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Identification and characterization of novel therapeutic targets and biomarkers in chronic myeloid leukemia Lin, Hanyang 2016

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IDENTIFICATION AND CHARACTERIZATION OF NOVEL THERAPEUTIC TARGETS AND BIOMARKERS IN CHRONIC MYELOID LEUKEMIA by  Hanyang Lin  B.Sc., The University of British Columbia, 2007 M.Sc., The University of British Columbia, 2010  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY  in  THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  July 2016  © Hanyang Lin, 2016 ii  Abstract  Chronic myeloid leukemia (CML) has long served as a paradigm for new insights into the cellular origin, pathogenesis and treatment of human cancers. ABL tyrosine kinase inhibitor (TKI) therapies have had remarkable effects on treatment of early phase CML. However, TKI monotherapies are not curative, and initial and acquired TKI resistance remain clinically challenging. Particularly, CML stem/progenitor cells are insensitive to TKIs. Therefore, novel treatments and predictive biomarkers are clearly needed. In this work, I studied the biological effects of dual BCR-ABL and JAK2 suppressions on TKI-nonresponder stem/progenitor cells, and identified and characterized novel microRNA (miRNA) biomarkers in these cells.  I examined the biological effects of a new JAK2 inhibitor, BMS-911543, in combination with TKIs on CD34+ CML cells from IM-nonresponders. I demonstrated that combination therapy significantly reduces JAK2/STAT5 and CRKL activities, induces apoptosis, inhibits colony growth, and eliminates leukemic stem cells in vitro, while sparing healthy counterparts. I further showed that oral BMS-911543 combined with dasatinib is more effective in eliminating leukemic cells in an aggressive mouse model of BCR-ABL+ human leukemia.  Next, I identified differentially expressed miRNAs in CD34+ CML cells using RNA-seq analysis, and validated the results in additional samples using high-throughput qPCR. Potential miRNA target genes were also identified by integrating miRNA expression profiles with gene expression profiles using strand-specific RNA-seq. These studies revealed that expression of miR-185 is significantly reduced in CD34+ CML cells from TKI-nonresponders compared to TKI-responders. Restoration of miR-185 expression by lentiviral transduction in CD34+ TKI-nonresponder cells significantly impairs survival of these cells and sensitizes them to TKI iii  treatment in vitro and in vivo. Additionally, I validated the target genes of miR-185 to rationalize its roles in CML. Lastly, I demonstrated that the expression levels of several miRNAs, including miR185, were restored in patients treated with nilotinib, suggesting their potential as biomarkers to predict clinical response to TKI therapies. These studies have uncovered the biological significance of JAK2 and miR-185 in regulation of the properties of drug-insensitive CML stem/progenitor cells, and their potential as therapeutic targets for improved treatments with TKIs especially in patients at risk of developing TKI resistance.   iv  Preface  I, Hanyang Lin, performed all the experiments except for the parts stated below. In this dissertation, I designed and conducted the experiments, analyzed and interpreted the data, and wrote and edited the thesis, under the supervision of Dr. Xiaoyan Jiang at the Terry Fox Laboratory, BC Cancer Research Centre. Dr. Xiaoyan Jiang also contributed to all the experimental designs, data interpretations, and thesis editing. In Chapter 1, figures 1.1, 1.2, and 1.5 are used with permission from applicable sources. A version of Chapter 3 has been published. Hanyang Lin, Min Chen, Katharina Rothe, Matthew V. Lorenz, Adrian Woolfson, and Xiaoyan Jiang (2014). “Selective JAK2/ABL dual inhibition therapy effectively eliminates TKI-insensitive CML stem/progenitor cells.” Oncotarget (18): 8637–8650. I contributed 90% of the work, as I designed and conducted the majority of the experiments, generated the figures, and wrote the paper. Dr. Min Chen assisted with the mouse experiments including tail-vein injection. Dr. Katharina Rothe performed purifications of the primary samples. Dr. Xiaoyan Jiang contributed to the experimental designs and manuscript writing. Drs. Matthew Lorenz and Adrian Woolfson from Bristol-Myers Squibb, Princeton, NJ, USA, provided the JAK2 inhibitor, offered intellectual advice, and assisted with manuscript editing. In Chapter 4, the studies include collaborations with Drs. Keith Humphries, Connie Eaves, Carl Hansen, Donna Forrest, Inanc Birol, and Ryan Brinkman. Drs. Keith Humphries and Carl Hansen provided expertise and full technical support on the high-throughput TaqMan qPCR system. Dr. Keith Humphries also provided the microRNA expressing lentiviral vectors. Dr. Connie Eaves offered advice on cell surface marker staining. Dr. Donna Forrest provided CML patient samples and clinical information. Dr. Inanc Birol offered intellectual advice on miRNA v  and mRNA sequencing. Dr. Brinkiman assisted with the statistical analysis. Dr. Min Chen assisted with the mouse work, including tail-vein injection and luciferase imaging. Dr. Katharina Rothe provided RNAs from the primary samples. Jonathan Zeng assisted with the in vitro study. I contributed 85% of the work in this chapter, as I designed and performed the majority of the experiments, analyzed the data, and generated the figures. A manuscript is currently under preparation for publication in a peer-reviewed journal based on the data of Chapter 4. All experiments that involved primary CML samples or healthy donors were approved by the University of British Columbia Clinical Research Ethics Board under the certificate number H12-02372. CML samples were provided by the Leukemia/BMT Program of British Columbia and the Hematology Cell Bank of British Columbia. All animal experiments were performed in the Animal Resource Centre of the BC Cancer Agency Research Centre, using procedures approved by the Animal Care Committee of the University of British Columbia under the certificate protocol number A15-0060. vi  Table of Contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ xi List of Figures .............................................................................................................................. xii List of Abbreviations ................................................................................................................. xiv Acknowledgements .................................................................................................................... xix Dedication .....................................................................................................................................xx Chapter 1: Introduction ................................................................................................................1 1.1 Chronic Myeloid Leukemia ............................................................................................ 1 1.1.1 Clinical Features of Disease........................................................................................ 1 1.1.2 CML Pathophysiology ................................................................................................ 2 1.1.3 Current CML Treatments and Challenges .................................................................. 7 1.1.4 Mechanism of Resistance to TKI .............................................................................. 12 1.2 Deregulated JAK2/STAT5 Activities in CML ............................................................. 14 1.2.1 JAK/STAT Signaling Pathway ................................................................................. 14 1.2.2 JAK/STAT Pathway in Human Diseases ................................................................. 17 1.2.3 Critical Roles of JAK2/STAT5 in CML ................................................................... 18 1.2.4 Existing JAK2 Inhibitors .......................................................................................... 20 1.3 Deregulated MicroRNA Expression in CML and Other Cancers ................................ 21 1.3.1 MicroRNA Biogenesis .............................................................................................. 21 vii  1.3.2 The roles of MicroRNA in Cancer............................................................................ 23 1.3.3 Deregulated Expression of MicroRNAs in CML ..................................................... 25 1.4 Thesis Objectives .......................................................................................................... 27 Chapter 2: Materials and Methods ............................................................................................30 2.1 Human Primary Samples .............................................................................................. 30 2.2 Cell Lines ...................................................................................................................... 31 2.3 Suspension Cultures ...................................................................................................... 31 2.4 MicroRNA Sequencing and Bioinformatic Analysis ................................................... 32 2.5 Microfluidic Quantitative PCR and Bioinformatic Analysis ........................................ 33 2.6 Strand Specific RNA Sequencing and Bioinformatic Analysis .................................... 34 2.7 MicroRNA Target Gene Prediction .............................................................................. 35 2.8 Inhibitors ....................................................................................................................... 36 2.9 Analysis of Drug Interactions ....................................................................................... 37 2. 10 Surface Marker Staining ............................................................................................... 37 2.11 Intracellular Staining ..................................................................................................... 38 2.12 RNA Extraction and Quantitative Real-Time PCR ...................................................... 38 2.13 Total Cell Lysate Extraction and Quantification .......................................................... 39 2.14 Western Blotting Analysis ............................................................................................ 40 2.15 miRNA Mimics and siRNAs Transfection ................................................................... 41 2.16 Cloning and Constructing miRNA Lentiviral Vectors ................................................. 42 2.17 Lentivirus Production.................................................................................................... 42 2.18 Generation of Stably Transduced Cells (cell lines and primary samples) .................... 43 2.19 Viability Assay.............................................................................................................. 44 viii  2.20 [3H]-thymidine Incorporation Assay ............................................................................. 44 2.21 Apoptosis Assays .......................................................................................................... 44 2.22 Colony-Forming Cell (CFC) Assay .............................................................................. 45 2.23 Long-Term Culture-Initiating Cell (LTC-IC) Assays ................................................... 45 2.24 Luciferase Reporter Assay ............................................................................................ 46 2.25 Cell Adhesion Assay ..................................................................................................... 47 2.26 Transplantation of Immunodeficient Mice with CML Cells ........................................ 47 2.27 Statistical Analysis ........................................................................................................ 48 Chapter 3: Selective JAK2/ABL Dual Inhibition Therapy Effectively Eliminates TKI-insensitive CML Stem/Progenitor Cells .....................................................................................49 3.1 Introduction ................................................................................................................... 49 3.2 Results ........................................................................................................................... 51 3.2.1 JAK2 Inhibitor in Combination with IM is More Effective in Reducing JAK2/STAT5 Activity and Inhibiting Proliferative Capacity of IM-insensitive CML Cells51 3.2.2 The Combination of BMS-911543 and TKIs Reduces BCR-ABL and JAK2/STAT5 Activity and Induces Apoptosis of CD34+ Treatment-naïve IM-nonresponder Cells .......... 53 3.2.3 Combined Exposure of a Selective JAK2 Inhibitor BMS-911543 and TKIs Eliminates IM-insensitive CML LSCs and Their Progenitor Cells ...................................... 59 3.2.4 Combined Treatment with the Selective JAK2 Inhibitor BMS-911543 and TKIs Significantly Enhances the Survival of Leukemic Mice ....................................................... 61 3.2.5 Combination Treatment with a Selective JAK2 Inhibitor BMS-911543 and Dasatinib Eradicates Infiltrated Leukemic Cells in Multiple Hematopoietic Tissues .......... 63 3.3 Discussion ..................................................................................................................... 68 ix  Chapter 4: Identification and Characterization of Novel MicroRNA Biomarkers and Candidate Target Genes in CML Stem/progenitor Cells .........................................................72 4.1 Introduction ................................................................................................................... 72 4.2 Results ........................................................................................................................... 75 4.2.1 Identification of Differentially Expressed miRNAs in CD34+ CML Cells .............. 75 4.2.2 Validation of Differentially Expressed miRNAs in CD34+ CML Cells ................... 80 4.2.3. In vitro Functional Screening of the Selected miRNAs in CML Cells Using a Transient Transfection System ............................................................................................. 83 4.2.4. In vitro Functional Screening of the Selected miRNAs Identified miR-185 as a Candidate Target in CML Using a Lentiviral-mediated Eexpression System ...................... 86 4.2.5. Restoration of miR-185 Expression by Lentiviral-mediated Transduction Sensitizes BCR-ABL+ Cells to IM-induced Killing and Apoptosis through a BCR-ABL-Kinase Dependent Mechanism ............................................................................................. 91 4.2.6. Restored Expression of miR-185 Reduces Colony Growth of IM-insensitive CML Stem and Progenitor Cells and Sensitize These Cells to TKIs ............................................. 95 4.2.7. Restored miR-185 Expression in BCR-ABL+ Blast Crisis Cells Decreases Leukemia Burden and Enhances Survival in Immunodeficient Mice and These Effects are enhanced by TKI ................................................................................................................... 98 4.2.8. Integrative Analysis of Gene Expression Profile and miRNA Expression Profile to Identify Predicted Target Genes ......................................................................................... 102 4.2.9. Validation of Predicted Target Genes and Potential Molecular Mechanisms .... 110 4.2.10. Identification of Potential miRNA Prognostic Markers in CD34+ CML Cells from a Large Cohort Study .......................................................................................................... 114 x  4.3 Discussion ................................................................................................................... 117 Chapter 5: General Conclusions and Future Directions ........................................................127 5.1 Summary ..................................................................................................................... 127 5.2 Significance and Limitations of the Work .................................................................. 128 5.3 Future Directions ........................................................................................................ 131 References ...................................................................................................................................135     xi  List of Tables   Table 2.1: Specific primers used for Quantitative Real-Time PCR .............................................. 39 Table 2.2: Oligonucleotide sequences used for luciferase reporter assay .................................... 46 Table 4.1. Differentially expressed miRNAs in CD34+ cells between NBM and CML .............. 76 Table 4.2. Differentially expressed miRNAs between IM-responders and nonresponders .......... 78 Table 4.3. Predicted target genes of the deregulated miRNAs ................................................... 106 Table 4.4. Enriched KEGG pathways among predicted target genes of the deregulated miRNAs..................................................................................................................................................... 109    xii  List of Figures  Figure 1.1: The Philadelphia chromosome ..................................................................................... 3 Figure 1.2: Development of CML and hematopoiesis .................................................................... 4 Figure 1.3: Major signaling pathways perturbed by BCR-ABL, including the AHI-1-mediated BCR-ABL/JAK2 pathway .............................................................................................................. 6 Figure 1.4: Structures of JAK and STAT ..................................................................................... 16 Figure 1.5: MicroRNA (miRNA) biogenesis pathway ................................................................. 23 Figure 2.1: Overview of sequencing data analysis and miRNA target gene identification .......... 36 Figure 2.2: Schematic representation of the miRNA expressing vector ....................................... 42 Figure 3.1: Combination treatment with BMS-911543 and imatinib (IM) is more effective at reducing p-STAT5 levels and inhibiting proliferative capacity of IM-resistant K562 and BV173 cells ............................................................................................................................................... 52 Figure 3.2: Effects of BMS-911543 alone or in combination with TKIs on normal CD34+ bone marrow cells .................................................................................................................................. 54 Figure 3.3: A combination of BMS-911543 and tyrosine kinase inhibitors (TKIs) results in a significant reduction in BCR-ABL and JAK2/STAT5 activities and induction of apoptosis of CD34+ treatment-naïve IM-nonresponder cells but not normal CD34+ cells ............................... 57 Figure 3.4: Efficacy of drug interactions by combined treatment with BMS-911543 and DA against CD34+ CML cells ............................................................................................................. 58 Figure 3.5: Combined exposure of BMS-911543 and TKIs eliminates CML stem and progenitor cells from IM-nonresponders ........................................................................................................ 60 Figure 3.6: A combination of BMS-911543 and TKIs significantly enhances survival of leukemic mice ............................................................................................................................... 62 xiii  Figure 3.7: Effects of oral treatment of BMS-911543 in combination with DA on the infiltration of leukemic cells into hematopoietic tissues of mice.................................................................... 65 Figure 3.8: Oral treatment of BMS-911543 in combination with DA significantly eliminates infiltrated leukemic cells in hematopoietic tissues 70 days post-transplant ................................. 67 Figure 4.1: Identification of differentially expressed miRNAs in CD34+ CML cells .................. 80 Figure 4.2: TaqMan qPCR validation of differentially expressed miRNAs in CD34+ cells between NBM and CML ............................................................................................................... 82 Figure 4.3: TaqMan qPCR validation of differentially expressed miRNAs in CD34+ cells between IM-responders and IM-nonresponders ........................................................................... 83 Figure 4.4: Functional screening of eight deregulated miRNAs in K562 cells ............................ 85 Figure 4.5: Lentiviral transduction of selected miRNAs in CML cells ........................................ 88 Figure 4.7: Forced expression of miR-185 inhibits proliferation and induces apoptosis in BCR-ABL+ cells but not in BCR-ABL T315I mutant cells in the presence of IM ................................ 92 Figure 4.8: Forced expression of miR-185 inhibits clonogenic growth of CML cells in the presence of IM .............................................................................................................................. 95 Figure 4.9: Forced expression of miR-185 reduces colony growth of IM-insensitive CML stem and progenitor cells and sensitizes these cells to TKIs ................................................................. 98 Figure 4.10: Combined over expression of miR-185 and DA treatment decreases leukemia burden and enhances survival of leukemic mice. ....................................................................... 102 Figure 4.11: Identification of high-probability predicted target genes of the deregulated miRNAs..................................................................................................................................................... 106 Figure 4.12: Validation of miR-185 target genes and their expression changes in CML cells. . 114 Figure 4.13: MicroRNA expression changes in CD34+ cells from patients treated with NL ..... 117 xiv  List of Abbreviations  ABL1 = Abelson Murine Leukemia Viral Oncogene Homolog 1 AGO = Argonaute AHI-1 = Abelson Helper Integration Site-1 AKT = Protein Kinase B ALL = Acute Lymphoid Leukemia AML = Acute Myeloid Leukemia AP = Accelerated Phase APC = Allophycocyanin ATR = Ataxia Telangiectasia and Rad3-related Protein BC = Blast Crisis  BCL2L15 = B-cell Lymphoma 2-Like 15 BCR = Breakpoint Cluster Region BFU-E = Erythroid-burst Forming Unit BM = Bone Marrow BMS = Bristol-Myers Squibb BP = Blastic Phase BrdU = Bromodeoxyuridine BSA = Bovine Serum Albumin C. elegans = Caenorhabditis elegans CBL = Casitas B-lineage Lymphoma CCND1 = Cyclin D1 CCNE1 = Cyclin E1  CD = Cluster of Differentiation CDKN2A = Cyclin-Dependent Kinase Inhibitor 2A CEBP = CCAAT/Enhancer Binding Protein CFC = Colony-Forming Cell CFU-GM = Granulocyte/Macrophage-colony Forming unit (CFU-GM ChIP = Chromatin Immunoprecipitation CI = Combination Index CLL = Chronic Lymphocytic Leukemia CLP = Common Lymphoid Progenitors CML = Chronic myeloid leukemia CMP = Common Myeloid Progenitors CNL = Chronic neutrophilic leukemia CP =chronic phase (CP) CRKL = V-Crk Avian Sarcoma Virus CT10 Oncogene Homolog-Like Ct = Cycle threshold DA = Dasatinib DAPI = 4',6-Diamidino-2-Phenylindole xv  DGCR8 = DiGeorge Syndrome Critical Region 8 DICER1 = Double-Stranded RNA-Specific Endoribonuclease DMEM = Dulbecco's Modified Eagle Medium DMSO = Dimethyl Sulfoxide DNA-PKcs = Deoxyribonucleic Acid-dependent Protein Kinase, catalytic subunit DNMT1 = DNA methyltransferase 1 DROSHA = Double-stranded Ribonuclease Type III ds = double-stranded E2F2 = E2F Transcription Factor 2 EPO = Erythropoietin ERK = Extracellular-signal-regulated Kinase ETV6 = ETS Translocation Variant 6 EUTOS = European Treatment and Outcome Study FACS = Fluorescence-activated Cell Sorter FBS = Fetal Bovine Serum FZD7 = Frizzled Class Receptor 7 G = Granulocyte GEP = Gene Expression Profile GFP = Green Fluorescent Protein GM-CSF = Granulocyte Macrophage Colony Stimulating Factor GMP = Granulocyte/Macrophage Progenitor GO = Gene Ontology GPA = Glycophorin A  GRB2 = Growth Factor Receptor-Bound Protein 2 GTP = Guanosine Triphosphate GW182 = Glycine-Tryptophan Protein Of 182 KDa H&E = Hematoxylin and Eosin HBSS = Hanks' Balanced Salt Solution HITS-CLIP = High-throughput Sequencing of RNAs Isolated by Crosslinking Immunoprecipitation HSC = Hematopoietic Stem Cell IC50 = Inhibitory Concentration IFNα = Interferon α IHC = Immunohistochemistry IL = Interleukin IM = Imatinib Mesylate JAK = Janus Kinase JH = JAK Homology Region K562R = IM-resistant K562 Lin = Lineage LPS = Lipopolysaccharides xvi  LSC = Leukemic Stem Cell LTC-IC = Long-Term Culture-Initiating Cell Luc = Luciferase M = Macrophage MAPK = Mitogen-Activated Protein Kinase MEG = Megakaryocyte MEK1 = Mitogen-Activated Protein Kinase Kinase  MEP = Megakaryocyte/Erythrocyte Progenitor MFI = Mean Fluorescence Intensity miRISC = miRNA-Induced Silencing Complex miRNA = microRNA mm = millimeter MPN = Myeloproliferative Neoplasm mRNA = messenger RNA MYC = V-Myc Avian Myelocytomatosis Viral Oncogene Homolog NGS = Next-Generation Sequencing NK = Natural Killer Cell NL = Nilotinib nM = nano Molar NOD/SCID = Nonobese Diabetic/Severe Combined Immunodeficiency NR = IM Nonresponder NR1D1 = Nuclear Receptor Subfamily 1 Group D Member 1 NSG = NOD/SCID-Interleukin 2 Receptor γ–chain-Deficient P = Phospho PAK6 = P21 Protein-Activated Kinase 6  PB = Peripheral Blood P-bodies = Processing Bodies PBS = Phosphate Buffered Saline PBX1 = Pre-B-Cell Leukemia Homeobox 1  PCM1 = Pericentriolar Material 1 PDK1 = Pyruvate Dehydrogenase Kinase Isozyme 1  PE = Phycoerythrin PEI = Polyethyleimine Ph+ = Philadelphia-positive PI = Propidium Iodide PI3K = Phospho-Inositide-3-Kinase PIAS = Protein Inhibitors of Activated STATs PIC = Protease Inhibitor Cocktail PIP5K1B = Phosphatidylinositol-4-Phosphate 5-Kinase Type I Beta PMSF = Phenylmethylsulfonyl Fluoride xvii  PP2A = Protein Phosphatase 2A PPT = Polypurine Tract pre-miRNA = precursor miRNA pri-miRNA = primary miRNA PSB = Phosphorylation Solubilization Buffer PTEN = Phosphatase and Tensin Homolog PTP = Protein Tyrosine phosphatase PVDF = Polyvinylidene Difluoride PXN = Paxillin   Q-RT-PCR = Quantitative Reverse Transcriptase Polymerase Chain Reaction R = IM Responder RAD51 = RAD51 Recombinase  RAS = Rat Sarcoma Viral Oncogene RB1 = Retinoblastoma 1  RBC = Red Blood Cells RNA Pol II = Ribonucleic Acid Polymerase II RNU48 = small nucleolar RNA (snoRNA) 48 RNU6B = small nuclear RNA (snRNA) 6B RPMI = Rosewell Park Memorial Institute SCF = Stem Cell Factor SDS-PAGE = Sodium Dodecyl Sulfate Polyacrylamide-Gel Electrophoresis SEM = Standard Error of the Mean SET = SET Nuclear Proto-Oncogene SFFV = Spleen Focus-Forming Virus SGMS1 = Sphingomyelin Synthase 1  SH2 = Src Homology 2 SHP-1 = Src homology region 2 domain-containing phosphatase-1 shRNA = short hairpin RNA  siRNA = small interfering RNA  SMAD =  Mothers Against Decapentaplegic Homolog SOCS = Suppressors of Cytokine Signaling ssRNA-seq = strand specific RNA sequencing STAT = Signal Transducer and Activator of Transcription STIM1 = Stromal interaction molecule 1  TANGO2 = Transport and Golgi Organization 2 Homolog TBE = Tris-Borate-Ethylenediaminetetraacetic Acid  TBST = Tris-Buffered Saline Tween 20 TEL = TEL1 Oncogene TERT = Telomerase Reverse Transcriptase TGFBR2 = Transforming Growth Factor, Beta Receptor II TKI = Tyrosine Kinase Inhibitor xviii  TP53 = Tumor Protein P53 TPO = Thrombopoietin TRBP = Transactivation-Responsive RNA-Binding Protein TYK2 = Tyrosine Kinase 2 UTR = Untranslated Region VSV-G = Vesicular Stomatitis Virus Glycoprotein Wpre = Woodchuck Hepatitis Virus Posttranscriptional Regulatory XPB = Xeroderma Pigmentosum Group B XPO 5 = Exportin 5 YFP = Yellow Fluorescent Protein μCi = micro Curie μg = micro Gram μL = micro Litre μM = micro Molar  Amino Acids: F = Pheynlalanine G = Glycine I = Isoleucine D = Aspartic acid R = Arginine T = Threonine V = Valine Y = Tyrosine     xix  Acknowledgements  I am most thankful to my supervisor Dr. Xiaoyan Jiang, for taking me as her graduate student, mentoring me throughout these years, and putting up with my ignorance time to time. Besides offering me scientific advice and training me to be a critical thinker, you also taught me many valuable life lessons, which made me a mature person and I am deeply grateful for this. I would also like to express my gratitude to my committee members, Drs. Keith Humphries, Aly Karsan, and Inanc Birol; my collaborators, Drs. Connie Eaves, Carl Hansen, Donna Forrest, and Ryan Brinkman; and members of their labs, for their support, advice, and intellectual input of my work. I thank the present and past members of the Jiang lab. I thank my co-authors Drs, Min Chen and Katharina Rothe, and Will for training me new techniques patiently and for always being genuinely happy to discuss science with me. I thank Kyi Min, Sharmin, Clark, Rachel, Josephine, and Vanessa for creating a positive working environment to make the hardship of research more bearable. I thank Sujie and Kelly for proofreading my thesis. I thank my co-op student Jonathan for his hard work. And Damian, I am still waiting for you at happy hour. I would also like to acknowledge CIHR, LLSC, CCSRI, BMS, and Novartis for funding my work; Experimental Medicine Program, UBC, and CIHR for funding my tuition and fellowship, and ESH, ISEH, ASH, and iGSN for funding my conference travels. And of course, my deepest gratitude goes to my family. I could not have made this far without supports and encouragements from my devoted parents, my loving sisters, and my beautiful wife-to-be.   xx  Dedication      To my supportive and caring parents, my loving sisters, and my beautiful wife-to-be.   1  Chapter 1: Introduction  1.1 Chronic Myeloid Leukemia 1.1.1 Clinical Features of Disease  Chronic myeloid leukemia (CML) is a clonal hematological malignancy which affects about 1-2 individuals per 100,000 people per year, and accounts for 15-20% of all new cases of leukemia in the Western world [1]. The median age at diagnosis is approximately 65 years, with an increased prevalence in males compared to females [2]. The incidence of CML is neither affected by geographic location nor ethnic group, but is affected by differences in the diagnostic technologies between countries [3]. The major risk factors for CML are largely unknown; although it has been suggested that radiation exposure may be a risk factor, as a higher incidence of CML was observed in Japan after the nuclear bombing in Hiroshima and Nagasaki [3]. CML is a triphasic disease, which is characterized by an earlier or chronic phase (CP) of the disease, followed by two advanced phases: the accelerated phase (AP), and the blastic phase (BP), also known as blast crisis (BC) phase. More than 90% of CML patients are diagnosed in CP. Typical symptoms include fatigue, anorexia, weight loss, unusual bleeding, and sweats. Typical abnormalities upon physical examination are splenomegaly and occasionally hepatomegaly. Interestingly, about 50% of patients are asymptomatic, and are diagnosed with CML only after blood tests for unrelated reasons. All CP patients have elevated white-cell counts and 30-50% of CP patients have elevated platelet counts. All the cells appear to be mature and fully differentiated on peripheral smear, and < 2% of myeloid blast cells are found in the bone marrow (BM) [4]. Without proper medical treatments, in 3-5 years CP patients progress to AP, during which patients may still respond to medications for 3-18 months, but eventually progress 2  to the most aggressive and final stage of CML, BP [5]. BP is highly aggressive and resembles acute leukemia, with a median survival of approximately 6 months and death due to BM failure [6, 7]. Different from CP, BP is characterized by an accumulation of the undifferentiated blast cells in the blood or BM. About two-thirds of BP patients have elevated myeloid blast cells, and about one-third of BP patients have elevated lymphoid blast cells [1, 8-10]. AP and BP differ by the proportion of blast cells. AP is diagnosed by the presence of 10-19% blasts in the blood or BM, whereas BP is diagnosed with ≥ 20% blasts in the blood or BM [6].  1.1.2 CML Pathophysiology  The defining hallmark of CML is the presence of the fusion gene BCR-ABL, which is the result of a chromosomal fusion and named the Philadelphia (Ph) chromosome, after the location of the institute where it was first described (Figure 1.1). The Ph chromosome was identified in karyotype analyses as a shortened chromosome 22 in 1960 by Peter C. Nowell and his graduate student, David Hungerford [11]. This abnormality was later discovered to be the result of a reciprocal translocation between chromosomes 9 and 22 designated as t(9;22)(q34;q11) [12]. In 1985, the Ph chromosome was shown to transcribe a fusion gene, BCR-ABL, which was later revealed to be the major cause of CP CML [13]. The Ph chromosome is present in 95% of CML patients. The other 5% of patients have different or more complex translocations, which would also yield BCR-ABL fusion gene. Consequently, every CML patient carries the BCR-ABL fusion gene [14].  It is generally believed that CML is a clonal, stem cell disorder. CML develops when a normal hematopoietic stem cell (HSC) acquires the Ph chromosome, which would impart the oncogenic potential to this cell and transform it into a leukemic stem cell (LSC) (Figure 1.2). 3  This LSC would then give rise to progeny with a similar oncogenic nature and would eventually allow Ph-positive (Ph+) clones to replace the normal cells [15]. This hypothesis is supported by the evidence that in CP CML patients, the Ph chromosome is detected in cells from different lineages, including myeloid, erythroid, megakaryocytic, and B lymphoid cells [14]. Furthermore, in BP CML, myeloid blast cells, lymphoid blast cells, or both are found in patients’ blood or BM, suggesting that the BCR-ABL oncogene arises in a primitive cell not yet committed to either myeloid or lymphoid lineages [8, 9].                              Figure 1.1: The Philadelphia chromosome. As a result of the reciprocal translocation between chromosomes 9 and 22, BCR-ABL fusion gene is created on the modified chromosome 22, which is termed the Philadelphia (Ph) chromosome. Adapted from Lydon, 2009 [16].  4  Figure 1.2: Development of CML and hematopoiesis. CML is a clonal disease originating from HSC harboring the BCR-ABL fusion gene. The HSC gives rise to common myeloid progenitors (CMPs) and common lymphoid progenitors (CLPs). CMPs then differentiate into granulocyte/macrophage progenitors (GMPs), which produce granulocytes (G) and macrophages (M), and megakaryocyte/erythrocyte progenitors (MEPs), which produce red blood cells (RBCs) and megakaryocytes (MEG). CLPs differentiate into T cells and B cells. CP CML is characterized by overproliferation of mature granulocytes. BP CML is characterized by expansion of myeloid (two-thirds of patients) or lymphoid blast cells (one-third of patients). HSC can be identified with surface marker Lineage-(Lin-)CD34+CD38-. Progenitor cells can be identified with Lin-CD34+CD38+. Lineage-restricted progenitors and mature cells are identified with lineage markers (Lin+). All progenies of CML HSC carry BCR-ABL fusion gene, but the production of lymphocyte progenies is compromised. Modified from Ren, 2005 [10].   The Ph chromosome encodes BCR-ABL fusion gene, which consists of BCR (Breakpoint Cluster Region), and a proto-oncogene ABL1 (Abelson murine leukemia viral oncogene homolog 1) [14, 17]. Wild type ABL1 protein is a non-receptor tyrosine kinase that shuttles between the nucleus and the cytoplasm to transduce signals from cell-surface growth factor and adhesion receptors to regulate cytoskeleton structure [18, 19]. Wild type ABL1 also contains a myristoyl 5  group at its N-terminus, which acts as an auto-inhibitory structure to regulate its kinase activity [20, 21]. When ABL1 is fused with BCR, the myristoyl group is lost and ABL1 becomes constitutively active and confined to the cytoplasm [22, 23]. Regulatory domains on BCR, such as tyrosine residue 177 (Y177), get subsequently phosphorylated by ABL1. This allows binding with GRB2, growth factor receptor-bound protein 2, through its Src Homology 2 (SH2) domain. GRB2 is an adaptor protein and is best known for its ability to link the epidermal growth factor receptor tyrosine kinase to the activation of RAS and PI3K and their downstream kinases [24]. Therefore, BCR-ABL drives the pathogenesis of CML by activating multiple signaling pathways, such as RAS/MAPK, PI3K/AKT, and JAK/STAT pathways [25-27]; and by directly phosphorylating multiple substrates, including CRKL, PDK1, PXN, and CBL [28-34], to confer proliferative advantages and resistance to apoptosis in CML, which is observed as a massive accumulation of myeloid cells in patients (Figure 1.3).  Depending on the breakpoints of the BCR gene, the BCR-ABL fusion gene can yield three different protein variants with different molecular weights: P190, P210, and P230 BCR-ABL [35]. All three variants have the same ABL tyrosine kinase, but differ in the length of the BCR sequence. The P210 form is predominately found in CP CML, as well as acute myeloid leukemia (AML) and acute lymphoid leukemia (ALL) patients derived from CML patients who have progressed from BP [36-38]. The P190 form is commonly found in primary Ph+ B-cell acute lymphoid leukemia and sometimes found in primary AML, which are not derived from BP CML [39-43]. The P230 form is found in a small subset of chronic neutrophilic leukemia (CNL) patients, and has a much more benign course compared to CML and rarely progresses to BP [44, 45]. Several in vitro and in vivo studies have demonstrated that P190 BCR-ABL has the greatest tyrosine kinase and oncogenic activities, and leads to a lymphoid-specific differentiation 6  program in primary mouse bone marrow cells, whereas P210 and P230 have weaker kinase activity, with preferential differentiation to myeloid cells, suggesting the level of tyrosine kinase activity directly affects the phenotypic expression of the disease [35, 46-48].             Figure 1.3: Major signaling pathways perturbed by BCR-ABL, including the AHI-1-mediated BCR-ABL/JAK2 pathway. Phospho (P) Y177 serves as a docking site for SH2 domain of GRB2. GRB2 binds to SOS and GAB2 through its SH3 domains. SOS in turn activates RAS/MAPK pathway. GAB2, phosphorylated by ABL, activates PI3K/AKT pathway. Protein complex composed of BCR-ABL/JAK2/AHI-1 also leads to activation of JAK2/STAT5 pathway [49]. These three overly active pathways lead to increased proliferation and reduced apoptosis and drive CML pathogenesis as observed by a massive accumulation of myeloid cells in circulation.  Upon acquisition of additional molecular or genetic abnormalities beyond BCR-ABL, CP CML evolves to BP CML. These abnormalities can be directly caused by either BCR-ABL or by 7  other factors, and include differentiation arrest, genomic instability, telomere shortening, and loss of tumour suppressor function [5, 50]. The differentiation arrest seen in BP CML can be caused by BCR-ABL inhibition of transcription factor CEBPA, which is required for granulocytic differentiation [51-54]. Another mechanism that leads to differentiation arrest is the activation of the β-catenin pathway, which causes an increase in the self-renewal activity of granulocyte-macrophage progenitors (GMP) [55]. Genomic instability seen in BP CML is due to improper genome surveillance and deficiencies of DNA repair, as BCR-ABL is also found to inhibit key DNA repair machinery, including ATR [56, 57], DNA-PKcs [58-60], RAD51 [61-63], and XPB [64, 65]. This could explain the non-random genomic abnormalities observed in CML patients, including trisomy 8 and duplications of the Ph chromosome [66]. The rate of telomere loss is also increased as CP CML progresses to AP CML, and this increase is 10-20-fold higher in Ph+ cells compared to normal cells [67]. Telomere shortening in CML is due to the reduced expression of TERT, the catalytic subunit of telomerase [68]. It has been speculated that BCR-ABL is able to suppress TERT activity, as wild type ABL has been shown to negatively regulate TERT activity [69]. Finally, the suppression of several tumour-suppressor genes is associated with BP CML progression, including loss of TP53 [70, 71], RB1 [72-74], CDKN2A [75-77], and PP2A [78]. The loss of PP2A in particular is caused by BCR-ABL-induced up-regulation of SET, a physiological inhibitor of PP2A.  1.1.3 Current CML Treatments and Challenges  The identification of BCR-ABL transcripts is one of the diagnostic criteria for CML and its reduction is used as a measure of treatment response. Understanding critical functions of the 8  abnormal activity of BCR-ABL also allows for the development of therapeutic drugs against BCR-ABL.  The first CML case was identified in 1845 by two pathologists, Dr. Rudolf Virchow and Dr. John Hughes Bennett [79, 80]. During the late 1800s, the recognized CML treatment was Fowler solution, which consisted mainly of arsenic containing compounds [81]. In the 1900s, radiotherapy, splenic irradiation, and the use of chemotherapy, such as busulfan and hydroxyurea were able to provide some symptomatic relief [82]. However, while these regimens were able to achieve hematologic response (i.e. normalization of blood count), they failed to delay the onset of disease progression, and were later found to be unable to achieve cytogenetic response, which is defined by undetectable Ph chromosome in metaphase analysis [1]. In the 1970s, two new treatment strategies, interferon α (IFNα), and allogeneic stem cell transplantation, were able to achieve some levels of cytogenetic response, and were able to prolong patient survival [3, 83]. IFNα, however, must be administered subcutaneously, and is associated with  a range of adverse side effects, including fever, myalgia, rash, depression, and thrombocytopenia, which greatly reduce quality of life in many patients and make long-term use impossible [1, 3, 4]. Allogeneic stem cell transplantation is believed to be the only curative therapy. Approximately 50% of CP CML patients who receive matched related donor are disease-free and achieve complete cytogenetic response for up to 15 years [84, 85]. However, the main disadvantages are the mortality and morbidity associated with the procedure [86], some restrictions to CP CML patients [87] or younger patients (< 20-40 years old, while the median age of CML is 65 years old), and the paucity of matched donors [1, 3, 4].  Fortunately, with the discovery of the BCR-ABL fusion protein, the very first selective BCR-ABL inhibitor, Imatinib Mesylate (IM), a tyrosine kinase inhibitor (TKI), was developed in 9  1996, and is the first-line of treatment for CML patients in Canada and other countries [88]. In several pioneering clinical studies by Druker et al., IM showed a high rate of complete hematologic and complete cytogenetic remission in CP patients, including some patients who had previously failed IFNα [89-91]. IM, along with second generation TKIs dasatinib (DA), nilotinib (NL), and bosutinib, and the third generation TKI, ponatinib, have revolutionized the management of CML and proven CP CML as a model disease for molecular-targeted therapy for other cancers [92-98].  Although IM and the newer generation of TKIs have had a major impact for treatment of CML, TKIs are not curative. Early relapse, and primary and acquired TKI resistance remain significant problems [99, 100]. Most patients harbor residual leukemic stem cells (LSCs), which are known to be genetically unstable and less responsive to TKI treatments [101]. Therefore, disease typically recurs if therapy is discontinued [102, 103]. Statistically, 15% of CP CML patients and up to 40% of AP CML patients fail TKI treatments [99, 104-106]. IM has very little effect on BP CML patients, as it merely increases survival from 2-3 months to 7.5 months [107]. Furthermore, intolerance to IM can cause further complications, including inflammatory skin reactions and fluid retention, which leads to IM discontinuation [90, 107, 108]. The major molecular mechanism of relapse and resistance is due to BCR-ABL kinase point mutations, in which more than 100 different amino acids have been described [109-111]. TKIs work by competing with ATP at the ATP-binding pocket of the kinase domain to prevent BCR-ABL from phosphorylating its substrate [88]. Some of the point mutations within the kinase domain, including T315I, are at contact points between the kinase domain and the TKI, preventing efficient binding of the TKI to the ATP-binding pocket [112]. The second generation TKIs, such as DA and NL, are more potent and more effective against certain BCR-ABL mutations, but they 10  still fail to inhibit BCR-ABL harboring the gatekeeper T315I mutation [98, 113]. The third generation TKI ponatinib is able to inhibit the T315I mutation, and clinically it has displayed impressive activity in patients who previously failed other TKI therapies [92]. Ponatinib, however, displays toxicity, as some patients in a phase 2 clinical trial developed arterial thrombosis, and further clinical trials are suspended until the cardiotoxicity problem is resolved [93].  Currently, defining and monitoring patient response to TKI is based on the transcript level of BCR-ABL gene by quantitative reverse transcriptase polymerase chain reaction (Q-RT-PCR) [114-116]. There are essentially four levels of response: complete hematologic response (normalization of white blood cell count), major cytogenetic response (< 30% cells in BM contain Ph chromosome, or a 1 log reduction in BCR-ABL transcript level in blood), complete cytogenetic response (no Ph chromosome is detected, or a 2 log reduction in BCR-ABL transcript level), and major molecular response (a 3 log reduction in BCR-ABL transcript level) [117, 118]. As most CP CML patients achieve hematologic response upon TKI treatment and conventional cytogenetic examination of patients’ BM is technically challenging and laborious, the European Leukemia Net now has utilized Q-RT-PCR to standardize the criteria to define an optimal responder: one who achieves complete hematologic response by 3 months, complete cytogenetic response by 12 months, and major molecular response by 18 months and remains in major molecular response thereafter, upon IM treatment [1, 6].  One who does not achieve any of these responses is considered as a nonresponder, and depending on the level of the responses, may seek alternate therapies, including second/third generations of TKIs, IFNα in combination with chemotherapy, or ultimately allogeneic stem cell transplantation while he/she is still in CP [1, 119]. 11   Traditionally, prognostic factors based on clinical parameters, such as spleen size, age, and white blood cell counts, were used to estimate survival of patients. The Sokal score, for example, uses age, spleen size, blast count, and platelet count to predict busulfan response, and the Hasford score was developed to predict IFNα response based on age, spleen size, blast count, platelet count, eosinophil count, and basophil count [120, 121]. Both scoring systems have some value to predict cytogenetic response with TKI therapy but not beyond, and they fail to predict patient survival [118]. The recently developed EUTOS (The European Treatment and Outcome Study) risk score, based on basophil count and spleen size only, was able to predict patient survival upon IM treatment in two studies, but still warrants further investigation [122, 123]. These historical clinical factors have limited predictive value because CML is a stem cell disease, and TKI-resistant clones reside in the stem cell compartment. Therefore, more studies have been focused on patients’ gene expression profile (GEP) in more primitive cells, especially CD34+ cells (stem/progenitor cells, Figure 1.2), in an attempt to identify molecular markers that may predict clinical response to TKI treatments. Investigators have directly compared GEP between IM-responders and IM-nonresponders, and found that a subset of genes have predictive value regarding IM response and patient survival [124-127]. However, among studies from different groups, there was little overlap of predictive genes, and only the pathways related to these predictive genes, including NF-κB, DNA repair, apoptosis, and cell adhesion pathways showed moderate levels of overlap [128]. For example, Yong et al. focused on highly purified CD34+ cells and attempted to redefine TKI response by categorizing CML patients as either indolent or aggressive based on CD7, PR3, and ELA2 expression, which correlate with patient survival [129]. These three genes, however, were not identified as survival predictors by other groups who also assessed   CD34+ cells. In short, the results from gene expression studies for 12  predicting IM response in CML remain inconclusive, possibly due to use of different GEP platforms (various microarray chips) and RNA preparation methods, as well as differences in CD34+ content due to patient sample availabilities. Interestingly, it has recently been reported that assessment of BCR-ABL1 transcript levels at 3 months may provide a useful predictive value for patients with CML treated with TKI therapies [130-132]. In addition, specific properties of pre-treatment CML stem/progenitor cells that correlate with subsequent response to IM therapy have also been reported [133].   Taken together, identification of new therapeutic targets and the development of mechanism-based combination therapeutic strategies are clearly needed to specifically target leukemic stem/progenitor cells, overcoming resistance and achieving long-term cure. Novel molecular tests to predict patient response to TKIs are also needed because if a particular patient was destined to be a poor TKI responder, alternate therapies could be offered upfront, prior to the development of resistant CML stem/progenitor clones, which make alternate treatments more difficult.  1.1.4 Mechanism of Resistance to TKI  One of the main reasons why TKI therapies fail to cure CML completely is that TKIs are only effective at eliminating mature CML cells, and ineffective at eliminating more primitive, undifferentiated cells, such as CML stem/progenitor cells [101, 134-136]. The proposed mechanisms for TKI resistance of CML stem cells can be divided into two main categories: BCR-ABL-dependent mechanisms, in which the CML stem cells rely on BCR-ABL activity for their survival; and BCR-ABL-independent mechanisms, wherein the CML stem cells do not rely on BCR-ABL for survival [105, 109, 137, 138]. The proposed BCR-ABL-dependent 13  mechanisms include an increase in BCR-ABL expression in the more primitive CML cells, lin-CD34+CD38- population, relative to the more mature and differentiated cells (Figure 1.2) [55, 101, 135]. The increased BCR-ABL transcript level leads to higher BCR-ABL activity, resulting in insufficient TKI concentration to properly suppress BCR-ABL kinase activity, thus allowing CML stem cells to survive in the presence of TKI. Secondly, the BCR-ABL kinase domain mutations, including T315I, prevent binding of TKIs to the kinase domain and make TKI inhibition of kinase activity unlikely. The kinase domain mutations can result from the inherent genomic instability of CML stem cells prior to initiation of TKI therapies. Administration of TKIs might actually provide a selective advantage to the CML stem cells containing kinase mutations over the CML stem cells containing wild type BCR-ABL [139-141]. In addition, under the selective pressure of TKIs, compound mutations (two or more different kinase point mutations on a single BCR-ABL molecule) can occur [142, 143], and these compound mutations have been shown to confer resistance to the third generation TKI ponatinib [144].  Another manner in which CML stem cells evade destruction by TKI therapy is through BCR-ABL-independent mechanisms. One such mechanism is through the intrinsic properties of leukemic stem cells, which are quiescent, non-cycling, and self-renewing [145-147]. TKIs can only target dividing and active cells, and they would have little effect on the stem cells with this quiescent nature [148, 149]. Furthermore, CML stem cells are well protected by the bone marrow microenvironmental niche, which consists of stromal cells, extracellular matrix, and growth factors, such as TGF-β, which together offer protection and maintenance of stem cell quiescence, self-renewal, and survival properties [150, 151]. Another BCR-ABL-independent mechanism is the activation of other pro-survival pathways, such as SRC [152, 153], PI3K/AKT [154], KRAS [155], JAK2/STAT5 [156], and autophagy pathways [157-159]. CML stem cells 14  could rely on these pathways for survival when BCR-ABL kinase activity is suppressed. Finally, several drug transporter genes are found to be deregulated, and are correlated with IM response in CML stem cells [101, 160, 161]. Drug efflux transporters ABCB1 and ABCG2 are up-regulated, whereas drug influx transporter OCT1 is down-regulated in CML cells compared to normal BM cells, and in the more primitive (CD34+CD38-) cells compared to the more mature cells [101]. This could confer TKI resistance due to insufficient concentration of TKIs within the cells.  It is interesting to note that both BCR-ABL dependent and independent mechanisms can also explain how CP CML progresses to BP CML, as BP CML cells are derived from and share similar properties to CP CML TKI-resistant subclones, including elevated BCR-ABL transcript levels and BCR-ABL kinase activity, point mutations on the kinase domain, acquisition of additional mutations beyond the Ph chromosome, and activation of other pro-survival pathways. Indeed, the GEP of resistance CP CML cells is similar to that of BP CML cells [162].  1.2 Deregulated JAK2/STAT5 Activities in CML 1.2.1 JAK/STAT Signaling Pathway  The Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway constitutes a signal transduction system through which diverse extracellular signals transmit information into the cells to elicit changes in gene expression that promote growth and proliferation [163]. The JAK/STAT signaling starts from binding of a cytokine to its corresponding receptor, which leads to a conformational change in the cytoplasmic portion of the receptor, bringing the attached JAKs in close proximity to autophosphorylate each other. The activated JAKs can now phosphorylate the receptor, creating docking sites for the SH2 domain 15  of STATs and subsequent STAT phosphorylation. Subsequently, STATs form either homo- or hetero- dimers through interactions between the SH2 domain of one STAT and the phosphotyrosine of another STAT. The dimers then translocate to the nucleus to bind to specific DNA promoters, resulting in transcription of genes, such as Bcl-xL, cyclin D1, and PIM1, which regulate cell proliferation, differentiation, and apoptosis [164-166].  JAKs are nonreceptor tyrosine kinases located in the cytoplasm, and are non-covalently bound to members of the cytokine receptor superfamily, which lack intrinsic tyrosine kinase activity and rely on JAKs to provide this activity [167, 168]. JAKs comprise a family of four structurally and functionally related proteins: JAK1, JAK2, JAK3, and TYK2 (tyrosine kinase 2) [169]. All JAKs contain seven highly conserved JAK homology regions (JH1-7, from C-terminal end to N-terminal end) (Figure 1.4) [170]. JH1 is the catalytic kinase domain, responsible for enzymatic reaction when JAKs are activated upon phosphorylations [171]. JH2 is a pseudo-kinase domain, which is structurally similar to JH1 but lacks enzymatic activity, hence the name “Janus”, from the two-faced Roman god, and is involved in inhibiting JH1 activity [172, 173]. JH3 shares homology with SH2 domain, which serves as docking sites for phosphorylated tyrosine residues on other proteins [174]. JH4-JH7 constitutes a receptor-binding domain, which helps JAKs non-covalently anchor to cytokine receptors and other kinases [175, 176]. Gene knockout studies have provided evidence that JAKs are involved in hematopoiesis and immune cell development. JAK2, in particular, plays a critical role in various stages of myelopoiesis. Mouse germline deletion of Jak2 is embryonic lethal and this is further supported by evidence that, JAK2 is actually responsible for transmitting signals from thrombopoietin (TPO), erythropoietin (EPO), granulocyte macrophage colony-stimulating factor (GM-CSF), IL-3, and IL-5 [177, 178]. Also, recent studies indicated that JAK2 is crucial in the function and 16  maintenance of hematopoietic stem cells by directly participating in TPO signaling and by indirectly modulating stem cell factor (SCF) signaling [179-181].    Figure 1.4: Structures of JAK and STAT. JAK protein is composed of JH1-7 domains. JH1 is the kinase domain responsible for enzymatic activity. STAT protein is composed of coiled-coil, DNA binding, SH2/tyrosine activation, and transcriptional activation domains (TAD).  There are seven mammalian STATs: STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B, and STAT6 [182]. STATs share structurally and functionally conserved domains, including a coiled-coil domain, the DNA binding domain, SH2/tyrosine activation domain, and a transcriptional activation domain (from N-terminal end to C-terminal end) (Figure 1.4) [183]. The coiled-coil domain facilitates interactions with other proteins and cytokine receptors [184, 185]. DNA-binding domain is responsible for the binding to the DNA promoter regions [186]. The SH2/tyrosine activation domain is responsible for homo- or hetero- dimerization between STATs [187], and is also able to recognize specific cytokine receptor phosphotyrosine motifs, resulting in a huge diversity of function between family members [188-191]. The transcriptional activation domain is the least conserved among STATs, and contributes to target gene specificity via interaction with different transcriptional regulators [192]. In vitro and in vivo studies indicated that STAT1 and STAT2 play an essential role in innate immune response, for they are 17  responsible for interferon signaling [193, 194]. Stat3 knockout mice are embryonic lethal, as Stat3 is responsible for the cell differentiation response to IL-6 and G-CSF [195-198]. Stat4 and Stat6 knockout mice would have defective T helper cells, as they both play key roles in IL-12 and IL-4 signaling, respectively [199-202]. Stat5a and/or Stat5b knockout mice show multiple lineage proliferation defects and show dysregulation in a number of cytokine signaling pathways, including IL-2, IL-3, IL-5, IL-7, G-CSF, and GM-CSF [203-207]. In fact, STAT5 has been implicated to be involved in many cancer types, including leukemia, prostate, and breast cancer [208-210].   1.2.2 JAK/STAT Pathway in Human Diseases  Since the essential role of JAK/STAT pathway is to mediate growth-promoting activities of various cytokines, it is not surprising that the constitutive activation of the JAK/STAT pathway may have pathological consequences. Indeed, a recurrent somatic point mutation, V617F, in the JH2 domain of JAK2 is present in a large proportion of myeloproliferative neoplasm (MPN) patients. The JAK2 V617F mutation is so prevalent (99% of polycythemia vera patients and more than 50% of essential thrombocythemia and primary myelofibrosis patients) that its detection has become a standard diagnostic procedure [211-216]. It is speculated that the V617 mutation makes the JH2 unable to regulate the JH1 kinase activity. Thus, JAK2 kinase is constitutively active, even in the absence of cytokines [217]. In addition to JAK point mutations, over-activation of JAK/STAT pathway independently of the presence of cytokines or receptors was observed in chimeric fusion transcripts involving JAK2, such as ETV6-JAK2, PCM1-JAK2, and TEL-JAK2, which have been described in ALLs, AMLs, multiple myeloma, and non-Hodgkin lymphoma [169, 218, 219]. Apart from hematopoietic malignancies, constitutive active 18  STAT1, STAT3, and STAT5 have been detected in solid tumours, including breast, lung, and head and neck cancers due to increased expression and abnormal activities of their upstream receptors and ligands. [220].  1.2.3 Critical Roles of JAK2/STAT5 in CML It has been reported that the JAK2/STAT5 signaling pathway plays an important role in CML leukemogenesis [25, 27]. STAT5, in particular, is found to be constitutively active in BCR-ABL-transduced cells [221, 222]. It is essential for both initiation and maintenance of BCR-ABL-induced leukemia [223-229], and forced expression of STAT5 in murine bone marrow (BM) can generate a disease that closely resembles a CML-like disease [230]. Additionally, STAT5 plays a critical role in mediating IM resistance in CML [231] as evidenced by a correlation both between STAT5 level and the response to TKI treatment [232, 233], and between STAT5 level and the occurrence of BCR-ABL mutations in CML patients [234]. Indeed, these studies suggest that STAT5 is an attractive drug target in CML. However, STAT5 is a transcription factor lacking enzymatic domains, which makes the development of a specific STAT5 inhibitor difficult. Fortunately, STAT5 is predominately activated by JAK2, which contains a kinase domain. JAK2, therefore, may be a more suitable target in CML. In fact, studies have shown the effectiveness of JAK2 inhibitors in treatment of CML in vitro and in vivo [235-239]. Although many reports have indicated the merit of JAK2 inhibition in CML, the exact contribution of JAK2 in CML pathogenesis remains controversial. Ilaria et al. provided the first evidence that both P190 and P210 BCR-ABL directly phosphorylated STAT5, rendering JAK2 dispensable by overexpression of BCR-ABL products [222]. A recent study by Hantschel et al. also confirmed the direct phosphorylation of STAT5 by BCR-ABL kinase [240].  This research 19  group further showed that, in a Jak2 conditional knockout model, loss of Jak2 had no impact on cell proliferation, cell cycle progression, and apoptosis, and that Jak2 was not required for initial myeloid transformation and leukemia maintenance when BCR-ABL is overexpressed in Jak2 deficient cells. In addition, this study also suggested potential off-target effects of some JAK2 inhibitors. However, these studies were all performed in murine cells using overexpression of BCR-ABL and the biological effects might be different from patient CML cells under control of a much weaker endogenous BCR promoter. Indeed, several other studies have provided molecular evidence that JAK2 might play critical roles in CML pathogenesis. Besides STAT5, JAK2 is found to be constitutively active in BCR-ABL expressing human cells [241, 242]. Also, JAK2 siRNA and shRNA knockdown in CML cells have demonstrated many crucial functional and biological consequences, including a reduction in STAT5 activity due to direct down-regulation of BCR-ABL by JAK2 [239], a reduction in Lyn activity [238, 243], which has been shown to induce apoptosis in BP CML cells [244], a reduction in β-catenin protein level in primitive CML stem/progenitor cells by suppression of JAK2 [245], possibly through activation of GSK-3β [237], and a reduction of BCR-ABL-mediated c-Myc expression [236, 237]. Above all, a BCR-ABL/JAK2 protein complex was identified indicating BCR-ABL phosphorylates JAK2 directly [246]. The newly identified protein complex, composed of AHI-1/BCR-ABL/JAK2, also contributes to the transforming activity of BCR-ABL and IM-resistance in CML stem/progenitor cells [49, 247]. Despite some initial controversy, JAK2 may still remain an attractive therapeutic target in CML.  20  1.2.4 Existing JAK2 Inhibitors Since the discovery of the JAK2 V617F mutant in MPNs, the JAK2 inhibitor Ruxolitinib  has been developed and shown effectiveness in treating MPNs [248]. Several other JAK2 inhibitors are currently in various stages of clinical trials; however, lack of specificity and off-target effects of some JAK2 inhibitors on normal primitive hematopoietic cells remain a concern [249]. Ruxolitinib remains the only FDA approved JAK2 inhibitor for treatment of myelofibrosis. Ruxolitinib, however, is a dual JAK1 and JAK2 inhibitor, and inhibition of JAK1 may have potential side effects as JAK1 is crucial for neural function and lymphoid development [250]. In addition, off-target inhibition of JAK1 may contribute to the anemia reported in some ruxolitinib-treated patients, as JAK2-specific inhibitors tend to correlate with less incidence of anemia [249]. Furthermore, a JAK2-specifc inhibitor is speculated to have less immunosuppressive potential than inhibitors targeting multiple JAK family members simultaneously [251]. TG101348 is another promising JAK2 inhibitor that is a highly selective with no inhibition against other JAK family members. TG101348, nevertheless, has a few reported non-JAK targets, including SRC family kinases LCK and FGR, as well as KIT and FLT3 receptor kinases, which are critical in hematopoietic cell signaling [252]. TG101348 also targets ABL and induces cell death in BCR-ABL-transformed cells lacking mouse Jak2 [240, 253]. Unfortunately, a higher incidence of adverse effects in patients treated with TG101348 resulted in the termination of a clinical trial as patients developed symptoms consistent with Wernicke’s encephalopathy, a neurological disease caused by brain lesions [254]. Other JAK2 inhibitors whose clinical trials were discontinued include AZD1480, Lestaurtinib, and XL019, possibly due to off-target effects and neurologic toxicities [254, 255]. Currently, JAK2 inhibitors that are still in ongoing clinical trials are Momelotinib, Pacritinib, LY2784544, NS-018, and 21  BMS-911543. Momelotinib, similar to Ruxolitinib, is also a dual JAK1 and JAK2 inhibitor, and has non-JAK targets, including PKD3, PRKD1, CDK2, and JNK1. Pacritinib is JAK2-specific, but similar to TG101348, it also has non-JAK targets, such as FLT3 [248, 255]. BMS-911543 is a promising selective JAK2 inhibitor which has an IC50 of 1.1 nM against JAK2 and is approximately 350-, 75-, and 65-fold less selective to JAK1, JAK3, and TYK2, respectively [251]. It is highly specific as no other non-JAK family targets with IC50 less than 100 nM have been reported [248]. BMS-911435 is currently under Phase I/II clinical trial for myelofibrosis (NCT01236352).   1.3 Deregulated MicroRNA Expression in CML and Other Cancers    1.3.1 MicroRNA Biogenesis  MicroRNAs (miRNAs) are single-stranded non-coding RNA of 18-25 nucleotides in length that have important functions in post-transcriptional regulation of gene expression [256]. miRNAs make up ~4% of the genes in the human genome (currently 1881 miRNAs have been annotated) and are highly evolutionarily conserved in nearly all organisms [257-259]. miRNA was first identified in Caenorhabditis elegans in 1993 by Victor Ambros and colleagues. They discovered that a gene, lin-4, does not code for a protein but produces a pair of small RNAs, which can control the timing of C. elegans larval development by inhibiting LIN-14 protein translation [260]. Similar to messenger RNA (mRNA), miRNAs are predominantly transcribed by RNA polymerase II (pol II) in the nucleus to yield primary miRNAs (pri-miRNAs), which can range from several hundred to several thousand nucleotides in length (Figure 1.5). Pri-miRNAs then fold into hairpin structures and are processed by DROSHA, a double-stranded (ds) ribonuclease (RNase) III, and DROSHA’s cofactor, DGCR8 (DiGeorge syndrome critical region 22  8) [261-264]. DROSHA specifically cleaves the dsRNA ~11bp from the basal junction connecting to the flanking single-stranded RNA to liberate precursor miRNAs (pre-miRNA), which range from 60-70 nucleotides in length and still maintain the hairpin structure [265-268]. The pre-miRNAs are then exported to the cytoplasm by exportin 5 (XPO5) in a RanGTP dependent manner [269-271]. In the cytoplasm, pre-miRNAs are further processed by another RNase III enzyme, DICER1, which removes the hairpin of the pre-miRNA to yield the mature ~22 nucleotide miRNA duplex with 2-nucleotide 3’ overhangs [272]. Transactivation-responsive RNA-binding protein (TRBP) physically bridges the Argonaute protein (AGO1-4) with DICER1 to form the miRNA-induced silencing complex (miRISC) [273]. AGO then selects one strand of the mature miRNA, which contains a seed sequence (2-8 nucleotides from the 5’ end) complimentary to the target mRNA’s 3’ UTR [274]. Once mature miRNAs and their target mRNAs are loaded into miRISC, the translation of the mRNA is suppressed [275-280]. The degradation of mRNAs occurs in cytoplasmic processing bodies (P-bodies), in which multiple proteins, including scaffold protein (GW182), mRNA-decapping enzymes, and RNA helicase, work together with AGO to mediate miRNA-induced gene repression [281-284].   23   Figure 1.5: MicroRNA (miRNA) biogenesis pathway. miRNA genes are transcribed by RNA polymerase II (Pol II) in the nucleus to yield primary miRNAs (pri-miRNAs). The long pri-miRNAs are cleaved and processed by Microprocessor DROSHA and DGCR8 to produce 60–70-nucleotide precursor miRNAs (pre-miRNAs). The pre-miRNAs are then exported out to the cytoplasm by exportin 5 (XPO5) in a RanGTP dependent manner. In the cytoplasm, pre-miRNAs are further processed by ribonuclease DICER1 and RNA binding protein TRBP to produce mature miRNAs. One strand of the mature miRNA (the guide strand) is selected and loaded to the miRNA-induced silencing complex (miRISC), which contains DICER1 and Argonaute (AGO) proteins. Once the targeted mRNA is loaded into the miRISC, the suppression of protein expression can be achieved by translational repression and/or mRNA degradation. The mRNAs degradation takes place in processing body (P-body), which contains multiple proteins and enzymes, including scaffold protein GW182. This results in reduced protein expression of the target gene. Adapted from Lin, 2009 [285].  1.3.2 The roles of MicroRNA in Cancer Since miRNAs can suppress target gene expression, they therefore have the potential to regulate multiple biological processes, including cellular development, differentiation, 24  proliferation, and survival. Aberrant expression of miRNAs contributes to many human diseases, including cancer. miRNAs can function either as tumour suppressors or as oncogenes in cancer development. Tumour suppressor miRNAs normally target oncogenes or proto-oncogenes, and their expression levels are found to be down-regulated in cancer cells, which leads to the up-regulation of oncogenes. Oncogenic miRNAs, or oncomiRNAs, on the other hand, are often amplified in cancer cells, which leads to a reduction in the translation of tumour suppressors [286]. The first study indicating miRNAs have a direct role in cancer development was the identification of frequently deleted miR-15a and miR-16-1 in chronic lymphocytic leukemia (CLL) [287]. These two tumour suppressor miRNAs were also reported to be down-regulated in prostate cancer and pituitary adenomas, and were later shown to target oncogenes BCL-2 and MCL1 [288-290]. Following this work, an in vivo study demonstrated that silencing of miR-15a and miR-16-1 caused CLL in mice [291]. miR-155 and the miR-17–92 cluster are typical examples oncomiRNAs. miR-155 was shown to be up-regulated in high risk CLL, AML, lymphoma, lung, colon, and breast cancer [292-299], and it targets tumour suppressors SHIP1 and CEBPB [300]. Ectopic expression of mir-155 in lymphocytes induces pre-B lymphoma and leukemia in mice [301]. miR-17–92 cluster expression is amplified in B-cell lymphoma, myeloma, AML, lung, breast, colon, and stomach cancer [295, 298, 299, 302-304]. The cluster targets tumour suppressors PTEN and BCL2L11. miRNAs thus have great therapeutic potential. In fact, restoring normal miRNA programs in cancer cells may be more comprehensive and feasible than targeting an individual gene or protein, as there are only a small number of miRNAs that are deregulated in cancer, compared with large perturbations of the transcriptome and proteome (e.g. ~1.5k miRNAs vs. ~50k genes).   25  miRNAs not only can act as tumour suppressors or oncogenes to play a direct role in cancer development, but also act as diagnostic or prognostic biomarkers [305]. Despite their smaller size, miRNAs actually have a slower turnover rate and are more stable than mRNAs, as they are protected by RNA-binding proteins, such as RISC, from ribonuclease [306, 307]. Indeed, based on the differentially expressed miRNAs from genome-wide profiling, several groups have identified diagnostic markers in various cancer types, including CLL, lung carcinoma, breast carcinoma, endocrine pancreatic tumours, hepatocellular carcinoma, papillary thyroid carcinoma, and glioblastoma [295, 299, 308-312]. For example, the aforementioned miR-15a and miR-16-1 can act as diagnostic markers to differentiate CLL cells from normal CD5+ B cells [292, 308]. Furthermore, the differentially expressed miRNAs identified from profiling can act as prognostic markers to predict patient outcome and patient response to specific therapies. For example, high miR-155 and low let-7a expression levels predict poor disease outcome in lung cancer [299], and liver cancer patients with low miR-26 expression respond better to IFNα treatment [313].  1.3.3 Deregulated Expression of MicroRNAs in CML  Several groups have performed miRNA expression profiling in leukemic cells to identify deregulated miRNAs that may play a role in CML pathogenesis. So far, studies have focused on the deregulated miRNAs that either directly target the BCR-ABL 3’ UTR, or are under BCR-ABL regulation. miR-203 was discovered to be down-regulated in Ph+ leukemic cells due to heavy methylation of its upstream region. It was the very first miRNA identified to target ABL mRNA. Overexpression of miR-203 in CML cells drastically lowers BCR-ABL protein level, reduces proliferation, and increases apoptosis [314]. Followed by this study, miRNAs, including 26  miR-29b, miR-30e, miR-138, and miR-320a were also found to target BCR-ABL mRNA directly, and their expression levels were down-regulated in CML cells. Similar to miR-203, ectopic expression of these miRNAs results in reduced proliferation and increased apoptosis in CML cells [315-318]. For the miRNAs that are regulated by BCR-ABL, their expression is normalized or restored to physiological levels following IM treatment. OncomiRNA miR-17–92 cluster was also found to be increased in CD34+ CML cells compared to normal CD34+ cells, and their expression was down-regulated (restored) by more than 2 fold upon either IM treatment or BCR-ABL knockdown. Overexpression of miR-17–92 cluster also results in increased proliferation in CML cells [319]. Other miRNAs that are regulated by BCR-ABL include miR-150, miR-130a/b, miR-451, miR-486, miR-30a, and miR-328 [320-326]. Among these BCR-ABL-regulated miRNAs, miR-30a was shown to target key autophagy genes Beclin 1 and ATG5 to inhibit autophagy, and miR-328 was shown to compete with CEBPA mRNA for binding to hnRNP E2 to allow translation of CEBPA to induce myeloid cell differentiation [320, 325]. Several studies have also utilized miRNA expression profile to look for potential miRNA biomarkers that distinguish CML patients from normal individuals, and to distinguish CML IM-responders from IM-nonresponders [321, 324, 327-331]. However, due to the different biological materials, microarray platforms and relatively small sample size applied in these studies, it is difficult to reach an agreement on the representative miRNA signature. Nevertheless, these studies highlight the importance of performing miRNA profiling in larger cohort of patient samples, which may help to better understand CML pathogenesis and identify potential biomarkers and therapeutic targets.  27  1.4 Thesis Objectives  As described above, CML stem/progenitor cells are insensitive to TKIs due to both intrinsic and acquired resistance properties of these cells, including BCR-ABL-dependent and BCR-ABL-independent mechanisms [105]. It is widely maintained that allogeneic stem cell transplantation is still the only curative treatment for CML, but it is of high risk and laborious [86]. Therefore, the overall goal of this study was to identify and characterize a novel therapeutic strategy to overcome TKI resistance and improve treatment of CML, and to identify potential molecular biomarkers to predict patient clinical response to TKI therapy. Therefore, I focused my work on two specific projects: 1. Targeting drug-insensitive CML stem/progenitor cells by a new combination treatment of JAK2 and ABL inhibitors in vitro and in vivo. 2. Identification and characterization of novel microRNA biomarkers in CML stem/progenitor cells.  In Chapter 3 of this thesis, I describe how I utilized a selective JAK2/ABL dual inhibition treatment strategy to effectively target treatment-naïve CML stem/progenitor cells obtained from IM-nonresponders both in vitro and in vivo. The Jiang laboratory has recently identified a new protein interaction complex between the oncoproteins AHI-1, BCR-ABL and JAK2, and this complex enhances the transforming activity of BCR-ABL and plays a key role in the IM response/resistance of primary CML stem cells [49, 332]. Therefore, I hypothesized that the combined suppression of BCR-ABL and JAK2 activities to destabilize this protein complex is more effective in eliminating leukemic CML stem cells. I first examined how the combination treatment affects JAK/STAT signaling in both CML cell lines and primary CD34+ CML cells by using western blot and intracellular staining. I then studied the biological effects of combination 28  treatment on CD34+ CML cell colony forming abilities and apoptosis. Finally, I assessed the ability of the combination treatment to eliminate BCR-ABL+ blast cells with in vivo leukemia propagating activity by injecting these cells intravenously into immunodeficient NSG mice and treating with inhibitors through oral gavage. In chapter 4, I describe how I identified potential miRNA biomarkers that may act as predictive biomarkers, and how I characterized their biological functions in CML cells. miRNAs play a critical role in cancer development, and various miRNA alterations have been identified in solid tumors and hematologic malignancies [256, 333, 334]. The role of miRNAs in the development of, or response to therapy in CML, has not been examined in depth. I first created miRNA expression profiles of CD34+ CML cells and CD34+ NBM cells using Illumina sequencing. I then identified differentially expressed miRNAs between CML and NBM using Bioconductor DESeq2. These deregulated miRNAs might be important for CML development. Therefore, I hypothesized that the deregulated miRNAs in CD34+ stem/progenitor cells may act as biomarkers to predict clinical response to TKI therapies, and these miRNAs may also be involved in CML pathogenesis and/or drug resistance. Next, I validated the sequencing data in a larger cohort of samples by using a high-throughput qPCR microfluidics device. I also identified potential miRNA target genes by performing strand-specific RNA sequencing and intersected miRNA profiles with corresponding mRNA expression changes using six prediction algorithms (mirBase, TargetScan, miRanda, tarBase, mirTarget2, and PicTar). Next, based on the validated miRNAs and their target genes, I selected eight miRNA candidates and performed extensive functional screening in CML cell lines and CD34+ CML stem/progenitor cells. These studies lead to the identification of miR-185 as a new, potential biomarker and therapeutic target in CML. Finally, I validated target genes of this miRNA to elucidate its possible role in CML 29  pathogenesis and drug resistance. Besides characterizing the biological functions of these miRNAs, I also aimed to identify potential miRNA prognostic markers that may predict clinical response of CML patients to TKI therapy. Total 195 patient samples before and after NL treatment were obtained from 65 CML patients. I again performed high-throughput microfluidics qPCR on CD34+ cells from these patients to evaluate the miRNA expressions changes before and after NL treatment.  30  Chapter 2: Materials and Methods  2.1 Human Primary Samples  Heparin-anticoagulated peripheral blood (PB) cells were obtained from CP CML patients. Bone marrow (NBM) cells were obtained from healthy adult donors (ALLCELL). Informed consent was obtained in accordance with the Declaration of Helsinki, and the procedures used were approved by the Research Ethics Board at the University of British Columbia. Mononuclear cells were isolated using Ficoll-Hypaque (Sigma-Aldrich) density gradient separation and CD34+ cells (>85%) were enriched immunomagnetically using the EasySep CD34 positive selection kit (STEMCELL Technologies). Purity was verified by restaining selected cells with an allophycocyanin-labeled (APC) anti-human CD34 antibody (BD Biosciences) and by using a fluorescence-activated cell sorter (FACS). In JAK2 inhibitor and miRNA validation/functional studies, CML samples were obtained from newly-diagnosed CML patients prior to TKI therapy, that were clinically classified, following IM monotherapy, as IM-nonresponders, based on the European Leukemia Net treatment guidelines [118, 335]. IM-responders achieved complete hematologic remission by 3 months, complete cytogenetic response by 12 months, and major molecular response by 18 months and remains in major molecular response thereafter. IM-nonresponders were individuals who either did not achieve these responses nor had subsequent evidence of loss of response to treatment. BCR-ABL mutations were not detected from the IM-nonresponders. In miRNA prognostic biomarker clinical study, CML samples were also obtained from newly-diagnosed CML patients prior to, 1 month after, and 3 months after nilotinib treatments. The status of IM response of these patients is not yet known. 31   2.2 Cell Lines Human CML cell lines used in this study include K562, IM-resistant K562 (K562R), and BV173 cells. K562 was derived from a BC CML patient carrying P210 BCR-ABL [336]. K562R, provided by Dr. A. Turhan, University of Poitiers, France, was derived from K562 cells culturing in low IM concentrations and does not carry the BCR-ABL kinase domain mutations [337]. BV173 was derived from a patient with Ph+ acute leukemia carrying P190 BCR-ABL [338]. Human AML cell lines used in this study include UT7, UT7-BCR-ABL, and UT7-BCR-ABL-T315I [339]. UT7-BCR-ABL cells are UT7 cells transduced with a P210 BCR-ABL retroviral vector, while UT7-BCR-ABL-T315I cells are UT7 cells transduced with P210 BCR-ABL carrying the T315I kinase domain mutation.  2.3 Suspension Cultures  CD34+ cells were cultured in Iscove’s medium plus bovine serum albumin (BSA), insulin, transferrin (STEMCELL Technologies) and 10-4 M 2-mercaptoethanol supplemented with four growth factors (20 ng/mL IL-3, 20 ng/mL IL-6, 100ng/mL Flt3-ligand, and 20 ng/mL G-CSF, STEMCELL Technologies), with or without drug treatments. CML and AML cell lines were maintained in Roswell Park Memorial Institute (RPMI) 1640 media supplemented with 10% fetal bovine serum (FBS, STEMCELL Technologies) unless stated otherwise, 100 U/mL penicillin, 0.1 mg/mL streptomycin, and 10-4 M 2-mercaptoethanol (STEMCELL Technologies) at 37˚C, 5% CO2 in a humidified cell culture incubator. Cell counts and viability were assessed using trypan blue dye exclusion.  32  2.4 MicroRNA Sequencing and Bioinformatic Analysis Three NBM samples, 3 CML IM-responders, and 3 CML IM-nonresponders, obtained from newly-diagnosed CML patients prior to TKI therapy, were purified for CD34+ cells and confirmed to be >90% CD34+ based on FACS analysis. Total RNA (1 µg) was extracted with miRNeasy Mini Kit (Qiagen) from each sample, and fractionated on a 15% tris-borate-EDTA (TBE) urea polyacrylamide gel to excise 15–30 base pair fractions. The smaller fractions of RNA were purified, ligated to 5’ and 3’ RNA adapters, and converted to cDNA using Superscript reverse transcriptase (Invitrogen). The resulting cDNA was PCR-amplified using Illumina’s small RNA primer set, made into small RNA libraries, and quantified using Genome Analyzer IIx or HiSeq 2000 (Illumina). Small RNA sequences were first pre-processed by removing adapter sequences. They were then aligned back to human genome version 19 coordinates, and annotated against reference database (miRBase v19). Sequences with overlapping annotations were classified into one of the following classes, listed in the priority used: miRNA, tRNA, rRNA, scaRNA, CDBox, scRNA, snoRNA, snRNA, srpRNA, genomic repeat or known transcript (exonic or intronic). Finally, sequence reads were summed by annotation and expression profiles were made [340]. To identify differentially expressed miRNAs between samples, Bioconductor DESeq2 package (version 1.6.3) was used to rank differentially expressed miRNAs based on negative binomial generalized linear models and Benjamini–Hochberg-adjusted p-values [341]. Differentially expressed miRNAs were clustered and made into heat maps using hierarchical clustering and heat map function in R (default parameters). miRNAs that reached adjusted p-values <0.05 were selected for microfluidic quantitative PCR validation.  33  2.5 Microfluidic Quantitative PCR and Bioinformatic Analysis  Microfluidic quantitative PCR (qPCR) was conducted as previously described [342]. CD34+ cells from NBM and CML samples were purified and total RNAs were extracted with TRIzol (Life Technologies) [343], or with miRNeasy Mini Kit (Qiagen). For miRNA reverse transcription (RT), 25 ng RNA of each sample was mixed with 0.5 µL of 10× RT buffer, 0.25 µL of 1% Tween 20, 0.25 µL of 100 mM dNTPs, 0.065 µL of 40 U/µL RNase inhibitor, 0.335 µL of 50 U/µL MultiScribe Reverse Transcriptase, 0.25 µL of pooled RT primer mix, and super Q water to fill to 5 µL total. All reagents are from High Capacity cDNA Reverse Transcription Kit and TaqMan® MicroRNA Assays Kit (Applied Biosystems). The pooled RT primer mix consisted of 92 distinct miRNA stem-loop primers plus RNU48 and RNU6B primers or 47 distinct miRNA stem-loop primers plus RNU48 primer to make 96 multiplexing (miRNA validation study) and 48 multiplexing (miRNA prognostic biomarker clinical study) arrays respectively (0.22 µL from each of stem-loop primers were mixed, concentrated with vacuum centrifuge at 60 ˚C, and dissolved in 0.22 µL of TE buffer). The RT reaction was performed under the following pulsed temperature conditions: 16 ˚C for 2 min, followed by 60 cycles of 20 ˚C for 30 seconds, 42 ˚C for 30 seconds, and 50 ˚C for 1 second. RT enzyme was then inactivated at 85 ˚C for 5 min.  Next, the cDNAs were preamplified using 5 µL of cDNA, 12.5 µL TaqMan® PreAmp Master Mix, 2.5 µL of pooled qPCR primer mix (all from Applied Biosystems), and 5 µL of super Q water to make a total reaction volume of 25 µL. The pooled qPCR primer mix consisted of 2.5 µL of each qPCR primers from TaqMan® MicroRNA Assays Kit plus TE buffer to dilute primers 100×. Preamplification PCR was performed under the following conditions: 95 ˚C for 10 min, 55 ˚C for 2 min, followed by 18 cycles of 95 ˚C for 15 seconds and 60 ˚C for 4 min. 34   Amplified cDNA were treated with ExoSAP-IT® PCR Product Cleanup (Affymetrix) according to manufacturer’s protocol, diluted five times with water, and 1.67 µL of the diluted samples were mixed with 3.7 µL of TaqMan Universal PCR Master No AmpErase UNG mix (Applied Biosystems), 0.37 µL of DA Sample Loading reagent (Fluidigm), and 0.93 µL of TE buffer. The mixture was then loaded to sample inlets of Fluidigm 48.48 Dynamic Array device. Another mixture composed of 2.5 µL of miRNA TaqMan qPCR primer and 2.5 µL of DA Assay Loading reagent (Fluidigm) was loaded to assay inlets of the Fluidigm device. The high-throughput quantitative real-time PCR was then performed using BioMark HD system (Fluidigm) to produce image-based fluorescent signals and raw Ct values.  For miRNA validation study, the raw Ct values obtained from 96 multiplexing array were organized using Bioconductor HTqPCR [344], and normalized using quantile normalization method from Bioconductor limma [345]. Nonparametric Mann–Whitney U test was performed to compare unpaired samples (e.g. NBM vs. CML; IM-responders vs. IM-nonresponders). Benjamini–Hochberg-adjusted p-values <0.05 were considered statistically significant. For miRNA prognostic biomarker clinical study, the raw Ct values were obtained from 48 multiplexing array, normalized to endogenous control RNU48 (ΔCt method), and log2 transformed. Parametric Student’s t test for paired samples was performed (same CML samples before vs. after niloitinb treatments). Benjamini–Hochberg-adjusted p-values <0.05 were considered statistically significant.  2.6 Strand Specific RNA Sequencing and Bioinformatic Analysis  Three NBM samples, 3 CML IM-responders, and 3 CML IM-nonresponders (same CML samples made for miRNA libraries) were CD34+ purified as above and total RNAs (1 µg from 35  each sample) were extracted. Polyadenylated mRNA was purified using MultiMACS mRNA Isolation Kit (Miltenyi Biotec). cDNA was synthesized from the purified polyadenylated mRNA using the Maxima H Minus First Strand cDNA Synthesis Kit (Thermo Fisher). The resulting cDNAs were PCR-amplified using Illumina’s PE primer set, and the purified PCR products were sequenced on the HiSeq 2000 platform (Illumina).  Paired-end RNA sequencing (RNA-seq) was utilized, and the sequencing data was aligned to GRCh37-lite genome-plus-junctions using BWA and human reference genome version 19 coordinates [346, 347]. Sequence reads were summed and coverage analysis including protein-coding genes, pseudogenes, processed transcripts, and other non-coding RNAs was generated.  To identify differentially expressed mRNAs between samples, protein-coding genes from the coverage analysis were selected for DESeq2 analysis (version 1.6.3) [341]. The identified differentially expressed mRNAs (adjusted p-values < 0.05 cutoff) were grouped into canonical and KEGG pathways using Ingenuity Pathway Analysis (Qiagen) and DAVID Bioinformatics Resources [348, 349].  2.7 MicroRNA Target Gene Prediction  Potential miRNA target genes were identified by intersecting the differentially expressed miRNAs, validated from microfluidic quantitative PCR, with the inversely deregulated predicted target genes, identified from RNA-seq (Figure 2.1). For example, miRNAs that were down-regulated in CML were matched with the predicted target mRNAs whose expressions are up-regulated in CML. Six prediction algorithms, mirBase [350], targetScan [274], miRanda [351], tarBase [352], mirTarget2 [353], and PicTar [354], were used to predict the target genes of the 36  differentially expressed miRNAs with Bioconductor RmiR package (version 1.22.0). Only the genes predicted by at least 3 out of the 6 algorithms were kept and considered as potential target genes to reduce false positive errors. Gene Ontology (GO) and pathway analysis were performed on the potential target genes using Ingenuity Pathway Analysis (Qiagen) and DAVID Bioinformatics Resources [348, 349].    Figure 2.1: Overview of sequencing data analysis and miRNA target gene identification. miRNA and mRNA expression profiles were made for NBM and CML samples. The differentially expressed miRNAs and mRNA were identified and matched with inverse expression relation and with prediction algorithms to identify potential miRNA target genes. The potential target genes were analyzed with GO enrichment and pathway analysis.   2.8 Inhibitors  BMS-911543 and dasatinib (DA) were provided by Bristol-Myers Squibb (Princeton, USA). IM and nilotinib (NL) were obtained from Novartis (Novartis, Basel, Switzerland). Stock solutions of 10 mM were prepared with water (IM) or with dimethyl sulfoxide (DMSO) (DA, NL, and BMS-911543) and stored at -20°C.   37   2.9 Analysis of Drug Interactions Analysis of drug interactions in the combination treatment group of BMS-911543 with DA was assessed after 72 hours of suspension culture with drug exposure using total viable cell counts. Data were analyzed using constant-ratio drug combinations and the median-effect method of Chou and Talalay [355]. The combination Index (CI) was calculated using CalcuSyn software (Biosoft, Cambridge, United Kingdom). CI< 1, CI = 1 or CI > 1 represent synergistic, additive or antagonistic effects respectively.  2. 10 Surface Marker Staining Samples were washed with Hanks' balanced salt Solution (HBSS) with 10 mM HEPES plus 2% FBS, blocked with blocking buffer, which consisted of 5% human serum (Sigma-Aldrich), anti-mouse CD16/CD32 (1:150 dilution, BD Biosciences), and anti-human CD32 antibodies (1:200 dilution, STEMCELL Technologies), and incubated with conjugated surface marker antibodies at 4°C for 1 hour, followed by Propidium iodide (PI, 1 µg/mL, Sigma-Aldrich) or DAPI (1 µg/mL, Invitrogen) staining prior to FACS analysis. Conjugated surface marker antibodies include anti-human CD19 PE (1:250 dilution, eBioscience), anti-human CD34 APC (1:100 dilution, BD Biosciences), anti-human CD45 PE (1:100 dilution, eBioscience), anti-human CD33 PE-Cyanine7 (1:100 dilution, eBioscience), anti-human CD15 PE-Cyanine7 (1:100 dilution, BD Biosciences), anti-human CD34 Alexa Fluor® (1:100 dilution, BioLegend), anti-human CD19 APC (1:50 dilution, eBioscience), anti-human CD3 eVolve™ (1:50 dilution, eBioscience), and anti-human GPA Pacific Blue™ (1:100 dilution, BioLegend).  38  2.11 Intracellular Staining After 72 hours of suspension culture with drug exposure, CD34+ CML cells were fixed with 4% paraformaldehyde, permeabilized with 0.2% saponin, and stained with p-CRKL (Cell Signaling) or p-STAT5 antibodies (Cell Signaling) at 4°C overnight, followed by incubation with a secondary antibody (anti–rabbit IgG FITC-conjugate, Invitrogen) at 4°C  for 1 hour prior to FACS analysis. P-CRKL and p-STAT5 levels were determined as the geometric mean fluorescence intensity (MFI) subtracted by the MFI of cells stained with isotype IgG control, and were normalized as a percentage of control cells incubated for the same time with DMSO only.  2.12 RNA Extraction and Quantitative Real-Time PCR Total RNA was extracted with TRIzol (Life Technologies) [343], or with miRNeasy Mini Kit (Qiagen). Glycogen (10µg/ml, Life Technologies) was added as a carrier to facilitate visibility of the RNA pellet. RNA pellet was then dissolved in RNase-free water (Life Technologies), and the concentrations were determined with a nanodrop ND-100 spectrophotometer (Thermo Scientific) measuring optical density at 260 nm and 280 nm.  For SYBR® Green system, 100-500 ng RNA was reversed transcribed into cDNA with SuperScript® VILOTM Master Mix (Life Technologies) according to manufacturer’s instructions. Quantitative real-time PCR was performed with 6 µL 2 × SYBR® Green PCR Master Mix (Life Technologies), 1 µL of 10-20 µM specific primers (Table 2.1), 1 µL cDNA, and RNase-free water (filled to 12 µL) on the 7500 Real Time PCR System (Applied Biosystems). Fluorescence signal was calculated using SYBR® Green as the reporter dye and ROX as the passive reference. GAPDH or B2M were used as endogenous controls to normalize mRNA input. 39  For singleplex TaqMan® system, 10-20 ng RNA was reversed transcribed into cDNA with TaqMan® MicroRNA Reverse Transcription Kit and miRNA specific primers (Applied Biosystems) according to manufacturer’s protocol. Real-time quantification of cDNA was carried out using 3.5 µL cDNA, 0.6 µL of primer from TaqMan® MicroRNA Assays Kit, 6 µL of 2 × TaqMan® Universal PCR Master Mix (Applied Biosystems), and RNase-free water (filled to 12 µL) on the 7500 Real Time PCR System (Applied Biosystems). The RNU6B small nuclear RNA (snRNA) or RNU48 small nucleolar RNA (snoRNA) were used as endogenous controls to normalize miRNA input. TaqMan® MicroRNA assays detecting mature miRNA expression include: hsa-miR-139-5p, hsa-miR-145-5p, hsa-miR-146b-5p, hsa-miR-185-5p, hsa-miR-340-5p, hsa-miR-452-5p, hsa-miR-628-3p, and hsa-miR-708-5p.  Table 2.1: Specific primers used for Quantitative Real-Time PCR Primer Name Sequence (5' to 3') GAPDH-F  CCCATCACCATCTTCCAGGAG GAPDH-R  CTTCTCCATGGTGGTGAAGACG BCR-ABL-F CATTCCGCTGACCATCAATAAG BCR-ABL-R GATGCTACTGGCCGCTGAAG B2M-F TAGCTGTGCTCGCGCTACT B2M-R TCTCTGCTGGATGACGTGAG PAK6-F  GGCCAGAGACAGGAATGTAAG PAK6-R  TTCGCCTTCTCTCCCAGATA NR1D1-F  CTCTGCACTTCTCTCTCCTTTAC NR1D1-R  GGGAGGCAGGTATTTACAAGAA SGMS1-F  CACAGCAGAGGGTGTGTAATAA SGMS1-R  GGCAGGTTCTCGTCTTCTATTT  2.13 Total Cell Lysate Extraction and Quantification Cells were washed with Dulbecco’s Phosphate Buffered Saline (PBS, STEMCELL Technologies) and lysed in lysis buffer on at rotator at 4˚C for 1 hour. The lysis buffer consisted 40  of 830 µL phosphorylation solubilization buffer (PSB), 50 µL of NP-40 Alternative Protein Grade Detergent (Calbiochem), 10 μL 200 mM phenylmethylsulfonyl fluoride (PMSF, Sigma-Aldrich), and 50 μL protease inhibitor cocktail (PIC, Sigma-Aldrich). The cell lysate was centrifuged at 15,000 × g for 10 min at 4°C, and the supernatant containing the released proteins were collected. Protein concentrations were quantified using Bradford assay (Bio-Rad) and were measured at 630 nm using an Elx808TM Absorbance Microplate Reader (BioTekk Instruments).  2.14 Western Blotting Analysis Western blotting samples were prepared using 30-50 µg of protein lysate, 4× loading dye, and super Q water. Samples were then heated at 90°C for 10 minutes and loaded to 8-15% sodium dodecyl sulfate polyacrylamide-gel electrophoresis (SDS-PAGE) gels with 1.5 mm wells (Bio-Rad) and PageRuler Pre-stained Protein Ladder (Fermentas). The gel was run at 100 V until loading dye reaches resolving gel, and then was run at 150 V until loading dye reaches the bottom of the resolving gel. Proteins were then transferred from the gel onto polyvinylidene difluoride (PVDF) membranes (Millipore) at 100 V for 1.5 hours. The PVDF membranes were then blocked with 5% skimmed milk powder (Sigma-Aldrich) in Tris-buffered saline Tween 20 (TBST) or with 5% BSA (phospho antibodies) in TBST at room temperature for 1 hour. The membranes were next probed with primary antibody overnight at 4°C. Next day, the membranes were washed with TBST, probed with corresponding secondary antibodies conjugated with horseradish peroxidise (Santa Cruz Biotechnology) at room temperature for 1 hour, and washed with TBST. Finally, proteins of interest were visualized by incubating with enhanced chemiluminescence reagent and exposing on KODAK BioMax Xar autogradiography film. 41  Antibodies used include anti-STAT5 (Millipore), anti-phospho-STAT5 (Cell Signaling and Abcam), anti-ABL (8E9, BD Biosciences), anti-phospho-tyrosine (4G10, Millipore), anti-phospho-CRKL (Cell Signaling), anti-ERK (Santa Cruz Biotechnology), anti-phospho-ERK (Cell Signaling), anti-AKT (Cell Signaling), anti-phospho-AKT (D9E XP, Cell Signaling), anti-β -catenin (BD Biosciences), anti-phospho-Y86-β-catenin (Cell Signaling), anti-PAK6 (Applied Biological Materials), anti-NR1D1 (Santa Cruz Biotechnology), anti-SGMS1 (Santa Cruz Biotechnology), anti-β-Actin (Applied Biological Materials), and anti-GAPDH (Sigma-Aldrich).  2.15 miRNA Mimics and siRNAs Transfection For transient transfection in cell lines, 100 nM of miRNA mimics (syn-hsa-miR-139-5p, syn-hsa-miR-145-5p, syn-hsa-miR-185-5p, syn-hsa-miR-340-5p, syn-hsa-miR-452-5p, syn-hsa-miR-628-3p, and syn-hsa-miR-708-5p,) 500 nM of miRNA inhibitor (anti-hsa-miR-146b-5p), 100 nM of siRNAs targeting PAK6 (Hs_PAK6_3, Hs_PAK6_11, Hs_PAK6_12, and Hs_PAK6_13), 100 nM of siRNAs targeting SGMS1 (Hs_SGMS1_1, Hs_SGMS1_4, Hs_TMEM23_4, and Hs_TMEM23_5), 100 nM of siRNAs targeting NR1D1 (Hs_NR1D1_1, Hs_ NR1D1_2, Hs_ NR1D1_5, and Hs_ NR1D1_9), or scrambled siRNA control (Qiagen) were transfected in 2 × 105 K562 or K562R cells in 600 µL of RPMI 1640 medium using 3 µL HiPerFect (Qiagen) per well according to manufacturer’s instructions. Cells were transfected for 48 hours prior to further experiments, including quantitative real-time PCR, western blot analysis, and proliferation assays.  42  2.16 Cloning and Constructing miRNA Lentiviral Vectors Precursor miRNA (pre-miRNA) sequence along with its upstream and downstream 100 bp flanking sequence were synthesized (GenScript) and cloned into pRRL lentiviral vector (kindly donated by Dr. K. Humphries, BC Cancer Agency, Vancouver) between BsrGI and SalI sites (Figure 2.2). The pRRL vector utilizes the murine spleen focus-forming virus (SFFV) promoter to drive the transcription of GFP and miRNA simultaneously so that the expression of GFP correlates with miRNA expression [356].      Figure 2.2: Schematic representation of the miRNA expressing vector. Third-generation lentiviral vector (pRRL) encoding GFP contain the central polypurine tract (PPT), the woodchuck hepatitis virus posttranscriptional regulatory (Wpre), and internal promoter of the murine spleen focus-forming virus (SFFV). PPT and Wpre provide increased transduction efficiency and transgene expression. Precursor miRNA sequence along with its upstream and downstream 100 bp flanking sequence were synthesized and cloned between BsrGI and SalI sites proceeded by GFP sequence.   2.17 Lentivirus Production Lentiviral particles were produced by polyethyleimine (PEI) transfection of 293T cells. 6 × 106 293T cells were plated in 10-cm plates (7 plates per construct) in 7 mL Dulbecco's Modified Eagle Medium (DMEM) with 10% FBS and 2 mM glutamine at 37°C, 5% CO2, in a humidified cell culture incubator. The next day, the culture medium was replaced with 4.5 mL fresh medium per plate 2-4 hours prior transfection. For each plate, in one tube, a total of 6 μg of 43  miRNA expressing plasmid or empty vector, two packaging constructs (3.9 μg of ∆R and 1.5 μg of REV), and 2.1 μg of vesicular stomatitis virus glycoprotein (VSV-G) envelope construct were mixed and topped up to a total volume of 250 μL Opti-MEM® medium (Thermo Fisher Scientific). Packaging and envelope plasmids were gifts from P. Leboulch (Harvard University, Boston, MA, USA). In a separate tube, 40 μL of PEI (1 μg/μL) was mixed with 210 μL Opti-MEM® medium. The viral constructs and PEI mixtures were mixed and incubated at room temperature for 20 min before it was added drop-wise to 293T cells. After 48 hours, the viral supernatant was collected and filtered with a 0.45 μm low protein-binding filter (Pall Corporation), followed by concentrating the virus by ultracentrifugation. The virus pellet was resuspended in 100 μL Iscove’s medium containing 5% DNase under gentle agitation for 1 hour at room temperature. Multiple aliquots of the concentrated virus were stored at -80°C.  2.18 Generation of Stably Transduced Cells (cell lines and primary samples) To generate stable miRNA expressing cell lines, 3 × 105 K562 cells, 3 × 105 K562R cells, 3 × 105 UT7 cells, 3 × 105 UT7-BCR-ABL cells, 3 × 105 UT7-BCR-ABL-T315I cells, 6 × 105 BV173, and 6 × 105 BV173YFP/Luc were seeded in 400 μL RPMI 1640 in 24-well plates. Cells were then infected with 5 μL concentrated lentiviral particles containing miRNA expressing plasmids or empty vectors in the presence of 5 µg/mL protamine sulfate and incubated at 37°C, 5% CO2, in a humidified cell culture incubator for 24 hours. Cells were then washed with PBS and resuspended in fresh medium for further expansion. FACS was used to determine the infection efficiency (GFP+ cells) and to purify GFP+/YFP+ double positive cells. For primary samples, 2 × 105 CD34+ CML cells were pre-stimulated in 300 μL medium containing growth factors in 24-well plates for 16 hours, and were later similarly transduced with lentiviral particles 44  for 6 hours. The cells were washed and recovered for 2 days prior to FACS sorting to purify GFP+ cells.  2.19 Viability Assay For cell lines, 0.5-1 × 105 cells were seeded in 0.5-1 mL medium in 24-well plates. For primary samples, 2-3 × 104 cells were seeded in 500 µL medium with growth factors in 24-well plates. Cell counts and viability were assessed using trypan blue dye exclusion method with Neubauer hemocytometer plus inhibitors.  2.20 [3H]-thymidine Incorporation Assay 2 × 104 K562 cells were seeded in triplicate in round-bottom 96 well plates in 100 µL of RPMI 1640 medium per well. The cells were cultured for 48 hours post miRNA mimics transfection at 37°C, 5% CO2 in a humidified cell culture incubator. The amount of tritiated [3H]-thymidine incorporation during a 4-hour pulse of the culture was measured. Briefly, 1 microcurie (μCi) of tritiated [3H]-thymidine was added to each well for 4 hours. The cells were then harvested onto a membrane using a Skatron instruments combi Harvester (LKB Wallace-PerkinElmer). The amount of tritiated [3H]-thymidine was measured with a LKB Betaplate scintillation counter.  2.21 Apoptosis Assays After 48, 72 and 96 hours of suspension culture with drug treatments, apoptosis assays were performed on CD34+ NBM cells, CD34+ CML cells, and CML cell lines using an Annexin V Apoptosis Detection Kit APC (eBioscience). Briefly, cells were stained with Annexin V APC 45  and Propidium Iodide (PI) and analyzed using a FACSCalibur (BD Bioscience). Total apoptotic cell populations were determined as the sum of the “early” apoptotic cells (Annexin V+ only) and “late” apoptotic cells (Annexin V+/PI+).  2.22 Colony-Forming Cell (CFC) Assay  600 K562 cells, 600 K562R cells, 600 UT7-BCR-ABL cells, 1,500 BV173 cells, or 3,000 CD34+ cells were mixed in 3 mL methylcellulose medium (STEMCELL Technologies) with or without inhibitors, and with (primary samples) or without (cell lines) growth factors (300 U EPO, 2 µg IL-6, 2 µg IL-3, 2 µg GM-CSF, and 2 µg G-CSF in 100 mL methylcellulose medium).  3-mL syringes with blunt end needles were then used to plate 1.2 mL of the mixture into CFC plates in duplicate. Colonies produced were counted after 14 to 16 days of incubation at 37°C, 5% CO2, in a humidified cell culture incubator Enumeration based on colony size (cell lines) or colony number and type (primary samples).  2.23 Long-Term Culture-Initiating Cell (LTC-IC) Assays LTC-IC assays were performed with CD34+ cells plated onto M2-10B4 stromal cells engineered to produce and secrete human IL-3, G-CSF, and stem cell factor. Cultures were maintained for six weeks with weekly half-medium changes, plus inhibitors for one to two weeks once weekly. Cells were then harvested and CFC assays were performed to obtain total numbers of LTC-IC-derived CFCs.  46  2.24 Luciferase Reporter Assay miRNA binding regions of PAK6 3’UTR/mutant, SGMS1 3’UTR/mutant, NR1D1 3’UTR/mutant, and PBX1 3’UTR/mutant oligos (IDT, Table 2.2) were annealed and cloned into a dual-luciferase reporter vector pmirGLO (Promega). HEK-293T cells were transfected in 24-well plates using Lipofectamine 3000 (Invitrogen) in three replicates according to manufacturer’s protocol. The transfection mixtures contained 0.6 µg of miRNA expression plasmid, 0.2 µg of reporter plasmid, and 1.5 µl of Lipofectamine. Luciferase activity was measured 48 hours after transfection using a dual-luciferase reporter assay kit (Promega) and presented as Firefly/Renilla ratio.  Table 2.2: Oligonucleotide sequences used for luciferase reporter assay  Oligos Sequence (5' to 3') PAK6 3’UTR-F  AAACCAGGACTTGCCTGCCTCCTCCTCTCAGTATTCTCTCCAAAGATTGAAATGTC PAK6 3’UTR-R  TCGAGACATTTCAATCTTTGGAGAGAATACTGAGAGGAGGAGGCAGGCAAGTCCTGGTTT PAK6 mutant-F  AAACCAGGACTTGCCTGCCTCCTCCTCTCAGTATTGAGAGGTAAGATTGAAATGTC PAK6 mutant-R  TCGAGACATTTCAATCTTACCTCTCAATACTGAGAGGAGGAGGCAGGCAAGTCCTGGTTT SGMS1 3’UTR-F  AAACACCTGGTCAGCACTGTGATCTTTTTTTCTCTCCAAAGGACCTGCGTTGGACC SGMS1 3’UTR-R  TCGAGGTCCAACGCAGGTCCTTTGGAGAGAAAAAAAGATCACAGTGCTGACCAGGTGTTT SGMS1 mutant-F  AAACACCTGGTCAGCACTGTGATCTTTTTTTGAGAGGTAAGGACCTGCGTTGGACC SGMS1 mutant-R  TCGAGGTCCAACGCAGGTCCTTACCTCTCAAAAAAAGATCACAGTGCTGACCAGGTGTTT NR1D1 3’UTR-F  AAACTTGTACAGAATCGAACTCTGCACTTCTCTCTCCTTTACGAGACGAAAAGGAC NR1D1 3’UTR-R  TCGAGTCCTTTTCGTCTCGTAAAGGAGAGAGAAGTGCAGAGTTCGATTCTGTACAAGTTT NR1D1 mutant-F  AAACTTGTACAGAATCGAACTCTGCACTTCTGAGAGGTTTACGAGACGAAAAGGAC NR1D1 TCGAGTCCTTTTCGTCTCGTAAACCTCTCAGAAGTGCAGAGTTCGATT47  mutant-R  CTGTACAAGTTT PBX1 3’UTR-F  AAACTTGGGGCAGGGGCAGGAGGGAGGGTTTCTCTCCCAACGCTGAAGCGGTCAGC PBX1 3’UTR-R  TCGAGCTGACCGCTTCAGCGTTGGGAGAGAAACCCTCCCTCCTGCCCCTGCCCCAAGTTT PBX1 mutant-F  AAACTTGGGGCAGGGGCAGGAGGGAGGGTTTGAGAGGCAACGCTGAAGCGGTCAGC PBX1 mutant-R  TCGAGCTGACCGCTTCAGCGTTGCCTCTCAAACCCTCCCTCCTGCCCCTGCCCCAAGTTT  2.25 Cell Adhesion Assay 24-well plates were coated with 12.5 µg/ml fibronectin (Roche) and incubated for 1.5 hours at 37°C. 30,000 cells per well were added to the coated plates and incubated for 1.5 hours at 37°C. The bound cells were then washed, trypsinized and counted by trypan blue dye exclusion method.  2.26 Transplantation of Immunodeficient Mice with CML Cells BV173 or BV173 cells expressing YFP and luciferase (BV173YFP/Luc, 2.5 × 106 cells per treatment condition) were injected intravenously into 8- to 10-week-old, sub-lethally cesium irradiated (315 cGy) NOD/SCID-interleukin 2 receptor γ–chain-deficient (NSG) mice. Two weeks post-transplant, mice were treated with vehicle and inhibitors once or twice a day for two weeks by oral gavage. The mice were monitored daily for body weight changes and survival during and after drug treatment. The level of engraftment of BV173 cells in the PB, BM, spleen, and liver was determined with anti-human CD19PE antibody (1:250 dilution) (eBioscience) and FACS analysis. The level of engraftment of BV173YFP/Luc cells was also determined by in vivo imaging using the Xenogen IVIS® 50 Bioluminescence Imaging System (1 second exposure time using Live Imaging Software Version 3.0) upon injection intraperitoneally with 2 µM D-Luciferin (Sigma-Aldrich) at 2 weeks, 6 weeks, and 8 weeks post transplantation. For 48  histopathology analysis, spleens and livers were fixed in 10% neutral buffered formalin, embedded in paraffin, and stained with hematoxylin and eosin (H-&-E), or with anti-human CD19 antibody (Abcam) for immunohistochemical (IHC) staining. Images of histological slides were captured on a Zeiss Axioplan 2 Imaging microscope (Göttingen, Germany) equipped with a Retiga EXi colour digital camera (Burnaby, Canada). Animal experiments were performed in the Animal Resource Centre of the BC Cancer Agency Research Centre, using procedures approved by the Animal Care Committee of the University of British Columbia (Vancouver).   2.27 Statistical Analysis   Unless otherwise indicated, results are shown as the mean ± standard error of the mean (SEM) of measurements from at least three independent experiments. Differences between groups were compared using the two-tailed Student’s t test for paired or unpaired samples, or one-way ANOVA with post-hoc testing for multiple comparisons. Log-rank tests were used to compare the median survival of mice from different groups. All statistical analyses were performed using GraphPad Prism version 6 (http://www.graphpad.com/scientific-software/prism/). P-values <0.05 were considered statistically significant.   49  Chapter 3: Selective JAK2/ABL Dual Inhibition Therapy Effectively Eliminates TKI-insensitive CML Stem/Progenitor Cells  3.1 Introduction Chronic myeloid leukemia (CML) is a lethal hematological malignancy defined by the presence of a BCR-ABL fusion gene originating in a hematopoietic stem cell (HSC) [14, 26]. The BCR-ABL oncoprotein has constitutively active tyrosine kinase (TK) activity, which drives the disease phenotype by perturbing multiple signaling pathways, including PI3K/AKT, RAS/MAPK, and JAK2/STAT5 [25, 27]. The treatment of chronic phase CML (CP CML) has been significantly improved by Imatinib Mesylate (IM) and other tyrosine kinase inhibitor (TKI) therapies [94, 96-98, 357]. TKI monotherapies are not, however, curative and early relapse and primary and acquired TKI resistance remain significant issues [99, 100]. Despite the effectiveness of TKI monotherapy, most patients harbor residual leukemic stem cells (LSCs), and disease typically recurs if therapy is discontinued [102, 103]. Furthermore, 15-20% of patients with early CP CML and up to 40% with accelerated phase (AP CML) disease fail treatment, indicating a need for alternative approaches. LSCs are known to be genetically unstable and less responsive to TKI treatments, and are of critical importance in mediating TKI resistance [101, 140, 358, 359]. Recent studies have indicated that CML LSCs might not be exclusively dependent on BCR-ABL TK activity for their survival [360, 361]. These observations emphasize the need to develop new therapeutic agents and combination strategies to target TKI resistant LSC subclones.   Evidence suggests that the Janus kinase 2/signal transducers and activators of transcription 5 (JAK2/STAT5) pathway plays a critical role in CML leukemogenesis [25, 27]. In 50  particular, JAK2 interacts directly with the C-terminal region of BCR-ABL and is a key interaction partner of BCR-ABL in CML [237, 239]. This complex enhances BCR-ABL TK activity and disrupts BCR-ABL-mediated signaling in BCR-ABL+ cells, possibly through direct phosphorylation of tyrosine 177 of BCR-ABL by JAK2 [237, 239].  We have recently identified an AHI-1-BCR-ABL-JAK2 protein complex that contributes both to the transforming activity of BCR-ABL and to IM-resistance in CML stem/progenitor cells. Disrupting this complex results in the elimination of IM-resistant BCR-ABL+ cells and primary CML stem/progenitor cells [49, 332]. Similarly, STAT5, a direct substrate of JAK2, is constitutively active in BCR-ABL-transduced cells [221, 222], and over-expression of Stat5 in murine BM cells generates a disease phenotype closely resembling BCR-ABL-induced CML [230]. High STAT5 levels were also found to mediate acquired IM resistance in CML cells and STAT5 inhibitors reduced their survival [231, 232]. Thus, targeting JAK2/STAT5 activity is consequently rational and complementary to the inhibition of BCR-ABL TK activity in CML stem/progenitor cells. Several JAK2 inhibitors are currently in various stages of clinical development in myeloproliferative neoplasms [248], including myelofibrosis [362, 363] and AML [364], but their off-target effects on healthy primitive hematopoietic cells remains challenging [248, 249, 365]. In this chapter, I examine the potential relevance of JAK2 inhibition in CML by examining the biological effects of a highly selective, orally bioavailable JAK2 inhibitor BMS-911543 alone and in combination with second generation TKIs, including dasatinib (DA), on CD34+ treatment-naïve IM-nonresponder cells. Dual inhibition of JAK2/STAT5 and BCR-ABL is shown more effective in eliminating CML LSCs, but not their healthy counterparts, than TKIs alone in vitro, and significantly enhances progression free survival in mice.  51  3.2 Results 3.2.1 JAK2 Inhibitor in Combination with IM is More Effective in Reducing JAK2/STAT5 Activity and Inhibiting Proliferative Capacity of IM-insensitive CML Cells   To determine the effect of a highly selective JAK2 inhibitor (BMS-911543) [251] alone or in combination with IM on CML cells, I examined changes in the phosphorylation of STAT5, which is activated by JAK2 kinase and can be used as a measure of JAK2 kinase activity. Phosphorylated STAT5 was analyzed by Western blot analysis in K562 cells and a spontaneously-derived cell line that is relatively resistant to IM (K562R), but has no BCR-ABL kinase domain mutations [337]. Combination treatment of BMS-911543 and IM was more effective at reducing p-STAT5 levels in K562R cells compared with IM or BMS-911543 alone (70% vs. 45% and 10% reduction, P<0.03, Figure 3.1A). This combination effect was not observed in IM-sensitive K562 cells.  Increased STAT5 protein expression was observed in K562R cells as compared with IM-sensitive K562 cells. BMS-911543 and IM together also resulted in a greater reduction than IM alone in both total colony numbers and colony size produced from K562R cells in CFC assays (2-3 fold, P=0.028, Figure 3.1B).  Similar results were obtained from BV173 cells, a BCR-ABL+ cell line derived from a patient with acute lymphoblastic leukemia (ALL, P=0.02, Figure 3.1B). These results indicate that the combination of BMS-911543 and IM result in a deeper suppression of p-STAT5 and more effectively reduce the proliferative capacity of IM-resistant cells than either single agent alone.    52    Figure 3.1: Combination treatment with BMS-911543 and imatinib (IM) is more effective at reducing p-STAT5 levels and inhibiting proliferative capacity of IM-resistant K562 and BV173 cells. (A) Western blotting analysis of p-STAT5 and STAT5 in K562 and K562 IM-resistant (K562R) cells cultured with or without IM (0.05 µM), BMS-911543 (1 µM), or a combination of IM and BMS-911543 for 2 hours (left panel). DMSO was used as a control. Protein expression of p-STAT5 relative to GAPDH was compared (right panel). Data shown are mean ± SEM of measurements from three independent experiments. (B) K562R and BV173 cells were plated in standard colony-forming cell (CFC) assays plus IM (2.5 µM for K562R; 0.5 µM for BV173) or BMS-911543 (5 µM) alone or in combination. Colonies produced were counted after 16 days of incubation, and the numbers obtained were expressed as a percentage of values obtained in untreated cells to which only DMSO was added (top panel). Colony numbers for large (>500 cells), medium (50-500 cells), and small (<50 cells) are indicated. Representative 53  photos of the size and morphology of colonies in each treatment is shown (bottom panel). Data shown are mean ± SEM of measurements from three independent experiments. P-values were calculated using a two-tailed paired Student’s t test.  3.2.2 The Combination of BMS-911543 and TKIs Reduces BCR-ABL and JAK2/STAT5 Activity and Induces Apoptosis of CD34+ Treatment-naïve IM-nonresponder Cells To investigate whether this dual BCR-ABL-JAK2 targeting approach may also be therapeutically effective for CML patients who do not respond adequately to treatment with a single TKI, I investigated the molecular and biological effects in primitive CML cells obtained at diagnosis from CML patients (n=7) classified retrospectively following initiation of IM monotherapy, as IM-nonresponders [118, 335]. A concentration of 300 nM for BMS-911543 was selected based on the 50% inhibitory concentration (IC50) obtained in BaF3 cells transduced with a constitutively active JAK2 construct but lacking V617F mutation [251]. Notably, this concentration (300 nM) showed no toxic effects on CD34+ normal bone marrow (NBM) cells (~2% Annexin V positive cells, Figure 3.2A). Interestingly, intracellular staining showed that combined exposure of CD34+ IM-nonresponder cells (n=4) to BMS-911543 (300 nM) and a TKI (5 µM IM; 150 nM DA) produced a deeper and more prolonged suppression of p-STAT5 activity (60-65%) than IM or DA alone (20-25%) after 72 hours (P<0.05, Figure 3.3A). P-CRKL activity, a direct target of BCR-ABL kinase was also suppressed more by combination treatment than single agents (70-90% vs. 45-65%, P<0.04).   While BMS-911543 (300 nM) had minimal effects on apoptosis and single TKIs (5 µM IM; 150 nM DA; 5 µM NL) increased the percentage of Annexin V positive cells by 10-20%, the combination treatment increased Annexin V positive cells to 20-30% at 48 hours, with a significant increase as a result of the combination treatment after 72 hours (2-fold, P<0.05, Figure 3.3B). These results indicate that the combination of BMS-911543 and TKIs markedly 54  and durably inhibits the activity of BCR-ABL and JAK2/STAT5 and is more effective in inducing apoptosis in IM-insensitive CML stem/progenitor cells. Critically, the combination of BMS-911543, at a clinically achievable concentration, with TKIs displayed minimal toxic effects in CD34+ NBM cells compared to CD34+ CML cells (4-8% vs. 32-36%, P<0.01, Figure 3.3B).                      Figure 3.2: Effects of BMS-911543 alone or in combination with TKIs on normal CD34+ bone marrow cells. (A) Percentage of total apoptotic cells determined by Annexin V/PI staining after 72 hours of BMS-911543 treatments (150 nM, 300 nM, 600 nM, or 1200 nM) in CD34+ normal bone marrow cells (NBM, n=2) and CD34+ CML cells (n=2). (B) CD34+ NBM cells 55  (n=4) were plated in standard CFC assays plus BMS (300 nM), IM (5 µM), DA (150 nM) or NL (5 µM) alone or in combination. Colonies produced were counted after 14 days of incubation, and the numbers obtained were expressed as a percentage of values obtained in untreated cells to which only DMSO was added.  56   57  Figure 3.3: A combination of BMS-911543 and tyrosine kinase inhibitors (TKIs) results in a significant reduction in BCR-ABL and JAK2/STAT5 activities and induction of apoptosis of CD34+ treatment-naïve IM-nonresponder cells but not normal CD34+ cells. (A) Phosphorylation of STAT5 and CRKL in CD34+ CML cells (n=4) was measured by intracellular flow cytometry after 72 hours of drug exposure, including BMS-911543 (300 nM), IM (5 µM), or dasatinib (DA, 150 nM) alone or in combination. Representative pSTAT5 fluorescence intensity histogram is shown (left panel). Phosphorylation levels were expressed as the geometric mean fluorescence intensity (MFI) subtracted by the MFI of cells stained with IgG control, and were normalized as a percentage of the untreated cells incubated with DMSO (right panel). Data shown are mean ± SEM of measurements from four individual patients. (B) Percentage of total apoptotic cells after 72 hours of drug treatments including BMS-911543 (300 nM), IM (5 µM), DA (150 nM) or nilotinib (NL, 5 µM) alone or in combination for CD34+ CML cells (n=3) and CD34+ normal bone marrow cells (NBM, n=2) as determined by Annexin V/PI staining (bottom panel). Top panel shows representative fluorescence-activated cell sorting (FACS) profiles. Y-axis represents PI, and X-axis represents Annexin V. Data shown are mean ± SEM of measurements from three individual patients. P-values were calculated using a two-tailed paired Student’s t test.  To further determine whether the combination of BMS-911543 and a TKI had synergistic or additive effect, I performed viability assays on CD34+ CML cells with graded doses of BMS-911543 and DA, alone or in combination, for 72 hours. The results were analyzed using CalcuSyn software (Biosoft) to calculate combination Index (CI), and an algebraic estimate and a conservative isobologram were generated (Figure 3.4A and B). At doses ranging from 150 to 600 nM of BMS-911543, and from 75 to 300 nM of DA, the average CI for ED50, ED75, and ED90 was calculated to be 0.597 (Figure 3.4A), indicating that the combination is highly synergistic (CI< 1, CI = 1 or CI > 1 represent synergistic, additive or antagonistic effects respectively). The conservative isobologram analysis further confirmed synergism between the two drugs, as CI values for each ED fall below the lines (Figure 3.4B). 58               Figure 3.4: Efficacy of drug interactions by combined treatment with BMS-911543 and DA against CD34+ CML cells. (A) Drug interactions for the combination of BMS-911543 with DA were assessed with drug exposure (BMS: 150 nM, 300 nM, 600 nM; DA: 75 nM, 150 nM, 300 nM; or DA + BMS) against CD34+ CML cells by viability assays after 72 hours of drug exposure. CI plots for BMS and DA were calculated using CalcuSyn software in an algebraic estimate. The combination of BMS plus DA showed synergistic activity (CI values <1) in CD34+ CML cells. (B) A conservative isobologram analysis for CD34+ CML cells indicated synergism between BMS and DA. 59  3.2.3 Combined Exposure of a Selective JAK2 Inhibitor BMS-911543 and TKIs Eliminates IM-insensitive CML LSCs and Their Progenitor Cells   To further determine if combined treatment of BMS-911543 and TKIs eliminates primitive LSCs and their progenitor cells from IM-nonresponders, I performed in vitro progenitor (CFC) and stem cell assays (LTC-IC). Combination treatment of BMS-911543 with IM, DA or NL demonstrated significant inhibition in colony growth of CD34+ cells compared to monotherapy alone (74-86% vs. 40-50% reduction, P<0.01, Figure 3.5A). Notably, the combination of BMS-911543 and a TKI almost completely inhibits erythroid-burst forming unit (BFU-E) colony formation compared to TKI alone (97-98% vs. 65-80%, P<0.03, Figure 3.5B). Granulocyte/macrophage-colony forming unit (CFU-GM) colonies were also more significantly reduced by the combination than single agents (45-60% vs. 20-40%, P<0.004, Figure 3.5C). Furthermore, LTC-IC stem cell assays showed that the more primitive cells were more significantly eliminated by combination treatments than single agents (3-6-fold, P<0.04, Figure 3.5D), indicating the potential benefit of combination therapy for targeting LSCs. Similar to the results obtained by apoptosis assay, the combination of BMS-911543 and TKIs has significantly less toxicity on CD34+ NBM cells than CD34+ CML cells (n=4, P<0.0001, Figure 3.2B). These results suggest that combination treatment with BMS-911543 and TKI is more effective in eliminating very primitive CML stem and progenitor cells from IM-nonresponders destined to develop TKI resistance.    60    Figure 3.5: Combined exposure of BMS-911543 and TKIs eliminates CML stem and progenitor cells from IM-nonresponders. (A-C) CD34+ CML cells (n=7) were plated in standard CFC assays plus BMS-911543 (300 nM), IM (5 µM), DA (150 nM) or NL (5 µM) alone or in combination. Colonies produced were counted after 14 days of incubation, and the numbers obtained were expressed as a percentage of values obtained in untreated cells to which only DMSO was added. The percentage of colonies containing erythroid-burst forming units (BFU-E) and granulocyte/macrophage-colony forming units (CFU-GM) are also presented (B & C). Data shown are mean ± SEM of measurements from seven individual patients. (D) CD34+ CML cells (n=3) were co-cultured with stromal cells and assayed for long term culture-initiating cells (LTC-ICs) in the presence of drug treatments including BMS-911543 (200 nM), IM (5 µM), DA (150 nM) or NL (5 µM) alone or in combination for two weeks. Total LTC-IC derived CFC numbers were determined from the LTCs harvested six weeks later and then expressed as a percentage of the LTC-IC CFC numbers obtained from cells in the absence of any added drug. 61  Data shown are mean ± SEM of measurements from three individual patients. P-values were calculated using a two-tailed paired Student’s t test.  3.2.4 Combined Treatment with the Selective JAK2 Inhibitor BMS-911543 and TKIs Significantly Enhances the Survival of Leukemic Mice To assess the ability of combination treatment to eliminate primitive BCR-ABL+ cells with in vivo leukemia propagating activity, Human BV173 cells, which have been shown to generate a lethal leukemia in NOD/SCID mice [49, 366], were utilized. BV173 cells (2.5x106) were intravenously injected into sub-lethally irradiated NSG mice, which were then treated with inhibitors (alone or in combination) or a vehicle control by oral gavage for two weeks. In the first pilot experiment, mice were treated with vehicle control (propylene glycol), BMS-911543 (15 mg/kg), IM (50 mg/kg), and IM (50 mg/kg) plus BMS-911543 (15 mg/kg) twice a day for two weeks by oral gavage two weeks after BV173 cells injection. The combination of IM plus BMS-911543 significantly enhanced survival of leukemic mice as compared with mice treated with single agents only (median survival of IM + BMS-911543 vs. IM or BMS-911543: 70 days vs. 53 days or 54 days, P<0.02, Figure 3.6A). Mice receiving combination treatment also had reduced weight loss as compared with mice treated with single agents (Fig 3.6A). I then investigated whether combining BMS-911543 with a more potent TKI, dasatinib (DA), might achieve better results in vivo. NSG mice (n = 5-6 mice per condition) injected with the same numbers of BV173 cells were treated with vehicle control, BMS-911543 (15 mg/kg), DA (15 mg/kg), and DA plus BMS-911543 once a day for two weeks by oral gavage. Significantly prolonged survival was observed in leukemic mice treated with DA plus BMS-911543 compared to DA alone (median survival of DA + BMS-911543 vs. DA: 96.5 days vs. 81 days, P=0.0007, Figure 3.6B), as well as significantly reduced weight loss.  62     Figure 3.6: A combination of BMS-911543 and TKIs significantly enhances survival of leukemic mice. CML BV173 cells (2.5×106 per mouse) were intravenously injected into sub-lethally cesium-irradiated NSG mice. Two weeks after transplantation, oral gavage treatment with or without inhibitors began and continued for two weeks. (A) Survival curve for leukemic mice (n=2-4 mice per group) treated with vehicle, BMS-911543 (15 mg/kg), IM (50 mg/kg), or IM (50 mg/kg) plus BMS-911543 (15 mg/kg) twice a day for two weeks (left panel). Body weights of mice in each treatment group were measured (right panel). (B) Survival curve for leukemic mice (n=5-6 mice per group) treated with vehicle, BMS-911543 (15 mg/kg), DA (15 mg/kg), or DA (15 mg/kg) plus BMS-911543 (15 mg/kg) once a day for two weeks (left panel). Body weights of mice in each treatment group were measured (right panel). Data shown are mean ± SEM. P-values were calculated using log-rank tests.  63  3.2.5 Combination Treatment with a Selective JAK2 Inhibitor BMS-911543 and Dasatinib Eradicates Infiltrated Leukemic Cells in Multiple Hematopoietic Tissues  A group of five mice transplanted with BV173 cells with or without drug treatment were sacrificed for analysis at 54 days post-transplant. I observed significantly enlarged hematopoietic organs, including spleen and liver, in mice treated with vehicle control or BMS-911543 alone but not in mice treated with DA and DA plus BMS-911543 (Figure 3.7A).  H&E staining revealed that mice treated with DA and DA plus BMS-911543 had no infiltration of leukemic cells to the spleen, whereas mice treated with vehicle or BMS-911543 alone had massive infiltrations of leukemic cells (Figure 3.7B). DA plus BMS-911543 treated mice had no infiltration of leukemic cells to the liver, while leukemic cells were detectable in DA-treated mice. In contrast, vehicle control and BMS-911543-treated mice were heavily infiltrated with leukemic cells (Figure 3.7B). IHC staining with a human CD19 antibody further confirmed that the infiltrating cells were indeed transplanted leukemic cells (Figure 3.7B). Although mice treated with either DA or DA plus BMS-911543 had fewer CD19+ cells in PB, BM, spleen, and liver than vehicle or BMS-911543-treated mice, the effect was more pronounced in mice receiving combination treatment (0.013% vs. 0.26%, 1.47% vs. 2.85%, 0.64% vs. 0.63%, and 14.3% vs. 56.6%, Figure 3.7D). Q-RT-PCR analysis further demonstrated statistically significant reduction in BCR-ABL transcript levels in mice treated with the combination compared to DA alone (BM: undetectable, P=0.0008; liver: 11-fold reduction, P=0.022, Figure 3.7C). Western blot analysis demonstrated high levels of phosphorylation and protein expression of BCR-ABL, p-STAT5 and p-CRKL in BM cells from vehicle control and BMS-911543-treated mice, but undetectable levels in mice treated with DA or DA plus BMS-911543 mice (Figure 3.7E).   64   65   Figure 3.7: Effects of oral treatment of BMS-911543 in combination with DA on the infiltration of leukemic cells into hematopoietic tissues of mice. At day 54 post-transplant, one mouse per treatment group, including no injection control (Ctrl), vehicle (no treatment), BMS-911543, DA and DA plus BMS-911543, was sacrificed and tissues were analyzed. (A) Spleen (top panel) and liver (bottom panel) weight of mice from each treatment group. (B) Hematoxylin and eosin (H&E) histology staining of spleen and liver from each treatment group (top two panels). Immunohistochemical (IHC) staining with CD19 antibody in spleen and liver (bottom two panels). (C) BCR-ABL transcript levels measured by Q-RT-PCR normalized to GAPDH. Data shown are mean ± SEM of measurements from three independent experiments.  P-values were calculated using a two-tailed paired Student’s t test. (D) FACS profiles of engrafted human CD19+ cells detected in peripheral blood (PB), bone marrow (BM), spleen, and liver. Y-axis represents CD19, and X-axis represents forward-scattered lights (FSC) (E) Western blot analysis was performed using protein lysates extracted from BM cells from each treated group and probed with specific antibodies as indicated.  Ctrl = no BV173 cell injection control; ND = not detectable.  At day 70 post-transplant, the difference in infiltration of leukemic cells in hematopoietic organs between mice injected with DA alone or DA plus BMS-911543 was more pronounced. Enlarged spleens and livers were observed in mice treated with DA alone, but not in mice treated with DA plus BMS-911543 (Figure 3.8A). H&E and IHC staining of spleen and liver revealed increased infiltration of leukemic cells in DA or vehicle-treated mice, but very low levels in DA plus BMS-911543-treated mice (Figure 3.8B). In addition, mice receiving combination treatment showed significantly reduced engraftment levels in PB, BM, and spleen compared to mice treated with DA alone (0.2% vs. 6.1%, 1.2% vs. 26%, and 2.5% vs. 52%, Figure 3.8C). It was observed that relatively higher levels of engrafted cells occurred in the liver as compared to other tissues under the combination treatment. Finally, BCR-ABL transcript levels in mice treated with DA plus BMS-911543 were much lower than mice treated with DA alone (BM: 20-fold reduction, P=0.0005; spleen: 32-fold reduction, P=0.006; liver: 2-fold reduction, P=0.0005; Figure 3.8D). Western blot analysis further showed highly increased levels of phosphorylation and protein expression of BCR-ABL, p-STAT5 and p-CRKL in mice treated with DA alone 66  compared to DA plus BMS-911543 (Figure 3.8E). Taken together, these results suggest that the oral combination treatment is much more effective than either agent alone in eliminating primitive human CML cells able to generate aggressive leukemia in mice, with significantly enhanced survival of leukemic mice.  67    Figure 3.8: Oral treatment of BMS-911543 in combination with DA significantly eliminates infiltrated leukemic cells in hematopoietic tissues 70 days post-transplant. At day 70 post-68  transplant, one mouse per remaining treatment group, including vehicle, DA, and DA plus BMS-911543 was sacrificed and tissues were analyzed. (A) Spleen and liver weights of mice from each treatment group. (B) H&E histology staining of spleen and liver from each treatment group (top two panels). IHC staining with CD19 antibody in spleen and liver tissues (bottom two panels). (C) FACS profiles of engrafted human CD19+ cells detected in PB, BM, spleen, and liver. (D) BCR-ABL transcript levels measured by Q-RT-PCR normalized to GAPDH. Data shown are mean ± SEM of measurements from three independent experiments. P-values were calculated using a two-tailed paired Student’s t test.  (E) Western blot analysis was performed using protein lysates extracted from BM cells from each treatment group and probed with specific antibodies as indicated.   3.3 Discussion In this study, I provide pre-clinical evidence that combination treatment with a selective JAK2 inhibitor (BMS-911543) and TKI more effectively eradicates IM-insensitive BCR-ABL+ cells and primary CML stem/progenitor cells compared to either BMS-911543 or TKI alone, suggesting a potential new treatment option for patients with CML. Specifically, I examined whether combination treatment might be a much better strategy for CML patients who are unlikely to respond to TKI monotherapies. These patients might benefit from such a treatment, which could more effectively reduce the CML stem cell burden, avoiding the development of TKI-resistance and disease relapse. My study on CD34+ treatment-naive IM-nonresponder cells supports this hypothesis. I demonstrated that BMS-911543, at clinically achievable concentrations, in combination with a TKI markedly reduced the output of progenitor colonies, and eradicated their more primitive stem cells in vitro (Figure 3.5). The combination was also more effective at reducing p-CRKL and p-STAT5 activities in these cells at the molecular level (Figure 3.3). In addition, the combination treatment displays synergism, suggesting that simultaneously targeting BCR-ABL and JAK2 activities in CML stem/progenitor cells is indeed more effective than using single agents (Figure 3.4).   69  Since transplantation of primary CML stem/progenitor cells is not able to generate leukemia in immunodeficient mice [49], I screened several BCR-ABL+ human cell lines to determine which could generate leukemia in vivo and found that human BV173 cells, but not K562 cells, are capable of infiltrating into multiple hematopoietic organs and generating a lethal leukemia in NSG mice. Thus, this is a useful model for examining efficacy of drug treatment in BCR-ABL+ human cells in vivo. Indeed, in vivo oral administration of BMS-911543 and TKI for two weeks significantly eliminated infiltrated leukemic cells to a greater extent in multiple hematopoietic tissues than TKI monotherapy (Figure 3.6 to Figure 3.8). A statistically significant prolonged survival of treated mice was obtained using the combination treatment, whereas IM or BMS-911543 alone was ineffective at preventing disease development. Compared with IM, dasatinib is not only a dual SRC-ABL inhibitor, but it is also 300-fold more potent in inhibiting ABL kinase in vitro, and induces much greater and faster rates of major molecular response in patients. [97, 367, 368]. Therefore, treatment with the potent TKI dasatinib alone appears to be more effective at prolonging disease survival than IM alone, and the combination of BMS-911543 and dasatinib even more significantly enhances survival of leukemic mice and prevents infiltration of leukemic cells in multiple hematopoietic tissues. Our study suggests a new strategy for treating CP-CML patients at risk of developing TKI resistance and for targeting more aggressive leukemic cells present in later stage CML patients, which are routinely only poorly responsive to TKI monotherapy [99, 100].  Consistent with my study, it has been reported that disrupting the BCR-ABL/JAK2-STAT5 network eliminates BCR-ABL-transduced cells and primitive CD34+ CML cells; JAK2 inhibitors have been shown to sensitize CML cells to TKIs in the BM microenvironment [156, 238, 369]. Knockdown of JAK2 using a shRNA approach reduced BCR-ABL expression, which 70  further down-regulated STAT5 activity [239], and suppression of JAK2 also reduced protein levels of β-catenin protein [245], possibly through activation of GSK-3β [237]. In addition, JAK2 siRNA knockdown reduced BCR-ABL-mediated c-Myc expression [236, 237]. On the other hand, it was reported that BCR-ABL directly phosphorylates STAT5 in BCR-ABL-transduced cells [222], and that Jak2 is not required for initial myeloid transformation and leukemia maintenance in a Jak2 conditional knockout model [240]. This discrepancy could be due to the different model systems and cell types used in these studies. For example, it is known that BCR-ABL-transduced murine BM cells, transduced using a retroviral transduction model, express much higher levels of BCR-ABL than physiologically primary CML cells from patients’ blood or BM samples and that the role of JAK2 in enhancing cell survival might not be required in these BCR-ABL over-expressing cells, rendering JAK2 dispensable. Indeed, increasing evidence indicates that the canonical JAK2/STAT5 pathway is critical for primary CML stem/progenitor cells, which rely on cytokine-activated JAK2/STAT5 signaling in addition to BCR-ABL signaling [156, 241, 365, 369, 370]. It has been reported that activation of BCR-ABL stimulates the production of cytokines, including IL-3, G-CSF and GM-CSF, which bind to their cognate receptors and contribute to TKI resistance of CML stem/progenitor cells through activation of the JAK2/STAT5 pathway [156, 370]. It was also reported that BCR-ABL interacts with the IL-3/GM-CSF receptor, which leads to the downstream activation of JAK2 [241], and that blockage of JAK2-mediated extrinsic survival signals using JAK2 inhibitors restores sensitivity of CML cells to TKIs [369]. Research from my laboratory and others has shown that destabilization of the BCR-ABL/JAK2 network with JAK2 inhibitors and TKIs dissociates their physical interaction and sensitizes CML LSCs to TKIs. These effects are not observed in the same cells treated with TKIs alone or even with combination of TKIs [49, 237-239]. 71  Consequently, these findings provide compelling evidence that JAK2/STAT5 signaling represents an important node supporting CML LSC growth and survival. Targeting BCR-ABL-JAK2 cooperative activities may reverse the innate TKI-resistance phenotype of CML LSCs and sensitize them to TKI.   BMS-911543 is a highly selective JAK2 inhibitor which has an IC50 of 1.1 nM with little effects on JAK1 (356 nM), JAK3 (73 nM), or TYK2 (66 nM), respectively [251]. It is highly specific; no other non-JAK family targets with an IC50 of less than 100 nM have been reported [248]. It is currently being investigated in a Phase 1/2a clinical trial in myelofibrosis (ClinicalTrals.gov identifier: NCT01236352) [249]. It thus provides a rational basis for a therapeutic combination strategy applying BMS-911543 and a TKI in CML. I found that BMS-911543 alone had limited inhibitory effects on primary CML stem/progenitor cells when its concentration was non-toxic to primitive healthy BM cells. Similarly, no significant changes in phosphorylation levels of the BCR-ABL kinase substrate CRKL were observed when these cells were treated with BMS-911543 monotherapy. This suggests specific inhibition of JAK2 downstream of BCR-ABL, and its effects can be significantly enhanced by combining with a TKI. This observation also reduces concerns of off-target effects of BMS-911543 on other kinases and signaling proteins that result in toxicity and have been reported for several other JAK2 inhibitors [240, 248, 249, 252, 254]. Taken together, this pre-clinical study provides strong scientific rational for the continued investigation of JAK2 inhibition as a therapeutic strategy in CML and demonstrates the potential merit of combining a highly JAK2-specific inhibitor with potent TKIs to specifically target CML stem/progenitor cells, especially in CML patients likely to develop TKI-resistance if treated with TKI monotherapy.   72  Chapter 4: Identification and Characterization of Novel MicroRNA Biomarkers and Candidate Target Genes in CML Stem/progenitor Cells    4.1 Introduction Patients with chronic myeloid leukemia (CML) contain a neoplastic clone that is sustained by rare hematopoietic stem cells carrying a BCR-ABL fusion gene. The BCR-ABL oncoprotein has constitutively elevated tyrosine kinase activity that drives disease development [4, 10]. Introduction of Imatinib mesylate (IM) and other ABL tyrosine kinase inhibitor (TKIs) therapies have had remarkable effects on treatment of early phase CML [88, 89, 94, 98, 371]. However, TKI monotherapies are not curative, most patients harbor residual leukemic cells, and disease usually recurs if TKI therapy is discontinued [102, 103]. Early relapse and IM resistance have been observed in 15% chronic phase (CP) patients and up to 40% in accelerated phase (AP) patients, once progresses to blastic phase (BP), IM has very little effect on survival of patients [107]. Allogeneic transplant thus remains the only curative therapy. However, the mortality and morbidity risks associated with the procedure [86], its restrictions to younger CP CML patients [87], and the lack of matched donors [1, 3, 4] question its feasibility. Extensive investigations have revealed that primary CML stem cells and their progenitors are insensitive to TKIs and hence remain a reservoir of cells able to generate resistant and or more aggressive subclones that contributes to disease progression and drug resistance [99, 100, 104-106]. Therefore, novel approaches to directly target CML stem/progenitor cells or to identify molecular biomarkers to predict patient response to TKIs are clearly needed.  MicroRNAs (miRNAs) are small, non-coding, single-stranded RNAs of 18-25 nucleotides that control gene expression by destabilizing targeted transcripts and inhibiting their 73  translation [256]. They play a key role in regulating multiple biological processes, including cell proliferation [372], survival [373], cell differentiation [374] and hematopoiesis [375]. Aberrant expression of miRNAs has been implicated in many human diseases, including both solid and hematologic malignancies [333, 334]. It has been reported that expression of let-7 miRNA family members were down-regulated and involved in lung, colon, breast, ovarian, and stomach cancers by targeting oncogenes KRAS, NRAS, CDK6, and MYC [295, 298, 303, 376-378]. miR-155, on the other hand, was found to be up-regulated and involved in high risk CLL, AML, lung, colon, and breast cancers by targeting tumour suppressors SHIP1 and CEBPB [292-300]. Besides their direct roles in cancer development, miRNAs can also act as diagnostic and/or prognostic biomarkers to predict cancer patient outcome or survival. For example, high miR-155 and low let-7a expression levels correlate with poor disease outcome in lung cancer [299]. In CML, several groups have utilized miRNA expression profiling or target gene predictions to identify both the deregulated miRNAs that directly target BCR-ABL (e.g. miR-203, miR-29b, miR-30e, miR-138, and miR-320a [314-318]) and the deregulated miRNAs that are under regulation of BCR-ABL (e.g. miR-17-92 cluster, miR-150, miR-130a/b, miR-451, miR-486, miR-30a, and miR-328 [319-326]). Other studies have involved miRNA expression profiling in CML cell lines or mononuclear cells obtained from patients at different stages of diseases or different TKI response status [321, 324, 327-331]. However, it has been difficult to reach a consensus on the miRNA signatures, due to different biological materials and microarray platforms used in these studies. Since CML is a stem cell disease, and the TKI resistance clones reside in the stem cell compartment, studies in CML should focus on more primitive cells to elucidate disease mechanisms. However, to date, only two studies have showed miRNA expression profiles in CD34+ cells using a limited number of patient samples [324, 327]. 74  Therefore, how miRNAs contribute to CML pathogenesis and drug resistance still remains poorly understood. In this study, deep sequencing of transcriptome (RNA-seq) was performed to create miRNA expression profiles in CD34+ CML cells obtained from IM-responders and IM-nonresponders in comparison to normal bone marrow CD34+ cells. Differentially expressed miRNAs were subsequently validated in additional normal and CML patient samples using a high-throughput TaqMan qPCR system. This analysis demonstrated that expression of miR-185 and miR-340 are significantly reduced in CD34+ CML cells from TKI-nonresponders vs. TKI-responders. I then performed integrative analysis of the miRNA profiles, and the gene expression profiles obtained from strand specific RNA sequencing (ssRNA-seq) using bioinformatics target prediction to identify potential target genes. Following extensive functional screening on selected miRNA candidates, my study discovered that restored expression of miR-185 by lentiviral-mediated overexpression in CML cell lines and in CD34+ TKI-nonresponder cells significantly impairs survival of these cells and sensitizes them to TKI treatment both in vitro and in vivo. The target genes of miR-185 were also validated to demonstrate the biological relevance of miR-185 in CML. Finally, significant restoration of miR-185 expression level in some CML patients after nilotinib (NL) treatments was observed, suggesting its potential to predict clinical response to TKI therapy. These findings have uncovered the biological significance of miR-185 in CML pathogenesis and drug resistance, its potential as a therapeutic target for combination treatment with TKIs and as a predictive biomarker for CML patients in response to TKI therapy.  75  4.2 Results 4.2.1 Identification of Differentially Expressed miRNAs in CD34+ CML Cells To identify differentially expressed miRNAs in CD34+ CML stem/progenitor cells that might serve as potential biomarkers and/or therapeutic targets, Illumina deep sequencing was performed to create miRNA expression profiles of highly purified CD34+ cells obtained from six CML patients at diagnosis. Three of the patients were classified retrospectively, after imatinib (IM) therapy, as IM-responders and three as IM-nonresponders. CD34+ cells isolated from three normal bone marrow (NBM) samples were used as controls. Bioconductor DESeq2 analysis revealed 66 differentially expressed miRNAs between CML and NBM samples with Benjamini–Hochberg-adjusted P-value < 0.05 from a total of 1,594 miRNAs sequenced (31 are up-regulated and 35 are down-regulated in CD34+ CML cells, Figure 4.1A and Table 4.1, where the first three columns represent average absolute read counts, fold change relative to NBM, and adjusted p-values, respectively). Interestingly, 26 differentially expressed miRNAs were identified in CD34+ cells between IM-responders and IM-nonresponders (P-value < 0.05, 7 are up-regulated and 19 are down-regulated in IM-nonresponders, Figure 4.1B and Table 4.2). An MA (Log ratio (M) versus mean average (A) expression) plot was generated to visualize the distribution of the data comparing NBM and CML samples with differentially expressed miRNAs (Figure 4.1A). A heatmap also shows fold changes of up-regulated or down-regulated miRNAs between CML vs. NBM samples by unsupervised dendrogram analysis. Similar analyses were performed on differentially expressed miRNAs between IM-responders and IM-nonresponders (Figure 4.1B).    76   miRNA Sequencing mean expression Sequencing  fold change (log2) p-value adjusted Validation  fold change (log2) p-value  (NBM vs.CML) p-value adjusted p-value  (R vs. NR) p-value adjusted hsa-mir-10b 5023.33526 -5.34230 1.03E-13 -4.50616 0.0661 0.0941 0.9999 0.9999 hsa-mir-139 263.08830 -4.24850 6.86E-10 -4.46536 0.0001 0.0007 0.8454 0.9999 hsa-mir-708 9.33923 -5.72087 1.33E-09 -1.24581 0.0120 0.0257 0.7756 0.9999 hsa-mir-22* 80577.47834 -2.69482 6.31E-09      hsa-mir-4491* 12.66969 -5.28585 9.94E-09          hsa-mir-151a 7699.05312 -2.82219 1.17E-08 -2.82822 0.0137 0.0267 0.1977 0.5627 hsa-mir-199a-2* 1603.14553 -2.59926 1.17E-08          hsa-mir-224* 20.15919 -4.25189 3.29E-07      hsa-mir-628 271.88739 -2.87807 5.42E-05 -3.50727 0.0125 0.0257 0.0878 0.3610 hsa-mir-1277§ 10.05632 -3.82857 0.00014      hsa-mir-199b* 2377.25216 -1.78272 0.00014          hsa-mir-375* 12.65896 -2.46313 0.00022      hsa-mir-584 5752.23065 -2.38004 0.00022 -1.33911 0.6235 0.6785 0.6571 0.9999 hsa-mir-106a 2222.11666 1.28705 0.00023 0.65382 0.0006 0.0028 0.4847 0.9460 hsa-mir-19b-1 260.32600 1.93676 0.00023 0.93849 0.0489 0.0724 0.7220 0.9999 hsa-mir-30a 2225.65144 -2.73408 0.00044 0.16100 0.7278 0.7480 0.4858 0.9460 hsa-mir-34a* 241.75427 2.66336 0.00044          hsa-mir-452 4.94564 -3.15943 0.00088 -3.21385 0.0005 0.0026 0.0619 0.3610 hsa-mir-1180* 106.73998 1.91018 0.00090          hsa-mir-148b* 414.75698 -1.02365 0.00154      hsa-mir-92b# 394.16309 1.50715 0.00154          hsa-mir-17 30592.00832 1.50198 0.00190 0.84210 0.0001 0.0007 0.8330 0.9999 hsa-mir-551b* 171.17827 -2.14676 0.00192          hsa-mir-23b 1787.69684 -1.60245 0.00210 0.99153 0.3975 0.4645 0.9611 0.9999 hsa-mir-6087# 1001.96222 2.49104 0.00217         hsa-mir-152 191.82108 -2.17390 0.00221 -0.59424 0.3929 0.4645 0.6805 0.9999 hsa-mir-135a-1* 19.61824 1.96616 0.00264         hsa-mir-412* 14.52354 -2.59728 0.00282      hsa-mir-5187§ 12.25999 -2.23888 0.00282          hsa-mir-20a 20075.88371 1.82374 0.00300 1.43521 0.0004 0.0025 0.9612 0.9999 hsa-mir-3648# 48.82058 2.51340 0.00370         hsa-mir-20b# 3767.84150 1.63264 0.00407      hsa-mir-2355* 460.82106 -1.85097 0.00481         hsa-mir-4446# 7.44276 -2.76674 0.00510      hsa-mir-378a* 4334.36921 1.04078 0.00608          hsa-mir-92a-2 714185.55300 1.52210 0.00672 0.85315 0.0071 0.0187 0.0444 0.3610 hsa-mir-3150b* 54.87551 -1.83220 0.00765          Table 4.1. Differentially expressed miRNAs in CD34+ cells between NBM and CML 77  miRNA Sequencing mean expression Sequencing  fold change (log2) p-value adjusted Validation  fold change (log2) p-value  (NBM vs.CML) p-value adjusted p-value  (R vs. NR) p-value adjusted hsa-mir-744 643.18377 -1.07630 0.00848 -0.63361 0.0737 0.0998 0.9872 0.9999 hsa-mir-3074* 217.88828 -1.00856 0.01148          hsa-mir-1271 61.76569 1.41385 0.01275 -0.50456 0.7683 0.7683 0.3027 0.7467 hsa-mir-4473# 14.56872 2.15847 0.01443          hsa-mir-146b 17183.65000 1.51444 0.01476 0.83071 0.0165 0.0305 0.5700 0.9999 hsa-mir-155 24984.59336 1.51661 0.01513 1.22204 0.0028 0.0094 0.0824 0.3610  hsa-mir-365a# 37.24056 1.36634 0.01513      hsa-mir-552# 2.60584 -3.17163 0.01513          hsa-mir-363 854.27178 1.26926 0.01693 1.88885 0.0001 0.0007 0.9197 0.9999 hsa-mir-365b 38.14708 1.28173 0.01980 -3.60628 0.0079 0.0187 0.9283 0.9999 hsa-mir-4521 57.53061 2.20947 0.01980 1.72833 0.0033 0.0102 0.4156 0.9045 hsa-mir-18a 432.98641 1.34042 0.02019 0.95279 0.0022 0.0081 0.0407 0.3610 hsa-mir-19b-2 2462.06444 1.73960 0.02057 0.93849 0.0489 0.0724 0.7220 0.9999 hsa-mir-185 737.40614 -1.11843 0.02079 -1.32569 0.0081 0.0187 0.0009 0.0333 hsa-mir-660 250.05706 1.44106 0.02079 -1.03515 0.4017 0.4645 0.1162 0.4279 hsa-mir-200b* 33.10156 1.59077 0.02422          hsa-mir-143 4454.89599 -1.55695 0.02826 -3.06319 0.0362 0.0609 0.0783 0.3610 hsa-mir-6513# 4.21578 -2.58072 0.02826          hsa-mir-885# 5.70528 2.45494 0.02895      hsa-mir-4781* 4.71521 -1.91809 0.02915          hsa-mir-6502§ 3.99144 -2.37375 0.02915      hsa-mir-1296# 43.86820 1.50460 0.03047          hsa-mir-4659a 5.82163 2.65871 0.03047 0.88425 0.0755 0.0998 0.1272 0.4279 hsa-mir-1249* 17.91292 -1.50574 0.03620          hsa-mir-32 112.76721 1.24854 0.03726 1.76536 0.2983 0.3806 0.1818 0.5606 hsa-mir-3920# 6.60191 -2.21149 0.03779          hsa-mir-4449# 21.46611 1.88711 0.03909      hsa-mir-141* 62.72660 1.24840 0.04947          hsa-mir-26a-1 113.02784 -1.58598 0.04990 2.25066 0.0190 0.0335 0.0582 0.3610 hsa-mir-3676 36.61823 1.31623 0.06130 0.31330 0.4392 0.4924 0.5233 0.9681 hsa-mir-192 3618.16688 1.00624 0.06939 2.69655 0.0001 0.0007 0.8065 0.9999 hsa-mir-16-1 10819.02573 -0.87024 0.08011 -0.85616 0.0001 0.0007 0.2461 0.6504 hsa-mir-92a-1 27807.95151 1.28520 0.08170 0.85315 0.0071 0.0187 0.0444 0.3610 hsa-mir-30e 75645.18624 -0.56835 0.09779 2.28063 0.0012 0.0049 0.3378 0.7812 hsa-mir-342 1437.82306 1.17879 0.09862 -0.67514 0.6535 0.6908 0.9301 0.9999 hsa-mir-191 13596.63876 -0.76726 0.09924 0.13209 0.0417 0.0671 0.9999 0.9999          * TaqMan qPCR assays are not optimal       § Probes for TaqMan qPCR assays are not available       # TaqMan qPCR Assays are not performed        78    miRNA Sequencing mean expression Sequencing  fold change (log2) p-value adjusted Validation  fold change (log2) p-value  (NBM vs.CML) p-value adjusted p-value  (R vs.NR) p-value adjusted hsa-mir-3607 348.30007 2.58397 6.98E-09 1.92358 0.1857 0.3343 0.7786 0.9535 hsa-mir-339 1939.04564 -2.51376 3.48E-07 -0.10956 0.6586 0.7903 0.6875 0.9519 hsa-mir-340 844.09310 -1.62569 2.98E-04 -1.37167 0.8521 0.9022 0.0010 0.0180 hsa-mir-100* 555.21982 -2.61735 3.29E-03      hsa-mir-4536-1 *18.52723 -2.93969 3.36E-03         hsa-mir-324 849.12071 -1.17179 5.87E-03 0.12818 0.0003 0.0018 0.8819 0.9656 hsa-mir-505* 1207.26306 -1.27679 1.23E-02          hsa-mir-598 789.71616 -1.91774 1.37E-02 -0.91496 0.0951 0.1902 0.3842 0.8750 hsa-mir-130b 1936.63099 -0.93811 1.38E-02 -1.11268 0.0001 0.0018 0.9656 0.9656 hsa-mir-145 319.60937 -1.54178 0.01377 -0.03163 0.0002 0.0018 0.6404 0.9519 hsa-mir-345 1165.85729 -1.37764 0.01466 -0.16394 0.4837 0.6808 0.7946 0.9535 hsa-mir-323b* 38.24995 2.33193 0.01799      hsa-mir-18a 660.92985 -1.08832 0.01943 -0.30616 0.0022 0.0099 0.4268 0.8750 hsa-mir-4662a* 162.01377 -1.36746 0.01961      hsa-mir-26a-1 77.54816 1.14130 0.02158 0.16438 0.0190 0.0570 0.0582 0.5238 hsa-mir-30a 827.44135 1.86149 0.02230 0.19473 0.7278 0.8188 0.4858 0.8750 hsa-mir-4521 95.29840 -1.93887 0.02251 0.08036 0.0033 0.0119 0.4156 0.8750 hsa-mir-2355* 280.82309 -1.12671 0.02295      hsa-mir-342 2150.78071 -1.21372 0.02295 0.75000 0.6535 0.7903 0.9301 0.9656 hsa-mir-3173# 71.05136 -1.52278 0.02548      hsa-mir-223 56427.10282 -1.49885 0.03869 0.32733 0.4917 0.6808 0.4861 0.8750 hsa-mir-605# 29.46873 2.36840 0.03869      hsa-mir-337# 118.91882 2.17386 0.04284          hsa-mir-125b-2 50.61790 1.49710 0.04785 0.86176 0.0365 0.0939 0.1487 0.6692 hsa-mir-660 386.38087 -1.05681 0.04785 -0.67378 0.4017 0.6573 0.1162 0.6692 hsa-mir-874# 917.11593 -1.60136 0.04785               * TaqMan qPCR assays are not optimal       # TaqMan qPCR Assays are not performed              Table 4.2. Differentially expressed miRNAs between IM-responders and nonresponders 79   80  Figure 4.1: Identification of differentially expressed miRNAs in CD34+ CML cells. (A) Deseq2 package was used to identify differentially expressed miRNAs comparing normal bone marrow (NBM) and CML samples and MA (Log ratio (M) versus mean average (A) expression) plots were constructed to show the distribution of differentially expressed mRNAs (left panel). The Y axis represents the average expression of each individual miRNA, and the X axis represents the fold change relative to NBM. The red dots represent the differentially expressed miRNAs with adjusted P-values < 0.05. Gplots package was used to plot Heatmap based on the differentially expressed miRNAs with adjusted P-values < 0.05. (right panel). Black bar represents NBM, blue bar represents IM-responders, and red bar represents IM-nonresponders. (B) Similar analyses were performed for the differentially expressed miRNAs between IM-responders and IM-nonresponders.  4.2.2 Validation of Differentially Expressed miRNAs in CD34+ CML Cells The sequencing data was next validated in CD34+ cells obtained from additional 11 normal individuals, 12 IM-responders, and 10 IM-nonresponders using a high-throughput TaqMan qPCR microfluidics device. A total of 66 differentially expressed miRNA candidates were selected for validation studies based on an adjusted P-value < 0.05 found in CD34+ CML cells vs. NBM or IM-responder vs. IM-nonresponder groups. A number of miRNAs were not validated due to no available probes for TaqMan assay or mean expression below 50 sequencing reads (e.g. miR-3648, Table 4.1 and Table 4.2). Additional miRNAs, including oncomiRNA miR-92a and tumour suppressor miR-16, were also selected for validation study based on literature search for their potential biological relevance in CML, although their adjusted P-values were between > 0.05 and < 0.1 (Table 4.1).  To establish a reliable validation system for the TaqMan qPCR method based on a new, multiplexing system, quality control of each TaqMan assay was performed by examining whether each miRNA assay’s amplification curve falls into a characteristic exponential phase, followed by a linear and a plateau phase. The amplification curves at exponential phase were converted to raw Ct (cycle threshold) values. The raw Ct values were then quantile normalized, nonparametric Mann–Whitney U tests were performed to compare unpaired samples, and the 81  deregulated miRNAs with P-values smaller than 0.05 after multiple hypothesis testing correction were identified. Twenty-one differentially expressed miRNAs were successfully validated in CD34+cells from NBM vs. CML samples (adjusted P<0.05, Figure 4.2 and Table 4.1, last five columns). Notably, only miR-185 and miR-340 were identified as differentially expressed miRNAs between IM-responders and IM-nonresponders (P<0.05, Figure 4.3 and Table 4.2, last five columns). miR-185 was not identified in deep sequencing due to limited sample size to reach statistical significance (Table 4.2). Interestingly, the fold change of differentially expression miRNAs (up- or down-regulation) from the TaqMan qPCR method correlated well with the fold change determined by the sequencing method (Table 4.1 and Table 4.2). The validated miRNAs such as increased levels of oncomirs miR-155 and miR-17-92 clusters, and a decreased level of tumor suppressors miR-145 and miR-16 are consistent with reported literature and provide confidence in the validity of a powerful high-throughput and multiplexing qPCR microfluidics system. 82   Figure 4.2: TaqMan qPCR validation of differentially expressed miRNAs in CD34+ cells between NBM and CML. Differentially expressed miRNAs identified from RNA-seq analysis  were validated through a TaqMan qPCR microfluidics device using CD34+ cells obtained from NBM samples (n=11), IM-responders (n=12), and IM-nonresponders (n=10). The raw Ct values obtained from 96 multiplexing microfluidics device were organized using HTqPCR package, and were normalized using quantile method from limma package. Nonparametric Mann–Whitney U test was performed to compare unpaired samples. Twenty differentially expressed miRNAs were successfully validated. Each data point represents quantile normalized Ct values relative to CD34+ NBM cells. All comparisons shown above are statistically significant (Benjamini–Hochberg-adjusted P-value < 0.05). 83        Figure 4.3: TaqMan qPCR validation of differentially expressed miRNAs in CD34+ cells between IM-responders and IM-nonresponders. Differentially expressed miRNAs identified from RNA-seq analysis were validated through a TaqMan qPCR microfluidics device using CD34+ cells obtained from  IM-responders (R, n=12), and IM-nonresponders (NR, n=10). The raw Ct values obtained from 96 multiplexing microfluidics device were organized using HTqPCR package, and were normalized using quantile method from limma package. Nonparametric Mann–Whitney U test was performed to compare unpaired samples. Each data point represents quantile normalized Ct values relative to CD34+ NBM cells. Two differentially expression miRNAs were successfully identified with statistically significant (Benjamini–Hochberg-adjusted P-value < 0.05).  4.2.3. In vitro Functional Screening of the Selected miRNAs in CML Cells Using a Transient Transfection System To elucidate the biological significance of the differentially expressed miRNAs identified in CD34+ CML stem/progenitor cells, I performed an initial screen of eight miRNAs by transiently transfecting K562 cells with chemically synthesized miRNA mimics for down-regulated miRNAs, including, miR-139-5p, miR-145-5p, miR-185-5p, miR-340-5p, miR-452-5p, miR-628-3p, and miR-708-5p; and by transfecting with miRNA inhibitor for one of the overexpressed miRNAs, miR-146b-5p. Based on our 21 validated deregulated miRNAs, these eight miRNAs were carefully selected for their novelty, highly deregulated expression levels in CML stem/progenitor cells, differentially expressed between IM-responders and IM-nonresponders, interesting target genes (cancer or CML related, see section 4.2.8), and their expression levels were significantly restored or no changes in patient samples treated with NL (see section 4.2.10). TaqMan qPCR analysis confirmed increased miRNA expression in cells 84  transfected with each miRNA mimic compared with cells transfected with scrambled control (SiC, > 500-fold), or reduced miR-146b expression in cells transfected with a miRNA inhibitor (> 2-fold, Figure 4.4A). The use of miR-145, miR-185, and miR-708 mimics moderately reduced growth of K562 cells determined by trypan-blue exclusion assay (Figure 4.4B). The results from viability assay were further validated by thymidine incorporation proliferation assay, in which miR-145 and miR-185 mimics showed a modest 20% reduction 48 hours post transfection,  compared to SiC (P < 0.01, Figure 4.4C). These results suggest that these miRNAs may play a role in the growth and proliferation of CML cells. 85   Figure 4.4: Functional screening of eight deregulated miRNAs in K562 cells. (A) Synthetic miRNA mimics or anti-miRNA inhibitor were transiently transfected to K562 cells. miRNA expression was quantified using TaqMan microRNA assay 48 hours post transfection. Data shown are mean ± SEM of measurements from technical duplicates. (B) Growth curves of K562 cells transfected with miRNA mimics or inhibitor. Cells were counted at the indicated time points after transfections using trypan blue dye exclusion method, and the numbers obtained were expressed as fold change relative to day 0. (C) Proliferation assay of K562 cells transfected with miRNA mimics or inhibitor. The amount of tritiated [3H]-thymidine was measured 48 hours post transfection. (B, C) Data shown are mean ± SEM of measurements from two to three 86  independent experiments. P-values were calculated using a two-tailed unpaired Student’s t test (*P < 0.05; **P < 0.01; ***P < 0.001).  4.2.4. In vitro Functional Screening of the Selected miRNAs Identified miR-185 as a Candidate Target in CML Using a Lentiviral-mediated Eexpression System    To further investigate the biological significance of specific miRNAs in CML cells, four miRNAs (miR-145, miR-185, miR-628, and miR-708) were selected for additional studies, based on my initial functional screening. A new, third-generation of lentiviral vector (from Dr. Humphries lab) was used to stably express these four miRNAs.  Precursor miRNA (pre-miRNA) sequence along with its upstream and downstream 100 bp flanking sequence were synthesized and cloned into pRRL lentiviral vector between BsrGI and SalI sites (Figure 4.5A). The pRRL vector utilizes the murine spleen focus-forming virus (SFFV) promoter to drive the transcription of GFP and candidate miRNAs simultaneously so that the expression level of GFP correlates with candidate miRNA expression. Three days after transduction into K562 cells, more than 95% GFP+ cells were observed in transduced cells by FACS analyses. Empty vector control, pRRL, was also transduced into K562 cells with a similar level of GFP+ cells as one of experimental controls (Figure 4.5B). TaqMan qPCR demonstrated increased expression of these miRNAs in the transduced cells compared with cells transduced with pRRL by at least 20-fold (P < 0.05, Figure 4.5C). Further studies to investigate biological functions of these selected miRNAs in primary CD34+ CML cells or other cell lines were conducted using the same expressing vectors. 87   88  Figure 4.5: Lentiviral transduction of selected miRNAs in CML cells. (A) Schematic representation of the miRNA expressing vector. Precursor miRNA sequence along with its upstream and downstream 100 bp flanking sequence were synthesized and cloned into pRRL lentiviral vector between BsrGI and SalI sites proceeded by GFP. (B) Representative FACS profiles indicating the percentage of GFP+ cells detected 48 hours post transduction in K562 cells using 1:20 dilution of concentrated virus. (C) TaqMan microRNA assay was used to quantify miRNA expression 72 hours post introduction in K562 cells transduced with miRNA expressing vectors and pRRL vector (pRRL). Data shown are mean ± SEM of measurements from three independent experiments. P-values were calculated using a two-tailed unpaired Student’s t test (*P < 0.05; **P < 0.01).  In order to identify which of the four miRNA candidates are critical drivers involved in regulating leukemic cell growth and TKI response/resistance, I transduced each miRNA expressing vector into three different CML cell lines: K562, IM-resistant K562 (K562R), and UT7-BCR-ABL (UT7 B/A) cells. K562 was derived from a BC CML patient carrying P210 BCR-ABL [336]. K562R was derived from K562 culturing in a low IM concentration condition and does not carry BCR-ABL kinase mutations [337]. UT7 B/A was a leukemia cell line transduced with a retroviral vector carrying P210 BCR-ABL [339].  I first monitored the growth curve of the transduced cells in regular culture conditions. Similar to my miRNA mimics studies (Figure 4.4B), overexpression of miR-145, miR-185, and miR-708 resulted in a modest reduction in cell growth in K562 cells (Figure 4.6A). The reduction was not obvious in K562R or UT7 B/A cells. In the presence of a low dose of IM, however, the miR-185-transduced cells showed reduced cell growth by more than 2-fold in all three cell lines after 4 days in culture  (P < 0.05, Figure 4.6A). These changes were not observed in other transduced cells. I then performed apoptosis assay and did not observe obvious changes in the miRNA transduced cells compared to control cells in the absence of IM. However, significantly increased apoptosis was observed in the presence of IM in both miR-185 overexpressing K562 and K562R cells compared to cells transduced with pRRL control vector 89  (30-40% vs. 20-25% Annexin V positive cells for K562 cells, P < 0.05 and 35-40% vs. 15-20% Annexin V positive cells for K562R cells, P < 0.001, Figure 4.6B). The similar results were observed in the absence of serum from these transduced cells (Figure 4.6B). These changes were not observed in the remaining three miRNA-transduced cells. These results indicate that forced expression of miR-185 affects survival of CML cell growth and enhances their sensitivity to TKI treatment, supporting further exploration of the biological function of miR-185 in additional CML cell line model systems and in primary CML stem/progenitor cells.        90   Figure 4.6: Functional screening of four deregulated miRNAs in CML cell lines. (A) Growth  curves of K562, K562R, and UT7 B/A cells transduced with indicated miRNA expressing vectors. Cells were counted at the indicated time points after IM treatments (0.5 µM for K562; 2.5 µM for K562R; 0.5 µM for UT7 B/A) using trypan blue dye exclusion method, and the numbers obtained were expressed as fold change relative to day 0. (B) Percentage of total apoptotic cells after 72 hours of IM treatments (1.0 µM for K562; 5.0 µM for K562R) in regular 91  medium (10% FBS) and after 48 hours of IM treatments in serum free medium (0% FBS) as determined by Annexin V/PI staining (right panel). Left panel shows representative FACS profiles in K562R cells. Data shown are mean ± SEM of measurements from three independent experiments. P-values were calculated using a two-tailed unpaired Student’s t test (*P < 0.05; **P < 0.01; ***P < 0.001).  4.2.5. Restoration of miR-185 Expression by Lentiviral-mediated Transduction Sensitizes BCR-ABL+ Cells to IM-induced Killing and Apoptosis through a BCR-ABL-Kinase Dependent Mechanism  To further characterize the biological effects of miRNA-185 in regulating TKI response/resistance of CML cells and disease progression, I transduced miR-185 expressing vectors to additional CML cell lines, including a more aggressive leukemia cell line, BV173, which was derived from a acute lymphoblastic leukemia (ALL) patient carrying a P190 BCR-ABL  [338]. Similar to K562 cells, a significant reduction in cell growth was found in miR-185 transduced cells (P < 0.001), and this effect was enhanced by IM compared with control cells (P < 0.01, Figure 4.7A). Apoptosis assay also revealed that miR-185 transduced cells sensitized BV173 cells to IM treatment in both serum-free and regular culture conditions compared to control cells (35-45% vs. 15% Annexin V positive cells, P < 0.05, Figure 4.7B).  I next investigated whether deregulated miR-185 directly contributes to IM-induced apoptosis in BCR-ABL+ cells as compared to cells that do not express BCR-ABL or are resistant to IM. I utilized parental UT7 cells, which do not carry BCR-ABL fusion protein, and UT7-BCR-ABL-T315I (UT7 T315I) cells, which are UT7 cells transduced with P210 BCR-ABL carrying the T315I kinase mutation. Unlike miR-185 transduced UT7 B/A cells, miR-185 transduced UT7 cells or UT7 T315I mutant cells did not display inhibition of cell growth or increase of apoptotic cells in the presence of IM regardless of culture conditions, as assessed by 92  both growth curve (Figure 4.7A) and apoptosis assay (Figure 4.7B). These results strongly suggest a selective role of miR-185 involved in a BCR-ABL-kinase dependent mechanism.   Figure 4.7: Forced expression of miR-185 inhibits proliferation and induces apoptosis in BCR-ABL+ cells but not in BCR-ABL T315I mutant cells in the presence of IM. (A) Growth curves of BV173, UT7 B/A, UT7 T315I, and UT7 cells transduced with miR-185 expressing vector. Cells were counted at the indicated time points after IM treatments (1.0 µM for BV173; 0.5 µM for UT7 B/A; 2.5 µM for UT7 T315I; 1.0 µM for UT7) using trypan blue dye exclusion method, and the numbers obtained were expressed as fold change relative to day 0. (B) Percentage of total apoptotic cells after 72 hours (96 hours for BV173) of IM treatments (2.0 µM for BV173; 1.0 µM for UT7 B/A; 5.0 µM for UT7 T315I; 2.0 µM for UT7) in regular medium (10% FBS) and after 48 hours (72 hours for BV173) of IM treatments in serum free medium (0% FBS) as determined by Annexin V/PI staining (right panel). Left panel shows representative FACS profiles in BV173 cells. Data shown are mean ± SEM of measurements from three independent experiments. P-values were calculated using a two-tailed unpaired Student’s t test (*P < 0.05; **P < 0.01; ***P < 0.001).  93   To further investigate the possible effect of these four miRNA candidates on CML clonogenic growth, colony forming assasys were carried out for K562 and K562R cells after transduction of miRNA expression vectors. There were no noticeable effects of any of the four miRNA candidates on CFC growth. Nevertheless, following IM treatment, miR-185 transduced cells markedly reduced CFC output in both K562 and K562R cells compared to pRRL vector control and the other miRNA candidates (Figure 4.8A and B). Notably, combined overexpression of miR-185 and IM treatment almost completely eradicated colonies in K562R cells (10-fold, P < 0.001, Figure 4.8B). Similarly, miR-185 transduced cells treated with IM also drastically reduced colony numbers and colony size in BV173 cells (5-fold, P < 0.001), and UT7 B/A cells (2-fold, P < 0.01). Like growth curve and apoptosis assays performed on UT7 T315I cells, forced expression of miR-185 in these cells displayed no effects on sensitizing cells to IM in CFC assays. All CFC colonies were observed to be GFP positive as determined by fluorescence microscopy confirming that all these transduced cell lines expressing miR-185 (Figure 4.8B). Cell adhesion assays were also performed to identify other biological importance of miR-185 and this assay resulted in an observation of enhanced adhesion ability of miR-185 transduced cells compared to pRRL vector control or miR-145 transduced cells (Figure 4.8C). Collectively these in vitro experiments demonstrated that restored miR-185 expression by lentiviral-mediated transduction sensitizes BCR-ABL+ cells to IM-induced killing and apoptosis and impairs colony-forming capacity through a BCR-ABL-kinase dependent mechanism.   94   95    Figure 4.8: Forced expression of miR-185 inhibits clonogenic growth of CML cells in the presence of IM. (A) Functional screening of four miRNA candidates using colony-forming cell (CFC) assays in K562 and K562R cells. K562 and K562R cells were plated in standard CFC assays plus IM (0.25 µM for K562; 2.5 µM for K562R). Colonies produced were counted after 16 days of incubation, and the numbers obtained were expressed as a percentage of values obtained in untreated cells transduced with pRRL vector. Data shown are mean ± SEM of measurements from technical duplicates. Representative photos of K562R colonies in each treatment are shown (right panel).  (B) K562, K562R, BV173, UT7 B/A, and UT7 T315I cells were plated in standard CFC assays plus IM (0.25 µM for K562; 2.5 µM for K562R; 1.0 µM for BV173 and UT7 B/A; 5.0 µM for UT7 T315I). Colonies produced were counted as described in (A).  Colony numbers for large (>500 cells), medium (50-500 cells), and small (<50 cells) are indicated. Representative photos of the GFP expression, size and morphology of K562 and K562R colonies in each treatment are shown. Data shown are mean ± SEM of measurements from three independent experiments. (C) Cell adhesion assay was performed in K562 and K562R cells. Cells were added to plates coated with fibronectin, washed, and trypsinized. The cells were counted using trypan blue dye exclusion method, and the numbers obtained were expressed in absolute number of cells recovered. Data shown are mean ± SEM of measurements from technical triplicates. P-values were calculated using a two-tailed unpaired Student’s t test (*P < 0.05; **P < 0.01; ***P < 0.001).   4.2.6. Restored Expression of miR-185 Reduces Colony Growth of IM-insensitive CML Stem and Progenitor Cells and Sensitize These Cells to TKIs To further investigate whether these biological changes may be observed in TKI-insensitive CML stem/progenitor cells, the effect of miR-185 was investigated in CD34+ cells from three samples of CML patients who were classified retrospectively as IM-nonresponders following IM monotherapy. These cells were transduced with lentiviruses containing miR-185 expressing vector or pRRL vector control. In all three patient samples studied, I observed a significant reduction of cell growth in miR-185 transduced cells compared to cells transduced with pRRL vector after 3 days in culture (P < 0.05, Figure 4.9A). These effects were further enhanced upon TKI treatments, including IM and DA (Figure 4.9A). Overexpression of miR-185 in these cells was confirmed by TaqMan qPCR (P < 0.01, Figure 4.9B).  96  Next, I examined whether miR-185 has an effect on stem/progenitor compartment of CML cells using a progenitor assay (the CFC assay), and a stem cell assay (the long-term culture-initiating cell (LTC-IC) assay). In CFC assay, overexpression of miR-185 in combination with all three TKIs (IM, DA, and NL) significantly reduce total number of colonies by more than two-fold compared to cells transduced with vector control plus TKIs (50-60% vs. 20-25% reduction, P < 0.05, Figure 4.9C). In LTC-IC assay, overexpression of miR-185 moderately suppressed the ability of stem/progenitor cells to form colonies from long-term cultures but greatly sensitized them to TKI treatments (70-85% vs. 45-60% suppression, P < 0.05, Figure 4.9D) Finally, whether forced expression of miR-185 affects cell differentiation of primitive leukemic cells in vitro was investigated. There was no apparent morphological difference in CD34+ CML cells transduced with either a miR-185 expressing vector or a pRRL vector after 5 days post-lentiviral transduction (Figure 4.9E). Although the percentage of differentiated cells, including CD33+/CD15+ (myeloid lineage) cells and other B- and T-cells were similar between miR-185 expressing and control cells, the percentage of CD34+ cells was noticeably decreased in two patient samples studied, suggesting that miR-185 might play a role in the maintenance of primitive CD34+ population (Figure 4.9F).  Taken together, these in vitro studies demonstrated that lentiviral-mediated expression of miR-185 significantly reduced viability and impaired survival of drug-insensitive CML stem and progenitor cells and these effects can be enhanced by TKI treatments, suggesting that miR-185 acts as a tumor suppressor and is directly involved in regulating TKI response/resistance of CML stem/progenitor cells. 97   98  Figure 4.9: Forced expression of miR-185 reduces colony growth of IM-insensitive CML stem and progenitor cells and sensitizes these cells to TKIs. (A) Growth curves of GFP+ cells sorted from CD34+ CML cells transduced with either a miR-185 expressing vector or a pRRL control vector from three CML patient samples. Cells were counted at the indicated time points after IM (2.5 µM) and DA treatments (75 nM) using trypan blue dye exclusion method, and the numbers obtained were expressed as fold change relative to day 0. Data shown are mean ± SEM of measurements from technical duplicates. (B) TaqMan microRNA assay was performed to quantify miR-185 expression in GFP+ cells transduced with a miR-185 expressing vector (miR-185) or  a pRRL vector (pRRL). (C) These GFP+ cells were plated in standard CFC assays plus IM (5 µM), DA (150 nM) or NL (5 µM). Colonies produced were counted after 14 days of incubation, and the numbers obtained were expressed as a percentage of values obtained in untreated cells transduced with pRRL vector. The percentage of colonies containing erythroid-burst forming units (BFU-E) and granulocyte/macrophage-colony forming units (CFU-GM) are also presented. (D) The same GFP+ cells were co-cultured with stromal cells and assayed for long term culture-initiating cells (LTC-ICs) in the presence of drug treatments including IM (5 µM), DA (150 nM) or NL (5 µM) for one week. Total colonynumbers were determined from the LTCs harvested six weeks later and then expressed as a percentage of the LTC-IC-derived CFCs obtained from untreated cells transduced with pRRL vector. (B-D) Data shown are mean ± SEM of measurements from three individual CML patients. P-values were calculated using a two-tailed unpaired Student’s t test (*P < 0.05; **P < 0.01; ***P < 0.001). (E) Wright-Giemsa stain of GFP+ cells from two CML patient samples after 5 days post lentiviral transduction. (F) CD34 surface marker staining was performed on two CML patient samples 5 days post lentiviral transduction. Cells were gated with PI- and GFP+ populations and the percentage of CD34+ cells in CD45+ population was then determined.  4.2.7. Restored miR-185 Expression in BCR-ABL+ Blast Crisis Cells Decreases Leukemia Burden and Enhances Survival in Immunodeficient Mice and These Effects are enhanced by TKI   To determine whether restored expression of miR-185, particularly in the presence of a TKI, could extend its in vitro biological effects to a mouse model mimicking a highly aggressive, late-stage leukemic disease, I utilized blast crisis patient-derived BV173 cells, which generate lethal leukemia in mice [49, 366]. A lentiviral-luciferase-YFP reporter was introduced into BV173 cells to enable close monitoring of engraftment of leukemic cells and treatment effects in multiple hematopoietic tissues. I intravenously injected BV173YFP/Luc cells co-transduced with a GFP-tagged miR-185 vector or a pRRL control vector into sub-lethally cesium-irradiated NSG 99  mice (2.5x 106 cells per mouse) (Figure 4.10A). Two weeks after transplant, I performed luciferase in vivo imaging in a subset of mice and observed that mice injected with pRRL-transduced cells displayed engraftment of leukemic cells in hematopoietic organs, mostly in the BM, as shown by strong bioluminescence signals (Figure. 4.10B). Strikingly, a significant reduced engraftment level was observed in mice injected with miR-185-tranduced BV173YFP/Luc cells (Figure 4.10B). Some mice injected with miR-185 or pRRL-transduced cells (5-6 mice per group) were then treated with DA (15 mg/kg) or with vehicle control by oral gavage for two weeks (Figure 4.10A). One mouse per each treatment group was imaged to monitor leukemia development three weeks after treatment. As expected, vehicle control mice progressed to develop leukemia, with detection of BV173YFP/Luc cells in all hematopoietic organs, while vehicle control mice treated with DA or mice injected with miR-185-transduced cells decreased the degree of leukemia progression, but was not able to prevent the spread of leukemic cells from the BM into liver and spleen as demonstrated by enlarged spleens and livers as well as increased GFP+/YFP+ cells detected in these organs (Figure 4.10B-D). In contrast, mice injected with miR-185-transduced cells and received DA treatment prevented leukemia progression with very low detectable engrafted cells (Figure 4.10B-D). To follow up on this finding, bioluminescence in vivo imaging was repeated on a few remaining mice (one per treatment group) four weeks after treatments. While vehicle control mice previously treated with DA or mice injected with miR-185-transduced cells revealed strong bioluminescence signals in the livers and spleens, indicative of high leukemia burden, mice injected with miR-185- transduced cells plus DA treatment continued to show very low detectable leukemic cells (Figure. 4.10E-F). These mice also showed significantly reduced engrafted leukemic cells in peripheral blood, BM, spleen, and liver 100  compared to vehicle control mice treated with DA or mice injected with miR185-transduced cells (Figure. 4.10E-F).   Furthermore, I monitored survival of 5-6 mice per treatment group. As expected, mice injected with pRRL-transduced cells treated with DA displayed prolonged survival compared to mice injected with pRRL control cells (median survival 60 vs. 47.5 days, P < 0.0014, Figure 4.10G). Similarly, mice that received miR-185-transduced cells treated with DA displayed prolonged survival compared to mice injected with miR-185-transduced cells (median survival 83 vs. 65 days, P < 0.0005, Figure 4.10G). Interestingly, significantly enhanced survival was observed in mice injected with miR-185- transduced cells compared to mice that received control pRRL-transduced cells (median survival 65 days vs. 47.5 days, P < 0.0005). Most importantly, mice injected with combined miR-185- transduced cells and DA treatment survived much longer than pRRL control mice treated with DA (median survival 83 days vs. 60 days, P < 0.0018). Taken together, these data indicate that combined restoration of miR-185 expression and DA treatment is highly effective for the treatment of an aggressive leukemia in mice. 101   102  Figure 4.10: Combined over expression of miR-185 and DA treatment decreases leukemia burden and enhances survival of leukemic mice.  (A) Schematic of in vivo experiments to assess the role of miR-185. BV173YFP/Luc cells transduced with miR-185 expressing vector or pRRL control vector were sorted for GFP+/YFP+ cells, and were intravenously injected into sub-lethally cesium-irradiated NSG mice (2.5 × 106 per mouse). Two weeks after transplantation, oral gavage treatment with or without dasatinib (DA, 15 mg/kg) began and continued for two weeks. (B) In vivo imaging of mice two weeks post-transplant. (C) At day 48 post-transplant, one mouse per treatment group, including no injection control (No cell ctrl), BV173YFP/Luc cells transduced with pRRL control vector treated with vehicle (pRRL vehicle), BV173YFP/Luc cells transduced with pRRL control vector treated with DA (pRRL DA), BV173YFP/Luc cells transduced with miR-185 expressing vector treated with vehicle (miR-185 vehicle), and BV173YFP/Luc cells transduced with miR-185 expressing vector treated with DA (miR-185 DA), were sacrificed and tissues were analyzed. Bioluminescence in vivo images of mice were taken a few days prior to sacrificing mice (top panel). Spleen and liver weight of mice from each treatment group (bottom panel) (D) FACS profiles of engrafted human GFP+/YFP+ cells detected in peripheral blood (PB), bone marrow (BM), spleen, and liver. (E) At day 60 post-transplant, one mouse per remaining treatment group was sacrificed and tissues were analyzed. In vivo images of mice were taken a few days prior to sacrificing mice (top panel). Spleen and liver weight of mice from each treatment group (bottom panel). (F) FACS profiles of engrafted human GFP+/YFP+ cells detected in PB, BM, spleen, and liver. (G) Survival curve for leukemic mice of each treatment group (n=5-6 mice per group). P-values were calculated using log-rank test.  4.2.8. Integrative Analysis of Gene Expression Profile and miRNA Expression Profile to Identify Predicted Target Genes To investigate the roles of the deregulated miRNAs in CML and to elucidate the potential mechanisms of the observed biological effects, I aimed to identify predicted target genes of the deregulated miRNAs. Global gene expression profiling was performed in 3 CD34+ NBM samples, 3 CML IM-responders, and 3 IM-nonresponders (the same 6 CML samples and 1 NBM sample made for miRNA libraries) using strand specific RNA sequencing, and differentially expressed genes were identified between NBM and CML samples and between IM-responders and IM-nonresponders using DESeq2 package. Of the total 21,727 protein coding genes, 3,795 genes were found down-regulated and 3,579 genes were found up-regulated in CD34+ CML cells compared to NBM using adjusted p-values < 0.05 cut-off, and 547 genes were found down-103  regulated and 560 genes were found up-regulated in IM-nonresponders compared to IM-responders using non-adjusted p-values < 0.05 cut-off (Figure 4.11A).  Next, I intersected the 21 validated deregulated miRNAs (Figure 4.2 and Figure 4.3) with the deregulated protein coding genes using six prediction algorithms, including mirBase [350], targetScan [274], miRanda [351], tarBase [352], mirTarget2 [353], and PicTar [354] using RmiR package. Finally, only the genes, which are predicted by at least 3 out of the 6 prediction algorithms and are inversely correlated with the deregulated miRNAs were kept and considered as potential target genes (Table 4.3). As an example, a list of miR-185’s predicted target genes is shown in heat maps in Figure 4.11C, indicating the predicted target genes are indeed up-regulated in CD34+ CML cells or in IM-nonresponders, as miR-185 expression is down-regulated in these cells (Figure 4.2 and 4.3). It is interesting to note that some miRNAs regulate more than 20 target genes, whereas some miRNAs have less than 5 target genes. Also, miRNAs belonging to the same family have very similar target genes (e.g. miR-17 and miR-106a). I then performed KEGG pathway analysis on the potential target genes using DAVID Bioinformatics Resources [348, 349], and found that there is a significant enrichment for genes involved in pathways in cancer, endocytosis, focal adhesion, mTOR signaling pathway, Wnt signaling pathway, and ErbB signaling pathway (Table 4.4). These results suggest that the deregulated miRNAs might contribute to CML pathogenesis and drug resistance by perturbing these signaling pathways. Based on my initial functional screening of 8 miRNA candidates, 4 miRNAs (miR-145, miR-185, miR-628, and miR-708) were identified, which may play a role in the growth and proliferation of CML cells (Figure 4.4). I therefore focused on the predicted target genes of these 4 miRNAs in order to rationalize the biological effects of these miRNA in CML and to elucidate 104  the potential mechanism of these target genes. I performed Gene Ontology (GO) analysis on these potential target genes using Ingenuity Pathway Analysis software, which not only groups the target genes into similar pathways, but also indicates if there is an overall activation or inhibition of these pathways. Among the predicted target genes that are differentially expressed between NBM and CML, I found an overall activation (shown in orange in Figure 4.11B) on proliferation of cells, organization of cytoplasm, transcription of DNA, and invasion of cells (Figure 4.11B left panel). For example, PAK6, which is up-regulated in CML samples, is able to activate proliferation of cells, organization of cytoplasm, and invasion of cells, whereas SGMS1 can only activate proliferation of cells. Among the predicted target genes that are differentially expressed between IM-responders and IM-nonresponders, I found an overall activation of proliferation of cells and microtubule dynamics, and an overall inhibition of cell death (shown in blue, Figure 4.11B right panel). NR1D1, for example, is up-regulated in IM-nonresponders and is able to inhibit cell death. These analyses provide new insights into how the predicted target genes might contribute to CML pathogenesis and assisted me on selecting genes for target gene validations and further studies.  105   106  Figure 4.11: Identification of high-probability predicted target genes of the deregulated miRNAs. (A) Deseq2 package was used to identify differentially expressed genes comparing CD34+ cells from NBM and CML samples, and comparing IM-responder and IM-nonresponder samples. In the MA plots, the Y axis represents the average expression of each individual gene, and the X axis represents the fold change relative to NBM or relative to IM-responders. The red dots represent the differentially expressed genes with adjusted P-values < 0.05 when comparing NBM and CML samples, and with non-adjusted P-value < 0.05 when comparing IM-responder and IM-nonresponder samples. (B) Gene Ontology analysis was performed on the potential target genes of miR-145, miR-185, miR-628, and miR-708 using Ingenuity Pathway Analysis software. Colour orange represents up-regulation or activation. Colour blue represents down-regulation or inhibition. Colour grey represents no direction is available. The gradient of the colour represents the degree of up- or down-regulation. (C) Heat maps of miR-185’s predicted target genes comparing NBM and CML samples (left panel), and comparing IM-responders (R) and IM-nonresponders (NR) (right panel).    Validated miRNA (NBM vs. CML) Predicted target genes hsa-mir-139 GDF10, TEK, NFIB, ENAH, DUSP19, ANK1, CBX1, SEPT11, UBE2F, SOCS2, PMP22,TOX, GPR56, ATP5G3, LMO4, MRPL42, MRPS25, CLPX hsa-mir-708 GOLT1A, CD34, TLN2, HTRA2, TEX261, MTCP1 hsa-mir-151a NIPAL2, RANBP17 hsa-mir-628 DMC1, SLC40A1, HPRT1, GAR1, CCBL2, PIP5K1B, MAMDC2, EFCAB13, RBFOX2 hsa-mir-106a GLIS3, SMAD7, CRIM1, EPHA4, TSHZ3, DOCK4, RGMB, RGL1, ZNF704, TLE4, ARL4C, PDE3B, CNN1, MAP3K2, ARHGAP26, GAB1, RASSF2, ABCA1, NFAT5, IL17RD, FBXO39, APBB2, CAMK2N1, PGBD5, BCL2L15, VASH2, SOWAHC, SSH2, TGFBR2, MMP2, ELK3, CDKN1A, E2F2, BMPR2, RB1CC1, KMT2C, ATP1A2, KLF10, ERBB3, WDFY3, ZNF697, FGL2, ZBTB18, IRF9, HECTD2, FAM126B, TNRC6B, CREB5, SPTBN1, PTGDR, TIMP2, EGLN1, ITGB8, ZNFX1, INTS6, NCOA3, CRK, FNDC3B, SNX9, LYST, STK38, RAB22A, SORL1, CCSER2, KAT2B, TNKS2, WDR37, CPEB3, BTBD7, TNRC6A, DENND5B, PHTF2, NR2C2, TMEM127, DCUN1D3, ANKRD12, DDHD1, ZNF148, MAP3K14, XRN1, CREBRF, UBE2W, GPR137B, FBXO21, PKN2, ATG2B, MTMR3, LIMK1, PURB, FBXW11, FNDC3A, ADAM9, TRIM8, SAR1B, PLAGL2, C11orf30, ARID4A, STX6, NAA30, SACS, ACBD5, CCNG2, USP3, DYNC1LI2, ITPKB, JAK1, MAP3K11, WAC, STAT3, ZFP91, HIF1A, IQSEC1, EFCAB14, GOSR1, ARID4B, NPAT, CHD9, TMCC1, YTHDF3, PHLPP2, C7orf43, TMEM245, DMTF1, YPEL2, ZBTB41, INO80, KIAA1191, DERL2, BICD2, BTBD10, CEP120, TUSC2, TAOK3, C7orf60, PTPN4, PPP6C, EEA1, MFN2, PLEKHA3, ZFYVE26, UBXN2A, MARK4, SIKE1, NRBP1, KLHL20, APP, EIF4G2, AGO1, GOLGA1, TMUB2, WDR82, TGOLN2, CDC40, MAPRE1, TMEM123, CAMTA1, ANKFY1, FBXL5 hsa-mir-452 LRRC7, INTS8, TRMT11, PRPF39 Table 4.3. Predicted target genes of the deregulated miRNAs 107  Validated miRNA (NBM vs. CML) Predicted target genes hsa-mir-17 GLIS3, SMAD7, CRIM1, EPHA4, TSHZ3, DOCK4, RGMB, RGL1, TLE4, ARL4C, PDE3B, CNN1, MAP3K2, ARHGAP26, GAB1, RASSF2, NFAT5, IL17RD, FBXO39, APBB2, CAMK2N1, PGBD5, BCL2L15, VASH2, SOWAHC, SSH2, TGFBR2, MMP2, ELK3, CDKN1A, BMPR2, RB1CC1, ATP1A2, KLF10, ERBB3, WDFY3, ZNF697, FGL2, ZBTB18, IRF9, HECTD2, FAM126B, TNRC6B, PTGDR, TIMP2, EGLN1, ITGB8, ZNFX1, INTS6, NCOA3, CRK, FNDC3B, SNX9, LYST, STK38, RAB22A, SORL1, CCSER2, KAT2B, TNKS2, WDR37, CPEB3, BTBD7, TNRC6A, DENND5B, PHTF2, NR2C2, TMEM127, DCUN1D3, ANKRD12, FURIN, TET3, DDHD1, ZNF148, MAP3K14, XRN1, CREBRF, UBE2W, POU6F1, FBXO21, PKN2, C16orf72, MTMR3, LIMK1, PURB, FBXW11, FNDC3A, ADAM9, TRIM8, SAR1B, PLAGL2, C11orf30, ARID4A, NAA30, SACS, ACBD5, CCNG2, USP3, DYNC1LI2, JAK1, MAP3K11, WAC, STAT3, ZFP91, HIF1A, IQSEC1, PIKFYVE, EFCAB14, GOSR1, ARID4B, NPAT, CHD9, TMCC1, YTHDF3, C7orf43, TMEM245, UBE2B, DMTF1, YPEL2, INO80, KIAA1191, DERL2, BICD2, BTBD10, CEP120, TUSC2, TAOK3, C7orf60, PTPN4, PPP6C, MFN2, PLEKHA3, ZFYVE26, UBXN2A, MARK4, SIKE1, NRBP1, KLHL20, APP, EIF4G2, AGO1, GOLGA1, TMUB2, WDR82, TGOLN2, CDC40, MAPRE1, TMEM123, CAMTA1, FBXL5 hsa-mir-20a CYP26B1, CORO2B, SMAD6, TRIM36, GLIS3, AHRR, SMAD7, CRIM1, EPHA4, TSHZ3, DOCK4, RGMB, PLAG1, RGL1, TLE4, RAB30, ARL4C, PDE3B, CNN1, MAP3K2, STC1, ARHGAP26, GAB1, RASSF2, ABCA1, NFAT5, PLEKHM1, IL17RD, FBXO39, APBB2, CAMK2N1, PGBD5, RASD1, BCL2L15, VASH2, SOWAHC, SSH2, TGFBR2, PTPRO, MMP2, ELK3, CDKN1A, BMPR2, RB1CC1, ATP1A2, KLF10, ERBB3, PDGFRA, WDFY3, NHLH1, ZNF697, FGL2, ZBTB18, IRF9, MAP3K3, HECTD2, FAM126B, TNRC6B, CREB5, PTGDR, TIMP2, EGLN1, ITGB8, ZNFX1, INTS6, MECP2, NCOA3, CRK, PAFAH1B1, FNDC3B, SNX9, ITCH, LYST, STK38, RAB22A, SORL1, CCSER2, TRPS1, KAT2B, TNKS2, FAM134C, ULK1, WDR37, KLHL2, BTBD7, TNRC6A, DENND5B, PHTF2, NR2C2, RAPGEFL1, TMEM127, MTF1, ZBTB7A, DCUN1D3, REEP3, ANKRD12, FURIN, TET3, DDHD1, ZNF148, MAP3K14, XRN1, CREBRF, UNKL, UBE2W, GPR137B, UBR5, CIC, SFMBT1, GATAD2B, FBXO21, FAM102A, ANKRD13C, FBXO31, RUNX1, PKN2, C16orf72, MTMR3, LIMK1, PURB, FBXW11, FNDC3A, ADAM9, TRIM8, SAR1B, PLAGL2, C11orf30, FOXJ3, ARID4A, KIF3B, CLOCK, RNF125, STX6, HECA, NAA30, SACS, C16orf70, CELF2, ACBD5, SUV420H1, KDM2A, CCNG2, USP3, DYNC1LI2, ITPKB, JAK1, MAP3K11, ATG16L1, WAC, STAT3, NAGK, FRS2, ZFP91, HIF1A, IQSEC1, EFCAB14, GOSR1, ARID4B, NPAT, CHD9, TMCC1, GBF1, YTHDF3, PHLPP2, C7orf43, PAFAH2, CACUL1, SCAMP2, LPGAT1, IKZF4, DYRK1A, DMTF1, YPEL2, ZBTB41, INO80, KIAA1191, SS18L1, PHF1, AGTPBP1, DERL2, NPLOC4, BICD2, PTEN, ZBTB4, BTBD10, DAZAP2, CEP120, CTDSPL2, TUSC2, TAOK3, C7orf60, CERS6, P2RX4, UBE2Q2, PTPN4, PPP6C, MFN2, PLEKHA3, ZFYVE26, UBXN2A, MARK4, ZNF236, SIKE1, EIF4H, NRBP1, RAP2C, KLHL20, APP, EIF4G2, AGO1, EZH1, GOLGA1, TMUB2, MARCH6, WDR82, TGOLN2, CDC40, MAPRE1, TMEM123, CAMTA1, ANKFY1, DCAF8, FBXL5 hsa-mir-92a RAG1, CHST1, TEAD1, CLDN11, RBM47, SMAD6, TRIM36, KSR2, SMAD7, ATP8B1, TACC2, NEFM, SLC32A1, AXL, SLC7A11, FAM110B, KLHL14, HCN2, COL1A2, SNN, MARK1, EDEM1, DSCAML1, PIK3AP1, PTPRO, ITGA5, DOCK9, KLF4, MAP2K4, ITGAV, CCNJL, FNDC3B, LYST, SESN3, FNIP1, GOLGA4, KAT2B, DYRK2, BCL11A, CPEB3, CHST7, STRN3, SYNJ1, PHTF2, CPEB4, BAZ2B, FRY, PLEKHA1, PAPD7, RNF11, UBE2W, NRF1, RGS3, PLEKHB2, CIC, JMY, CNEP1R1, TRIO, EPG5, SETD5, MYO5A, TBL1XR1, FMR1, TRAF3, MAN2A1, APPL1, RNF38, COG3, LATS2, PEAK1, LMBR1L, RFX1, TGIF1, FBXW7, SNX13, NPTN, PIKFYVE, REV3L, KLHDC10, PHLPP2, PPP1R12A, PAPD5, QSER1, ZFC3H1, TSC1, DNAJB12, PCMTD1, SRPR, WASL, CD2AP, ATXN3, SGK3, SCAF11, WWP2, PTAR1, TMEM87A, PTEN, DMXL1, RNF4, GOLGA3, C6orf62, HERPUD2, KIAA1109, KIF5B, ARID1B hsa-mir-146b CDK9, TMEM189-UBE2V1, UBE2V1, RUNX1T1, MARK1, ARID3A, MPO, KLF7,  LRRTM2, FBXO3, ZBTB2, SLC10A3, PHF20L1, KDM2B, USP3, HMBOX1, FBXW2, FAF2, SMAD4, IRAK1, EIF4G2 108  Validated miRNA (NBM vs. CML) Predicted target genes hsa-mir-155 RBM47, RBMS3, GPRIN3, GPM6B, FGF7, TSHZ3, HIVEP2, SOX11, BOC, TLE4, S1PR1, CSF1R, NAV3, MYO10, RAPH1, NFAT5, GDF6, CEBPB, MYLK, ABCD2, MEF2A, NKX3-1, SMAD1, ZBTB18, TSPAN14, CSRNP2, MECP2, RICTOR, ZNF652, ANTXR2, SATB1, TAPT1, TRPS1, STRN3, VEZF1, LCORL, AGO4, MAP3K14, CREBRF, PKN2, BRD1, ETS1, KPNA1, KRAS, ZMYM2, KANSL1, CARHSP1, KBTBD2, WDFY1, MBNL2, ARID2, TRAM1, FBXO11, NUFIP2, KRCC1, HIF1A, N4BP1, RAB11FIP2, CHD9, ELL2, H3F3A, RPS6KA3, CACUL1, PHC2, SP3, DMTF1, ZBTB41, CDC73, SGK3, SARAF, RCOR1, G2E3, ADD3, CSNK1A1, FAM91A1, JADE1, CSNK1G2, ZNF236, YWHAZ, FAR1, PAXBP1, MARCH6, GNAS, CAMTA1, ANKFY1 hsa-mir-363 RAG1, CHST1, TEAD1, CLDN11, RBM47, SMAD6, KSR2, SMAD7, ATP8B1, TACC2, NEFM, RGL1, GATA6, SLC32A1, AXL, SLC7A11, FAM110B, KLHL14, COL1A2, SNN, GHR, MARK1, RNF180, EDEM1, PIK3AP1, PTPRO, ITGA5, CXXC5, DOCK9, KLF4, MAP2K4, ITGAV, CCNJL, FNDC3B, LYST, SESN3, FNIP1, CCSER2, GOLGA4, DAB2IP, KAT2B, DYRK2, HIVEP1, CHST7, STRN3, SYNJ1, PHTF2, TESK1, BAZ2B, FRY, PLEKHA1, PAPD7, RNF11, UBE2W, NRF1, RGS3, PLEKHB2, CIC, JMY, CNEP1R1, TRIO, EPG5, SETD5, MYO5A, ZDHHC5, TBL1XR1, FMR1, TRAF3, MAN2A1, APPL1, RNF38, COG3, LATS2, PEAK1, LMBR1L, RFX1, DOCK5, TGIF1, FBXW7, NPTN, PIKFYVE, REV3L, KLHDC10, PHLPP2, PAPD5, GALNT7, QSER1, ZFC3H1, TSC1, DNAJB12, PCMTD1, SRPR, WASL, GPBP1L1, CD2AP, ATXN3, SGK3, SCAF11, TMEM87A, PTEN, DMXL1, RNF4, GOLGA3, C6orf62, ASB7, HERPUD2, VPS4B, KIAA1109, RPS6KA4, KIF5B, ARID1B, FAR1, PAXBP1 hsa-mir-4521 No predicted targets hsa-mir-18a CAMSAP2, CRIM1, TSHZ3, SH3BP4, PAPPA, CTGF, MEF2D, MYLK, DAAM2, NAV1, EPB41L1, UBTD2, ESR1, TNRC6B, FAM3C, NR3C1, TRIOBP, MAP7D1, ANKRD13C, TMEM2, ETV6, PHF20L1, ASXL2, TOR1B, TRAPPC8, RAB5C, FRS2, HMBOX1, HIF1A, CCDC88A, SEL1L3, RAB11FIP2, ORAI3, BRWD3, FAM73A, DICER1, ADD3, ZBTB4, NCOA1, NR1H2, SON, ZCCHC3, KLHL20, KPNA6 hsa-mir-185 ASTN2, PBX1, PAK6, KIAA1328, LRRC16B, PHF7, DTX3, SGMS1, MLLT11, LPCAT3, STMN1, HP1BP3 hsa-mir-192 SH2D1A, TCF7, NRIP3, CREB5, FNDC3B, NIPBL, DYRK3, RAB2A, WNK1, ARFGEF1, BLCAP, H3F3B, CTCF, DICER1, KPNA6 hsa-mir-16 FGF2, SLC4A4, HTR2A, HSPG2, TFAP4, MAMSTR, ENAH, LIPE, KIF5A, CCND1, ITGA2, ANKRD13B, UBXN10, STRADB, SLC25A22, SLC35G1, FASN, STK33, MAP7, STXBP1, AK4, DNAJB4, ARL2, ISOC1, HIGD1A, SPTBN2, PTPRD, MTMR4, CCNE1, FAM189B, PEX5, GLUD2, SMYD5, VAMP8, CTDSPL, ARHGEF9, ARL3, FSD1, SERBP1, PCBP4, SLC39A10, UBQLNL, SEH1L, PVRL1, SLC11A2, PNPLA6, AHCYL2, SUPT16H, EIF2B5, TRIP10, LMAN2L, MTFR1L, TARBP2, C12orf76, SH3BGRL2, FAM60A, DVL1, MINK1, GLUD1, PDIA6, PPT2, ACTR2, TXN2, TCAIM, VAT1, PPP1R11, PBX3, SPAG7, PSMD7, SRPK1, EIF3A, SEPT2, MTCP1, PPIF, WDTC1 hsa-mir-324 GSG1L, MYLK2, RARG, SUPT6H, STAG2, YTHDC1 hsa-mir-130b NRP1, CHST1, PMEPA1, SHANK2, CAMSAP2, IGF1, WNT1, FRZB, MET, BACH2, RAI2, SYT6, RUNX3, HIVEP2, FAM43A, RAB30, PFKFB3, S1PR1, WNT2B, POU4F1, TGFA, STC1, NPNT, ABCA1, PTPRG, LRIG1, RNF180, SNAP25, SOX5, TGFBR2, ELK3, ARAP2, CLUL1, BMPR2, KLF13, ADAM12, KMT2C, DLL1, MAFB, PDGFRA, WDFY3, EPB41L1, ZCCHC14, UCP3, CYLD, ZBTB18, ESR1, CPEB2, TNRC6B, TIMP2, PAN3, PLCL2, ITGB8, MECP2, PAFAH1B1, SLC25A44, TRPS1, AGFG1, BTBD7, TNRC6A, BAZ2A, CLIP1, RNF145, LDLRAD4, SLC6A6, CPEB4, LCORL, LMTK2, AGO4, MTF1, SBF2, SECISBP2L, ANKRD12, ACSL1, B4GALT5, UBE2W, POU6F1, LNPEP, PRR5L, MTMR9, BTG1, STX12, ATG2B, BIRC6, MDFIC, WDR47, EIF4E3, TBL1XR1, WDR20, PHF12, FAM179B, EPC2, FMR1, CLOCK, STX6, KMT2A, HECA, MPPED2, DENND4C, APPL1, RNF38, C16orf70, E2F7, ACBD5, GPATCH8, BTAF1, ASXL2, SUV420H1, KDM2A, DYNC1LI2, HOXB3, SNX27, ATG16L1, SESTD1, ZFP91, NPTN, ATP6V1B2, CUL3, QKI, PIKFYVE, BLCAP, N4BP1, RFX7, CCDC88A, SEL1L3, NPAT, CHD9, STIM2, CSNK1G1, CNOT6, HOXA3, MIER1, SLMAP, VPS13D, ZFC3H1, IKZF4, TSC1, FBXO28, INO80, CEP170, WASL, TMEM55B, GPCPD1, SNX2, FAM73A, DICER1, LIMD2, PTEN, ZBTB4, BTBD10, CEP120, ARHGAP21, NCOA1, PHF3, RAB5A, C7orf60, PRR14L, SPEN, UBE2D1, ATRN, PHACTR2, PTPN4, WHSC1L1, FAM104A, JADE1, PRKAA1, CASD1, RAP2C, KLHL20, KIAA1468, AGO1, DCP2, MBNL1, MMGT1, GMFB, CAMTA1, MEMO1, USP33 109  Validated miRNA (NBM vs. CML) Predicted target genes hsa-mir-145 SCN2A, NFIB, RBPMS2, ANGPT2, FZD7, FUCA2, MYCN, GGT7, MEST, EPB41L5, FSCN1, NET1, SNX15, RASAL2, TLN2, ABRACL, TMEM178A, FKBP3, RAD51B, LRRC16A, GPHN, PRPSAP2, CCDC25, SPATS2, DPYSL2, KATNBL1, LDB1, SSBP3, ZDHHC9, IVNS1ABP, PAN2, CAPZB, ATXN7L1, DDX31, UPF3A, HTATSF1, AP2B1, UBTF, ORC2, SCAMP3, HIST1H3B Validated miRNA (R vs. NR) Predicted target genes hsa-mir-340 FAM184A, THAP2, CXorf57, SLC30A4, ITGA9 hsa-mir-185 NR1D1, LRRC16B, FAM184A, ATP1A3, THTPA, ATP6V1F     KEGG pathway Count P-value Genes Pathways in cancer 34 1.29E-07 E2F2, FGF7, EGLN1, PTEN, MMP2, CCNE1, WNT1, KRAS, ITGAV, NKX3-1, TGFA, RUNX1, FGF2, TRAF3, CSF1R, TCF7, MET, TGFBR2, RUNX1T1, SMAD4, ITGA2, IGF1, APPL1, FZD7, STAT3, WNT2B, DVL1, CDKN1A, CCND1, HIF1A, ETS1, PDGFRA, JAK1, CRK  Regulation of actin cytoskeleton 20 4.20E-04 ENAH, FGF7, LIMK1, SSH2, PIP5K1B, ITGA2, MYLK2, PAK6, ITGA9, KRAS, ITGA5, ITGB8, ITGAV, PIKFYVE, PDGFRA, PPP1R12A, WASL, CRK, FGF2, MYLK Prostate cancer 12 4.63E-04 E2F2, CCNE1, CDKN1A, TCF7, CCND1, KRAS, PDGFRA, NKX3-1, TGFA, IGF1, CREB5, PTEN Melanoma 10 1.27E-03 E2F2, CDKN1A, CCND1, FGF7, KRAS, MET, PDGFRA, IGF1, FGF2, PTEN Endocytosis 17 1.43E-03 ERBB3, RAB5C, MET, TGFBR2, PIP5K1B, EEA1, RAB11FIP2, AP2B1, PIKFYVE, RAB22A, PDGFRA, RAB5A, VPS4B, ITCH, ARAP2, IQSEC1, CSF1R Focal adhesion 17 3.55E-03 TLN2, MET, ITGA2, IGF1, MYLK2, PTEN, PAK6, ITGA9, CCND1, ITGA5, ITGB8, ITGAV, COL1A2, PDGFRA, PPP1R12A, CRK, MYLK  Colorectal cancer 10 4.11E-03 TCF7, CCND1, KRAS, MET, TGFBR2, PDGFRA, SMAD4, APPL1, FZD7, DVL1 Renal cell carcinoma 9 4.53E-03 PAK6, HIF1A, KRAS, ETS1, MET, GAB1, TGFA, EGLN1, CRK  Glioma 8 9.06E-03 E2F2, CDKN1A, CCND1, KRAS, PDGFRA, TGFA, IGF1, PTEN mTOR signaling pathway 7 0.01286 RPS6KA3, HIF1A, TSC1, ULK1, IGF1, PRKAA1, RICTOR Ubiquitin mediated proteolysis 12 0.01390 CUL3, FBXW7, WWP2, UBR5, BIRC6, UBE2W, UBE2F, ITCH, UBE2D1, UBE2Q2, FBXW11, UBE2B Pancreatic cancer 8 0.01816 E2F2, CCND1, KRAS, TGFBR2, SMAD4, TGFA, JAK1, STAT3  Chronic myeloid leukemia 8 0.02228 E2F2, CDKN1A, CCND1, KRAS, TGFBR2, SMAD4, RUNX1, CRK Wnt signaling pathway 12 0.02674 CSNK1A1, WNT1, TBL1XR1, TCF7, CCND1, NFAT5, SMAD4, FBXW11, DAAM2, FZD7, DVL1, WNT2B Table 4.4. Enriched KEGG pathways among predicted target genes of the deregulated miRNAs 110  KEGG pathway Count P-value Genes p53 signaling pathway 7 0.04223 CCNE1, CDKN1A, CCND1, IGF1, CCNG2, PTEN, SESN3 ErbB signaling pathway 8 0.04510 PAK6, CDKN1A, KRAS, ERBB3, GAB1, MAP2K4, TGFA, CRK SNARE interactions in vesicular transport 5 0.05350 STX6, STX12, VAMP8, GOSR1, SNAP25 Hedgehog signaling pathway 6 0.05888 CSNK1A1, WNT1, CSNK1G1, CSNK1G2, FBXW11, WNT2B Acute myeloid leukemia 6 0.06656 TCF7, CCND1, KRAS, RUNX1T1, RUNX1, STAT3 Bladder cancer 5 0.07234 E2F2, CDKN1A, CCND1, KRAS, MMP2 Neurotrophin signaling pathway 9 0.09567 IRAK1, RPS6KA3, YWHAZ, RPS6KA4, KRAS, MAP3K3, GAB1, CRK, FRS2 Small cell lung cancer 7 0.09651 E2F2, CCNE1, CCND1, ITGAV, ITGA2, PTEN, TRAF3 ECM-receptor interaction 7 0.09651 ITGA9, ITGA5, ITGB8, ITGAV, COL1A2, HSPG2, ITGA2    4.2.9. Validation of Predicted Target Genes and Potential Molecular Mechanisms Based on my extensive functional screening (Figure 4.6 to Figure 4.9), lentiviral-mediated expression of miR-185 significantly reduced viability and impaired survival of drug-insensitive CML stem and progenitor cells and sensitize these cells to TKI treatments. I therefore focused on miR-185’s predicted target genes to further investigate its role in CML biology and drug resistance. Among the eighteen predicted target genes (Figure 4.11C and Table 4.3), I selected PBX1, PAK6, SGMS1, and NR1D1 for validation study based on their relevant roles to the properties of cancer stem cells, including CML (Figure 4.11B, and Table 4.4), the fold change difference, and their mean expression in CD34+ CML stem/progenitor cells. PBX1, or pre-B-cell leukemia transcription factor 1, is a transcription factor whose fusion gene with E2A is involved in development of pre- and pro-B cell acute lymphoblastic leukemias [379]. PAK6, or p21-activated protein kinase 6, is a serine/threonine-protein kinase that has been shown to regulate apoptosis and the MAPK signaling in cancer [380]. SGMS1, or sphingomyelin synthase 1, synthesizes sphingomyelin for plasma membrane, and has been implicated to be involved in 111  cell proliferation and caspase-dependent apoptosis [381, 382]. NR1D1, or nuclear receptor subfamily 1 group D member 1, is a transcription factor which has been demonstrated to be important for the survival of breast cancer cells [383]. All these four genes may be biological relevant to CML and have oncogenic potential to contribute to CML pathogenesis. I cloned PBX1, PAK6, SGMS1, and NR1D1 wild-type or mutant 3’ UTR, containing miRNA binding sites, into a luciferase reporter construct (Figure 4.12A), and found that luciferase activity in HEK-293T cells co-transfected with miR-185 expressing vector and PAK6 wild-type, SGMS1 wild-type, or NR1D1 wild-type was reduced by at least 50% (P < 0.05, Figure 4.12B), but not in cells co-transfected with a mutant construct. miR-185 did not affect luciferase activity in cells co-transfected with wild-type PBX1 (data not shown). I also examined the mRNA transcript levels of these genes by Q-RT-PCR, and found that PAK6, SGMS1, and NR1D1 mRNA levels were reduced in K562R cells transduced with miR-185 expressing vector compared to cells transduced with pRRL control vector (P < 0.05, Figure 4.12C). Western blot analysis further showed that miR-185 overexpression caused a markedly decrease in protein levels of PAK6, SGMS1, and NR1D1 in both miR-185-transduced K562R and BV173 cells (P < 0.05, Figure 4.12D). These data indicate that PAK6, SGMS1, and NR1D1 are bona fide targets of miR-185, and the loss of miR-185 expression in CML might lead to up-regulation of these three genes, which in turn contribute to disease progression and development of drug resistance. To further explain the biological function of deregulated miR-185, including its effect on sensitizing CML cells to TKI in a BCR-ABL-kinase dependent manner, I examined several critical proteins known to be involved in BCR-ABL-mediated signaling pathways. First, I found that miR-185 expression might be regulated by BCR-ABL, since miR-185 expression was found to be up-regulated in K562R cells upon IM treatment (Figure 4.12E). Also, Western blotting 112  revealed that after 24 and 48 hours of IM treatments, K562R cells transduced with miR-185 expressing vector displayed a reduction of p-ERK level compared to cells transduced with pRRL control vector, while p-BCR/ABL and p-AKT levels remained the same (Figure 4.12F). These data suggest that miR-185 could be up-regulated by IM treatment, which in turn sensitizes K562R cells to IM-induced apoptosis through suppression of the RAS/MAPK pathway.  113   114  Figure 4.12: Validation of miR-185 target genes and their expression changes in CML cells. (A) Schematic representation of PAK6 3’ UTR, SGMS1 3’ UTR, and NR1D1 3’ UTR sequences containing potential miR-185 binding sites. The positions of mutations generated are marked in red. (B) Luciferase reporter assay of PAK6, SGMS1, and NR1D1. HEK-293T cells were cotransfected with reporter vectors each containing PAK6, SGMS1, or NR1D1 binding sites and their corresponding mutations along with miR-185 expressing vector or pRRL control vector. Luciferase activity was measured 48 hours after transfection and normalized to Renilla luciferase activity. Data shown are mean ± SEM of measurements from three independent experiments. (C) Q-RT-PCR analysis on mRNA levels of PAK6, SGMS1, and NR1D1 in K562R cells transduced with miR-185 expressing vector or pRRL control vector. Data shown are mean ± SEM of measurements from technical duplicates. (D) Western blot analysis of protein expression of PAK6, SGMS1, and NR1D1 in K562R and BV173 cells transduced with miR-185 expressing vector or pRRL control vector. Protein expression of PAK6, SGMS1, and NR1D1 relative to ACTIN was quantified and compared. Data shown are mean ± SEM of measurements from two independent experiments. (E) K562R cells were treated with IM (5 µM) and miR-185 expression level was analyzed by TaqMan microRNA assay at 48 hours. Data shown are mean ± SEM of measurements from technical duplicates. (F) Western blotting analysis of protein expression of phosphotyrosine (4G10), p-ERK, ERK, p-AKT, AKT and BCR-ABL in transduced K562R cells cultured with or without IM (5.0 µM) for 24 and 48 hours. ACTIN serves as loading control. P-values were calculated using a two-tailed unpaired Student’s t test (*P < 0.05; **P < 0.01; ***P < 0.001).    4.2.10. Identification of Potential miRNA Prognostic Markers in CD34+ CML Cells from a Large Cohort Study   To further validate differentially expressed miRNAs identified in this study as potential miRNA prognostic markers that may predict clinical response of newly diagnosed patients to current TKI therapy, miRNA expression changes were next examined in a large cohort of samples obtained at 3 different time point (before, after 1 month, and after 3 months of nilotinib treatments) from 65 newly diagnosed CML patients, which is part of a clinical trial (CAMN107E2401 – ENESTxtnd). CD34+ cells were purified and the high-throughput qPCR microfluidics device was utilized to obtain miRNA expression profile. Interestingly, almost all of the differentially expressed miRNAs examined (a total of 19 miRNAs) showed a significant degree of restoration upon nilotinib (NL, Figure 4.13A). For instance, miR-139 expression level is found reduced in CD34+ CML cells before NL treatment; however, its expression is greatly 115  increased after NL treatment (Figure 4.13A). Likewise, miR-106a expression is found up-regulated in CML, and after NL treatment, the patients’ miR-106a expression is reduced (Figure 4.13A),  Based on the limited information on patients’ response status, I compared miRNA expression changes before and after NL treatment in a subset of patients with known major molecular responses as NL-responders or NL-nonresponders. Interestingly, I found that restoration levels of miR-145 and miR-185 are different upon NL treatment between NL-responders and NL-nonresponders, particularly comparing before and after three months of NL treatment (Figure 4.13B). The expression levels of miR-145 and miR-185 were increased to a greater extent in NL-responders than the expression levels in NL-nonresponders. Indeed, miR-185 expression level was restored in CD34+ cells from all NL-responders but there was no change or further reduced miR185 expression found in NL-nonresponders after three months NL treatment (P=0.03). These data suggest that some of the differentially expressed miRNAs, including miR185 could be applied to predict subsequent clinical response to TKI therapy. 116   117  Figure 4.13: MicroRNA expression changes in CD34+ cells from patients treated with NL. (A) miRNA expression levels of CD34+ cells obtained from newly diagnosed CML patients (n=65) prior to (BL), 1 month after (M1), and 3 months (M3) after NL treatment were detected using the high-throughput qPCR microfluidics device. Raw Ct values were obtained from 48 multiplexing array, normalized to endogenous control RNU48, and log2 transformed. P-values were calculated using a two-tailed paired Student’s t test. (B) Representative miRNA expression levels comparing NL-responders (R) at M3 with NL-nonresponders (NR) at M3 of selected patients. P-values were calculated using a two-tailed unpaired Student’s t test.  4.3 Discussion In this study, I have extensively described how differentially expressed miRNAs were identified between CD34+ CML cells and CD34+ NBM cells, and between IM-responders and IM-nonresponders using combined powerful technologies of next-generation sequencing (NGS) and high-throughput TaqMan qPCR system. I also integrated miRNA expression profiles with gene expression profiles obtained from RNA-seq of the same CML samples, to identify potential target genes and pathways which might contribute to disease development and confer TKI resistance. I then combined this expression profiling with a functional characterization of eight miRNA candidates in multiple leukemic cell lines, including IM resistant cells. This led to the discovery that expression of miR-185 is significantly reduced in CD34+ CML stem/progenitor cells from TKI-nonresponders compared to TKI-responders. This finding is further supported by additional studies in a larger cohort of samples from CML patients before and after nilotinib treatment, suggesting its potential to predict clinical response to TKI therapy. Importantly, restoration of miR-185 expression by lentiviral transduction in CD34+ TKI-nonresponder cells significantly impairs survival of these cells and sensitizes them to TKI treatment in vitro. The tumor suppressor role of miR-185 also extends to a mouse model of an aggressive, late-stage human leukemia, combined overexpression of miR-185 and TKI dasatinib treatment effectively eliminate infiltrated leukemic cells in hematopoietic tissues and significantly enhance survival of 118  leukemic mice. Finally, several predicted target genes of miR-185, including PAK6, SGMS1and NR1D1, were validated to further define the role of miR-185 in CML pathogenesis and drug resistance.        One of the most commonly used high-throughput techniques to create miRNA expression profile is miRNA microarray. Although the results obtained from microarray are highly reproducible, the sensitivity and specificity of detecting miRNA expression are not as satisfactory compared to other technical options, such as NGS and real-time PCR [384]. Here, NGS, which offers more depth, was initially performed for identification of deregulated miRNAs, including novel miRNAs (Figure 4.1). High-throughput TaqMan qPCR, which offers more sensitivity, were then performed using a larger cohort of samples to validate these deregulated miRNAs (Figure 4.2 and 4.3). A strong correlation between the two technologies was observed. Even for the miRNA candidates that did not meet adjusted P-values < 0.05 cut-off, between these two technologies, the detection (up- or down-regulation) of fold change comparing NBM and CML almost always agrees with each other especially among the miRNA candidates that are at higher ranking based on DESeq2 (Table 4.1). In addition, based on my validation data using 11 NBM and 22 CML samples, I found a great number of deregulated miRNA candidates overlap with the deregulated miRNAs published by the two groups who also used CD34+ CML cells to create expression profiles [324, 327]. The overlapping deregulated miRNAs include reduced expression of miR-16, miR-151, and miR-10b; and increased expression of miR-155, miR-130b, and miR-17-92 cluster and paralogs (including miR-18a, miR-19b, miR-17, miR-20a, miR-363, and miR-92a). Notably, miR-10b and miR-19b are borderline significant but the detection of the fold change is the same as the published data. Indeed, up-regulation of miR-17-92 cluster families and their oncogenic roles in CML have been 119  previously characterized [319, 385]. The roles of oncomiRNA miR-155 and tumor suppressor miR-16 have not been studied in CML but were well investigated in other cancers and hematological diseases [291, 301]. Other deregulated miRNAs found in these two studies, such as miR-199a, miR-224, miR-551b, and miR-22, were also found in our sequencing data, but I could not validate these miRNAs due to the suboptimal quality of the TaqMan qPCR assays in this 96 multiplexing array used for my validation study. Additional miRNA candidates published by other groups overlap with my sequencing data include down-regulation of miR-199b in CML patients bearing 9q deletion [386, 387], and hypermethylation of miR-34a found in CML [388]. In order to validate these miRNAs in our CML samples, I could perform individual TaqMan qPCR assay on each specific miRNA, or attempt different combinations and lower levels of multiplexing (96 to 48) to reduce signal-to-noise ratio to improve the quality of the TaqMan assays. Regardless, for the miRNA candidates that passed through stringent quality control of the TaqMan qPCR assays, and met Benjamini-Hochberg-adjusted P-values < 0.05 cut-off, they are truly differentially expressed in CML stem/progenitor cells. Other borderline significant miRNA candidates might also be differentially expressed and important in CML biology, but a larger cohort of samples are needed to validate their significance. Integration of miRNA-mRNA expression data led to the identification of high-probability predicted target genes that might play key roles in CML pathogenesis and drug resistance (Figure 4.11). Pathway analysis revealed that the majority of predicted target genes clustered into multiple cancer pathways, including prostate cancer, melanoma, colorectal cancer, renal cell carcinoma, glioma, pancreatic cancer, and CML (Table 4.3 and Table 4.4). Many overlapping genes found in these pathways include tumour suppressors, which are down-regulated in CML based on our RNA-seq data, and are predominately predicted to be targeted by oncomiRNA 120  miR-17-92 cluster and its paralogs, and by oncomiRNA miR-155. For examples, tumour suppressors PTEN, E2F2, SMAD7, BCL2L15, and TGFBR2 were potential target genes of miR-17-92 cluster, and tumour suppressors SMAD1 and CEBPB were potential target genes of miR-155. In fact, PTEN and TGFBR2 (along with other components of the TGF-β pathway) have been shown to be a direct target of miR-17-92 cluster [389, 390], and PTEN has been show to be involved in regulating functions of CML stem cells [391]. Similarly, CEBPB, which has been validated to be a target of miR-155 [300], is known to inhibit proliferation and promote differentiation of BCR-ABL-expressing cells [52]. In addition, a number of proto-oncogenes and oncogenes found across these multiple pathways are cyclin E1 (CCNE1), cyclin D1 (CCND1), PAK6, PIP5K1B, and FZD7, which were predicted targets of miR-16 (CCNE1 and CCND1), miR-185, miR-628, and, miR-145, respectively. Particularly, PAK6 and PIP5K1B are kinases, and FZD7 is part of the receptor for Wnt signaling pathway, and all three genes have been implicated in cancers [380, 392, 393]. This integration analysis further supports the oncogenic roles of miR-17-92 cluster and miR-155, and the tumour suppressor role of miR-16 in CML development. It also led to the identification of miRNAs with novel oncogenic or tumour suppressor functions in CML pathogenesis. To identity novel miRNAs with therapeutic and predictive potential, I have focused on the miRNAs that are less characterized but with good correlations with patient’s response to therapies in cancers or in CML. Among the validated deregulated miRNAs, I selected 8 candidates for further functional screening based on: their significant ranking according to DESeq2, the degree of fold change, their absolute expression levels, their novelty in leukemia, and the importance of their predicted target genes. The extensive biological assays revealed that forced overexpression of miR-185 suppresses leukemic cell growth, sensitizes TKI-induced apoptosis, and impairs colony growth 121  upon TKI treatments (Figure 4.4 to Figure 4.8). In fact, from my validation study, miR-185 is the only miRNA that is down-regulated in CD34+ CML cells compared to NBM, and its expression levels distinguish IM-nonresponders from IM-responders, suggesting its biological relevance to CML biology, drug resistance and potential as a useful biomarker. Although miR-185 has been reported to be deregulated and have tumor-suppressive effects in several cancers [394], it has not been reported in any hematopoietic malignancies nor in CML. miR-185 was found to be down-regulated and suppress tumour growth by directly targeting DNMT1 (DNA methyltransferases 1) in triple-negative breast cancer [395], gastric cancer [396], glioma [397], and hepatocellular carcinoma [398]. Other known targets of miR-185 include MYC [399], and STIM1 [400], whose overexpression correlate with poor prognosis in colorectal cancer patients. miR-185 was also found to be involved in caspase-dependent apoptosis in prostate cancer [401], radiation-induced apoptosis in kidney cancer [402], and chemotherapy-induced apoptosis in ovarian cancer [403]. Here I showed that miR-185 sensitizes leukemic cells to IM in a BCR-ABL-kinase dependent manner in all four BCR-ABL+ leukemic cell lines examined using several biological assays. This is supported by no effect of miR-185 on inducing apoptosis in BCR-ABL null or UT7-BCR-ABL-T315I mutant cells in the presence of IM. The role of miR-185 on cell proliferation is not as evident, for it moderately reduces growth in K562 and BV173 cells, but it does not seem to affect growth of K562R and UT7 B/A cells. The suppression of cell proliferation was also apparent in the mouse model using BV173 cells, but was not noticeable in CFC assay in all cell lines. This discrepancy could be due to different levels and variants of BCR-ABL or different intrinsic miR-185 level in these different cell lines. UT7 B/A cells are generated by transduction of a retroviral BCR-ABL vector, which might lead to higher BCR-ABL kinase activity than K562 or BV173 cells with endogenous level of BCR-ABL. In CFC methylcellulose medium 122  where no serum is provided and paracine signaling among cells is not possible, the condition might be too stringent for miR-185 to display any effect. Other more sensitive assays, such as Bromodeoxyuridine (BrdU) incorporation assay and cell cycle analysis, are needed to examine the role of miR-185 in cell growth more carefully. Similar to cell lines, forced expression of miR-185 significantly impairs the survival of CML stem/progenitor cells upon TKI treatment (Figure 4.9). I observed a decrease of the percentage of CD34+ CML cells transduced with miR-185 expressing vector in vitro, while its potential to block cell differentiation was not observed. This suggests that tumor suppressor function of miR-185 might be required for CML leukemic stem cell maintenance or survival, and re-expression of miR-185 might lead to increased differentiation of CML stem cells and therefore sensitize the more differentiated cells to TKIs. Whether the target genes of miR-185 or other functions of miR-185 are involved in the pathways responsible for maintaining the stemness of CML primitive cells, such as through β-Catenin [404], ALOX5 [405], and hedgehog signaling [406], warrant further investigations. miR-185 expression level in more primitive CML cells (CD34+CD38- vs. CD34+CD38+) should also be examined. Using six different target gene prediction algorithms and pathway analysis, I successfully validated PAK6, SGMS1, and NR1D1 to be target genes of miR-185 in CML. PAK6 and SGMS1 are up-regulated in CML samples compared to NBM samples, while NR1D1 is up-regulated in IM-nonresponders compared to IM-responders based on our RNA-seq data (Figure 4.11). PAK6 is a serine/threonine-protein kinase belongs to PAK family proteins, which has been shown to be up-regulated and correlated with poor patient survival in many solid tumours, which require PAK for the efficient activation of ERK, AKT and β-catenin signaling [380]. In fact, several pan-PAK inhibitors have been developed, and one has advanced to Phase I clinical trials, which was 123  shown to reduce tumour growth in xenograft mouse models using colon, breast, lung, melanoma, and stomach cancer cells [380, 407]. PAK6 in particular has been shown to be involved in the development of prostate cancer and chemoresistance of colon cancer [408-410]. SGMS1 is a sphingomyelin synthase, which synthesizes sphingomyelin for plasma membrane by combining ceramide and phosphorylcholine. The role of SGMS1 in cancer is not well characterized but studies have indicated that increased activity of SGMS1 correlates well with cell proliferation, and that inhibition of SGMS1 activity induces caspase-mediated apoptosis in Jurkat T cells [381, 382, 411]. NR1D1 is a nuclear receptor and a transcriptional repressor that was originally identified to regulate adipogenesis [412]. It was later identified to play a key role in apoptosis in neural and breast cancer cells [383, 413]. While these three target genes display oncogenic potential in CML, their roles in CML pathogenesis and drug response/resistance anticipate further studies. The study conducted by Venturini et al. indicated that IM treatment reduces or restores miR-17-92 cluster expression levels in CD34+ CML cells, and claimed that miR-17-92 cluster is under BCR-ABL regulation [319]. In agreement with this study, I not only found normalization of miR-17-92 cluster expression after TKI treatment, but also found that miR-185 expression is restored upon TKI treatments in K562R and CD34+ CML cells, suggesting miR-185 is also regulated by BCR-ABL (Figure 4.12 and Figure 4.13). miR-185 is an intragenic miRNA located on chr22: 20033139-20033220 [+], which is within the introns of TANGO2 (Transport and golgi organization 2 homolog) gene. RNA Polymerase II chromatin immunoprecipitation (ChIP)-chip analysis revealed that miR-185 is co-transcribed with its host gene TANGO2 [414]. Therefore, it might be of interest to further study how BCR-ABL regulates the transcription factors that activate TANGO2 gene and miR-185 expression. One possible explanation of how miR-185 124  promotes IM-induced apoptosis might be due to suppression of MAPK pathway, as I observed that overexpression of miR-185 plus IM reduces pERK activity in K562R cells (Figure 4.12F). Since IM treatment leads to up-regulation of miR-185, which in turn contributes to down-regulation of PAK6, and since PAKs are known to directly phosphorylate MEK1, which phosphorylates ERK [380]. Overexpression of miR-185 plus IM might consequently reduce pERK activity and therefore, enhance IM-induced apoptosis. To support this hypothesis, the expression level of downstream targets of pERK, such as cyclin D1, need to be examined. As a matter of fact, a recent review described that the deregulated miRNAs in CML might contribute to disease progression by targeting several components of MAPK signaling pathway [415]. In addition, the observation that overexpression of miR-185 can reduce leukemic cell proliferation in the presence of IM might be due to the effect of the other target gene, SGMS1. SGMS1 activity was recently shown to be under BCR-ABL regulation, since IM treatment in K562 cells reduce SGMS1 activity and knockdown of SGMS1 in K562 cells reduces cell proliferation [416]. Thus, IM treatment would lead to up-regulation of miR-185, which would in turn reduce SGMS1 protein level, overexpressing miR-185 plus IM may augment the effect of miR-185 to further impair leukemic cell growth. Taken together, the target genes PAK6 and SGMS1 might help explain the biological effects of miR-185 overexpression in CML cells, and their functions shall be better characterized as they might also allow development of novel therapeutic agents. Further validation of these miRNAs as predictive markers has also been carried out   using a larger cohort of samples obtained from newly diagnosed 65 CML patients before and after NL treatment. Almost all the miRNAs examined (a total of 19) have a significant degree of normalization upon NL treatment, particularly after three months of treatment (Figure 4.13). The miRNAs that were found down-regulated in CML, such as miR-139, miR-708, and miR-151, 125  were up-regulated after NL treatment, and miRNAs that were found up-regulated in CML, such as miR-155 and members of miR-17-92 cluster, were down-regulated after NL treatment. This suggests that these miRNAs might be regulated by BCR-ABL tyrosine kinase activity, and may act as potential biomarkers to predict clinical response of newly diagnosed CML patients to TKI therapy. Most interestingly, miR-185 level was not restored in CD34+ cells from NL-nonresponders as compared to the level detected in the responders after three months of NL treatment from some patients known their response status (Figure 4.13B), suggesting its potential to predict clinical outcome. Currently, we are obtaining complete clinical data of the CML patients, including, Sokal score, white blood cell counts, patient survival, and NL response status, and collecting laboratory data, such as BCR-ABL transcript level and CFC output upon NL treatment, which has been previously shown to correlate with clinical IM response [133]. Once these parameters are collected, I will perform multivariable logistic regression model to identify prognostic miRNA biomarkers that are able to predict clinical outcome of TKI therapies. Identification of prognostic markers that predict response of TKI is clinically valuable, thus alternate therapies could be offered upfront to patients deemed to be poor TKI responders before the development of greater refractoriness that might render the alternate therapies obsolete after TKI treatments. An alternate explanation for the observed miRNA expression changes in patients upon NL treatment could be due to the killing of leukemic cells. This leads to an increase of the percentage of normal CD34+ cells and a decrease of the percentage of leukemic CD34+ cells, thus, normalizing miRNA expression levels. However, this scenario is less likely, as several groups have reported that miRNA expression levels can be affected by TKIs without detection of cell death [319-326], and I also observed miR-185 expression changes in K562R cells in the presence of IM (Figure 4.12E). Furthermore, even if this scenario is real, those normalized 126  miRNAs could still act as prediction markers: more leukemic cells are killed in the responder pool compared to that of the nonresponder pool. Nevertheless, prior to multivariable statistical analyses and other comparisons, the miRNA expression levels can be normalized to BCR-ABL transcript level, or to the levels of other CML leukemic stem cell specific markers, including CD26 [417] and CD93 (ASH meeting abstract: Blood 2015; 126(23):49). In conclusion, by integrating miRNA expression profiles with gene expression profiles, my study has provided novel insights into CML disease progression and TKI resistance mechanisms. The identification of miR-185 as a new tumour suppressor and potential prognosis predictive biomarker opens the avenue for development of new therapeutic targets for improved treatment options and new guidelines for monitoring and management of CML patients.   127  Chapter 5: General Conclusions and Future Directions  5.1 Summary CML is a clonal stem cell hematological malignancy driven by elevated tyrosine kinase activity of fusion protein BCR-ABL. The development of ABL TKIs has revolutionized treatment options for CP CML patients. However, TKI monotherapy is not curative, with relapse and persistence of CML stem/progenitor cells remain to be challenging. Therefore, efforts have been made to identify novel therapeutic strategies to overcome TKI resistance, and to identify molecular biomarkers to predict clinical response of newly diagnosed patients to TKIs. This work has made some contributions to the field by identifying JAK2 and miR-185 to be key players in CML pathogenesis and TKI resistance, and as potential therapeutic targets for improved treatment options in CML.  In chapter 3, I examined the biological effects of a highly selective JAK2 inhibitor, BMS-911543, in combination with TKIs in CD34+ treatment-naïve IM-nonresponder cells. I have shown that combined treatment of BMS-911543 and TKIs can reduce JAK2/STAT5 and CRKL activities, induce apoptosis, inhibit proliferation and colony growth, and eliminate CML stem/progenitor cells in vitro, while sparing normal stem/progenitor cells. I further showed that the combined treatment of BMS-911543 and TKI is more effective in eliminating infiltrated leukemic cells in hematopoietic tissues than TKI treatment alone in vivo, and in enhancing survival of leukemic mice. Therefore, dual targeting of BCR-ABL and JAK2 kinase activities in CML stem/ progenitor cells may consequently lead to more effective disease eradication, especially in patients with higher potential to develop TKI resistance. 128   In chapter 4, I focused on identifying the deregulated miRNAs in CML stem/progenitor cells, which may play a role in CML biology and act as predictive biomarkers. I first identified a list of aberrant expressed miRNAs in CML CD34+ cells and in IM-nonresponders using next-generation sequencing. I validated the sequencing data in a larger cohort of samples using a high-throughput qPCR microfluidics device. After performing a comprehensive functional screening of several miRNA candidates in CML cell lines, I discovered that miR-185 acts as a tumour suppressor, and its overexpression suppresses cell growth, enhances IM-induced apoptosis, and impairs colony-forming ability upon TKI treatments. The biological significance of miR-185 also extends to CD34+ TKI-nonresponder cells and to an aggressive leukemic mouse model. In addition, target gene prediction algorithms revealed a list of potential target genes of the deregulated miRNAs, and I validated some of the target genes of miR-185 in order to explain the observed biological effects in CML cells. Finally, I found significant expression level changes in some of the deregulated miRNAs, including miR-185, in CD34+ cells from 65 CML patients after NL treatment, suggesting their potential to predict clinical response to TKI therapy. This work demonstrates the possibility of miR-185 to act as a therapeutic target for combination treatments with TKIs, and as a biomarker to predict clinical outcome prior to TKI therapy.  5.2 Significance and Limitations of the Work  JAK2/STAT5 signaling pathway has been implicated to play a critical role in CML pathogenesis due to over activation of STAT5. Since BCR-ABL can phosphorylate STAT5 directly, rendering JAK2 dispensable, the merit of JAK2 inhibition in CML has been questioned. In fact, the function of JAK2 in CML remains controversial. While some groups argued that the success of JAK2 inhibitors in CML cells is due to non-specificity and off-target inhibition of 129  BCR-ABL itself, other groups claimed that canonical JAK2/STAT5 pathway is critical for the survival of CML stem/progenitor cells, which depend on cytokine-activated JAK2/STAT5 signaling on top of BCR-ABL signaling. In the first part of my thesis, I provided strong pre-clinical evidence that while the BMS-911543 alone has limited effect on CML stem/progenitor cells, suggesting its specific inhibition of JAK2, combination treatment of BMS-911543 and TKI is much more effective in eliminating IM-insensitive BCR-ABL+ cells and CD34+ treatment-naïve IM-nonresponder cells compared to TKI alone. This work is significant, for it supports the notion that CML stem/progenitor cells rely on canonical JAK2/STAT5 pathway when their BCR-ABL signaling is inhibited, and that combination treatment needs to be offered upfront to potential CML IM-nonresponders in order to prevent the development of TKI-resistance CML stem/progenitor clones and disease relapse.  Even though BMS-911543 displayed high specificity based on this work, inhibitors are bound to have some toxicity or minor off-target effects. Therefore, a more specific genetic approach, such as JAK2 siRNA or shRNA knockdown, in combination with TKIs, should be performed in CML stem/progenitor cells to determine whether similar biological effects observed with dual inhibition of JAK2 and BCR-ABL can also be achieved. In addition, in order to further elucidate the mechanisms of the combination effects, and to identify other potential novel therapeutic targets, it might be interesting to perform DNA microarray to examine the downstream targets, and to perform mass cytometry, CyTOF [418], or high-throughput (phospho) proteomics [419] to study in vitro and in vivo changes in other signaling pathways and phosphorylation patterns in CML stem/progenitor cells upon combination treatments. To my knowledge, the miRNA component of my project is the first study to utilize highly purified CD34+ CML and NBM cells to perform a large scale miRNA expression profiling using 130  both next-generation sequencing and a high-throughput TaqMan qPCR device in order to identify differentially expressed miRNAs in CML and in IM-nonresponders with high validity. I also utilized gene expression profiles from the same CML samples using RNA-seq to identify potential target genes of the deregulated miRNAs to shed light on the potential mechanisms of CML pathogenesis due to the deregulated miRNAs. This work contributes greatly to the CML field. As CML is a stem cell disease and the TKI resistance clones reside in the stem-cell compartment, the deregulated miRNAs identified in CD34+ cells may be responsible for TKI resistance, and may act as predictive markers to TKI treatments. Indeed, from these comprehensive studies, I found one of the deregulated miRNAs, miR-185, whose function has not been explored in any hematopoietic malignancy, acts as tumour suppressor and its overexpression inhibiting cell proliferation and sensitizing primitive CML cells to TKI in vitro and in vivo, indicating its role in TKI resistance and its potential to act as a novel therapeutic target for combination treatments. Also, the expression level of miR-185 in CML patients treated with NL was restored in NL-responders but not in potential NL-nonresponders, demonstrating its ability to act as a prognostic marker.  In most biological experiments, the lentiviral-mediated miR-185 overexpressing system was used to achieve stable overexpression. While the results from the biological assays seem promising, the degree of overexpression using lentivirus system is usually greater than what is achieved physiologically (> 10 fold). This might introduce non-specific binding and suppression of genes that are not physiological targets of miR-185. Therefore, it might be of interest to perform additional biological assays using miRNA sponges to knock down miR-185 expression in CML cells to within physiological level [420], and to examine whether the biological effects observed in miR-185 expressing cells can be reversed. Likewise, knocking down miR-185 131  expression in CD34+ NBM cells could be performed to see if the NBM cells would display oncogenic phenotypes. Moreover, the mechanisms of these biological effects need to be better understood. Although three predicted target genes of miR-185, namely PAK6, SGSMS1, and NR1D1, have been validated, their roles and how they contribute to the tumour suppressor function of miR-185 in CML cells remains to be understood. siRNA or shRNA knockdown of these three target genes can be used to examine whether the biological effects observed in miR-185 overexpressing system can be replicated, and if so, whether the effects are also involved in the RAS/MAPK pathway by examining pERK activity and its downstream targets, such as cyclin D1.  Almost all the deregulated miRNAs found in the miRNA expression profiles showed restorations in their expression levels in patients treated with NL, indicating the possibility for some of them to act as predictive biomarker signatures. However, in order to claim this, more clinical parameters, such as patients’ TKI response status, need to be obtained so that logistic regression models or other statistical tests can be performed. Proper normalization to the number of leukemic cells, such as the miRNA expression levels relative to BCR-ABL transcript level, might also be needed prior to any further analyses.  5.3 Future Directions In both parts of my project, I utilized a xenotransplant mouse model engrafted with BCR-ABL+ blast crisis cells (BV173) to generate a lethal leukemia in mice. Although I have successfully showed that combined treatments of TKI plus BMS-911543, and combined treatments of TKI plus miR-185 overexpression significantly reduce leukemia burden and enhance survival of leukemic mice, I could also use a different xenotransplant mouse model with 132  transplantation of primitive human CML cells instead [421], which resembles more closely to the heterogeneity and pathology characteristic of primary leukemias. Additionally, I could use the inducible transgenic BCR-ABL mouse model [422], in which the mice are able to develop CML-like leukemic disease, including neutrophilia and splenomegaly. Similar to the BV173 xenotransplant mouse model, this transgenic model has short disease latency, which is ideal for testing therapeutic drugs to evaluate the effects of therapeutic interventions. This transgenic model, however, would require generation of miR-185 overexpressing or knockout mice. Alternatively, the BCR-ABL retroviral transduction/transplantation model could be used for the miR-185 project, for this model uses ex vivo retroviral transduction of mouse BM which is ideal for studying the biological functions of single or multiple genes [35]. This model also generates a CML-like disease with short latency, which is also suitable for drug testing.   I have successfully identified differentially expressed miRNAs in CD34+ CML cells using CD34+ NBM cells as controls and showed that some deregulated miRNAs indeed play functional roles in CML biology and are under regulation of BCR-ABL. However, the relative heterogeneous nature of CD34+ cells and the variations among normal control samples are concerns and might mask the truly differentially expressed miRNAs in the primitive cell populations. Therefore, it might be ideal to perform single cell whole transcriptome analysis on Lin-CD34+CD38- populations obtained from the same CML patient to identify differentially expressed miRNAs comparing the expression profiles of BCR-ABL expressing cells to that of BCR-ABL non-expressing cells (ASH meeting abstract: Blood 2015; 126(23):13). This would lead to direct identifications of the differentially expressed miRNAs that are under the influence of BCR-ABL, while reducing background noise due to genetic variations among different individuals. 133  Since functional screening has revealed miR-185 to be the most biologically significant in CML among other miRNA candidates, instead of examining the predicted target genes obtained from gene expression profiles comparing CML samples to NBM samples, I could evaluate the predicted target genes obtained from CD34+ CML cells transduced with miR-185 expressing vector and CD34+ CML cells transduced with pRRL control vector. In addition, to reduce false-positive rate from the target gene predictions algorithms, I could transduce cells using different virus titers, or infect cells for different period of time before harvesting RNAs to create gene expression profiles. I could therefore select the predicted target genes whose expression levels are down-regulated in cells transduced with miR-185 expressing vector, and whose down-regulated expression fall in a dosage- or time-dependent manner. Alternatively, instead of using gene expression profiles, I could perform high-throughput sequencing of RNAs isolated by crosslinking immunoprecipitation (HITS-CLIP) using argonaute proteins [423]. This technique would allow identification of the target mRNAs that are directly bound to both argonaute proteins and miRNAs simultaneously. Moreover, the regulation and biological roles of miR-185 could be further explored. I observed that miR-185 expression is affected by TKIs, suggesting it might be under BCR-ABL regulation. To further support this hypothesis, I could overexpress BCR-ABL in CD34+ NBM cells or cell lines not expressing BCR-ABL to examine whether miR-185 expression level would be changed upon overexpression. Also, since miR-185 shares the same promoter as its host gene TANGO2, I could study how BCR-ABL activates TANGO2 and miR-185 expression by interacting or by promoting the activity of transcription factors for TANGO2. Besides exploring the possibility of miR-185 being regulated by BCR-ABL, it might be interesting to see whether DICER1 is responsible for the global miRNA expression changes in CML since DICER1 has 134  been found to be down-regulated in CML samples studied based on RNA-seq analysis (data not shown). DICER1 is a major processing machinery responsible for yielding mature miRNAs from their precursors, and its reduced expression is associated with lung [424], ovarian [425], and breast cancer [426], possibly by altering miRNA expression. Therefore, it might be interesting to restore DICER1 level in CD34+ CML cells to see if the deregulated miRNAs can be normalized, and to knockdown DICER1 in CD34+ NBM cells to determine if miRNA expressions can be altered. Lastly, the biological functions of other miRNA candidates can be explored. Particularly, the role of miR-155 in CML has not been studied although it has been characterized as oncomiRNA in other cancer types [301]. Similarly, although miR-17-92 cluster has been shown to be involved in CML development [319, 385], the function of the individual member and its paralogs has not been extensively studied. There are three paraglos of miR-17 family, including miR-17-92 cluster (miR-17, miR-18a, miR-19a, miR-20a, miR-19b-1, and miR-92a-1), miR-106a-363 cluster (miR-106a, miR-18b, miR-20b, miR-19b-2, miR-92a-2, and miR-363), and miR-106b-25 cluster (miR-106b, miR-93, and miR-25) located at chromosome 13, chromosome X, and chromosome 7, respectively [427]. Many members of the miR-17 family are found to be up-regulated in CML based on my validation study, and many of their target tumour suppressor genes are found to be down-regulated in CML. 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