synergy.

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CHAPTER 4: DISCUSSION

4.1 Megakaryoblastic leukemia develops in the Runx1 Tg/Gata-1.05 mouse

model

Earlier studies have demonstrated that Gata1 mutations or trisomy 21 alone are insufficient to promote TAM or leukemia in human and mouse, indicating that additional cooperating mutations are required for leukemogenesis. Therefore, compound mice (Runx1 Tg/Gata-1.05) were subjected to the RIM study to identify genetic changes associated with AMKL.

Morphological analysis of peripheral blood smears from diseased mice demonstrated the presence of megakaryoblasts with prominent cytoplasmic blebs. In addition, the most widely used markers for examining megakaryoblast such as c-Kit,

CD61 and CD41 were shown to be positive in most of the compound mice. Therefore, flow cytometric analysis confirmed that these compound mice had a disease similar to the human AMKL-like leukemia. This result also indicates that Gata-1-knockdown and

Runx1-overexpressing status drives myeloid features in leukemias despite the strong T- lymphoid tropism of MoMuLV virus.

It is important to emphasize that CD41/CD61 were thought to be an integrin specific and selective phenotypic markers for the MK/platelet lineage, these markers should be used with caution in identifying MK cells as there is increasing evidence that

CD41 expression is not restricted to cells with potential to differentiate into MK cells, but also in hematopoietic stem and progenitor cells (HSPCs) with a myeloid and lymphoid potential.

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Overall, data from my study demonstrate that Gata-1-knockdown and Runx-1 overexpressing compound mice along with RIM are useful model for the study of the molecular mechanisms of multistep AMKL leukemogenesis.

4.2 RIM identifies CIS associated with JAK-STAT and Notch pathways

Identification of RIS in mouse leukemias by inverse PCR revealed several hundreds of integration sites in my experimental cohorts of RIM mice. CIS that occur with high frequency in compound mice (Runx1 Tg/Gata-1.05), compared to WT controls, would provide an insight into the additional genetic alterations required for facilitating the progression of compound cells from pre-leukemic state to overt leukemia. Therefore,

RIM studies on compound mice would be useful to unveil the currently unknown mechanistic basis for leukemogenesis in Gata-1-knockdown and Runx1-overexpressing mice.

The RIM result suggested interferon-γ receptor 2 (Ifngr2) as the highest potential AMKL gene. IFNGR2 encodes a type I integral membrane protein which forms a part of the interferon-γ receptor (IFN-γR). IFN-γ and its downstream signaling molecules JAK1/2 and STAT1 are primarily immunity related factors, but they are also shown to play a role in the maturation of megakaryocytes145. Of note, IFNGR2 is located on human chromosome 21, within the critical region for DS related clinical features. It is reported that a small number of DS-AMKL patients carry the mutation of

JAK2 or JAK3.

Rorc encodes the TH17-lineage specific transcription factor, RORγt, a member of orphan nuclear receptor. TH17 cell is a recently identified IL-17 producing, subclass of helper T cells, and has been the subject of much attention, mainly because IL-17 and

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other cytokines released from TH17 cells are important to the pathogenesis of autoimmune diseases and cancer immunity. Notably, TH17 cell differentiation is induced by IFN-γ secreted by another helper T cells, TH1 cells. Therefore, Rorc functions as a downstream factor of IFN-γ in TH17 cells. Moreover, RORγt is shown to physically interact with Runx1 and induce IL-17 expression together375. IL-17 function in myeloid cells is not well documented, but earlier studies suggested that IL-17 also stimulates megakaryopiesis376. Although IL-17 production in megakaryoblasts needs further investigation, the link of IFN-γ-RORγt-Runx1 is likely to be kept in megakaryoblast and its deregulation could lead to AMKL.

Interestingly, one of the CIS identified was Notch1 gene, where retrovirus integrations resulted in gain-of-function mutations that cluster in the carboxy-terminal

(C-terminal) region of Notch1 containing negative regulatory sequences generating a stable expression of its activated product, NICD1. The altered expression in these domains is likely to contribute to leukemogenesis by increasing the half-life of transcriptionally active NICD1377. NOTCH1 was also suggested to be a potential

AMKL gene. Notch family of transmembrane signaling receptors comprises four distinct genes. Of these, NOTCH1 is shown to be involved in human solid cancers and is frequently mutated in human childhood T-cell leukemia. Recently, Gilliland’s group showed that NOTCH1 and NOTCH4 are involved in megakaryocyte differentiation365.

In summary, the RIM study identified JAK-STAT and Notch as the key pathways in

AMKL leukemogenesis.

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4.3 Deep sequencing validates genetic alterations associated with JAK-

STAT and Notch pathways in human AMKL samples

A role for the loss of function of Notch in AMKL would predict that Notch signaling pathway alterations might be found; indeed, NOTCH2 was found to be frequently mutated in AMKL (29.5%) characterized by missense mutations in the EGF-like repeats of the Notch ectodomain which is required for ligand interaction378. Mutations in other Notch signaling-related genes were also detected by deep sequencing and these mutations were validated in my study. Previous studies have shown that point mutations of MAML1 were found in three patients with aggressive 5q-AML379 and also mutations were found in NOTCH2, MAML1, NCSTN and APH1A in patients with CMML263.

These Notch inactivating alterations suggest that Notch signaling inhibits AMKL growth as well as survival and mutations in a subset of AMKL cells may contribute to

AMKL leukemogenesis.

Most of the NGS studies on AMKL mentioned in Introduction were performed by exome and transcriptome sequencing. Remarkably, none of the above studies have identified Notch signaling pathway genes. Whilst exome sequencing provides a platform for genome-wide comprehensive mutation studies; targeted deep sequencing offer significant advantages where studies are designed to fulfill specific aims such as investigations into the mutational analysis of specific pathways or genes in a clinical context. The advantages include increased read depth and decreased off-target noise leading to improved sensitivity and specificity for variant detection380 as well as the ability to capture and sequence regions not covered by exome capture kits381. My understanding through personal communication is that Notch mutations are difficult to be detected due to the low capturing efficiency by baits in targeted deep sequencing. In

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my study, however, I have employed targeted deep sequencing using 20-mer biotinylated RNA baits specifically designed to increase the coverage for Notch related genes by tiling across targeted genomic regions for Notch, because RIM result has suggested that Notch pathway is one of the key driver mechanisms for AMKL leukemogenesis. For the same reason, I took the approach to prioritize confirmatory

Sanger sequencing in Notch signaling pathway genes. In contrast, it is likely that earlier

NGS studies did not perform confirmatory Sanger sequencing on Notch mutations due to the lack of specific interest in Notch signaling pathway.

Notch alterations in the mouse AMKL model are likely to cause Notch activation, whereas alterations in NOTCH2 and NOTCH4 in human AMKL are considered to be loss-of-function mutations, based on the domains where mutations are mapped. This discrepancy may highlight differential requirement for Notch signaling in mouse and human megakaryopoiesis. Observations from previous study has suggested a function for Notch/Dll1 signaling in the MK lineage by showing that plating mouse

Lin−Sca-1+c-Kit+ (LSK) cells on OP9-Dll1 cells resulted in the appearance of mature

MKs that was not seen in the parental OP9 cells without Notch ligands or in the presence of γ-secretase inhibitors365. Conversely, experiments using human CD34+ progenitor differentiation assays have shown that Dll4 stimulation inhibited the generation of platelets and mature CD41a+ CD42b+ MKs which could be rescued by pharmacologic inhibition of Notch signaling using γ-secretase inhibitors382.

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4.4 Retroviral insertional mutagenesis still remains powerful

Leukemogenesis is a multistep molecular process that requires combinations of oncogenic mutations, loss-of-function mutations of tumor suppressors, epigenetic alterations of gene expression and structural abnormalities of chromosomes331,339. It is therefore important to understand how individual genetic alterations cooperate with each others. Different high throughput experimental approaches have been developed to discover novel cancer genes. The emergence of NGS technologies has revolutionized

DNA sequencing which has boosted the analysis of cancer genomes at different levels, including identification of point mutations, chromosomal aberrations and epimutations335.

Yet there are important limitations to the use of NGS technologies such as the presence of a vast background of random mutational noise comprising of single- nucleotide polymorphisms, germline mutations, PCR errors, or DNA duplications which complicates the identification of somatic mutations that actually drive leukemogenesis and to distinguish driver mutations from neutral passenger mutations.

In addition, these approaches require massive amount of tumors to be analyzed to detect infrequently mutated driver genes as well as functional interconnection and regulation among cancer genes383. RIM on genetically engineered animal models that are predisposed to cancer development has been shown to be a powerful strategy to identify cooperative genes with pre-existing genetic alteration(s) involved in leukemogenesis because the retroviral integrations serve to add specific cooperative genetic changes to the pre-existing alterations which can be monitored by observing the development and progression of tumors in mice114,384,389. Functional validation for implicated genes, in the pathogenesis of the disease is necessary for its clinical relevance.

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RIM began early in the 20th century by Peyton Rous and others, as a tool for cancer gene discovery. However, the immense popularity of NGS has accelerated the transition from RIM to NGS leading to the declining use of RIM. In spite of that, NGS studies cannot simply substitute functional studies in model organisms as the successful identification of Notch signaling pathway in AMKL in my study demonstrates that RIM still remains powerful. Consequently, data from functional approaches such as RIM serves as a very important complement to other high throughput experimental approaches as the RIM studies may be more rational, cost-effective and essential to reduce morbidity and mortality from cancer385.

4.5 Potential therapeutic efficacy of JAK inhibition and Notch activation in

treating AMKL

As JAK/STAT and Notch pathways are suggested to be key pathways in AMKL leukemogenesis, JAK inhibitor and Notch agonist were expected to inhibit cell proliferation as well as induce apoptosis in AMKL cell lines, and indeed the observed results lend further support to my data of the mutation status in AMKL patients.

4.5.1 JAK inhibition suppresses AMKL cell proliferation

In this study, I presented the anti-leukemic activity of a synthetic pan-inhibitor of JAK-

STAT pathway, JAK inhibitor 1 whereby treatment with JAK inhibitor 1 significantly reduced AMKL cell proliferation accompanied by an increased in apoptosis as observed by the increased cleaved PARP. In the absence of JAK inhibitor 1, three downstream

STAT proteins were all activated (phosphorylated). Such activation of STAT1 was not

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changed in the presence of JAK inhibitor 1, whereas activation of STAT3 and STAT5 were significantly reduced when treated in many AMKL cells, except for MEG-01 cell line which is resistant to JAK inhibitor 1. This result suggests that STAT3 and STAT5, but not STAT1, may play a dominant role for AMKL cell proliferation. Expression of

JAK1, JAK2 and TYK2 is ubiquitous; however, expression of JAK3 is restricted to hematopoietic cells. JAK inhibitor 1 was shown to induce suppression of JAK1 and paradoxical hyperphosphorylation of JAK3 in AMKL cells bearing activating JAK3 mutations. These data support a crucial role of JAK-STAT in the process of proliferation and transformation of AMKL cells which further underlie the significance of JAK-STAT in these AMKL cells as a potential therapeutic target and thus provide a rationale for attempts to block cytokine activation via the JAK-STAT pathway.

4.5.2 Notch activation attenuates AMKL cell expansion

PEITC is a natural and clinically useful product for chemoprevention of cancer, which has recently shown to not only activate Notch pathway but also known to modulate other pathways386. Consistent with the detected types of mutation which are considered to result in inactivation of Notch pathway, the present study reveals that activation of

Notch signaling induced by PEITC has been shown to inhibit proliferation of AMKL cells. Previous studies have shown that NOTCH1 expression regulates cell death through apoptosis387-389. In my study, the results indicate that PEITC efficiently promoted the cleavage of caspase-8, an initiator caspase for extrinsic apoptotic pathway, resulting in the direct activation of caspase-3 in AMKL cells, which is an important effector of apoptotic cell death. Proper functioning of active caspase-3 was subsequently demonstrated by the detection of one of its cleaved substrates, PARP. In

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contrast, there were no obvious changes in the expression of Bcl-2 or Bcl-XL in AMKL cells treated with PEITC, which is known to block the release of cytochrome c and thereby inhibit activation of caspase-9, an initiator caspase for intrinsic apoptotic pathway. Although minimal activation of the intrinsic apoptotic pathway was observed, these results suggest that PEITC execute apoptosis mainly by the extrinsic apoptotic pathway via caspase activation. In order to confirm the critical involvement of extrinsic

(caspase-8) or intrinsic (caspase-9) apoptotic pathways in PEITC-induced apoptosis, caspase-8 inhibitor (Z-IETD-FMK) and caspase-9 inhibitor (Z-LEHD-FMK) can be used to suppress apoptosis in AMKL cells following PEITC treatment. In a parallel fashion, Notch signaling in AMKL cells treated with PEITC results in a profound G1 cell cycle arrest associated with p21waf1/cip1, similar to findings in a previous study on

Notch signaling in small cell lung cancer cells390. In another study on human prostate cancer, PEITC was also shown to promote cell cycle arrest via p53 expression391.

Moreover, PEITC treatment promotes cleavage of NOTCH1 leading to transcriptional activation of Notch target genes, HES1 and HEY1. On the contrary, suppression of

NOTCH1 activation with gamma-secretase inhibitor which blocks the Notch pathway,

DAPT, has no effect on the proliferation and apoptosis in AMKL cells. In addition, both PEITC and DAPT treatment did not down-regulated the protein expression of cleaved NOTCH2. These results suggest that growth inhibitory effect of PEITC is independent of NOTCH2 but through the activation of NOTCH1. In conclusion, the present study has established a novel tumor suppressor role for Notch signaling in

AMKL, and demonstrated that activation of Notch signaling could represent a therapeutic strategy in AMKL.

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4.5.3 Synergistic cytotoxic effect of JAK inhibitor 1 and PEITC against AMKL

As with other agents, dose-limiting toxicity and the development of resistance limits its long-term utility as well as increased mortality in DS-AMKL as did autologous and allogeneic transplant392. My study has demonstrated that the combination of low doses of JAK inhibitor 1 and PEITC induces apoptosis in AMKL cells that is only achievable at much higher doses of either agent alone. Our data demonstrate synergistic cytotoxicity of JAK inhibitor 1 and PEITC on AMKL cells, thereby suggesting clinical applicability of this combined therapeutic regimen in AMKL.

JAK-STAT activation and Notch inactivation due to respective genetic mutation appear to be the key driver mechanism for AMKL leukemogenesis. Therefore, I hypothesize that inhibition of JAK-STAT and activation of Notch are expected to lead to cell differentiation and apoptosis (Figure 4.1).

Figure 4.1: A proposed model for the role(s) of JAK-STAT and Notch pathways in AMKL leukemogenesis and treatment. JAK-STAT activation and Notch inactivation due to respective genetic mutation appear to be the key driver mechanism for AMKL leukemogenesis. Inhibition of JAK-STAT and activation of Notch are expected to lead to cell differentiation and apoptosis.

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4.6 Future work

To grasp a comprehensive landscape of genetic alterations in AMKL, I am also planning to carry out high-resolution single nucleotide polymorphism (SNP) array which identifies copy umber variations. In addition, there are several known fusion genes such as OTT-MAL and CBFA2T3-GLIS2324 which are common in AMKL. Such fusion complementary DNA (cDNA) will also be examined using patient’s cDNAs or gDNAs.

To better define the role of pan-JAK inhibitor compared to more specific JAK-

1,-2 and -3 inhibitors, I intend to use FDA approved clinical agents such as selective

JAK3 inhibitor, Tofacitinib (CP-690550) or selective JAK1/2 inhibitor, Ruxolitinib

(INCB018424) on AMKL cell lines. Such comparison with different JAK inhibitors will likely provide additional insight into the disease mechanism, as it is conceivable that more selective JAK inhibitors might be efficacious with reduced adverse effects, which are more highly favorable for translational or clinical application.

As one of the CIS identified is near the Notch1 gene locus in the mouse AMKL model which suggests that Notch1 gene is likely to be activated or upregulated in the mouse model, additional experiments to determine the status of Notch1 or Notch2 activation in mouse AMKL cells and to test both PEITC and DAPT on these cells will be carried out.

In contrast, Notch may be inactivated in human AMKL through mechanisms other than mutations, therefore, expression changes in Notch pathway and target genes will be further examined. I will next investigate whether the inhibitory effect on cell proliferation of PEITC is Notch-specific (independent of NOTCH2 but through the activation of NOTCH1), using RNA interference (RNAi) method by introducing small

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interfering RNA (siRNA)-mediated knockdown of NOTCH1 and NOTCH2 to AMKL cells or using plasmid-based approaches for overexpression of NOTCH1 to examine the effects on cell proliferation and downstream signaling pathways in AMKL cells. I also planned to examine the functional property of the mutated NOTCH2 in human AMKL cells by generating stable cell lines expressing the mutant or wildtype variant of

NOTCH2 using site-directed mutagenesis and to perform functional analysis on proliferation and in vitro ligand binding assays.

To further confirm Notch activation dependent cell killing effect on AMKL cells, other Notch agonists, such as anti-Notch antibody which is shown to activate

Notch pathway, synthetic Notch ligands (Dll4-Fc)393, Notch agonist peptide394 are currently listed to be tested. Majority of those molecules have been used to treat cancer cells which are caused by loss-of-function mutation in Notch pathway.

As my results showed that JAK inhibitor and PEITC inhibit cell proliferation and induce apoptosis in AMKL cell lines, drug efficacy will be further confirmed using in vivo xenograft model employing NOD/SCID/IL2 receptor gamma chain knockout

(NSG) mice. AMKL cell lines and subsequently, primary AMKL patient cells will be subjected to the drug testing.

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4.7 Conclusions

Gata-1-knockdown and Runx-1 overexpressing compound mice is a useful mouse model for the study of the molecular mechanisms of multistep AMKL leukemogenesis.

Both RIM and targeted deep sequencing studies revealed that JAK-STAT activation and Notch inactivation due to respective genetic mutation appear to be the key driver mechanism for AMKL leukemogenesis. Moreover, in vitro drug screening has revealed the potential therapeutic efficacy utilizing drugs targeting JAK inhibition and Notch activation in treating AMKL. Such hitherto unknown link between JAK-STAT and

Notch signaling pathways, unveiled in my study, would provide insights into fundamental mechanisms of AMKL leukemogenesis, and lead to the improvement of therapeutic regimen for AMKL.

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APPENDICES

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Appendix I: Characteristics of Malaysia-Singapore (MASPORE) AML patients study 2006

No. DOB Date of Dx Age Age Revised Dx Cytogenetics Risk by Maspore Date of 1st Date of Date of Date of No CR/ Immuno-phenotype WBC Dx PB Blasts Dx at Dx <1, 1-10, (ANALYSIS) Cytogenetics Status after Event 2nd Event Death Relapse Relapse count % BMA (yrs) >10 (ANALYSIS) ADE#2 SJVF at Dx % analysis (DR Chan)

D472 13/08/2007 04/06/2008 0.81 <1 AML-M7 47,XY,+21[9]/46,XY[21 HR CR (after#2), CD34+ blasts: CD34+/SSc- 62.03 10.4 Not ] BMT lo/45-/cyMPO-/19-/cy79a- done /cy3-/7-/56++/2-/65-/7.1-/11b- /13dim/117+/HLADR- /33++/123+/36-/64-/GlyA- /41+/42a-/61-/15-/16- Megakaryoblasts (0.16%): CD45-/CD34+/FSc-lo/56++/2- /65-/ Mab7.1-/11b- /13dim/117+/HLADR- /33++/123dim/36-/64-/41+/ 42a-dim/61 dim/15-/16-

KL319 13/02/1996 04/10/2005 9.64 1-10 AML-M7 Not done HR CR (after#3), 18/06/2006 18/06/2006 CD117 CD34 CD45 CD61 2.60 41 occ Death (Sepsis)

KL327 20/04/2005 30/09/2005 0.45 <1 AML-M7 Not done HR CR (after#2), 09/05/2006 09/07/2006 09/07/2006 09/05/2006 09/05/2006 CD117 CD61 8.30 Occasionally 30 Relapsed, Seen Death

KL347 17/08/2004 25/11/2005 1.27 1-10 AML-M7 Not done HR CR (after#1), 15/05/2006 01/06/2006 01/06/2006 15/05/2006 15/05/2006 CD117 CD33 CD34 CD56 24.10 23 46 Relapse, CD61 Death

KL399 29/12/1998 12/06/2006 7.45 1-10 AML-M7 47XX, +mar[9] / 47, HR CCR (after#2) CD33 CD45 CD61 HLADR 6.90 12 90 idem, t(12;15)(q24.1;q15)[10] / 46XX[1]

KL468 27/06/2005 21/02/2007 1.65 1-10 AML-M7 No metaphase available HR CR (after#1), 04/08/2008 03/12/2008 03/12/2008 04/08/2008 04/08/2008 CD117 CD33 CD34 CD52 16.4 33 90 for analysis Relapse, CD61 (Enrolment Death form written 67%)

KL473 11/06/2004 08/03/2007 2.74 1-10 AML-M7 46,XY,t(7;14)(q22;q32), HR No CR 09/03/2007 14/11/2007 14/11/2007 09/03/2007 CD33 CD45 CD61 HLA DR 36.70 41 80 del(9)(p22),del(13)(q12q (after#2), 22)[19]/46,XY[1] Death

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KL532 13/07/2006 28/09/2007 1.21 1-10 AML-M7 46,XX,der(8)t(8;18)(q13; HR CCR 05/06/2008 05/06/2008 05/06/2008 CD117 CD13 CD33 CD41a 5.00 3 46 q11.2),add(12)(p?12)[5], (after#3), CD61 add(13)(q?31)[8],add(15) Relapse, (q22)[2],der(16)t(8;16)(q Defaulted 13;q12.1),del(18)(q11.2)[ cp12]/46,XX[8]

D307 11/01/2005 01/06/2005 0.39 <1 AML-M7- 47,XY,+21c[36]/43,XY,- Down's CCR (after#1) NA 9.60 25 56 Down's 10,-11,-15,- 21[1]/45,XY,-15,- 21[1]/,46,XY,- 21[1]/48,XY,- 7,+18,+21c,+mar[1], Down's syndrome

D507 13/12/2007 06/01/2009 1.07 1-10 AML-M7- 48,XY,+8,+21c[2]/47,X Down's CCR (after#1) Myeloblasts: CD45dim/SSC- 16.58 27 Hemo Down's Y,+21c[48] lo/19-/cy79a-/cyMPO-/7dim/ diluted cy3-/13dim/11b-/33+/65-/64- , 10% /15-/16-/117+/HLADR-var/ blast. 61-/36+/NG2-/2-/56+/41-/42a- dim

D526 11/03/2008 27/04/2009 1.13 1-10 AML-M7- 48,XY,+8,+21c[15]/47,X Down's CR (after#1) Myeloblast: SSC-lo/FSC- 10.19 Occasionally 33 Down's Y,+21c[5] int/CD34-/45dim/56+/2-/65- Seen /NG2-/13-/11b-/HLADR- /117+/33+/61dim/123dim/36+/ 64-/14-/15-/16-/41+/42a+/19- /cy79a-/cyMPO-/7+/cy3-

KKH002 20/06/2004 01/08/2006 2.11 1-10 AML-M7- 47,XX,?der(1)t(1;1)(p36. Down's CCR (after#3) FLOW CYTOMETRY OF 12.60 22 31 Down's 3;q32),+21c [15]/ MARROW 47,XX,+21c[7] DEMONSTRATES ~22% BLASTS WHICH EXPRESS CD117+ CD33+ CD34+ MPO+ CD36+ PARTIAL CD13+(~32%) CD7+ TDT- CYTOPLASMIC CD3- CD41+. THEREFORE THIS IS SUGGESTIVE OF ACUTE MYELOID LEUKAEMIA WITH MEGAKARYOCYTIC DIFFERENTIATION.

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KKH032 24/05/2005 04/01/2008 2.61 1-10 AML-M7- Down's (47,XY,- Down's CCR (after#2) FLOW CYTOMETRY OF 4.59 13 23 Down's 7,+21c,+21[20] All MARROW metaphases showed DEMONSTRATES ~23% monosomy 7 and an BLASTS WHICH extra chromosome 21 in EXPRESS addition to constitutional CD33+CD117+CD34+ trisomy 21. This has PARTIAL CD13+ (~30%) been associated with PARTIAL WEAK MPO+ MDS, AML and MPO. ) (40%) CD14-CD64- CD36+CD41+ CYTOPLASMIC CD3-Tdt-CD19-CD10-CD7+. THEREFORE ACUTE MYELOID LEUKAEMIA WITH MEGAKARYOCYTIC DIFFERENTIATION HAS TO BE CONSIDERED.

KL334 05/02/2004 20/10/2005 1.70 1-10 AML-M7- Not done Down's CCR (after#1) CD19 CD61 7.90 Down's

KL555 24/12/2004 21/01/2008 3.07 1-10 AML-M7- 47,XX,del(7)(p15),del(9) Down's CCR (after#1) CD13 CD33 CD34 7.3 22 38 Down's (q13q22),+21c[10]/47,X X,+21c[10]

KL606 26/09/2006 31/07/2008 1.84 1-10 AML-M7- 47,XX,del(7)(q33~35),de Down's CCR (after#3) CD117 CD13 CD33 CD41 26.6 36 35 Down's r(14)t(1;14)(q25;q32),+2 CD61 1[20]

KL638 03/05/2005 08/01/2009 3.68 1-10 AML-M7- 48,XY,+10,+21c[15]/47, Down's CCR (after#1) CD117 CD33 CD45 154.00 82 80 Down's XY,dup(7)(q11.2q32),+2 1c[4]/47,XY,+21c[1]

KL648 27/10/2006 17/03/2009 2.39 1-10 AML-M7- 47,XX,t(3;17)(q25;q25), Down's No CR 18/03/2009 04/06/2009 04/06/2009 18/03/2009 CD117 CD11b CD13 CD33 200.30 93 Down's del(6)(q13q23),add(21)(q (after#2), CD34 CD45 CD61 HLADR 22),+21c[19] Death

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Appendix II: Characteristics of Malaysia-Singapore AMKL patients (May 2011)

Lab ID Sex Ethnicity DOB Age Date Dx FAB subtype GATA1 Notch4 Notch4 Jak3 Jak2 cMPL FLT3- FLT3- c-kit Ex17 NPM1 WT1 ex7 Notch2 Notch2 Micro Dx HD PEST ex3-6 ex12-15 ITD TKD D816 insertion insertions HD PEST array

AML- Deletion after D210 F Chinese 26/6/1997 5.6 23/1/2003 None None None None None not done not done None None None M7+Down's 70bp

AML- V617F D235 M Malay 21/2/1999 4.8 18/12/2003 None None None not done None not done None None M7+Down's (G1849T)

D295 M Chinese 23/01/2003 2.11 04/03/2005 AML-M7 None None None None None None None None None None None

D298 M Chinese 03/04/2002 3.02 12/04/2005 AML-M7 None None None None None None None None None

AML- D307 M Chinese 11/06/2004 0.97 01/06/2005 None None None None None None None None None None None M7+Down's

D349 M Malay 18/04/2005 1.06 09/05/2006 AML-M7 None None None None None None None None None None

AML M7- D352 M Malay 04/10/2004 1.77 12/07/2006 None None None None None None None Relapse

D400 F Vietnamese 26/09/2004 2.59 30/04/2007 AML-M7 None None None None None None None

AML-M7- KKH002 F Chinese 20/06/2004 2.11 01/08/2006 None None None None None None None Down's

Deletion after Insertion KL187 F Chinese 04/03/1987 16.99 02/03/2004 AML-M7 None None None None None None None None None None 70bp 3+9bp

KL267 M Malay 07/12/2001 3.21 23/02/2005 AML-M7 None None None None None None not done None None None done

AAT- D835N- KL319 M Chinese 13/02/1996 9.60 20/09/2005 AML-M7 None None None None None not done not done not done None None done Asp to Asn

KL327 M Chinese 20/04/2005 0.45 30/09/2005 AML-M7 None None None None None None None None None None done

AML- KL334 F Malay 05/02/2004 1.70 20/10/2005 None None None None None None None None None M7+Down's

KL347 M Indian 17/08/2005 0.27 25/11/2005 AML-M7 None None None None None None None None None None done

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KL399 F Chinese 29/12/1998 7.45 12/06/2006 AML-M7 None None None None None None None None None done

KL468 F Malay 27/06/2005 1.65 21/02/2007 AML-M7 None None None None None None None

KL473 M Malay 11/06/2004 2.74 08/03/2007 AML-M7 None None None None None None None done

KL532 F Chinese 13/07/2006 1.21 28/09/2007 AML-M7 None None None None None None None

KL555 F Chinese 24/12/2004 3.07 21/01/2008 AML-M7 None None None None None None None

AML-M7, KKH 32 M Malay 24/05/2005 2.61 04/01/2008 Down's None None syndrome

R 523 Others 03/07/2008 0.71 18/03/2009 AML-M7 None None

R 400 Vietnamese 26/09/2004 2.59 30/04/2007 AML-M7 None None

AML-M7, KL 606 F Chinese 26/09/2006 1.84 31/07/2008 Down's None None Syndrome

KL52 done

R 210 done

R 307 done

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Appendix III: Characteristics of AMKL cell lines used in this study

Name Source Cell type Remarks Cytogenetics Immunology CMK Homo sapiens Acute megakaryoblastic Established from the Human flat-moded hypotetraploid karyotype with 8% polyploidy CD3 -, CD13 +, CD14 -, leukemia peripheral blood of a 10- - 85-90<4n>XY, -X, -Y, -2, -3, +5, -6, -6, -8, +11, -15, -15, +16, - CD15 -, CD19 -, CD33 +, month-old boy with Down's 17, -19, +21, +22, +7-11mar, add(1)(q31), add(1)(p36), CD34 +, CD41 +, CD71 +, syndrome and acute add(3)(q11), del(3)(p14)x2-3, add(5)(q11), add(5)(q13), CD235a + megakaryocytic leukemia dup(8)(q11q21), add(8)(q13-21), del(8)(q11), del(9)(p21)x2, (AML M7) at relapse add(9)(q11)x2, del(10)(q22q24), der(11;17)(q10;q10), der(11)dup(11)(p13p15)t(5;11)(q11;p15)x1-2, del(11)(q23), add(12)(p13)x2, add(17)(p1?), add(18)(q23)x2-3, add(19)(p13), der(20)t(1;20)(q2?5;q1?2)x2, add(22)(q13) - disomic rearrangement of 20q1 close to common deleted segment, together with additional rearrangements of ch 20 present in markers - matches published karyotype

CMK-11-5 Homo sapiens Acute megakaryoblastic Differentiated subclone of Not available Expression of CD41a+ and leukemia CMK, morphologically larger CD61+ greater in CMK-11- than the parent clone CMK 5 than in CMK cells, and contains multinucleated positive for platelet giant cells as well as peroxidase (PPO) activity, possesses more mature thrombospondin (TSP) and characteristics than CMK-86 von Willebrand factor cells (vWF)395,396

CMK-86 Homo sapiens Acute megakaryoblastic Differentiated subclone of Not available CD41+, CD61+, leukemia CMK, morphologically larger Prostaglandin (PG) D than the parent clone CMK synthase activity highest in and contains multinucleated CMK-86 compared to giant cells as well as CMK and CMK-11-5397 possesses more mature characteristics than CMK-86 cells

M-07e Homo sapiens Acute megakaryoblastic Established from the Human near-diploid karyotype - 46(45-46)<2n>XX, CD3 -, CD13 +, CD14 -, leukemia peripheral blood of a 6- t(11;21)(p11;p13), add(13)(p13), add(22)(p13) CD19 -, CD33 +, HLA-DR month-old girl with acute - megakaryoblastic leukemia (AML M7) at diagnosis

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Name Source Cell type Remarks Cytogenetics Immunology MEG-01 Homo sapiens Chronic myeloid Established from the bone Human hyperdiploid karyotype with 12% polyploidy - 54(53- CD3 -, CD13 +, CD15 +, leukemia in marrow of a 55-year-old man 56)<2n>XY, +6, +19, +19, +21, +3-4mar, t(1;15)(p13;p13), CD19 -, CD33 + megakaryocytic blast with chronic myeloid ?inv(3)(p25q26), i(4q), add(5)(p15), der(9)t(9;22)(q34;q11)x2, crisis leukemia (CML) in add(10)(p14), dup(13)(q13q33-34), add(14)(p11), megakaryocytic blast crisis der(22)t(9;22)(q34;q11) - r/dmin x1 present at 57% - sideline with i(5)(q10)del(5)(q11q13) instead of add(5) - resembles published karyotype; cells are described to carry t(9;22) leading to BCR- ABL1 e13-a2 (b2-a2) fusion gene

MG-S Homo sapiens Acute megakaryoblastic Established from a boy with Expresses only a truncated mutant of GATA-1 (GATA-1ΔNT) Not available leukemia Down's syndrome and acute which blocks differentiation toward the erythroid lineage100 and megakaryocytic leukemia contains activating JAK3 mutations (JAK3(Q501H) and (AML M7) JAK3(R657Q))210

MOLM16 Homo sapiens Acute myeloid leukemia Established in 1999 from the Human hypotetraploid karyotype with hypodiploid sideline - 83- CD3 -, CD13 +, CD14 (+), peripheral blood of a 77-year- 88<4n>XXX, -X, -4, -4, -5, +6, +7, -9, -18, +2mar, CD19 -, CD33 +, CD34 +, old Japanese woman at add(5)(q11)x2, add(6)(q11-13), der6)t(6;9(q16;p13), CD71 +, CD235a + relapse of acute myeloid t(6;8)(q15;q24.3), der(8)t(6;8)(q15;q24.3), der(9;18)(p10;q10), leukemia (AML M0) after der(16)del(16)(p13)add(16)(q11), der(16)(del16)(p13)dup(16) failed chemotherapy (q?11q?24)ins(16;?)(q11;??), der(18)t(9;18)(q13;q21), der(19)add(19)(p13)add(19)(q13)x2 - resembles published karyotype

UT-7 Homo sapiens Acute myeloid leukemia Established from the bone Human flat-moded hypotetraploid karyotype - 72-88<4n>XXYY, CD3 -, CD4 -, CD13 +, marrow of a 64-year-old man +1, -8, -9, -9, -9, -11, -11, -12, -13, -13, -14, -18, -21, -22, +6mar, CD14 -, CD15 -, CD19 -, with acute myeloid leukemia del(1)(q42)x2, t(2;4)(p16-23;q27-31)x1-2, der(2)t(2;5)(p16- CD33 +, CD34 -, CD41 (AML M7) at diagnosis 23;q12-14)x1-2, add(5)(q11), del(5)(q13), der(6;13)(p10;q10), (+), CD42 (+), CD68 +, i(7q)x1-2, add(8)(q24)x1-3, del(9)(q22), add(12)(p12-13), CD235a (+), HLA-DR (+) der(13)t(13;13)(p11;q14), add(14)(p11), add(15)(p11), add(16)(p1?), add(17)(p11), add(19)(p13)x2, add(19)(q13), i(21q)x1-2 - extensive subclonal variation present - resembles published karyotype; cells are constitutively cytokine-dependent and responsive to various cytokines Source of information from “DSMZ:Catalogue Human and Animal Cell Lines” website (www.dsmz.de/home.html), otherwise cited.

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Appendix IV: Copyright clearances

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