Characterization of Signal Transduction Abnormalities Revealed Spleen Tyrosine as a Therapeutic Target in High-Risk Precursor B Cell Acute Lymphoblastic Leukemia

by

Tatiana Perova

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Medical Biophysics University of Toronto

© Copyright by Tatiana Perova 2013

Characterization of signal transduction abnormalities revealed spleen as a therapeutic target in high-risk precursor B cell acute lymphoblastic leukemia

Tatiana Perova

Doctor of Philosophy

Department of Medical Biophysics University of Toronto

2013

ABSTRACT Currently, the intensive chemotherapy remains the first line treatment for B cell acute lymphoblastic leukemia (B-ALL). Although these regimens have significantly improved patient outcomes, their use is associated with debilitating morbidities and fatal relapses, highlighting the great need in new agents that target essential survival signals in leukemia. Thus, the overall goal of my project was to gain insights into the signaling abnormalities that regulate aberrant proliferation and survival of B-ALL cells in an effort to identify novel targets in this malignancy.

This study demonstrated that pre-B cell receptor (pre-BCR)-independent spleen tyrosine kinase (SYK) activity was required for the survival and proliferation of a -/-PrkdcSCID/SCID mouse model of B-ALL. I extended this discovery to human disease, demonstrating that SYK was activated in primary B-ALL, independent of the pre-BCR expression. The small molecule

SYK inhibitor fostamatinib (fosta) significantly attenuated proliferation of 79 primary diagnostic

B-ALL samples at clinically achievable concentrations. Importantly, fosta treatment reduced dissemination of engrafting B-ALL cells into the spleen, liver, kidney and central nervous system

(CNS) in a NOD.Prkdcscid/scidIl2rgtm1Wjl/SzJ xenotransplant model of B-ALL. Analysis of signaling abnormalities using a high-throughput phospho-flow cytometry platform demonstrated that pediatric and adult B-ALL samples exhibit variable basal activation of BCR, ii

PI3K/AKT/mTOR, MAPK and JAK/STAT pathways. Importantly, we identified that fosta- mediated inhibition of SYK, PLC 2, CRKL and EIF4E phosphorylation in B-ALL was predictive of its anti-leukemic activity, and was distinct from the cellular actions of other small molecule inhibitors of key nodal signaling pathways. Examination of molecular mechanism of fosta action by expression profiling revealed transcriptional effects of fosta treatment that included, most notably, potent inhibition of pathways involved in lymphocyte activation and inflammation. In conclusion, this study demonstrates that SYK signaling is crucial for B-ALL survival and provides detailed characterization of cellular and molecular mechanisms of fosta action in B-ALL. These data argue in favor of testing small molecule SYK inhibitors in pediatric and adult B-ALL.

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ACKNOWLEDGEMENTS The famous adage says “It takes a village to raise a child”. My personal experience in graduate school fits well with this proverb, since my personal and professional growth took contributions from an inspiring group of people that made my PhD a productive and exciting experience. To my mentors, Jayne Danska and Cynthia Guidos, thank you for giving me a unique opportunity to work on an exciting project that allowed me to explore in depth the world of translational research, and providing me with an exceptional academic environment and inspiring guidance. This experience helped me to build essential skills required for successful research career and fully realize my scientific passion. My committee members Dr. Dwayne Barber, Dr. Jane McGlade and Dr. Ben Neel have been exceptional mentors, providing me with valuable advise and ideas that helped to structure this PhD thesis. I am extremely lucky to have had a privilege to collaborate with Dr. Johann Hitzler and Dr. Mark Minden, who made this project possible by providing access to valuable patient samples. Their perspectives and scientific knowledge were essential for the identification of the clinical contribution of my project. I am extremely humbled to have an opportunity to work with all of these inspiring individuals. I am grateful to share the many years of PhD with a fantastic group of people. To my “bestie” Eniko Papp, I don‟t think it would be possible to endure graduate school without your friendship. Your passion for science, intelligence and constant encouragement have motivated me to persevere, even in the hardest of times. I want to thank Ildiko Grandal, who welcomed and supported me in the lab from the day 1, and provided much-needed help in lengthy experiments. Phil Kousis, Andrea Wong, Sara Suliman and Peggy Wong have all been great friends and colleagues that provided support on a daily basis. I want to thank all the remarkable members of the lab, past and present, for providing a great working environment. Most importantly, I would like to acknowledge the limitless support of my family. My parents have given me an opportunity to pursue my dreams and ambitions, even if they took me thousands of miles away from home. You have given me love, encouragement and support. I am forever in your debt. I am lucky to have an incredible sister and a loving spouse, who both have unwearyingly provided tremendous daily motivation and personal support. I love you both and I am grateful to have you in my life. Thank you all for patiently waiting for me to finally finish graduate school. To your relief, I will not be an eternal student or “вечный студент”, as my dad once called me!

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TABLE OF CONTENTS

ABSTRACT ...... ii ACKNOWLEDGEMENTS ...... iv TABLE OF CONTENTS ...... v LIST OF TABLES ...... viii LIST OF FIGURES ...... ix LIST OF ABBREVIATIONS ...... xii CHAPTER I: INTRODUCTION ...... 1 I. 1 Thesis Overview ...... 2 I. 2 Overview of B cell development ...... 2 I.2.1 Regulatory networks promoting B lineage specification and commitment ...... 3 I.2.2 Assembly of immunoglobulin through V(D)J recombination ...... 3 I.2.3 Expression of pre-BCR and BCR controls B cell differentiation program ...... 4 I.3 Initiation of signaling through pre-BCR and BCR complexes ...... 5 I.3.1 Contribution of Ig and Ig to pre-BCR and BCR signaling ...... 5 I.3.2 Src family are early effectors of pre-BCR and BCR signaling ...... 6 I.3.3 SYK is an obligatory component of pre-BCR and BCR signaling ...... 7 I.4 Complexity of SYK-dependent signaling pathways in B cells ...... 9 I.4.1 Essential role of BLNK and BTK in SYK signaling ...... 9 I.4.2 SYK and PLC 2-mediated activation of NF B ...... 10 I.4.3 SYK and activation of MAPK signaling ...... 11 I.4.4 SYK and activation of survival PI3K/AKT/mTOR pathway ...... 12 I.5 Targeting kinases in hematologic malignancies ...... 14 I.5.1. Protein kinases as therapeutic targets ...... 14 I.5.2 Discovery of imatinib for the treatment of BCR-ABL1 leukemia...... 15 I.5.3 Targeting aberrant BCR signaling in mature B cell malignancies ...... 15 I.6 Use of phospho-flow cytometry to study signaling perturbations ...... 18 I.7 B-ALL: clinical presentation and current treatment options ...... 18 I.7.1 Incidence and clinical presentation of B-ALL ...... 19 I.7.2 Prognostic factors ...... 19 I.7.3 Current treatment options and challenges ...... 20 I.8 Discovery of novel therapeutic targets in B-ALL ...... 21 I.8.1 Beginning of the genomics era in B-ALL ...... 21 I.8.2 Identification of a novel subgroup of high-risk B-ALL ...... 21 1.8.3 Genetic alterations reveal novel therapeutic targets in B-ALL ...... 22 1.8.4 It’s a long way to go: the search for personalized medicine in B-ALL continues...... 22 I.9 Project goals ...... 23 I.10 FIGURES ...... 24 CHAPTER II: Therapeutic potential of spleen tyrosine kinase inhibition for the treatment of high-risk precursor B-cell acute lymphoblastic leukemia ...... 32 II.1 ABSTRACT ...... 33 II.2 INTRODUCTION ...... 33 II.3 METHODS ...... 35 II.3.1 Human B-ALL Samples ...... 35 v

II.3.2 Cell lines...... 35 II.3.3 Kinase Inhibitors ...... 36 II.3.4 Proliferation Assay ...... 36 II.3.5 siRNA Transfection ...... 36 II.3.6 Mice ...... 36 II.3.7 Lymphocyte isolation ...... 37 II.3.8 Lymphoblast Isolation from CNS, Liver and Kidneys ...... 37 II.3.9 Histology ...... 38 II.3.10 Flow Cytometry and adoptive transfer of murine cells ...... 38 II.3.11 Flow cytometric analyses of human samples ...... 39 II.3.12 assay ...... 39 II.3.13 Magnetic beads depletions ...... 39 II.3.14 Phospho-specific Flow Cytometry ...... 39 II.3.15 Fluorescence Compensation ...... 40 II.3.16 B-ALL Xenograft Assays ...... 40 II.3.17 Western Blot Analysis ...... 41 II.3.18 Microarrays ...... 42 II.3.19 Statistical Analysis ...... 42 II.4 RESULTS ...... 43 II.4.1 pre-BCR-independent SYK activation in a p53-/-; Prkdcscid/scid model of early B-ALL ...... 43 II.4.2 Pre-BCR-independent SYK activation in human B-ALL ...... 44 II.4.3 SYK activation promotes B-ALL survival and proliferation ...... 45 II.4.4 Kinase specificity of SYK inhibitor effects ...... 45 II.4.5 Fosta limits B-ALL growth after xenotransplantation ...... 46 II.5 DISCUSSION ...... 49 II.6 TABLES ...... 52 II.7 FIGURES ...... 57 CHAPTER III: Phospho-flow cytometric profiling of SYK-dependent signaling networks in high-risk precursor B-cell acute lymphoblastic leukemia ...... 81 III.1 ABSTRACT ...... 82 III.2 INTRODUCTION ...... 83 III.3 METHODS ...... 86 III.3.1 Patient samples ...... 86 III.3.2 Cell lines ...... 86 III.3.3 Small molecule kinase inhibitors ...... 86 III.3.4 Proliferation assay ...... 87 III.3.5 Antibodies ...... 87 III.3.6 Phospho-flow analysis in cell lines ...... 87 III.3.7 Phospho-flow analysis in B-ALL patient samples ...... 88 III.3.8 Data normalization and visualization ...... 89 III.3.9 Mutation analysis ...... 90 III.3.9 Statistical analysis ...... 90 III.4 RESULTS ...... 90 III.4.1 Optimization and validation of phospho-flow platform for detection of basal signaling 90 III.4.2 Dissection of pre-BCR-independent SYK signaling in high-risk B-ALL ...... 93 III.4.3 Aberrant SYK-independent MAPK signaling networks in B-ALL ...... 95 III.4.4 Role of basally active BCR-related in proliferation of B-ALL ...... 96 III.4.5 AKT-independent inhibition of pS6 and pEIF4E by fosta ...... 97 III.4.6 Aberrant activation of pSTAT5 in high-risk BCR-ABL- B-ALL ...... 99 III.4.7 B-ALL patients organized by similarities in basal phosphorylation signature display similar clinical characteristics ...... 101

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III.5 DISCUSSION ...... 102 III.6 TABLES ...... 107 III.7 FIGURES ...... 111 CHAPTER IV: Identification of transcriptional effects of fostamatinib in high-risk precursor B-cell acute lymphoblastic leukemia ...... 139 IV.I ABSTRACT ...... 140 IV.II INTRODUCTION ...... 140 IV.3 METHODS ...... 142 IV.3.1 Patient samples ...... 142 IV.3.2 Reagents ...... 142 IV.3.3 Proliferation Assay ...... 142 IV.3.4 Flow Cytometry ...... 143 IV.3.5 Apoptosis Assay ...... 143 IV.3.6 Treatment and RNA extraction ...... 143 IV.3.7 Microarray experiments ...... 144 IV.3.8 Statistical analyses of microarray data ...... 144 IV.3.9 Gene set enrichment analyses of expression data ...... 145 IV.3.10 Validation by quantitative real-time PCR (qRT-PCR) ...... 145 IV.4 RESULTS ...... 146 IV.4.1 Optimization of treatment conditions to profile expression signature of fosta effects . 146 IV.4.2 Analysis of fosta effects in high-risk adult and pediatric B-ALL ...... 148 IV.4.3 Distinct inhibition signatures of fosta and dexamethasone ...... 150 IV.5 DISCUSSION ...... 151 IV.6 TABLES ...... 155 IV.7 FIGURES ...... 174 Chapter V: CONCLUSIONS AND FUTURE DIRECTIONS ...... 197 V.1 Thesis overview ...... 198 V.2 Clinical relevance of this study ...... 199 V.3 Phospho-flow: a move towards personalized medicine ...... 200 V.4 Cellular effects of fosta in B-ALL: SYK inhibition or off-target effects? ...... 201 V.5 Identification of transcriptional consequences of fosta treatment in B-ALL ...... 202 V.6 Mechanisms of pre-BCR-independent SYK signaling in B-ALL ...... 203 V.7 Targeting of leukemia initiating cells (LICs) by fosta ...... 204 V.8 Signal transduction therapies in B-ALL: the road ahead ...... 205 CHAPTER VI: REFERENCES ...... 207

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LIST OF TABLES Table II.1 Summary of B-ALL patient samples ...... 52 Table II.2 List of antibodies used for compensation in flow cytometry experiments ...... 53 Table II.3 Clinical characteristics of B-ALL patient samples used for in vivo experiments with fosta ...... 54 Table II.4 Summary of regimen 1 effects on the weight organs in a xenotransplant model of human B-ALL ...... 55 Table II.5 Summary of regimen 2 effects on the weight of organs in a xenotransplant model of early human B-ALL ...... 56 Table III.1 List of small molecule inhibitors used in phospho-flow profiling of early B-ALL samples ...... 107 Table III.2 List of phospho-specific and intracellular antibodies for profiling of early B-ALL samples ...... 108 Table III.3 List of antibodies used for compensation in flow cytometry experiments ...... 109 Table III.4 Clinical characteristics of B-ALL patient samples ...... 110 Table IV.1 Sequences of primers used for quantitative real-time PCR ...... 155 Table IV.2 Plasmids used to prepared standard curved for quantitative real-time PCR ...... 156 Table IV.3 A list of top 60 differentially expressed gene at 4 h fosta treatment ...... 157 Table IV.4 A list of top 60 differentially expressed genes at 8 h fosta treatment ...... 160 Table IV.5 Top 60 gene sets enriched vehicle-treated pediatric B-ALL samples ...... 163 Table IV.6 Top 60 gene sets enriched vehicle- versus fosta-treated adult B-ALL ...... 165 Table IV.7 Top 60 gene sets enriched vehicle- versus DEX-treated adult B-ALL samples ..... 167 Table IV.8 A list of unique fosta-sensitive genes in adult B-ALL ...... 169 Table IV.9 A list of unique DEX-sensitive genes in adult B-ALL ...... 171

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LIST OF FIGURES Figure I.1 Simplified overview of transcriptional and signaling networks that control generation of B-cell progenitors ...... 24 Figure I.2 Critical checkpoints in early B cell development ...... 26 Figure I.3 Schematic representation of SRC and SYK protein structure ...... 28 Figure I.4 SYK is a central component of pre-BCR and BCR-mediated signaling ...... 29 Figure I.5 BLNK acts as an adaptor protein linking SYK to downstream signaling molecules . 30 Figure I.6 Composite overview of signal transduction cascades activated by pre-BCR/BCR signaling ...... 31 Figure II.1 DM leukemias display a pre-BCR-independent pro-B to pre-B cell transition ...... 57 Figure II.2 pre-BCR-independent SYK activity drives proliferation and survival of DM B-ALL cells ...... 59 Figure II.3 Analysis of expression on BCR signaling components in human early B-ALL ...... 61 Figure II.4 Phospho-flow analysis of SYK-dependent signaling in B-ALL ...... 62 Figure II.5 pre-BCR-independent SYK activity in human early B-ALL ...... 63 Figure II.6 SYK activity is necessary for proliferation and survival of human early B-ALL ..... 64 Figure II.7 Anti-proliferative effects on SYK inhibitors in B-ALL ...... 66 Figure II.8 SYK inhibitors‟ effects on B-ALL proliferation are SYK-specific and are not due to off-target effects on FLT3 or SRC ...... 67 Figure II.9 SYK knockdown reduces proliferation of early B-ALL cell lines ...... 69 Figure II.10 In vivo inhibition of SYK activity shows therapeutic potential in a xenotransplant model of early B-ALL ...... 70 Figure II.11 Fostamatinib reduces tumor burden in a xenograft model of early B-ALL ...... 72 Figure II.12 Therapeutic potential of inhibition of SYK activity in a well-established xenograft model ...... 74 Figure II.13 Tumor burden in NSG mice at the start of fosta regimen 2 ...... 76 Figure II.14 Fostamatinib reduces burden of an established leukemia...... 77 Figure II.15 Fostamatinib reduced leukemia-initiating cells in CNS and spleen ...... 79 Figure III.1 High-throughput phospho-flow protocol for analysis of signaling networks in B- ALL ...... 111 Figure III.2 Optimization of phospho-flow antibodies to detect basal signaling responses ..... 113

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Figure III.3 Validation of high-throughput phospho-flow protocol to detect basal signaling responses in B-ALL ...... 115 Figure III.4 Profiling of BCR signaling responses in B-ALL ...... 117 Figure III.5 Analysis of BCR signaling in B-ALL ...... 119 Figure III.6 Distinct inhibition profiles of fosta and PD184352 ...... 121 Figure III.7 Analysis of potentiated ERK signaling in early B-ALL ...... 123 Figure III.8 Comparison of inhibition profiles of fosta and DAS in B-ALL ...... 125 Figure III.9 Profiling of basal PI3K/AKT/mTOR signaling in B-ALL ...... 127 Figure III.10 Differential in vitro sensitivity to fosta and LY ...... 129 Figure III.11 Validation of phospho-flow platform to detect basal pSTAT3 and pSTAT5 signaling responses ...... 131 Figure III.12 Identification of aberrant pSTAT5 responses in B-ALL ...... 132 Figure III.13 Distinct inhibition profiles of fosta and SAR ...... 134 Figure III.14 Differential inhibition of BCR signaling by fosta and SAR ...... 136 Figure III.15 Phospho-flow profiling of B-ALL reveals five groups with distinct phosphorylation signatures that correlate with clinical outcomes ...... 137 Figure IV.1 Protocol for optimization of treatment conditions to detect fosta-induced changes in gene expression ...... 174 Figure IV.2 Comparison of gene expression genes following fosta treatment at 4h and 8h time- points ...... 176 Figure IV.3 Gene set enrichment analysis (GSEA) of fosta effects following 4 h and 8 h treatment ...... 178 Figure IV.4 Zoom in of the lymphocyte activation and innate immune response clusters in the fosta treatment enrichment map ...... 180 Figure IV.5 Validation of microarray results using quantitative real-time PCR ...... 181 Figure IV.6 Analysis of fosta effects in pediatric B-ALL ...... 183 Figure IV.7 Representative GSEA plots showing enrichment score for vehicle-enriched gene sets after 6h fosta treatment in pediatric B-ALL ...... 185 Figure IV.8 Representative examples of gene sets enriched in fosta-group ...... 186 Figure IV.9 Analysis of fosta effects in high-risk adult B-ALL ...... 187 Figure IV.10 Enrichment map for fosta treatment in pediatric and adult B-ALL ...... 189

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Figure IV.11 Heatmap of fosta genes differentially regulated by fosta and DEX in adult B-ALL ...... 191 Figure IV.12 Analysis of DEX effects in high-risk adult B-ALL ...... 192 Figure IV.13 Enrichment map for fosta and DEX treatment in adult B-ALL ...... 194 Figure IV.14 Anti-proliferative effects of combination treatment with fosta and DEX ...... 196 Figure V.1 Platform for pre-clinical drug development in B-ALL ...... 206

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LIST OF ABBREVIATIONS

ABC DLBCL Activated B-cell diffuse large B-cell lymphoma ALL Acute lymphoblastic leukemia AML Acute myeloid leukemia ANOVA Analysis of variance ATP Adenosine-5‟-triphosphate B-ALL B cell acute lymphoblastic leukemia BAY BAY613606, SYK inhibitor BCL-10 B-cell lymphoma 10 BCR B cell receptor BCR-ABL1- BCR-ABL1-negative leukemia BCR-ABL1+ BCR-ABL1-positive leukemia BLNK B cell linker BM Non-injected bones BTK Bruton‟s tyrosine kinase CARMA1 Caspase recruitment domain, CARD, membrane-associated , MAGUK, protein 1 CBL Casitas B-lineage lymphoma CLL Chronic lymphocytic leukemia CLP Common lymphoid progenitors CML Chronic myeloid leukemia CMP Common myeloid progenitors CNS Central nervous system CRLF2 Receptor Like Factor 2 DAG Diacylglycerol DAS Dasatinib, SFK/BCR-ABL inhibitor DEX Dexamethasone DM Double mutant p53-/-; Prkdcscid/scid mouse DMSO Dimethyl sulfoxide EIF4E Eukaryotic transcription-initiation factor 4E ER Endoplasmic reticulum ERK Extracellular signaling-related kinase FC Fold-change FDR False discovery rate FLT3 FMS-like tyrosine kinase receptor-3 FMO Fluorescence minus one Fosta Fostamatinib, SYK inhibitor GDP Guanosine diphosphate GSEA Gene set enrichment analysis GTP Guanosine triphosphate HD Hyperdiploid HR High risk HSC Hematopoietic stem cells Ig Immunoglobulin IgH or Ig Immunoglobulin heavy chain IgL Immunoglobulin light chain xii

IL7 Interleukin-7 IP3 Inositol-1,4,5-triphosphate ITAM Immunoreceptor tyrosine-based activation motif JAK Janus kinase LIC Leukemia-initiating cell LY LY294002, PI3K inhibitor MALT1 Mucosa-associated lymphoid tissue lymphoma translocation protein 1 MAPK Mitogen activated MEK Mitogen-activated protein kinase kinase MFI Median fluorescence intensity MLL Mixed lineage leukemia MNK Mitogen activated protein kinase-interacting kinase MPP Multipotent progenitors mTOR Mammalian target of rapamycin NF B Nuclear factor- B NHEJ Non-homologous end-joining NSG NOD.Prkdcscid/scidIl2rgtm1Wjl/SzJ mouse PD PD184352, MEK1/2 inhibitor PI3K Phosphoinositide-3 kinase PKC Protein kinase C PLC 2 Phospholipase C- 2 PMA Phorbol 12-myristate 13-acetate Pre-BCR Pre-B cell receptor PTK Protein tyrosine kinase PTP Protein tyrosine phosphatase PTPROt Protein tyrosine phosphatase receptor-type O truncated qRT-PCR Quantitative real-time chain reaction RAG Recombinase activating gene RF Injected right femur RSK Ribosomal S6 kinase family S Serine S6 Ribosomal subunit S6 SAR SAR302502, JAK2 inhibitor SC Signaling cluster SCID Severe combined immunodeficiency SEM Standard error of the mean SFK Src family of kinases SFK Src family of kinases SFM Phenol red-free, serum-free media SH2 Src homology domain 2 SHP1 SH2 domain-containing phosphatase 1 SLC Surrogate light chain SOS Son of sevenless homologue SPL Spleen SR Standard risk STAT Signal transducer and activator of transcription SYK Spleen tyrosine kinase TKI Tyrosine kinase inhibitor

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V(D)J Variable, Diversity, Joining v/v Volume/volume VEH Vehicle Y Tyrosine

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CHAPTER I: INTRODUCTION

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I. 1 Thesis Overview Over the past 10 years, development of novel therapeutic agents for cancer treatment, driven by the identification of molecular mechanisms involved in leukemogenesis, has come to the forefront of biomedical cancer research. These efforts were motivated by the success of imatinib, the first tyrosine kinase inhibitor used to successfully treat chronic myelogenous leukemia. It is, therefore, reasonable to hypothesize that identification of signal transduction abnormalities in leukemia will facilitate the discovery of rational therapeutic targets. Thus, my thesis focused on defining signal transduction abnormalities in B cell acute lymphoblastic leukemia (B-ALL). The goal of introductory chapter (Chapter I) is to describe relevant background of normal B cell development followed by an overview of B-ALL pathophysiology in an effort to provide rationale for the studies presented in this thesis. Chapter II describes identification and pre-clinical evaluation of spleen tyrosine kinase (SYK) as a potential therapeutic target in B- ALL. Chapter III focuses on elucidation of signal transduction abnormalities in B-ALL using phospho-flow cytometry. Finally, chapter IV describes the use of gene expression analysis to define molecular consequences of SYK inhibition by fostamatinib in B-ALL. Collectively, this thesis provides compelling evidence for the therapeutic efficacy of SYK inhibitors in B-ALL.

I. 2 Overview of B cell development B lymphocytes comprise a vital component of the adaptive immune system, which is largely attributed to their unique ability to produce pathogen-specific antibodies, first demonstrated in late 1960s (Coombs et al., 1969). B cell generation begins in fetal liver and omentum of the developing human embryo (Gathings et al., 1977; Solvason and Kearney, 1992). It is later taken over by the bone marrow, with final maturation occurring in peripheral lymphoid organs. Development of B cells continues throughout life and is a multi-step maturation process. It is tightly regulated by critical developmental checkpoints and specific signaling mechanisms, which act in concert to ensure successful proliferation and differentiation of developing B cell progenitors.

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I.2.1 Regulatory networks promoting B lineage specification and commitment B cell development involves differentiation of multipotent hematopoietic stem cells (HSC) to more restricted lymphoid progenitors through a series of intermediate stages (Kondo et al., 2003) (Figure I.1a). Studies in mice have been pivotal in defining distinct stages of B cell differentiation. Early in hematopoiesis, HSC produce multipotent progenitors (MPP) that give rise to common myeloid progenitors (CMP) or common lymphoid progenitors (CLP) with the latter developing into committed B cell progenitors (Akashi et al., 2000; Kondo et al., 1997). During this process, lineage specification is tightly controlled by complex regulatory networks and involves activation and silencing of transcription factors including Ikaros, PU.1, E2A, EBF and Pax5 (Figure I.1). Ikaros and PU.1 are necessary for the development of multiple hematopoietic lineages, whereas E2A, EBF and Pax5 regulate differentiation of CLP into committed B-lineage cells (Busslinger, 2004). The essential role of these transcription factors in B cell development has been demonstrated through the use of knockout mouse models (Bain et al., 1997; Kirstetter et al., 2002; Lin and Grosschedl, 1995; Scott et al., 1994; Urbanek et al., 1994) (Figure I.1b). Transcriptional regulators modulate B cell fate by acting in concert with cytokine signaling. Two , including FMS-like tyrosine kinase receptor-3 (FLT3) ligand and interleukin-7 (IL7), are secreted by the bone marrow microenvironment and provide essential cues for B cell development (Kang and Der, 2004). The importance of these signaling pathways in B cell development is illustrated by the complete absence of B cells in mice lacking Flt3 and Il7 receptor (Sitnicka et al., 2003; Vosshenrich et al., 2003). Importantly, IL7 signaling alone was essential and sufficient for differentiation of CLP into early pro-B cells (Miller et al., 2002), highlighting the critical function of this pathway in B lymphopoiesis. The above observations highlight that B cell fate choices are thus tightly regulated by complex regulatory networks, which include combinatorial activities of transcription factors and cytokine-mediated signaling (Figure I.1c). Disruptions in any components of these regulatory circuits result in abnormal or inhibited B cell development.

I.2.2 Assembly of immunoglobulin genes through V(D)J recombination Successful B cell differentiation is governed by highly ordered and strictly regulated assembly of immunoglobulin (Ig) variable (V), diversity (D) and joining (J) gene segments, known as V(D)J recombination (Schatz and Ji, 2011). This somatic rearrangement is of fundamental importance to efficient adaptive immune responses that rely on the generation of a diverse

3 repertoire of antibodies by B cells. Assembly of Ig heavy (IgH) and Ig light (IgL) gene segments displays developmental stage specificity and begins at the pro-B cell stage with the rearrangement of the IgH locus, followed by recombination of IgL gene segments at the pre-B cell stage (Figure I.2a) (Schatz and Ji, 2011). V(D)J recombination is initiated by lymphocyte-specific recombinase activating genes 1 and 2 (RAG1/2) that recognize and cleave DNA at recombination signal sequences (RSS), flanking each V, D and J gene segments (Bassing et al., 2002). Cleaved DNA ends are subsequently repaired by proteins of non-homologous end joining (NHEJ) machinery (Malu et al., 2012). Importantly, mutations affecting V(D)J recombination machinery lead to impaired lymphocyte development, characteristic of severe combined immunodeficiency (SCID) in mice (Blunt et al., 1995; Chang et al., 1993; Mombaerts et al., 1992). In humans, SCID is diagnosed in 1 of 75,000 births and is associated with poor prognosis with patients requiring bone marrow transplant for survival (Fischer et al., 2005). Importantly, 20% of SCID patients have a deficiency in V(D)J machinery, including mutations in RAG genes (Schwarz et al., 1996; Villa et al., 2001) and NHEJ proteins (Malu et al., 2012). Collectively, these studies suggest that fidelity of V(D)J recombination is essential for the maintenance of normal B cell development in mice and humans.

I.2.3 Expression of pre-BCR and BCR controls B cell differentiation program As mentioned above, B cell differentiation is characterized by the stepwise recombination of the IgH and IgL gene segments. Productive in-frame V(D)J recombination of IgH locus, initiated at the pro-B cell stage, results in Ig chain expression that assembles with the surrogate light chains (SLC) 5 and VpreB to form a tetrameric pre-B cell receptor (pre-BCR) (Nishimoto et al., 1991) (Figure I.2a,b). The expression of the pre-BCR marks the first critical checkpoint in B- cell development and differentiation into pre-B cell stage. Indeed, the pre-BCR transduces signals necessary to initiate survival and clonal expansion of large pre-B cells, allelic exclusion at the IgH locus, downregulation of SLC genes and subsequent initiation of recombination at the IgL chain locus (Herzog et al., 2009). Productive IgL gene rearrangement leads to the expression of a mature BCR, marking the second developmental checkpoint and differentiation to immature B-cells (Figure I.2a) that migrate from the bone marrow to the spleen for further maturation. Importantly, mice that lack 5, VpreB or carry deletion of the transmembrane region of Ig exhibit block at the pro-B cell stage (Kitamura et al., 1992; Kitamura et al., 1991; Mundt

4 et al., 2001; Papavasiliou et al., 1996). Furthermore, defects in Ig and 5 are found in 10% of patients with congenital agammaglobulinemia, characterized by failure of B cell development (Lopez Granados et al., 2002; Minegishi et al., 1998; Yel et al., 1996). Collectively, this evidence emphasizes that formation and expression of pre-BCR and BCR forms a foundation for the proper B cell development in mice and humans.

I.3 Initiation of signaling through pre-BCR and BCR complexes Successful differentiation, survival and maturation of developing B cells are dependent on the initiation and propagation of signal transduction by pre-BCR and BCR complexes. Two mechanisms for signal initiation have been proposed, including antigen-dependent (Dal Porto et al., 2004) and antigen-independent (tonic) signaling (Bannish et al., 2001; Rolink et al., 2000). Most studies have mainly focused on examining signaling initiation and propagation by BCR; however, existing evidence suggests that pre-BCR signals are similar to those initiated by BCR (Benschop and Cambier, 1999; Guo et al., 2000; Meffre et al., 2000). Antibody responses are initiated by antigen-dependent activation of B cells following BCR engagement. In addition to antigen-driven signaling, BCR in resting cells provide provides tonic signals essential for B cell survival (Monroe, 2006; Pierce and Liu, 2010). In contrast, the pre-BCR is unable to bind conventional antigen and mechanisms triggering the initiation of pre- BCR activation are not clearly defined. In this regard, several groups have suggested the existence of pre-BCR stroma-derived ligands, such heparan sulfates in mice (Bradl et al., 2003) and galectins in mice and humans (Espeli et al., 2009; Gauthier et al., 2002). In contrast, other studies suggest that autonomous co-aggregation of pre-BCR complexes on the cell surface is sufficient to initiate signaling (Bradl et al., 2007). Taken together, these studies support the existence of tonic and ligand-dependent pre-BCR and BCR signaling in B cells.

I.3.1 Contribution of Ig and Ig to pre-BCR and BCR signaling The pre-BCR and BCR are hetero-oligomeric receptors that form complexes with non- covalently-associated Ig (CD79a) and Ig (CD79b) heterodimer (Figure I.2b). This assembly is required for the export of pre-BCR/BCR complexes from Golgi apparatus to the cell surface. Initial observations of Ig and Ig phosphorylation upon BCR engagement suggested an important role for these signaling molecules in BCR signaling (Flaswinkel and Reth, 1994).

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More recent studies reported that Ig and Ig were necessary and sufficient to elicit tonic pre- BCR/BCR signaling (Fuentes-Panana et al., 2006; Fuentes-Panana et al., 2004; Kraus et al., 2004). Ig and Ig are transmembrane signaling proteins that contain immunoreceptor tyrosine- based activation motifs (ITAMs, D/Ex0-2YxxL/I x6-8YxxL/I) in their cytoplasmic tails (Cambier, 1995; Reth, 1989) (Figure I.2b). Upon phosphorylation of tyrosine residues, ITAMs serve as docking sites for proteins containing Src homology 2 (SH2) domains that propagate signaling by activating downstream pathways. Given the important role of ITAM-mediated signal transduction, it is not surprising that Ig and Ig are absolutely essential for proper B cell development. In this regard, in vivo ablation of Ig or Ig resulted in a complete block in differentiation at the pro-B cell stage (Gong and Nussenzweig, 1996; Pelanda et al., 2002). Furthermore, mutations in Ig and Ig have been identified in patients with agammaglobulinemia that exhibit a complete block in B cell development at the pro-B cell stage (Ferrari et al., 2007; Minegishi et al., 1999a; Wang et al., 2002), further emphasizing the functional importance of Ig / Ig signaling elements in B cell development. Ig and Ig do not posses catalytic activity and, therefore, act in concert with other signaling modules for signal propagation. Extensive characterization of pre-BCR and BCR signaling have identified SRC and SYK/ZAP70 families of cytoplasmic tyrosine kinases that play a pivotal role in conveying downstream responses from these receptors (Hsueh and Scheuermann, 2000).

I.3.2 Src family kinases are early effectors of pre-BCR and BCR signaling A proposed model for antigen receptor signaling involves phosphorylation of ITAMs on Ig and Ig by the Src family of tyrosine kinases (SFKs) (Weiss and Littman, 1994). SFKs consist of nine members that share significant structural homology and are composed of one SH4 domain, a unique N-terminal region, followed by one of each SH3, SH2 and kinase domains (Lowell, 2004) (Figure I.3a). SFK activity is tightly regulated by phosphorylation of two key regulatory tyrosines: the active form is characterized by phosphorylation of a tyrosine within the kinase domain, wheras phosphorylation at a C-terminus keeps the kinase in an inactive conformation (Xu et al., 1999). SFKs are found proximal to the plasma membrane, which is attributed to N-terminal acylation (Resh, 1999) and, through their catalytic activity, regulate vital cellular functions, such as survival, metabolism, proliferation, differentiation and migration (Ingley, 2008). Importantly, SFKs play an important role in transducing pre-BCR/BCR signals.

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Regulation of SFK activity: As mentioned earlier, the catalytic activity of SFKs is regulated by tyrosine phosphorylation. Indeed, CD45 and CD148 transmembrane protein tyrosine phosphatases (PTPs) play an important role in B cell development (Zhu et al., 2008) and enhance BCR signaling by preferentially dephosphorylating the C-terminus inhibitory tyrosine on SFKs (Yanagi et al., 1996). Actions of CD45 and CD148 are opposed by c-SRC tyrosine kinase (CSK)-mediated phosphorylation of the inhibitory tyrosine residue (Veillette et al., 2002). SFKs in pre-BCR/BCR signaling: B cells express several SFKs including LYN, BLK, FYN, HCK, FGR and LCK (Lowell, 2004). Evidence from early biochemical studies suggests that LYN, BLK and FYN are most relevant in B cell development and antigen receptor signaling (Burkhardt et al., 1991; Clark et al., 1992; Kurosaki et al., 1994; Takata and Kurosaki, 1995; Yamanashi et al., 1991). The critical role of these SFKs in B cell development was demonstrated in Lyn, Blk and Fyn triple-knockout mice that were characterized by significant loss of pre-B cells due to an increase in apoptosis (Saijo et al., 2003). Unexpectedly, no defect in phosphorylation of Ig and Ig was observed in these mice, suggesting an SFK-independent BCR signaling that relied on spleen tyrosine kinase (SYK), another critical component of B cell signaling. These observations were consistent with other reports describing SFK-independent SYK-mediated ITAM phosphorylation (Rolli et al., 2002; Takata et al., 1994), thereby bringing into question the role of SFKs in initiation of pre-BCR/BCR signaling. A recent publication by Mukherjee et al. sought to resolve the contribution of SFKs to B cell signaling and demonstrated that a delayed SFK-independent initiation of BCR signaling requires receptor multimerization, thereby highlighting a critical role of SFKs in enhancing speed and sensitivity of BCR responses in the absence of receptor clustering (Mukherjee et al., 2013). Collectively, these data show that, although SFK function may not be necessary to initiate BCR signaling, their efficient phosphorylation of ITAMs augments BCR activation. These studies also highlight an indispensable role of SYK in pre-BCR/BCR signaling.

I.3.3 SYK is an obligatory component of pre-BCR and BCR signaling Strong evidence supports a fundamental role of SYK in immunoreceptor signaling (Mocsai et al., 2010). SYK belongs to a SYK/ZAP70 family of cytoplasmic non-receptor protein tyrosine kinases and is highly expressed in hematopoietic, as well as non-immune cells (Coopman and Mueller, 2006; Mocsai et al., 2010). Extensive literature reviews SYK activation downstream of various ITAM and hemITAM (single YxxL) receptors (Kerrigan and Brown, 2011; Lowell, 7

2011; Mocsai et al., 2010). For the purpose of this thesis, I will focus on role of the SYK in pre- BCR/BCR signaling. Initial studies identified SYK in association with BCR, thereby providing the first clues into the function of this kinase in B cells (Hutchcroft et al., 1991; Hutchcroft et al., 1992; Law et al., 1994). Important evidence for the role of SYK in B cell development came from studies demonstrating a block at the pro-B cell stage in mice lacking syk (Cheng et al., 1995; Turner et al., 1995). These studies prompted further investigations into the role of SYK in B cell signaling. Activation: The structure of SYK includes two tandem SH2 domains and a C-terminal kinase domain separated by linker regions, called interdomain A (inter-SH2 linker) and interdomain B (SH2-kinase domain linker) (Figure I.3b). Recent studies solved the first crystal structure of SYK (Gradler et al., 2013) and identified a total of 32 phosphorylation sites, including 15 tyrosine (Y), 11 serine (S) and 6 threonine (T) residues (Bohnenberger et al., 2011), providing insights into molecular regulation of SYK activity. In this regard, phosphorylation of several key residues is essential for SYK activation and propagation of signaling. In its inactive form, SYK is maintained in an autoinhibitory conformation formed by intermolecular interactions of the two SH2 and the catalytic domains (Arias-Palomo et al., 2009) (Figure I.4). Upon receptor activation, phosphorylated ITAMs recruit the SH2 domains of SYK resulting in conformational rearrangements to an active form accessible for autophosphorylation. Robust phosphorylation at Y348/Y352 in interdomain B and Y525/Y526 in the kinase domain of SYK are the earliest events upon BCR ligation (Bohnenberger et al., 2011; Hong et al., 2002; Kurosaki et al., 1995), resulting in SYK activation and autophosphorylation. Furthermore, SYK is capable of phosphorylating neighboring ITAMs leading to recruitment of more SYK proteins, thus creating a positive feedback loop, which results in prolonged activation of signaling (Mukherjee et al., 2013; Rolli et al., 2002). Negative regulators of SYK activity: Several negative regulators of SYK activity that control intensity and duration of BCR signaling have been described (Mocsai et al., 2010). Indeed, a B-cell specific cytoplasmic PTP receptor-type O truncated (PTPROt) (Aguiar et al., 1999) diminishes BCR signaling through inhibition of SYK phosphorylation (Chen et al., 2006). In addition, SYK phosphorylation at Y317 provides docking sites for an E3 CBL, which attenuates BCR signaling through SYK ubiquitination and degradation (Lupher et al., 1998; Rao et al., 2001; Yankee et al., 1999). Finally, SH2 domain-containing phosphatase 1 (SHP1) is activated through binding to immunoreceptor-based inhibitory motifs (ITIMs),

8 including CD22, CD72 and Fc RIIB receptors on B cells (Siminovitch and Neel, 1998), and acts as a negative regulator of BCR signaling, at least in part, by dephosphorylating SYK (Adachi et al., 2001; Dustin et al., 1999; Maeda et al., 1999). Collectively, these data highlight that tightly regulated mechanisms control SYK activity and, therefore, the strength and duration of pre- BCR/BCR-driven signaling in B cells. Maintaining the balance between positive and negative regulators is essential for proper B cell development and B cell function.

I.4 Complexity of SYK-dependent signaling pathways in B cells As described above, SYK is absolutely essential in transducing signaling downstream of pre- BCR and BCR. Activated SYK drives pathways dependent on B cell linker (BLNK) adaptor protein, Bruton‟s tyrosine kinase (BTK), phosphoinositide-3 kinase (PI3K) and RAS-mitogen activated protein kinase (MAPK)/extracellular signaling-related kinase (ERK) to orchestrate multiple crucial B cell responses including proliferation, survival and differentiation. Although SYK-activated pathways will be described individually, it is important to emphasize that complex networks and cross-talk mechanisms exist that link these pathways together.

I.4.1 Essential role of BLNK and BTK in SYK signaling Pre-BCR/BCR signaling leads to formation of multiprotein signalosomes through specific protein-protein interactions and catalytic activities facilitated by adapter proteins and cytoplasmic kinases (Dal Porto et al., 2004). BLNK is one of the major players within this signaling complex, given its function as an SH2-containg adapter protein that couples SYK with downstream signaling mediators including phospholipase C- 2 (PLC 2), VAV, BTK, growth factor receptor-bound protein 2 (GRB2) and NCK adapter protein (Chiu et al., 2002; Koretzky et al., 2006) (Figure I.5). While blnk-/- mice display an arrest in B cell development at the pro-B cell stage (Jumaa et al., 1999; Pappu et al., 1999), BLNK deficiency in humans results in the lack of pre-B and mature B cells (Minegishi et al., 1999b) and impairs downstream signaling activation (Taguchi et al., 2004), which highlights critical role of BLNK in B cell development. Phosphorylated BLNK recruits and mediates SYK-dependent phosphorylation of BTK (Baba et al., 2001), a member of the TEC family of non-receptor tyrosine kinases that has an indispensable role in B cell development. Indeed, mutations in BTK result in X-linked agammaglobulinemia (XLA) in humans, characterized by a block at the pro-B cell stage

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(Conley et al., 2005). Furthermore, mutations or deletion of btk in mice lead to impaired maturation of B cells and deficiency in BCR signaling (Conley et al., 2000; Ellmeier et al., 2000). BLNK phosphorylation at Y84, Y178 and Y189 recruits PLC 2 (Chiu et al., 2002) and facilitates its activation by SYK and BTK (Figure I.5). In turn, PLC 2 generates second 2+ messengers diacylglycerol (DAG) and inositol-1,4,5-triphosphate (IP3), required for Ca flux and regulation of protein kinase C-dependent (PKC) signaling (Kurosaki et al., 2000; Scharenberg et al., 2007). PKC activation and Ca2+ release regulate activation of mitogen- activated protein kinases (MAPKs) and nuclear factor- B (NF B) and nuclear factor of activated T cells (NFAT) transcription factors (Figure I.5). Collectively, the sequential SYK- dependent phosphorylation of BLNK, BTK and PLC 2 is essential for proper organization of signaling complexes to propagate pre-BCR/BCR-dependent signaling that regulates B-cell fate decisions.

I.4.2 SYK and PLC 2-mediated activation of NF B Activation of the NF B family of transcription factors in B cells depends on PLC 2 (Petro and Khan, 2001) and controls expression of genes essential for growth, proliferation and survival (Figure I.6). The NF B family is composed of NF B1 (p50), NF B2 (p52), RELA (p65), REL (cREL) and RELB that form homo- or heterodimers (Hayden and Ghosh, 2008). Importantly, deficiency in NF B leads to block in B-cell development and maturation (Vallabhapurapu and Karin, 2009). In resting cells, NF B is found in the cytoplasm in association with inhibitors of NF B (I B). In a canonical activation pathway, pre-BCR/BCR engagement leads to I B phosphorylation by I B kinases (IKK) and subsequent degradation, which is followed by release and nuclear translocation of p50-RELA heterodimer to regulate gene transcription (Hayden and Ghosh, 2008). Activity of IKK is regulated by CARMA1/BCL-10/MALT1 signaling complex, which is absolutely essential for BCR-mediated NF B activation (Thome, 2004). Although non-canonical activation of NF B is pre-BCR/BCR-independent, it is, nonetheless, important for B cell survival and maturation, and involves tumor-necrosis factor (TNF) family of receptors-mediated activation of p50-p52 heterodimer (Vallabhapurapu and Karin, 2009).

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I.4.3 SYK and activation of MAPK signaling MAPKs ERK1/2, Jun N-terminal kinases (JNKs) and p38 are signaling proteins that convert extracellular stimuli into a variety of cellular responses by activating signal transduction pathways (Wagner and Nebreda, 2009). Their primary function is to regulate gene expression through phosphorylation and activation of the transcription factors Elk-1 and c- by ERK, c- jun and ATF-2 by JNK, and ATF-2 and MAX by p38 (Dal Porto et al., 2004). In B cells, MAPKs are activated by BCR signaling. However, pathways leading to activation of ERK appear to be distinct from those promoting JNK and p38 signaling (Hashimoto et al., 1998). Activation of ERK and its targets: ERK1 and ERK2 are serine/threonine kinases that regulate survival, proliferation and differentiation of hematopoietic progenitors (Chan et al., 2013). In B cells, ERK is activated in a SYK-dependent manner following pre-BCR/BCR engagement. Functional significance of ERK in B cell development was illustrated by the impaired pro- to pre-B cell transition in erk1/2-deficient mice (Yasuda et al., 2008). Furthermore, Rowland et al. demonstrated that ERK transduces tonic BCR signaling to promote differentiation of immature B cells (Rowland et al., 2010). In B cells, ERK is regulated by membrane-bound RAS guanosine triphosphatase (GTPase) in a SYK-dependent manner (Chiu et al., 2002; Jiang et al., 1998). Following BCR engagement, GDP-bound RAS is converted to the active GTP-bound form through the action of SOS RAS guanyl nucleotide exchange factors (RasGEF), which are found in a complex with adapter protein GRB2 (Kolch, 2000). Recruitment of SOS-GRB2 to the plasma membrane, likely through GRB2 binding to phosphorylated BLNK (Fu et al., 1998), promotes SOS activation of RAS. Binding of RAS by BLNK has recently been implicated as the mechanism of BCR-induced ERK activation (Imamura et al., 2009). In addition to SOS, RasGRP3 is another RasGEF that regulates RAS activity in a PLC 2-dependent manner (Oh-hora et al., 2003; Teixeira et al., 2003). Finally, SYK-independent mechanisms of RAS activation have also been described (Yokozeki et al., 2003), highlighting distinct modes of RAS-dependent ERK activation in B-cells. Active RAS propagates signaling through RAF/MEK1/2/ERK1/2 kinase cascade (Figure I.6). ERK phosphorylates 90-kDa ribosomal S6 kinase family (RSK), which, in turn, regulates transcription, , proliferation and migration (Romeo et al., 2012). Activated ERK also phosphorylates effectors MAPK-interacting kinases 1 and 2 (MNK1/2), which, in turn, phosphorylate eukaryotic transcription-initiation factor EIF4E on S209, the only phosphorylation site on this protein (Joshi et al., 1995; Waskiewicz et al., 1999). EIF4E forms 11

EIF4F initiation factor complex with the scaffold protein EIF4G and RNA helicase EIF4A, and facilitates translation by binding to the 5‟ cap of mRNA (Gingras et al., 1999). Importantly, the functional significance of EIF4E phosphorylation in translation initiation has not been clearly defined (Scheper and Proud, 2002), but several studies suggest its role in translational control (Furic et al., 2010; Herdy et al., 2012; Matsuo et al., 1997). Finally, regulation of EIF4E is complex and provides an integration point for RAS/ERK and PI3K-regulated pathways (Raught and Gingras, 1999), as will be discussed in section I.4.4.

Activation of JNK and p38: In contrast to ERK, JNKs and p38 kinases are weakly activated by BCR (Sutherland et al., 1996) and appear to be dispensable for B cell development (Kim et al., 2005). Nonetheless, existing evidence suggests that JNK and p38 are activated downstream of SYK in B cells. A recent study by Khiem et al. demonstrated that p38-mediated activation of the MEF2C regulated B-cell proliferation following BCR engagement (Khiem et al., 2008). In addition, activation of p38 and JNK downstream of BCR was dependent on BLNK and PLC 2 since loss of expression of either one of these proteins completely abolished BCR-mediated activation of p38 and JNK (Hashimoto et al., 1998; Ishiai et al., 1999; Jiang et al., 1998). Recent evidence implicates JNK and p38 in BCR signaling following their compartmentalization within endosomes (Chaturvedi et al., 2011). This notion was further supported by the observation of ezrin-dependent activation of JNK and its localization to endosomes following BCR engagement (Parameswaran et al., 2013). Furthermore, BCR endocytosis is essential for optimal signaling and proper regulation of gene transcription (Murphy et al., 2009) and, in B cells, it depends on MAPK signaling.

I.4.4 SYK and activation of survival PI3K/AKT/mTOR pathway Proper B cell development requires BCR-dependent activation of PI3K pathway. This notion is strongly supported by the observations of B cell deficiencies in mice and humans lacking p85 regulatory subunit of PI3K (Conley et al., 2012; Fruman et al., 1999; Suzuki et al., 1999). PI3K is activated following pre-BCR/BCR engagement by several mechanisms (Dal Porto et al., 2004). CD19 glycoprotein positively regulates pre-BCR/BCR signaling by recruiting p85 subunit of PI3K to its phosphorylated YxxM motifs in the cytoplasmic tail, thereby bringing PI3K in proximity to plasma membrane (Buhl and Cambier, 1999; Buhl et al., 1997). In addition, SYK-dependent phosphorylation of p110 catalytic subunit of PI3K is essential for

12 maximal and sustained PI3K activity (Craxton et al., 1999; Pogue et al., 2000). Activated PI3K phosphorylates phosphatidylinositol-4,5-bisphosphate (PIP2) to generate phosphatidylinositol-

3,4,5-phosphate (PIP3) that subsequently binds to pleckstrin-homology (PH) domains of AKT serine/threonine kinases and phosphoinositide-dependent kinases (PDK1) (Fayard et al., 2010) (Figure I.6). AKT regulates a broad range of cellular functions by propagating signals downstream of PI3K (Figure I.6). Full activation of AKT requires phosphorylation at T308 (by PDK1) and S473 (by mammalian target of rapamycin complex 2, mTORC2) (Sarbassov et al., 2005; Stephens et al., 1998). Activated AKT interacts with multiple substrates in the cytoplast and nucleus, which are beyond the scope of this thesis. Importantly, AKT-dependent regulation of cell growth and metabolism is dependent on its activation of the serine/threonine kinase mTORC1 complex (Bononi et al., 2011). The mTORC1 regulates phosphorylation of ribosomal protein S6 kinase (S6K) and EIF4E binding protein 1 (4EBP1). S6K promotes mRNA translation by phosphorylating ribosomal S6 protein (S6). On the other hand, mTORC1- mediated phosphorylation of 4EBP1 leads to its inactivation resulting in EIF4E release (Bononi et al., 2011). Indeed, binding of 4EBP1 to EIF4E prevents assembly of EIF4F complex and initiation of translation. Collectively, this evidence highlights a critical role of PI3K/AKT signaling in regulating mRNA translation that is essential for proper B cell function.

Summary of Section I.4: This section was devoted to the overview of SYK-dependent signaling downstream of pre-BCR/BCR complexes. SYK is positioned at the top of a signaling hierarchy, composed of a multitude of critical signaling pathways with multiple levels of interactions (Figure I.6). Undoubtedly, tight regulatory mechanisms must be in place to ensure proper B cell survival, proliferation and differentiation. The consequences of an imbalance in BCR regulation include aberrant activation of SYK-dependent signaling networks, a common feature of B cell malignancies, as evident from the fast growing body of literature (Choi and Kipps, 2012; Efremov and Laurenti, 2011; Stevenson et al., 2011; Wiestner, 2012; Young and Staudt, 2013). This knowledge prompted the development of therapies targeting aberrant kinase activation and will be discussed below.

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I.5 Targeting protein kinases in hematologic malignancies Protein kinases are essential regulators of fundamental cellular processes including survival, proliferation, differentiation, antigen-receptor signaling (section I.4) and transcription, to name a few (Hunter, 1998). As a result, protein kinases have emerged as primary targets of signaling- based therapies in B cell malignancies and other diseases.

I.5.1. Protein kinases as therapeutic targets The human genome encodes for over 500 protein kinases (Manning et al., 2002) that are critical players in nearly all signaling pathways. There are 90 protein tyrosine kinases (PTKs) classified into transmembrane receptor and cytoplasmic non-receptor tyrosine kinases. The activity of PTKs is regulated by phosphorylation of regulatory residues that acts as an on/off switch for the kinase function and its catalytic activity. Given an essential role of protein kinases in signaling homeostasis, it is not surprising that they have been implicated in pathogenesis of many diseases. Dysregulation of PTKs in cancer can occur by several mechanisms. Commonly, PTKs form fusion oncoproteins with another partner, which typically replaces N-terminal domain of a kinase and plays an important role in mediating constitutive oligomerization (Medves and Demoulin, 2011). This results in autophosphorylation and constitutive activation of the fusion oncoprotein, frequently accompanied by aberrant overexpression (Krause and Van Etten, 2005). PTKs can also be activated by a mutation in a regulatory residue resulting in disruption of autoinhibition and constitutive phosphorylation. A third mechanism involves impairment in negative regulators that balance tyrosine kinase activity. Indeed, frequent dysregulation of protein tyrosine phosphatases is implicated in aberrant tyrosine kinase activation in cancer (Julien et al., 2011). Common consequences of these alterations include increased cellular survival, proliferation and drug resistance. Early discoveries of aberrant protein kinase activation in cancer have provided a compelling rationale for the development of inhibitors of these “oncogenic” kinases (Krause and Van Etten, 2005). All of the clinically approved tyrosine kinase inhibitors (TKIs, 13 in total) are adenosine-5‟-triphosphate (ATP)-mimics that have high affinity for ATP-binding pocket within the catalytic domain. Although ATP-competitive TKIs may provide an attractive less-toxic therapeutic options in cancer, they also come with several caveats. First, TKI resistance is a growing problem that develops as a result of acquired “gatekeeper” mutations and requires preventative treatment strategies (Krause and Van Etten, 2005). Second, ATP binding sites are

14 highly conserved among different tyrosine kinases, suggesting a high likelihood that TKIs will inhibit other PTKs (off-target effects), as was recently revealed (Davis et al., 2011). The off- target effects are likely to result in an unanticipated and unfavorable toxicity, but they may also be beneficial or even essential for efficacy (Karaman et al., 2008), highlighting the need to clearly define selectivity profiles of TKIs, thereby revealing the full spectrum of their biological activities.

I.5.2 Discovery of imatinib for the treatment of BCR-ABL1 leukemia The approval of imatinib (Gleevec), targeting BCR-ABL1 oncoprotein, for clinical use in 2001 generated significant interest in TKIs. The pathological role of BCR-ABL1 fusion protein, resulting from a reciprocal translocation of a t(9;22), in chronic myeloid leukemia (CML) was first defined over 20 years ago (Daley et al., 1990). Subsequent studies provided evidence for aberrant signaling downstream of this oncogenic kinase, which included activation of PI3K/AKT/mTOR, RAS/RAF/MAPK, JAK/STAT, WNT/ -catenin and hedgehog pathways (Ahmed and Van Etten, 2013). Recognition that BCR-ABL-driven signaling underlies the pathogenesis of CML prompted rapid development of small-molecule inhibitors targeting kinase activity of BCR-ABL. Imatinib was the first tyrosine kinase inhibitor to be introduced to clinical practice, significantly improving outcomes in CML and BCR-ABL-positive acute lymphoblastic leukemia (BCR-ABL+) patients (Druker et al., 2006; Druker et al., 2001a; Druker et al., 2001b; Talpaz et al., 2002). Importantly, this landmark event was a critical factor in a paradigm shift toward targeted treatment of cancer, prompting exponential increase in development of kinase- targeted drug therapies. To this end, five BCR-ABL-specific tyrosine kinase inhibitors have been approved for treatment of CML and BCR-ABL1+ ALL. Collectively, these studies highlight that identification of specific aberrantly active cellular pathways that regulate survival and proliferation of tumor cells will provide valuable molecular targets for future drug development.

I.5.3 Targeting aberrant BCR signaling in mature B cell malignancies Section I.4 highlighted some of the critical protein kinases that are essential for proper signaling through the pre-BCR/BCR complexes in B cells, regulating their survival and proliferation. Importantly, strong evidence implicates aberrant activation of BCR signaling pathway in the pathophysiology of mature B cell malignancies including B cell lymphomas and chronic lymphocytic leukemia (CLL) (Burger, 2011a; Woyach et al., 2012; Young and Staudt, 2013). 15

B cell lymphomas originate from malignant transformation of mature B cells and are characterized by the expression of BCR on the cell surface. Early studies demonstrated that BCR expression was essential for positive selection of malignant B cells (Bahler and Levy, 1992). These observations suggested that, similar to normal B cells, B cell lymphomas might rely on BCR signaling for survival and proliferation. In this regard, overexpression and/or constitutive activation of multiple components of BCR pathway have been described in this malignancy. In particular, constitutive phosphorylation of SFK, SYK and their downstream targets has been seen in B-cell lymphomas (Chen et al., 2008; Cheng et al., 2011; Fruchon et al., 2012; Gururajan et al., 2006; Ke et al., 2009; Leseux et al., 2006; Rinaldi et al., 2006; Yang et al., 2008; Young et al., 2009). In addition, SFK- and SYK-specific inhibitors abrogated activation of tonic BCR signaling, resulting in apoptosis. More recently, Davis et al. described chronic active BCR signaling in an activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) that lead to the activation of NF B survival pathway (Davis et al., 2010). Importantly, this aberrant signaling was dependent on expression of Ig /Ig , SYK, BTK, BLNK, PLC 2, PKC and CARD11, because deletion of these proteins ablated NF B target genes and reduced survival of ABC DLBCL. Similarly, treatment with inhibitors of SFK, BTK or NF B also reduced survival of ABC DLBCL (Davis et al., 2010). Although the specific mechanisms leading to constitutive BCR signaling remain to be investigated, some studies implicate dysregulation or mutations in regulatory elements of BCR signaling. Indeed, transcriptional repression of PTPROt, a negative regulator of SYK activity, has been detected in DLBCL (Chen et al., 2006; Juszczynski et al., 2009). In addition, frequent mutations in Ig observed in ABC DLBCL lead to increased surface BCR expression likely by preventing negative autoregulation and BCR internalization (Davis et al., 2010; Gazumyan et al., 2006). Antigen stimulation through BCR also plays an important role in CLL pathogenesis and progression, as suggested by the expression of similar or identical BCRs among CLL patients (Messmer et al., 2004; Tobin et al., 2004; Widhopf et al., 2004). In particular, continuous BCR stimulation by antigen results in enhanced capacity for BCR-mediated signaling, leading to an increased expression of BCR-regulated genes, and is associated with aggressive disease (Damle et al., 2002; Le Roy et al., 2012; Rosenwald et al., 2001). These data are further substantiated by the observations of overexpression (Buchner et al., 2009) and constitutively active SFK/SYK signaling in CLL patient samples that lead to an increase in pro-survival regulators, including myeloid leukemia cell differentiation protein (MCL1) (Baudot et al., 2009; Gobessi et al., 2009;

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Herishanu et al., 2011; Petlickovski et al., 2005). Furthermore, accumulating evidence suggests that SYK is activated by signals from stromal microenvironment of CLL cells and SYK-specific inhibition prevents CLL activation, survival and migration (Buchner et al., 2010; Hoellenriegel et al., 2012; Quiroga et al., 2009). Most recently, Jumaa and colleagues described antigen- independent SYK-dependent activation of BCR signaling in a subset of human CLL cases, suggesting that basal BCR activity also promotes CLL survival (Duhren-von Minden et al., 2012). Overall, the downstream consequences of SYK-dependent BCR signaling in CLL include activation of PI3K/AKT, NF B and ERK survival pathways (Woyach et al., 2012). Collectively, these data suggest that there may be a therapeutic potential of targeting BCR and its effector pathways in CLL. Indeed, strong pre-clinical data urged evaluation of the therapeutic potential of targeting pathological BCR signaling in B cell lymphoma and CLL, which represents an exciting area of clinical research. In this regard, a plethora of kinase inhibitors of BCR pathway are being currently tested in Phase I/II/III clinical trials. For example, fostamatinib, an ATP-competitive inhibitor of SYK activity (Braselmann et al., 2006), has shown promising effects in Phase I/II clinical trials in patients with relapsed B cell lymphoma and CLL, demonstrating good tolerability and minimal toxicity (Baluom et al., 2012; Friedberg et al., 2010). The specificity of fostamatinib was recently demonstrated by a reduction in BCR signature markers following in vivo fostamatinib treatment in CLL patients (Herman et al., 2013). Another TKI undergoing clinical trials is ibrutinib. Ibrutinib (PCI-32765) is a BTK-specific inhibitor that interferes with its enzymatic activity and prevents B cell signaling (Pan et al., 2007). Therapeutic potential of BTK inhibition was suggested by studies demonstrating that BTK kinase activity was necessary for survival of B lymphoma and CLL cells in pre-clinical studies (Davis et al., 2010; de Rooij et al., 2012; Herman et al., 2011; Ponader et al., 2012). Ongoing Phase I/II clinical trials have demonstrated its efficacy in patients with CLL and non-Hodgkin lymphoma (NHL) (Winer et al., 2012). In addition, clinical trials with inhibitors of PI3K (GS-1101, Phase II/III), SFK (dasatinib, Phase II), among others, for the treatment of B lymphomas and CLL are ongoing and have been extensively reviewed (Burger and Montserrat, 2013; Kenkre and Kahl, 2012; Reeder and Ansell, 2011; Wiestner, 2012; Witzig and Gupta, 2010; Woyach et al., 2012). Collectively, these studies highlight that response rates to these agents vary significantly among patients, suggesting difference in signaling networks downstream of BCR activation. Thus, development of biomarkers predictive of response to these agents is of utmost importance.

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I.6 Use of phospho-flow cytometry to study signaling perturbations As described above, B lymphomas and CLL are characterized by constitutive activation of pathways downstream of BCR. The detailed characterization of these pathways in most studies was enabled by the use of phospho-flow cytometry. For many decades, western blotting has been a conventional technique to study protein phosphorylation, averaged across a whole cell population. A growing appreciation for the cellular heterogeneity within a tumor highlighted the need to study signaling perturbations in discrete cell populations found within a tumor sample. In this regard, phospho-flow cytometry, first introduced by Dr. Gary Nolan‟s group 10 years ago, enables the detection of phosphorylated proteins at a single-cell level, thereby revealing heterogeneity within a population of cancer cells (Krutzik and Nolan, 2003; Nolan, 2006). Since then, multiple phospho-specific antibodies have become available, providing the means to interrogate signaling networks in cancer cells. Phospho-flow cytometry has been used to define aberrant BCR signaling in B lymphomas and CLL (Baudot et al., 2009; Blix et al., 2012; Cesano et al., 2013; Chen et al., 2008; Cheng et al., 2011; Irish et al., 2006a; Le Roy et al., 2012; Song et al., 2010) that was introduced in section I.5.3. However, all of these studies focused on a limited number of phospho-proteins, thereby failing to provide a complete overview of alterations in signaling networks downstream of BCR. The most extensive analysis of potentiated signaling networks in lymphoma was performed by Irish et al. and revealed prognostically significant heterogeneity within lymphoma subsets (Irish et al., 2010), previously described in AML (Irish et al., 2004). These studies demonstrated that phospho-flow cytometry is a powerful technology that can dissect signaling perturbations in cancer cells. Furthermore, it can be used to screen novel targeted therapies as a way to define their signaling targets and develop predictive biomarkers (Krutzik et al., 2008).

I.7 B-ALL: clinical presentation and current treatment options Thus far, I described pre-BCR/BCR-dependent signaling pathways controlling proliferation and survival of normal B cells. In addition, strong evidence supports a pathological role for constitutive BCR signaling in mature B cell malignancies. B cell acute lymphoblastic leukemia (B-ALL) represents another group of B-cell-derived malignancies that, unlike mature B cell tumors, occurs as a result of abnormal proliferation and clonal expansion of B cell progenitors

18 that lack expression of pre-BCR/BCR. Thus, these leukemias subvert the normal developmental checkpoints, enabling their growth and survival. However, the precise signaling mechanisms that regulate aberrant proliferation in B-ALL have not been clearly defined and are the main focus of my thesis.

I.7.1 Incidence and clinical presentation of B-ALL B-ALL is defined by the presence of at least 20% blasts in the bone marrow. Accumulating blasts replace normal hematopoietic cells in the bone marrow leading to anemia, infection (neutropenia), thrombocytopenia and bleeding, common symptoms present at diagnosis. In addition, frequent infiltration of extramedullary sites including central nervous system (CNS), lymph nodes, spleen, liver and gonads is characteristic of B-ALL (Zhou et al., 2012). There are three distinct age groups of B-ALL: infant (< 1 year of age), pediatric (1-19 years of age) and adult (>19 years of age). B-ALL is the most common childhood malignancy accounting for about 30% of all pediatric . On the other hand, the incidence in adults is rare comprising about 1% of all cancers.

I.7.2 Prognostic factors B-ALL patients can be categorized into standard-risk (SR) and high-risk (HR) groups based on the current National Institute of Cancer (NCI) risk criteria that uses age and white blood cell count. Patients that are between 1 to 9.99 years of age with WBC < 50 109/L are classified into SR group, whereas those with WBC 50 109/L or patients that are 10 years or older are considered HR (Smith et al., 1996). In addition, B-ALL is characterized by recurrent chromosomal alterations that have prognostic implications and guide therapy choices. Favorable cytogenetic abnormalities include TEL-AML1, hyperdiploidy (>50 ) and E2A- PBX1, whereas presence of BCR-ABL1, hypodiploidy (<44 chromosomes) or MLL rearrangements is associated with poor prognosis in children and adults. In addition to these six most common cytogenetic groups of B-ALL, recent studies have identified additional submicroscopic genetic alterations that are predictive of patient outcome (Inaba et al., 2013) and will be presented in section I.7. Contemporary risk-adaptive treatments have steadily improved outcomes in children with B-ALL as evident from current survival rates of over 80%. Despite such great progress in improving cure rates, long-term disease-free survival rates in adult B-ALL are less than 40%,

19 highlighting the ineffectiveness of conventional treatments for these patients (Bassan and Hoelzer, 2011). Similarly, children with high-risk chromosomal abnormalities, including BCR- ABL1 or MLL fusions, are at greater risk of treatment failure and, as a result, have dismal prognosis (Pui et al., 2004). Although treatment failure and subsequent relapses are frequently associated with HR B-ALL, relapses occur in all B-ALL risk groups and are not exclusively associated with a particular cytogenetic lesion. Importantly, relapsed B-ALL represents a major clinical challenge as it is currently incurable and remains a leading cause of morbidity and mortality in children (Fielding et al., 2007; Gaynon et al., 1998). Clearly, there is an unmet need to develop new therapeutic approaches that improve clinical outcomes in B-ALL patients and prevent fatal relapses.

I.7.3 Current treatment options and challenges The modern era of chemotherapy began in 1940‟s with the landmark studies demonstrating effectiveness of mustards and antifolate compounds in inducing remission in leukemias (Farber and Diamond, 1948; Goodman et al., 1946). At the same time, corticosteroids were promptly introduced into clinical practice in light of the discovery of their pro-apoptotic effects in leukemias (Pearson et al., 1949). This was followed by the discovery of purine and pyridine analogues, platinum compounds, antitumor antibiotics and vinca alkaloids (Chabner and Roberts, 2005), culminating in the introduction of the first combination regiment for childhood ALL (methotrexate, vincristine, 6-mercaptopurine, prednisone) in 1965 (Frei et al., 1965). Over 50 years later, significant improvement in the survival rates of B-ALL patients is largely due to the use intensified combination chemotherapy that consists of agents discovered decades ago, illustrating the lack of progress in novel treatment development. Current treatment regiments consist of three phases of treatment including remission induction, consolidation and maintenance, spanned over the course of two to three years (Bassan and Hoelzer, 2011; Pui et al., 2008). In addition, patients with CNS involvement at diagnosis receive intrathecal chemotherapy with methotrexate combined with systemic high-dose chemotherapy during the first treatment phase (Pui et al., 2009). Consequently, although continuous rounds of chemotherapy eliminate leukemic cells, they also result in acute and long- term toxicities that affect virtually every organ of the body and lead to debilitating consequences in B-ALL survivors. Some of the common complications include endocrinopathy, growth failure in children, obesity, osteoporosis, osteonecrosis, cardiomyopathy, neurological impairment and development of second cancer (Hoelzer et al., 2002). Clearly, further

20 intensification of chemotherapy as a way to improve outcomes is not a viable option and will produce more deleterious rather than beneficial effects. Thus, identification of novel targeted therapies may provide much needed treatment options to extend survival and improve quality of life in B-ALL survivors. Importantly, the development of targeted therapies requires elucidation of molecular and signal transduction abnormalities that govern survival and proliferation of B- ALL.

I.8 Discovery of novel therapeutic targets in B-ALL It is evident that characterization of molecular mechanisms involved in leukemogenesis requires identification of all genetic lesions found in a tumor. Whereas 75% of B-ALL cases harbor prognostic genetic alterations, tumors from the remaining B-ALL patients lack any known or identifiable lesions. Over the past 7 years, multiple studies provided a detailed overview of genomic alterations in B-ALL in order to characterize genetic basis of leukemogenesis, which is of particular importance in samples with no known cytogenetic lesions.

I.8.1 Beginning of the genomics era in B-ALL First comprehensive analysis of genomic lesions in childhood B-ALL identified over 50 genomic alterations regulating lymphoid differentiation, , apoptosis and tumor suppression (Kuiper et al., 2007; Mullighan et al., 2007). Importantly, lesions in genes regulating B-cell development and differentiation pathways, including PAX5, IKZF1, EBF1, were found in over 40% of B-ALL cases, suggesting that dysregulation of B cell development program drives B-cell leukemogenesis. Importantly, these alterations were found in cases with previously undefined lesions. Collectively, these data provided an unprecedented overview of genetic alterations in B-ALL and raised important questions regarding the prognostic values of these alterations.

I.8.2 Identification of a novel subgroup of high-risk B-ALL Unfortunately, most of the novel alterations have limited prognostic value with the exception of IKZF1 (Mullighan, 2012). In this regard, additional studies demonstrated a highly significant association between alterations of IKZF1, encoding IKAROS, and relapse (Mullighan et al., 2009b). Interestingly, IKZF1 mutations characterize BCR-ABL1+ ALL (Mullighan et al., 2008) as well as a newly identified subgroup of high-risk BCR-ABL1-negative B-ALL (Den Boer et

21 al., 2009; Martinelli et al., 2009; Mullighan et al., 2009b). Collectively, these studies suggest that IKZF1 alterations will be a useful prognostic indicator in B-ALL, particularly in cases with unknown chromosomal alterations.

1.8.3 Genetic alterations reveal novel therapeutic targets in B-ALL Importantly, gene expression analysis of BCR-ABL1-negative samples (BCR-ABL1- ALL) harboring IKZF1 alterations was highly similar to BCR-ABL1+ ALL cases (Den Boer et al., 2009; Mullighan et al., 2009b). These observations lead to the hypothesis that this Ph-like group of B-ALL may harbor additional lesions that result in constitutively active kinase signaling, similar to BCR-ABL1 . Follow-up studies identified rearrangements of CRLF2, encoding cytokine receptor like factor 2, in 50% of Ph-like ALL with concomitant JAK2 mutations (Harvey et al., 2010; Mullighan et al., 2009a; Mullighan et al., 2009c; Russell et al., 2009). Surprisingly, tyrosine kinome sequencing in Ph-like ALL revealed that, apart from JAK2, kinase-activating point mutations are rare in this group of B-ALL (Loh et al., 2013). In contrast, transcriptome and whole-genome sequencing revealed that remaining cases of Ph-like ALL were characterized by the presence of fusions (EBF1- PDGFRβ, BCR-JAK2, STRN3-JAK2, ETV6-ABL1, IGH-EPOR) and activating IL7R and FLT3 mutations (Roberts et al., 2012). Thus, evidence from genomic analyses suggests that survival and progression of HR B-ALLs is likely dependent on aberrant kinase and cytokine signaling. This notion was substantiated by the observation of aberrant activation of JAK/STAT and PI3K/mTOR signaling pathways in CRLF2-rearranged samples (Tasian et al., 2012). Furthermore, these studies led to identification of the therapeutic potential of JAK2 and mTOR inhibitors in Ph-like ALL (Maude et al., 2012). Taken together, these studies highlight that identification of aberrant signaling networks of B- ALL may identify important targets for rational therapeutic interventions.

1.8.4 It’s a long way to go: the search for personalized medicine in B-ALL continues It is clear that subversion of normal lymphocyte signaling pathways forms the foundation of B- ALL pathophysiology. Thus, identification of molecular changes that disrupt signal transduction pathways controlling abnormal survival and proliferation of leukemia cells will promote identification of novel therapeutic targets. Recent examples of success with this strategy are identification of genetic lesions that cause aberrant JAK/STAT and PI3K/mTOR pathways in Ph-like ALL (Roberts et al., 2012; Tasian et al., 2012) and activation of RAS/MAPK signaling in hypodiploid HR B-ALL (Holmfeldt et al., 2013). However, most of theses studies focused

22 only on specific, frequently rare subtypes of B-ALL. Importantly, developing specific inhibitors to target each new type of signaling mutation may take several years and is likely unrealistic. Thus far, extensive examination of basal leukemia-associated perturbations in signaling in major cytogenetic groups of B-ALL has not been reported. Thus, there is a great need for comprehensive analysis of abnormalities in signaling networks across a spectrum of cytogenetic groups of B-ALL, including SR and HR patients. Such unbiased approach may reveal common signaling alterations in B-ALL that can be targeted with a single agent. Given that B-ALL is a developmental disorder, exploration of major signaling networks crucial for B cell development is warranted, especially in light of the pathological role of the BCR-mediated signaling pathway in other B cell malignancies.

I.9 Project goals At the start of my project, little was known about signaling pathways that contribute to B-ALL pathogenesis. My research was driven by the hypothesis that identification of protein kinases that contribute to aberrant survival and proliferation of all cytogenetic subtypes of B-ALL will provide a foundation for the discovery of rational therapeutic targets in this disease. Thus, the major goal of my thesis was to elucidate signal transduction abnormalities across a spectrum of cytogenetic subtypes of precursor B-ALL leukemia in an effort to identify novel targets. In Chapter II, I present an integrated analyses of mouse model and human B-ALL that lead to the identification of SYK pathway as a therapeutic target. Subsequent chapters focus on defining cellular and molecular consequence of SYK inhibition in B-ALL. Specifically, I describe the development and the use of high-throughput phospho-flow platform to interrogate signaling perturbations in B-ALL and provide evidence for the potential use of this technique as a “personalized medicine” platform to screen targeted therapies for high-risk B-ALL (Chapter III). Chapter IV describes the use of gene expression profiling to elucidate molecular consequences of SYK inhibition by fostamatinib in high-risk B-ALL and to compare these effects to chemotherapeutic agents. Collectively, the data presented in this thesis provide a thorough overview of complexity in signaling architecture as well as describe the cellular and molecular mechanisms of SYK inhibitor action in B-ALL.

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I.10 FIGURES

Figure I.1 Simplified overview of transcriptional and signaling networks that control generation of B-cell progenitors (a) Requirements for transcription factors and cytokine receptors at four stages (hematopoietic stem cells (HSC), multipotent progenitors (MPP), common lymphoid progenitors (CLP), pro-B

24 cells) are depicted. Each transition requires distinct combination of regulatory factors (Ikaros, PU.1, EBF, E2A, Pax5) and signaling receptors (IL7 receptor (IL7R) and FLT3 receptor (FLT3R)). Cytokine receptors regulate transcription factors, which, in turn, induce or suppress target genes (Nutt and Kee, 2007). (b) B cell phenotype of mice harboring deficiencies of B- lineage transcription factors (Nutt and Kee, 2007). (c) Regulatory interactions implicated in B cell development. Transient activity of Ikaros, PU.1 and FLT3 induces expression of Pax5, EBF1, E2A and IL7R that work in concert through auto- and cross-regulation to support B- lineage program. Pax5 expression is required for suppression of FLT3R-dependent alternative lineage genes, thereby ensuring B-cell lineage commitment (Medina and Singh, 2005; Tijchon et al., 2013).

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Figure I.2 Critical checkpoints in early B cell development (a) The development of B cells is characterized by stepwise recombination of the immunoglobulin (Ig) gene segments (Variable (V), Diversity (D), Joining (J)). Productive in-

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frame VDJH rearrangement of Ig heavy chain (IgH) generates Ig chain, which forms a pre-B cell receptor (pre-BCR) on the cell surface, marking a pro- to pre-B cell transition and a first checkpoint in B cell development. Signaling from pre-BCR is essential for clonal proliferation, downregulation of SLC genes and initiation of IgL rearrangement. Second checkpoint is the formation of BCR following successful VJ rearrangement of Ig light chain (IgL), which marks immature B cell stage. (b) Pre-BCR is comprised of 2 IgH and 2 surrogate light chains (SLC: 5 and VpreB), whereas BCR includes 2 IgH and 2 IgL chains. Pre-BCR and BCR form signaling complexes with Ig /Ig heterodimer. Ig and Ig contain immunoreceptor tyrosine-based activation motifs (ITAMs) in their cytoplasmic tail. Upon receptor engagement, ITAMs become phosphorylated on two tyrosine (Y) residues that provide binding sites for downstream signaling mediators.

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Figure

Figure I.3 Schematic representation of SRC and SYK protein structure (a) The Src family of kinases (SFKs) consists of nine members. A representative structure is shown for LYN, which is expressed in B cells. SFKs consists of 6 functional domain: an N- terminus SH4 domain, which is a site of myristoylation and anchors SFKs to plasma membrane; the unique domain; Src homology 3 (SH3) and SH2 domains facilitate homo- and heterodimerization by binding proline-rich sequences and phosphorylated tyrosine, respectively; the kinase domain that contains a regulatory tyrosine (Y) residue and is responsible for catalytic activity; and C-terminus that contains regulatory Y residue. (b) SYK contains tandem SH2 domains separated by interdomain A, interdomain B and kinase domain. SH2 domains have high affinity for diphosphorylated ITAMs. The thirty-two phosphoacceptor sites have been identified (S: serine; T: threonine; Y: tyrosine) (Bohnenberger et al., 2011). Several Y residues are known to recruit SH2 domains of other proteins leading to formation of signaling complexes.

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Figure I.4 SYK is a central component of pre-BCR and BCR-mediated signaling In resting cells, SYK is found in autoinhibitory conformation. Receptor engagement promotes SFK-dependent phosphorylation of ITAMs on Ig /Ig . Dual-phosphorylated ITAMs bind SH2 domains of SYK leading to its rapid activation through autophosphorylation. CD19 and CD45/CD148 are positive regulators of BCR signaling, whereas negative co-regulators include CD22, CD72 and Fc RIIB. In addition, SYK activity is tightly regulated by cytoplasmic protein tyrosine phosphatases SHP1 and PTPROt, whereas SFK activity is regulated by CSK tyrosine kinase.

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Figure I.5 BLNK acts as an adaptor protein linking SYK to downstream signaling molecules Upon receptor engagement, SYK phosphorylates BLNK on five Y residues, which serve as docking sites for BTK, PLC 2,VAV, NCK and GRB2. SYK-dependent phosphorylation and activation of PLC 2 leads to generation of second messengers DAG and IP3 that propagate SYK signaling by activating MAPK and NF B signaling pathways as well as transcription factor NFAT. Collectively, these pathways regulate transcription of genes essential for B-cell fate decisions. In addition, recruitment and activation of VAV and NCK regulates cytoskeletal and morphological reorganization, whereas SYK-dependent GRB2 phosphorylation activates RAS signaling.

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Figure I.6 Composite overview of signal transduction cascades activated by pre-BCR/BCR signaling Pre-BCR and BCR signal transduction is mediated by SYK recruitment to dual-phosphorylated ITAMs on Ig /Ig resulting in its activation. Active SYK propagates signaling by phosphorylation and activation of signaling intermediates. SYK-dependent activation of PLC 2 triggers activation of PKC- and Ca2+-dependent networks including MAPK, NK B and RAS signaling pathways. SYK also recruits and phosphorylates regulatory subunit of PI3K, resulting in signal propagation through AKT/mTORC1 network. SYK also regulates activation of RAS/MEK/ERK-dependent pathways. Collectively, SYK recruitment and activation leads to initiation of orchestrated series of cellular processes that regulate survival, proliferation and differentiation of B cells.

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CHAPTER II: Therapeutic potential of spleen tyrosine kinase inhibition for the treatment of high-risk precursor B-cell acute lymphoblastic leukemia

Tatiana Perova1,2, Ildiko Grandal1, Lauryl Nutter1, Eniko Papp1,2, Joseph Beyene3, Paul E. Kowalski1, Johann K. Hitzler1,4, Mark D. Minden2,5, Cynthia J. Guidos1,6 and Jayne S. Danska2,6,7

1Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, ON, Canada 2Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada 3Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, ON 4Division of Hematology & Oncology, The Hospital for Sick Children, Toronto, ON, Canada 5Ontario Cancer Institute and Princess Margaret Hospital, University Health, Network, Toronto, ON, Canada 6Department of Immunology, University of Toronto, Toronto, ON, Canada 7Program in Genetics & Genomic Biology, The Hospital for Sick Children, Toronto, ON, Canada

Contributions: TP, CJG and JSD designed study. TP conducted all experiments and analyzed all data with the following exceptions: IG performed western immunolabelling with DM ALL (Figure II.2a), LN performed microarray experiments and immonophenotyping of DM ALL (Figures II.1a,b), EP performed and analyzed DM adoptive transfers for fosta treatment (Figure II.2d). JB analyzed microarray data (Figure II.1a), PEK performed analysis of SYK mutations. JKH and MDM provided primary B-ALL patient samples.

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II.1 ABSTRACT Intensified and central nervous system (CNS)-directed chemotherapy has significantly improved outcomes for pediatric B-acute lymphoblastic leukemia (B-ALL), but confers significant late-effect morbidities. Moreover, many patients suffer relapses, underscoring the need to develop novel, molecularly targeted B-ALL therapies. Using a mouse model, we showed that leukemic B-cells require pre-B-cell receptor (pre-BCR)-independent spleen tyrosine kinase (SYK) signaling in vivo. In diagnostic samples from human B-ALL patients, SYK and downstream targets were phosphorylated regardless of pre-BCR expression or genetic subtype. Two different SYK inhibitors, fostamatinib (fosta) and BAY613606 (BAY), significantly attenuated proliferation of 69 B-ALL samples, including several high-risk (HR) subtypes, at clinically achievable concentrations. Orally administered fosta significantly reduced disease burden after xenotransplanting HR B-ALL samples into immune-deficient mice, and decreased leukemia dissemination into spleen, liver, kidneys and the CNS. Thus, SYK activation sustains growth of multiple HR B-ALL subtypes, suggesting that SYK inhibitors may improve outcomes for HR and relapsed B-ALL.

II.2 INTRODUCTION B-ALL is the most common malignancy of childhood. Use of intensified systemic chemotherapy and central nervous system (CNS)-directed prophylaxis now yields survival rates of 75-80% (Pui et al., 2008), but these treatments also cause acute and long-term treatment-related complications (Pui et al., 2003). However, ~20% of pediatric and over 60% of adult B-ALL patients fail current front- line therapies and relapse with a highly unfavorable prognosis (Liew et al., 2012; Locatelli et al., 2012; Pui and Evans, 2006), highlighting the ineffectiveness of current treatments. The most aggressive treatments are typically reserved for children (~20% of cases) deemed to be at high-risk for treatment failure or relapse based on the clinical features, the presence of particular cytogenetic abnormalities or minimal residual disease levels after induction therapy (Borowitz et al., 2008; Schultz et al., 2007). However, relapses also occur in standard-risk (SR) patients that present with favorable clinical or cytogenetic prognostic criteria (Chessells, 1998; Seeger et al., 2001). In both adults and children, relapses often originate from leukemic blasts that have disseminated to extramedullary sites such as the CNS, and are associated with low survival rates (Domenech et al., 2008). Thus, there is an unmet need to develop therapies that more specifically target signaling pathways that promote survival and extramedullary dissemination of HR B-ALL to improve survival and quality of life for both children and adults.

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The discovery that aberrant protein kinase activation is prevalent in many types of cancer has spurred development of targeted kinase inhibitors for several malignancies (Krause and Van Etten, 2005). Addition of lesion-specific tyrosine kinase inhibitors to conventional chemotherapy has greatly improved outcomes for HR B-ALL patients whose blasts harbor BCR-ABL1 translocations (Druker et al., 2001a). Recent studies have identified mutations and/or aberrant activation of cytokine and JAK/STAT signaling pathways in subsets of HR B-ALL patients (Roberts et al., 2012; Tasian et al., 2012), and pre-clinical studies have suggested that inhibitors targeting JAK2 or mTOR signaling may also hold promise for certain HR subtypes of BCR-ABL1-negative B- ALL (Maude et al., 2012). However, these cases harbor a mechanistically diverse array of mutations in several different signaling pathways, suggesting the need to develop inhibitors that target several different classes of lesions in this poor prognosis group of patients. Therapeutic targeting of signal transduction abnormalities common to many HR subtypes of B-ALL would be a more attractive solution to this problem, but this has not yet been achieved. B-ALL results from abnormal accumulation and proliferation of transformed progenitor-B (pro-B) or precursor-B (pre-B) lymphocytes. During normal B cell development, pro-B cells are programmed to die unless they successfully rearrange and express the immunoglobulin heavy chain. The resulting Ig protein interacts with 5, VpreB and CD79A/CD79B dimer to form the transmembrane pre-B cell receptor (pre-BCR) signaling complex. The pre-BCR expression induces SRC-family kinases (SFKs) to dually phosphorylate tyrosines in the cytoplasmic tails of CD79A/CD79B, which bind paired SH2 domains in the SYK N-terminus, promoting a conformational change that induces SYK activation (Herzog et al., 2009). Pre-BCR-induced SYK signaling is required for survival, proliferation and differentiation of pro-B into pre-B-cells (Herzog et al., 2009). Consequently, B-cell development is arrested at the pro-B-cell stage in mice with genetic defects in DNA repair that impair Ig expression or pre-BCR signaling (Matei et al., 2006). However, combined disruption of DNA repair and p53-mediated DNA damage checkpoints causes mice to spontaneously develop pre-BCR-negative early B-ALL (Guidos et al., 1996; Nacht et al., 1996). Interestingly, nearly 70% of pediatric and adult B-ALL lack cytoplasmic Ig and, consequently, do not express a pre-BCR (Pui et al., 2002). Thus, B-cell leukemogenesis in mice and humans frequently involves subversion of the pro-B developmental checkpoint, enabling progenitors to survive and proliferate in a pre-BCR-independent fashion. These considerations prompted us to explore mechanisms of pre-BCR-independent growth in B-ALL to identify new therapeutic targets. Here, we report that pre-BCR-independent SYK

34 signaling enabled aberrant B-ALL survival and proliferation in p53-/-; Prkdcscid/scid “double mutant” (DM) mice. Furthermore, we found that diagnostic human B-ALL displayed pre-BCR-independent SYK activation, and that SYK inhibitors potently attenuated proliferation of a large cohort of B- ALL samples belonging to multiple HR subgroups in vitro. Oral administration of fosta, a small molecule SYK inhibitor (Braselmann et al., 2006), potently reduced disease burden for several HR genetic subtypes of B-ALL in xenotransplant studies. In particular, fosta reduced B-ALL dissemination from bone marrow (BM) to spleen, liver, kidneys and the CNS. These results document pre-BCR-independent SYK activation across many genetic subtypes of B-ALL, and provide pre-clinical in vivo evidence that SYK inhibition may improve outcomes for HR B-ALL.

II.3 METHODS II.3.1 Human B-ALL Samples The research ethics boards at the Hospital for Sick Children (Toronto, Canada) and University Health Networks (Toronto, Canada) approved this study. Bone marrow or peripheral blood samples from newly diagnosed B-ALL patients were obtained with informed consent. Mononuclear cells were isolated from heparinized whole blood or bone marrow using Ficoll-Paque Plus density gradient separation (GE Healthcare, Baie d‟Urfe, Canada), according to manufacturer‟s instructions. Mononuclear cells were viably frozen in 90% fetal bovine serum (FBS, v/v) containing 10% DMSO (v/v) and stored long-term in a vapor phase of liquid nitrogen. Summary of B-ALL samples used is provided in Table II-1.

II.3.2 Cell lines KOPN-8 and NALM-6 B-ALL cell lines were purchased from Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ, the German Collection of Microorganisms and Cell Culture, Braunschweig, Germany), whereas MV4;11, RS4;11, K562 and Ramos were from the American Tissue Culture Collection (Manassas, VA, USA). KOPN-8, NALM-6, RS4;11 and Ramos were maintained in RPMI 1640 supplemented with 10% FBS (Wisent Inc., Laval, Canada), 10 mM HEPES (pH 7.2, Wisent Inc.), 2 mM L-glutamine (Gibco, Gaithersburg, MD) and 1 mM sodium pyruvate (Gibco). MV4;11 cells were grown in Iscove‟s modified Dulbecco‟s medium (IMDM, Wisent Inc.) supplemented with 10% FBS, 10 mM HEPES (pH 7.2), 2 mM L-glutamine and 1 mM sodium pyruvate. K562 cells were maintained in Dulbecco‟s modified eagle medium (DMEM,

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Wisent) supplemented with 10% FBS and 5.5 10-5 M 2-mercaptoethanol. All cells were grown in a

95% air/5% CO2 humidified incubator at 37 C.

II.3.3 Kinase Inhibitors AstraZeneca kindly provided two different formulations of fostamatinib (Alderley Park, Macclesfield, UK). R406 was used for in vitro experiments, and R788, an orally bioavailable form, impregnated into AIN-76A rodent diet (2-8 g R788/kg diet; Research Diets, New Brunswick, NJ) was used for in vivo experiments. Both forms are referred to generically as „fosta‟. BAY613606 and AGL2043 were purchased from EMD Chemicals (Philadelphia, PA). Dasatinib was purchased from Toronto Research Chemicals (Toronto, ON, Canada).

II.3.4 Proliferation Assay Patient samples were thawed and incubated at 37 C overnight in StemSpan media (Stem Cell Technologies, Vancouver, BC), containing 25 mM HEPES (pH 7.2), 1 mM sodium pyruvate, 2 mM L-glutamine and 0.1 mM non-essential amino acids. Cells were cultured in triplicate (1.5 105/well) in 96-well flat-bottom plates with vehicle or inhibitors for 72 h. Methyl- [3H] Thymidine (1 µCi/well) was added 16 h prior to harvesting onto glass fiber paper using Inotech Cell Harvester (Inotech Biosystems, Rockville, MD). Proliferation was measured as disintegrations per minute (DPM) on Beckman LS 6500 Scintillation Counter (GMI Inc, Ramsey, MN).

II.3.5 siRNA Transfection SMARTpool siRNA against SYK and non-targeting siRNA pool were purchased from Dharmacon RNAi Technologies/Thermo Scientific (Lafayette, CO). NALM6, KOPN-8 and RS4;11 were cultured in 12-well plates in the presence of 1 M SYK SMARTpool siRNA or non-targeting siRNA pool for 5 days. Cells were analyzed for knockdown efficiency using flow cytometric analysis following intracellular staining with total SYK-FITC (4D10, BD Biosciences). Effects of SYK knockdown on proliferation were assesses by [3H]-Thymidine incorporation. Triplicates were used for each condition.

II.3.6 Mice DM and Rag2-/- mice were bred and housed in specific pathogen-free conditions at the Hospital for Sick Children animal facility as previously described (Gladdy et al., 2003; Guidos et al., 1996).

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NOD.Prkdcscid/scidIl2rgtm1Wjl/SzJ (NSG) mice were bred and housed in pathogen-free conditions at the Max Bell Research Centre animal care facility. All experiments using mice were conducted in accordance with and approval from the Hospital for Sick Children or UHN Animal Care Committees.

II.3.7 Lymphocyte isolation Single cell suspensions were prepared from spleen and lymph nodes by gentle disruption of the organs through a 70 m mesh cell strainer (BD Falcon) using a 3cc syringe plunger. Bone marrow cells were isolated from 2 tibias and 2 femurs by flushing bones with staining media (Hank‟s balanced salt solution [Gibco], 10 mM HEPES [pH 7.2], 2% calf serum [Wisent Inc.]) using a 27½G needle (VWR, Mississauga, ON). Cells were further disrupted by gentle pipetting. All cell suspensions were filtered through an 80 m nitex nylon mesh (Dynamic Aqua Supply LT, Surrey, Canada) and centrifuged (5 min, 400 g, 4 C). Red blood cells were removed by resuspending cell pellets in 1 Gey‟s lysis solution (Stock A: 0.65M NH4Cl; 0.025M KCl; 4.2mM Na2HPO4•7H2O;

0.9mM KH2PO4; 0.03M glucose, Stock B: 0.02M MgCl2•6H2O; 0.01M MgSO4•7H2O; 0.02M CaCl2,

Stock C: 0.27M NaHCO3 1 Gey‟s Solution: 20 parts Stock A; 5 parts Stock B, 5 parts Stock C; 70 7 parts sterile ddH2O) at 2 10 cells/mL and incubating on ice for 3 minutes. Cells were then washed in staining media, pelleted by centrifugation (5 min, 400 g, 4 C) and resuspended in staining media. The number of viable cells was determined by trypan blue exclusion (Sigma Aldrich, Oakville, Canada).

II.3.8 Lymphoblast Isolation from CNS, Liver and Kidneys

Liver, kidneys, brain and spinal cord were removed from mice sacrificed by CO2 inhalation. Organs were dissociated by gently disruption with a 3cc syringe plunger and passing through a 70 m mesh cell strainer in staining media. All cell suspensions were pelleted by centrifugation (5 min, 400 g, 4 C). Isolation of leukemic cells from CNS tissue was performed using layered Percoll gradient (70%/37%/30% Percoll (GE Healthcare)). After centrifugation (400 x g, 20min, RT), cells were collected from the 37%:70% interface, washed, pelleted (400 x g, 10 min, RT) and resuspended in RPMI 1640 supplemented with 10% calf serum. Isolation of leukemic cells from liver and kidneys was performed by resuspending cell pellets in 33% Percoll gradient and subsequent centrifugation (400 x g, 20min, RT). Cell pellets were washed in RPMI 1640 supplemented with 10% calf serum and pelleted (400 x g, 10 min, RT). Red blood cells were removed by resuspending cell pellets in 1 Gey‟s lysis solution. Cells were then washed in RPMI 1640 supplemented with 10% calf serum,

37 pelleted by centrifugation (10 min, 400 g, RT) and resuspended in RPMI 1640 supplemented with 10% calf serum.

II.3.9 Histology

Liver and kidneys were dissected from mice sacrificed by CO2 inhalation. Organs were fixed in 15% formaldehyde solution (Sigma Aldrich) for 24 hours. Fixed organs were washed and stored in 70% ethanol. Tissues were embedded in paraffin, sectioned and stained with Hematoxylin and Eosin at the Centre for Phenogenomics Pathology Department (Toronto, ON).

II.3.10 Flow Cytometry and adoptive transfer of murine cells Flow cytometric analyses of murine BM, spleen and lymph node cells were performed as previously described (Danska et al., 1994; Guidos et al., 1996). All samples used in this study contained >85% CD19+ leukemic blasts. In some experiments, total BM from littermate (LM) control mice (p53+/+ or p53+/-; Prkdcscid/scid) containing 5-10% CD19+ cells was used as a control. For surface immunophenotyping of DM B-ALL, cells were stained with fluorochrome- conjugated antibodies specific for CD19-PE (1D3), CD43-FITC (S7), CD22-biotin and -PE (Cy34.1), CD2-biotin and -PE (RM2-5) and MHC II-PE (M5/114.15.2), all purchased from BD Biosciences. Biotinylated primary antibodies were revealed by streptavidin-Alexa Fluor® 633 (Molecular Probes). Data was collected on a FACSCalibur flow cytometer with CellQuest software (BD Biosciences), equipped with a 15 mW blue 488 nm laser and 20 mW red diode 635 nm laser (BD Biosciences) and analyzed using FlowJo software v 9.1 (TreeStar, Ashland, OR). To purify DM B-ALL cells for adoptive transfer experiments, BM cell suspensions from moribund DM mice were stained with antibodies specific for murine CD19-PE-Cy™7 (1D3), CD43- FITC, CD22-PE and CD11b-APC (M1/70). Viable CD19+CD11b-CD43-CD22+ cells were sorted (purity > 97%) and injected intravenously (103/mouse) into sublethally irradiated (650 cGy) Rag2-/- mice. Recipient mice received AIN-76A rodent diet (Research Diets) impregnated with fosta (3 g/kg) or vehicle 36 h later. Mice were sacrificed 3 weeks later to analyze leukemia engraftment in single cell suspensions from BM and spleen by staining with murine CD19-APC, CD22-PE and CD45.2- +FITC (104). Viable cell counts were determined by trypan blue exclusion and absolute numbers of CD19+CD22+CD45.2+ donor cells were calculated using the formula: total # live cells % CD19+CD45+ /100.

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II.3.11 Flow cytometric analyses of human samples For engraftment analysis of human leukemias in transplanted mice, cells were stained with human- specific antibodies for CD19-PE (4G7) and CD45-FITC (2D1), purchased from BD Biosciences. Live cells were discriminated by staining with 5 µg/ml DAPI (Molecular Probes) or 1 µg/ml Fixable Blue Viability dye (Molecular Probes), as per manufacturer‟s instruction. Flow cytometry was performed on either LSRII equipped with with a 100 mW blue 488 nm laser, a 20 mW red diode 633 nm laser, a 25 mW violet 407nm laser and a 20 mW UV 355 nm laser or LSRFortessa equipped with 100 mW blue 488 nm laser, 150 mW yellow/green 561 nm laser, 40 mW red 640 nm laser, 100 mW violet 405 nm laser, and 50 mW UV 355 nm laser (BD Biosciences).

II.3.12 Apoptosis assay For apoptosis assays, B-ALL samples were cultured with fosta (1-3 M) or DMSO vehicle (0.1% DMSO v/v) for 24 hours. Treatment with staurosporine (1 M, 2 hours) was included as a positive control for caspase-3 activation. Fixable Blue viability stain was added 30 minutes prior to fixing and permeabilizing cells, using Cytofix/Cytoperm™ Fixation and Permeabilization Solution (BD Biosciences), and then staining with a FITC-conjugated antibody specific for active caspase-3 (BD Biosciences active caspase-3 apoptosis kit), according to manufacturer‟s instructions. Cellular fluorescence was measured with an LSRFortessa analyzer.

II.3.13 Magnetic beads depletions B Cell Isolation Kit (Miltenyi Biotec, Cologne, Germany) was used to enrich for B cells in PBMCs that were used in immunoblot analyses. For intrafemoral injections, B-ALL samples were depleted of CD3+ cells using CD3 MicroBeads (Miltenyi Biotec), as per manufacturer‟s protocol. All magnetic separations of labeled cells were performed using “depletes” program on an AutoMACS Pro Separator (Miltenyi Biotec). Purity of eluted cells was assessed by flow cytometric analysis, using antibodies against CD3 (clone SK7, BD Biosciences) and CD19 (clone 4G7, BD Biosciences) and was >98% in all cases.

II.3.14 Phospho-specific Flow Cytometry All monoclonal phospho-specific antibodies were purchased from BD Biosciences and include SRC(Y418)-Alexa Fluor 488 (K98-37), SYK(Y352)/ZAP70(Y319)-Alexa Fluor 488 (17a), SYK(Y348)-Alexa Fluor 488/PE (I120-722), BLNK(Y84)-Alexa Fluor 647 (J117-1278) and

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PLC 2(Y759)-Alexa Fluor 488 (K86-689.37). CD45-APC-Cy™7 (2D1) was used to discriminate human leukemic cells. Murine DM B-ALL samples were stained with anti-B220- PE-Cy™7 (RA3- 6B2) to identify leukemic B-cells. Prior to phospho-flow analysis, B-ALL cell lines were serum-deprived for 18-24 h in serum- and phenol red-free RPMI-1640, supplemented with 25 mM HEPES (pH 7.2), 1 mM sodium pyruvate, 2 mM L-glutamine and 0.1 mM non-essential amino acids (SFM). BM cells from murine DM B-ALL and patient samples were rapidly thawed and allowed to recover for 1 hour in SFM. Cells were then cultured with fosta (10 µM), BAY613606 (10 µM) or DMSO (0.1% v/v) vehicle in SFM for 2 h at 37 C. Fixable Blue Viability dye (1:1000) was added for the last 30‟ of culture. Cells were then fixed with BD Cytofix buffer (10‟, 37 C, 1:1 v/v) prior to permeabilization on ice (30‟) with Perm Buffer III (107/ml; BD Biosciences). Cells were rehydrated and washed with PBS containing 1% BSA (w/v) (PBS/BSA), collected by centrifugation (400 x g, 10 min, RT) and stained with pre-determined optimal concentrations of antibodies (30‟, RT). Cells were washed, pelleted by centrifugation (400 x g, 10 min, RT), resuspended in PBS/BSA and filtered through 80 m nitex nylon mesh into round bottom tubes for flow cytometric analysis on an LSRII flow cytometer. FCS3.0 data files were analyzed using FlowJo v 9.1 and Cytobank (http://cytobank.org/) (Kotecha et al., 2010).

II.3.15 Fluorescence Compensation For compensation, anti-mouse, anti-rat or anti-hamster Igκ and negative control compensation particles (BD Bioscience) were stained with fluorochrome-conjugated antibodies. All antibodies used for compensation were purchased from BD Biosciences or Molecular Probes and are listed in Table II.2. The ArC™ Amine Reactive Compensation Bead Kit (Molecular Probes) was used for Fixable Blue stain compensation, according to manufacturer‟s instructions. Compensation setup function in BD FACSDiva software (BD Biosciences) was used to calculate spectral overlap for automated compensation of acquired fluorescence.

II.3.16 B-ALL Xenograft Assays NSG male and female mice (8-12 weeks old) were sublethally irradiated (200 cGy) from a 137Cs - irradiator 24 hours before transplantation. The intrafemoral transplantations were performed as previously described (Mazurier et al., 2003). Briefly, mice were anesthetized by isoflurane inhalation. The area at injection site was cleaned and a 27½G needle was used to drill through a knee. Cells

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(range, 3 105 – 5 106, depending on leukemia sample) were injected with a 28½G insulin syringe (VWR). For fosta regimen 1, animals were treated with control or fosta-impregnated (2 g fosta/kg feed) AIN-76A rodent diet starting on the day of B-ALL injection. For fosta regimen 2, B-ALL engraftment was monitored by flow cytometry for 2-8 weeks post-injection. After xenografts were well established (> 60% CD19+CD45+ blasts in injected right femur), mice were given control AIN- 76A diet, fosta feed with 5 kg fosta/kg feed and fosta feed with 8 g/kg. Under both regimens, mice were monitored daily for signs of morbidity (weight loss, ruffled coat, difficulty breathing, inactivity, abnormal posture) and sacrificed 4-12 weeks after transplantation. The injected right femur (RF), non-injected bones (BM), spleen, brain, liver and kidneys from each recipient were analyzed for the presence of human leukemia cells by flow cytometry using human-specific CD45 and CD19 antibodies (see Section II.3.11). Viable cell counts were determined by trypan blue exclusion and absolute numbers of CD19+CD45+ blasts were calculated using the formula: total # live cells % CD19+CD45+ /100.

II.3.17 Western Blot Analysis Total protein extracts were prepared by lysing cells in modified RIPA buffer (50 mM TrisCl, pH 7.4, 1% NP-40, 0.25% sodium deoxycholate, 150 mM NaCl, 1mM EDTA) with added protease inhibitor cocktail (Roche Applied Science, Laval Canada) and Halt phosphatase inhibitor cocktail (Pierce/Thermo Scientific, Rockford, IL) at 1-3 x 107 cells/ml. Extracts were quantified using the DC protein assay reagents (VWR) with bovine serum albumin (BSA) for the standard curve. Equal amounts of protein were loaded onto a 10% (37.5:1) SDS-PAGE with stacking gel and electrophoresed until the bromophenol blue dye front eluted from the bottom of the gel. Proteins were transferred to PVDF membrane in 25 mM Tris-HCl, 190 mM glycine, 10% (v/v) methanol at 75V for 1.5 hours. Membranes were blocked in 5% (w/v) BSA powder in 1X TBST (20 mM Tris- HCl, 137 mM NaCl, 1% (w/v) Tween-20) at room temperature for 1 hour. Membranes were incubated at 4 C overnight with antibodies against CD79a, BLNK, PLC 2, anti-pSYK(Y352), GAPDH, -actin (Cell Signaling), CD79b (Santa Cruz Biotechnology) and total SYK (SYK-01, Monosan) diluted in blocking buffer. Membranes were washed 4 times for 15 minutes each wash prior to incubation with anti-mouse or anti-rabbit HRP-conjugated antibodies (Santa Cruz and Cell Signaling, respectively) diluted in blocking buffer for 45 minutes at RT. Detection was performed with ECL chemiluminescent reagents (GE Healthcare). Blots were imaged by exposure to BioMax Light film (GE Healthcare).

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II.3.18 Gene Expression Microarrays RNA was isolated from BM or lymph node single cell suspensions, lysed at 5-10 x 106 cells/ml in Trizol® reagent (Invitrogen) per manufacturer‟s instructions and cleaned up using the RNeasy isolation kit (Qiagen). All procedures were conducted at The Centre for Applied Genomics at the Hospital for Sick Children (TCAG, Toronto, ON). TCAG. Biotin-labeled cRNA probes were prepared as recommended by the chip manufacturer (Affymetrix). Briefly, 3.5 to 5 µg of total cellular

RNA were used to generate double-stranded cDNAs with oligodeoxythymidine (oligo(dT)12-18) primers and SuperScript reverse transcription reagents (Invitrogen). The resulting dsDNA was used to prepare biotinylated cRNA probes with a BioArray™ HighYield™ RNA transcript labelling kit (Enzo Life Sciences). 15 µg of biotin-labeled cRNA was fragmented and hybridized to the Mouse Genome 430 2.0 array as recommended by the manufacturer. Chips were washed using an automated fluidics workstation and the EukGE-WS2v4 protocol (Affrymetrix) and arrays were immediately scanned on a GeneChip Scanner 3000 (Affymetrix). All statistical and graphical analysis was carried out in the R computing environment for Windows, version 2.4. One-way analysis of variance (ANOVA) was implemented using the linear models for microarray analysis (LIMMA) package (Smyth, 2004) part of the Bioconductor project (http://www.Bioconductor.org) (Gentleman et al., 2004). Affymetrix CEL files were pre-processed using Robust MultiChip Analysis (Irizarry et al., 2003a; Irizarry et al., 2003b). Statistical analysis was performed on log2-transformed data and differential expression of genes was determined using an empirical Bayes approach and moderated F statistics within LIMMA (Smyth, 2004). These approaches are robust for small sample situations typical of microarray experiments and are in general preferred to the traditional t test or F test. After EB analysis within LIMMA, genes are ranked as being differentially expressed in decreasing order of the value of the F statistics (equivalently in increasing order of the associated p-value). The F-tests were carried out using a one-factor ANOVA design, utilizing contrasts from three pair-wise comparisons via the 'decideTests' command within LIMMA. FDR for the F-tests were carried out within LIMMA using the Benjamini-Hochberg multiple testing correction method (Benjamini and Hochberg, 1995). Venn diagram comparison was used to identify the degree of overlap among differentially expressed probe sets at FDR-adjusted p ≤ 0.001.

II.3.19 Statistical Analysis All data are presented as mean standard error of the mean (SEM). Gaussian distribution was assessed by D‟Agostino-Pearson normality test. Two-group comparisons were preformed using 42 paired Student‟s t-test. The significance of the differences between two groups non-Gaussian distribution was analyzed by using Mann-Whitney test. One-way analysis of variance (ANOVA) with Tukey‟s post-hoc test was used to perform three-group comparisons, whereas repeated- measures ANOVA was used to compare matched groups. Statistical differences with two-tailed probability values of p < 0.05 were considered significant. All data were analyzed using GraphPad Prism software, version 5.0 for Mac OS X (La Jolla, CA).

II.4 RESULTS II.4.1 pre-BCR-independent SYK activation in a p53-/-; Prkdcscid/scid model of early B-ALL DM mice spontaneously develop early B-ALL with 100% incidence and a median latency of eight weeks (Gladdy et al., 2003; Guidos et al., 1996). Consistent with their defect in Igh recombination due to the scid mutation in Prkdc, DM leukemias lack Ig expression (Guidos et al., 1996). We therefore expected DM B-ALL blasts to be developmentally arrested at the pro-B stage. However, global gene expression profiles of DM B-ALL blasts resembled normal pre-B cells more closely than normal pro-B cells (Figure II.1a). Indeed, many genes normally down-regulated during the pro-B to pre-B transition, such as Tdt, Vpreb, 5, Socs-2 and Pkc-eta (Hardy and Hayakawa, 2001) were under-expressed in DM leukemias compared to pro-B-cells. Furthermore, genes normally upregulated as pro-B cells differentiate into pre-B cells, such as Cd2, Cd22, MhcII and Ig (Hardy and Hayakawa, 2001), were over-expressed in DM B-ALL blasts relative to normal pro-B cells. Flow cytometric evaluation confirmed that DM leukemias have a pre-B-like rather than pro-B-like immunophenotype (Figure II.1b). Thus, despite their failure to express the pre-BCR, DM leukemias resembled normal pre-B-cells, suggesting that they aberrantly traversed the pro-B developmental checkpoint in a pre-BCR-independent fashion. Given the critical role of SYK in transducing pre-BCR-signals during normal pre-B-cell development (Mocsai et al., 2010), we asked if DM B-ALL cells exhibit pre-BCR-independent SYK activation, which is accompanied by phosphorylation of two tyrosine (Y) residues (Y348 and Y352) in the linker region, separating the paired SH2 domains from the kinase domain (Furlong et al., 1997). We detected phospho-SYK (Y352) (pSYK (Y352)) by western blotting ex vivo DM leukemia cells, but not in BM cells of p53-proficient littermate (LM) control Prkdcscid/scid mice (Figure II.2a). To obtain a single-cell resolution, we used phospho-specific flow cytometry (Krutzik et al., 2004) to examine basal (stimulus-independent) phosphorylation of SYK (pSYK) and its downstream

43 mediators BLNK and PLC 2 in ex vivo DM leukemias, since these residues are defined targets of pre-BCR or BCR-dependent signaling cascades (Herzog et al., 2009). We optimized and validated our phospho-flow protocol using Igµ+ Ramos B-cell lymphoma cells, which exhibited rapid and robust anti-IgM-induced phosphorylation of SRC, SYK, BLNK and pPLC 2 (See Chapter III). Importantly, staining with antibodies specific for pSYK (Y348) and pSYK (Y352) was significantly higher in all DM leukemias than in background fluorescence minus one (FMO) controls lacking phospho-antibody (Figure II.2b). Furthermore, levels of pBLNK and pPLC 2 were also significantly above background in DM leukemic cells (Figure II.2b). Phosphorylation of all 4 epitopes was decreased by a 2h treatment with fosta and BAY (Figure II.2b). Furthermore, proliferation of B-ALL cells isolated from moribund DM mice was significantly inhibited by both fosta and BAY, at clinically achievable concentrations (Figure II.2c). Thus, proliferation of DM B- ALL cells requires pre-BCR-independent SYK signaling that can be attenuated by small molecule SYK inhibitors. Fosta is currently in clinical trials for and B-cell lymphoma (Efremov and Laurenti, 2011), so its pharmacokinetic and pharmacodynamic properties are well-defined. Therefore, we used fosta to test the SYK dependence of growth after transplantation of CD19+CD22+ B-cells sorted from leukemic DM mice into irradiated B6.CD45.1-Rag2-/- recipient mice. Administration of fosta in feed beginning 36h after transplant achieved consistent therapeutic serum levels. Fosta-treated recipients exhibited lower spleen weights and reduced engraftment of leukemic CD19+CD22+ B-cells in BM and spleen, compared to vehicle treated recipients (Figure II.2d). Together, these data suggest that fosta-sensitive SYK activity is required for DM leukemia survival in vivo.

II.4.2 Pre-BCR-independent SYK activation in human B-ALL We next asked if cIg - B-ALL samples from SR and HR pediatric and adult patients exhibit evidence of SYK activation (Table II.1). All samples expressed high levels of SYK, but variable levels of CD79A, CD79B, BLNK and PLC 2 (Figure II.3a-c). Variable BLNK and CD79A/CD79B expression in B-ALL has been previously reported (Jumaa et al., 2003; Trageser et al., 2009). We used phospho-flow cytometry protocol to measure the impact of fosta and BAY on basal levels of pSYK and downstream targets in pre-B-ALL cell lines and leukemic blasts from diagnostic B-ALL samples. Because SYK is often activated by SFKs (Mocsai et al., 2010), we also measured levels of auto-phosphorylated site SRC (Y418), using an antibody that detects several related SFKs. The

44 gating strategy used to positively identify live, single CD45low leukemic blasts is shown in Figure II.4a. Although occasional samples contained discrete subsets with differing levels of the phospho- proteins examined, most displayed unimodal staining patterns, suggesting lack of cellular heterogeneity (Figure II.4a). Human pre-B-ALL cell lines expressed low levels of phosphorylated SYK (Y348) and several other phospho-proteins that were substantially reduced by a 2h pre- treatment with two small molecule ATP-competitive SYK inhibitors, fosta and BAY (Figure II.4b), suggesting dependence on basal SYK activation. Furthermore, we observed variable levels of basally phosphorylated SRC, SYK and PLC 2 in all 6 HR cIg - samples (Figure II.5a,b). Consistent with our B-ALL cell line data (Figure II.4b), fosta pre-treatment acutely reduced phosphorylation of these proteins in all samples (Figure II.5a,b), suggesting SYK dependence. Although fosta pretreatment reduced pBLNK levels in some samples, overall the reductions were not statistically significant (Figure II.5b), likely due to low BLNK levels in some patients (Jumaa et al., 2003) (Figure II.3b,c). Collectively, these phospho-flow studies demonstrated basal SYK signaling promotes phosphorylation of several pre-BCR signaling proteins in human B-ALL.

II.4.3 SYK activation promotes B-ALL survival and proliferation We next tested the SYK dependence of survival and proliferation for a large cohort of SR and HR B-ALL samples in vitro. Fosta and BAY both robustly attenuated proliferation of pediatric and adult - cIg B-ALL samples in a dose-dependent manner (Figure II.6a-c). Importantly, IC50 values, calculated using non-linear regression, revealed that effective fosta concentrations in this assay (Figure II.7a,b) were within plasma concentrations achieved in clinical trials (Friedberg et al., 2010). Both inhibitors also markedly decreased proliferation of cIg + B-ALL samples in a dose- dependent manner (Figure II.7c). ). Fosta‟s effects on B-ALL proliferation were likely due, at least in part, to apoptosis induction, since fosta rapidly induced expression of cleaved (activated) caspase- 3 (Figure II.6d). Collectively, these data suggest that, similar to murine DM B-ALLs, human B- ALL samples (both Ig - and Ig +) depend on SYK activation for proliferation and survival in vitro.

II.4.4 Kinase specificity of SYK inhibitor effects In addition to SYK, fosta inhibits FLT3, SFKs and several other kinases (Braselmann et al., 2006). Although aberrant FLT3 activation is rare in B-ALL (Armstrong et al., 2004), pre-BCR-mediated SFK activation plays a critical role in promoting survival in some cIg + B-ALL samples (Bicocca et al., 2012). Indeed, a 2h pre-treatment with 10 M fosta acutely reduced pSRC levels in most

45 samples tested (Figure II.5 and Chapter III), suggesting this drug also targets SFKs. In contrast, BAY has a much more restricted target spectrum that does not include FLT3 or SFKs (Yamamoto et al., 2003). Nonetheless, we asked whether FLT3 inhibition by AGL2043 (Gazit et al., 2003) and ABL/SFK inhibition using dasatinib (Lombardo et al., 2004) could impair B-ALL proliferation as potently as SYK inhibition by fosta and BAY. Although treatment with 1 M AGL2043 potently inhibited proliferation of MV4;11 acute myeloid leukemia (AML) cells harboring activated mutant FLT3 (Figure II.8a), 3-10-fold higher doses did not impair B-ALL proliferation (Figure II.8b). Moreover, 10 nM dasatinib potently inhibited proliferation of BCR-ABL+ K562 cells (Drexler et al., 1999) (Figure II.8c) and 13/13 BCR-ABL+ B-ALL samples, but only 3/30 BCR-ABL- B-ALL samples were sensitive to this dose (Figure II.8d), suggesting that ABL/SFK activation does not drive proliferation in most BCR-ABL- cases. It will be interesting to determine if the 3 dasatinib- sensitive BCR-ABL- samples harbor other activating mutations of the ABL kinase (Roberts et al., 2012). Collectively, these data suggest that SYK, rather than FLT3 or SFKs, is the primary target of fosta and BAY in SR and HR B-ALL samples. To complement the pharmacologic assessment of fosta targets in primary B-ALL, we used RNA interference to reduce SYK protein levels in B-ALL cell lines in which we had observed both basal and fosta-sensitive phosphorylation of SYK and its downstream mediators (Figure II.9). The cell lines NALM6, KOPN8 and RS4;11 were exposed to a pool of SYK siRNA (siSYK) or to non- silencing control siRNA (siNonSil) and SYK protein levels were examined by flow cytometry. The siSYK reduced total SYK protein levels in all three cells lines by at least 60%, relative to siNonSil (Figure II.9a). Proliferation was then assessed by [3H]-thymidine incorporation assay. The siSYK treatment reduced NALM6, KOPN8 and RS4;11 proliferation to levels comparable to the effects of

0.3-1 M (the IC50 range) of BAY (Figure II.9b). Collectively, these data further support the contention that SYK is a primary growth mediator of B-ALL and the therapeutic target of fosta and BAY.

II.4.5 Fosta limits B-ALL growth after xenotransplantation We used the well-established NOD.Prkdcscid/scidIl2rgtm1Wjl/SzJ (NSG) xenotransplantation assay to test fosta‟s effects on B-ALL growth and dissemination in vivo (Agliano et al., 2008; Ito et al., 2002; le Viseur et al., 2008; Notta et al., 2011; Quintana et al., 2008; Shultz et al., 2005). B-ALL samples were depleted of CD3+ cells and injected into right femurs (RF) of NSG recipients to maximize engraftment in BM niches and allow evaluation of leukemia dissemination to other bones and

46 tissues (McKenzie et al., 2005). These studies focused on B-ALL samples from HR subgroups (Table II.3), where there is greatest clinical need for novel therapies. Some SR pediatric samples were included since some patients in this category also relapse. All samples were pre-screened to ensure that they engrafted robustly (> 80% human leukemic cells in NSG BM) and caused leukemia-related morbidities within 10 weeks (data not shown). Rapid engraftment kinetics after xenotransplantation has been shown to be an independent prognosticator of poor outcome for B- ALL patients (Meyer et al., 2011). In pilot experiments, NSG recipients were given vehicle or fosta-impregnated feed immediately after transplantation, and analyzed 4-10 weeks later for B-ALL engraftment in hematopoietic organs (RF, BM from non-injected bones and spleen) (Figure II.10a). Significant fosta-specific reduction of spleen weight was observed for 8 of 9 patient samples examined (Table II.4). Fosta reduced leukemia engraftment for all 9 samples in at least one tissue, as manifested by a significant decrease in the number of engrafted leukemic B-cells (Figure II.10b; II.11). Strikingly, fosta reduced disease burden in 2-3 tissues for 4/9 samples examined. Leukemia dissemination into non-hematopoietic tissues (liver and kidney) causes significant morbidity, and fosta also significantly decreased disease burden to these sites (Table II.4; Figure II.10c,d). Leukemia dissemination to CNS is also a serious complication and an impediment to disease-free remission in pediatric and adult B-ALL (Locatelli et al., 2012; Sancho et al., 2006). In these pilot studies, we detected CNS infiltration of B-ALL cells in 3/9 samples. Fosta demonstrated a trend toward reduction of CNS infiltration of these samples (Figure II.10b; II.11). Together, these results suggested that fosta treatment can reduce B-ALL survival and expansion in vivo, and limit dissemination to hematopoietic and non-hematopoietic tissues, including the CNS. Next we asked if fosta could reduce the burden of well-established leukemic grafts, a more clinically relevant scenario (Figure II.12a). For this second regimen, 7 aggressive, fast-engrafting samples were injected into NSG mice (25-31 recipients/sample). This HR cohort included 1 BCR- ABL+ adult case, 1 MLL rearranged infant case, 1 low hypodiploid adult case, and 4 cases with normal or complex karyotypes. To ensure that disease was well established before beginning treatment, we assessed engraftment in sentinel recipients by timed sacrifice from 2-9 weeks after injection. Once sentinels displayed > 60% CD19+CD45+ blasts in RF and robust leukemia dissemination to other bones and/or spleen (Figure II.13), the remaining cohort was placed on vehicle (n=85) or fosta-containing feed (n=108, 2 different concentrations). Recipients were evaluated for disease burden 14 days later.

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Strikingly, even the lower fosta dose significantly reduced leukemia burden in all tissues for sample the MLL rearranged sample 9037, with a clear dose-dependent reduction of dissemination to non-injected bones (Figure II.12b). More impressively, fosta significantly reduced leukemia dissemination to other bones for 4/7 samples and to spleen in 7/7 samples. Fosta-specific reduction in spleen weight was observed for 7 of 7 patient samples examined (Table II.5). In addition, fosta significantly reduced leukemia burden in the injected RF and/or other bones for 4 of the remaining 6 samples, and dose-dependent responses were also evident (Figure II.14a,b). Although fosta did not decrease leukemia burden of the low hypodiploid sample in the RF or BM, dissemination to spleen, CNS and liver were robustly reduced. Indeed, fosta significantly reduced dissemination of all 7 leukemias from BM to spleen, and in 5/6 cases fosta reduced dissemination from BM to liver and/or kidneys (Figure II.14a,b). Finally, fosta also significantly reduced leukemia dissemination to the CNS for 4/6 samples examined. Collectively, these data demonstrate that fosta treatment impairs survival, proliferation and extramedullary dissemination of multiple genetic subtypes of HR B-ALL in vivo. Therefore, addition of SYK inhibitors to chemotherapy regimens may improve outcomes for poor prognosis pediatric and adult patients. Compelling evidence suggests that relapses develop as a result of failure to eradicate leukemia-initiating cells (LICs). We, therefore, investigate if fosta treatment reduced tumor- initiating ability of 2 high-risk B-ALL samples by transplanting BM or SPL cells isolated from fosta- or vehicle-treated mice into secondary recipient and evaluating leukemia engraftment in RF, BM from non-injected bones, spleen and CNS (Figure II.15a). All recipients were analyzed by flow cytometry to evaluate disease burden 4-5 weeks later. Strikingly, we observed significant decrease in CNS leukemia burden of mice that received fosta-treated cells compared to robust engraftment of cells taken from vehicle-treated mice in secondary recipients. These results suggested that fosta exerts an anti-leukemic effect by eliminating CNS leukemia-initiating cells (Figure II.15b). It should be noted that our studies examined fosta effects on a bulk tumor population (5 105) that likely contains an excess of LICs. Indeed, a recent study demonstrated that most B-ALL samples can initiate leukemia with 102-103 cells (Rehe et al., 2013), suggesting that the full potential of fosta may be masked by the overload of LICs present. To address this issue, future studies will test fosta effects on HR B-ALL under limiting dilution conditions.

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II.5 DISCUSSION Identification of kinase-dependent signaling pathways required for survival and proliferation of leukemic B-cells cells is critical for clinical development of targeted therapies to improve outcomes for patients with HR B-ALL. Here, we identified aberrant SYK signaling in a mouse model of B- ALL as well as in multiple subtypes of HR B-ALL, implicating SYK as a potential therapeutic target for this disease. We provide evidence that SYK activation is pre-BCR-independent and linked to activation of several downstream targets in murine and human B-ALL cells. Most importantly, fosta significantly reduced the burden of established disease in NSG xenograft experiments, and prevented B-ALL dissemination to spleen, kidney, liver and CNS, sites that are associated with significant pathology (Redaelli et al., 2005). This fosta-sensitive cohort included HR B-ALL samples belonging to several poor prognosis subgroups, including samples with MLL rearrangements as well as those with low hypodiploid, normal and complex karyotypes. The ability of fosta as a single agent to decrease the burden of established HR B-ALL in xenografts, suggests that adding fosta (or other SYK inhibitors) to current front-line therapies may improve patient outcomes. Similar to most ATP-competitive kinase inhibitors, fosta inhibits a variety of other kinases in addition to SYK (Davis et al., 2011), raising the possibility that SYK is not the relevant target in B- ALL. We provide several lines of evidence that argue against this notion. First, although fosta inhibits activation of SFKs nearly as potently as SYK (Braselmann et al., 2006), proliferation of all B-ALL samples tested was also potently inhibited by BAY, a SYK inhibitor that does not inhibit SFKs and has many fewer “off-target” effects than fosta(Lau et al., 2012). Furthermore, proliferation of 17/30 BCR-ABL- samples was not sensitive to the SFK/ABL inhibitor dasatinib at doses that potently inhibited proliferation of BCR-ABL+ samples. Thus, the ability of fosta to inhibit SFKs does not explain its potent anti-proliferative effect in our study. Fosta also inhibits activation of the receptor tyrosine kinase FLT3, but proliferation of the B-ALL samples in our cohort was not affected by a FLT3-targeted inhibitor. This cohort included three samples with MLL rearrangements that express high levels of FLT3, suggesting that fosta-mediated inhibition of FLT3 activation does not account for its potent anti-proliferative effect in B-ALL. Importantly, we showed that fosta and BAY decrease basal phosphorylation of known SYK targets such as PLC 2 and BLNK, in murine and human leukemic B-cells, confirming that both inhibitors acutely interfere with SYK-dependent signaling. Collectively, these data strongly suggest that inhibition of SYK, and not other kinases, explains the potent anti-proliferative effects of fosta and BAY on multiple HR B-ALL subtypes.

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We did not find SYK coding mutations in DM B-ALL samples (not shown), and it unlikely that the broad sensitivity of HR B-ALL samples to SYK inhibitors reported here reflects recurrent SYK mutations. Indeed, despite extensive genome-wide profiling studies, SYK mutations have not been reported in HR B-ALL (Loh et al., 2013). Furthermore, SYK is not a frequent or recurrent target of point mutation or copy number aberrations in epithelial and other solid tumor (http://www.cbioportal.org; http://cancer.sanger.ac.uk/cosmic/gene/analysis), although epigenetic dys-regulation of SYK expression leading to lineage-inappropriate SYK dependence has been reported in (Zhang et al., 2012). Interestingly, chronically active SYK-dependent BCR signaling, likely driven by self-antigens in a cell autonomous fashion, is essential for survival of B-cell chronic lymphocytic leukemia (CLL) (Duhren-von Minden et al., 2012), as well as one subtype of diffuse large B-cell lymphoma (DLBCL) (Davis et al., 2010). Although 20% of these DLBCL cases harbor gain-of-function mutations in CD79A or CD79B ITAMs, mutational mechanisms that promote BCR signaling have not been identified in the majority of cases. Similarly, some AML cases express active SYK and are sensitive to SYK inhibition(Hahn et al., 2009), but SYK mutations have not been identified in this disease (Cancer Genome Atlas Research Network 2013). It is important to note that SYK can be activated by many receptors expressed on B- cells, including Fc receptors, integrins, C-type lectins and cytokines (Mocsai et al., 2010; Schweighoffer et al., 2013). Although many of these receptors contain paired or single ITAMs, ITAM-independent mechanisms for SYK activation have also been described (Mocsai et al., 2010). Furthermore, like many kinases, SYK is regulated by phosphatases and ubiquitin (Chen et al., 2006; Mocsai et al., 2010). Thus, while we cannot exclude the possibility that some HR B-ALLs harbor mutations in SYK or CD79A/CD79B (Davis et al., 2010), we favor the notion that non- mutational mechanisms, akin to ligand-independent “tonic” signaling, promote widespread SYK activation in HR B-ALL. Prior evidence for the role of SYK in early B-ALL has been controversial. SYK deficiency was reported in an early study of pediatric B-ALL, suggesting that it might function as a tumor suppressor (Goodman et al., 2001) and down-regulation of B-cell signaling genes was identified as a signature associated with IKAROS deletion in adult B-ALL (Iacobucci et al., 2012). Although SYK deletion was reported in 1/22 cases of BCR-ABL+ B-ALL (Trageser et al., 2009), 8/9 of the BCR- ABL+ cases we have profiled expressed fosta/BAY-sensitive pSYK (348) and pPLC 2 (Chapter III), suggesting that SYK signaling is active in most cases of BCR-ABL+ B-ALL. In contrast, SYK served as a proto-oncogene in a model of transduction-mediated TEL-SYK B-ALL supporting a tumorigenic role of SYK signaling (Wossning et al., 2006). Our findings provide direct evidence 50 that pre-BCR-independent SYK activation is a common event in human B-ALL. Moreover, SYK- inhibition reduced proliferation of samples, among which 70% came from poor-prognosis groups at high risk for relapse. Further studies are needed to define the upstream mechanisms of pre-BCR- independent SYK activation in B-ALL as this may reveal additional targets for therapeutic development. Regardless of the mechanisms of activation, SYK appears to regulate key signal transduction pathways of abnormal proliferation and survival in B-ALL. Thus, small molecule SYK inhibitors represent promising agents for treatment for poor-prognosis and relapsed B-ALL. CNS involvement with ALL is associated with poor prognosis and increased risk of ALL relapse (Locatelli et al., 2012). There has been little therapeutic innovation of CNS-directed therapy since the introduction of intrathecal chemotherapy and cranial irradiation, which contemporary treatment seeks to avoid due to its significant late effects (Pui et al., 2009). Our findings have two important implications for B-ALL in the CNS. First, we demonstrated the utility of xenografted NSG mice to model CNS disease, providing a valuable screen for pre-clinical testing of known or novel therapeutic agents. Second, we describe, that SYK inhibition by fosta can impair dissemination and/or survival of leukemic blasts in the CNS. Although not uniformly effective in all B-ALL samples tested, fosta substantially reduced CNS dissemination of 4/4 HR B-ALL samples after engraftment into NSG mice, using a regimen designed to mimic treatment of patients who present with a significant leukemia cell burden. This cohort included an MLL-rearranged infant case with CNS involvement at diagnosis as well as other poor prognosis samples. Our data suggest that SYK inhibitors may provide an appropriate treatment option to study in poor prognosis B-ALL patients with CNS involvement.

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II.6 TABLES

Table II.1 Summary of B-ALL patient samples Pediatric SR Pediatric HRa Adult Total (n) 28 22 41 Age (years) 1-9 0-16 19-82 Gender (n) Male 17 14 14 Female 11 8 21 N/A 0 0 6 Outcome (n) Deceased 0 4 23 Alive 28 18 12 N/A 0 0 6 Cytogenetics (n) TEL-AML1 15 3 0 Hyperdiploidy 6 2 0 Hypodiploid 0 0 1 E2A-PBX1 2 2 0 MLL rearrangement 1 3 4 BCR-ABL 0 1 12 Normal 0 3 5 Complex 4 8 15 N/A 0 0 4

Legend: a HR group criteria: age 1 or 10 years and/or white blood cell count 50 109/L. n, number of samples per group; SR, standard-risk group; HR, high-risk group; N/A, not available.

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Table II.2 List of antibodies used for compensation in flow cytometry experiments Antibody Clone Conjugate Manufacturer Dilution 104 FITC 1:25 BD Biosciences Cy34.1 PE 1 : 100 BD Biosciences 1D3 PE-Cy 7 1 : 100 BD Biosciences RA3-6B2 PE-Cy 7 1 : 100 BD Biosciences HI10a APC 1:25 BD Biosciences 2D1 APC-Cy 7 1 : 100 BD Biosciences Cy34.1 Biotin 1 : 100 BD Biosciences SK7 PerCP-CY 5.5 1 : 10 BD Biosciences Avidin/Streptavidin Alexa Fluor® 647 1:100 Molecular Probes Avidin/Streptavidin Alexa Fluor® 488 1:100 Molecular Probes

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Table II.3 Clinical characteristics of B-ALL patient samples used for in vivo experiments with fosta

Age at Risk Patient Sample Diagnosis Gender Cytogenetics Group Status (years) 7016 12 M Normal SR A 6020 15 M Complex HR D 7055 11 F Complex HR A 6004 11 F Complex SR A 9037 0.25 F MLL rearrangement HR D 6006 12 M Normal HR A 080048 54 F MLL rearrangement HR A 534061 44 M Normal HR D 5806 59 F Complex HR D 090419 23 M Complex HR D 090255 18 M Complex HR A 0523 60 M Low hypodiploid HR D 090318 24 F BCR-ABL HR A

Legend: This table summarizes clinical data for thirteen B-ALL patient samples used to examine in vivo efficacy of Fostamatinib. M, male; F, female; SR, standard-risk group; HR, high-risk group; D, diseased; A, alive.

Table II.4 Summary of regimen 1 effects on the weight organs in a xenotransplant model of human B-ALL

Spleen (mg) Liver (mg) Kidneys (mg) Sample Treatment N Mean SEM Statistics Mean SEM Statistics Mean SEM Statistics 7016 Vehicle 4 199 22 2135 58 518 16 Fosta 3 111 10 0.02 1811 128 0.052 429 10 0.007 6020 Vehicle 5 153 20 1783 69 448 26 Fosta 5 96 11 0.034 1535 50 0.020 359 18 0.023 7055 Vehicle 5 156 17 1754 50 468 28 Fosta 5 115 16 0.122 1475 59 0.007 428 5 0.193 6004 Vehicle 10 250 10 2183 99 497 14 Fosta 9 174 7 <0.0001 1903 38 0.022 429 12 0.002 080048 Vehicle 10 61 4 1717 52 455 21 Fosta 8 49 4 0.049 1526 32 0.014 369 9 0.005 534061 Vehicle 6 471 35 2526 120 619 27 Fosta 5 351 14 0.016 2136 88 0.033 464 11 0.0008 5806 Vehicle 15 353 11 2401 54 474 10 Fosta 17 217 10 <0.0001 1852 78 <0.0001 454 17 0.338 090419 Vehicle 9 126 7 1912 54 458 13 Fosta 10 85 2 <0.0001 1754 31 0.018 427 14 0.005 090255 Vehicle 9 190 10 2225 50 531 13 Fosta 3 109 19 0.003 1944 88 0.018 444 16 0.005

Legend: Cohorts of mice (N: number of mice) were intrafemorally injected with four pediatric and five adult primary early B-ALL samples. Treatment with fosta at 2 g/kg of AIN-76A diet or vehicle was initiated at the time of xenotransplantation. Mice were sacrificed 4-12 weeks after the start of treatment and their organs isolated and weighted. Data (in milligrams, mg) are presented as treatment group mean SEM. Data were analyzed using unpaired t-test with p- values shown in Statistics column.

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Table II.5 Summary of regimen 2 effects on the weight of organs in a xenotransplant model of early human B-ALL

Spleen (mg) Liver (mg) Kidneys (mg) Sample Treatment N mice Mean SEM Statistics Mean SEM Statistics Mean SEM Statistics 090419 Vehicle 10 140 5 1607 55 341 10 Fosta, 5k/kg 9 74*** 5 <0.0001 1479 71 0.110 299** 9 0.002 Fosta, 8g/kg 8 48*** 3 1649 26 296** 7 090255 Vehicle 12 244 16 1520 67 308 15 Fosta, 5k/kg 10 191* 10 0.010 1641 52 0.180 301 10 0.840 Fosta, 8g/kg 9 192* 13 1692 80 299 9 534061 Vehicle 10 323 18 2062 45 385 11 Fosta, 5k/kg 10 219** 10 <0.0001 1963 53 0.080 335* 6 <0.0001 Fosta, 8g/kg 8 158*** 27 1834 104 279*** 21 9037 Vehicle 11 304 13 2114 79 385 15 Fosta, 5k/kg 8 148*** 8 <0.0001 1784** 43 0.001 335* 8 <0.0001 Fosta, 8g/kg 10 112*** 5 1768** 49 292*** 5 0523 Vehicle 14 348 20 1968 57 368 14 <0.0001 0.002 0.001 Fosta, 5k/kg 11 110*** 8 1681** 58 319** 8 090318 Vehicle 14 112 4 1608 35 341 6 <0.0001 0.443 0.009 Fosta, 5k/kg 12 72*** 16 1569 37 306** 7 6006 Vehicle 14 106 6 1554 140 341 9 <0.0001 0.656 0.003 Fosta, 5k/kg 13 47*** 2 1531 31 304** 6

Legend: Cohorts of mice (N: number of mice) were intrafemorally injected with two pediatric and five adult primary early B-ALL samples.

Treatment with fosta at 5 g/kg or 8 g/kg of AIN-76A diet or vehicle was initiated after leukemia establishment. Mice were sacrificed 14 d after the start of treatment and their organs isolated and weighted. Data (in milligrams, mg) are presented as treatment group mean SEM.

Data were analyzed using one-way ANOVA (p-values in Statistics column) with Tukey‟s post-hoc test for all pairwise comparisons for three-group comparisons or Mann-Whitney test for two-group comparisons; *p < 0.05, **p < 0.01, ***p < 0.001 (Vehicle vs Fosta 5 g/kg or vehicle vs Fosta 8g/kg).

II.7 FIGURES

Figure II.1 DM leukemias display a pre-BCR-independent pro-B to pre-B cell transition

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(a) The venn diagram shows the number of probe sets differentially expressed (based on Affymetrix gene expression profiles) in each pair-wise comparison (ANOVA q 0.001). Genes upregulated (top) and downregulated (bottom) during the pro-B to pre-B transition are shown. (b) Flow cytometric analysis of BM cells from DM mice showing signs of leukemia compared to wild-type C.B-17 and p53+/+; Rag2+/+; Prkcdscid/scid littermate (LM) control mice. Cells were stained with antibodies specific for CD19 and CD43, which marks pro-B-cells in the CD19+ population, and antibodies for the pre-B-cell markers CD22, MHCII or CD2. Contour plots (5% probability with bi-exponential scaling) were pre-gated on viable singlets. Gates used to define the pro-B, pre-B, and immature B-cell subsets are shown.

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Figure II.2 pre-BCR-independent SYK activity drives proliferation and survival of DM B- ALL cells

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(a) Immunoblot analysis of SYK phosphorylation in DM B-ALL samples. BM lysates made from leukemic DM mice (> 80% CD19+ leukemic blasts, n=5) and 2 LM control mice were immunoblotted with antibodies specific for pSYK (Y352), total SYK or -actin. Murine splenic

B-cells treated with and anti-IgM and H2O2 (+) or untreated (-) provided a positive control for anti- pSYK (Y352). (b) Phospho-flow analysis of SYK signaling in DM B-ALLs. BM cells from leukemic DM mice (n=3–5) were cultured with vehicle (Veh) or SYK inhibitors for 2h, prior to staining with anti-B220 (FMO control) or anti-B220 plus the indicated phospho-specific antibodies and analyzed by flow cytometry. Graphs show median fluorescence intensities (MFI) of B220+ viable singlets for the FMO control versus phospho-antibody stained cells after treatment with DMSO vehicle (Veh), 10 M fosta or 10 M BAY. Each symbol represents one sample. Variance across groups was analyzed by repeated-measures ANOVA. Tukey‟s post-hoc comparisons are shown: * p < 0.05, ** p < 0.01, *** p < 0.001. SYK inhibitors decrease proliferation of DM B-ALL cells. BM cells from leukemic DM mice (containing > 80% CD19+ leukemic blasts) were treated with fosta (n=7), BAY (n=6) or Veh for 72 h before assessing proliferation [H3]-thymidine incorporation assay. Graphs show normalized proliferation (DPM Inhibitor/DPM Veh 100). Each symbol represents mean of triplicate measurements for one DM sample. (d) SYK-dependent growth of DM B-ALL growth in vivo. The Rag2-/- mice were transplanted with sorted CD19+CD22+ DM B-ALL cells, and after 36h they were given fosta (3 g/kg feed) or vehicle feed for 3 weeks prior to being sacrificed. Graphs show weight (spleen) and leukemic cell engraftment (BM and spleen) in each group (Veh: n=6; fosta: n=7). Unpaired two-tailed t-test: ** p < 0.01, ***p < 0.001. For b-d and subsequent scatter graphs, horizontal lines depict mean SEM for each treatment group.

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Figure II.3 Analysis of expression on BCR signaling components in human early B-ALL (a) Immunoblot analyses were performed with antibodies against CD79a, CD79b and GAPDH using protein lysates prepared from seven viably frozen primary diagnostic pediatric (P1-P3) and adult (A1-A4) early B-ALL patient samples. Lysates prepared from Ramos Burkitt‟s lymphoma cell line (R) and normal B cells isolated from peripheral blood mononuclear cells (PBMC) were used as controls. (b-c) Immunoblot analyses were performed with antibodies against total SYK, BLNK, PLC- 2 and -actin using protein lysates prepared from five viably frozen primary diagnostic pediatric (panel b: P1-P5) and six adult (panel c: A1-A6) early B- ALL patient samples. Lysates prepared from Ramos Burkitt‟s lymphoma cell line (R) and normal B cells isolated from peripheral blood mononuclear cells (PBMC) were used as controls.

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Figure II.4 Phospho-flow analysis of SYK-dependent signaling in B-ALL (a) Gating strategy used for phospho-flow analyses of B-ALL samples shown in Fig. 2d,e. Dead cells were gated out using displays of fixable blue viability dye vs forward scatter area (FSC-A). Debris and doublets were gated out based on side scatter area (SSC-A) vs FSC-A and FSC- height (FSC-H) vs FSC-A, respectively. Finally, plots of FSC-A vs CD45 were used to identify live CD45low blasts. (b) Phospho-specific antibody staining of NALM6, KOPN8 and RS4;11 early B-ALL cell lines. Cells were treated with fosta (10 M), BAY (10 M) or DMSO vehicle for 2h prior to staining with the indicated phospho-specific antibodies. Data displays were pre- gated on live single cells and scaled by normalizing to FMO controls to generate log2 MFI ratios

(Log2 MFI of phospho-antibody stained sample/MFI of FMO control without phospho- antibody).

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Figure II.5 pre-BCR-independent SYK activity in human early B-ALL (a) Phospho-flow analysis of SYK-dependent signaling in diagnostic B-ALL samples. Histograms depict background (FMO) vs phospho-specific antibody staining of live CD45low singlets from B-ALL samples (3 BCR-ABL-; 3 BCR-ABL+) after a 2h culture with Veh or 10 M fosta. MFI values for each histogram are shown in (b). Each symbol represents one sample. Tukey‟s post-hoc comparisons are shown: *p < 0.05, ** p < 0.01, ***p < 0.001.

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Figure II.6 SYK activity is necessary for proliferation and survival of human early B-ALL (a-c) Viably frozen cIg - B-ALL samples were thawed and recovered for 24 h. Cells were treated with increasing concentrations of fosta, BAY or vehicle for 72 h, followed by measurement of [3H]-thymidine uptake. Proliferation (DPM) was normalized to vehicle for each patient sample (Inhibitor/Vehicle 100). Each symbol represents an average of triplicate cultures for one patient sample. Data are presented as the mean of all samples per dose ± SEM. (a) Effects of fosta (top panel: n=25 independent samples) and BAY (bottom panel: n=20 independent samples) on proliferation of standard-risk (SR) pediatric B-ALL samples (b)

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Effects of fosta (top panel: n=19 independent samples) and BAY (bottom panel: n=18 independent samples) on proliferation of high-risk (HR) pediatric B-ALL samples (c) Effects of fosta (top panel: n=39 independent samples) and BAY (bottom panel: n=31 independent samples) on proliferation of HR adult B-ALL samples. (d) Fosta induces apoptosis in early human B-ALL. Six independent viably frozen adult B-ALL samples were thawed and recovered for 1 h prior to a 24 h treatment with fosta. At the end of treatment, apoptosis was evaluated by flow cytometry by active caspase-3-FITC intracellular staining. Cells were also treated with staurosporine (1 M, 2 h) as a positive control. Histograms show a representative example of fosta effect on caspase activation. Bar graph (right panel) shows summary for six independent patient samples, presented as mean ± SEM. Repeated measures ANOVA: p = 0.004; Tukey‟s multiple comparison test (shown on graph): NS, not significant, ** p < 0.01.

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Figure II.7 Anti-proliferative effects on SYK inhibitors in B-ALL

(a-b) IC50 values for fosta and BAY were determined for pediatric (a) and adult (b) B-ALL samples using data from Figure II-6a,b and Figure II-6c, respectively. IC50 values were calculated using non-linear regression analysis. Data (normalized proliferation) are displayed as mean of all samples per dose ± SEM (c) Proliferation of primary cIg + samples is reduced in the presence of SYK inhibitors. Anti-proliferative effects of SYK inhibitors on cIg + early B-ALL samples were measured by [3H]-thymidine uptake and expressed as disintigrations per minute (DPM) normalized to vehicle for each patient sample (Inhibitor/Vehicle 100). Effects of fosta (left panel: n=8 independent samples) and BAY (right panel: n=6 independent samples) are

66 shown as scatter plots. Each symbol represents an average of triplicate cultures for one patient sample. Data are presented as the mean of all samples per dose ± SEM.

Figure II.8 SYK inhibitors’ effects on B-ALL proliferation are SYK-specific and are not due to off-target effects on FLT3 or SRC (a) Determination of optimal concentration for AGL2043 (FLT3 inhibitor). Proliferation of the FLT3-ITD-containing MV4-11 AML cell line was measured after 72 h culture with increasing concentrations of AGL2043 by [3H]-thymidine uptake. Proliferation (DPM) was normalized to vehicle treatment for each patient sample (Inhibitor/Vehicle 100). Data are presented as normalized proliferation ± SEM for the triplicate measurements. (b) AGL2043 does not inhibit

67 proliferation of primary diagnostic early B-ALL samples. Anti-proliferative effects of 72 h treatment with AGL2043 on 8 pediatric and 23 adult independent samples were measured by [3H]-thymidine incorporation assay. Each symbol represents an average of triplicate cultures for an individual patient sample. Data are presented as the mean of all samples per dose ± SEM.(c) Determination of optimal concentration for dasatinib. Proliferation of BCR-ABL-dependent K562 cell line was measured after 72 h culture with increasing concentrations of dasatinib by [3H]-thymidine uptake. Data are presented as normalized proliferation ± SEM for the triplicate measurements. (d) Dasatinib effects on proliferation of primary diagnostic early B-ALL samples. Anti-proliferative effects of 72 h dasatinib treatment on 30 pediatric and adult BCR- ABL- and 13 BCR-ABL+ samples were measured by [3H]-thymidine incorporation assay. Each symbol represents an average of triplicate cultures for an individual patient sample. Data are presented as the mean of all samples per dose ± SEM.

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Figure II.9 SYK knockdown reduces proliferation of early B-ALL cell lines NALM6, KOPN-8 and RS4;11 cell lines were cultured for five days in the presence of SYK siRNA (siSYK) or non-silencing siRNA (siNonSil). (a) The effect of SYK knockdown on total SYK protein levels was determined by intracellular staining and the degree of knockdown was calculated by the formula (1-median fluorescence intensity (MFI) of siSYK/ siNonSil MFI). Data are presented as histogram overlays (left panels). (b) Effects of SYK knockdown on proliferation of NALM6, KOPN-8 and RS4;11 cell lines was determined by [3H]-thymidine incorporation assay five days after culture in the presence of siSYK or siNonSil. Proliferation (DPM) was normalized to siNonSil. Anti-proliferative effects of BAY on these cells lines are included for comparison and were measured by [3H]-thymidine incorporation. Data are presented as normalized proliferation ± SEM for the triplicate measurements.

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Figure II.10 In vivo inhibition of SYK activity shows therapeutic potential in a xenotransplant model of early B-ALL (a) Schematic of fosta treatment regimen 1. CD3-depleted cells from 9 B-ALL samples were intrafemorally (IF) injected into male NSG mice. At the time of transplantation, mice were divided into cohorts that were given either vehicle or fosta containing diet (2g/kg feed). Recipient animals were monitored and sacrificed at the first gross signs of leukemia. Flow cytometric analyses of live cells in injected right femur (RF), non-injected bones (BM), spleen and central nervous system (CNS) were performed to examine engraftment of human cells. (b) A representative example of fosta regimen 1 effects on the engraftment and dissemination of sample 534061 is shown (Vehicle n=6; fosta n=5). Live cells from RF, BM, spleen and CNS were analyzed for the presence of CD19+CD45+ blasts by flow cytometry. Absolute numbers of CD19+CD45+ blasts were calculated using the formula: total # cells % CD19+CD45+ /100.

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Each symbol represents an individual NSG mouse. Data are presented as treatment group mean SEM. Mann-Whitney test, *p < 0.05, **p < 0.01. (c-d) Analysis of fosta regimen 1 effects on tumor dissemination to liver and kidneys. Liver (c) and kidney (d) were isolated at sacrifice and fixed in formaldehyde solution. Tissues were sectioned and stained with hematoxylin and eosin. Representative sections are shown. Scale bars: 400 M.

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Figure II.11 Fostamatinib reduces tumor burden in a xenograft model of early B-ALL Human leukemia engraftment was assessed in RF, BM, spleen and CNS after intrafemoral transplantation of early B-ALL cells from 4 pediatric (a) and 4 adult (b) patient samples into

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NSG mice and continuous fosta treatment initiated at the time of transplantation. The absolute numbers of CD45+CD19+ human leukemia cells (total # cells % CD19+CD45+ /100) from vehicle- and fosta-treated animals are shown as scatter plots for each patient sample. Each symbol represents one NSG recipient mouse. Data are presented as treatment group mean SEM. Each column of graphs corresponds to an individual patient sample with sample ID number indicated on top. Mann-Whitney test, *p < 0.05, **p < 0.01, ***p < 0.001.

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Figure II.12 Therapeutic potential of inhibition of SYK activity in a well-established xenograft model (a) Schematic of fosta treatment regimen 2. CD3-depleted cells from 7 high-risk B-ALL samples were IF injected into female NSG mice. Mice with established disease (>60% CD19+CD45+ in RF) were divided into cohorts treated with vehicle, fosta (5 g/kg diet) or fosta (8 g/kg diet). Mice were sacrificed 14 d after treatment and engraftment of human cells in RF, BM, spleen, liver, kidneys and CNS was examined by flow cytometry. (b) Fosta reduces burden of an established leukemia. A representative example of fosta regimen 2 effects on the engraftment and metastasis of infant B-ALL 9037 sample is shown (Vehicle n=11; fosta 5 g/kg

74 n=8; fosta 8 g/kg n=10). Cell suspensions were prepared from RF, BM, spleen, liver, kidneys and CNS of recipient mice at sacrifice. Absolute numbers of CD19+CD45+ blasts were determined as described in Figure 5b. Each symbol represents an individual mouse. Data are presented as treatment group mean SEM. Data were analyzed using one-way ANOVA with Tukey‟s post-hoc p values shown on graphs: *p < 0.05, **p < 0.01, ***p < 0.001.

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Figure II.13 Tumor burden in NSG mice at the start of fosta regimen 2 Seven primary diagnostic early B-ALL patient samples were injected into NSG mice, as described in Figure II-12a. Human B-ALL engraftment was monitored in RF, BM and spleen by flow cytometry. Summary of human leukemic engraftment (%CD45+CD19+) in RF, BM and spleen prior to the fosta treatment initiation is shown for seven patient samples.

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Figure II.14 Fostamatinib reduces burden of an established leukemia. (a-b) Human leukemia engraftment was assessed in RF, BM, spleen, CNS, liver and kindeys after IF transplantation of early B-ALL cells from six patient samples into NSG mice and subsequent 14 d treatment with fosta. The absolute numbers of CD45+CD19+ human leukemia cells (total # cells % CD19+CD45+ /100) from vehicle- and fosta-treated animals are shown as scatter plots for each patient sample. Each symbol represents one mouse. Data are presented as treatment group mean SEM. Each column of graphs corresponds to an individual patient sample with sample ID number indicated on top. (a) Three-group comparisons: ANOVA with Tukey‟s post-hoc test or (b) Mann-Whitney test: *p < 0.05, **p < 0.01, ***p < 0.001.

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Figure II.15 Fostamatinib reduced leukemia-initiating cells in CNS and spleen

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(a) Overview of experimental design. Effect of fosta treatment on LICs from B-ALL samples 534061 (left) and 9037 (right) was assessed by serial IF transplantation of BM or spleenic cells (5 105 CD19+CD45+) harvested from mice treated with fosta (8 g/kg) or vehicle. (b) Human leukemia engraftment was assessed in RF, BM, spleen and CNS 4-5 weeks after transplantation. The absolute numbers of CD45+CD19+ human leukemia cells (total # cells % CD19+CD45+ /100) are shown as scatter plots for each patient sample. Each symbol represents one mouse. Data are presented as treatment group mean SEM. Mann-Whitney test: *p < 0.05.

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CHAPTER III: Phospho-flow cytometric profiling of SYK- dependent signaling networks in high-risk precursor B-cell acute lymphoblastic leukemia

Tatiana Perova1,2, Goce Bogdanoski1, Stevan Lauriault1, Julie S. Yuan1, Chen Shochat3, Shai Izraeli3, Johann K. Hitzler1, Mark D. Minden2,4, Jayne S. Danska2,5,6 and Cynthia J. Guidos1,6

1Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada 2Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada 3Department of Pediatric Hemato-Oncology, Functional Genomics and Childhood Leukemia Research, Cancer Research Center, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel 4Ontario Cancer Institute/Princess Margaret Hospital, University Health Network, Toronto, Ontario, Canada 5Program in Genetics and Genome Biology, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada 6Department of Immunology, University of Toronto, Toronto, Ontario, Canada

Contributions: TP, CJG and JSD designed study. TP performed all experiments and analyzed all data. GB, SL and JSY assisted with phospho-flow profiling of cell lines, CS and SI performed mutation analysis of CLRF2 and JAK2. JKH and MDM provided primary B-ALL patient samples.

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III.1 ABSTRACT B-cell acute lymphoblastic leukemia (B-ALL) is a heterogeneous malignancy that consists of multiple genetic subtypes characterized by distinct prognostic cytogenetic abnormalities. Currently, over 60% of adults and 20% of children with B-ALL do not respond to conventional chemotherapy and often develop fatal relapse. Thus, there is a great need to identify novel therapeutic targets in B-ALL. Towards that end, we used phospho-flow cytometry to measure expression of phosphorylated proteins involved in the pre-BCR/BCR, PI3K/AKT/mTOR, MAPK and JAK/STAT signaling networks in 68 diagnostic B-ALL samples, and to measure patient-specific responses to small molecule inhibitors targeting these pathways. We present evidence for widespread activation of pre-BCR/BCR signaling across all cytogenetic groups of B-ALL that could be abrogated by SYK (fosta, BAY) or SRC (dasatinib) inhibitors. In contrast, we observed high, fosta-insensitive levels of pERK in a small number of samples, revealing pre- BCR/BCR independent regulation of RAS/ERK pathway in B-ALL. High basal levels of p4EBP1 were reduced by LY294002 but not fosta. Interestingly, most samples with high basal p4EBP1 had low basal phosphorylation of the ribosomal S6 protein, suggesting that distinct mechanisms regulate mTOR activation and S6 phosphorylation in B-ALL. We detected high basal pS6 levels in a subgroup of samples that correlated with phosphorylated SYK levels and was consistently reduced by pre-treatment with fosta. In addition, a subset of samples had high basal STAT5 phosphorylation with several unique patterns of drug sensitivities that did not correlate with known genomic alterations, revealing distinct mechanisms of STAT5 activation on B-ALL. Importantly, fosta and BAY613606 robustly reduced levels of pEIF4E (S209), a translational regulator, which was more potent than the effects of SRC, PI3K or MEK1/2 inhibitors. Here we present strong evidence that the potent anti-leukemic effects of SYK inhibitors fosta and BAY613606 that we previously identified, may be linked to their combined inhibition of BCR signaling and EIF4E phosphorylation, a feature not shared by other inhibitors targeting BCR, PI3K/AKT/mTOR or JAK signaling that minimally affected proliferation of B- ALL samples. Collectively, out data revealed, for the first time, complex alterations in basal signaling networks in B-ALL patient samples that likely explain their distinct sensitivities to small molecule kinase inhibitors.

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III.2 INTRODUCTION B cell acute lymphoblastic leukemia (B-ALL) is caused by the abnormal accumulation or expansion of malignant B-cell progenitors in the bone marrow. Leukemic B-cells exhibit a diverse spectrum of genetic alterations that affect clinical presentation, treatment response and patient outcomes. Currently, the presence of certain cytogenetic abnormalities and clinical features are used to assign patients into standard-risk (SR) or high-risk (HR) treatment groups. However, relapses occur in all cytogenetic subgroups of pediatric and adult B-ALL patients (Chessells, 1998), and are associated with dismal survival rates of approximately 10% (Fielding et al., 2007; Nguyen et al., 2008; Oriol et al., 2010). Although current multi-agent chemotherapy regimens have increased the cure rates for pediatric B-ALL patients to around 85% (Hunger et al., 2012; Pui et al., 2009; Pui et al., 2010), further dose escalations or incorporation of additional cytotoxic agents are unlikely to improve these outcomes (Marshall et al., 2013). Thus, there is a great need to develop novel agents that will further increase survival rates in B-ALL patients. Recent profiling of copy number alterations in leukemic B-cells revealed that B-ALL pathogenesis is driven by genetic or epigenetic disruption of transcriptional and signaling networks important for normal B-cell development (Kuiper et al., 2007; Mullighan et al., 2007). For example, loss-of-function mutations in transcription factors important for B-lineage specification, such as PAX5, IKAROS and EBF1, are common and likely contribute to developmental arrest, which is an important step in B-ALL pathogenesis. Unfortunately, transcription factors are not easily “druggable”. Aberrant signal transduction pathways that regulate survival and proliferation of B-ALL blasts provide more attractive therapeutic targets. Indeed, the discovery that most chronic myelogenous leukemias (CML) and some B-ALL cases harbor BCR-ABL gene rearrangements lead to the development of small molecule kinase inhibitors, such as imatinib and dasatinib (DAS), which in combination with conventional chemotherapy, have greatly improved clinical outcomes for CML and BCR-ABL+ B-ALL patients (Biondi et al., 2012; Druker et al., 2001a; O'Hare et al., 2005; Ravandi et al., 2010; Thomas et al., 2004). The therapeutic efficacy of BCR-ABL inhibitors is attributable to the disruption of stimulus-independent (also known as basal) BCR-ABL tyrosine kinase activity and its activation of PI3K/mTOR, RAS/MAPK and JAK/STAT signaling in the absence of growth factors and other cell-extrinsic cues (Goldman and Melo, 2003; Hazlehurst et al., 2009). Thus,

83 the BCR-ABL paradigm provides a compelling rationale for the development of inhibitors targeting other basally active “oncogenic” kinases in B-ALL. Towards that end, next-generation sequencing technologies have recently identified mutations that cause aberrant activation of a variety of kinase and cytokine signaling pathways in some HR B-ALL samples (Holmfeldt et al., 2013; Roberts et al., 2012). Similar to BCR- ABL, these genetic events were shown to cause aberrant activation of common core signaling pathways, including JAK/STAT, RAS/MAPK and PI3K/mTOR (Holmfeldt et al., 2013; Roberts et al., 2012). Although small molecule kinase inhibitors targeting these pathways are already in clinical development for a variety of malignancies (Ahmed and Van Etten, 2013; Balakrishnan and Gandhi, 2012; Reeder and Ansell, 2011), these mutations occur at a low frequency, presenting practical challenges for rapid identification, functional validation and identification of suitable targeted therapies. To date, comprehensive characterization of basal signaling perturbations across the major cytogenetic groups of HR B-ALL has not been reported. In addition to cytokine signaling, B-cell progenitors require pre-B-cell receptor (pre- BCR)-mediated activation of Src family kinases (SFK) and spleen tyrosine kinase (SYK) to survive and proliferate beyond the pro-B cell stage of development. SYK activates several downstream pathways by phosphorylating effector proteins including B cell linker (BLNK), phospholipase C- 2 (PLC 2), Bruton‟s tyrosine kinase (BTK) and the p85 subunit of phosphoinositide-3 kinase (PI3K), leading to activation of PI3K/AKT/mTOR and RAS/MAPK survival and growth pathways (Mocsai et al., 2010). The central role of SYK as a major upstream regulator of B-cell progenitor survival and proliferation likely explains why ectopic SYK activation can drive B-cell leukemogenesis in mice (Wossning et al., 2006). Although mutations in SYK or other BCR signaling proteins have not yet been identified in B-ALL (Loh et al., 2013; Mullighan et al., 2007), we have shown that fostamatinib (fosta), an ATP- competitive SYK inhibitor (Braselmann et al., 2006), significantly attenuates proliferation of HR B-ALL in vitro and in vivo (Chapter II), suggesting that SYK might represent an attractive therapeutic target in B-ALL. Indeed, orally available small molecule SYK inhibitors have been developed for clinical use in B-cell mediated autoimmune diseases (Ruzza et al., 2009; Singh et al., 2012). Furthermore, some mature B-cell malignancies display readily detectable basal (stimulus-independent) SYK phosphorylation (Chen et al., 2008; Cheng et al., 2011), which in some cases has been linked to mutations in BCR signaling mediators (Davis et al., 2010). However, the extent to which SYK-dependent pre-BCR/BCR signaling is active across different cytogenetic groups of HR B-ALL has not been investigated. 84

Phospho-flow cytometry provides a high-throughput platform that can rapidly characterize signaling networks in single cells using antibodies specific for activation-induced phosphorylation of key signaling proteins (Krutzik et al., 2004; Krutzik and Nolan, 2003). Importantly, phospho-flow cytometric profiling studies have demonstrated that leukemia- associated signaling mutations can alter cytokine-potentiated phosphorylation of signaling proteins in myeloid leukemia and B-ALL samples (Irish et al., 2004; Kotecha et al., 2008; Rosen et al., 2010; Tasian et al., 2012). Phospho-flow cytometric profiling also identified higher basal levels of several phospho-proteins in B-ALL samples with rearrangements of CRLF2, encoding the thymic stromal lymphopoietin (TSLP) receptor, as compared to CRLF2 wild-type samples (Tasian et al., 2012), demonstrating the ability of phospho-flow to detect mutation- dependent perturbation in basal signaling. Thus, phospho-flow cytometry can identify leukemia- associated abnormalities in both cytokine-potentiated and basal signaling. In this study, we used phospho-flow cytometry to evaluate SYK-dependent signaling networks in a large cohort of SR and HR B-ALL samples belonging to all major cytogenetic subgroups. Using a carefully optimized high throughput phospho-flow platform, we demonstrate that several key proteins involved in pre-BCR/BCR signaling, including SRC, SYK, PLC- 2, and CRKL, exhibited basal phosphorylation that was highly sensitive to fosta and DAS in most samples. However, only rare samples displayed evidence of high basal expression of phospho- ERK, and this was relatively fosta-insensitive, suggesting pre-BCR/BCR-independent regulation of RAS/MAPK activation in these samples. In contrast, most samples displayed basal phosphorylation of 4EBP1 that was decreased by the PI-3K/mTOR inhibitor LY294002 but not fosta, suggesting widespread but pre-BCR/BCR-independent mTOR activation in B-ALL. Although it has been suggested that S6 phosphorylation reflects mTOR activation in B-ALL, in this cohort most samples exhibiting LY-sensitive 4EBP1 phosphorylation had low basal pS6, suggesting that mTOR activation and S6 phosphorylation are largely uncoupled in B-ALL. Nonetheless, a small subset of samples exhibited high basal levels of pS6 that was decreased by multiple inhibitors, suggesting complex regulation. In addition, we revealed that inhibition of phosphorylation of translational protein EIF4E in combination with attenuation of BCR signaling was a unique signature of fosta, likely explaining its potent anti-proliferative activity in comparison to SFK/BCR-ABL, PI3K, MEK1/2 and JAK2 inhibitors. Furthermore, we demonstrate that high basal pSTAT5 levels displayed several distinct patterns of tyrosine kinase inhibitor sensitivity to that did not correlate with genetic lesions. Finally, we revealed that profiling of basal phospho-proteins could be used to classify B-ALL 85 samples into distinct phospho-protein cluster groups, predictive of patient outcome. Collectively, our data provides support for the use of phospho-flow cytometry to detect aberrations in basal signaling pathways driven by multiple genetic lesions in B-ALL, thereby providing a high throughput platform for patient-specific screening of novel targeted therapies.

III.3 METHODS III.3.1 Patient samples Peripheral blood or bone marrow aspirates were collected from 68 newly diagnosed B-ALL patients at Hospital for Sick Children and Princess Margaret Hospital (Toronto, Canada). Samples were obtained with informed consent according to guidelines established and approved by the Research Ethics Boards at these institutions. Mononuclear cells were isolated from all samples using Ficoll-Paque Plus density gradient (GE Healthcare, Baie d‟Urfe, Canada), according to manufacturer‟s instructions. Isolated cells were viably frozen in 90% fetal bovine serum (v/v) containing 10% DMSO (v/v) and stored long-term in liquid nitrogen until used. Cell viability after thaw was determined by trypan blue (Sigma Aldrich) exclusion method and was greater than 80% in all cases.

III.3.2 Cell lines Human leukemia cell lines Ramos and NALM6 were purchased from the American Tissue Culture Collection (Manassas, VA, USA) and DSMZ German Collection of Microorganisms and Cell Cultures (Braunschweig, Germany), respectively. Cells were maintained in RPMI 1640 (Wisent Inc., Laval, Canada) supplemented with 10% fetal bovine serum (FBS, Wisent Inc), 10 mM HEPES (Wisent Inc), 2 mM L-glutamine and 1 mM sodium pyruvate (Gibco). Murine BaF3 cells, provided by Dr. Robert Rottapel (Toronto, Canada), were grown in RPMI 1640 supplemented with 10% FBS and 10 ng/mL recombinant mouse IL-3 (Peprotech, Rocky Hill,

NJ). All cells were grown in a 95% air/5% CO2 humidified incubator at 37 C.

III.3.3 Small molecule kinase inhibitors All small molecule inhibitors are listed in Table III.1. Active form of fostamatinib (previously known as R406) was kindly provided by AstraZeneca (Alderley Park, Macclesfield, UK). BAY613606 and LY294002 were purchased from EMD Chemicals (Philadelphia, PA). PD184352 was obtained from US Biological (Swampscott, MA). Dasatinib was purchased from

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Toronto Research Chemicals (Toronto, Canada). SAR302502 was supplied by Sanofi Pasteur (Bridgewater, NJ). All inhibitors were reconstituted with DMSO at 10 mM and stored at -80 C.

III.3.4 Proliferation assay Viably frozen B-ALL samples were thawed and recovered at 37 C overnight in StemSpan media (Stem Cell Technologies, Vancouver, BC) supplemented with 25 mM HEPES pH 7.2, 1mM sodium pyruvate, 2 mM L-glutamine and 0.1 mM non-essential amino acids. Cells were then plated at 1.5 105 cells per well in a flat-bottom 96-well plate and cultured in the presence of indicated inhibitors for 72h. For the final 16 h of culture, [methyl-3H]-Thymidine (Perkin Elmer, Woodbridge, ON) was added to each well (1 Ci/well). At the end of treatment, cells were harvested onto glass fiber filter mat using Inotech Cell Harvester (Inotech Biosystems, Rockville, MD). Proliferation was measured as disintegrations per minute (DPM) on Beckman LS 6500 Scintillation Counter (GMI Inc, Ramsey, MN). Determinations were made in triplicates for each treatment condition.

III.3.5 Antibodies All antibodies used in the phospho-flow profiling of B-ALL samples were purchased from BD Biosciences, with the exception of I B that was obtained from Cell Signaling. All antibodies are listed in Table III.2. Each phospho-antibody was tested at 1:5-1:40 dilution range. Optimal concentration was determined by calculating staining index (SI) using formula 1.645

[(MFIpositive-MFInegative)/(95%negative-5%negative)]. CD45-APC-Cy™7 (2D1, BD Biosciences) was used to discriminate human leukemic cells in B-ALL patient samples. For compensation, we stained anti-mouse Ig, κ and negative control compensation particles (BD Bioscience) with fluorochrome-conjugated antibodies (listed in Table III.3) for 30 minutes. We used ArC amine reactive compensation bead kit (Molecular Probes) to prepare fixable blue compensation sample according to manufacturer‟s instructions. A total of 20,000 events were collected for each compensation control.

III.3.6 Phospho-flow analysis in cell lines Ramos and NALM6 cells were serum-deprived for 18-24h in serum- and phenol red-free free RPMI 1640 media (SFM, 25 mM HEPES pH 7.2, 2 mM L-glutamine, 1 mM sodium pyruvate, 0.1 mM non-essential amino acids) at 37 C. Cells were kept at 37 C during treatment,

87 stimulation and fixation. Where indicated, cells (1 106/ml) were incubated for 2h with fostamatinib (10 M), dasatinib (200 nM), LY294002 (20 M) or DMSO vehicle (0.2% v/v). Fixable Blue viability dye (1:1000; Molecular Probes, Eugene, OR) was added for the last 30 minutes of culture. For BCR crosslinking, Ramos cells were treated with goat F(ab‟)2 anti- human anti-IgM (10 g/ml; SouthernBiotech, Birmingham, AL) for the last 8 minutes of culture. BaF3 cells were serum- and IL-3- deprived for 4h and subsequently stimulated with IL- 3 (10 ng/ml; Peprotech, Rocky Hill, NJ) for 10 minutes. At the end of stimulation, cells were fixed with BD Cytofix buffer (1:1, v/v) for 10 minutes. Cells were permeabilized in ice-cold BD Perm Buffer III (BD Biosciences) at 1 107 cells/mL on ice for 30 minutes. Cells were rehydrated and washed in staining media (SM, PBS supplemented with 1% bovine serum albumin) and stained in SM with phospho-specific antibodies for 30 minutes at room temperature (RT). Cells were washed, pelleted by centrifugation (10 min, 400 g, RT), resuspended in SM and filtered through 80 m nitex nylon mesh (Dynamic Aqua Supply LT, Surrey, Canada) to remove cell aggregates and debris. Immunofluorescence was analyzed on LSRFortessa cell analyzer (BD Biosciences) equipped with 100 mW blue 488 nm laser, 150 mW yellow/green 561 nm laser, 40 mW red 640 nm laser, 100 mW violet 405 nm laser, and 50 mW UV 355 nm laser. Compensation setup function in BD FACSDiva software (BD Biosciences) was used to calculate spectral overlap for compensation of acquired fluorescence data. Compensated data were then gated in FlowJo software v 9.1 (TreeStar, Ashland, OR) and exported to Cytobank (http://cytobank.org/), a web-based analytical software for flow cytometry data analysis and visualization.

III.3.7 Phospho-flow analysis in B-ALL patient samples The overview of the phospho-flow protocol is shown in Figure III.1. Viably frozen B-ALL patient samples were thawed, resuspended at 1 106/ml in SFM and rested for 1h at 37 C. Cells were then treated for 2h with fostamatinib (10 M), dasatinib (200 nM), LY294002 (20 M), PD184252 (10 M), SAR302502 (200 nM) or DMSO vehicle (0.2% v/v). Fixable Blue viability dye was added for the final 30 minutes of treatment. Where specified, PMA (Sigma Aldrich) stimulation (50 nM) was performed for the final 15 minutes of culture. Cells were immediately fixed with BD Cytofix buffer (10 min 37 C) and permeabilized in BD Perm Buffer III at 1 107 cells/mL (30 min, ice). Cells were stored in BD Perm Buffer III overnight at -20 C.

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The following day cells were rehydrated by addition of SM, washed, pelleted by centrifugation (10 min, 400 g, RT), resuspended at 107 cells/ml in SM and filtered through 80 m nitex mesh. For each stain, 4 105 - 1 106 cells were transferred to a round-bottom 96-well plate (Evergreen Scientific, Los Angeles, CA). Cells were pelleted as above and stained (30 minutes, room temperature) in a 50 l antibody mix containing saturating concentrations of phospho-specific antibodies in combination with CD45. Cells were washed twice, pelleted and resuspended in 200 l of SM and acquired (30,000 events/well) in 96-well plates using high throughput sampler (HTS) on LSRFortessa cell analyzer. FACSDiva-compensated data were exported and gated in FlowJo software v 9.1. Compensated and pre-gated data were exported to Cytobank (http://cytobank.org/) for analysis and visualization. For analysis of CRLF2 surface expression in human leukemias, cells were stained with human-specific antibodies for CD19-PE (4G7, BD Biosciences), CD45-FITC (2D1, BD Biosciences) and CRLF2-biotin (1B4, Biolegend). Biotinylated CRLF2 antibody was revealed by streptavidin PE-Texas Red (BD Biosciences). Live cells were discriminated by staining with 5 µg/ml DAPI (Molecular Probes). Immunofluorescence was quantified using LSRFortessa cell analyzer.

III.3.8 Data normalization and visualization Levels of basal phosphorylation for each protein were determined as difference between median fluorescence intensity (MFI) of cell stained with CD45 in combination with phospho-antibody and MFI of vehicle-treated cells stained with CD45 alone (fluorescence minus one, FMO).

Normalized basal phosphorylation levels were calculated as log2 (MFIstained-MFIFMO). For hierarchical clustering of basal phosphorylation, data were first row-normalized. This normalization was performed for every row (phospho-protein) by calculating a z-score for each patient (patient MFI-row mean MFI)/ standard deviation. Overall minimum and maximum z- scores were used to establish color scale for heatmap visualization of basal phospho-protein levels. Hierarchical clustering of basal phospho-proteins was performed using R project statistical computing software. To determine changes in phosphorylation of each protein following treatment with small molecule kinase inhibitors, we calculated the log2 fold-change (FC) ratio of MFI of inhibitor- treated cells divided by MFI of cells treated with DMSO vehicle (log2 [MFITreatment/MFIVehicle]). Cytobank was used for visualization of multidimensional data sets.

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III.3.9 Mutation analysis Mutation analsysis was performed as previously described (Bercovich et al., 2008; Hertzberg et al., 2010). We used intronic primers of human CRLF2 exon 6 (F: 5‟- GTACAGCCGCACGTCATGTT-3‟; R: 5‟-TCGTACTTCACAGACATTGTGC-3‟) and JAK2 exon 14 (F: 5‟-CATGCTGAAAGTAGGAGAAAGTGC-3‟; R: 5‟-TACACTGACACCTAGCTGTGATCC-3‟) for amplification with PCR. Fast start Taq DNA polymerase (Roche) was used with the following thermal cycling conditions: 1 cycle of 94°C for 5 min, followed by 5 cycles of 94°C for 30 s, 58°C for 30 s, and 72°C 30 s, followed by 30 cycles of 94°C for 30 s, 55°C for 30 s, and 72°C 30 s, followed by 1 cycle of 72°C for 7 min. PCR fragments were sequenced directly by the primer used for PCR.

III.3.9 Statistical analysis Gaussian distribution was assessed by D‟Agostino-Pearson normality test. Two-group comparisons were preformed using Wilcoxon matched pairs rank test for non-Gaussian data. Repeated measures one-way analysis of variance (ANOVA) with Tukey post-hoc test was used to perform multi-group comparisons. Statistical differences with two-tailed probability values of p < 0.05 were considered significant, unless specified otherwise. Bonferroni correction was used to adjust for multiple testing. The relationships between phospho-proteins were assessed by two- tailed Pearson correlation. Data are presented as mean standard error of the mean (SEM) in all scatter graphs. All data were analyzed using GraphPad Prism software, version 5.0 for Mac OS X (La Jolla, CA).

III.4 RESULTS III.4.1 Optimization and validation of phospho-flow platform for detection of basal signaling Phospho-flow cytometry can be used to evaluate leukemia-associated changes in basal or potentiated signaling networks (Irish et al., 2006b). Although potentiated signaling is more robust and thus straightforward to measure, leukemic B-cells, like normal B-cell progenitors, respond to a wide array of hematopoietic and inflammatory cytokines, and the costs and cell numbers needed to measure B-ALL responses to many different cytokines would be prohibitive. Therefore, we chose to profile basal phosphorylation of signaling proteins in the BCR,

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RAS/MAPK, PI3K/AKT/mTOR and JAK/STAT pathways in diagnostic B-ALL samples, an approach which does not require screening responses to large panels of agonists. We rigorously optimized antibody concentrations to ensure that we could reliably detect low levels of staining above background, since basal levels of signaling are often low. For BCR- regulated phospho-proteins including pSRC, pSYK, pBLNK, pBTK, pPLC 2, pCRKL, pERK and pp38 as well as a total phospho-tyrosine (pTYR), we defined optimal antibody concentrations as those yielding the maximum difference in staining of unstimulated versus BCR-stimulated Ramos cells (Figure III.2a). We optimized staining for phosphorylated PI3K/mTOR proteins S6, 4EBP1 and the translational regulator EIF4E in Ramos cells based on the reduction in BCR-potentiated phosphorylation, achieved with PI3K-specific inhibitor LY294002 (LY) (Vlahos et al., 1994) and/or on robust IL3-induced phosphorylation in BaF3 lymphoid progenitor cells (Rosa Santos et al., 2000) (Figure III.2b,c). The pEIF4E (S209) was included in this study because phosphorylation of this epitope, regulated by mitogenic RAS/MAPK signaling pathway (Joshi et al., 1995; Waskiewicz et al., 1997), is required for selective translation of mRNAs involved in innate immunity and tumorigenesis (Furic et al., 2010; Herdy et al., 2012). IL3 also induced strong ERK phosphorylation in BaF3 cells, providing ideal conditions to optimize pEIF4E staining (Figure III.2c). Previous studies have documented ligand-independent basal BCR signaling in B cell lymphoma lines (Chen et al., 2008; Schmitz et al., 2012). We therefore used our optimized staining conditions to determine if we could detect basally active BCR signaling in BCR- positive B cell lymphoma cell line (Ramos). Unstimulated Ramos cells exhibited staining above the fluorescence minus one (FMO) background with antibodies specific for pTYR and 8 phospho-proteins belonging to BCR pathway (pSRC, pSYK, pBLNK, pBTK, pPLC 2, pCRKL, pERK and pp38; Figure III.2d). Importantly, a 2 hour pre-treatment with ATP-competitive small molecule inhibitors targeting SYK (fosta) or SFKs (dasatinib, DAS) substantially reduced or completely abrogated staining for pTYR, pSRC, pSYK, pBLNK, pPLC 2 and pCRKL, but not pBTK, pERK or pp38. We observed similar fosta- and DAS-sensitive basal phosphorylation of pTYR, pSRC, pSYK, pBLNK, pPLC 2 and pCRKL in NALM6, a pre-BCR-positive B-ALL cell line (Figure III.2d), suggesting that our approach can also detect tonic pre-BCR signals. Thus, using phospho-flow cytometry we could readily detect basal phosphorylation of several BCR signaling mediators in transformed B-cell lines.

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Although Ramos and NALM6 cells expressed similar basal levels of BCR-regulated phospho-proteins, levels of PI3K/mTOR pathway phospho-proteins were more variable, most notably pS6 (Figure III.2d). Although fosta and DAS reduced phosphorylation of AKT (S), 4EBP1 and S6 in NALM6, only fosta reduced pS6 levels in Ramos cells, suggesting that distinct upstream mechanisms regulate PI3K/mTOR signaling networks in the two cell lines, likely in a pre-BCR/BCR-independent fashion. Intriguingly, fosta reduced EIF4E phosphorylation more potently than DAS in both cell lines, suggesting regulation by a SYK-dependent, but SRC- independent, mechanism. Collectively, these findings demonstrate that phospho-flow cytometric profiling of responses to kinase inhibitors can reveal differences in the architecture of SYK- and SRC-dependent basal signaling networks in transformed B-cells, and suggest that SYK signaling may uniquely regulate EIF4E (S209) phosphorylation. Using these optimized conditions, we analyzed the architecture of SYK and SRC- dependent signaling networks in diagnostic B-ALL samples using a high-throughput 96-well plate format. Specifically, we measured 16 phospho-proteins belonging to the BCR/pre-BCR, RAS/MAPK, PI3K/mTOR and JAK/STAT pathways under basal or kinase-inhibited conditions to yield 96 distinct phospho-protein states, or nodes, in 30,000 cells per sample (Table III.2). Data files were pre-gated to exclude dead cells, debris, doublets and normal CD45hi hematopoietic cells, allowing quantification of phospho-protein levels specifically in live single CD45lo leukemic cells (Figure III.3a). The basal median fluorescence intensity (MFI) for each marker was normalized to account for background fluorescence and expressed as log2 MFI, whereas inhibitor effects were represented as the log2-fold change (FC) MFI ratio of inhibitor- treated cells divided by basal MFI. We observed considerable variation in basal levels of pSYK, pSRC, pPLC 2 and pCRKL in a small cohort of B-ALL samples (log2 MFI 5.2-9.4; Figure III.3b). However, in all cases, a 2-hr pre-treatment with fosta decreased basal signals

(Log2 FC -1.1 to -2.6; Figure III.3c), demonstrating that these low signals reflect kinase- dependent true basal signaling. Furthermore, this optimized protocol showed good precision for measuring low levels of basal phosphorylation of 16 proteins in Ramos cells across nine independent experiments, since measurements between runs clustered tightly together for all phospho-proteins (coefficient of variation 17%; Figure III.3d). We also observed a similar pattern of basal phosphorylation of 9 proteins in one patient sample profiled in 3 independent experiments (Figure III.3e). Thus, variations in abundance of phospho-proteins observed between patients are likely due to differences in signaling network architecture rather than assay

92 variations. Collectively, these data demonstrate that the optimized phospho-flow protocol can generate highly reproducible data suitable for high-throughput analysis of signaling pathways in diagnostic B-ALL patient samples.

III.4.2 Dissection of pre-BCR-independent SYK signaling in high-risk B-ALL We, next, characterized basally active signaling in diagnostic B-ALL samples and their sensitivity to clinically relevant tyrosine kinase inhibitors. To compare architecture of signaling networks within and between cytogenetic subgroups of B-ALL, we selected 68 diagnostic B- ALL samples with known prognostic cytogenetic alterations such as BCR-ABL, MLL, TEL- AML1 and hyperdiploidy (Table III.4). However, patients that lack prognostic cytogenetic alterations are at the greatest risk of relapse; therefore, there is an urgent need to understand signaling abnormalities in these patients in order to develop novel therapies. As a result, we also included samples with complex cytogenetic and those lacking any known alterations. Collectively, 57 out of 68 samples were from high-risk B-ALL patients, as determined by the current NCI risk criteria (age (<1 or >10 years), white blood cell (WBC) count (> 50 109/L) and cytogenetics) (Smith et al., 1996), whereas remaining 11 samples were classified as standard-risk cases. Our earlier studies identified SYK as the necessary driver for in vitro and in vivo proliferation of multiple cytogenetic subgroups of cytoplasmic Ig -negative B-ALL, which was abrogated in the presence of fosta, demonstrating pre-BCR/BCR-independent SYK activation The pre-BCR/BCR-dependent SYK activation leads to phosphorylation of BCR-related proteins SRC, SYK, pPLC 2, CRKL, BLNK, BTK, ERK and pp38 in B cells (Mocsai et al., 2010). To gain insight into downstream effects of basal SYK activation in B-ALL, we used optimized high-throughput phospho-flow assay to quantify basal levels of these phospho-proteins and the their responses to fosta treatment in 68 B-ALL patient samples, previously shown to respond to fosta in vitro. The pSYK levels were significantly correlated with levels of other BCR-regulated phospho-proteins (SRC, PLC- 2, CRKL, BLNK, BTK and ERK: Figure III.4a), suggesting that SYK activity might regulate their phosphorylation. This notion was supported by the robust

(defined as log2 FC -0.5) fosta–induced reduction in phosphorylation of SRC, SYK, PLC 2 and CRKL in most samples (Figure III.4b). This fosta-induced decrease in phosphorylation of these proteins was observed across all cytogenetic subgroups, but was consistently larger for the TEL-AML and HD subgroups (Figure III.5a,b), suggesting that basal SYK-dependent

93 signaling may be higher in these pediatric patients. Although BCR-induced and SYK-dependent BLNK phosphorylation is prominent in normal B-cells (Chiu et al., 2002), only the HD subgroup showed consistently high BLNK phosphorylation (Figure III.4c), probably accounting for the less significant correlation between pSYK and pBLNK abundance (Figure III.4a). Notably, although the magnitude of basal phosphorylation of SRC, SYK, PLC 2, CRKL and BLNK was highly variable among B-ALL samples, they were directly proportional to the fosta-induced decrease in phosphorylation of each protein (Figure III.4d). Collectively, these data suggest that SYK activation, though variable in magnitude, is widespread in B-ALL and regulates phosphorylation of several key BCR/pre-BCR signaling proteins. Although basal pSYK abundance was also strongly correlated with pBTK and pERK levels (Figure III.4a), fosta did not consistently decrease phosphorylation of these proteins (Figure III.4b; Figure III.5a). Furthermore, the correlations between basal and fosta-induced decreased levels of pSYK, pBTK and pERK were less significant than for pSRC, pPLC 2 and pCRKL (Figure III.4d). These data suggest that SYK, BTK and ERK may be phosphorylated by similar upstream mechanisms that are largely SYK-independent. In contrast to pERK, phosphorylation of the related p38 MAPK was not significantly correlated with pSYK levels, and fosta pre-treatment did not consistently reduce pp38 levels, and in several cases increased its phosphorylation (Figure III.4a,b; Figure III.5a), suggesting that SYK-independent mechanisms regulate p38 phosphorylation in B-ALL. Taken together, we identified aberrant activation of pre-BCR-independent SYK signaling in B-ALL that can be abrogated by fosta resulting in inhibition of pSRC, pSYK, pPLC 2 and pCRKL across multiple subgroups of B- ALL. Although SYK is the primary target of fosta, it has been shown to inhibit activity of multiple kinases, including SFKs (Braselmann et al., 2006; Davis et al., 2011), raising a possibility that some of the fosta responses described above occurred as a result of its off-target effects. To examine this possibility, we compared fosta-induced effects on pSRC, pSYK, pPLC 2 and pCRKL to those elicited by BAY613606 (BAY), a more specific ATP-competitive inhibitor of SYK activity (Lau et al., 2012; Yamamoto et al., 2003), previously shown attenuate proliferation of B-ALL samples from multiple cytogenetic subgroups (Chapter II). We observed that levels of pSRC were unaffected by BAY as compared to fosta (Figure III.5c), which is in agreement with previously described specificity of BAY (Yamamoto et al., 2003). In contrast, fosta-dependent decrease in levels of pSYK, pPLC 2 and pCrkL significantly correlated with

94 the effects of BAY on these phospho-proteins (Figure III.5d), demonstrating that regulation of these pre-BCR/BCR proteins occurs downstream of SYK activation and is a shared mechanism of SYK inhibitors in B-ALL.

III.4.3 Aberrant SYK-independent MAPK signaling networks in B-ALL In contrast to the reports of SYK-dependent ERK activation in B cells (Chiu et al., 2002; Jiang et al., 1998; Yasuda et al., 2008), we observed low levels of pERK that were fosta-insensitive across all cytogenetic subgroups of B-ALL (Figure III.4b,d; Figure III.5a), raising a possibility of dysregulation networks that control MEK/ERK signaling. To further probe ERK- dependent signaling casade in B-ALL, we quantified sensitivity of pERK to fosta as compared to PD184352 (PD), an allosteric inhibitor of MEK1/MEK2 (Sebolt-Leopold et al., 1999), which are upstream kinases that phosphorylate ERK (Lange-Carter et al., 1993; Zheng and Guan, 1993). We detected high basal PD-sensitive pERK in only a small subset of samples (Figure III.6a), suggesting basal activation of SYK-independent MEK/ERK signaling. On the other hand, low basal pERK levels were drug-insensitive (Figure III.6b). These observations prompted us to investigate whether MEK/ERK pathway was intact by stimulating a cohort of 12 high-risk B-ALL samples with PMA, which is known to mediate PKC-dependent activation of RAS/MAPK signaling (Sansbury et al., 1997). We initially verified that phosphor-flow cytometry can detect PMA-induced RAS/MAPK signaling in normal PBMCs and observed induced pERK, pp38, pS6, pEIF4E, pNF B and reduced total I B , as expected (Figure III.7a) (Chang et al., 2005; Fonseca et al., 2011; Krutzik and Nolan, 2003). Interestingly, PMA treatment increased pERK and pS6, which were PD-sensitive, in all B-ALL samples (n=12), including those with BCR-ABL, MLL and complex/normal cytogenetics (Figure III.7b,c,d, respectively). Unexpectedly, PMA augmented pp38 levels and pNF B signaling in only a few samples (Figure III.7b-d), suggesting variability in MAPK signaling architecture in B-ALL. Indeed, we detected PMA-induced pp38 in only 3 of 12 samples (all complex/normal), implicating PKC-dependent p38 activation in a subset of samples. Similarly, we detected increased pNFkB with decrease in total IkB in 2 of 12 samples (complex/normal), further highlighting differences in PKC-dependent networks in B-ALL. Taken together, our observations suggest an occasional SYK-independent basal activation of MAPK signaling and emphasize significant variability in architecture of PMA-induced MAPK signaling networks in high-risk B-ALL.

95

Given an important role of ERK signaling in regulating cancer survival (Balmanno and Cook, 2009) and our observations of high basal pERK in a subset of samples, we investigated the effect of PD on proliferation of 19 B-ALL samples and compared it to fosta. We detected modest inhibition of proliferation by PD at highest dose as compared to pronounced anti- proliferative effects of fosta (Figure III.6c). Importantly, clinically achievable concentration of PD (1 M) (Lorusso et al., 2005; Rinehart et al., 2004) reduced proliferation by > 50% in only 7 of 19 samples in contrast to inhibition of all samples by fosta (Figure III.6d). Notably, all 7 PD- sensitive samples exhibited very low PD-insensitive pERK levels, demonstrating that pERK doesn‟t predict in vitro sensitivity to PD in B-ALL. In contrast to fosta, PD had negligible effect on basal levels of pSRC, pSYK, pPLC- 2 and pCRKL (Figure III.6e), consistent with its specificity and mechanisms of action (Davies et al., 2000). However, PD treatment robustly increased pp38 in 13 samples, confirming a previously described “seesaw” cross-talk between MEK/ERK and p38-dependent pathways in other malignancies (Ding and Adrian, 2001; New et al., 2001; Shimo et al., 2007). Collectively, our data suggest that inhibition of BCR-related proteins may be necessary for anti-proliferative effects of small molecule kinase inhibitors in B- ALL.

III.4.4 Role of basally active BCR-related proteins in proliferation of B-ALL We next wanted to further examine the possibility that reduced phosphorylation of BCR signaling proteins may be associated with anti-proliferative properties of small molecule kinase inhibitors. Given the important contribution of SFKs to pre-BCR and BCR signaling and observations of high basal pSRC in B-ALL, we examined the effects of dasatinib (DAS), a dual SFK and BCR-ABL inhibitor (Lombardo et al., 2004), on phosphorylation of basally active BCR signaling proteins, including pSRC, pSYK, pPLC 2 and pCRKL in 40 B-ALL samples. Both fosta and DAS robustly decreased phosphorylation of pSRC, pSYK, pPLC 2 and pCRKL, although DAS-induced pSRC inhibition was significantly greater compared to fosta (Figure III.8a,b). In contrast, fosta decreased pPLC 2 levels more potently than DAS, suggesting stronger dependence on SRC-independent SYK signaling. Interestingly, the anti-proliferative effects of DAS were largely restricted to BCR-ABL+ samples, whereas fosta consistently inhibited proliferation of BCR-ABL- and BCR-ABL+ samples (Figure III.8c). Furthermore, 3 M fosta reduced proliferation by > 50% in all BCR-ABL+ samples and 16 of 17 BCR-ABL- samples, whereas 10 nM DAS (clinically relevant) (Luo et al., 2006) inhibited only 2 /17 BCR-

96

ABL- samples (Figure III.8d). Collectively, these data suggest that proliferation of BCR-ABL- B-ALL samples requires SYK-dependent but SRC-independent signaling, and show that SYK- dependent signaling also regulates proliferation of BCR-ABL+ samples. Furthermore, these observations highlight that inhibition of BCR-related proteins alone is not sufficient to exert anti-proliferative effects in B-ALL, suggesting that other signaling pathways must be regulated by fosta and not DAS.

III.4.5 AKT-independent inhibition of pS6 and pEIF4E by fosta Strong evidence implicates SYK-dependent regulation of PI3K activity downstream of pre- BCR/BCR signaling in B cells, that, in turns, controls cellular translation, metabolism and survival through activation of AKT/mTOR pathway (Dal Porto et al., 2004; Limon and Fruman, 2012). Recently, pre-clinical evaluation of INK128, an mTOR-specific inhibitor, revealed its anti-leukemic properties in a xenograft model of B-ALL (Janes et al., 2013), thus, suggesting therapeutic potential of targeting mTOR pathway in this disease. In an effort to identify signaling pathways that are uniquely regulated by fosta, we next investigated SYK involvement in PI3K/AKT/mTOR signaling cascade in B-ALL by using high-throughput phospho-flow assay to quantify basal levels in pAKT, p4EBP1, pEIF4E and pS6 in 68 B-ALL samples. We detected minimal fosta effect on pAKT (T) and p4EBP1 (Figure III.9a), which is likely attributed to SYK-independent phosphorylation of these proteins. Phosphorylation of AKT (S) was most significantly inhibited in TEL-AML subgroup (Figure III.9a), suggesting that basal pAKT (S) was higher and SYK-regulated in these patient samples (Figure III.10a). Importantly, fosta robustly inhibited pEIF4E across all genetic subgroups (Figure III.9a), whereas it had more variable effect on pS6 levels (Figure III.9b). A few samples from the complex and normal groups showed very strong fosta-induced inhibition of S6 phosphorylation (Figure III.9b). Furthermore, the effect of fosta on pS6 was significantly greater in the normal subgroup as compared to the BCR-ABL+ and HD patient samples. Interestingly, basal levels of pEIF4E were less variable across cytogenetic subgroups ( 2 = 0.8), whereas basal levels of pS6 showed greater overall variation ( 2 = 4.4), with particularly low levels in the HD and BCR-ABL+ subgroups (Figure III.9b). Nonetheless, the magnitude of the fosta-induced decrease in pEIF4E and pS6 was directly proportional to basal levels of each phospho-protein (Figure III.9c). These observations suggest that fosta most strongly decreased pS6 levels in the subset of samples with high basal S6 phosphorylation. Collectively, these data show that fosta robustly decreases

97 phosphorylation of EIF4E across all genetic subtypes, but has more minimal and/or variable effects on other PI3K/mTOR-regulated signaling proteins. In normal cells, phosphorylation of S6 (S235/S236) and 4EBP1(T36/T45) is induced downstream of mTORC1 activation (Averous and Proud, 2006), whereas EIF4E (S209) phosphorylation is controlled by MAPK pathway activation (Joshi et al., 1995; Waskiewicz et al., 1999). Therefore we compared the ability of LY and PD to decrease basal levels of pAKT, p4EBP1, pEIF4E and pS6, as compared to fosta and DAS, in a cohort of 39 B-ALL samples. Fosta and LY both decreased pAKT (S) levels, with significant correlation between their effects (Figure III.10b), in B-ALL cells more robustly than DAS, whereas PD had no effect on this phospho-protein (Figure III.9d). As expected, treatment with LY, but not fosta, DAS or PD, robustly decreased p4EBP1 levels in most patient samples, supporting PI3K-dependent phosphorylation of this protein. Notably, fosta decreased pEIF4E very robustly (mean of -1.0

Log2 FC), whereas DAS had more modest effects (mean of -0.3 log2FC; Figure III.9d,e), suggesting SYK-dependent, but SRC-independent regulation of EIF4E phosphorylation. Furthermore, compared to fosta, LY had weaker effect on EIF4E phosphorylation (mean of -0.5 log2FC), likely because the LY-induced decrease in 4EBP1 phosphorylation would enhance 4EBP1 binding of EIF4E and limit access for S209 kinases (Furic et al., 2010). Surprisingly, PD had a much weaker effect on EIF4E phosphorylation than fosta (Figure III.9d,e), suggesting SYK-dependent, ERK-independent EIF4E activation. Furthermore, we noted that in many patient samples (marked as asterisk), fosta induced a larger decrease in pEIF4E levels than PD (Figure III.9e), providing further evidence that SYK activation can regulate EIF4E phosphorylation independently of ERK-dependent signaling in these samples. Finally, fosta, LY and PD robustly decreased pS6 levels in only a few samples (Figure III.9e), consistent with our findings that pS6 levels were basally high in only some samples. Surprisingly, however, LY or PD also robustly increased pS6 levels in some samples, suggesting that inhibition of PI3K/mTOR or ERK signaling can have divergent effects on S6 phosphorylation in different patient samples. Collectively, these data demonstrate the complexity and patient-specific variation of signaling networks that regulate phosphorylation of AKT (S), 4EBP1, EIF4E and S6 in B-ALL. Despite the ability of LY to robustly decrease pAKT (S) and p4EBP1 levels in B-ALL samples, it was much less effective than fosta at inhibiting proliferation (Figure III.10c). Notably, fosta (3 M) reduced proliferation by > 50% of all samples, whereas LY (3 M) inhibited only 7 of 22 samples (Figure III.10d). Thus, inhibition of PI3K signaling is not 98 sufficient to attenuate proliferation of B-ALL samples. Furthermore, in contrast to fosta, LY did not decrease abundance of BCR-regulated phospho-proteins (Figure III.10e). Furthermore, fosta-dependent decrease in levels of pEIF4E significantly correlated with the effects of BAY on these phospho-protein (Figure III.10f), demonstrating that regulation of EIF4E phosphorylation occurs downstream of SYK activation and is a shared mechanism of SYK inhibitors in B-ALL, but not DAS (Figure II.9d,e). Collectively, these data suggest that fosta effectiveness in inhibiting B-ALL proliferation may be explained by its ability to inhibit BCR signaling and EIF4E phosphorylation.

III.4.6 Aberrant activation of pSTAT5 in high-risk BCR-ABL- B-ALL STATs transduce signaling responses downstream of cytokine and growth factor receptor s and have been implicated in pathogenesis of many diseases. Importantly, recent evidence from genomic analyses revealed deregulated cytokine signaling in high-risk B-ALL, which includes aberrant activation of JAK2/STAT5 signaling cascade in CRLF2-positive B-ALL (Roberts et al., 2012; Tasian et al., 2012). Therefore, used high-throughput phospho-flow assay to quantify basal levels of pSTAT3 and pSTAT5 and their drug responses in B-ALL. We initially optimized staining for pSTAT3 and pSTAT5 based on their robust IL3- dependent phosphorylation in BaF3 cells (Figure III.11a). Several studies suggest that lymphocyte antigen receptor signaling induces phosphorylation of STAT proteins in a JAK- independent fashion (Su et al., 1999; Wang et al., 2007). Using Ramos cells, we confirmed that BCR signaling induced phosphorylation of STAT3 and STAT5, which was attenuated by fosta treatment (Figure III.11b), demonstrating SYK-dependent regulation in these cells. Although STAT tyrosine phosphorylation is predominantly regulated by JAK kinases, surprisingly, treatment with pan-JAK inhibitor (JAKI) had no effect of BCR-induced phosphorylation of tyrosine residues, suggesting JAK-independent regulation. Notably, PD treatment reduced tyrosine and, most robustly, serine phosphorylation, consistent with previous reports of ERK- and RSK-dependent STAT3 (S) phosphorylation (Chung et al., 1997; O'Rourke and Shepherd, 2002; Zhang et al., 2003). Analysis of STAT3 responses in 68 B-ALL samples revealed that serine and tyrosine phosphorylation of STAT3 was fosta-insensitive in most samples (Figure III.12a), suggesting SYK-independent regulation in these samples. Interestingly, fosta treatment consistently and + robustly decreased pSTAT5 in BCR-ABL samples (log2FC = -0.65; Figure III.12b). In marked contrast, we observed heterogeneous response to fosta in samples with complex/normal

99 cytogenetics, which can be attributed to significant heterogeneity of basal pSTAT5 abundance in these samples (Figure III.12b). Importantly, the magnitude of fosta-induced decrease in pSTAT5 strongly correlated with basal pSTAT5 (Figure III.12c), indicating that, in a subset of samples, pSTAT5 may be regulated in a SYK-dependent manner. In agreement with BCR-ABL/SFK-dependent pSTAT5 activation (Hantschel et al., 2012; Nam et al., 2007; Nieborowska-Skorska et al., 1999), DAS reproducibly decreased STAT5 phosphorylation in BCR-ABL+ samples (Figure III.12d). However, pSTAT5 was also decreased by PD in 2 of 8 BCR-ABL+ samples, revealing heterogeneity in signaling networks that regulate pSTAT5 in samples that share an common oncogenic driver. Strikingly, BCR- ABL- samples showed considerable variability in pSTAT5 sensitivity to fosta, DAS and PD that did not correlate with JAK2 mutant (JAK2mut) status (Figure III.12d). For example, in 3 of 3 CRLF2-positive samples (samples 1-3), fosta and PD but not DAS robustly decreased pSTAT5 levels, (Figure III.12d). One CRLF2-negative sample expressing wild-type JAK2 (JAK2WT) (sample 4) showed the same pattern of pSTAT5 drug sensitivity as the CRLF2-positive samples.

On the other hand, in the reaming three CRLF2-negative JAK2WT samples, pSTAT5 was decreased by DAS, but responses to fosta and PD were variable and resembled those observed in BCR-ABL+ samples (Figure III.12d). Thus, we observed CRLF2-like and BCR-ABL-like patterns of pSTAT5 drug sensitivity in samples lacking these genetic abnormalities. Our surprising finding that PD decreased pSTAT5 in some samples is consistent with MEK1/2- induced STAT5 tyrosine phosphorylation in T cells (Maki and Ikuta, 2008). Collectively, these data reveal unanticipated complexity in signaling networks regulating STAT5 phosphorylation in HR B-ALL samples that did not strictly correlate with the presence of known genetic lesions Recent studies in high-risk B-ALL demonstrated that chemical inhibition of JAK2 activity results in decrease of pSTAT5 and pS6 abundance and displays anti-tumor activity in a xenograft model of B-ALL carrying activating JAK2 mutations (Maude et al., 2012; Tasian et al., 2012). Therefore, we tested the ability of SAR302502 (SAR), a JAK2-specific inhibitor

(Wernig et al., 2008), to decrease pSTAT5 and pS6 levels in samples with JAK2mut versus those expressing JAK2WT. We selected samples with complex/normal cytogenetics that displayed drug-sensitive high pSTAT5 (Figure III.12d), three of which were CRLF2-positive samples with (n=2, indicated by *) or without (n=1) JAK2mut. Interestingly, SAR treatment robustly decreased pSTAT5 in all CRLF2-positive samples (samples 1-3), as well as in sample 4, which had a CRLF2-like pSTAT5 inhibition pattern (Figure III.12e), suggesting JAK2 involvement in STAT5 phosphorylation in these patients. In contrast, pSTAT5 was SAR-insensitive in the

100 remaining three CRLF2-negative samples (samples 5-7) that displayed DAS-sensitive pSTAT5

(Figure III.12e). Thus, sensitivity of pSTAT5 to SAR did not distinguish samples with JAK2mut versus those expressing JAK2WT, but did distinguish samples with DAS-insensitive and DAS- sensitive pSTAT5. We also noted that basal pS6 levels varied considerably across this cohort of samples, but did not correlate with either expression of CRLF2 or JAK2 status (Figure III.12f). Although SAR robustly decreased pSTAT5 levels in CRLF2-posistive and CRLF2-like samples, it had minimal impact on pS6 in these samples (Figure III.12e,f), suggesting that S6 phosphorylation is largely JAK2-independent. Given our observations of comparable pSTA5 inhibition by fosta, PD and SAR in CRLF2-positive samples, we compared biological effects of these inhibitors on proliferation of these samples. Interestingly, whereas fosta robustly inhibited proliferation of all samples, SAR and PD displayed variable effects that did not correlate with JAK2 status (Figure III.13a), suggesting that pSTAT5 inhibition alone cannot predict in vitro sensitivity to these inhibitors. We validated this notion in a larger cohort of samples demonstrating that SAR was less effective than fosta at inhibiting proliferation (Figure III.13b,c). Furthermore, 6 of 7 SAR-sensitive samples (Figure III.13c) displayed low basal pSTAT5 levels that were SAR-insensitive (Figure III.13d). Further analysis of SAR effects on signaling networks in B-ALL revealed that although SAR inhibition of pEIF4E was similar to fosta, it had not effect on phosphorylation of BCR signaling proteins, in marked contrast to fosta (Figure III.14a,b). Collectively, these data further substantiate the notion that combined inhibition of BCR signaling and EIF4E phosphorylation is required for anti-proliferative effects of small molecule kinase inhibitors.

III.4.7 B-ALL patients organized by similarities in basal phosphorylation signature display similar clinical characteristics Our data demonstrated fosta-dependent reduction in BCR signaling and pEIF4E in all B-ALL samples, regardless of cytogenetic subgroup, whereas reduction in pS6 and pSTAT5 was only evident in some samples due to varying degree of basal activity of these proteins. These observations indicated significant heterogeneity in basal phosphorylation between and within cytogenetic groups, suggesting that cytogenetic-based clustering may not be sufficient. A demonstration of the prognostic relevance of patient stratification based on potentiated signaling signatures in AML (Irish et al., 2004) prompted us to examine the clinical value of basal phosphorylation profiles in B-ALL. Thus, we performed unsupervised hierarchical clustering of

101 normalized basal levels of 15 phospho-proteins (pSRC, pSYK, pPLC 2, pCRKL, pBTK, pERK, pp38, pAKT (S/T), p4EBP1, pEIF4E, pS6, pSTAT3 (S/Y), pSTAT5) using R statistical computing software (R Development Core Team, 2010). This analysis revealed five major phospho-protein clusters (PC1-PC5), each associated with a unique phosphorylation signature (Figure III.15a). Samples in PC1 displayed high phosphorylation of S6 and AKT with concomitant low BCR signaling, whereas PC2 had heterogeneous pS6 but low overall basal phosphorylation of remaining proteins, which implies low basal signaling in this group. Interestingly, 7 of 10 MLL samples clustered in PC2, indicating low basal phosphorylation of signaling proteins in this cytogenetic subgroup of B-ALL. PC3 cluster displayed high levels of BCR-regulated phospho-proteins with low S6 phosphorylation. The main feature of PC4 was robust phosphorylation of STAT5 and low pS6 levels. Finally, PC5 group had low p4EBP1 levels. Notably, samples with similar cytogenetics did not always cluster together, indicating heterogeneity of basal phosphorylation levels within the same cytogenetic group of patients. We next examine correlation between clinical parameters, including presence of relapse and treatment failure, and phospho-protein clusters by performing 2-correlation test. Importantly, presence of relapse significantly correlated with PC groups and was predominantly observed in PC2 and PC4 (Figure III.15b). Similarly, we observed significant correlation between treatment failure and clustered phospho-protein groups, which occurred primarily in patients that clustered to PC2 and PC4 (Figure III.15b). Collectively, these data suggest that profiling of basal levels of phospho-proteins may be used as an additional prognostically relevant stratification strategy for B-ALL patients.

III.5 DISCUSSION Small molecule kinase inhibitors represent promising therapeutic approach for the treatment of B-ALL. However, the clinical application of these inhibitors is challenging due to significant complexity and heterogeneity in signaling network alterations among B-ALL patients, which contributes to variable treatment responses. Previous studies investigating signaling responses in B-ALL focused on a single pathway within rare subtypes (Holmfeldt et al., 2013; Tasian et al., 2012), thereby failing to provide comprehensive overview of basal signaling perturbations across different cytogenetic groups of B-ALL. In this study, we used phospho-flow cytometry to characterize basal signaling networks in B-ALL, including BCR, PI3K/AKT/mTOR, ERK/MAPK and JAK/STAT pathways, and their responses to clinically relevant small molecule

102 kinase inhibitors. BCR signaling was basally active across all cytogenetic subgroups of B-ALL and was robustly inhibited by fosta and DAS. In contrast, fosta-sensitive S6 phosphorylation varied greatly in B-ALL samples, and, surprisingly, did not correlate with activation of other PI3K/AKT/mTOR proteins. Furthermore, we have shown that potent anti-proliferative effects of fosta in B-ALL may be explained by its unique ability to robustly inhibit phosphorylation of BCR-related proteins as well as EIF4E, effects that were not observed in the presence of DAS, LY, PD or SAR. Importantly, in vitro sensitivity of small number of B-ALL samples to PD or SAR did not correlate with the presence of high PD-sensitive pERK or SAR-sensitive pSTAT5, respectively. Finally, we identified five prognostically relevant groups of B-ALL samples with distinct phosphorylation profiles that highlighted significant basal signaling variability within cytogenetic subgroups of B-ALL. Our study is the first to profile basal signaling networks and their responses to kinase inhibitors in a spectrum of cytogenetics groups of B-ALL. Identification of basally active BCR-related signaling proteins in B-ALL provides further support to the pathological role of BCR signaling in lymphoid malignancies. The mature B cell lymphomas that display chronic active (Davis et al., 2010) or tonic BCR signaling (Chen et al., 2008) express BCR on the cell surface. On the other hand, all B-ALL samples used in our study lacked surface and cytoplasmic Ig , indicating pre-BCR/BCR-independent activation of BCR proteins, which included SYK, SRC, PLC 2 and CRKL. The inhibition of SRC phosphorylation may be an off-target effect of fosta (Davis et al., 2011), since a more specific SYK inhibitor BAY had no effect on pSRC levels, whereas DAS robustly inhibited phosphorylation of this protein, as expected. On the other hand, inhibition of pSYK, pPLC 2 and pCRKL was share by the two SYK inhibitors, demonstrating SYK-dependent regulation of their phosphorylation in B-ALL. Interestingly, an adapter protein CRKL is a major of BCR-ABL and is essential for leukemic transformation by this oncogene (Seo et al., 2010). Observations of decrease in CRKL phosphorylation by fosta and BAY suggest that it may be a SYK target in B-ALL, which is substantiated by the earlier report of BCR-dependent CRKL phosphorylation (Ingham et al., 1996), consistent with our observations on Ramos cells, and the identification of CRKL as a SYK binding partner (Galan et al., 2011; Oda et al., 2001). The basal BLNK phosphorylation was highly sensitive to SYK inhibitors and was restricted to pediatric HD samples, revealing cytogenetic group-specific activation of this adapter protein. Interestingly, these samples carry multiple copies of 10, where BLNK is located, which may explain its robust basal activation in HD samples. Based on these data, we propose

103 pre-BCR-independent activation of SYK in B-ALL that results in phosphorylation of PLC 2, CRKL, and, in HD samples, BLNK. Our analyses revealed low levels of basal ERK phosphorylation that were not sensitive to SYK inhibitors or DAS. PMA stimulation of these samples revealed ERK-dependent S6 phosphorylation, consistent with previous studies (Kalaitzidis and Neel, 2008; Roux et al., 2007). Furthermore, induction of pp38 and pNF B in a subset of patients highlights the heterogeneity in MAPK-dependent signaling network in B-ALL that can be revealed by phospho-flow cytometry. Observations of PD-sensitive pERK responses in a small number of samples suggested MEK-dependent regulation of ERK phosphorylation in B-ALL, and are in agreement with SYK-independent regulation of RAS/MEK/ERK cascade in normal and leukemic B cells (Cesano et al., 2013; Yokozeki et al., 2003). Additional work will be necessary to determine if ERK activation in these samples was due to mutations in RAS, which occur at high frequency in high-risk B-ALL (Zhang et al., 2011). Although previous studies suggested that PD-dependent pERK inhibition predicts response to this inhibitor in solid tumors (Lorusso et al., 2005; Rinehart et al., 2004; Sebolt-Leopold et al., 1999), our data demonstrated PD- insensitive low ERK phosphorylation in all samples that displayed PD sensitivity in vitro, arguing against the usefulness of pERK as a biomarker in B-ALL, as previously described for another MEK1/2 inhibitor (Dry et al., 2010) Our analysis of ribosomal protein S6 revealed significant heterogeneity of its phosphorylation and drug sensitivity among B-ALL patients. The phosphorylation of S6 on S235/S236 can be regulated by ERK and mTOR signaling cascades (Roux et al., 2007). Despite low basal pERK, we detected pS6 inhibition by PD is some samples, consistent with observations by Kalaitzidis and Neel (Kalaitzidis and Neel, 2008). Concomitant pS6 sensitivity to LY and PD suggests that inputs from mTOR and ERK pathway likely regulate phosphorylation of this protein in some samples. Notably, fosta robustly inhibited pS6 in all samples, regardless of the magnitude, in an AKT-independent manner. Interestingly Markova et al. (Markova et al., 2010) demonstrated PLC -dependent phosphorylation of S6 by calcium/Calmodulin-dependent kinase (CaMK) in chronic myeloid leukemia (CML). Given our observations of robust fosta-induced inhibition of PLC 2, it is tempting to speculate that S6 phosphorylation is SYK/PLC 2-dependent, as previously suggested in other malignancies (Carnevale et al., 2013; Fruchon et al., 2012; Leseux et al., 2006). However, in view of wide selectivity of fosta (Davis et al., 2011), additional genetic-based biological assays will be

104 required to validate this notion. Collectively, these data demonstrate complexity of signaling networks that regulate pS6 in B-ALL. Fosta-induced pS6 inhibition suggests that this inhibitor may modulate protein translation in B-ALL. This notion was substantiated by robust inhibition of EIF4E (S209) phosphorylation by fosta in all samples. Interestingly, regulation of pEIF4E appears to occur in a MEK1/2-independent manner in many samples, as evident by the lack of PD effect on EIF4E serine phosphorylation. These observations are intriguing considering that MNK1/2, activated by MEK1/2-dependent signaling, are the sole kinases known to regulate EIF4E (S209) phosphorylation to-date (Joshi et al., 1995; Waskiewicz et al., 1999). Thus, our data suggest, for the first time, MNK1/2-independent phosphorylation of this translational protein in B-ALL. In addition, pEIF4E inhibition was a shared effect of SYK inhibitors. Given high selectivity of BAY for SYK (Lau et al., 2012), our data suggest SYK-dependent regulation of this protein. Our data are consistent with SYK-dependent regulation of mTOR signaling in B cell lymphoma and AML (Carnevale et al., 2013; Leseux et al., 2006). Given the oncogenic role of EIF4E (Carroll and Borden, 2013), the possibility SYK-dependent regulation of translation is worth exploring. Therefore, future experiments measuring protein synthesis in B-ALL in the presence of chemical and genetic SYK inhibitors will be necessary to investigate this point. Our data also revealed complexity of signaling networks that regulate STAT5 phosphorylation. High basal STAT5 phosphorylation was observed in a subset of samples and did not correlate with CRLF2 over-expression and/or JAK2 mutations, as previously suggested (Tasian et al., 2012). Furthermore, pSTAT5 responses displayed patient-specific sensitivities to kinase inhibitors, revealing JAK2-dependent and –independent mechanisms of STAT5 phosphorylation. Notably, we identified novel MEK1/2-dependent inputs that regulated STAT5 phosphorylation in some samples, previously only described in murine T cells (Maki and Ikuta, 2008). Interestingly, fosta inhibited high pSTAT5 in all samples. Recent examination of SYK interactome in AML reveled direct interaction between SYK and STAT5 (Oellerich et al., 2013), raising a possibility of SYK-dependent STAT5 regulation in some B-ALL, which will require further analyses. Although pSTAT5 inhibition was suggested to predict sensitivity to JAK2 inhibitors (Maude et al., 2012; Wernig et al., 2008), our data argues against this notion. Since JAK2 inhibitors may have therapeutic potential of in B-ALL, we suggest future pre- clinical studies should not be limited to samples with basally high STAT5 phosphorylation. In concordance with AML studies (Irish et al., 2004), we demonstrated prognostic values of phospho-flow profiling of basal signaling responses in B-ALL. Our study revealed significant

105 heterogeneity of basal phosphorylation among patients with distinct cytogenetic abnormalities. More importantly, samples belonging to the same cytogenetic subgroups did not always cluster together, suggesting variability of basal signaling networks within cytogenetic groups. Future validations of the signatures associated with distinct phospho-protein clusters as prognostic biomarkers in B-ALL will be necessary in the large cohort of B-ALL samples. Collectively, we described an optimized phospho-flow cytometry platform that allowed us to perform a comprehensive analysis of basal signaling network in B-ALL samples. Using this platform, we demonstrated significant patient-specific differences in architecture of signaling networks and their responses to small molecule kinase inhibitors. Furthermore, we identified fosta signaling signature in B-ALL and revealed that inhibition of BCR signaling and EIF4E phosphorylation is predictive of response to small molecule kinase inhibitors.

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III.6 TABLES Table III.1 List of small molecule inhibitors used in phospho-flow profiling of early B-ALL samples Inhibitor Inhibitor Mode of Structure Manufacturer Name Target inhibition ATP- Fostamatinib SYK AstraZeneca competitive

ATP- BAY61-3606 SYK Calbiochem competitive Toronto SRC, BCR- ATP- Dasatinib Research ABL competitive Chemicals ATP- LY294002 PI3K Calbiochem competitive Non- PD184352 MEK1/2 US Biologicals competitive JAK1/2/3, ATP- JAK inhibitor I Calbiochem TYK2 competitive ATP- SAR302503 JAK2 Sanofi Pasteur competitive

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Table III.2 List of phospho-specific and intracellular antibodies for profiling of early B- ALL samples

Antibody Clone Protein Name Phospho-epitope Conjugate M31-16 4EBP1 T36/T45 Alexa Fluor 647 M89-61 AKT S473 Alexa Fluor 488 J1-223.371 AKT T308 PE J117-1278 BLNK Y84 Alexa Fluor 647 24a/BTK(Y551) BTK/ITK Y551/Y511 Alexa Fluor 488 K30-391.50.80 CrkL Y207 PE J77-925 EIF4E S209 PE 20A ERK1/2 T202/Y204 Alexa Fluor 488 L35A5 I B N/A Alexa Fluor 488 36/p38(pT180/pY182) p38 MAPK T180/Y182 Alexa Fluor 647 K10-895.12.50 p65 NF- B S529 PE K86-689.37 PLC- 2 Y759 Alexa Fluor 488 N7-548 S6 S235/S236 Alexa Fluor 647 K98-37 SRC Y418 Alexa Fluor 488 49/p-STAT3 STAT3 S727 Alexa Fluor 488 4/P-STAT3 STAT3 Y705 PE 47/Stat5(pY694) STAT5 Y694 Alexa Fluor 647 I120-722 SYK Y348 PE 17A/P-ZAP70 SYK/ZAP70 Y352/Y319 PE

108 Table III.3 List of antibodies used for compensation in flow cytometry experiments Clone Conjugate Dilution Manufacturer 104 FITC 1:25 BD Biosciences Cy34.1 PE 1 : 100 BD Biosciences 2D1 APC-Cy 7 1 : 100 BD Biosciences Cy34.1 Biotin 1 : 100 BD Biosciences Avidin/Streptavidin Avidin Alexa® Fluor 647 1:100 Molecular Probes Avidin/Streptavidin Avidin Alexa® Fluor 488 1:100 Molecular Probes Avidin/Streptavidin Avidin PE-Texas Red 1:100 BD Biosciences

Table III.4 Clinical characteristics of B-ALL patient samples Pediatric SR Pediatric HRa Adult Total 11 17 40 Age (years) 2-7 0-17 18-68 Gender Male 6 10 24 Female 5 7 16 Outcome Deceased 0 2 28 Alive 11 15 12 Cytogenetics TEL-AML1 6 1 0 Hyperdiploidy 3 4 0 MLL rearrangement 1 1 8 BCR-ABL 0 0 9 Normal 0 4 6 Complex 1 7 17

Legend: Sixty-eight viably frozen patient samples were profiled using phospho-flow cytometry. This table summarizes clinical and genetic features of all samples used in the study. a HR group criteria: age 1 or 10 years and/or white blood cell count 50 109/L. Number of samples per group is shown; SR, standard-risk group; HR, high-risk group.

110

III.7 FIGURES

Figure III.1 High-throughput phospho-flow protocol for analysis of signaling networks in B-ALL B-ALL patient samples were thawed and rested for 1h in serum-free media (SFM). Cells were then treated with DMSO vehicle (0.2%) or an indicated inhibitor for 2h. A viability dye was

111 added for the final 30 minutes of treatment period. Cells were then fixed, permeabilized and stained in a 96-well plate with CD45 alone (FMO) or CD45 in combination with optimized phospho-specific antibodies and analyzed on high-throughput samples (HTS) by flow cytometry.

112

Figure III.2 Optimization of phospho-flow antibodies to detect basal signaling responses (a) Optimization of phospho-antibodies belonging to BCR pathway. Ramos cells were deprived of serum for 18-24h prior to stimulation with anti-human IgM (@IgM: 10 g/ml, 8 min) followed by fixation, permeabilization and staining. Histogram overlays were generated in Cytobank and show phosphorylation of an indicated protein in live cells following staining with an optimal concentration of phospho-antibodies. Cells stained with fluorescence minus one (FMO) excluding the phospho-antibody provide baseline for color scale. (b) Optimization of phospho-antibodies belonging to mTOR pathway. Serum-deprived Ramos cells were treated with 0.2 % DMSO vehicle (Basal) or LY294002 (LY, 20 M) for 2h. Cells were stimulated 113 with @IgM for the last 8 minutes of treatment. Vehicle-treated cells were used to establish baseline for color scheme of histogram overlays. (c) Optimization of phospho-antibodies belonging to AKT/mTOR pathway. BaF3 cells were deprived of serum and IL3 for 4h prior to stimulation with anti-mouse IL3 (10 ng/ml; 10 min). Cells stained with FMO excluding the phospho-antibody provide baseline for color scale. (d) Phospho-flow analysis of basal responses. Ramos and NALM6 cells were deprived of serum for 18-24h followed by a 2h treatment with DMSO vehicle (Basal), fostamatinib (Fosta, 10 M) or dasatinib (DAS, 200 nM). Basal and inhibited responses in Ramos (top row) and NALM6 (bottom row), measured in live cells, were normalized to FMO to establish color scheme baseline.

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Figure III.3 Validation of high-throughput phospho-flow protocol to detect basal signaling responses in B-ALL

115

Overview of the gating strategy. Gating used to discriminate live (Fixable blue dead cell stain vs. forward scatter area (FSC-A)), intact (side scatter area (SSC-A) vs FSC-A)), singlet (FSC- height (FSC-H) vs FSC-A) and CD45low leukemic blast cells (FSC-A vs CD45) was performed in FlowJo. (b-c) Phospho-flow platform validation. Four B-ALL patient samples were thawed and rested for 1h in SFM. Cells were then treated with DMSO vehicle (0.2%) or fosta (10 M) for 2h. A viability dye was added for the final 30 minutes of treatment period. Cells were then fixed, permeabilized and stained in a 96-well plate with CD45 alone (FMO) or CD45 in combination with optimized phospho-specific antibodies and analyzed on HTS by flow cytometry. (b) Basal response for each antibody was normalized (log2[median fluorescence intensity (MFI) of stained sample minus MFI of FMO]) and displayed for each histogram. (c) Changes in phosphorylation of each protein in response to fosta treatment were calculated as log2 (fold-change (FC) ratio of MFI inhibitor-treated cells and MFI of vehicle-treated cells) and shown on each histogram. Data normalization and visualization were performed using GraphPad Prism and Cytobank software. (d) Reproducibility of basal signaling responses using phospho- flow platform. Basal phosphorylation of 16 proteins was measure in serum-deprived Ramos cells in nine independent experiments using optimal concentrations of phospho-antibodies. We normalized basal responses by calculating log2 (MFIstained-MFIFMO). Each symbol represents an independent experiment (n=9). Data are presented as mean standard error of the mean (SEM) for each phospho-protein examined. (e) Phosphorylation of 9 proteins in one B-ALL sample was measured in three independent experiments. Normalized basal responses, calculated as log2

(MFIstained-MFIFMO), are presented as scatter plots with mean SEM. Each symbol represents basal phosphorylation from an independent experiment.

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Figure III.4 Profiling of BCR signaling responses in B-ALL Phospho-flow profiling was performed as described in Supplementary Figure 2. Briefly, cells were treated with DMSO vehicle (0.2%) or fosta (10 M) for 2h, fixed, permeabilized and stained with phospho-specific antibodies recognizing pSRC(Y418), pSYK(Y348), pPLC 2(Y759), pCrkL(Y207), pBLNK(Y84), pBTK(Y551), pERK(T202/Y204), pp38(T180/Y182) belonging to BCR pathway. (a) Coexpression of pSYK (x-axis) with other BCR-related phospho-proteins (y-axis) was analyzed by two-tailed Pearson correlation.

Normalized MFI (log2) values were calculated as log2 (MFIstained-MFIFMO). Each dot represents one sample. P-values are shown for each correlation. Correlations with Bonferroni-corrected p- value < 0.007 were considered significant. (b) Fosta inhibition of BCR signaling. Heatmap displays changes in basal phosphorylation of BCR proteins following 2h treatment with fosta (10 M). Samples (n=68) were ordered by cytogenetic group. Colored boxes below each heatmap represent cytogenetic groups. Names of proteins are listed to the right. Data are 2 presented as log2 (MFIFosta/MFIVehicle). The corresponding mean log2FC values and variance ( ) are shown on the right for each marker. (c) Basal (Log2 MFI, top) and fosta (Log2 FC, bottom) pBLNK responses were compared across different cytogenetic groups of B-ALL using one-way ANOVA with Tukey pair-wise comparisons. Data are presented as scatter plots with mean SEM for each cytogenetic group. Each symbol is one patient sample. HD vs other groups:

*p<0.05, **p<0.01, ***p<0.001. (d) Relationship between basal (x-axis, log2 [MFIstained-

MFIFMO]) and inhibited (y-axis, log2 [MFIFosta/MFIVehicle]) phosphorylation of BCR proteins was assessed by two-tailed Pearson correlation. Each dot represents one sample. P-values are shown on each graph. Correlations with Bonferroni-corrected p-value < 0.006 were considered significant.

Figure III.5 Analysis of BCR signaling in B-ALL Fosta responses of pSRC(Y418), pSYK(Y348), pPLC 2(Y759), pCrkL(Y207), pBLNK(Y84), pBTK(Y551), pERK(T202/Y204) and pp38(T180/Y182) were calculated as log2

(MFIFosta/MFIVehicle) and plotted on y-axis for each cytogenetic group (indicated in bottom right corner of each graph). Each dot is one sample. (b) Fosta-induced changes (Log2 FC) in levels of pSRC(Y418), pSYK(Y348), pPLC- 2(Y759) and pCrkL(Y207) were compared across different cytogenetic groups of B-ALL using one-way ANOVA with Tukey pair-wise comparisons. Data are presented as scatter plots with mean SEM for each cytogenetic group. Each symbol is one patient sample. *p<0.05, **p<0.01, ***p<0.001. (c-d) Comparison of response signatures between SYK inhibitors. Phospho-flow cytometry was used to evaluate changes in pSRC(Y418), pSYK(Y348), pPLC 2(Y759) and pCrkL(Y207), belonging to BCR pathway in 34 B-ALL samples following treatment with fosta (10 M) or BAY613606 (BAY, 10 M) for 2h. (c) Effects of fosta and BAY on phosphorylation levels of SRC were compared using two- tailed Wilcoxon matched pairs test. Each symbol is one sample. Dotted horizontal line indicates baseline (no change in phosphorylation). Data are presented as mean SEM. ***p<0.001. (d) Correlation between fosta- (x-axis) and BAY-induced (y-axis) reduction in levels of pSYK(Y348), pPLC 2(Y759) and pCrkL(Y207) were assessed by two-tailed Pearson correlation. Each symbol is one sample. P-values are shown on each graph. Correlations with Bonferroni-corrected p-value < 0.01 were considered significant.

Figure III.6 Distinct inhibition profiles of fosta and PD184352 (a-b) B-ALL samples (n=40) were thawed, rested for 1h at 37 C and treated with PD (10 M), fosta (10 M) or DMSO vehicle (0.2%) for 2h. Cells were fixed, permeabilized and stained with

121 phospho-specific antibody for ERK(T202/Y204). Histogram overlays show effects of PD and fosta on basal pERK in a representative cohort of samples with high basal pERK (n=5, a) and low basal pERK (n=7, b). FMO was used to establish background fluorescence. The response to inhibitor was calculated as log2 (MFITreatment/MFIFMO). Cytogenetic group is indicated on each plot. (c-d) Viably frozen B-ALL samples (n=19) were thawed and recovered for 24 h. Cells were treated with increasing concentrations of fosta, PD or vehicle for 72 h, followed by measurement of [3H]-thymidine uptake. Proliferation (DPM) was normalized to vehicle for each patient sample (Inhibitor/Vehicle 100). Each symbol represents an average of triplicate cultures for one sample. Data are presented as the mean of all samples per dose ± SEM. I Effects of PD (left panel) and fosta (right panel) on proliferation. (d) Effects of clinically achievable concentration of fosta (3 M, x-axis) and PD (1 M, y-axis) on proliferation of 19 B-ALL samples (right panel). Data are presented as normalized proliferation (Inhibitor/Vehicle 100). Boxes indicate 50% inhibition in proliferation by fosta (blue) or PD (orange), relative to vehicle. (e) Effects of 2h treatment with fosta and PD on phosphorylation of BCR proteins were compared using two-tailed Wilcoxon matched pairs test, with Bonferonni-corrected p-value < 0.006 considered significant. Each symbol is one sample. Dotted horizontal line indicates baseline (no change in phosphorylation). Data are presented as mean SEM. ***p<0.001.

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123

Figure III.7 Analysis of potentiated ERK signaling in early B-ALL PBMC from healthy subjects (n=2) and B-ALL samples (n=12) were thawed, rested for 1h at 37 C and treated with PD (10 M) or DMSO vehicle (0.1%) for 2h. Cells were stimulated with PMA (50 nM) for the final 15 minutes of treatment, fixed, permeabilized and stained with phospho-specific antibodies for ERK, p38, S6, NF B p65 and total I B for analysis by flow cytometry. (a) Histogram overlays show a representative response of PBMC to PMA stimulation, gated on live cells. FMO was used to establish background fluorescence. The response to PMA at each marker was calculated as log2 (MFIPMA/MFIFMO). (b-d) Histogram overlays display changes in basal and PMA-induced phosphorylation in response to PD treatment in MLL (b), BCR-ABL+ (c) and 3 complex/ 1 normal B-ALL samples (d). Treatment conditions are shown in rows, markers are indicated in columns. The response to stimulation and/or inhibition was calculated as log2 (MFItreatment/MFIBasal).

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Figure III.8 Comparison of inhibition profiles of fosta and DAS in B-ALL (a-b) Phospho-flow cytometry was used to evaluate changes in pSRC(Y418), pSYK(Y348), pPLC- 2(Y759) and pCrkL(Y207) belonging to BCR pathway in 40 B-ALL samples following treatment with fosta (10 M) or DAS (200 nM) for 2h. (a) Effects of fosta and DAS, calculated as log2FC, on phosphorylation of BCR proteins were compared using two-tailed Wilcoxon matched pairs test with Bonferonni-corrected p-value < 0.01 considered significant. Each

125 symbol is one sample. Dotted horizontal line indicates baseline (no change in phosphorylation). Data are presented as mean SEM. ***p<0.001. (b) Marker-specific heatmaps display changes in basal phosphorylation of each protein following 2h treatment with fosta (top) or DAS (bottom). Samples (n=40) were ordered by cytogenetic group. Colored boxes below each heatmap represent cytogenetic group. Names of proteins are listed to the left. The corresponding 2 mean log2FC values and variance ( ) are shown on the right for each treatment. (c-d) Viably frozen B-ALL samples (n=24) were thawed and recovered for 24 h. Cells were treated with increasing concentrations of fosta, DAS or vehicle for 72 h, followed by measurement of [3H]- thymidine uptake. Proliferation (DPM) was normalized to vehicle for each patient sample (Inhibitor/Vehicle 100). Each symbol represents an average of triplicate cultures for one sample. Data are presented as the mean of all samples per dose ± SEM. (c) Effects of DAS (left panel) and fosta (right panel) on proliferation of 17 BCR-ABL- (black circles) and 7 BCR-ABL+ samples (blue squares). (d) Effects of clinically achievable concentration of fosta (3 M, x-axis) and DAS (10 nM, y-axis) on proliferation of 7 BCR-ABL+ (left panel) and 17 BCR-ABL- samples (right panel). Data are presented as normalized proliferation (Inhibitor/Vehicle 100). Boxes indicate 50% inhibition in proliferation by fosta (blue) or DAS (orange), relative to vehicle.

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Figure III.9 Profiling of basal PI3K/AKT/mTOR signaling in B-ALL (a-c) Cells were treated with DMSO vehicle (0.2%) or fosta (10 M) for 2h, fixed, permeabilized and stained with phospho-specific antibodies recognizing pAKT(T308), pAKT(S473), p4EBP1(T36/T45), pEIF4E(S209), pS6(S235/S236) belonging to PI3K/AKT/mTOR pathway. (a) Fosta-induced changes in phosphorylation levels of AKT,

4EBP1 and EIF4E were calculated as log2FC and plotted on y-axis. Data are presented as scatter plots with mean SEM for each cytogenetic group. Each symbol is one patient sample. Data were analyzed using one-way ANOVA with Tukey pair-wise comparisons. (b) Basal (Log2

MFI, bottom) and fosta (Log2 FC, top) pS6 responses were compared across different cytogenetic groups of B-ALL using one-way ANOVA with Tukey pair-wise comparisons. Data are presented as scatter plots with mean SEM for each cytogenetic group. Each symbol is one patient sample. (c) Relationship between basal (x-axis, log2MFI) and inhibited (y-axis, log2FC) phosphorylation of EIF4E and S6 proteins was assessed by two-tailed Pearson correlation. Each dot represents one sample. P-values are shown on each graph. (d-e) Phospho-flow cytometry was used to evaluate changes pAKT(S473), p4EBP1(T36/T45), pEIF4E(S209) and pS6(S235/S236) in 39 B-ALL samples treated with fosta (10 M), DAS (200 nM), LY (20 M) or PD (10 M) for 2h. Effect of each inhibitor was normalized to vehicle by calculating log2FC. (d) Effects of fosta, DAS, LY and PD on phosphorylation of AKT, 4EBP1, EIF4E and S6 were compared using repeated measures ANOVA with Tukey multiple comparison. Each symbol is one sample. Dotted horizontal line indicates baseline. Data are presented as mean SEM. *p<0.05, **p<0.01, ***p<0.001. (e) Each marker-specific heatmap, generated in Cytobank, illustrates effects of 4 inhibitors (rows) on phosphorylation of an indicated protein (left labels). Each column is a patient sample. Colored boxes below each heatmap represent cytogenetic 2 group. The corresponding mean log2FC values and variance ( ) are shown on the right for each treatment.

Figure III.10 Differential in vitro sensitivity to fosta and LY

(a) Basal (Log2 MFI) pAKT(S), pAKT(T) and pEIF4E responses were compared across different cytogenetic groups of B-ALL using one-way ANOVA with Tukey pair-wise

129 comparisons. Data are presented as scatter plots with mean SEM for each cytogenetic group. Each symbol is one patient sample. *p<0.05, **p<0.01, ***p<0.001. (b) Correlation between LY- (x-axis) and fosta-induced (y-axis) reduction in levels of pAKT(S) were assessed by two- tailed Pearson correlation. Each symbol is one sample. P-value is shown on the graph. (c-d) Viably frozen B-ALL samples (n=22) were thawed and recovered for 24 h. Cells were treated with increasing concentrations of fosta, LY or vehicle for 72 h, followed by measurement of [3H]-thymidine uptake. Proliferation (DPM) was normalized to vehicle for each patient sample (Inhibitor/Vehicle 100). Each symbol represents an average of triplicate cultures for one sample. Data are presented as the mean of all samples per dose ± SEM. (c) Effects of LY (left panel) and fosta (right panel) on proliferation of 22 B-ALL samples. (d) Effects of clinically achievable concentration of fosta (3 M, x-axis) and LY (3 M, y-axis) on proliferation of 22 B-ALL samples. Data are presented as normalized proliferation (Inhibitor/Vehicle 100). Boxes indicate 50% inhibition in proliferation by fosta (blue) or LY (orange), relative to vehicle. (e) Phospho-flow cytometry was used to evaluate changes pSRC, pSYK, pPLC 2 and pCrkL in 39 B-ALL samples following treatment with fosta (10 M) or LY (20 M) for 2h.

Data are presented as log2FC. Effects of fosta and LY were compared using Wilcoxon matched pairs test with Bonferonni-corrected p-value < 0.01 considered significant. Each symbol is one sample. Dotted horizontal line indicates baseline (no change in phosphorylation). Data are presented as mean SEM. ***p<0.0001. (f) Correlation between fosta- (x-axis) and BAY- induced (y-axis) reduction in levels of pEIF4E were assessed by two-tailed Pearson correlation. Each symbol is one sample. P-value is shown on the graph.

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Figure III.11 Validation of phospho-flow platform to detect basal pSTAT3 and pSTAT5 signaling responses BaF3 cells were deprived of serum and IL3 for 4h prior to stimulation with anti-mouse IL3 (10 ng/ml; 10 min). Cells stained with FMO excluding the phospho-antibody provide baseline for color scale. (b) Serum-deprived Ramos cells were treated with 0.2 % DMSO vehicle (Basal), fosta (10 M), PD (10 M) or JAKI inhibitor (JAKI, 100 nM) for 2h. Cells were stimulated with @IgM for the last 8 minutes of treatment. Data are presented as histogram overlays for each phospho-specific protein. Vehicle-treated cells were used to establish baseline for color scheme of histogram overlays. 131

Figure III.12 Identification of aberrant pSTAT5 responses in B-ALL (a-c) Cells were treated with DMSO vehicle (0.2%) or fosta (10 M) for 2h, fixed, permeabilized and stained with phospho-specific antibodies recognizing pSTAT3 (S727), pSTAT3 (Y705) and pSTAT5 (Y694). (a) Fosta-induced changes in phosphorylation levels of

132

STAT3 were calculated as log2FC and plotted on y-axis. Data are presented as scatter plots with mean SEM for each cytogenetic group. Each symbol is one patient sample. (b) Basal (Log2

MFI, bottom) and fosta (Log2 FC, top) pSTAT5 responses were compared across different cytogenetic groups of B-ALL using one-way ANOVA with Tukey pair-wise comparisons (**p<0.01). Data are presented as scatter plots with mean SEM for each cytogenetic group.

Each symbol is one patient sample. (c) Relationship between basal (x-axis, log2MFI) and inhibited (y-axis, log2FC) phosphorylation of STAT5 protein was assessed by two-tailed Pearson correlation. Each dot represents one sample. (d) B-ALL samples (n=40) were treated with DMSO vehicle (0.2%), fosta (10 M), DAS (200 nM), LY (20 M) or PD (10 M) for 2h, fixed, permeabilized and stained with phospho-specific antibodies recognizing STAT5. Effect of each inhibitor was normalized to vehicle by calculating log2 (MFITreatment/MFIVehicle). Data

(log2FC) are presented as a heatmap. Each column is one sample, each row is an indicated inhibitors. Colored boxes below each heatmap represent cytogenetic group. Samples numbered

1 through 7 (top of heatmap) were tested for SAR sensitivity. The corresponding mean log2FC values and variance ( 2) are shown on the right for each treatment. (e-f) Differential sensitivity of basal pSTAT5 and pS6 response in high-risk B-ALL. B-ALL samples (samples 1-3: CRLF2+; samples 4-7 CRLF2-; *mutant JAK2) were thawed, rested in SFM for 1h at 37 C and treated with SAR302502 (SAR, 200 nM) or DMSO vehicle (0.2%) for 2h. Cells were fixed, permeabilized and stained with pSTAT5 (e) and pS6 (f) antibodies. Data are presented as histogram overlays. Vehicle-treated cells (basal) were used to establish baseline for color scheme.

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Figure III.13 Distinct inhibition profiles of fosta and SAR (a-c) Viably frozen B-ALL samples were thawed and recovered for 24 h. Cells were treated with increasing concentrations of fosta, SAR, PD or vehicle for 72 h, followed by measurement of [3H]-thymidine uptake. Proliferation (DPM) was normalized to vehicle for each patient sample (Inhibitor/Vehicle 100). Data are presented as the mean of triplicate cultures per dose ± SEM. (a) Analysis of drug sensitivity in three CRLF2+ samples (1-3, Figure 5). Bar graphs summarize patient-specific responses to fosta (top), SAR (middle) and PD (bottom). JAK2 status

134 is indicated as mutant (mut) or wild-type (WT). (b) Effects of SAR (left panel) and fosta (right panel) on proliferation of 18 B-ALL samples. (c) Effects of clinically achievable concentration of fosta (3 M, x-axis) and SAR (300 nM, y-axis) on proliferation of 18 B-ALL samples. Data are presented as normalized proliferation (Inhibitor/Vehicle 100). Boxes indicate 50% inhibition in proliferation by fosta (blue) or SAR (orange), relative to vehicle. (d) Phospho-flow cytometry was used to measure pSTAT5 response to SAR treatment in 8 samples that showed in vitro sensitivity to this drug ( 50% inhibition in proliferation by SAR). Data are presented as histogram overlays. Vehicle-treated cells (basal) were used to establish baseline for color scheme. The pSTAT5 responses to 2 of 8 samples are shown in Figure 5 E (samples 3 and 5).

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Figure III.14 Differential inhibition of BCR signaling by fosta and SAR Phospho-flow cytometry was used to evaluate changes in pSRC(Y418), pSYK(Y348), pPLC- 2(Y759) and pCrkL(Y207) belonging to BCR pathway (a) as well as pAKT(T308), pAKT(S473), p4EBP1(T36/T45), pEIF4E(S209), pS6(S235/S236) belonging to PI3K/AKT/mTOR pathway (b) following 2h treatment with fosta (10 M), SAR (200 nM) or

DMSO vehicle in 19 B-ALL samples. Data are presented as log2FC (MFIInhibitor/MFIVehicle). Effects of fosta and SAR were compared using Wilcoxon matched pairs test with Bonferonni- corrected p-value < 0.01 considered significant. Each symbol is one sample. Dotted horizontal line indicates baseline (no change in phosphorylation). Data are presented as mean SEM. ***p<0.0001.

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Figure III.15 Phospho-flow profiling of B-ALL reveals five groups with distinct phosphorylation signatures that correlate with clinical outcomes Phospho-flow cytometry was used to determine basal phosphorylation of 15 markers (shown in rows) belonging to BCR, PI3K/AKT/mTOR, JAK/STAT and MAPK pathways in 68 B-ALL patient samples (shown in columns). B-ALL samples were thawed, rested in SFM for 1h at 37 C and treated with DMSO vehicle (0.2%) or an indicated inhibitor for 2h. Cells were fixed, permeabilized and stained with phospho-specific antibodies for analysis on HTS by flow cytometry. Basal phosphorylation for each antibody was normalized by calculating log2

(MFIstained-MFIFMO). Hierarchical clustering of normalized basal phosphorylation was performed in R project to group samples based on similarities in basal phosphorylation signatures. Data was row (marker)-normalized by calculating each patient‟s z-score for an indicated marker (see Materials and Methods). Heatmap was scaled by z-score. Five groups were labeled phospho- protein cluster (PC) 1 through PC5. (b) Clinical parameters including occurrence of relapse (top row) and treatment response (bottom row) were mapped for each patient in the same order as they were organized within designated PCs. Significance in correlation signaling clusters and clinical parameters was determined by the 2 test. P-values for each correlation are shown.

CHAPTER IV: Identification of transcriptional effects of fostamatinib in high-risk precursor B-cell acute lymphoblastic leukemia

Tatiana Perova1,2, Shaheena Bashir3, Veronique Voisin4, Johann K. Hitzler1, Mark D. Minden2,3, Cynthia J. Guidos1,5 and Jayne S. Danska2,5,6

1Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada 2Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada 3University Health Network, Toronto, Ontario, Canada 4The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada 5Department of Immunology, University of Toronto, Toronto, Ontario, Canada 6Program in Genetics and Genome Biology, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada

Contributions: TP, CJG and JSD designed the study. TP conducted all experiments and analyzed the data with the following exceptions: SB performed statistical analyses of microarray data and generated Venn diagrams/heatmaps; TP and VV performed GSEA and enrichment map analyses. JKH and MDM provided primary B-ALL patient samples.

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IV.I ABSTRACT Although conventional chemotherapy has lead to a substantial increase in survival rates of B- ALL patients, its use is associated with debilitating systemic toxicities. Thus, development of alternative targeted approaches may circumvent toxic side effects of chemotherapy. Small- molecule kinase inhibitors provide an attractive treatment option for B-ALL, but they frequently exhibit off-target activities, making it difficult to attribute the beneficial effects to the inhibition of their primary target. We previously reported potent anti-leukemic effects of fostamatinib (fosta; Chapter II), an oral inhibitor of spleen tyrosine kinase (SYK), in high-risk B-ALL. Although SYK is reportedly its primary target, fosta exhibits broad selectivity profile by inhibiting other kinases. In order to elucidate full spectrum of fosta effects, we performed gene expression profiling to identify changes in gene expression induced by fosta treatment in pediatric and adult high-risk B-ALL. Here, we describe a broad fosta inhibition effects that included down-regulation of genes involved in lymphocyte activation, cytokine signaling, innate/adaptive immunity and immune response. Collectively, this study provides novel insights into fosta-response genes in high-risk precursor B-ALL that warrant future validation.

IV.II INTRODUCTION The use of intensified chemotherapeutic regiments resulted in a remarkable progress in precursor B cell acute lymphoblastic leukemia (B-ALL) treatment over the past four decades. In particular, dexamethasone is one of the most commonly used glucocorticoids in B-ALL treatment protocols, including in CNS prophylaxis (Balis et al., 1987; Cortes et al., 1995; Mitchell et al., 2005; Teuffel et al., 2011). However, deleterious acute and long-term toxicities associated with the use of chemotherapy, including dexamethasone, is a major hurdle in improving B-ALL patient care (Inaba and Pui, 2010). Thus, major efforts are underway to identify targeted therapies with novel mechanisms of action that will result in better patient outcomes and minimal treatment-related side effects. In this regard, small molecule kinase inhibitors targeting signaling perturbations have emerged as potentially effective treatment options in cancer. Over the past 20 years, it has become apparent that abnormalities in signal transduction pathways promote cancer development and progression. Indeed, cancer cells exhibit hyperactive kinase-regulated signaling networks, necessary for their proliferation and survival. This knowledge prompted an exponential increase in the development of targeted therapies that

140 ameliorate these alterations (Levitzki and Klein, 2010). At present, all clinically approved small molecule kinase inhibitors are ATP-competitive and block catalytic kinase activity, preventing tyrosine phosphorylation of substrates (Zhang et al., 2009). Significant conservation of ATP- binding sites among kinases poses a considerable challenge in developing highly specific kinase inhibitors (Davis et al., 2011). As a result, most kinase inhibitors interact with multiple partners, resulting in off-target interactions that frequently contribute to unfavorable side effects and toxicity. On the other hand, target promiscuity can result in inhibition of multiple oncogenic pathways and lead to therapeutically desirable effects (Karaman et al., 2008), highlighting the need to clearly define the full spectrum of cellular and molecular targets of small molecule kinase inhibitors. Recently, we showed that fosta significantly reduced proliferation and survival of high- risk B-ALL cells in vitro and in vivo (Chapter II), suggesting therapeutic potential of fosta in this disease. Fosta (pro-drug of the active metabolite R406) is an orally available ATP- competitive inhibitor of spleen tyrosine kinase (SYK) (Braselmann et al., 2006). Importantly, fosta has shown therapeutic efficacy in phase 2 trials of rheumatoid arthritis (Weinblatt et al., 2013; Weinblatt et al., 2010) and phase I/II trials of chronic lymphocytic leukemia and B cell lymphoma (Friedberg et al., 2010; Herman et al., 2013). Although fosta preferentially targets SYK, it also interacts with other kinases, including FLT3, SFKs, JAK1, JAK3 and c-KIT, in a cell-type specific manner (Braselmann et al., 2006). Importantly, this selectivity profile of fosta was evaluated across a limited part of the kinome, thereby failing to define the full spectrum of its effects. More recently, Davis et al. tested 72 kinase inhibitors, including fosta, against a panel of 442 kinases and revealed broad inhibition patterns of fosta (Davis et al., 2011), alluding to the possibility that some of these off-target activities may contribute to fosta‟s pre-clinical efficacy in B-ALL, as previously suggested in B cell lymphoma (Davis et al., 2010). We observed potent anti-proliferative activity of fosta in high-risk B-ALL, which included inhibition of extramedullary dissemination, yet the precise molecular mechanisms responsible for these anti- leukemic effects have not been evaluated. Importantly, identification of the full range of fosta effects may reveal novel therapeutic targets in B-ALL. In this study, in an effort to delineate molecular mechanisms of fosta action, we took advantage of gene expression profiling to study changes in gene expression following fosta treatment. Several studies have used this high-throughput technology to reveal dexamethasone response genes in B-ALL, including MYC, TSC22D3, FKBP5, SOCS1, DDIT4, that predicted apoptotic sensitivity to this agent (Bhadri et al., 2011; Miller et al., 2007; Schmidt et al., 2006;

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Wei et al., 2006). We used primary diagnostic B-ALL samples, previously shown to be fosta- sensitive, thereby allowing us to study transcriptional changes that relate to fosta response in B- ALL. We identified a large number of fosta-modulated genes in pediatric and adult high-risk B- ALL, including genes involved in lymphocyte activation, cytokine signaling, innate/adaptive immunity and immune response. Surprisingly, fosta treatment potently reduced interferon response genes, raising the possibility that inflammation may promote B-ALL proliferation and survival. Our results also revealed distinct molecular consequences of fosta and dexamethasone treatments in high-risk B-ALL, suggesting their different mechanisms of action. Collectively, this study described an unbiased approach to delineate transcriptional targets of fosta treatment in B-ALL.

IV.3 METHODS IV.3.1 Patient samples All B-ALL samples were obtained according to guidelines approved by research ethics boards of University Health Networks and Hospital for Sick Children (Toronto, ON). Informed consent was obtained from newly diagnosed patients prior to collecting peripheral blood or bone marrow samples. Low-density mononuclear cells were isolated from peripheral blood or bone marrow aspirates using Ficoll-Paque Plus gradient according to manufacturer‟s instructions. Cells were viably frozen in 90% FBS/10% DMSO (v/v) and stored long-term in a vapor phase of liquid nitrogen. Cell viability, assessed by trypan blue exclusion method after thawing, was greater than 80% for all samples.

IV.3.2 Reagents Fostamatinib was provided by AstraZeneca. BAY613606 was purchased from EMD chemicals, while dexamethasone was from BioVision (Mountain View, CA). All inhibitors were reconstituted with DMSO at 10 mM and stored at -80 C.

IV.3.3 Proliferation Assay Patient samples were thawed and incubated at 37 C overnight in StemSpan media (Stem Cell Technologies) containing 25 mM HEPES (pH 7.2), 1 mM sodium pyruvate and 2 mM L- glutamine and 0.1 mM non-essential amino acids. Cells were cultured in triplicates (1.5 105/well) in 96-well flat-bottom plates with vehicle or inhibitors for 72 h. Methyl-

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[3H]Thymidine (1 µCi/well) was added 16 h prior to harvesting onto glass fiber paper using Inotech Cell Harvester. Disintegrations per minute (DPM) were measured using a Beckman LS 6500 Scintillation Counter. Data are presented as mean of triplicates SEM.

IV.3.4 Flow Cytometry To evaluate percentage of CD19+CD45low blast cell in each tumor, patient samples were thawed and stained with saturating concentration of human-specific antibodies for CD19-PE (4G7) and CD45-FITC (2D1) for 30 minutes on ice. Cells were washed in staining media (SM: HBSS, 2% calf serum, 10mM HEPES, pH 7.2) and collected by centrifugation (400 x g, 5 min, 4 C). Cells were resuspended in staining media containing 5 µg/ml DAPI (Molecular Probes) to discriminate live cells and filtered through 80 m nitex nylon mesh into round bottom tubes for flow cytometric analysis on LSRFortessa. Compensation was performed using anti-mouse Ig and negative control compensation particles. All data were analyzed using FlowJo v 9.1. All samples included in the microarray analysis contained at least 95% of CD19+CD45low blast population.

IV.3.5 Apoptosis Assay B-ALL samples were thawed, recovered for 1 hour in serum- and phenol red-free RPMI-1640 supplemented with 25 mM HEPES (pH 7.2), 1 mM sodium pyruvate, 2 mM L-glutamine and 0.1 mM non-essential amino acids (SFM). Cells were then treated with fosta (3 M) or DMSO vehicle (0.1% v/v) for 6 hours. Fixable Blue viability stain was added for the last 30 minutes of treatment. Cells were immediately fixed and permeabilized cells using Cytofix/Cytoperm™ Fixation and Permeabilization Solution (BD Biosciences) and subsequently stained with a FITC-conjugated antibody specific for active caspase-3 (BD Biosciences active caspase-3 apoptosis kit) according to manufacturer‟s instructions. Cellular fluorescence was measured with an LSRFortessa analyzer.

IV.3.6 Treatment and RNA extraction Patient samples were quickly thawed and resuspended in SFM at 1 106/ml. Cells were recovered for 1 h at 37 C before treatment initiation. Cells were treated with fostamatinib (1 -10 M), BAY613606 (3 M), dexamethasone (100 nM) or DMSO vehicle (0.1% v/v) and harvested at the appropriate time points by centrifugation (400 x g, 5 min, RT). Total RNA was

143 prepared using Rneasy Plus Universal extraction kit with Rnase-free Dnase set (Qiagen, Santa Clara, CA) or Rneasy Plus Universal kit, according to manufacturer‟s instructions. The quality and integrity of RNA was assessed using Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA).

IV.3.7 Microarray experiments All procedures were conducted at the microarray facility at the Hospital for Sick Children Centre for Applied Genomics (TCAG, Toronto, ON) using standard protocols. Illumina HumanHT-12v4 gene expression arrays (San Diego, USA) were used for global gene expression studies. Briefly, 250 ng total RNA was used to prepare amplified and labeled cRNAs using Illumina TotalPrep -96 RNA amplification kit (Applied Biosystems, USA). Then, 1.5 μg cRNA was hybridized to each microarray chip, according to manufacturer‟s instructions. The signals were detected and gathered using BeadArray Reader (Illumina, USA). The data were uploaded to GenomeStudio data analysis software (Illumina, USA) and after qualitative evaluation, text files with data were extracted for statistical analyses.

IV.3.8 Statistical analyses of microarray data All statistical analyses, preprocessing and finding of differential expression, were conducted in statistical software R version 2.13.0 with Bioconductor packages Lumi (Illumina Microarrray Data) and LIMMA (Linear Models for Microarray Data) (Gentleman et al., 2004; Smyth, 2004; Wettenhall and Smyth, 2004). Probes undetected at 30% detection p-value threshold were removed from further analysis. The statistical analyses were carried out on log2-transformed data.

Pilot experiment: This experimental design included 3 time points (2, 4 and 8 hours) and 4 treatment levels (vehicle, fosta 1-10 M) in three B-ALL samples. All samples were processed and analyzed simultaneously to avoid confounding batch effect variation (Johnson et al., 2007). A linear fitting model was used to adjust for the different factors and levels. ANOVA on time points 4 and 8 hours followed by pair-wise comparisons was used to find differentially gene expression of treated samples compared to control ones. The 2 hours time-point is too early to detect treatment effect and was not further used in this analysis. The ranked gene list for each factor was extracted by ranking all probes in increasing order of adjusted p-values (q-values).

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The adjustment for multiple testing was done using Benjamini and Hochberg‟s method to control the FDR (Benjamini and Hochberg, 1995).

Large scale experiment: 18 pediatric B-ALL samples were treated for 6 hours with 3 M fosta or DMSO vehicle (0.1%, v/v), whereas 16 adult B-ALL samples were treated for 6 hours with 3 M fosta, 100 nM dexamethasone or DMSO vehicle. Statistical analyses were performed similarly as for the pilot experiment. First, a linear fitting model was used to adjust for the different factors and levels, followed by an ANOVA (LIMMA) and pair-wise comparisons to find differentially expressed genes. The ranked gene lists were extracted with ranking based on increasing order of FDR-adjusted p-values (q-values). The adjustment for multiple testing was done using Benjamini and Hochberg‟s correction method.

Venn diagrams: Venn diagrams were created to identify the degree of overlap among non- redundant differentially expressed genes. For genes with multiple probe sets, the probe with the highest q-value was selected for the analysis. Probes with missing annotation, LOC and RIKENs were removed form the analysis.

IV.3.9 Gene set enrichment analyses of expression data The gene expression data were analyzed using GSEA (Subramanian et al., 2005) with parameters set to 2,000 gene-set permutations and gene-set size between 8 and 500. Genes were ranked using t-statistic values obtained from pair-wise comparisons. The gene-sets included in the GSEA analyses were obtained from KEGG, MsigDB-c2, NCI, Biocarta, IOB, Netpath, HumanCyc, Reactome and the (GO) databases, updated January 2012 (http://baderlab.org/GeneSets). Cytoscape (Smoot et al., 2011), an open source software for analysis and visualization of networks, was used to generate an enrichment map (version 1.2 of Enrichment Map software (Merico et al., 2010)) for each comparison using enriched gene-sets with a nominal p-value < 0.005, FDR < 0.1 and the overlap coefficient set to 0.5.

IV.3.10 Validation by quantitative real-time PCR (qRT-PCR) Double-stranded cDNA was generated from 2-5 g of total RNA using SuperScriptIII and (oligo(dT)12-18) primers (Invitrogen, Burlington, ON), according to manufacturer‟s instructions. Purity of synthesized cDNA was confirmed by performing PCR

145 with GAPDH primers and Taq polymerase (Qiagen) on an AB Thermocycler (Applied Biosystems) using the following conditions: 94°C for 3‟ followed by 40 cycles at 94°C for 30 sec, 55°C for 45 sec, 72°C for 45 sec and 72°C for 5‟. PCR products were dissolved on a 2% agarose gel. A single bad corresponding to size of 184 bp was detected for all cDNA preparations. STAT1, OAS2, IFIT2, IFIT3, MYD88 and HPRT1 were analyzed by quantitative real- time PCR using SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA) on an ABI Prism 7900HT sequence detection system (Applied Biosystems) using universal cycling conditions: Step 1: 50°C for 2‟, Step 2: 95°C for 10‟, Step 3: 95°C for 15, Step 4: 60°C for 1‟ (repeat Step 3 and 4: 40 cycles). Primer sequences are included in Table IV.1. Template dilution standard curves (100-106 range), generated using gene-specific plasmids (Table IV.2), were included for each primer set to ensure a linear relationship between Ct values and concentration (R2 0.99). Assays were performed in triplicates. Concentration of each transcript was calculated using the relative standard-curve method. Relative expression levels were then normalized to HPRT1 expression as an endogenous control followed by calculation of the fold- difference between vehicle- and fostamatinib-treated samples. Expression data are presented as mean of triplicates SEM.

IV.4 RESULTS IV.4.1 Optimization of treatment conditions to profile expression signature of fosta effects Identification of the full range of intracellular targets of a small molecule inhibitor is essential for complete understanding of its molecular mechanisms of action. Importantly, these effects must be studied at clinically relevant concentrations and within a time frame that appropriately reflects modulation of primary transcription responses. Therefore, we first used gene expression profiling to perform time-course and dose-response analyses in an effort to identify optimal conditions that elicit maximal changes in gene expression in response to fosta treatment. We compared the effect on gene expression of three different fosta concentrations (1-10 M) at three time-points (Figure IV.1a) in three high-risk adult B-ALL samples (1 complex, 2 BCR- ABL1). We selected these samples based on their in vitro sensitivity to fosta, as evident by dose- dependent inhibition of proliferation by fosta (Figure IV.1b), and high purity of CD19+CD45+ blast population within each sample ( 95%; Figure IV.1c). We incubated each sample with 1, 3

146 or 10 M, isolated total RNA after 2, 4 or 8 hours of treatment and analyzed changes in gene expression by Illumina Human HT-12 arrays. Hierarchical clustering revealed that the three independent B-ALL samples clustered on their own, likely reflecting the underlying heterogeneity of each leukemia samples (Figure IV.2a). The 4h and 8h time points clustered together and distinctly from the 2hr time point, suggesting that 4h and 8h treatments may yield more robust and interesting changes in gene expression. We consequently focused our further analyses on these two time-points (Figure IV.2b-d). Forty out of top 60 genes down-regulated at 4h time-point (Table IV.3) were also reduced after 8h treatment (Table IV.4), and same biological pathways were enriched within the list of differentially expressed genes at 4hr and 8hr, confirming that either treatment duration is sufficient to induce robust changes in gene expression (Figure IV.3). The 4h treatment with 3 M fosta demonstrated the highest number of differentially expressed genes (245) compared to other conditions (Figure IV.2b-d). Of these 245 genes (at q <0.05), 68% were downregulated by fosta, highlighting its predominantly inhibitory role in B-ALL. The 3 M dose is within maximum attainable therapeutic concentration observed in clinical trials, suggesting that any changes in the gene expression likely reflects the clinical effects of this inhibitor. The genes downregulated by the fosta treatment were involved in a broad variety of biological pathways (Figure IV.3; IV.4a). These significantly altered pathways included the cytokine signaling and lymphocyte activation (Figure IV.3; IV.4a). These data are consistent with our observations of fosta-mediated inhibition of basal signaling pathways (Chapter III), which may very well explain anti-proliferative effects of fosta in B-ALL. Surprisingly, the pathway map also highlighted pronounced enrichment of genes belonging to immune response pathways in vehicle-treated group, and included inflammatory response, defense response and interferon production (Figure IV.3; IV.4b), implicating inflammation in B-ALL survival and progression. Collectively, these data suggest that fosta exhibits broad effects by modulating expression of genes involved in multiple specialized cellular processes. Given similar effects of 4h and 8h fosta treatment, an intermediate time-point (6h) was used for validation and all subsequent experiments. Importantly, 6h fosta treatment did not result in significant apoptosis in B-ALL, as indicated by low levels of activated caspase-3 following fosta treatment (Figure IV.5a). Therefore, any changes in gene expression measured were likely related to the direct mechanisms of action of fosta rather than consequences of drug-induced apoptosis. Using this duration, we next investigated the reproducibility of microarray results by

147 qRT-PCR validation of fosta effects on the expression of inflammatory response genes STAT1, OAS2, IFIT2, IFIT3 and MYD88, which were among top differentially expressed genes in the pilot microarray analysis (Figure IV.5b). Notably, fosta treatment reduced expression of STAT1 and OAS2 by at least 60% (relative to vehicle) in three independent samples (Figure IV.5c). Furthermore, fosta treatment robustly decreased STAT1, OAS2, IFIT2, IFIT3 and MYD88 in two high-risk B-ALL samples (Figure IV.5d,e). These inhibitory effects were shared by BAY613606 (BAY), a potent and highly selective ATP-competitive inhibitor of SYK activity (Yamamoto et al., 2003), which suggests SYK-dependent regulation of inflammatory responses in B-ALL. Taken together, we identified 6 h treatment with fosta (3 M) as an optimal treatment to induce robust changes in gene expression in B-ALL that can be used to profile molecular consequences of fosta inhibition in larger cohort of high-risk B-ALL.

IV.4.2 Analysis of fosta effects in high-risk adult and pediatric B-ALL High-risk B-ALL patients poorly respond to modern chemotherapy protocols and are, therefore, at greatest risk of fatal relapses, highlighting an outstanding need for new treatment options in this group of B-ALL. Since we previously showed an anti-leukemic effect of fosta in high-risk B-ALL, we sought to gain insight into transcriptional consequences of fosta treatment in this disease by selecting 18 pediatric B-ALL samples, which included 14 high-risk cases (Figure IV.6a). In vitro proliferation of all selected samples was robustly inhibited by fosta (Chapter II). All pediatric samples were processed simultaneously to monitor the global changes in gene expression associated with fosta treatment (3 M, 6h). The reduction in the expression of previously validated genes STAT1, OAS2, IFIT2, IFIT3 and MYD88 by fosta in this cohort of B- ALL samples confirmed strong fosta-mediated inhibition of interferon response genes (q 0.004; Figure IV.6b). To further analyze which of the biological pathways were affected by fosta treatment, we performed pathway enrichment analysis. The resulting map includes 139 pathways significantly enriched in vehicle and 272 pathways significantly enriched in fosta group (p-value < 0.005 and FDR < 10%). Again, confirming the findings of the pilot study, functional pathway analysis revealed significant enrichment of immune response, interferon signaling and lymphocyte signaling pathways in vehicle group (genes down-regulated by the treatment, blue circles; Figure IV.6c, Table IV.5). Notably, we also observed down-regulation of genes belonging to B cell receptor (BCR) signaling pathway (Figure IV.7a), consistent with our own observations of fosta-mediated

148 inhibition of phosphorylation of BCR signaling proteins in B-ALL (Chapter III). The anti- inflammatory effects of fosta were evident by its pronounced inhibition of interferon-stimulated genes (ISGs), including MX1, IFITM1, OAS2, IFIT2, ISG15, IRF7/9 and GBP2 (Figure IV.7b). Although these effects were unexpected in B-ALL, emerging body of evidence implicates deregulated interferon signaling in leukemogenesis (Kim et al., 2011; Ohmine et al., 2001; Schurch et al., 2013; Tomic et al., 2011), suggesting possible therapeutic potential of its inhibition in B-ALL. Importantly, many of these ISGs were induced upon BCR activation of chronic lymphocytic leukemia (CLL) cells (Herishanu et al., 2011), further supporting fosta- mediated inhibition of BCR pathway in B-ALL. A substantial number of pathways (272 gene sets) were also significantly enriched in genes upregulated in fosta-treated group (red nodes on Figure IV.6c). They were not significantly enriched in the small case study, likely due to small sample size. These pathways represented diverse biological processes – ER stress response, protein degradation, mRNA surveillance and cell cycle regulation – highlighting again a broad effect of fosta treatment on B-ALL samples (Figure IV.8a). Noteworthy, unfolded protein response (UPR), which is essential for maintaining ER homeostasis, was enriched in fosta-treated group and was associated with increased expression of CALR, encoding ER protein calreticulin, involved in ER-associated degradation and apoptosis of cancer cells (Luo and Lee, 2013). Ribosomal biogenesis was significantly over-represented in fosta-treated group and was associated with increased expression of small and large ribosomal protein genes (Figure IV.8b). We next examined fosta effects in 16 high-risk adult samples belonging to four cytogenetic groups of B-ALL, previously shown to respond to fosta in vitro (Figure IV.9a). Similar to pediatric B-ALL, fosta treatment significantly reduced the expression of STAT1, OAS2, IFIT2, IFIT3 and MYD88 in adult B-ALL samples (q 0.009; Figure IV.9b), supporting reproducibility of fosta-mediated inhibition of interferon response genes across spectrum of cytogenetic groups of adult and pediatric B-ALL. Functional pathway analysis in vehicle- versus fosta-treated samples revealed that many genes enriched in vehicle group (blue circle) map to immune response, migration, interferon and cytokine signaling pathways (Figure IV.9c, Table IV.6), confirming our observations in the cohort of pediatric B-ALL samples. Although gene sets found enriched in fosta group comprised only a small part of the map, they related to increase in UPR and oxidative metabolism, effects that were also observed in pediatric samples. Enrichment mapping allows evaluation of gene-sets that have similar or distinct enrichment in two data sets (Merico et al., 2010). Therefore, we used this approach to evaluate differences 149 in gene expression enrichment between pediatric and adult B-ALL samples in an effort to identify shared responses to fosta that are most likely relevant to its efficacy in high-risk B- ALL. Thus, we have created a pathway map that contains both pediatric and adult B-ALL samples (Figure IV.10). Notably, there was an apparent agreement between adult and pediatric responses to fosta as the majority of pathways enriched in the pediatric samples were also enriched in the adult ones (node borders and centers are of same color, Figure IV.10). It included gene sets belonging to lymphocyte signaling, immune response, interferon signaling and ER stress response, highlighted earlier. It should be noted that there were also some differences in enriched pathways that likely reflected the genetic heterogeneity across different genetic subtypes of adult and pediatric B-ALL (Boissel et al., 2003; De Braekeleer et al., 2012; Moos et al., 2002; Yeoh et al., 2002). Collectively, we revealed molecular effects of fosta in B- ALL that included modulation of a large number of biological processes and functions such as lymphocyte signaling, interferon signaling, immune response and ER-stress response.

IV.4.3 Distinct inhibition signatures of fosta and dexamethasone Dexamethasone (DEX) is a glucocorticoid that is used as a mainstay treatment for B-ALL, including CNS relapse. Despite its success in B-ALL treatment, the use of DEX is associated with significant clinical morbidities (Inaba and Pui, 2010). We previously showed that fosta might have therapeutic potential in B-ALL (Chapter II), suggesting that addition of fosta to conventional chemotherapy may be warranted. Although transcriptional consequences of DEX treatment have been previously reported, no studies have compared its effects to fosta. We therefore investigated whether DEX and fosta elicit similar or distinct changes in gene expression in high-risk B-ALL. To compare gene expression programs regulated by DEX and fosta, we treated 16 high-risk adult B-ALL samples (Figure IV.9a) with DEX, which was performed in parallel with fosta treatment described in previous section. We identified 2456 probes that were differentially expressed in at least one of the three comparisons (FDR < 0.05). We determined the DEX effect on the expression levels of STAT1, OAS2, IFIT2, IFIT3 and MYD88 and observed minimal effect of DEX on the expression of these genes (Figure IV.12a), compared to fosta (Figure IV.7b, IV.9b), suggesting differential regulation of ISGs by DEX and fosta. Within the top 60 differentially expressed probes, many showed a different expression pattern in response to fosta and DEX treatment, revealing significant differences in their effects (Figure IV.11). Indeed, at the FDR threshold of q<0.001, only four genes (FDR p<0.001) were similarly affected by DEX

150 and fosta treatment (Figure IV.12b,c), whereas 51 genes were uniquely regulated by fosta and 61 genes were unique to DEX treatment (Figure IV.12b, Tables IV.8-9). Functional pathway analysis of differentially expressed genes in DEX-treated samples revealed significant enrichment of cell cycle regulation, non-homologous end joining (NHEJ), immune response, mitochondrial function and lymphocyte signaling pathways in the vehicle group (Figure IV.12d, Table IV.7), which is in agreement with previously described glucocorticoid target genes (Bhadri et al., 2011; Miller et al., 2007; Rainer et al., 2012; Schmidt et al., 2006; Wasim et al., 2010; Wei et al., 2006). To further probe differences between DEX and fosta, we generated a composite enrichment map for gene sets passing significance cut-off (p<0.005, FDR<10%) by mapping enrichment for fosta effects in node center and DEX effects in node border (Figure IV.13). Notably, this analysis highlighted distinct effects of DEX and fosta on gene sets belonging to cell cycle, NHEJ, IL1B production, DNA damage response and nitric oxide synthesis. Because GSEA analyses highlighted notable differences in the genes affected by fosta and DEX treatment, we hypothesized that the combination of DEX and fosta may result in more potent inhibition than either of the drug alone. Therefore, we cultured 5 high-risk B-ALL samples with fosta (1 M, clinically relevant dose) alone, DEX (10-10-10-6) alone or in combination for 72h. Using a [3H]-thymidine assay, we observed that DEX alone inhibited proliferation of BCR-ABL+ (Figure IV.14a) and BCR-ABL- (Figure IV.14b) samples in a dose dependent-manner, although poor inhibition ( 50%) was observed at the lowest doses for 4 of 5 samples. Although fosta alone robustly ( 50%) inhibited proliferation of 4 of 5 samples, we observed more potent inhibition by fosta when combined with DEX, particularly at low concentration (10-10-10-9M). Collectively, these data suggest that lower doses of DEX may be used in combination with fosta to more effectively suppress proliferation of B-ALL cells than either of these agents alone.

IV.5 DISCUSSION Although fosta, an ATP-competitive inhibitor of SYK activity, has emerged as a valuable therapeutic option in autoimmune disorders and mature B cell malignancies (Singh et al., 2012), the precise nature of transcriptional responses associated with fosta activity have not been elucidated. Although our studies suggest anti-proliferative activity of fosta in high-risk B-ALL is SYK-specific, off-target effects of this small molecule kinase inhibitor have been described

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(Davis et al., 2011). Thus, the goal of this study was to capture global overview of fosta effects using gene expression profiling and to reveal its unidentified molecular targets, or hidden phenotype (MacDonald et al., 2006), in B-ALL. Here, we report fosta-dependent regulation of a multitude of biological processes, including BCR, cytokine, interferon and ER stress response pathways. To our knowledge, this is the first comprehensive examination of gene expression signature of fosta in B-ALL. The strength of this study is highlighted by the careful considerations given to cell type, fosta dose, treatment duration and data analysis, which were necessary optimization parameters. First, we selected fosta-sensitive primary diagnostic B-ALL samples that were previously screened for their responsiveness in vitro and, in several cases, in vivo. This selection criterion was necessary to ensure the analysis of fosta action in a disease-relevant model, while, at the same time, eliminating potentially confounding fosta effects in non-responding samples. Second, to probe therapeutically relevant fosta actions, we selected a treatment dose that was within the clinically achievable concentrations. The optimization of treatment duration was necessary to detect changes reflective of the direct mechanisms of action on transcription. Third, we used GSEA to identify fosta-associated changes in gene expression. This method is far superior to the traditionally used hierarchical clustering since it allows to study coordinated alterations in biological pathways and functions, rather than focusing on individual genes (Lamb et al., 2006; Mootha et al., 2003; Subramanian et al., 2005). Furthermore, enrichment map visualization of GSEA data allowed us to directly compare fosta effect in adult and pediatric B- ALL samples, revealing marked similarity of cellular processes modulated by fosta in these two groups of patients. Finally, this method allowed comprehensive comparison between fosta and DEX, a commonly used cytotoxic agent in B-ALL treatment, revealing similarities and notable differences in their mechanisms of action. Although the discovery nature of this study, in the absence of functional validations, cautions against definitive conclusions of molecular mechanisms of fosta action, the results presented here provide compelling evidence for the broad spectrum of fosta effects and implicate regulation of BCR signaling and immune response pathways in its potential mechanisms of action in B-ALL. Earlier studies suggested reduced expression of BCR-related genes in high-risk B-ALL cases, including those with BCR-ABL1 translocation and/or IKZF1 deletion (Iacobucci et al., 2012; Mullighan et al., 2009b), bringing into question the relevance of this pathway in B-ALL. In marked contrast, we report fosta-mediated attenuation of expression of genes belonging to the BCR pathway, confirming our earlier observations of decrease in

152 phosphorylation of proteins belonging to BCR signaling network, including SRC, SYK, PLC 2 and CRKL, in high-risk adult and pediatric samples belonging to different cytogenetic groups (Chapter III). In this regard, 10 of 18 pediatric and all adult samples used in the gene expression analysis were also profiled for fosta responsiveness by phospho-flow and exhibited fosta- sensitive phosphorylation of BCR proteins. Collectively, our observations of pronounced fosta- driven inhibition of BCR pathway by two independent high-throughput methods strongly implicate it as a direct target of fosta action in high-risk B-ALL. In addition to the BCR pathway regulation, we observed significant inhibition of genes belonging to immune response/interferon signaling in adult and pediatric B-ALL. In particular, gene expression analysis detected significant inhibition of interferon-stimulated genes (ISGs) by fosta, observations that were independently confirmed by qRT-PCR. Such potent anti- inflammatory signature of fosta is intriguing, considering that chronic inflammation has been implicated in survival, proliferation, angiogenesis and metastasis of tumor cells (Coussens and Werb, 2002; Mantovani, 2005). Findings that this effect was identified in pediatric and adult samples suggest that regulation of interferon response genes may be at the core of the fosta inhibition signature in B-ALL. The observations of shared inhibitory effects of fosta and BAY on ISGs‟ expression implicates SYK-dependent regulation of these genes in B-ALL. Interestingly, SYK signaling is required for toll-like receptor (TLR)/MYD88-depedent interferon-beta (IFN ) production in dendritic cells (Weiss et al., 2012). Another study identified oncogenic MYD88-dependent pathway that induces interferon-response signature, thereby regulating survival signals in B cell lymphoma (Ngo et al., 2011). Interestingly, our analysis revealed a significant decrease in MYD88 and IFNB1 by fosta in adult and pediatric B- ALL samples, suggesting that MYD88-driven IFN production may depend on SYK signaling. Further functional experiments are clearly warranted to examine the role of interferon signaling in B-ALL survival and proliferation. Among pathways enriched in fosta-treated samples were ribosomal biogenesis and ER stress responses/ apoptosis. Although biological significance of enhanced ribosomal biogenesis in B-ALL is unclear, several studies implicated ribosomal proteins in p53-mediated apoptosis (Shenoy et al., 2012; Zhang et al., 2011). We introduced the notion that fosta exhibits of pro- apoptotic effects in Chapter II. In support of this, fosta decreased expression of genes encoding known proto-, including CCND1, CDC16, CDC20 and MYC, with concomitant increase in CSNK1E, a gene encoding casein kinase that phosphorylates p53 to promote its

153 dissociation from Mdm2 and subsequent activation (Alsheich-Bartok et al., 2008; Knippschild et al., 1997). Collectively, these data suggest that anti-leukemic effect of fosta may be attributed to its ability to induce apoptosis in B-ALL. Despite the clinical importance of glucocorticoid agents, including DEX, in B-ALL treatment, their use is associated with debilitating toxicities. Therefore, identification of novel less-toxic therapeutics with new mechanisms of action is essential to improve treatment outcomes in B-ALL. Here, we compared molecular mechanisms of action of fosta and DEX in the same cohort of B-ALL samples. In contrast to fosta, DEX had no effect on BCR signaling with minimal inhibition of interferon-response genes. On the other hand, in agreement with previous reports, DEX activity included potent regulation of genes belonging to apoptosis, metabolism and cell cycle (Bhadri et al., 2011; Miller et al., 2007; Tissing et al., 2007). In addition, we observed that fosta, used within a clinically relevant dose, enhanced anti- proliferative activity of DEX in high-risk B-ALL, including at lowest doses, suggesting potential clinical benefit in combining fosta with cytotoxic agents. In conclusion, these data reveal that compared to DEX, fosta treatment regulates unique molecular mechanisms in high- risk B-ALL, including prominent modulation of genes belonging to lymphocyte activation/signaling, immune response/interferon signaling and ER stress response. Further functional studies will be necessary to define which pathways are necessary for fosta‟s anti- proliferative activity in B-ALL. In addition, observations of enhanced anti-leukemic properties of DEX in combination with fosta, combined with their distinct molecular mechanisms, warrant pre-clinical evaluation of this combination treatment regimen.

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IV.6 TABLES

Table IV.1 Sequences of primers used for quantitative real-time PCR Gene Direction Sequence (5’-3’) Product size (bp) S GAG CTT CAC TCC CTT AGT TTT GA STAT1 84 AS CAC AAC GGG CAG AGA GGT S TGG TGG CAG AAG AGG AAG AT IFIT2 122 AS GTA GGC TGC TCT CCA AGG AA S TTC AGA ACT GCA GGG AAA CA IFIT3 83 AS ATG GCA TTT CAG CTG TGG A S CCT GCC TTT AAT GCA CTG G OAS2 71 AS ATG AGC CCT GCA TAA ACC TC S TGA GCT CAT CGA AAA GAG GTG MYD88 78 AS AAG TCA CAT TCC TTG CTC TGC S TGA CCT TGA TTT ATT TTG CAT HPRT1 ACC 102 AS CGA GCA AGA CGT TCA GTC CT S GTC GGA GTC AAC GGA TT GAPDH 184 AS AAG CTT CCC GTT CTC AG

Legend: S, sense; AS, antisense. All primers were used at a concentration of 100 nM.

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Table IV.2 Plasmids used to prepared standard curved for quantitative real-time PCR Direction Accession Clone ID Vector STAT1 BC002704 3627218 pOTB7 IFIT2 BC032839 4838844 pBluescriptR IFIT3 BC004977 2906188 pOTB7 OAS2 BC049215 5517804 pCMV-SPORT6LB MYD88 BC013589 3900951 pCMV-SPORT6LB HPRT1 BC000578 3163726 pOTB7

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Table IV.3 A list of top 60 differentially expressed gene at 4 h fosta treatment

Gene Log FDR Probe ID 2 t Description Symbol FC q value ILMN_1691364 STAT1 -2 -14 3.80E-10 signal transducer and activator of transcription 1 ILMN_1701789 IFIT3 -2.6 -12.9 1.57E-09 interferon-induced protein with tetratricopeptide repeats 3 ILMN_1707695 IFIT1 -2.9 -11.5 1.53E-08 interferon-induced protein with tetratricopeptide repeats 1 ILMN_1799467 SAMD9L -1.4 -11.5 1.53E-08 sterile alpha motif domain containing protein 9-like ILMN_1657871 RSAD2 -2.1 -11.4 1.59E-08 radical S-adenosyl methionine domain-containing protein 2 ILMN_1674811 OASL -2.3 -11 3.16E-08 2'-5'-oligoadenylate synthase-like protein ILMN_1745242 PLSCR1 -1.5 -10.7 4.52E-08 Ca2+-dependent phospholipid scramblase 1 ILMN_2289093 KIAA1618 -1.3 -10.7 4.52E-08 RING finger protein 213 ILMN_2231928 MX2 -2.6 -10.6 5.38E-08 interferon-induced GTP-binding protein Mx2 ILMN_1787509 PRIC285 -1.9 -10 1.13E-07 helicase with zinc finger 2, transcriptional coactivator ILMN_1729749 HERC5 -1.7 -10 1.24E-07 HECT and RLD domain containing E3 ubiquitin protein ligase 5 ILMN_1674063 OAS2 -1.6 -9.9 1.24E-07 2'-5'-oligoadenylate synthetase 2 ILMN_1675640 OAS1 -1.8 -9.6 2.27E-07 2'-5'-oligoadenylate synthetase 1 ILMN_3240420 USP18 -2.2 -9.6 2.27E-07 ubiquitin specific peptidase 18 ILMN_1781373 IFIH1 -1 -9.6 2.27E-07 interferon induced with helicase C domain 1 ILMN_1783621 CMPK2 -1.8 -9.5 2.27E-07 cytidine monophosphate (UMP-CMP) kinase 2, mitochondrial ILMN_1731224 PARP9 -1.2 -9.5 2.63E-07 poly (ADP-ribose) polymerase family, member 9 ILMN_2054019 ISG15 -2.7 -9.1 6.54E-07 interferon-induced 15 kDa protein ILMN_1760062 IFI44 -2 -8.9 8.37E-07 interferon-induced protein 44 ILMN_1813625 TRIM25 -1.1 -8.8 1.14E-06 E3 ubiquitin/ISG15 ligase tripartite motif containing protein 25 ILMN_1797001 DDX58 -1.3 -8.7 1.25E-06 DEAD (Asp-Glu-Ala-Asp) box polypeptide 58 ILMN_1745148 ZNFX1 -0.7 -8.7 1.36E-06 NFX1-type zinc finger-containing protein 1 ILMN_1696654 IFIT5 -1.1 -8.6 1.66E-06 interferon-induced protein with tetratricopeptide repeats 5 ILMN_2388547 EPSTI1 -2 -8.5 2.01E-06 epithelial stromal interaction protein 1 ILMN_1659913 ISG20 -1.8 -8.4 2.57E-06 interferon stimulated exonuclease gene 20kDa ILMN_1814305 SAMD9 -1.1 -8.3 3.16E-06 sterile alpha motif domain containing 9

ILMN_1758418 TNFSF13B -1.6 -8.2 3.53E-06 BAFF; (ligand) superfamily, member 13b ILMN_1731418 SP110 -1.2 -8.2 3.87E-06 interferon-induced protein 41 ILMN_1678422 DHX58 -1.3 -8.1 4.54E-06 DEXH (Asp-Glu-X-His) box polypeptide 58 ILMN_1654639 HERC6 -1.7 -8.1 4.66E-06 HECT and RLD domain containing E3 ubiquitin protein ligase family member 6 ILMN_1659960 IL4I1 -1 -8 5.13E-06 IL4-induced protein 1 ILMN_1690365 USP41 -1.3 -8 5.13E-06 ubiquitin specific peptidase 41 ILMN_2349061 IRF7 -1.4 -8 5.26E-06 interferon regulatory factor 7 ILMN_1683792 LAP3 -1.3 -8 5.34E-06 leucine aminopeptidase 3 ILMN_1784380 DTX3L -0.9 -7.9 7.39E-06 E3 ubiquitin-protein ligase deltex3-like ILMN_1795181 DDX60 -1.4 -7.8 9.26E-06 DEAD (Asp-Glu-Ala-Asp) box polypeptide 60 ILMN_1801307 TNFSF10 -1.7 -7.7 9.84E-06 TRAIL; tumor necrosis factor (ligand) superfamily, member 10 ILMN_1701455 FBXO6 -1.2 -7.7 1.15E-05 F-box protein 6 ILMN_1745397 OAS3 -1.7 -7.6 1.46E-05 2'-5'-oligoadenylate synthetase 3 ILMN_1795464 LTA -1 -7.5 1.58E-05 lymphotoxin alpha (TNF superfamily, member 1) ILMN_1810608 PNPT1 -1 -7.5 1.74E-05 polyribonucleotide 1, mitochondrial ILMN_1662358 MX1 -2 -7.4 2.02E-05 interferon-induced GTP-binding protein Mx2 ILMN_1738523 MYD88 -0.8 -7.4 2.12E-05 myeloid differentiation primary response gene 88 ILMN_1765994 ZBP1 -0.9 -7.4 2.19E-05 DNA-dependent activator of IFN-regulatory factors ILMN_1810191 PLA2G4C -0.6 -7.3 2.45E-05 cytosolic phospholipase A2 gamma ILMN_2320964 ADAR -0.9 -7.3 2.45E-05 adenosine deaminase, RNA-specific ILMN_2370573 XAF1 -1.2 -7.3 2.47E-05 XIAP associated factor 1 ILMN_1723912 IFI44L -2.8 -7.2 3.08E-05 interferon-induced protein 44-like ILMN_1695404 LY6E -1.5 -7.2 3.09E-05 lymphocyte antigen 6 complex, locus E ILMN_2342695 PDGFA -0.8 -7.2 3.47E-05 platelet-derived growth factor alpha polypeptide ILMN_3243928 DDX60L -1.1 -7.1 3.81E-05 DEAD (Asp-Glu-Ala-Asp) box polypeptide 60-like ILMN_1705241 TDRD7 -0.9 -7.1 4.05E-05 tudor domain-containing protein 7 ILMN_1718558 PARP12 -0.8 -7.1 4.11E-05 poly (ADP-ribose) polymerase family, member 12 ILMN_1745374 IFI35 -1.3 -7 4.33E-05 interferon-induced protein 35 ILMN_1663618 STAT3 -0.8 -7 4.47E-05 signal transducer and activator of transcription 3 ILMN_1731064 CABC1 0.6 6.9 6.12E-05 chaperone, ABC1 activity of bc1 complex like

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ILMN_1801246 IFITM1 -1.5 -6.9 6.28E-05 interferon induced transmembrane protein 1 ILMN_2347798 IFI6 -2.1 -6.9 6.66E-05 interferon, alpha-inducible protein 6 ILMN_1691731 PARP14 -0.9 -6.7 9.09E-05 poly (ADP-ribose) polymerase family, member 14 ILMN_2053415 LDLR -0.8 -6.7 0.0001 low density lipoprotein receptor

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Table IV.4 A list of top 60 differentially expressed genes at 8 h fosta treatment

Log FDR Probe ID Gene 2 t Description FC q value ILMN_1691364 STAT1 -1.6 -11 1.3E-07 signal transducer and activator of transcription 1 ILMN_1787509 PRIC285 -1.8 -9.7 1.6E-06 helicase with zinc finger 2, transcriptional coactivator ILMN_1701789 IFIT3 -1.7 -8.3 2.3E-05 interferon-induced protein with tetratricopeptide repeats 3 ILMN_1739428 IFIT2 -1.5 -8.3 2.3E-05 interferon-induced protein with tetratricopeptide repeats 2 ILMN_1799467 SAMD9L -1.0 -8.1 2.4E-05 sterile alpha motif domain containing protein 9-like ILMN_1707695 IFIT1 -2.0 -8.1 2.7E-05 interferon-induced protein with tetratricopeptide repeats 1 ILMN_1736729 OAS2 -1.2 -7.5 9.0E-05 2'-5'-oligoadenylate synthetase 2 ILMN_1674811 OASL -1.6 -7.5 9.6E-05 2'-5'-oligoadenylate synthase-like protein ILMN_1731224 PARP9 -0.9 -7.4 0.0001 poly (ADP-ribose) polymerase family, member 9 ILMN_1657871 RSAD2 -1.3 -7.0 0.0002 radical S-adenosyl methionine domain-containing protein 2 ILMN_1783621 CMPK2 -1.3 -6.9 0.0003 cytidine monophosphate (UMP-CMP) kinase 2, mitochondrial ILMN_1729749 HERC5 -1.1 -6.7 0.0005 HECT and RLD domain containing E3 ubiquitin protein ligase 5 ILMN_2231928 MX2 -1.6 -6.6 0.0006 interferon-induced GTP-binding protein Mx2 ILMN_2054019 ISG15 -1.9 -6.4 0.0008 interferon-induced 15 kDa protein ILMN_1760062 IFI44 -1.4 -6.4 0.0009 interferon-induced protein 44 ILMN_1701114 GBP1 -1.6 -6.3 0.0009 guanylate binding protein 1, interferon-inducible ILMN_1701455 FBXO6 -1.0 -6.3 0.0010 F-box protein 6 ILMN_1759250 TAP2 -0.7 -6.2 0.0012 ransporter 2, ATP-binding cassette, sub-family B ILMN_2115005 FGD2 -0.7 -6.2 0.0012 FYVE, RhoGEF and PH domain containing 2 ILMN_1683792 LAP3 -1.0 -6.2 0.0012 leucine aminopeptidase 3 ILMN_1659960 IL4I1 -0.7 -6.2 0.0012 IL4-induced protein 1 ILMN_1797001 DDX58 -0.9 -6.1 0.0015 DEAD (Asp-Glu-Ala-Asp) box polypeptide 58 ILMN_1659913 ISG20 -1.3 -6.0 0.0017 interferon stimulated exonuclease gene 20kDa ILMN_2347798 IFI6 -1.8 -6.0 0.0017 interferon, alpha-inducible protein 6 ILMN_1716815 CEACAM1 -0.7 -5.9 0.0019 carcinoembryonic antigen-related 1 ILMN_3243928 DDX60L -0.9 -5.9 0.0019 DEAD (Asp-Glu-Ala-Asp) box polypeptide 60-like

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ILMN_1758418 TNFSF13B -1.1 -5.9 0.0019 BAFF; tumor necrosis factor (ligand) superfamily, member 13b ILMN_1675640 OAS1 -1.1 -5.9 0.0020 2'-5'-oligoadenylate synthetase 1 ILMN_1745148 ZNFX1 -0.5 -5.9 0.0020 NFX1-type zinc finger-containing protein 1 ILMN_1810608 PNPT1 -0.8 -5.8 0.0022 polyribonucleotide nucleotidyltransferase 1, mitochondrial ILMN_1784380 DTX3L -0.6 -5.8 0.0024 E3 ubiquitin-protein ligase deltex3-like ILMN_1654639 HERC6 -1.2 -5.8 0.0024 HECT and RLD domain containing E3 ubiquitin protein ligase family member 6 ILMN_1723912 IFI44L -2.2 -5.7 0.0027 interferon-induced protein 44-like ILMN_2052208 GADD45A 0.6 5.7 0.0030 growth arrest and DNA-damage-inducible, alpha ILMN_1691731 PARP14 -0.8 -5.7 0.0030 poly (ADP-ribose) polymerase family, member 14 ILMN_1656287 SPOCK2 0.7 5.6 0.0034 sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 2 ILMN_1684278 SNORD38A -0.6 -5.6 0.0035 small nucleolar RNA, C/D box 38A ILMN_2053415 LDLR -0.7 -5.6 0.0036 low density lipoprotein receptor ILMN_1690921 STAT2 -0.7 -5.6 0.0037 signal transducer and activator of transcription 2 ILMN_1696654 IFIT5 -0.7 -5.6 0.0038 interferon-induced protein with tetratricopeptide repeats 5 ILMN_1731418 SP110 -0.8 -5.5 0.0040 interferon-induced protein 41 ILMN_1791759 CXCL10 -2.8 -5.5 0.0040 chemokine (C-X-C motif) ligand 10 ILMN_1776723 PHF11 -0.5 -5.5 0.0040 PHD finger protein 11 ILMN_1798181 IRF7 -0.9 -5.5 0.0043 interferon regulatory factor 7 ILMN_2201966 N4BP1 -0.5 -5.4 0.0046 NEDD4 binding protein 1 ILMN_2058782 IFI27 -0.7 -5.4 0.0046 interferon, alpha-inducible protein 27 ILMN_1667825 MLKL -0.5 -5.4 0.0048 mixed lineage kinase domain-like protein ILMN_1695404 LY6E -1.1 -5.4 0.0048 lymphocyte antigen 6 complex, locus E ILMN_2193591 UNC93B1 -0.6 -5.4 0.0048 unc-93 homolog B1 ILMN_1801246 IFITM1 -1.2 -5.4 0.0048 interferon induced transmembrane protein 1 ILMN_2289093 KIAA1618 -0.6 -5.4 0.0048 RING finger protein 213 ILMN_1795181 DDX60 -1.0 -5.4 0.0051 DEAD (Asp-Glu-Ala-Asp) box polypeptide 60 ILMN_2053527 PARP9 -0.8 -5.3 0.0065 poly (ADP-ribose) polymerase family, member 19 ILMN_1690365 USP41 -0.9 -5.2 0.0082 ubiquitin specific peptidase 41 ILMN_1725090 CTHRC1 0.5 5.1 0.0089 collagen triple helix repeat containing 1 ILMN_1813625 TRIM25 -0.7 -5.1 0.0098 E3 ubiquitin/ISG15 ligase tripartite motif containing protein 25

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ILMN_1682935 LYPLAL1 0.5 5.1 0.0105 lysophospholipase-like protein 1 ILMN_3240420 USP18 -1.1 -5.0 0.0106 ubiquitin specific peptidase 18 ILMN_1801307 TNFSF10 -1.1 -5.0 0.0110 TRAIL; tumor necrosis factor (ligand) superfamily, member 10 ILMN_1687201 APOL6 -0.5 -5.0 0.0110 apolipoprotein L, 6 Legend: gene symbols highlighted in bold indicate genes that are also among top 60 genes downregulated by fosta treatment after 4h (Table IV.3).

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Table IV.5 Top 60 gene-sets enriched vehicle-treated pediatric B-ALL samples DATABASE PATHWAY NAME SIZE NES FDR GO:0016810 ACTIVITY 65 -2.27 0.00 GO:0016514 SWI SNF COMPLEX 15 -2.19 0.01 GO:0050854 REGULATION OF ANTIGEN RECEPTOR-MEDIATED SIGNALING PATHWAY 17 -2.18 0.01 GO:0016814 HYDROLASE ACTIVITY 21 -2.15 0.01 GO:0071357 CELLULAR RESPONSE TO TYPE I INTERFERON 64 -2.12 0.01 GO:0019239 DEAMINASE ACTIVITY 19 -2.12 0.01 GO:0060337 TYPE I INTERFERON-MEDIATED SIGNALING PATHWAY 64 -2.11 0.01 GO:0034340 RESPONSE TO TYPE I INTERFERON 65 -2.11 0.01 GO:0008360 REGULATION OF CELL SHAPE 57 -2.11 0.01 GO:0006337 NUCLEOSOME DISASSEMBLY 17 -2.10 0.01 GO:0032986 PROTEIN-DNA COMPLEX DISASSEMBLY 17 -2.10 0.01 GO:2000242 NEGATIVE REGULATION OF REPRODUCTIVE PROCESS 35 -2.09 0.01 GO:0042641 ACTOMYOSIN 31 -2.06 0.02 REACT:25162.1 INTERFERON ALPHA BETA SIGNALING 61 -2.05 0.02 GO:0009164 NUCLEOSIDE CATABOLIC PROCESS 26 -2.04 0.02 GO:0051607 DEFENSE RESPONSE TO VIRUS 30 -2.03 0.02 GO:0010464 REGULATION OF MESENCHYMAL CELL PROLIFERATION 17 -2.02 0.03 GO:0031663 LIPOPOLYSACCHARIDE-MEDIATED SIGNALING PATHWAY 17 -2.01 0.03 GO:0070603 SWI/SNF SUPERFAMILY-TYPE COMPLEX 21 -2.01 0.03 GO:0045121 MEMBRANE RAFT 77 -2.00 0.03 NETPATH BCR 161 -1.99 0.03 MSIGDB C2 ST GA12 PATHWAY 22 -1.99 0.03 GO:0001725 STRESS FIBER 27 -1.98 0.04 GO:0071565 NBAF COMPLEX 12 -1.98 0.04 REACT:197.4 BRANCHED-CHAIN AMINO ACID CATABOLISM 17 -1.97 0.04 GO:0002698 NEGATIVE REGULATION OF IMMUNE RECEPTOR PROCESSES 28 -1.97 0.04 GO:0032432 ACTIN FILAMENT BUNDLE 27 -1.96 0.04 IOB EPO 52 -1.96 0.04 GO:0005925 FOCAL ADHESION 77 -1.95 0.05 KEGG:HSA04670 LEUKOCYTE TRANSENDOTHELIAL MIGRATION 113 -1.94 0.05 GO:0014910 REGULATION OF SMOOTH MUSCLE CELL MIGRATION 14 -1.94 0.05 REACT:25229.1 INTERFERON SIGNALING 168 -1.94 0.05 REACT:1698.8 NUCLEOTIDE METABOLISM 67 -1.94 0.05 GO:0009065 GLUTAMINE FAMILY AMINO ACID CATABOLIC PROCESS 16 -1.93 0.05 GO:0043393 REGULATION OF PROTEIN BINDING 55 -1.93 0.05

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KEGG:HSA00280 VALINE, LEUCINE AND ISOLEUCINE DEGRADATION 44 -1.93 0.05 IOB IFN-GAMMA 41 -1.93 0.05 GO:0035065 REGULATION OF HISTONE ACETYLATION 20 -1.92 0.05 GO:0006164 PURINE NUCLEOTIDE BIOSYNTHETIC PROCESS 68 -1.92 0.05 GO:0030224 MONOCYTE DIFFERENTIATION 11 -1.92 0.06 GO:0050853 B CELL RECEPTOR SIGNALING PATHWAY 20 -1.91 0.06 GO:0009112 NUCLEOBASE METABOLIC PROCESS 56 -1.91 0.06 GO:0019198 TRANSMEMBRANE RECEPTOR PROTEIN TYROSINE PHOSPHATASE ACTIVITY 17 -1.91 0.06 GO:0016836 HYDRO- ACTIVITY 16 -1.91 0.06 GO:0032755 POSITIVE REGULATION OF INTERLEUKIN-6 PRODUCTION 15 -1.91 0.06 GO:0019221 CYTOKINE-MEDIATED SIGNALING PATHWAY 243 -1.90 0.06 GO:0046135 PYRIMIDINE NUCLEOSIDE CATABOLIC PROCESS 19 -1.90 0.06 NCI IL8- AND CXCR2-MEDIATED SIGNALING EVENTS 33 -1.89 0.06 GO:0018108 PEPTIDYL-TYROSINE PHOSPHORYLATION 40 -1.89 0.07 GO:0005001 TRANSMEMBRANE RECEPTOR PROTEIN TYROSINE PHOSPHATASE ACTIVITY 17 -1.89 0.07 GO:0009083 BRANCHED CHAIN FAMILY AMINO ACID CATABOLIC PROCESS 19 -1.89 0.07 GO:0035239 TUBE MORPHOGENESIS 70 -1.88 0.07 GO:0005924 CELL-SUBSTRATE ADHERENS JUNCTION 79 -1.88 0.06 GO:0048525 NEGATIVE REGULATION OF VIRAL REPRODUCTION 19 -1.88 0.06 REACT:18342.1 NUCLEOTIDE-LIKE (PURINERGIC) RECEPTORS 16 -1.88 0.07 REACT:20580.1 REGULATION OF CYTOSKELETAL REMODELING AND CELL SPREADING BY IPP COMPLEX 8 -1.88 0.07 COMPONENTS GO:0045335 PHAGOCYTIC VESICLE 15 -1.88 0.07 GO:0045069 REGULATION OF VIRAL GENOME REPLICATION 27 -1.87 0.07 GO:0045071 NEGATIVE REGULATION OF VIRAL GENOME REPLICATION 19 -1.87 0.07 GO:0072522 PURINE AND DERIVATIVE BIOSYNTHETIC PROCESS 83 -1.87 0.07

Legend: Gene sets were obtained from Gene Ontology (GO), Reactome (REACT), Kyoto encyclopedia of genes and genomes (KEGG), National Cancer Institute (NCI), NetPath, the Molecular Signatures Database (MSigDB) and Institute of Bioinformatics (IOB) databases. The table lists top 60 gene sets significantly enriched in vehicle-treated group of pedatric B-ALL samples as compared to fosta. Normalized enrichment score (NES), which accounts for the gene set size, and false discovery rate (FDR q-value) are shown for each gene set.

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Table IV.6 Top 60 gene-sets enriched vehicle- versus fosta-treated adult B-ALL DATABASE PATHWAY NAME SIZE NES FDR GO:0071357 CELLULAR RESPONSE TO TYPE I INTERFERON 63 -3.36 0 GO:0034340 RESPONSE TO TYPE I INTERFERON 64 -3.36 0 REACT:25162.1 INTERFERON ALPHA BETA SIGNALING 60 -3.34 0 GO:0060337 TYPE I INTERFERON-MEDIATED SIGNALING PATHWAY 63 -3.30 0 REACT:25229.1 INTERFERON SIGNALING 167 -3.15 0 GO:0071346 CELLULAR RESPONSE TO INTERFERON-GAMMA 77 -3.03 0 GO:0034341 RESPONSE TO INTERFERON-GAMMA 89 -2.98 0 GO:0019221 CYTOKINE-MEDIATED SIGNALING PATHWAY 243 -2.92 0 GO:0060333 INTERFERON-GAMMA-MEDIATED SIGNALING PATHWAY 71 -2.90 0 GO:0071345 CELLULAR RESPONSE TO CYTOKINE STIMULUS 280 -2.87 0 REACT:25078.1 INTERFERON GAMMA SIGNALING 73 -2.84 0 GO:0034097 RESPONSE TO CYTOKINE STIMULUS 321 -2.82 0 REACT:75790.2 CYTOKINE SIGNALING IN IMMUNE SYSTEM 276 -2.77 0 GO:0009615 RESPONSE TO VIRUS 104 -2.60 0 KEGG:HSA05164 INFLUENZA A 171 -2.57 0 GO:0051707 RESPONSE TO OTHER ORGANISM 165 -2.56 0 GO:0051607 DEFENSE RESPONSE TO VIRUS 29 -2.54 0 GO:2000242 NEGATIVE REGULATION OF REPRODUCTIVE PROCESS 35 -2.52 0 GO:0045071 NEGATIVE REGULATION OF VIRAL GENOME REPLICATION 19 -2.51 0 GO:0045069 REGULATION OF VIRAL GENOME REPLICATION 27 -2.46 0 GO:0048525 NEGATIVE REGULATION OF VIRAL REPRODUCTION 19 -2.46 0 REACT:115831.1 ISG15 ANTIVIRAL MECHANISM 68 -2.44 0 REACT:115676.1 ANTIVIRAL MECHANISM BY IFN-STIMULATED GENES 68 -2.42 0 GO:0003725 DOUBLE-STRANDED RNA BINDING 18 -2.39 0 GO:0048020 CCR CHEMOKINE RECEPTOR BINDING 13 -2.37 0 IOB CCR1 30 -2.37 0 REACT:25359.1 RIG-I MDA5 MEDIATED INDUCTION OF IFN-ALPHA BETA PATHWAYS 74 -2.35 0 GO:0016814 HYDROLASE ACTIVITY 21 -2.32 0 KEGG:HSA05162 MEASLES 129 -2.32 0 KEGG:HSA04622 RIG-I-LIKE RECEPTOR SIGNALING PATHWAY 67 -2.31 0 REACT:25271.1 NEGATIVE REGULATORS OF RIG-I MDA5 SIGNALING 34 -2.29 0 GO:0032479 REGULATION OF TYPE I INTERFERON PRODUCTION 49 -2.28 0 GO:0009607 RESPONSE TO BIOTIC STIMULUS 223 -2.28 0 GO:2000401 REGULATION OF LYMPHOCYTE MIGRATION 15 -2.26 0.001 GO:0032480 NEGATIVE REGULATION OF TYPE I INTERFERON PRODUCTION 32 -2.25 0.001

165

GO:0045121 MEMBRANE RAFT 75 -2.22 0.001 REACT:25039.1 NF-KB ACTIVATION THROUGH FADD RIP-1 PATHWAY MEDIATED BY CASPASE-8 AND -10 12 -2.13 0.005 GO:0051704 MULTI-ORGANISM PROCESS 288 -2.12 0.006 GO:0019239 DEAMINASE ACTIVITY 19 -2.12 0.006 REACT:24938.1 TRAF6 MEDIATED IRF7 ACTIVATION 27 -2.12 0.006 GO:0045088 REGULATION OF INNATE IMMUNE RESPONSE 172 -2.12 0.006 KEGG:HSA05160 HEPATITIS C 127 -2.11 0.007 GO:0042379 CHEMOKINE RECEPTOR BINDING 31 -2.09 0.009 GO:0002548 MONOCYTE CHEMOTAXIS 13 -2.07 0.011 KEGG:HSA05332 GRAFT-VERSUS-HOST DISEASE 48 -2.06 0.014 REACT:25026.1 TRAF3-DEPENDENT IRF ACTIVATION PATHWAY 14 -2.05 0.014 GO:2000403 POSITIVE REGULATION OF LYMPHOCYTE MIGRATION 12 -2.05 0.016 GO:0097035 REGULATION OF MEMBRANE LIPID DISTRIBUTION 15 -2.04 0.017 KEGG:HSA04623 CYTOSOLIC DNA-SENSING PATHWAY 59 -2.03 0.019 REACT:6802.3 INNATE IMMUNE SYSTEM:REACTOME 249 -2.03 0.019 GO:0006955 IMMUNE RESPONSE 495 -2.02 0.019 MSIGDB C2 INFLAMMATION PATHWAY 28 -2.02 0.020 GO:0042590 ANTIGEN PROCESSING AND PRESENTATION OF EXOGENOUS PEPTIDE ANTIGEN VIA MHC 74 -2.02 0.021 CLASS I GO:0002252 IMMUNE EFFECTOR PROCESS 117 -2.02 0.021 REACT:111119.1 ANTIGEN PROCESSING-CROSS PRESENTATION 75 -2.01 0.021 REACT:397.1 POST-ELONGATION PROCESSING OF INTRON-CONTAINING PRE-MRNA 33 -2.01 0.022 GO:0050776 REGULATION OF IMMUNE RESPONSE 405 -2.01 0.022 GO:0002685 REGULATION OF LEUKOCYTE MIGRATION 55 -2.01 0.022 GO:0032647 REGULATION OF INTERFERON-ALPHA PRODUCTION 12 -2.00 0.022 REACT:197.4 BRANCHED-CHAIN AMINO ACID CATABOLISM 17 -2.00 0.024

Legend: Gene sets were obtained from Gene Ontology (GO), Reactome (REACT), Kyoto encyclopedia of genes and genomes (KEGG), National Cancer Institute (NCI), NetPath, the Molecular Signatures Database (MSigDB) and Institute of Bioinformatics (IOB) databases. The table lists top 60 gene sets significantly enriched in vehicle-treated group of adult B-ALL samples as compared to fosta. Normalized enrichment score (NES), which accounts for the gene set size, and false discovery rate (FDR q-value) are shown for each gene set.

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Table IV.7 Top 60 gene-sets enriched vehicle- versus DEX-treated adult B-ALL samples DATABASE PATHWAY NAME SIZE NES FDR GO:0071357 CELLULAR RESPONSE TO TYPE I INTERFERON 63 -2.87 0 GO:0060337 TYPE I INTERFERON-MEDIATED SIGNALING PATHWAY 63 -2.84 0 GO:0034340 RESPONSE TO TYPE I INTERFERON 64 -2.84 0 REACT:25162.1 INTERFERON ALPHA BETA SIGNALING 60 -2.79 0 GO:0042590 ANTIGEN PROCESSING AND PRESENTATION OF EXOGENOUS PEPTIDE ANTIGEN VIA MHC 74 -2.64 0 CLASS I GO:0006977 DNA DAMAGE RESPONSE, SIGNAL TRANSDUCTION BY P53 CLASS MEDIATOR RESULTING IN 67 -2.63 0 CELL CYCLE ARREST REACT:20549.1 AUTODEGRADATION OF THE E3 UBIQUITIN LIGASE COP1.1 52 -2.63 0 KEGG:HSA04940 TYPE I DIABETES MELLITUS 44 -2.62 0 REACT:111178.1 ER-PHAGOSOME PATHWAY 64 -2.62 0 GO:0072422 SIGNAL TRANSDUCTION INVOLVED IN DNA DAMAGE CHECKPOINT 68 -2.62 0 GO:0019884 ANTIGEN PRESENTATION, EXOGENOUS ANTIGEN 75 -2.62 0 GO:0072431 SIGNAL TRANSDUCTION INVOLVED IN MITOTIC CELL CYCLE G1/ S TRANSITION 67 -2.61 0 CHECKPOINT GO:0072401 SIGNAL TRANSDUCTION INVOLVED IN DNA INTEGRITY CHECKPOINT 68 -2.61 0 GO:0072404 SIGNAL TRANSDUCTION INVOLVED IN G1 S TRANSITION CHECKPOINT 68 -2.61 0 GO:0072474 SIGNAL TRANSDUCTION INVOLVED IN MITOTIC CELL CYCLE CHECKPOINT 67 -2.60 0 GO:0072413 SIGNAL TRANSDUCTION INVOLVED IN MITOTIC CELL CYCLE CHECKPOINT 67 -2.60 0 GO:0002478 EXOGENOUS PEPTIDE ANTIGEN PROCESSING AND PRESENTATION 75 -2.59 0 REACT:25229.1 INTERFERON SIGNALING 167 -2.58 0 REACT:111119.1 ANTIGEN PROCESSING-CROSS PRESENTATION 75 -2.58 0 KEGG:HSA03050 44 -2.57 0 GO:0072395 SIGNAL TRANSDUCTION INVOLVED IN G2/M TRANSITION CHECKPOINT 69 -2.57 0 KEGG:HSA05330 ALLOGRAFT REJECTION 38 -2.57 0 REACT:2160.2 P53-INDEPENDENT DNA DAMAGE RESPONSE 52 -2.57 0 REACT:1614.4 UBIQUITIN MEDIATED DEGRADATION OF PHOSPHORYLATED CDC25A 52 -2.57 0 REACT:309.2 STABILIZATION OF P53 53 -2.56 0 REACT:111056.1 CROSS-PRESENTATION OF SOLUBLE EXOGENOUS ANTIGENS (ENDOSOMES) 49 -2.55 0 GO:0002479 ANTIGEN PROCESSING AND PRESENTATION OF EXOGENOUS PEPTIDE ANTIGEN VIA MHC 71 -2.54 0 CLASS I, TAP-DEPENDENT GO:0071158 POSITIVE REGULATION OF CELL CYCLE ARREST 78 -2.54 0 REACT:1208.1 P53-INDEPENDENT G1 S DNA DAMAGE CHECKPOINT 52 -2.54 0 REACT:4.1 UBIQUITIN-DEPENDENT DEGRADATION OF CYCLIN D1 50 -2.52 0 REACT:938.4 UBIQUITIN-DEPENDENT DEGRADATION OF CYCLIN D1 50 -2.51 0

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REACT:9453.2 VIF-MEDIATED DEGRADATION OF APOBEC3 52 -2.49 0 REACT:25325.1 DESTABILIZATION OF MRNA BY AUF1 (HNRNP D0) 54 -2.47 0 REACT:1221.2 CDK-MEDIATED PHOSPHORYLATION AND REMOVAL OF CDC6 50 -2.46 0 REACT:85.1 P53-DEPENDENT G1 S DNA DAMAGE CHECKPOINT 58 -2.46 0 GO:0072331 P53-DEPENDENT INTRACELLULAR SIGNALING 100 -2.45 0 REACT:2254.1 G1 S DNA DAMAGE CHECKPOINTS 60 -2.45 0 GO:0030330 DNA DAMAGE RESPONSE, SIGNAL TRANSDUCTION BY P53 CLASS MEDIATOR 94 -2.44 0 REACT:13464.2 REGULATION OF ACTIVATED PAK-2P34 BY PROTEASOME MEDIATED DEGRADATION 49 -2.44 0 REACT:1625.3 P53-DEPENDENT G1 DNA DAMAGE RESPONSE 58 -2.44 0 REACT:9031.1 VPU MEDIATED DEGRADATION OF CD4 51 -2.43 0 REACT:6821.1 SCF-BETA-TRCP MEDIATED DEGRADATION OF EMI1 54 -2.43 0 REACT:13565.1 REGULATION OF ORNITHINE DECARBOXYLASE (ODC) 50 -2.43 0 REACT:12582.2 PHOSPHORYLATION OF CD3 AND TCR ZETA CHAINS 25 -2.42 0 GO:0000502 PROTEASOME COMPLEX 29 -2.39 1.22E-05 REACT:19324.1 PD-1 SIGNALING 26 -2.38 1.20E-05 GO:0002474 ANTIGEN PROCESSING AND PRESENTATION OF PEPTIDE ANTIGEN VIA MHC CLASS I 91 -2.37 1.17E-05 REACT:25078.1 INTERFERON GAMMA SIGNALING 73 -2.37 2.28E-05 GO:0033238 REGULATION OF AMINE METABOLISM 63 -2.37 2.23E-05 REACT:12596.2 TRANSLOCATION OF ZAP-70 TO IMMUNOLOGICAL SYNAPSE 23 -2.36 2.19E-05 REACT:13648.3 REGULATION OF APOPTOSIS 59 -2.35 2.14E-05 REACT:1949.3 CDT1 ASSOCIATION WITH THE CDC6:ORC:ORIGIN COMPLEX 57 -2.35 2.10E-05 GO:0034341 RESPONSE TO INTERFERON-GAMMA 89 -2.35 2.06E-05 GO:0071346 CELLULAR RESPONSE TO INTERFERON-GAMMA 77 -2.34 2.02E-05 GO:0006521 REGULATION OF CELLULAR AMINO ACID METABOLIC PROCESS 56 -2.34 1.99E-05 REACT:9003.1 SCF(SKP2)-MEDIATED DEGRADATION OF P27 P21 57 -2.33 1.95E-05 GO:0042770 SIGNAL TRANSDUCTION IN RESPONSE TO DNA DAMAGE 111 -2.33 1.92E-05 KEGG:HSA05416 VIRAL MYOCARDITIS 68 -2.31 1.89E-05 GO:0090068 POSITIVE REGULATION OF CELL CYCLE PROCESS 128 -2.31 1.85E-05

Legend: Gene sets were obtained from Gene Ontology (GO), Reactome (REACT) and Kyoto encyclopedia of genes and genomes (KEGG) databases. The table lists top 60 gene sets significantly enriched in vehicle-treated group of adult B-ALL samples as compared to DEX-treated samples. Normalized enrichment score (NES), which accounts for the gene set size, and false discovery rate (FDR q-value) are shown for each gene set.

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Table IV.8 A list of unique fosta-sensitive genes in adult B-ALL

Gene Log FDR Probe ID 2 t Description Symbol FC q value ILMN_1776777 ADAR -0.9 -9.3 3.3E-07 double-stranded RNA-specific adenosine deaminase ILMN_1799467 SAMD9L -1.9 -9.2 3.3E-07 sterile alpha motif domain containing 9-like ILMN_2239754 IFIT3 -2.8 -9.1 3.8E-07 interferon-induced protein with tetratricopeptide repeats 3 ILMN_2248970 OAS2 -2.1 -8.7 7.8E-07 2'-5'-oligoadenylate synthetase 2, 69/71kDa ILMN_1657871 RSAD2 -2.3 -8.5 1.1E-06 radical S-adenosyl methionine domain containing 2 ILMN_1781373 IFIH1 -1.4 -8.4 1.3E-06 interferon induced with helicase C domain 1 ILMN_2070044 PPM1K -0.9 -8.4 1.3E-06 PP2C domain-containing protein phosphatase 1K ILMN_1760062 IFI44 -2.2 -8.3 1.6E-06 interferon-induced protein 44 ILMN_1814305 SAMD9 -1.3 -8.2 1.9E-06 sterile alpha motif domain containing 9 ILMN_3240420 USP18 -2.1 -8.0 2.7E-06 ubiquitin specific peptidase 18 ILMN_1769520 UBE2L6 -1.4 -7.8 4.4E-06 ubiquitin-conjugating E2L 6 ILMN_1797001 DDX58 -1.5 -7.8 4.5E-06 DEAD (Asp-Glu-Ala-Asp) box polypeptide 58 ILMN_1683678 SPATS2L -1.1 -7.7 5.0E-06 spermatogenesis associated, serine-rich 2-like ILMN_2415144 SP110 -1.5 -7.7 6.0E-06 interferon-induced protein 41, 30kD ILMN_1739428 IFIT2 -2.6 -7.5 8.0E-06 interferon-induced protein with tetratricopeptide repeats 2 ILMN_1664294 LEPRE1 0.5 7.3 1.3E-05 leucine proline-enriched proteoglycan (leprecan) 1 ILMN_1697268 EMILIN2 0.7 7.3 1.4E-05 elastin microfibril interfacer 2 ILMN_2347798 IFI6 -2.5 -7.3 1.4E-05 interferon, alpha-inducible protein 6 ILMN_1696654 IFIT5 -1.1 -7.2 1.6E-05 interferon-induced protein with tetratricopeptide repeats ILMN_1701455 FBXO6 -1.4 -7.2 1.7E-05 F-box protein 6 ILMN_1682245 IFNB1 -1.1 -7.2 1.8E-05 interferon, beta 1, fibroblast ILMN_1654639 HERC6 -1.9 -7.1 2.1E-05 HECT and RLD domain containing E3 ubiquitin protein ligase family member 6 ILMN_1738712 GPR180 -0.7 -7.0 2.5E-05 G protein-coupled receptor 180 ILMN_1729749 HERC5 -2.0 -7.0 2.9E-05 HECT and RLD domain containing E3 ubiquitin protein ligase 5 ILMN_1745374 IFI35 -1.6 -6.9 3.8E-05 interferon-induced protein 35 ILMN_1742929 HESX1 -1.1 -6.8 4.7E-05 homeobox expressed in ES cells 1

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ILMN_1766505 COMMD10 -0.6 -6.8 4.8E-05 COMM domain-containing protein 10 ILMN_3243928 DDX60L -1.0 -6.7 6.7E-05 DEAD (Asp-Glu-Ala-Asp) box polypeptide 60-like ILMN_1690365 USP41 -1.3 -6.7 6.8E-05 ubiquitin specific peptidase 41 ILMN_1691731 PARP14 -1.1 -6.5 9.2E-05 poly (ADP-ribose) polymerase family, member 14 ILMN_1718558 PARP12 -1.1 -6.5 9.3E-05 poly (ADP-ribose) polymerase family, member 12 ILMN_1723480 BST2 -1.2 -6.5 9.5E-05 bone marrow stromal cell antige ILMN_2139100 SHISA5 -0.8 -6.5 0.0001 protein shisa-5; putative NF-kappa-B-activating protein 120 ILMN_1791759 CXCL10 -3.2 -6.4 0.0002 chemokine (C-X-C motif) ligand 10, interferon gamma-induced ILMN_2148785 GBP1 -2.1 -6.3 0.0002 guanylate binding protein 1, interferon-inducible ILMN_2383290 TRIM14 -0.5 -6.3 0.0002 tripartite motif containing 14 ILMN_1681721 OASL -1.7 -6.2 0.0002 2'-5'-oligoadenylate synthetase-like ILMN_1699331 IFIT1 -0.9 -6.2 0.0002 interferon-induced protein with tetratricopeptide repeats 1 ILMN_1681437 DCXR 0.9 6.2 0.0002 dicarbonyl/L-xylulose reductase ILMN_2349061 IRF7 -1.7 -6.1 0.0003 interferon regulatory factor 7 ILMN_1666206 GSDMB 0.6 6.1 0.0003 gasdermin B ILMN_1745242 PLSCR1 -1.2 -6.0 0.0004 ca(2+)-dependent phospholipid scramblase 1 ILMN_1779979 SLC37A3 -0.5 -6.0 0.0004 solute carrier family 37 (glycerol-3-phosphate transporter), member 3 ILMN_2112301 DRAP1 -0.7 -6.0 0.0004 DR1-associated protein 1 (negative 2 alpha) ILMN_2058512 PSMA2 -0.5 -6.0 0.0004 proteasome (prosome, macropain) subunit, alpha type, 2 ILMN_2038777 ACTB -0.6 -5.9 0.0005 actin, beta ILMN_1780769 TUBB2C 0.6 5.9 0.0005 tubulin, beta 4B class IVb ILMN_2151818 PSMA6 -0.5 -5.9 0.0005 proteasome (prosome, macropain) subunit, alpha type, 6 ILMN_1694432 CRIP2 -0.8 -5.8 0.0006 cysteine-rich protein 2 ILMN_2326512 CASP1 -0.8 -5.8 0.0006 caspase 1, apoptosis-related cysteine peptidase ILMN_1685258 TMEM14B -0.4 -5.8 0.0006 transmembrane protein 14B ILMN_1809437 RHBDD2 0.6 5.7 0.0007 rhomboid domain containing 2 ILMN_1744006 GFOD2 0.5 5.7 0.0009 glucose-fructose domain containing 2 ILMN_1699226 UBR4 0.5 5.6 0.0009 ubiquitin protein ligase E3 component n-recognin 4

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Table IV.9 A list of unique DEX-sensitive genes in adult B-ALL

Probe ID Gene Log2 t FDR Description Symbol FC q value ILMN_1758250 TRAFD1 -0.9 -9.0 2.5E-07 TRAF-type zinc finger domain containing 1 ILMN_2114568 GBP5 -1.5 -7.8 3.7E-06 guanylate binding protein 5 ILMN_2052717 GRAMD1C 0.6 7.7 4.3E-06 GRAM domain-containing protein 1C ILMN_1691860 SPRY1 2.1 7.5 7.9E-06 sprouty homolog 1, antagonist of FGF signaling ILMN_1808707 FSCN1 -1.1 -7.5 8.0E-06 fascin homolog 1, actin-bundling protein ILMN_1720708 CSNK1D 0.8 7.3 1.2E-05 casein kinase 1, delta ILMN_1708375 IRF1 -1.1 -7.3 1.2E-05 interferon regulatory factor 1 ILMN_1771385 GBP4 -1.3 -7.1 1.7E-05 guanylate binding protein 4 ILMN_1730917 KMO -0.7 -7.1 1.8E-05 kynurenine 3-monooxygenase (kynurenine 3-hydroxylase) ILMN_1780368 GPR18 -1.1 -7.0 2.4E-05 G protein-coupled receptor 18 ILMN_1756541 MXD4 1.4 6.9 2.8E-05 MAX dimerization protein 4 ILMN_2414325 TNFAIP8 -1.1 -6.9 2.8E-05 tumor necrosis factor, alpha-induced protein 8 ILMN_2083469 IRS2 1.3 6.8 3.4E-05 insulin receptor substrate 2 ILMN_2184049 COX7B -0.6 -6.7 4.6E-05 cytochrome c oxidase subunit VIIb ILMN_1668639 TBC1D10B 0.5 6.7 4.8E-05 TBC1 domain family, member 10B ILMN_1794707 ATHL1 0.9 6.6 5.5E-05 acid trehalase-like protein 1 ILMN_1784300 TUBA4A 1.6 6.6 5.5E-05 tubulin, alpha 4a ILMN_1724493 LYSMD2 -1.2 -6.6 6.0E-05 LysM, putative peptidoglycan-binding, domain containing 2 ILMN_1805148 CLHC1 0.5 6.5 7.0E-05 clathrin heavy chain linker domain containing 1 ILMN_1805410 NMES1 -1.4 -6.5 7.6E-05 normal mucosa of esophagus-specific gene 1 protein ILMN_1660114 MMRN1 1.1 6.4 8.0E-05 multimerin 1 ILMN_1697491 PRR5L -1.3 -6.4 8.7E-05 proline rich 5 like ILMN_1807042 MARCKS -0.9 -6.4 8.9E-05 myristoylated alanine-rich protein kinase C substrate ILMN_1777342 PREX1 0.8 6.4 8.9E-05 phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 1 ILMN_2198376 PSMA4 -0.4 -6.2 0.0001 proteasome (prosome, macropain) subunit, alpha type, 4 ILMN_1754529 SPG7 0.5 6.2 0.0001 spastic paraplegia 7 (pure and complicated autosomal recessive) ILMN_1711988 KCNK12 -1.3 -6.2 0.0002 potassium channel, subfamily K, member 12

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ILMN_1721636 TSC22D4 1.1 6.1 0.0002 TSC22 domain family, member 4 ILMN_1793201 HAGHL 0.8 6.1 0.0002 hydroxyacylglutathione hydrolase-like ILMN_2131177 GUCY1A3 -0.8 -6.1 0.0002 guanylate cyclase 1, soluble, alpha 3 ILMN_1747775 STX2 0.6 6.1 0.0002 syntaxin 2 ILMN_1746515 DNTT -1.3 -6.0 0.0002 deoxynucleotidyltransferase, terminal ILMN_1795937 VIL2 0.6 6.0 0.0002 ezrin ILMN_1772131 IL1R2 2.2 5.9 0.0003 interleukin 1 receptor, type II ILMN_3250023 CLEC2D -0.9 -5.9 0.0003 C-type lectin domain family 2, member D ILMN_1786612 PSME2 -0.6 -5.9 0.0003 proteasome (prosome, macropain) activator subunit 2 (PA28 beta) ILMN_1791306 IDNK 0.5 5.8 0.0003 dnK, gluconokinase homolog ILMN_1742052 SERPINB9 -0.8 -5.8 0.0003 serpin peptidase inhibitor, clade B (ovalbumin), member 9 ILMN_1815283 SULT1A3 0.7 5.8 0.0004 sulfotransferase family, cytosolic, 1A, phenol-preferring, member 3 ILMN_1782050 CEBPD 1.0 5.8 0.0004 CCAAT/enhancer-binding protein delta ILMN_1806432 NT5C 0.5 5.8 0.0004 5', 3'-nucleotidase, cytosolic ILMN_1779356 TP53 -0.6 -5.8 0.0004 tumor protein p53 ILMN_1661646 BANK1 -0.6 -5.8 0.0004 B-cell scaffold protein with ankyrin repeats 1 ILMN_1652198 CCM2 -1.1 -5.8 0.0004 cerebral cavernous malformation 2 ILMN_1730622 EVL -0.7 -5.7 0.0004 Enah/Vasp-like ILMN_1747347 MILR1 -0.8 -5.7 0.0004 mast cell immunoglobulin-like receptor 1 ILMN_1694325 NFIX -0.6 -5.7 0.0005 nuclear factor I/X (CCAAT-binding transcription factor) ILMN_2366388 PRDX1 -0.7 -5.7 0.0005 peroxiredoxin 1 ILMN_1714108 TP53INP1 1.7 5.6 0.0006 tumor protein p53 inducible nuclear protein 1 ILMN_1703244 MAP1LC3B 0.7 5.6 0.0006 microtubule-associated protein 1 light chain 3 beta ILMN_1676718 CD97 0.8 5.6 0.0006 leukocyte antigen CD97 ILMN_1762594 NOD2 -0.9 -5.6 0.0007 nucleotide-binding oligomerization domain containing 2 ILMN_1912737 HIPK2 0.9 5.5 0.0007 homeodomain interacting protein kinase 2 ILMN_1656902 HECTD3 0.4 5.5 0.0007 HECT domain containing E3 ubiquitin protein ligase 3 ILMN_1723874 MRPS6 -0.6 -5.5 0.0007 mitochondrial ribosomal protein S6 ILMN_1774077 GBP2 -1.1 -5.5 0.0007 guanylate binding protein 2 ILMN_1712887 SLC10A3 0.5 5.5 0.0007 solute carrier family 10 (sodium/bile acid cotransporter family), member 3

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ILMN_2093775 ZNF831 0.6 5.5 0.0008 zinc finger protein 831 ILMN_2317580 SHANK3 0.7 5.5 0.0008 SH3 and multiple ankyrin repeat domains 3 ILMN_1723969 PLCB1 -0.8 -5.5 0.0008 phospholipase C, beta 1 (phosphoinositide-specific) ILMN_1760121 RRAGC 0.5 5.4 0.0009 Ras-related GTP binding C ILMN_2382990 HK1 0.5 5.4 0.0009 1 ILMN_1703246 SBF1 0.6 5.4 0.0009 SET binding factor 1 ILMN_2401618 MLX 0.4 5.4 0.0009 MLX, MAX dimerization protein ILMN_2412214 LGALS9 -0.8 -5.4 0.0009 lectin, galactoside-binding, soluble, 9 ILMN_2412384 CCNE2 0.8 5.4 0.0009 cyclin E2 ILMN_1765547 IRF2 -0.5 -5.4 0.0010 interferon regulatory factor 2 ILMN_1700144 ITGA10 0.4 5.4 0.0010 integrin, alpha 10 ILMN_1717639 SIK1 1.4 5.4 0.0010 salt-inducible kinase 1 ILMN_1766275 PIK3CD -0.9 -5.4 0.0010 phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit delta

173 IV.7 FIGURES

Figure IV.1 Protocol for optimization of treatment conditions to detect fosta-induced changes in gene expression (a) Schematic representation of a 3 (time) 4 (treatment) factorial experiment design. Adult B- ALL samples (n = 3) were treated with fostamatinib (fosta: 1, 3 and 10 M) or DMSO vehicle

(0.1%v/v). RNA was extracted at appropriate time-points (2, 4 and 8 h) and hybridized to Illumina HumanHT-12v4 gene expression arrays. (b-c) Characteristics of patient samples used in the optimization experiments. (b) The in vitro sensitivity of three adult B-ALL samples (1 complex; 2 BCR-ABL+) to fosta was measured by [H3]-thymidine incorporation after 72 h of treatment. Proliferation was expressed as disintegrations per minute (DPM), normalized to vehicle for each patient sample (Inhibitor/Vehicle 100). Data are presented as the mean of triplicate measures per dose ± SEM. (c) Flow cytometric analysis of live cells in three adult B- ALL samples following staining with antibodies specific for CD45 and CD19.

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Figure IV.2 Comparison of gene expression genes following fosta treatment at 4h and 8h time-points

(a) Data were pre-processed using quantile normalization and then log2-transformed. Dendrogram hierarchical clustering was performed on normalized data for all samples. Veh:

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vehicle, F1-F10, fosta 1-10 M. (b-d) The statistical analysis was performed on log2- transformed data for 4 h and 8 h time-points. Differential expression of genes was determined using an empirical Bayes approach with moderated F-statistic/t-statistic. Benjamini-Hochberg false discovery rate (FDR) method was used to correct for multiple testing. Venn diagram comparisons show the degree of overlap amongst non-redundant differentially expressed genes in each of the three pair-wise comparisons (FDR adjusted q-value ≤ 0.05) following treatment with fosta for 4 h (b) and 8 h (c). (d) The number of differentially expressed genes at q-value ≤ 0.05 in response to fosta treatment (1, 3 and 10 M), compared to DMSO vehicle, at 4h and 8h time-points.

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Figure IV.3 Gene set enrichment analysis (GSEA) of fosta effects following 4 h and 8 h treatment The gene expression data were analyzed using GSEA with parameters set to 2000 gene-set permutations and gene-set size between 8 and 500. We used a list of 28223 non-redundant genes to perform GSEA analysis of pathways modulated by fosta at 4h and 8h as compared to vehicle. Genes were ranked using the t-statistic. An enrichment map was created for each comparison (Veh vs fosta@4h; Veh vs fosta@8h) using enriched gene-sets with a nominal p-value <0.005, FDR<10% and the overlap coefficient set to 0.5. This network map represents the enrichment results of differential expression of fosta- vs vehicle-treated samples. The results are visualized using Enrichment Map, a cytoscape plug-in. Each node represents a gene-set (group of genes with similar function/annotation). An edge (green line) represents the number of genes that are overlapping between two nodes (the thicker the line, the greater the number of genes in

178 common). The node color is associated with the enrichment p-value for each node (calculated with the GSEA algorithm). Blue color represents gene-sets enriched in the vehicle and red represents gene-sets enriched in the treated samples. White means that the enrichment p-value is not significant. The nodes that share genes and functions form distinct clusters on the map (encircled manually in dark grey). These clusters correspond to pathway/biological functions activated or inhibited in the treated or control samples. Enrichments for 4h and 8h treatments were mapped to the inner and outer nodes, respectively.

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Figure IV.4 Zoom in of the lymphocyte activation and innate immune response clusters in the fosta treatment enrichment map Blue color represents gene-sets enriched in the vehicle-treated group. Color intensity is proportional to the enrichment significance. (a) Zoom in on cell activation gene set within the lymphocyte activation cluster shows a list of differentially expressed genes following 8h fosta treatment (log2 fold-change (FC) of fosta/vehicle is shown for each gene). (b) Zoom in on regulation of inflammatory response gene set within the innate immure response cluster shows a list of differentially expressed genes following 8h fosta treatment (log2 FC of fosta/vehicle is shown for each gene).

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Figure IV.5 Validation of microarray results using quantitative real-time PCR (a) 6h treatment with fosta induces minimal apoptosis in B-ALL samples. Two viably frozen B- ALL samples were treated with DMSO vehicle (0.1%) or fosta (3 M) for 6h, and apoptosis

181 was measured by flow cytometry following intracellular staining with active caspase-3 FITC. (b) Summary of microarray results of fosta effects on the expression of five candidate genes after 4h and 8h treatment. Log2 fold-change (FC) of fosta/vehicle and q-values are shown. (c) Validation of candidate genes by qRT-PCR in three B-ALL samples. Changes in transcript abundance of STAT1 and OAS2, induced by 6h treatment with fosta (3 M), were quantified by SYBR Green detection. Expression was normalized to HPRT1. The fold-change of transcript abundance, relative to vehicle-control, is shown on y-axis. All measurements were performed in triplicates, and data are presented as triplicate mean SEM. (d-e) Comparison of the effects of SYK inhibitors on the expression levels of candidate genes in two B-ALL samples. Changes in transcript abundance of STAT1, OAS2, IFIT2, IFIT3 and MYD88, induced by 6h-treatment with fosta (3 M) or BAY613606 (BAY), were quantified by SYBR Green detection. Each graph depicts patient-specific alterations (fold-change relative to vehicle) in transcript levels in response to fosta and BAY. All measurements were performed in triplicates, and data are presented as triplicate mean SEM.

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Figure IV.6 Analysis of fosta effects in pediatric B-ALL (a) Characteristics of pediatric B-ALL samples. A total of 18 samples (14 high risk (HR), 4 standard risk (SR)) belonging to different cytogenetic subgroups were included in the analysis. M: male, F: female. (b-c) Samples were treated with DMSO vehicle (0.1%) or fosta (3 M) for 6h, and changes in gene expression were assessed by Illumina HumanHT-12v4. Pair-wise comparisons were used to find differentially regulated genes for fosta versus vehicle

183 comparison. Probe sets were ranked using FDR q-value. (b) Summary of microarray results for fosta effects on the expression of five candidate genes. Log2-normalized expression values for each treatment, log2 FC of fosta/vehicle and q-values are shown. (c) Enrichment map of the 6h fosta response. Gene expression data were analyzed by GSEA using a ranked list of 29175 genes. The map displays the enrichment of gene-sets (nominal p-value < 0.005 and FDR < 10%) in fosta vs. vehicle comparison. Each node represents a gene-set (group of genes with similar function/annotation). An edge (green line) represents the number of genes that are overlapping between two nodes (the thicker the line, the greater the number of genes in common). The node color is associated with the enrichment p-value for each node. Blue color represents gene-sets enriched in the vehicle and red represents gene-sets enriched in the fosta-treated samples. The nodes that share genes and functions form distinct clusters on the map (encircled manually in black). These clusters correspond to pathway/biological functions activated or inhibited in the treated or vehicle samples.

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Figure IV.7 Representative GSEA plots showing enrichment score for vehicle-enriched gene sets after 6h fosta treatment in pediatric B-ALL The enrichment score (ES), calculated by GSEA, reflects the degree to which a gene set is overrepresented at the top or bottom of a ranked gene list. The green line represents the running ES score. Black lines demonstrate where genes fall within the ranked gene list. Top of the ranked list (red) indicates gene set enrichment in fosta, whereas bottom of the ranked list (blue) indicates gene set enrichment in the vehicle group. The plots illustrate enrichment of BCR (a) and interferon (b) signaling pathways in vehicle-treated samples. Normalized ES (NES, corrected for gene set size) and FDR q-value are shown on graphs. A list of leading edge genes (that contribute most to the ES) is shown below each plot.

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Figure IV.8 Representative examples of gene sets enriched in fosta-group (a) Zoom in of the ER stress response cluster in the fosta treatment enrichment map. Red color represents gene-sets enriched in the fosta-treated group. Color intensity is proportional to the enrichment significance. Top-ten differentially expressed genes within response to unfolded protein gene ser (boxed) are shown below. (b) The GSEA plot illustrating enrichment of large ribosomal subunit pathway in fosta-treated samples. The green line represents the running ES score. Black lines demonstrate where genes fall within the ranked gene list. Top of the ranked list (red) indicates gene set enrichment in fosta, whereas bottom of the ranked list (blue) indicates gene set enrichment in the vehicle group. NES, FDR q-value and a list of leading edge genes are shown.

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Figure IV.9 Analysis of fosta effects in high-risk adult B-ALL (a) Characteristics of adult B-ALL samples. A total of 16 samples (all high risk (HR)) belonging to four cytogenetic subgroups were included in the analysis. M: male, F: female. (b-c) Samples were treated with DMSO vehicle (0.1%) or fosta (3 M) for 6h, and changes in gene expression were assessed by Illumina HumanHT-12v4. Pair-wise comparisons were used to find differentially regulated genes for fosta versus vehicle comparison. Probe sets were ranked using FDR q-value. (b) Summary of microarray results for fosta effects on the expression of five

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candidate genes. Log2-normalized expression values for each treatment, log2 FC of fosta/vehicle and q-values are shown. (c) Enrichment map of the 6h fosta response. Gene expression data were analyzed by GSEA using a ranked gene list. The map displays the enrichment of gene-sets (with FDR < 0.1) in fosta vs. vehicle comparison. Each node represents a gene-set (group of genes with similar function/annotation). An edge (green line) represents the number of genes that are overlapping between two nodes (the thicker the line, the greater the number of genes in common). The node color is associated with the enrichment p-value for each node. Blue color represents gene-sets enriched in the vehicle and red represents gene-sets enriched in the fosta- treated samples. The nodes that share genes and functions form distinct clusters on the map (encircled manually in black). These clusters correspond to pathway/biological functions activated or inhibited in the treated or vehicle samples.

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Figure IV.10 Enrichment map for fosta treatment in pediatric and adult B-ALL The gene expression data were analyzed using GSEA with parameters set to 2000 gene-set permutations and gene-set size between 8 and 500. Genes were ranked using the t-statistic. An enrichment map was created for each comparison (Veh vs fosta pediatric; Veh vs fosta adult) using enriched gene-sets with a nominal p-value <0.005, FDR<10% and the overlap coefficient set to 0.5. This network map represents the enrichment results of differential expression of fosta- vs vehicle-treated samples. The results are visualized using Enrichment Map, a cytoscape plug- in. Each node represents a gene-set (group of genes with similar function/annotation). An edge

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(green line) represents the number of genes that are overlapping between two nodes (the thicker the line, the greater the number of genes in common). The node color is associated with the enrichment p-value for each node (calculated with the GSEA algorithm). Blue color represents gene-sets enriched in the vehicle and red represents gene-sets enriched in the treated samples. White means that the enrichment p-value is not significant. The nodes that share genes and functions form distinct clusters on the map (encircled manually in dark grey). These clusters correspond to pathway/biological functions activated or inhibited in the treated or control samples. Enrichments for pediatric and adult samples were mapped to the inner and outer nodes, respectively.

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Figure IV.11 Heatmap of fosta genes differentially regulated by fosta and DEX in adult B- ALL Sixteen adult B-ALL samples were treated with fosta (3 M), DEX (100 nM) or DMSO vehicle (0.1%v/v) for 6h. Gene expression changes were analyzed by Illumina HumanHT-12v4. ANOVA-based analysis was performed to identify differentially expressed probe sets. The ranked gene list was extracted (ranking based on FDR q-value for F-test). Top 60 non-redundant genes were used for heatmap visualization with samples organized by treatment condition. Gene names are listed to the right.

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Figure IV.12 Analysis of DEX effects in high-risk adult B-ALL Samples were treated with DMSO vehicle (0.1%), DEX (100 nM) or fosta (3 M) for 6h, and changes in gene expression were assessed by Illumina HumanHT-12v4. ANOVA-based analysis with pair-wise comparisons was used to find differentially regulated genes for fosta versus vehicle comparison. Probe sets were ranked using FDR q-value for each pair-wise comparison. 192

(a) Summary of microarray results for DEX effects on the expression of five candidate genes.

Log2-normalized expression values for each treatment, log2 FC of fosta/vehicle and q-values are shown. (b) Venn diagram shows the number of unique and overlapping non-redundant genes for the three comparisons at FDR q-value < 0.001. (c) Four genes corresponding that were commonly regulated by fosta and DEX (q < 0.001). Log2 FC for each drug is shown. (d) Enrichment map of the 6h DEX response. Gene expression data were analyzed by GSEA using a ranked gene list. The map displays the enrichment of gene-sets (with FDR < 0.1) in fosta vs. vehicle comparison. Each node represents a gene-set (group of genes with similar function/annotation). An edge (green line) represents the number of genes that are overlapping between two nodes (the thicker the line, the greater the number of genes in common). The node color is associated with the enrichment p-value for each node. Blue color represents gene-sets enriched in the vehicle and red represents gene-sets enriched in the fosta-treated samples. The nodes that share genes and functions form distinct clusters on the map (encircled manually in black). These clusters correspond to pathway/biological functions activated or inhibited in the treated or vehicle samples.

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Figure IV.13 Enrichment map for fosta and DEX treatment in adult B-ALL The gene expression data were analyzed using GSEA with parameters set to 2000 gene-set permutations and gene-set size between 8 and 500. Genes were ranked using the t-statistic. An enrichment map was created for each comparison (Veh vs fosta; Veh vs DEX) using enriched gene-sets with a nominal p-value <0.005, FDR<10% and the overlap coefficient set to 0.5. This network map shows selected enrichment results of differential expression of fosta- vs vehicle- treated and DEX- vs vehicle-treated samples. The results are visualized using Enrichment Map, a cytoscape plug-in. Each node represents a gene-set (group of genes with similar function/annotation). An edge (green line) represents the number of genes that are overlapping between two nodes (the thicker the line, the greater the number of genes in common). The node color is associated with the enrichment p-value for each node (calculated with the GSEA

194 algorithm). Blue color represents gene-sets enriched in the vehicle and red represents gene-sets enriched in the treated samples. White means that the enrichment p-value is not significant. The nodes that share genes and functions form distinct clusters on the map (encircled manually in dark grey). These clusters correspond to pathway/biological functions activated or inhibited in the treated or control samples. Enrichments for fosta and DEX treatments were mapped to the inner and outer nodes, respectively.

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Figure IV.14 Anti-proliferative effects of combination treatment with fosta and DEX Viably frozen high-risk B-ALL samples (a: n=2 BCR-ABL+; b: n=3 BCR-ABL-) were treated with r fosta (1 M) with or without increasing concentrations of DEX (10-10-10-6 M), followed by measurements of [H3]-thymidine uptake. Proliferation was normalized to vehicle for each patient (Inhibitor/Vehicle 100). Each symbol is an average of triplicate cultures per dose SEM.

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Chapter V: CONCLUSIONS AND FUTURE DIRECTIONS

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V.1 Thesis overview After several long decades of ever-unchanging chemotherapy protocols that dominated the cancer field, a new consensus among scientific community is emerging that signal transduction therapies may provide much needed viable treatment options for incurable cancer, including B- ALL. With that in mind, this study aimed to uncover signaling pathways that regulate survival and proliferation of B-ALL cells. The studies in DM mouse model of B-ALL, presented in Chapter II, provided strong evidence for the role of SYK in murine B cell leukemogenesis, demonstrating for the first time, pre-BCR/BCR-independent SYK activation. The subsequent studies in Chapter II were designed to translate this discovery in human precursor B-ALL, with the use of primary diagnostic patient samples. I showed that SYK was expressed and phosphorylated in B-ALL. Furthermore, SYK inhibitors fosta and BAY robustly attenuated the in vitro proliferation of B-ALL samples belonging to a spectrum of cytogenetic groups. Importantly, I demonstrated potent anti-leukemic properties of fosta in the NSG xenotransplant system, providing the first evidence for the therapeutic potential of this inhibitor in precursor B- ALL. In Chapter III, I used phospho-flow cytometry to explore basal activation of BCR, PI3K/AKT/mTOR, MAPK and JAK/STAT signaling pathways in B-ALL and their response to small molecule kinase inhibitors. Using this approach, I revealed complex signaling networks in B-ALL that varied greatly between and within cytogenetic groups. I identified that fosta treatment robustly inhibited phosphorylation of key BCR proteins (SYK, PLC 2, CRKL) and EIF4E in all samples, implicating modulation of these pathways in anti-leukemic properties of fosta. I also identified fosta-sensitive pS6 and pSTAT5 responses in some patients, suggesting broad inhibition selectivity of this drug. Observations of patient-specific differences in architecture of signaling networks that regulate MAPK, mTOR and STAT5 activation that did not correlate with genetic abnormalities lead to the identification of distinct phospho-protein clusters of B-ALL samples, grouped together by similarities in basal phosphorylation signatures. Importantly, these clusters were predictive of patient outcome. Together, these data provide the first comprehensive overview of basal signaling perturbations in B-ALL and demonstrate the potential clinical application of phospho-flow cytometry in characterizing signaling networks in B-ALL. Finally, in Chapter IV, I used gene expression profiling to investigate, for the first time, the transcriptional effects of fosta in B-ALL. I showed that, in agreement with findings in Chapter III, fosta inhibited genes belonging to the BCR pathway, further highlighting the importance of this signaling cascade in its mechanism of action. Importantly, these effects were

198 not shared by dexamethasone, suggesting distinct targets of these agents in B-ALL. I also identified pronounced inhibition of interferon response pathways and enhanced ER stress response/apoptosis by fosta, revealing novel fosta-regulated pathways. Collectively, the approaches described in this thesis, which include the use of primary diagnostic patient samples, intrafemoral NSG xenograft assays, extensive high-throughput proteomic analyses and gene expression profiling, provide a new “personalized” platform for pre-clinical drug development to screen novel therapies and to identify novel targets in B-ALL (Figure V.1).

V.2 Clinical relevance of this study Identification of targeted therapies that will improve outcomes in B-ALL patients has come to the forefront of leukemia research. Motivated by the hypothesis that B-ALL occurs as a result of deregulation in signaling pathways controlling B cell development, we focused out attention on SYK. Prior to this study, the role of SYK in precursor B-ALL has not been considered. In fact, several studies suggested deficiency in BCR-related proteins in B-ALL (Iacobucci et al., 2012; Mullighan et al., 2009b; Trageser et al., 2009), including SYK itself (Goodman et al., 2001). However, in view of the lack of functional characterization of these abnormalities, their contribution to B-ALL pathobiology remained unclear. The data presented here is a significant advance over previous studies, demonstrating not only the presence of SYK and its targets in B- ALL but also the critical role of SYK-dependent signaling in proliferation of B-ALL belonging to different cytogenetics groups. Collectively, this study revealed that the alterations in SYK signaling are a common feature of B-ALL. While this thesis was in preparation, another group substantiated our findings by identifying SYK as a therapeutic target in B-ALL (Uckun et al., 2013). Fosta is the first SYK inhibitor to demonstrate efficacy and good tolerability in clinical trials for autoimmune diseases (Weinblatt et al., 2013; Weinblatt et al., 2010) and mature B-cell malignancies (Friedberg et al., 2010; Herman et al., 2013). This study is the first to provide support for fosta as a promising agent in B-ALL. Extensive evidence regarding its pharmacokinetic and pharmacodynamic properties as well as tolerance and side effects in humans rationalizes its clinical evaluation in high-risk B-ALL. Such possibility highlights the need to develop biomarkers that can be used to monitor fosta response in patients. For example, Herman et al. recently demonstrated that inhibition of BCR signaling correlated with response to fosta in vivo in CLL patients (Herman et al., 2013).

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Thus, although all of the samples used in the xenograft studies were pre-screened for active SYK signaling by phospho-flow and an in vitro response to fosta, it would be important to evaluate whether the in vivo response to fosta is directly related to target inhibition of SYK signaling. Our observations of robust attenuation of pSYK, pPLC 2 and pCRKL responses in vitro across all samples provide strong rational for testing the phosphorylation status of these proteins following in vivo treatment with fosta. These studies will yield biomarkers predictive of fosta response, which could be directly applicable to clinical trials with this inhibitor in B-ALL. The phospho-flow cytometry platform described in this study will be an invaluable tool in evaluating real-time, reliable and reproducible responses in B-ALL patients in a high- throughput, automated manner.

V.3 Phospho-flow: a move towards personalized medicine Phospho-flow cytometry has emerged as a powerful tool for studying signaling processes in cancer at a single-cell level. The interest in this technology is driven largely by its potential clinical application in cancer diagnostics. Phospho-flow has enabled the creation of patient- specific signaling maps and stratification of patients based on signaling responses into distinct subtypes, associated with clinical characteristics (Irish et al., 2004). Although this technique has been extensively used to profile AML and B cell lymphomas, limited data existed on the basal activation of signaling pathways in B-ALL. In this study, I developed and extensively optimized a phospho-flow platform to characterize, for the first time, basal signaling in primary B-ALL samples, and to assess the impact of small molecule kinase inhibitors on key signaling pathways in these cells. I demonstrated that this assay allows studying patient-specific perturbations in the basal signaling networks and screening of targeted therapies that ameliorate these alterations, regardless of genetic lesions. Complex genetic alterations promote leukemia development and progression. These alterations vary greatly among patients, but the ultimate result is the aberrant activation of signaling pathways controlling survival and proliferation of leukemic cells. Phospho-flow cytometry represents an essential tool for rapid deciphering of heterogeneity in basal signaling responses in B-ALL. In this regard, I identified significant variation in basal signaling networks in patients with similar genetic alterations, suggesting that these samples may harbor yet unidentified mutations. Thus, phospho-flow cytometry can used to reveal signaling abnormalities even in the absence of the completely characterized cytogenetic lesions. Further

200 clinical application of this technique is demonstrated by identification of prognostic signaling groups, thereby revealing signaling differences in patients with favorable outcome and those that are likely to develop relapse. It would be essential to validate these signatures as possible prognostic biomarkers to identify B-ALL patients at greatest risk of relapse. As the field moves forward, more phospho-specific antibodies against key signaling proteins will become available, allowing their rapid incorporation into the screening of targeted therapies. At the same time, the development of more sophisticated technologies that allow assessment of over thirty phospho-proteins at the same time is fast growing. In this regard, single-cell mass cytometry technique to rapidly screen drug responses has been recently described (Bendall et al., 2011; Bodenmiller et al., 2012; Qiu et al., 2011). In contrast to fluorescence-based phospho-flow, this technique avoids the problem of spectral overlap by using antibodies labeled with metal isotopes that are detected by a CyTOF mass cytometer (Bendall et al., 2012). Bodenmiller et al. used this technology to profile 18,1816 phosphorylation states in PBMCs (Bodenmiller et al., 2012). The investigation of sensitivity of mass cytometry to detect low basal phosphorylation in B-ALL will be the first step towards its use in studying signaling abnormalities in B-ALL.

V.4 Cellular effects of fosta in B-ALL: SYK inhibition or off-target effects? The work presented in this thesis revealed complex and broad inhibitory effects of fosta in B- ALL. Although a recent demonstration of over 100 off-targets of fosta (Davis et al., 2011) cautions against explaining our findings as SYK-specific effects, the inclusion of additional chemical and genetic SYK inhibitors allows to draw solid conclusions. Indeed, many experiments presented here directly compared fosta to BAY in the same patient samples. Importantly, BAY exhibits much greater specificity than fosta (Lau et al., 2012). Thus, potent anti-proliferative effects of fosta, BAY, and siRNA-mediated SYK knockdown, described in Chapter II, provide strong evidence for the role of SYK in B-ALL survival and proliferation. Furthermore, inhibition of pSYK, pPLC 2, pCRKL and pEIF4E was observed in the presence of both inhibitors (Chapter III), supporting SYK-dependent regulation of phosphorylation of these proteins, which likely regulates proliferation in B-ALL. If this is true, then combination of agents that inhibit BCR signaling and pEIF4E, such as DAS and SAR (Chapter III) should exhibit potent anti-proliferative effects in B-ALL and are worth consideration. On the other hand, the pSTAT5 and pSRC inhibition by fosta likely reflects an off-target inhibition of JAK2

201 and SFKs (Davis et al., 2011). Further experiments using genetic SYK inhibitors will be necessary to delineate which of these effects are due to the inhibition of SYK, its primary target, and which reflect consequences of off-target inhibition of other kinases. Nonetheless, this study provides the first insights into SYK-dependent networks as well as fosta targets in high-risk B- ALL.

V.5 Identification of transcriptional consequences of fosta treatment in B- ALL Although understanding the full spectrum of effects that an inhibitor elicits is a challenging task, it is important to decipher the cellular and molecular mechanisms of their action. A powerful method to explore transcriptional effects of a drug is gene expression profiling, which has previously been used to study effects of glucocorticoids. I used this methodology to investigate the transcriptional effects of fosta in B-ALL. In Chapter IV, I presented GSEA findings that revealed a fosta-dependent regulation of multiple pathways involved in lymphocyte activation, interferon response and apoptosis. Additional studies will be necessary to elucidate which of these pathways contribute to the anti-leukemic properties of fosta in B-ALL. The ability to correlate findings between phospho-flow and gene expression profiling strengthens the clinical relevance of fosta mechanisms in B-ALL. Importantly, both techniques revealed substantial attenuation of BCR signaling in high-risk B-ALL samples, strongly implicating this pathway in B-ALL pathophysiology. The evidence for anti-inflammatory effects of fosta in B-ALL is intriguing, but perhaps not entirely surprising. Fosta was originally developed for the treatment of autoimmune diseases (Bajpai, 2009), where SYK was described as a key mediator of acute and chronic inflammation (Riccaboni et al., 2010; Ruzza et al., 2009; Singh et al., 2012; Wong et al., 2004). Although the role of SYK-dependent inflammation in chronic inflammatory diseases is well defined, its contribution to B-ALL pathogenesis is a relatively unexplored area. The outstanding question that warrants further exploration is whether anti-inflammatory fosta effects are due to its inhibition of SYK. The use of genetic SYK inhibitor will be necessary to definitively resolve this issue, nonetheless, the observations that fosta and BAY treatment inhibited expression of interferon-response genes argues in favor of the SYK-dependent regulation of pro-inflammatory networks in B-ALL. In view of the growing evidence supporting the role of inflammation in

202 cancer initiation and progression, future studies must aim to provide insights into the role of inflammation in B-ALL survival and proliferation.

V.6 Mechanisms of pre-BCR-independent SYK signaling in B-ALL Mature B cell malignancies rely of SYK-dependent signals originating from the BCR for survival and proliferation (Efremov et al., 2012; Young and Staudt, 2013). It is not surprising then that therapeutic agents targeting oncogenic BCR pathway have emerged as promising treatment options for patients with lymphoid malignancies. Evidence presented in Chapters II and III revealed, for the first time, a constitutive activation of BCR-related signaling proteins in B-ALL cells that was necessary for their proliferation and survival. In marked contrast to mature lymphoid malignancies that express surface BCR, all B-ALL samples examined in this work lack intracellular and surface expression of BCR or pre-BCR, revealing, for the first time, pre-BCR-independent activation of SYK signaling. These findings pose an obvious question: how is SYK activated in the absence of pre-BCR and BCR in B-ALL. There are several possibilities that are worth considering, including 1) SYK over-expression or activating mutations in SYK; 2) aberrant expression of negative regulators of SYK activity; 3) activating mutations in Ig and/or Ig ; and 4) SYK activation by association with an unknown ITAM- containing molecule. It is well established that over-expression and/or activating mutations results in deregulated kinase activity in cancer (Krause and Van Etten, 2005). However, although SYK over-expression has been described in other hematologic malignancies (Buchner et al., 2009; Feldman et al., 2008; Hahn et al., 2009; Rinaldi et al., 2006), it is unlikely that these mechanisms contribute to SYK activation in B-ALL given the lack of apparent SYK over- expression (presented in Chapter II) and reported absence of SYK mutations in B-ALL samples (Loh et al., 2013). Although translocations resulting in constitutively active SYK have been described (TEL-SYK, ITK-SYK) (Kuno et al., 2001; Pechloff et al., 2010; Streubel et al., 2006), our data argues against this possibility in B-ALL, given the observations of the expected molecular weight band, corresponding to full-length SYK (Chapter II). An intriguing possibility for a mechanism promoting SYK activation is aberrant expression of PTPROt that is known to modulate SYK phosphorylation and tonic BCR signaling in vivo (Chen et al., 2006). PTPROt expression is substantially reduced in B cell lymphomas, leading to enhanced tonic BCR signaling that regulates survival and proliferation of

203 these tumors (Aguiar et al., 1999; Juszczynski et al., 2009). Given the tumor-suppressor function of PTPROt, it would be of particular interest to examine its expression in B-ALL. Despite the absence of pre-BCR/BCR, human B-ALL express Ig and Ig signaling molecules. In B cells, SYK binds to phosphorylated ITAMs on Ig /Ig . This binding, in turn, facilitates a positive feedback loop through SYK phosphorylation of neighboring ITAMs (Rolli et al., 2002). Thus, it remains to be investigated whether Ig and Ig are phosphorylated and interact with SYK, thereby maintaining its activity in B-ALL. In addition, examination of possible mutations in Ig and Ig are warranted, considering recent evidence demonstrating that mutations in these proteins in B cell lymphoma enhanced BCR signaling (Davis et al., 2010). It is also worth considering the possibility that other ITAM-containing molecules may substitute for the pre-BCR/BCR in B-ALL. In this regard, SYK is activated by integrins, and pattern recognition receptors (PPRs) that regulate adhesion/migration and innate immune responses, respectively, in a variety of hematopoietic cells (Lowell, 2011; Mocsai et al., 2010). In addition, SYK has been implicated in chemokine receptor signaling (Matsusaka et al., 2005). Importantly, integrin and chemokine signaling play critical role in leukemia by conveying survival signals from the tumor microenvironment (Ayala et al., 2009; Burger, 2011b). In this regard, Buchner et al. demonstrated that SYK is phosphorylated and activated by CXCL12/CXCR4 and VCAM-1/VLA-4 pathways in BCR-independent manner in CLL cells

(Buchner et al., 2010). In addition, SYK activation downstream of MAC1, a 2 integrin receptor that transduces its signals through ITAM-containing Fc chain, was required for proliferation of AML cells (Oellerich et al., 2013), emphasizing the need to evaluate the role of integrin signaling in SYK activation in B-ALL. Importantly, preliminary data from our laboratory suggests that B-ALL cells express high levels of VLA-4 and CXCR4, urging evaluation of their role in SYK activation. Clearly, identification of the upstream regulators of SYK activity may provide additional insights into the signaling pathways involved in B cell leukemogenesis and reveal novel therapeutic or prognostic indicators in B-ALL.

V.7 Targeting of leukemia initiating cells (LICs) by fosta Data presented in Chapter II demonstrated that fosta diminished engraftment and dissemination of the bulk tumor population in a xenograft mouse model of high-risk B-ALL, providing the first evidence for the therapeutic potential of fosta in this disease. An important outstanding question is whether discontinuing fosta treatment will results in reemergence of leukemia. In

204 other words, is fosta effective in eliminating disease-sustaining leukemia initiating cells (LICs)? Indeed, the central role of LICs in the leukemia pathogenesis has been well established (Dick, 2008; McCubrey et al., 2012; Valent, 2011; Wang and Dick, 2005). In fact, failure of the traditional chemotherapy regiments to eradicate LICs leads to leukemia progression and fatal relapse. Since relapse constitutes the major obstacle to successful treatment of B-ALL, therapies targeting LICs may prove to be most effective in improving outcomes in these patients. I began to answer this question in preliminary serial transplantation studies with two high-risk B-ALL samples, presented in Chapter II. I demonstrated that fosta-treated cells showed reduce ability to disseminate to the CNS and, in one case, to the spleen, but not other sites. It is essential to point out that a bulk tumor population (5 105 cells) was used in these experiments, likely containing saturating amounts of LICs. Indeed, recent studies demonstrated that as little as 102- 7 103 cells of aggressive B-ALL initiated leukemia in NSG mice (Notta et al., 2011; Rehe et al., 2013), suggesting that the dose used in our studies was at least 70-fold greater. Thus, future serial transplantation studies using limited dilution of B-ALL cells harvested from fosta-treated mice will be necessary to unequivocally investigate fosta effects on LICs. Ultimately, these experiments will reveal whether SYK signaling is essential for the survival of LICs in B-ALL.

V.8 Signal transduction therapies in B-ALL: the road ahead Over the last decades, the threshold for chemotherapy toxicity has been reached and, if exceeded, will only lead to deleterious rather than beneficial effects in B-ALL patients. Dysregulation of signaling pathways underlies cancer development by aberrantly activating networks that regulate survival, proliferation and invasion of tumor cells. Constantly growing compelling evidence supports the development of signal transduction therapies as an alternative treatment option for B-ALL patients. As multiple agents become available, it will be essential to determine the complete spectrum of their effects in order to provide mechanistic insights into their action and clinical efficacy. At the same time, much work is still required to unravel the complexity of signaling networks in B-ALL imposed by the significant amount of genetic heterogeneity among patients. The methodologies described in this study (Figure V.1) can be used to establish personalized screening platforms to test patient responses to new agents; moreover, they are readily applicable in the clinic as diagnostic tools.

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Figure V.1 Platform for pre-clinical drug development in B-ALL We developed a platform for high-throughput screening of targeted therapies in B-ALL. This platform involves large-scale analysis of primary B-ALL samples for their response to small molecule kinase inhibitors including three approaches: intrafemoral (IF) xenograft assays (pre- clinical testing), analysis of perturbations in basal signaling networks (phospho-flow cytometry) and analysis of transcriptional effect (gene expression profiling/GSEA). This platform was developed to deliver comprehensive overview on in vivo drug effects as well as to reveal full spectrum of cellular and molecular targets of fosta in B-ALL.

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