Hyper-Methylation of the Socs2 Promoter in Aml: an Unexpected

HYPER-METHYLATION OF THE SOCS2 PROMOTER IN AML: AN UNEXPECTED

ASSOCIATION WITH THE FLT3-ITD MUTATION.

Courtney Amanda Lynne McIntosh

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Graduate Department of Medical Biophysics

University of Toronto

FLT3-ITD mutation, Master of Science 2009, Courtney Amanda Lynne McIntosh, Department

of Medical Biophysics, University of Toronto.

Abstract

Haematopoiesis requires strict regulation in order to maintain a balanced production of the

various blood cell components. Escape from this regulation contributes to the development of

cancers such as leukemia. SOCS2 is a member of the Suppressor of cytokine signalling (SOCS)

family, and normally functions as a negative regulator of the JAK/STAT pathway. I examined

gene expression and promoter methylation in acute myeloid leukemia (AML) cell lines and

patient samples. SOCS2 expression was quite variable in AML patients, and very low in acute

promyelocytic leukemia (APL) patients. Promoter hyper-methylation was found in these

patients, particularly those with high white blood cell count and a FLT3-ITD. I speculate that

SOCS2 interacts with an aspect of the signalling complex to inhibit cell growth in these patients,

and silencing SOCS2 is necessary for leukemia progression. Treating these patients with a de-

methylating agent, such as decitabine, may show promise in the clinic.

Acknowedgements

I would like to thank my supervisor, Mark Minden, for taking me into his lab when I

thought all was lost, and for always treating me with kindness and respect. Thank you for all of

your guidance and ideas, and for getting me through this process.

Also a big thank you to my committee members, Dwayne Barber and Tony Pawson, for

thier support, assistance, and guidance.

I appreciate everything that the Minden lab members, past and present (Lin, Fernando,

Serban, Jian Liu, YouQi, Haytham, Jenny, Rajesh, Ruijuan, Vicky, Xiu-Zhi, Yuhui, Samantha,

Meng, Mr. Hu) have done for me. Especially Lin Yang for her expertise in lab proceedures, as

well as for being a good friend. Also thank you to Fernando Suarez for all of his technical

support and endless knowledge on datasets, microarrays, statistics and bioinformatics. Thank you

also to YouQi Han, Serban San-Marina, and Jian Liu for thier help with many of the

proceedures. I appreciate all the endless discussions which helped me focus on the bigger

picture.

A special thanks goes to my partner and best friend, Porya Rajabi, for being there to share

the good times, and being a shoulder to cry on through the hard times. Thank you for your

endless support. Also thank you to my roommates and dear friends, Andrea Para, Chris

Marriage, and Frank Goytisolo. I don’t know what I would have done without your friendship!

A special thank you to my brother, Graham, and my parents Lynne and Clark for being there for me both finanically and emotionally through these tough couple of years. Thanks for believing in me and never giving up hope! ☺

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Table of Contents

Title………………………………………………………………………………………..………i Abstract…………………………………………………………………………………….…….ii Acknowledgements……………………………………………………………………………...iii List of figures…………………………………………………………………...………………..vi List of Abbreviations……………………………………………………………..………....….vii

Chapter 1: Introduction……………………………………………………………...……….....1 Overview…………………………………………………………………………………………..2 PART 1: NORMAL CELLS…………………………………………………………………….3 Haematopoiesis, stem cells……………………………………………………..…………………3 Cell surface receptors and signalling pathways…………………………………………….……..5 JAK/STAT………………………………………………………………………………………...8 Negative Regulation……………………………...………………………………………………14 Negative Regulators…………………………………………………………..…………….……15 SOCS knock-outs……………………………………………………………………..………….23 SOCS2……………………………………………………………………………………..…….24 PART 2: CANCER………………………………………………………………………….….25 When the steady state is compromised………………………………………………………..…25 Leukemia……………………………………………………………………………….………...25 AML Classification…………………………………………………………………………...….26 Biology of AML…………………………………………………………………………………30 AML as a genetic disease………………………………………………………………………..32 Aberrant negative regulation……………………………………………………………………..34 Methylation………………………………………………………………………………………34 Random vs. directed……………………………………………………………………………..36 SOCS family and methylation………………………………………………………………...…38 PART 3: MY THESIS………………………………………………………………………….40 Rationale…………………………………………………………………………………………40 Hypothesis………………………………………………………………………………………..41 Chapter 2: Manuscript…………………………………………………………………………42 Title………………………………………………………………………………………………43 Abstract…………………………………………………………………………………………..44 Introduction…………………………………………………………………………………..….45 Materials and Methods…………………………………………………………………………..48 Results…………………………………………………………………………………………...53 Discussion………………………………………………………………………………….…….76 Chapter 3: Summary and Future Work…………………………………………………...….86 Significance/Impact on field……………………………………………………………………..87 Future work…………………………………………………………………………..…….…….88 Summary…………………………………………………………………………….…………...92 References………………………………………………………………………………………..94 Chapter 4: Appendix…………………………………………………………………………...98 Appendix 1 : Valk………………………………………………………………………………..99

List of Figures Chapter 1 Figure 1.1: The haematopoietic hierarchy in normal and leukemic cells………………….………..4 Figure 1.2: The domain structure of the JAK protein family………………………………….………8 Figure 1.3: The domain structure of the STAT protein family…………………………………..…….9 Figure 1.4: The JAK/STAT pathway, signalling through the EPO-R…………………………….…11 Figure 1.5: The domain structure of the 8 members of the suppressor of cytokine signalling…16 Figure 1.6: The role of the SOCS family members on the JAK/STAT pathway……….…………..18 Figure 1.7: The ubiquitin-proteosome pathway and SOCS………………………………………….20 Figure 1.8: AML classifications…………………………………………………………………………27 Table 1.1: JAK and STAT family members used in various cytokine signalling pathways……...12 Table 1.2: Factors that induce SOCS expression……………………………………………………..21 Table 1.3: Features of SOCS knock-out mice………………………………………………………….23

Table 1.4: The eight FAB subtypes of AML……………………………………………………………………..28 Table 1.5: WHO classification of AML………………………………………………………………...28 Chapter 2 Figure 2.1: The SOCS2 gene and promoter methylation…………………………………………….52 Figure 2.2: The SOCS family in CD34 positive and NBM samples in Valk……………….………54 Figure 2.3: Gene expression in the Valk dataset………………………………………………………55 Figure 2.4: SOCS2 and CD34 expression across Valk dataset………………………………….….56 Figure 2.5: SOCS2 protein, RNA, and methylation in cell lines…………………………………….59 Figure 2.6: SOCS2 expression and promoter methylation in patient samples………….…………62 Figure 2.7: SOCS2 expression in Group 4 & 12 ………………………………………….………….66 Figure 2.8: SOCS2 methylation in Group 4 & 12 ………………………...………………...……..…68 Figure 2.9: Induction of SOCS2 by Decitabine………………………………………….…………….72 Figure 2.10: SOCS2 expression in AML patients treated with Decitabine…………….…………..74 Figure 2.11: SOCS2 expression for FLT3-ITD patients in Valk…………...... …78

Figure 2.12: SOCS2 expression for 16 Valk groups………………………………………………….82 Table 2.1: Characteristics of Valk patients in each of the 16 groups……………………………....57

Table 2.2: Group 4 patient information……………………………………………………………..….63 Table 2.3: Group 12 patient information……………………………………………………………….64 Table 2.4: WBC and FLT3-ITD in Valk patients……………………………………………………...70 Table 2.5 Patient info for decitabine clinical trial…………………….……………………………...75 Chapter 4 Figure 4.1: FAB classification in each Valk group………………………………………………….100 Figure 4.2: Valk group in each FAB classification…………………………………………….……101

List of Abbreviations 5-aza- 5-Aza-2'-Deoxycytidine (Decitabine) ABL- ABelson Leukemia AKT- A viral oncoprotein originally isolated from a murine T CELL LYMPHOMA infected with the acutely transforming retrovirus AKT8 AML- Acute Myeloid Leukemia APL- Acute Promyelocytic Leukemia BCR- Breakpoint cluster region BCR-ABL- fusion protein of the abl and bcr genes BM- Bone marrow CD- Cluster of differentiation molecule CDK- cyclin-dependent kinase CFU- colony forming unit CFU-Meg- colony forming unit-megakaryocyte CFU-E- colony forming unit-erythroid CIS-Suppressor of Cytokine Signaling c-kit- Receptor, Stem Cell Factor (p145) CML- Chronic Myeloid Leukemia CpG- stands for cytosine and guanine separated by a phosphate, which links the two nucleosides together in DNA. CXCL12/SDF-1- stromal cell-derived factor-1 DNA- Deoxyribonucleic acid DNMTs- DNA-methyltransferases E1- ubiquitin-activating enzyme E2- Ub conjugating enzyme E3- Ub ligase protein ELOC- Elongin C EPO- erythropoietin EPO-R- erythropoietin receptor ERK-extracellular signal-regulated kinases (ERKs) or classical MAP kinases FAB- French-American-British classification system FAK- Focal adhesion kinase FAS- Part of the tumour necrosis family, a ligand inducing apoptosis FERM- Four-point-one, Ezrin, Radixin, Moesin FLT3- Fms-like tyrosine kinase receptor 3 (CD135) FLT3L- ligand for FLT3 receptor FLT3-ITD- Internal tandem duplication of the FLT3 receptor FMS- GP140 GAS- gamma interferon activation site G-CSF- granulocyte-colony stimulating factor G-CSF-R- granulocyte-colony stimulating factor receptor GH- growth hormone HSC- Hematopoetic stem cell IFN-α- interferon alpha IFN-γ- Interferon gamma IGF-1- insulin-like growth factor-1

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IL- interleukin ITD- internal tandem duplication JAK- Janus Kinase JH- JAK homology KIR- kinase inhibitory region LIF- Leukemia initiating factor LSC- Leukemia stem cell LTC-IC- long term culture-initiating cell Lnk- Lymphocyte adaptor protein MAP-K- mitogen-activated protein kinase MDS- myelodysplastic syndromes MPL- thrombopoietin receptor MS-SSCP- Methylation specific- single strand conformation polymorphism NBM- normal bone marrow NIH-3T3- Murine fibroblast cell line OSM- Oncostatin M PIAS- Protein Inhibitor of activated STATs Pim1- Pim-1 oncogene PTPs- Protein tyrosine phosphatises PRL- prolactin PRL-R- prolactin receptor RA- retinoic acid RARE- retinoic acid response element RAS- Retrovirus Associated Sequences RNA- ribonucleic acid RNAi- RNA interference SC- stem cell SH2- Src homology 2 SHPs- 2-containing phosphatises SOCS- Suppressor of Cytokine Signaling STAT- Signal Transducers and Activator of Transcription TC-PTP- T-cell protein tyrosine phosphatase TPO- thrombopoietin TYK2- JAK family member UB- ubiquitin Valk- gene expression study of AML blast cells from the peripheral blood or bone marrow of 285 patients at diagnosis WBC- white blood cell count WHO- World Health Organization Y- phosphorylation site

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CHAPTER 1: Introduction

Overview

Haematopoiesis is a lifelong orderly process of functional blood cell production. Cell

signalling mediates cell growth, and requires tight regulation so that a steady state is maintained;

failure of this regulation can contribute to the development of cancers. Leukaemias are cancers

of the blood, and of the blood forming organ, the bone marrow. These develop as the result of a

variety of mutations that are manifested by failure of normal blood cell production, and the

accumulation of immature cells in the blood and marrow. As acute myeloid leukemia (AML)

develops due to quite distinct genetic abnormalities, it is now clear that AML is not one, but

multiple diseases. This is clearly evident from gene expression profiling in which the patterns of

increased or decreased gene expression are characteristic of AML subtypes [1]. While the genes

increased compared to normal cells are frequently growth promoting oncogenes, the genes that

are decreased are often growth limiting, such as tumour suppressor genes. Recently, epigenetic

changes in gene expression, such as promoter hyper-methylation, are thought to play an

important role in the silencing process and are implicated in a wide variety of cancers.

The Suppressor of Cytokine Signalling (SOCS) family of proteins are thought to

normally function as negative regulators of cytokine signalling, and their loss in experimental

animals results in generalized hyper-proliferation [2-4]. Previous hyper-methylation of the

SOCS1 [5] and SOCS2 [6] promoter has been found in myelodysplastic syndromes (MDS), and

SOCS1 in AML [5, 7, 8] but so far not in SOCS2. My analysis of a public domain gene

expression dataset for AML showed a small subset of cases with very low levels of SOCS2 gene

expression [1]. I hypothesise that this decrease in gene expression may be in part due to promoter methylation.

PART 1: NORMAL CELLS

Haematopoiesis, stem cells

Haematopoiesis (from Ancient Greek: haima blood; poiesis to make) is the effect of stringently regulated signalling pathways mediated by cytokines and their receptors, which generate cells in all of the various blood cell components (Figure 1.1). Haematopoietic stem cells

(HSCs) are found at the top of the hierarchy, and are constantly maintained in the bone marrow in a quiescent state. HSCs are characterized by their potential to differentiate and self renew, to produce blood cells of all lineages through their progeny, long term culture-initiating cells (LTC-

IC) [9]. These multipotent cells differentiate in a step-wise fashion, generating what is depicted as the haematopoietic hierarchy, as shown in Figure 1.1. Unlimited replicative potential is restricted to this small fraction of stem cells, which infrequently divide asymmetrically to generate one pluripotent daughter cell to replace the original, and one that commits to terminal differentiation. By confining cell expansion to cells ultimately committed to death, stem cells provide a sufficient number of cells to maintain and replace tissues, while preventing exposure to mutagenic risk by limiting the number of cell divisions [9-12].

The committed progenitor cell retains many stem cell-like properties, however, its fate becomes restricted towards a particular lineage: lymphoid (T- and B- white blood cells), and myeloid (erythrocytes, granulocytes, megakaryocytes, and macrophages). Both lineages are derived from a common haematopoietic stem cell, however, it is possible to isolate progenitor cells that display a preference towards a particular lineage [13, 14].

Figure 1.1. The haematopoietic hierarchy in normal and leukemic cells. HSC- Haematopoietic stem cell, LSC- Leukemic stem cell, LTC-IC- long term culture-initiating cells, CFU- colony forming units. Figure by Ryan Pinto, adapted from JC Wang and JE Dick [9].

As the committed daughter stem cell matures towards its differentiated cell fate, it undergoes changes in gene expression that limit the type of cell that it can become. Each successive change moves the cell closer to its final choice of mature cell and further limits its replicative potential until it becomes fully differentiated. It is well established that cell growth and differentiation is mediated by a group of soluble polypeptide growth factors known as cytokines. These changes can often be tracked by the presence of proteins on the surface of the cell. For example, the CD34 cell surface marker is found on immature cells, but is lost as cells

commit to a defined lineage [15]. This commitment towards a certain type of blood cell depends

on gene expression, as well as the external signals within its environment. Usually a balance

exists between cell production and cell death, creating a steady state situation. However, the

system is responsive to stress. During a bacterial infection for example, the production of

granulocytes increases rapidly to eliminate invading organisms. Once the infection is under

control, the immune response must be shut off to regain equilibrium.

Cell surface receptors and signalling pathways

Cell signalling is the complex method of communication used by cells to govern cellular

behaviour within a complex multi-cellular organism. Cytokine receptors have been classified

based on their three-dimensional structure, and my thesis will focus on the Type 1 haematopoietic growth factor family of receptors, which consist of a single subunit that forms a homo-dimer upon ligand binding, and whose members consist of the erythropoietin (EPO),

thrombopoietin (MPL), growth hormone (GH), prolactin (PRL) and granulocyte-colony

stimulating factor (G-CSF) receptors [16].

The importance of cell signalling begins with fertilisation and lasts throughout life. It is

crucial for cells to develop into differentiated structures, to detect and respond to their

environment, and to maintain a homeostatic state. Problems in relaying cellular information can

lead to various diseases such as autoimmunity, diabetes, and cancer, which are caused by

aberrant signalling in the cell. Cells receive environmental information through a variety of cell

surface protein receptors that can bind molecules in a specific manner, such as hormones,

neurotransmitters, cytokines, and growth factors. These molecules bind to their cognate receptors

and mediate cell signalling events that result in changes in gene expression. In haematopoietic

cells, the trans-membrane protein receptors involved in signal transduction may be divided into those with intrinsic kinase activity like c-Kit, or receptors like the EPO-R that lack kinase activity but interact with cytoplasmic protein kinases like Janus kinases (JAKs). The interaction of a ligand with its receptor begins the propagation of information from the cell surface into the cytoplasm and nucleus by way of reversible phosphorylation of serine, threonine or tyrosines on target proteins.

In addition to classifying haematopoietic receptors by their mode of signalling, they can be separated according to their place in the haematopoietic hierarchy. At the top of the pyramid are pluripotent stem cells capable of self renewal and differentiation into all types of myeloid and lymphoid cells. For the purpose of my thesis, I will focus on the myeloid branch, which produces terminal cells including erythrocytes, monocytes, granulocytes, and platelets. Between the uncommitted stem cell and the terminally differentiated cell exists a series of committed cells, some of which are morphologically recognizable, and others that are not.

The movement from one stage in the hierarchy to another is associated with the presentation of a specific cell surface receptor. For example, the c-Kit receptor is needed for the proliferation and self-renewal of the haematopoietic stem cell. It is present on pluripotent cells like pre-colony forming units and committed progenitors, but disappears in differentiated cells

[17, 18]. EPO is required for a myeloid progenitor cell to differentiate into an erythrocyte, and thrombopoietin (TPO) is required for a myeloid progenitor cell to differentiate into a megakaryocyte (thrombocyte-forming cells) [19]. The EPO receptor is present on colony forming unit-erythroid (CFU-E) cells, and the MPL receptor is present on colony forming unit-

megakaryocyte (CFU-Meg) cells, and disappears as the cells terminally differentiate. These

growth factors are important for cell growth and survival.

As previously touched upon above, there exists a steady state of production and

destruction of white blood cells, red blood cells, and platelets, in the normal individual. This

process is governed by EPO, the cytokine responsible for the increase in terminally differentiated erythrocytes, to ensure that cell numbers are enough to sustain adequate oxygen levels in tissues, but not too high as to cause a stroke. However, stress conditions do arise, and the body has developed adaptive/protective mechanisms to deal with these situations. A simplified example is when an individual sustains a major cut with significant blood loss. The body compensates by increasing the production of red blood cells. The production of EPO increases from the kidney in response to low oxygen levels. EPO binds circulating red blood cells, so low numbers of circulating cells leads to relatively high levels of unbound EPO. This results in the expansion of the CFU-E compartment, and a burst in downstream terminally differentiated erythrocytes. This process returns haemoglobin back to a normal level in the body. Once the stress has been alleviated and the defence is no longer needed, the system requires a way to slow down erythrocyte production, and return back to steady state. The kidney decreases EPO production until its level returns to normal, and the signalling pathways are dampened by negative regulators such as the SOCS proteins. Such negative regulation is important in maintaining control of the system; this is in contrast to events in some malignant cells, which will be described below.

In the following sections, I will describe some of the positive and negative regulators involved in haematopoiesis, and some the consequences that ensue when this regulation is compromised.

JAK/STAT

Studies conducted over the past 15 years have revealed that haematopoietic cytokine

signalling is largely mediated by a family of tyrosine kinases, Janus kinases (JAKs), and their downstream transcription factor targets, Signal transducers and activators of transcription

(STATs). The JAK/STAT pathway plays an important role in haematopoiesis at various points in

the hierarchy, through its ability to propagate positive growth signals. The JAK/STATs are

crucial for signalling through Type I cytokine receptors, such as the EPO-R, which do not

contain intrinsic tyrosine kinase activity, and must therefore associate with cytoplasmic proteins

possessing this property; however, trans-membrane receptors with intrinsic tyrosine kinase

activity may also engage the JAK/STAT pathway.

Figure 1.2. The domain structure of the JAK protein family. JH= JAK homology, TK= tyrosine

kinase, SH2= src homology 2 domain, FERM= band Four-point-one, Ezrin, Radixin, Moesin

domain [16].

The JAK family of cytoplasmic tyrosine kinases carry out this role by generating signals

from cytokine and growth factor receptors. The family is comprised of four family members,

JAK1, JAK2, JAK3, and TYK2. Structurally these proteins are characterized by seven JAK

homology (JH) domains JH1-JH7, as depicted in Figure 1.2. The C-terminal domains, JH1 and

JH2, show extensive homology to tyrosine kinase domains. JH1 is a functional tyrosine kinase 8

domain, whereas JH2 has many conserved amino acids found in functional kinases but lacks any

enzymatic activity. It has been shown to have a negative regulatory role in JAK/STAT

signalling, however, and is critical for JAK molecules to self-regulate and mediate cytokine-

induced responses [20]. JAK2 V617F is a somatic mutation in the JH2 auto-inhibitory domain,

which results in constitutive activation of the tyrosine kinase, phosphorylation of STAT5, and factor-independent growth of haematopoietic cells. It has been described in polycythemia vera, essential thrombocythemia, and primary myelofibrosis [21, 22]. It is believed to disrupt the inhibitory role that the pseudo-kinase domain JH2 has on the JH1 domain, and leads to increased phosphorylation and activation of JH1 by an altered conformation [21].

The N-terminal portion of JAK molecules contain an SH2 domain, encompassing the JH3

and JH4 domains, and a band Four-point-one, Ezrin, Radixin, Moesin (FERM) domain,

encompassing JH6 and JH7. This regulates catalytic activity and mediates receptor association.

The effect of the activated JAK is mediated by its ability to engage a number of

downstream pathways including AKT, ERK, and STAT. For the purposes of my thesis, I will

limit my discussion to the STAT portion as the JAKs are the major activators of these proteins.

Figure 1.3. The domain structure of the STAT protein family [16].

The STAT proteins are a family of 7 latent cytoplasmic transcription factors; STAT1,

STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6, each containing six conserved

domains, shown in figure 1.3. The first domain is the N-terminal domain, which is important for

STAT phosphorylation, receptor binding, nuclear import, and co-operates with the DNA-binding

domain [16]. The coiled-coil region associates with regulatory proteins, and helps as well with receptor binding [23]. The third domain is the DNA binding domain, which recognizes the conserved STAT binding site with the consensus sequence of TTNCNNNAA [24, 25]. The fourth is a linker domain that maintains proper orientation between the DNA binding domain and the following fifth domain, the SH2 domain. The tyrosine containing SH2 domain binds to phosphorylated receptors. Through the phosphorylated SH2 domain, the STATs can form homo-

and hetero-dimers. Once dimerized, the protein is then able to enter the nucleus and bind to

DNA. At the C-terminus is the sixth domain, the transcriptional activation domain; this region

varies between family members, and modulates the activation of target genes. The C-terminus

houses the highly conserved tyrosine, which is the substrate for phosphorylation by the JAK

kinases.

Figure 1.4. A schematic representation of the JAK/STAT pathway, signalling through the EPO-R

[26].

For simplicity, I will describe the EPO pathway as an example, as it encompasses the basis of the JAK/STAT pathway (Figure 1.5). The EPO receptor (EPO-R) exists on the surface of the cell as a single chain that associates in a preformed dimer, and the signalling event is initiated when EPO molecules bind to the extracellular portion of the EPO-R. Upon ligand binding, the receptor undergoes a conformational change, allowing two receptor-associated

JAK2 molecules to be brought in close proximity of each other, allowing them to auto- phosphorylate themselves and their cognate receptor at several tyrosine resides in the EPO-R tail, such as Y343, Y401, and Y431 as seen in figure 1.5. The activated receptors are then able to

recruit STAT molecules such as STAT1, STAT3 and STAT5a/b in the cytoplasm, which bind to

the phospho-tyrosines through their SH2 domains. STAT5 associates with Y343 and Y401 and

STAT3 binds to Y431. STAT1 is activated downstream of JAK2 but does not require tyrosine

phosphorylation by the EPO-R. The STAT5 proteins become phosphorylated on tyrosine residues (STAT5a on Y694 and STAT5b on Y699), allowing them to form homo-dimers. These activated dimers complete a nuclear localization signal, allowing them to migrate into the

nucleus. Here they bind to STAT binding sites called gamma interferon activation site (GAS)

elements on the promoters of genes such as CIS, SOCS1, SOCS3, Pim1, and oncostatin M and

modify gene expression of various targets involved in erythropoesis [27]. Translation of these

and other protein products control processes such as proliferation, differentiation, and apoptosis

of the erythroid linage.

Table 1.1. JAK and STAT family members used in various cytokine signalling pathways [16].

Cytokine JAK STAT

CNTF JAK1, JAK2, TYK2 STAT3

CT-1 JAK1, JAK2, TYK2 STAT3

EPO JAK2 STAT1, STAT3, STAT5a, STAT5b

GH JAK2 STAT3, STAT5a, STAT5b

G-CSF JAK2, JAK3 STAT3

GM-CSF JAK2 STAT5a, STAT5b

IFN-α/β JAK1, TYK2 STAT1, STAT2

IFN-γ JAK1, JAK2 STAT1

IL-2 JAK1, JAK2, TYK2 STAT3, STAT5a, STAT5b

IL-3 JAK2 STAT3, STAT5a, STAT5b

IL-4 JAK1, JAK3 STAT6

IL-5 JAK2 STAT1, STAT3, STAT5a, STAT5b

IL-6 JAK1, JAK2, TYK2 STAT1, STAT3

IL-7 JAK1, JAK3 STAT3, STAT5a, STAT5b

IL-9 JAK3 STAT3, STAT5a, STAT5b

IL-10 JAK1, TYK2 STAT1, STAT3

IL-11 JAK1, JAK2, TYK2 STAT3

IL-12 JAK2, TYK2 STAT4

IL-13 JAK1, JAK2, TYK2 STAT6

IL-15 JAK1, JAK3 STAT3, STAT5a, STAT5b

LIF JAK1, JAK2, TYK2 STAT3

OSM JAK1, JAK2, TYK2 STAT3

PRL JAK2 STAT5a, STAT5b

TPO JAK2, TYK2 STAT5a, STAT5b

Similarly, other cytokine receptors utilize a similar pathway, but may encompass

different combinations of JAK/STAT proteins (Table 1.1). Receptors in the same receptor

family, such as the EPO-R and the PRL-R for instance, may both use the same JAK, JAK2, to

activate STAT5a and STAT5b. Other family members, such as the MPL-R, may activate

different JAK molecules, like TYK2. The GH-R activates JAK2, which leads to the activation of

STAT3 in addition to STAT5a and STAT5b. Additionally, receptors in different families, such as GH-R and IL3-R (a cytokine in the gp140 family that signals through the gp140β-subunit and

a ligand-binding α-subunit) can also activate similar JAKs and STATs, although it is not as

common. The fact that there is no specific JAK or STAT molecule for every receptor suggests a

higher level of specificity (Darnell, 1997; Kohlhuber et al., 1997). However, studies with

chimeric receptor molecules with the same STAT binding site but a different JAK binding site

can activate the same STAT (Kotenko et al., 1996; Kohlhuber et al., 1997). Therefore, some

specificity for STAT phosphorylation appears to be determined by the docking sites for STATs

present in the receptors themselves.

Another way to control for specificity is to regulate the gene expression of a molecule in the cell at a particular time. However, as the expression of the JAKs and STATs are commonly

constitutively expressed in most cell types due to the nature of their function, there are other

levels of regulation that exist to allow specificity.

Studies have supported the notion that different STATs may be phosphorylated by

different tyrosines under different conditions. For example, v-Abl mediated transformation of B-

cells exhibit constitutive activation of STAT1, STAT3, and STAT5a/b through JAK1

phosphorylation. Additionally, dominant negative studies of have validated this theory, as STAT

activation is inhibited in the presence of mutated JAK1. In contrast, BCR-ABL has been shown

to constitutively activate STAT1 and STAT5 with little to no effect on the JAKs, and dominant negative JAK mutants did not effect STAT activation, suggesting a JAK-independent mechanism

(reviewed in [16]). These examples provide insight into just some the complexities in place to govern the specificity of the JAK/STAT pathway.

Negative regulation

Alongside the requirement for positive growth signals, such as the JAK/STAT pathway,

lies a multitude of growth inhibitory factors. These act as a gate to protect against the

proliferative response to mitogens, which must be overcome in order for the cell to enter into the cell cycle [28]. Some examples of these are the interferons [29], which possess anti-oncogenic properties and are produced in response to mitogens and other cytokines, such as Interleukin-1,

Interleukin-2 , Interleukin-12, Tumour Necrosis Factor, and Colony-Stimulating Factor. These 14

are synthesised in response to the appearance of various antigens in the body. Negative

regulation can act on the cell cycle by suppressing phosphorylation (and thus inactivating) the

tumour suppressor retinoblastoma protein (pRB), exert inhibitory effects on cyclin-dependent kinases (CDKs), induce various CDK inhibitors, and also suppresses the c-Myc oncogene [30].

Other mechanisms of suppression of the cell cycle include inositol phosphatases, which function as second messengers for a variety of extracellular signals, trigger numerous cellular

processes by regulating calcium release from internal stores, and E3 ubiquitin ligases, which

target a protein for degradation by the proteasome. For the purposes of my thesis, I will focus on

the later proteasomal pathway for my discussion.

Negative Regulators

Negative regulators of various signalling pathways are also important. At least three

classes of negative regulators of cytokine signalling exist: protein tyrosine phosphatases, protein

inhibitors of activated STATs (PIAS), and the suppressors of cytokine signalling (SOCS) [31].

Protein tyrosine phosphatases (PTPs)

Protein tyrosine phosphatases (PTPs) regulate kinases and other proteins by removing phosphorylation on tyrosine residues. Three types of these molecules are in place to exert negative regulation on the JAK/STAT pathway: SH2-containing phosphatases (SHPs), trans- membrane PTPase CD45, and phosphoprotein phosphatase 1B and T-cell protein tyrosine phosphatase (TC-PTP) [32]. SHP proteins are constitutively expressed, and act by removing phosphorylation on JAKs or their receptors. They are composed of two N-terminal SH2

domains, and a C-terminal protein-tyrosine phosphatase domain. SH1 binds to cytokine receptors such as IL4R, c-kit, and EpoR through its SH2 domains [33].

Protein Inhibitor of activated STATs (PIAS)

These were originally found to exert negative regulation on STAT signalling proteins, but more recently have been described to have an added negative regulatory role toward a variety of transcription factors. There are five family members, and all bind to activated STAT dimers and inhibit STAT-mediated transcription by their own distinct mechanism. SUMOs (small ubiquitin- related modifiers) and their conjugation pathway components act as co-regulator proteins for a number of transcription factors that are also targets for SUMO modification. PIAS proteins promote SUMOylation in a manner resembling ubiquitin E3 ligases.

Suppressors of Cytokine Signalling (SOCS)

Figure 1.5. The domain structure of the 8 members of the suppressor of cytokine signalling

family. KIR=kinase inhibitory region, SH2= src homology 2 domain. Adapted from [34].

The suppressor of cytokine signalling (SOCS) protein family regulate the JAK/STAT

pathway, and they are induced in response to a large variety of cytokines, hormones, and growth

factors. The members of the SOCS family were discovered independently in 1997 by three

separate groups [35-38]. The family is comprised of 8 family members, CIS, and SOCS1-7, all

sharing common domain structure. An amino-terminal variable region, a central SH2 domain,

and a SOCS box at the carboxy-terminus [39-41]. SOCS1 and SOCS3 also share an N-terminal

kinase inhibitory region (KIR) not found in the other family member [39, 42] (Figure 1.5). Other

proteins, such as VHL, Elongin-A, ASB, SSB and WSB, have also been described as having a

SOCS box, however, they do not contain an SH2 domain.

SOCS proteins can inhibit the action of many cytokines, as seen through the reduced

activity of JAKs and STATs. SOCS mRNA and proteins are normally expressed at constitutively

low levels in un-stimulated cells, but are rapidly induced by a wide spectrum of growth factors

and cytokines, thereby creating a negative feedback loop. The mechanism of action is partly due

to their ability to bind tyrosine phosphorylated proteins through their SH2 domain, and also to bind Elongin BC through their SOCS box. SOCS can block cytokine signalling by acting as (i) kinase inhibitors of JAK proteins (SOCS1 and SOCS3), (ii) binding competitors against STATs

(SOCS3 and CIS) and (iii) by acting as ubiquitin ligases, thereby promoting the degradation of their partners (SOCS1, SOCS3, and CIS).

Figure 1.6. A representation of the role of the SOCS family members on the JAK/STAT pathway.

A) Signal activation, where cytokine binding eventually leads to increased expression of negative regulators of cytokine signalling (SOCS), and B) the completion of the negative feedback loop, where SOCS family members act upstream of the pathway to inhibit signalling and re-sensitize the cell for future signalling events Adapted from [43].

The SOCS proteins complete a negative feedback loop, shown in figure 1.6. The activation of cytokine signalling induces the transcription of SOCS genes, which in turn feedback on the various signalling molecules involved in activating the pathway. For example,

SOCS1 blocks receptor activation by binding to JAK2, SOCS3 competes with other SH2 containing proteins for binding to phospho-tyrosines on the activated receptor, and CIS also binds the phosphorylated receptor and competes for binding and activation of STATs. The role of SOCS2 in this pathway is still unclear, however, studies with a yeast two-hybrid system revealed that SOCS2 interacts with the cytoplasmic domain of the IGF-1 receptor, only when the

receptor is activated [44]. Therefore, SOCS2 may block access of STATs to the receptor, similar to the action of CIS. SOCS2 has also been found to associate with all of the other SOCS members, suggesting that it may play an important role in shuttling the other SOCS for protein degradation and renew cytokine sensitivity in the cell [6].

SOCS molecules also have E3 ubiquitin ligase function through their SOCS box-

mediated interaction with Elongin B and C [45]. This allows them to help shuttle signalling

proteins such as JAKs and other SOCS molecules to the proteasome for degradation, thereby regulating the action of various cytokines and terminating the negative feedback [46, 47].

Ubiquitin-Proteasome Pathway

The ubiquitin-proteasome pathway is an important mechanism in place to shut off cell signalling. It is comprised of 2 steps: The covalent attachment of a 8.5 kD ubiquitin polypeptide

(Ub) to the target protein (ubiquitination), and the degradation of the ubiquitinated protein by the

26S proteasome complex with the release of Ub that is then re-used [48]. The proteasome is an enzyme complex within the nucleus and cytoplasm of eukaryotic cells, and is in charge of degrading ubiquitinated proteins. Ubiquitination signals on proteins targeted for degradation can be genetic, or can be aquired by phosphorylation or the binding of an adaptor protein [48]. Thus,

the proteolysis pathway is tightly knit amongst other signalling pathways and regulation systems.

Ubiquitination is the post-translational modification that results in the covalent

attachment of Ub to lysine residues on target proteins. It requires 3 enzymes: E1 (ubiquitin-

activating enzyme), E2 (Ub conjugating enzyme) and E3 (Ub ligase protein, which in most cases

is a protein complex). E1 activates Ub by forming a bond between the carboxy-terminal glycine

of the Ub molecule by adenylation, and a reactive cytosine present in E1. Then the activated Ub 19

moiety is transferred from E1 to the E2 enzyme. Finally, the E2 is bound by one of several dozen

E3 ubiquitin liagases, which mediates the attachment to the target protein by specific factor

proteins, such as SOCS molecules, which interact with the target protein and E2. The covalent

attachment of Ub to the target protein is a result of an amide linkage to the e-amino group on an

internal lysine in the target protein. Substrate specificity is determined by E3.

Figure 1.7. A representation of how the SOCS family are thought to exert their effects on the ubiquitin-proteasome pathway. KIR= kinase inhibitory region, clu5=cullin 5 , Rbx1=ring=box

1, EB= elongin B (E1), EC=elongin C ( E2), SOCS Box= E3 [43].

As shown in Figure 1.7, the SOCS box in each SOCS molecule is thought to act as the

E3, or UB ligase protein complex, and binds to elongin C (EC). The SOCS molecules provide specificity, as other proteins that are to be targeted for proteosomal degradation bind via the SH2 domain, and are tagged for transport to the proteasome by the action of the E3 ligase.

SOCS induction

SOCS gene expression is normally low in un-stimulated cells, but is rapidly induced upon cytokine stimulation. They consist of only a few introns, making them small and easy to transcribe quickly. Since they require a rapid turnover rate, the proteins are unstable with a short half life. It has been found that SOCS proteins target the JAK/STAT pathway, thereby regulating the action of many cytokines induced by STAT-responsive elements within their promoters [49].

Table 1.2 displays a list of the proteins which have been found to induce SOCS expression.

Additionally, SOCS interactions and factors inhibited by the SOCS are compared. As can be seen, a wide variety of cytokines and growth factors induce SOCS expression. Often, the molecules that induce SOCS expression also interact with, and are inhibited by the SOCS molecule. For example, CIS is induced by GH and EPO, interacts with the GHR and EPOR, and both of these receptors are inhibited by CIS.

Previous work has looked at the action of each SOCS protein individually, and has determined that over-expression in diverse cell lines leads to inhibition of a wide range of cytokines. Some of these effects can be demonstrated by a reduction in JAK and STAT phosphorylation, STAT dimerization, nuclear translocation and STAT transcriptional activity.

Some growth factors and cytokines that have been shown to be inhibited by SOCS2 over- expression are GH, PRL, IGF-1, IL-6 and LIF (Table 1.2). There is a visible redundancy between the SOCS proteins, as many of them have been shown to inhibit similar cytokine and growth

factors, as displayed below.

Table 1.2. Factors that induce SOCS expression

SOCS Induced by Interacts with Inhibits

CIS GH, PRL, Leptin, EPO, EPO-R, IL2R, GHR, CSF3R, GH, PRL, EPO, IL2, IL3 TSLP, IL2, IL3, IL6, IL9, SMAD2, EGFR, ERBB2,

IFNα, TNFα LPS PRLR, IL3RA, MK1L1, KPCB, KPCA, KPCT, KIT, IL3RB SOCS GH, PRL, Insulin, CNTF, JAK2, CSF1R, ELOC, IRS1, GH, PRL, Insulin, Leptin, 1 cadiotropin, TSH, EPO, CSF1, VAV, TEC, INSR, EPO, TPO, TSLP, IL2, IL3, TPO, TSLP, G-CSF, GM- COMD1, ELOB, CLU5, IL4, IL6, IL7, IL12, IL15, CSF, IL2, IL4, IL6, IL7, RBX1, K1C18, ALK, IFNα/β, IFNγ, LIF, TNFα, IL9, IL13, IFNα/β, IFNγ, ERBB2, TF65, IGF1R, IGF1 GRB2, EGFR, FLT3, FYN, LIF, TNFα, thyrotropin, IL2RB, JAK1, KIT, ITK, CXXL12 JAK3, TIE2, VAV2, NCK1, GHR, TRIM8, Q5T1R9, P85B, UFO, P85A, PIM2, INGR1, CXCR4, PGFRB, TIRAP, TPOR, IRS2 SOCS GH, PRL, Insulin, EPO, PRLR, IGF1R, GHR, GHR, PRLR, IGF-1R, IL6R, 2 CNFT, cadiotropin, IL1, Q658W2, FAK1, EPO-R, LIFR IL2, IL3, IL4, IL6, IL9, ELOC, INSR, SOCS3 IL10, IFNα, IFNγ, LIF, Estrogen, CXCL12 SOCS GH, PRL, Insulin, Leptin, JAK2, EPO-R, INSR, GH, PRL, Insulin, Leptin, 3 CNTF, EPO, GM-CSF, IL6RB, IGF1R, PP2BB, EPO, IL2, IL3, IL4, IL6, IL9, IL1, IL2, IL3, IL4, IL6, EGFR, RASA1, IRS1, IL10, IL11, IL12, IFNα/β, IL7, IL9, IL10, IL11, CXCR4, ELOB, ELOC, IFNγ, LIF, IGF-1 IL12, IL13, IFNα, IFNγ, LEPR, KC1E, PIN1, CSF1R, PRLR, GHR, IRS2, JAK1, LIF, LPS, TPO, CXCL12 CSF3R, PTN11, IL2RB, BCL10, SOCS2, JAK3 SOCS EGF ELOC EGF 4 SOCS IL6, EGF MYLK, IF4B, ELOC, IL4, IL6, LIF, EGF 5 IL4RA, KIT SOCS Insulin KIT, ELOB, ELOC, 014654, PRL, GH, Leptin, binding to 6 INSR, ACVR1, BMP1B, Nck SMUF2, IRS2, P85B, TGFR1, TBB2C, P85A, TBA1A SOCS Unknown VINEX, SEPT2, SEPT6, Unknown 7 GRB2, PLCG1, NCK1, TBB2C, P85B, P85A, IRS2, TBA1A

[31, 50, 51]

Knock-outs

The use of knock-out or transgenic mice have been important in deciphering the role of

each SOCS member, as in vitro results suggested that various SOCS proteins can inhibit a

variety of cytokine receptors and in turn they could be induced by a wide range of stimuli. In

vivo data, however, displayed that individual SOCS proteins could show some degree of

specificity (See table 1.3 for features of SOCS knock-out mice) [3, 49].

Table 1.3: Features of SOCS knock-out mice

Knockout phenotype Transgenic phenotype CIS • No significant phenotype • Reduced weight • Disturbed lactation • Altered IL-2 signalling SOCS • Neonatal lethality • Disturbed T-lymphocyte 1 • Fatty liver degeneration development • Haematopoietic infiltrations • Decreased number of γδ T-cells • Lymphopenia, accelerated apoptosis • Spontaneous T-cell activation • Increased IFN-γ sensitivity SOCS • Gigantism • Gigantism 2 • Deregulated growth hormone and IGF-1 • Deregulated growth hormone signalling signalling SOCS • Embryonic lethality • Embryonic lethality 3 • Placenta defects • Increased Th2 differentiation • Disturbed erythropoiesis • Altered IL-2/NFAT signalling • Altered IL-6 and LIF signal transduction SOCS • Disturbed IL-4 signalling and Th2 5 differentiation SOCS • Slightly decreased growth 6 SOCS • 10% smaller 7 • 50% lethality within 15 weeks post-natal due to hydrocephalus

SOCS2

My thesis explores the role of SOCS2 specifically. The SOCS2 was discovered in 1997

independently by three separate groups [35-38]. SOCS2 is found on chromosome 12q22, and the

genomic full length gene consists of 5804 base pairs, with 3 exons (only 2 coding exons) making

up a 597 bp coding region number (accession # AB451318). The full length protein is 22.2 kDa,

and is composed of 198 amino acids.

SOCS2 is induced rapidly in response to various cytokines such as GH, PRL, EPO, and

others (see Table 1.2). SOCS1 and SOCS3 expression is induced rapid and transiently after stimulation, however, SOCS2 turns on later and is more prolonged [52, 53]. SOCS2 has been shown to interact with all of the other members of its family, including itself. It was also found to

interfere with the inhibitory actions the other SOCS family members in GH, Leptin, and IFN

signalling. This finding was SOCS-box dependent, required the recruitment of elongins B/C, and

coincided with the degradation of the SOCS family members. It has therefore been suggested

that SOCS2 is responsible for the restoration of cellular sensitivity to cytokines by overcoming

the negative effects of other SOCS family members [54]. Apart from being able to bind to and

inhibit the other SOCS family members, SOCS2 is also thought to bind to cytokine receptors

[44]. It is thought to inhibit contact between cytokine receptors (type 1) and JAK2, and also leads

to targeting of signalling molecules for degradation by the proteasome.

SOCS2 knock-out mice (Table 1.3) show mainly a defect in GH/IGF-1 signalling,

suggesting an essential negative regulatory role in this pathway. Phenotypically, these mice

displayed increased body weight, increased long bone length, and enlargement of most organs.

Also, a decreased production of major urinary protein, increased local IGF-1 protection, collagen

accumulation in the dermis represented a deregulation in GH/IGF-1 signalling [3]. Interestingly,

SOCS2 knockout mice exhibit an overgrowth phenotype due to GH induced STAT5 activation,

and over-expression of SOCS2 in transgenic mice also leads to a similar effect. This dual effect

is also found in vitro, where low doses of SOCS2 inhibit GH, PRL, and IL-3 signalling, whereas high doses appear to have a positive effect. This is thought to occur because SOCS2 is able to partially overcome the negative effects of SOCS1 on the GH-R and SOCS1 and SOCS3 on the

PRL-R, by binding them with its SOCS box domain and shuttling these negative regulators to the proteasome for degradation.

This dual effect of SOCS2 seen in vitro and in vivo allows this protein to have oncogenic potential, either when silenced or over-expressed.

PART 2: CANCER

When the steady state is compromised

Up until now, I have been describing processes that occur in the normal healthy cell.

Positive signals exist which are in turn regulated and shut off appropriately. However, changes that compromise the steady state of the system can arise, resulting in diseases like cancer

(including leukemia), diabetes, and autoimmunity.

Leukemia

Leukemia is clinically divided into four broad forms. This division is based upon the origin of the cells involved, myeloid or lymphoid, and on the clinical pace of the disease, acute or chronic. For the purposes of my thesis, I have focused on acute myeloid leukemia (AML), an aggressive cancer of the peripheral blood and bone marrow. It is characterized by a reduction in normal haematopoiesis, and an accumulation of non-functional immature myeloid precursors

(blasts) in the bone marrow and often in the peripheral blood, as depicted on the right hand side

of figure 1.1.

Clinically, patients present with symptoms such as fatigue, bleeding, and infection as a

result of the loss of red blood cells, platelets and neutrophils, respectively. AML is not a single disease, but represents a heterogeneous group of haematopoietic stem cell disorders that differ in presentation, treatment response, and clinical outcome. These differences can be explained by examining the underlying genetic heterogeneity in AML cells, such as chromosomal

abnormalities, fusion proteins, or aberrant gene expression. Because the underlying cause of the

disease differs among patients, treatment options and outcomes are highly disease-dependent.

Initially, to recognise the morphologic heterogeneity of the disease, and more recently, to help

direct treatment and to inform about prognosis, a series of classification systems have evolved;

these are discussed below and a flow chart is depicted in Figure 1.8.

AML Classification Systems

Figure 1.8. The progression from AML classification from morphology (FAB) to recurrent chromosomal abnormalities (WHO) to gene expression profiling [1]. Figure adopted from Ryan

Pinto.

FAB

Traditionally, AMLs have been classified according to the French-American-British

(FAB) classification system, into subtypes from M0 to M7 [55]. This is based primarily on leukemia cell morphology and histochemical features, such as degree of differentiation and extent of maturation.

Table 1.4. The eight FAB subtypes of AML [55]

FAB Cell Morphology M0 undifferentiated AML M1 myeloblastic, without maturation

M2 myeloblastic, with maturation M3 promyelocytic), or acute promyelocytic leukemia (APL M4 myelomonocytic M4 myelomonocytic together with bone marrow

eosinophilia M5 monoblastic leukemia (M5a) or monocytic leukemia (M5b)

M6 erythrocytic), or erythroleukemia M7 megakaryoblastic

WHO

With improvements in cytogenetic methods it became evident that leukemia is a genetic

disease. Moreover, the presence of specific cytogenetic abnormalities predicted the response to

therapy. In some subtypes, like APL for example, there was excellent concordance between morphology and cytogenetics, however, in most other subtypes this was not the case. To recognize this and to provide a clinically useful tool, the World Health Organization (WHO)

proposed a new classification system that encompassed not only morphology but all available

information. They included genetic, immunophenotypic, biologic, and clinical features to define

the disease [56]. As seen in the order of presentation in the table below, the greatest weight is

given to molecular and cytogenetic abnormalities.

Table 1.5. WHO classification of AML [56]

WHO Classification Acute myeloid leukemia with recurrent genetic abnormalities Acute myeloid leukemia with t(8;21)(q22;q22), (AML1/ETO) Acute myeloid leukemia with abnormal bone marrow eosinophils and inv(16)(p13q22) or t(16;16)(p13;q22), (CBF /MYH11) 28

Acute promyelocytic leukemia with t(15;17)(q22;q12), (PML/RAR ) and variants Acute myeloid leukemia with 11q23 (MLL) abnormalities Acute myeloid leukemia with multi-lineage dysplasia Following MDS or MDS/MPD Without antecedent MDS or MDS/MPD, but with dysplasia in at least 50% of cells in 2 or more myeloid lineages Acute myeloid leukemia and myelodysplastic syndromes, therapy related Alkylating agent/radiation-related type Topoisomerase II inhibitor-related type (some may be lymphoid) Others Acute myeloid leukemia, not otherwise categorized Classify as: Acute myeloid leukemia, minimally differentiated Acute myeloid leukemia without maturation Acute myeloid leukemia with maturation Acute myelomonocytic leukemia Acute monoblastic/acute monocytic leukemia Acute erythroid leukemia (erythroid/myeloid and pure erythroleukemia) Acute megakaryoblastic leukemia Acute basophilic leukemia Acute panmyelosis with myelofibrosis Myeloid sarcoma

Classification by Gene Expression

With recent advances in high-throughput microarrays, gene expression profiles are now

being used to further sub-classify AML patients. Although several studies of this type have been

reported, for the purpose of illustration I will focus on a study carried out by Peter Valk et al. in

2004 [1]. In their manuscript, they presented the gene expression profile of AML blast cells from

the peripheral blood or bone marrow of 285 patients at diagnosis. An Affymetrix GeneChip was

used, containing 13,000 unique genes or signature tags; unsupervised cluster analyses revealed

16 distinct types of AML based on patterns of gene expression. Interesting correlations emerged

when clustering results were compared to known cytogenetic abnormalities. For example, groups

9, 12, and 13 almost exclusively contained cases that had inv(16), t(15;17) and t(8;21)

respectively. For example, 18 of the 19 patients in group 12 carried the t(15;17). This 29

concordance of a defined chromosomal abnormality with a particular gene expression subgroup helps to validate the clustering algorithm. Furthermore, the clinical relevance of their methodology was illustrated in that some clusters were associated with particularly poor outcomes with standard therapy, while improved survival was found in other subgroups.

While a strong relationship was seen between specific cytogenetic subgroups and gene expression, there was no discussion of any relationship between expression signature and cell morphology. In Appendix 1 of this thesis, I performed and analysis that integrated gene expression into the FAB classification (figure 4.1) and the FAB classification into the Valk gene expression classification (figure 4.2). The pictorial presentation of this assessment demonstrates the problem with reliance on morphology to describe a highly heterogeneous condition such as

AML.

Biology of AML

The growth and differentiation of normal cells, as discussed above, is under the control of a variety of cytokines and growth factors acting at all levels of the differentiation hierarchy.

These proteins are not only crucial for effecting differentiation, but are critical in maintaining cell viability.

Leukemia is a clonal genetic disease requiring a number of mutations to occur in a cell, mainly resulting in an increased ability to proliferate and evade apoptosis. The target cell in which leukemia inducing mutations occur is controversial. There is evidence that the disease may develop due to alterations within the haematopoietic stem cell itself, or in committed progenitors. Regardless of the exact nature of the leukemia initiating cell, AML, like normal haematopoiesis, is organized as a hierarchy. A small proportion of cells with extensive 30

proliferative and self renewal capacity give rise to cells with limited ability to proliferate and lack the ability to self renew. Morphologically, the cells are similar with blast like features, however, it is possible to segregate cells with stem cell properties from the bulk population of cells based on physical characteristics and the pattern of proteins expressed on their surface.

Like normal cells, the proliferation and survival of leukemic blast cells is dependent upon the engagement of signalling pathways within the cell. In leukemia, these signals can come from surrounding cells, similar to the normal situation, or may be generated through the production of growth factors by the leukemic cells (autocrine growth), mutation of receptor molecules on the surface of the cell, or mutated kinases within the cell. In addition to positive regulators, there is a loss of the normal mechanisms that limit cell growth. For example, reduced expression of proteins such as p15, p16 and SOCS1 may occur due to hyper-methylation of their promoters.

The growth of normal haematopoietic cells is dependent upon signals from outside the cell that are transmitted into the cell by way of cell membrane receptors. The ligands for these receptors may be expressed by cells within the bone marrow stroma, or may be produced by distant cells. Leukemia is a bone marrow disease and a growth factor dependent disease.

Leukemias begin in the bone marrow and, in many cases, do not shed large numbers of blast cells into the peripheral blood. In other cases, there are large numbers of leukemic cells circulating in the blood and replacing the bone marrow. Leukemic cells in the bone marrow express many of the same receptors as normal haematopoietic progenitors, and are exposed to the same membrane bound ligands as normal cells. Studies comparing the properties of leukemic cells in suspension compared to those adherent to stromal cells in culture show that survival is enhanced outside the patient when in contact with stroma. This indicates that leukemic cells can,

and do, respond to ligands presented by the stroma. However, the signalling within the leukemic cells is not normal, and is perturbed in a number of ways. In some cases, the proteins that regulate the extent of proliferation may be suppressed. For example SOCS1, p15 or p16 may be silenced by promoter hyper-methylation. In other cases, there may be mutations of growth factor receptors or their downstream targets, leading to constitutive activity of the pathway. This can permit cells to escape from control by reliance on the bone marrow stroma. An example of a mutated receptor is FLT3.

FLT3 is a receptor tyrosine kinase expressed normally on immature haematopoietic cells.

It is highly expressed in some cases of AML, and mutations are quite common. The most common mutations are point mutations in the tyrosine kinase domain (TKD), or an internal tandem duplication (ITD) of the juxtamembrane region, rendering the kinase constitutively active. ITD mutations not only render the receptor active, but also alter the degree of phosphorylation of downstream targets. Wild-type FLT3 normally signals through MAPK, and

AKT, but has minimal effect on STAT5a. However, in cells carrying a FLT3-ITD mutation, there is a high level of phosphorylation and activation of STAT5a (in addition to the activation of

MAPK and AKT). It is of note that this activity is in the absence of added FLT3 ligand [57].

Other trans-membrane tyrosine kinases involved in the growth of AML cells include c-

Kit and FMS. Like FLT3, they may exist in the wild-type form or acquire mutations that render them active.

AML as a genetic disease

AML arises as the consequence of acquired genetic changes in normal bone marrow cells. These changes alter the growth and differentiation potential of those cells. As pointed out 32

in the discussion of the WHO classification of AML, one form of genetic change frequently

found in AML are chromosome translocations, in which DNA from one chromosome is

exchanged with a second chromosome in a balanced manner. In general, the translocations can

alter gene behaviour in two ways. In the first, the translocation brings a tissue specific regulatory

region into the domain of an oncogene, leading to the constitutive expression of that gene. An

example of this type of translocation is t(8;14) that activates myc. The second type of

translocation is the form most often seen in AML, and results in the development of a fusion protein. One example of this is the t(9;22) seen in CML and rare cases of AML. In this translocation, the BCR promoter and 5’ coding sequences of the BCR gene is fused to the normally tightly regulated ABL tyrosine kinase, resulting in the formation of the BCR-ABL fusion protein. This protein is localized to the cytoplasm, and has a constitutively active ABL kinase. This creates deregulation of the downstream mitogenic and anti-apoptotic pathways, such as RAS/MAP, JAK/STAT, and PI3K/AKT.

Transcription factors are also common targets for activation in this fashion. One example is the t(15;17) defining the AML subtype APL. In this case, the retinoic acid receptor (RARα) on chromosome 17 becomes fused to the promyelocytic leukemia (PML) gene on chromosome 15

(or others). The fusion protein, which has decreased responsiveness to retinoic acid, binds to

RAR binding sites of genes important in normal differentiation. The result is suppression of the downstream target genes required for differentiation into granulocytes, and is seen as an abnormal accumulation of immature promyelocytes. All-trans retinoic acid (ATRA) is a drug used as a treatment for this illness, as is it reverses this block, and allows the cells to differentiate and eventually succumb to death; pharmacologic levels of ATRA are required to observe this effect. 33

Aberrant Negative Regulation

So far I have discussed the importance of having a “gas pedal” in terms of a chromosomal translocation, or a constitutively active tyrosine kinase, leading to enhanced

growth and survival of leukemic cells. However, negative regulators must also be surpassed in

order for the cell to evade apoptosis and become fully deregulated. Silencing of tumour

suppressor genes by mechanisms such as mutation or promoter hyper-methylation have been

shown to be important in the development of all cancers, including leukemia. For example,

SOCS1 was investigated in CML due to its important role in the JAK/STAT pathway and

potential role downstream of the BCR-ABL tyrosine kinase. Mutation analysis, CpG island

methylation, and expression of SOCS1 was analyzed in 112 CML samples, 5 leukemia cell lines,

and 30 normal controls [58]. No mutations in SOCS1 were found in any of the CML samples,

suggesting that this is not the mechanism used to evade the negative effects of SOCS1. However, promoter hyper-methylation was found in 67% of blast phase, and 46% of chronic phase CML patient samples, and no methylation was detected in the normal controls or patients in remission.

SOCS1 expression correlated strongly with promoter methylation. These results suggest that

SOCS1 silencing by hyper-methylation is necessary for disease progression. In remission the hyper-methylation of SOCS1 is not evident in the normal progenitor cells that now populate the bone marrow.

Methylation

Epigenetics is defined as reversible, heritable changes in gene expression that are not due to alterations in the DNA sequence. The mechanisms involved can affect DNA in the form of methylation of cytosines, or DNA associated histones that can be modified by methylation,

acetlyation, or phosphorylation, for example. For the purpose of this thesis I will concentrate on

methylation of DNA.

DNA methylation is found in both prokaryotic and eukaryotic cells, however, the only

modification found in mammalian genomic DNA is the addition of a methyl group at the 5’

position of the cytosine reside within a cytosine-guanine dinucleotide (CpG), leading to the formation of 5-methylcytosine (m5C). CpGs are not spread randomly throughout the genome, but

exist in CpG-rich regions called CpG islands, which are often found in the 5’ regulatory region

of many genes. In normal cells, these regions are usually hypo-methylated, and methylation is

used for processes such as tissue specificity or silencing of “junk DNA”. Normally, 60-90% of

CpGs are methylated in mammalian genomic DNA.

The methylation of mammalian DNA is catalyzed by a class of enzymes called DNA-

methyltransferases (DNMTs) that are divided into de novo DNMTs, responsible for methylating

C to m5C after replication in un-methylated DNA, or maintenance DNMTs, which preferentially

adds a methyl group to hemi-methylated DNA during replication. De novo DMNTs, including

DNMT3A and DMNT3B, are responsible for the acquisition of new DNA-methylation.

Maintenance DNMTs, represented by DNMT1, maintain the DNA-methylation pattern during replication. DNMT2 is the smallest DNMT and has very weak methylating ability, and is thought

to be involved in the recognition of DNA damage, DNA recombination and mutation repair.

Methylation of a gene promoter or early exon may affect transcription either directly or

indirectly. In the direct mechanism, m5CpGs interfere with the binding of transcription factors to

the promoter of genes. The indirect mechanism involves the binding of m5CpG specific proteins

to DNA, which then block the interaction of transcription factors to their specific recognition

sequences in DNA. These protein suppressors of promoters include: m5CpG-binding domains 35

(MBD1,2,3,4) that act as transcription repressors, m5CpG-binding proteins (MeCP1,2), which are able to form complexes with histone-deacetylases, co-repressors like Sin3a, and the ATP- dependent chromatin remodelling proteins.

DNA methylation normally plays a role in gene expression during normal development; however, it can also mediate epigenetic silencing of CpG island containing genes in cancer and other diseases. CpG island regions of genes are prone to mutation due to spontaneous deamination of m3C to thymine (T), resulting in the loss of methylated CpG sites, and increased genomic instability. The transformation of a normal cell to a cancer cell occurs in several stages, often involving the activation of oncogenes and the suppression of tumour-suppressor genes.

Baylin and his group have shown that early on in transformation, there is a general reduction in the total methylation of DNA within a cell (baylin ref). This most likely leads to the increase and aberrant expression of growth promoting genes. Over time however, hyper-methylation of CpG islands in the promoter of tumour-suppressor genes becomes evident. The consequence of this methylation is the silencing of these genes’ expression. Often times, the genes that are suppressed by methylation are bona fide tumour suppressor genes like p15, p16, and SOCS1.

It is still unclear as to whether silencing by methylation is a random event, offering the cells a growth advantage which is then selected for, or whether it is directed through a specific pathway initiated by an oncogene.

Random vs. Directed Hyper-methylation:

Random

The theory of random hyper-methylation suggests that non-specific errors result in the de novo methylation of CpG islands that are un-methylated in normal cells. This is followed by the selection for cells with down-regulated genes important in growth-inhibition or apoptosis, and an increased growth rate. This abnormal methylation results in heritable changes and clonal selection of cells with increased growth rates, due to a decrease in the negative regulatory effects of growth suppressive genes. This mechanism suggests that the methylation errors are purely stochastic, but once they occur they result in clonal succession. As such, hyper-methylation is likely to be important in tumour progression.

Directed

Alternatively, specific genes or areas of the genome may actually be targeted for repression through a trans-activating instructional pathway. Current research is underway, attempting to resolve the full complement of genes that undergo de novo methylation, to better understand this complex question. Two examples of directed hyper-methylation are presented below.

In acute promyelocytic leukemia (APL) it has been found that the RARβ promoter is silenced due to hyper-methylation. It would appear that this change in methylation is directed by the oncogene PML-RARα, which binds to the retinoic acid response element (RARE) in the

RARβ promoter. The bound PML-RARα recruits proteins that ultimately cause repression and hyper-methylation of RARβ [59].

A second example relates to the Ras oncogene. Previous studies have shown that Ras is activated by recurrent mutation in about 30% of human tumours, and this is associated with the epigenetic silencing of the tumour-suppressor gene, Fas, preventing Fas-mediated apoptosis. 37

Michael Green’s group has postulated that this silencing of Fas in mutant Ras cells is directed by

the constitutively active form of Ras. To test this, they took a genome-wide approach with an

RNAi screen in K-ras transformed NIH-3T3 cells, and identified a group of 28 genes important for Ras-mediated epigenetic silencing of the pro-apoptotic Fas gene. Of the 28 identified genes, at least 9 were directly associated with specific regions of the Fas promoter in K-ras transformed cells, but not untransformed NIH-3T3 cells. Knock-down of any one of the 28 genes resulted in failure to recruit DNMT1 to the Fas promoter, loss of Fas promoter hyper-methylation, and a

decrease in Fas expression. They suggest that their results show that Ras is able to direct

epigenetic silencing through a specific pathway [60]. However, although this paper claims to

demonstrate that Ras directs methylation, the study was carried out on cells selected for their

ability to demonstrate a transformed phenotype when transfected with Ras. As such, it is not

clear whether Ras is mediating the methylation, or whether the cells that were able to become

transformed were selected for already for during transformation for having a methylated Fas

promoter.

SOCS Family & Methylation

Among the genes that have been found to be silenced by hyper-methylation at a high

frequency in different cancer types, are members of the SOCS family. As discussed above, the

SOCS proteins serve as negative regulators of a variety of signalling molecules, and hence, their

silencing may elicit increased cellular proliferation and/or survival in the presence of cytokines

or a constitutively active kinase. Previous studies have focussed on SOCS 1, and have found

increased methylation in hepatocellular carcinoma (65%), multiple myelomas (62%) [61], a

subset of AML (60%) [62], pancreatic ductal neoplasms (21%) [63], primary ovarian (23%) [64],

and breast cancers (9%) [64], providing evidence that it may play a role in oncogenesis [65].

Only one study has addressed changes in SOCS2.

Jane Visvader and colleagues addressed this question in their study in 2004. They sought to investigate whether silencing or inactivation of the SOCS genes occurs in ovarian and breast carcinomas by examining for somatic mutation, allelic loss, and methylation-associated silencing of SOCS1-3 [64].

The expression of SOCS1 and SOCS2 transcripts were low to negligible in many of the ovarian cell lines, and high in primary mammary epithelial cells, but low in many breast cancer cell lines examined, suggesting that these genes are silenced or mutated in these cancers. To investigate the mechanism of loss of gene expression, they first sequenced the genes. No mutations were found in SOCS2 or SOCS3 in any of the cell lines or primary tumours. For

SOCS1 in 3 (4.4%) of the primary breast cancers and one ovarian cell line, a recurrent mutation was found, which upon further analysis was found to be a novel single nucleotide polymorphism.

Another mechanism for loss of gene expression is loss of heterozygosity. However this was not found to be a relevant mechanism for SOCS 1 and 2; low level LOH was seen for SOCS3.

They used MS-SSCP analysis to examine aberrant methylation of CpG islands in cell lines and primary patient samples. They found that SOCS1 was hyper-methylated in 4/6 ovarian cell lines and 8/11 breast cancer cell lines, and 10/43 ovarian, and 4/43 breast cancers. SOCS2 was hyper- methylated in 4/6 ovarian and 8/10 breast cancer cell lines, and 6/42 primary ovarian samples.

No methylation was observed in adjacent tissues. Transcript expression was significantly lower in methylated samples. Further evidence of the importance of methylation in silencing of the gene was obtained by treatment of cells with the de-methylating agent 5-azadeoxycytidine (5- 39

aza-dC). This resulted in restoration of expression of SOCS1 and SOCS2 in methylated but not un-methylated samples. They also showed the importance of SOCS1 and SOCS2 silencing on the behaviour of cells, as the re-introduction of SOCS1 or SOCS2 into cells with a silenced gene resulted in markedly reduced growth.

A similar study looked the SOCS1 and SOCS2 promoters in 12 primary melanoma specimens to determine whether methylation was involved in silencing gene transcription.

Hyper-methylation was found in 75% of SOCS1, and 43% of SOCS2 promoters in the melanoma samples [66]. Also, transcription of SOCS1 and SOCS2 genes were significantly reduced in those samples with hyper-methylation. This suggests that epigenetic silencing in the

form of methylation was important for the down-regulation of these potent negative regulators,

and contributes to the formation and maintenance of the disease.

PART 3: MY THESIS (rationale, hypothesis)

Rationale

The rationale behind my thesis began with an inspection of the expression of various regulators of cytokine signalling, such as Lnk and SOCS2. My analysis of Lnk is presented in

Appendix 2 of this thesis. The focus of my thesis is on SOCS2, as it appeared to be show greater

variation in cases of AML, with some patient’s blasts having very high levels, while levels were

very low in other samples. In addition there appeared to be a clustering of these samples

suggesting a biologic significance.

Based on inconsistencies in the literature with regards to the role of some of the SOCS

family, including SOCS2 as a negative regulator of cytokine signalling, I hypothesize that

SOCS2 has disease-specific effects on the growth of leukemic cells depending on cell context.

For example, high expression of SOCS2 in CML may have a growth enhancing role, whereas

low expression in some AML subtypes may be due to active suppression.

Hypothesis

Because of the negative effects that SOCS2 plays in cytokine signalling, I hypothesized that SOCS2 expression is aberrantly silenced in some forms AML by means of promoter hyper- methylation.

CHAPTER 2: Manuscript

Hyper-methylation of the SOCS2 promoter in AML: an unexpected association with the

FLT3-ITD mutation.

Courtney McIntosh, Dr. Mark Minden

Department of Medical Biophysics, University of Toronto

Princess Margaret Hospital

January, 2009

Abstract

Haematopoiesis requires strict regulation in order to maintain a balanced production of the

various blood cell components. Escape from this regulation contributes to the development of

cancers such as leukemia. SOCS2 is a member of the Suppressor of cytokine signalling (SOCS)

family, and normally functions as a negative regulator of the JAK/STAT pathway. I examined

gene expression and promoter methylation in AML cell lines and patient samples. SOCS2

expression was quite variable in AML patients, being very low in some types and very high in

others. I focused on two subtypes displaying very low to minimal SOCS2 expression, APL,

referred to as group 12, and an immature FAB M1 subtype, referred to as group 4. Promoter

hyper-methylation was found in these patients, particularly those with high white blood cell

count and a FLT3-ITD mutation. I speculate that SOCS2 interacts with an aspect of the

signalling complex to inhibit cell growth in these patients, and silencing it is necessary for

leukemia progression. Treating these patients with a de-methylating agent, such as decitabine,

may show promise in the clinic, either alone or in combination with chemotherapy or a FLT3

inhibitor.

Introduction

Haematopoietic cytokine signalling is largely mediated by a variety of receptors, Janus

kinases (JAKs), and their downstream transcription factor targets, Signal transducers and

activators of transcription (STATs). The JAK/STAT pathway plays an important role in haematopoiesis at various points in the hierarchy, through its ability to propagate positive signals for growth and survival. The JAK/STATs are crucial for signalling through Type I cytokine receptors, such as the EPO-R, MPL-R, GH-R and PRL-R. They do not contain intrinsic tyrosine kinase activity, and must therefore associate with cytoplasmic proteins possessing this property.

Trans-membrane receptors with intrinsic tyrosine kinase activity, however, may also engage the

JAK/STAT pathway.

The Janus kinase (JAK) family of cytoplasmic tyrosine kinases carry out their role by generating signals from cytokine and growth factor receptors. Cytokine receptors exist on the surface of the cell, and a signalling event is initiated when a cytokine or growth factor molecule binds to the extracellular portion of the receptor. The receptor undergoes a conformational change, bringing two receptor-associated JAK molecules in close proximity of each other, allowing them to cross-phosphorylate themselves and their cognate receptor at several tyrosine residues in the receptor tail. The activated receptors can then recruit STAT molecules in the cytoplasm, which bind to the phospho-tyrosines through their SH2 domains. The STAT proteins become phosphorylated on tyrosine residues, allowing them to form dimers. These activated dimers complete a nuclear localization signal, allowing them to be transported into the nucleus.

Here they bind to STAT binding sites (GAS elements) on the promoters of various genes such as the Suppressor of Cytokine Signalling (SOCS) family of genes, and modify gene expression.

Translation of these and other proteins ultimately control processes such as proliferation,

differentiation, and apoptosis of various blood cell lineages in the hematopoietic hierarchy.

Among the functions of the SOCS protein family is the ability to negatively regulate the

JAK/STAT pathway. Family members can bind to phosphorylated tyrosines in receptors or in

JAKs, and inhibit signal propagation, or can act as an E3 ubiquitin ligase, shuttling proteins for

proteosomal degradation. The SOCS family of negative regulators was discovered in 1997

independently by three separate groups [35-38]. The family is comprised of 8 family members,

CIS, and SOCS1-7, all sharing common domain structure: an amino-terminal variable region, a central SH2 domain, and a SOCS box at the carboxy-terminus. SOCS1 and SOCS3 also share an

N-terminal kinase inhibitory region (KIR) not found in the other family members. SOCS2 knock-out studies have yielded an overgrowth phenotype in mice, mainly due to deregulated GH signalling.

While negative regulation is important in normal physiology, negative regulators must be bypassed in order for the cell to evade apoptosis and become fully deregulated in the malignant state. Among the mechanisms used to silence tumour suppressor genes, promoter hyper- methylation has been shown to be increasingly important in the development of all cancers, including leukemia. Along these lines, the effect of methylation on the regulation of the SOCS

family of proteins has been investigated in various types of cancers, including breast and ovarian cancers, melanoma, and leukemia. SOCS1 was investigated in CML due to its important role in the JAK/STAT pathway, and potential role downstream of the BCR-ABL protein tyrosine kinase. Mutation analysis, CpG island hyper-methylation, and expression of SOCS1 was analyzed in 112 CML samples, 5 leukemia cell lines, and 30 normal controls [58]. No mutations

in SOCS1 were found in any of the CML samples. However, promoter hyper-methylation was

found in 67% of blast phase, and 46% of chronic phase CML patient samples, and no

methylation was detected in the normal controls or patients in remission. These results suggest

that the silencing of SOCS1 by hyper-methylation is necessary for disease progression in this

case. In remission, the hyper-methylation of SOCS1 is not evident in the normal progenitor cells

that now populate the bone marrow. Additional studies that have focussed on the expression of

SOCS1 have found increased methylation in hepatocellular carcinoma (65%), multiple

myelomas (62%) [61], a subset of AML (60%) [62], pancreatic ductal neoplasms (21%) [63],

primary ovarian (23%) and breast cancers (9%) [64], providing evidence that it may play a role

in oncogenesis [65].

While SOCS1 has been the focus of studies in AML, little has been reported with regards

to SOCS2 in this disease. I hypothesize that SOCS2 exerts negative effects on the complex

driving signalling in the instances where SOCS2 expression is low. In order for the cell to

become fully malignant and escape regulation, SOCS2 needs to be silenced by promoter

methylation.

An examination of SOCS2 expression in a publicly available AML database indicated

large variations in the level of RNA across patients [1]. Based on this, I chose to explore SOCS2 expression in various types of Leukemic cell lines and patient samples. I focused on cases with extremely low levels of SOCS2, as previous studies of SOCS1 in AML, and SOCS2 in breast cancer and ovarian cancer cell lines have revealed that low to absent levels of expression are associated with promoter hyper-methylation. In keeping with previous work, I identified AML patients in which the SOCS2 promoter is highly methylated. In general, these patients tended to

have higher peripheral blast counts than in patients where SOCS2 was not methylated. In many

cases, this was associated with the presence of a mutation of the FLT3 receptor. Treating the

methylated APL cell line NB4 with a de-methylating agent, decitabine, lead to the re-induction of SOCS2 transcript, and a reduction in cell viability and cell growth.

Materials and Methods

Valk Dataset

A study was conducted by Peter Valk and colleagues in 2004, which examined peripheral blood or bone marrow from 285 AML patient samples using Affymetrix U133A GeneChips containing 13,000 unique genes or signature tags, and unsupervised cluster analyses revealed 16 distinct types of AML based on gene expression profiles [1]. I searched this publically available dataset to discover the expression level of Lnk and the SOCS family in the 16 AML subtypes.

The clustering was driven in some cases by the presence of chromosomal lesions, such as t(8;21), t(15;17), and inv(16). In other cases, the clustering was not as clear cut. This dataset was also used to examine the expression of other genes that we then used to select patients from our tissue bank that fell into various groups of interest.

Cell Culture

OCI AML1-5 were cell lines from my lab. AML2 and 3 were cultured in α-MEM growth medium supplemented with antibiotics and 10% fetal calf serum (FCS). AML1, 4 and 5 were grown in Alpha medium supplemented with antibiotics and 10% FCS, and 10% 5637 conditioned medium at 37 degrees Celsius, and 5% CO2.

CMLT1, Meg01, and MC3 were obtained from Karla Badger in Dr. Dwayne Barber’s

lab, and cultured in RPMI growth medium supplemented with antibiotics and 10% FCS at 37

degrees Celsius and 5% CO2.

The clinical patient peripheral blood and bone marrow samples were obtained from the

Tissue Bank, maintained by Dr. Mark Minden at the Ontario Cancer Institute at Princess

Margaret Hospital. These were collected following informed consent as per institutional research

ethics board guidelines. Mononuclear cells were separated by Ficol-Hypaque density

centrifugation and re-suspended in freezing medium, and stored in the liquid nitrogen tissue bank

until use. Before use, they were thawed rapidly in a 37 degree Celsius water bath, re-suspended

in RPMI medium supplemented with antibiotics and 10% FCS, and spun at 2500 rpm for 5

minutes. The cell pellet was re-suspended in ACK red blood cell lysis buffer (0.15 M NH4Cl, 10 mM KHCO3, 0.1 mM EDTA in 1 L ddH2O, pH to 7.2-7.4 ) for 5 minutes at room temperature,

washed twice and prepared as stated below for DNA, RNA and protein isolation.

RNA Isolation

Cells were pelleted at 1500 rpm for 5 minutes. Total RNA was isolated from cells using

an RNeasy kit (Qiagen Inc., Canada) as per protocol provided, and stored at -70 degrees. RNA

concentration and purity (260/280 ratio of ~2 or above) was measured using the ND-1000 nanodrop spectrophotometer.

DNA Isolation

Cell line and patient sample DNA was purified using the Qiagen DNeasy® Tissue purification kit, as per protocol. DNA concentration was measured for concentration and purity

(260/280 ratio of ~ 1.8 or above) using the ND-1000 nanodrop spectrophotometer. 1 μg of DNA

was sent for analysis.

Real time quantitative PCR

Total RNA (2µg) was converted into 100µl of cDNA using the Applied Biosystems

TaqMan® One-Step RT-PCR master mix reagents kit (cat#4309169), using random hexamers.

Primers used for real time PCR are as follows: SOCS1 F 5’GAGAGCTTCGACTGCCTCTT3’,

R- 5’AGGTAGGAGGTGCGAGTTCA3’ [67], SOCS2 F- 5’CTGCCACCATTTCGGACACC3’

R- 5’CGTCCCTTCCCCGCCATTCC3’ [68], SOCS3 F- 5’AGGTAGGAGGTGCGAGTTCA3’

R-5’TTCTCATAGGAGTCCAGGT3’ [67], GAPDH F- 5’GAAGGTGAAGGTCGGAGTC3’

R- 5’GAAGATGGTGATGGGATTTC3’, CTNNA1 F- 5’CTGGGCATCTTAGGAAGCAG3’

R- 5’TGGCATCGAGACACTGTAGC3’, FLT3 F 5'

AGCAATTTAGGTATGAAAGCCAGCTA 3', R 5'CTTTCAGCATTTTGACGGCAACC 3

[69].

In all, 2 µl of cDNA was used per PCR reaction with 2 x SYBR®Green (Applied

Biosystems, USA) as per manufacturer’s protocol, using 10 µm primers, and an ABI PRISM™

sequence detector. The program was as follows: 1 cycle for 10 minutes at 95 degrees, 40 cycles

for 30 seconds at 95 degrees and 1 minute at 60 degrees, and 1 cycle for 3 minutes at 72 degrees.

A dissociation curve was performed to ensure that the primers were only amplifying a single

target.

Antibodies/Western Blot

The rabbit polyclonal SOCS2 antibody was purchased from Abcam (ab3692), and detects

a band of approximately 23 kDa, and corresponds to amino acids 184-198 of human, rat, and

mouse SOCS2, and was used at the concentration of 1 µg/ml.

β-actin antibody was a monoclonal antibody obtained from Sigma, (A5441) (mouse IgG1

isotope), and used at a concentration of 1:50,000 for 1 hr, room temperature.

The secondary Anti-mouse (NA931) and Anti-rabbit (NA934) IgGs were purchased from

Amersham Biosciences, and were used at a concentration of 1:5000. Immunoblots were performed on a 10% SDS-page gel. 50 µg of lysates were boiled for 5 minutes in 6X sample buffer and loaded equally. The gel was run at 125V for 95 minutes, and transferred overnight in transfer buffer using 30V onto a PDVF membrane at 4 degrees Celsius.

SOCS2 primary antibody was used at the above concentration, and incubated at room temperature for 2 hours with 5% milk. The membrane was washed 3X for 15 minutes in TBST.

Secondary antibody was incubated at room temperature for 1 hour. The membrane was washed

3X for 15 minutes. A Perkin Elmer Western Lightning Chemiluminescence Regant Plus

(NEL104) kit was used as a non-radioactive light-emitting system, as per protocol provided, to detect proteins on the membrane. Kodak autoradiography film was used to detect the signal.

Exposure ranged from 10-60 seconds.

Drug Treatments

NB4 and AML2 cells were plated at a concentration of 2.0 x 105 cells/ml for a total of 3 ml,

and treated with 0.2 µm of the de-methylating agent 5-Aza-2'-Deoxycytidine (Decitabine) for 24

hours for 1 day. In order to induce SOCS2 expression, 1 µm of all-trans-retinoic acid (ATRA)

was added as well for 24 hours for 1 day. Cells were counted at 0, 24, 48, 72, and 96 hours for cell number and viability, and harvested for RNA preparation for qPCR analysis at the 24 hour time point.

Promoter Methylation

C) -166 -> 208 relative to initiation codon CCAGGATCTGGGGAGAAAGAGCCCCATCCCTTCTCTCTCTGCCACCATTTCGGACACCCCGCAGG GACTCGTTTTGGGATTCGCACTGACTTCAAGGAAGGACGCGAACCCTTCTCTGACCCCAGCTCGG GCGGCCACCTGTCTTTGCCGCGGTGACCCTTCTCTCATGACCCTGCGGTGCCTTGAGCCCTCCGG GAATGGCGGGGAAGGGACGCGGAGCCAGTGGGGGACCGCGGGGTCGGCGGAGGAGCCATCCCC GCAGGCGGCGCGTCTGGCGAAGGCCCTGCGGGAGCTCGGTCAGACAGGTAGGGAGCCGATCGG CCGCGACGCGTGCGGGAGGGAGCGCCTCCCCAAGGAAGCAGCTAGGAAGCGGG

Figure 2.1. A) The vertical bar indicates where on chromosome 12 the SOCS2 gene is located. B) The grey bar indicates the region of the SOCS2 gene examined in our study. C) The -166 to 208 base pair region used to examine

SOCS2 promoter methylation relative to the start codon indicated in bold.

The region we chose to focus on was -166 -> 208 relative to initiation codon, as depicted in figure 2.1. We selected this region because these CpG islands were shown to be hyper- methylated in 14% of primary ovarian cancers in the study by Jane Visvader and colleagues [64].

The method we chose to employ for our methylation analysis was a mass spectrometry based method called EpiTYPER. Is a tool for the discovery and quantitative analysis of DNA 52

methylation using a base-specific cleavage and matrix-assisted laser desorption/ionization time- of-flight mass spectrometry (MALDI-TOF MS) [70]. It uses the speed and accuracy of the

MassARRAY® system, making it ideal for the discovery of methylation, for discrimination between methylated and non-methylated samples, and for quantifying the methylation levels of

DNA. As a negative control, we purchased both un-methylated and universally methylated DNA samples from Chemicon, which were both blindly determined to be almost 100% un-methylated and methylated respectively.

Results

To begin to explore variations in the expression of proteins involved in limiting cytokine signalling, I examined a previously published array of AML patient samples to look at the expression of Lnk and SOCS family members in normal cells and different AML subtypes [1].

700

600

500 Dataset

SOCS1

Valk SOCS2 400 SOCS3 from

CISH 300 SOCS5 Expression SOCS6 200 SOCS7 Gene Lnk 100

0 CD34 CD34 CD34 NBM NBM NBM NBM Cell Type

Figure 2.2. The expression of the SOCS family of negative regulators in immature CD34 positive cells and in mature normal bone marrow samples examined in Valk’s gene expression dataset [1].SOCS expression was measured on an Affy array, and expression values over 400 is thought to be expressed above background.

The transcript expression of the SOCS family and Lnk is compared across the immature

CD34 sorted fraction, and bulk differentiated normal bone marrow in the Valk dataset [1]

(Figure 2.2). Valk’s group used Affymetrix Microarray Suite software for their analysis, which

predicted the average intensity values across all samples was 150, and that they could reliably

identify samples with an average intensity value of 30 or more. So for the purposes of my

analysis, I have considered values below 30 as non-expressed, values around 150 as averagely

expressed, and values around 300 or more as highly expressed for this dataset. As seen in figure

2.2, CISH, SOCS5, and SOCS3 are all expressed above background, at consistent but low levels

in both immature and mature cell types. SOCS1, SOCS6, and SOCS7 are considered negligible.

In contrast, SOCS2 expression showed variation based upon the differentiation stage of the cells,

with high expression in immature cells, and consistent but low levels in the bulk population of

bone marrow cells, which is made up of predominantly differentiated forms. This pattern

suggests that SOCS2 has an important function in early haematopoietic cells that is distinct from

the role of other SOCS family members. This finding is consistent with the literature on the role

of SOCS2 in regulating growth hormone and IGF-1 signalling, and the gigantism phenotype that

exists in both transgenic and knock-out mince [3]. It is interesting to note that the pattern of Lnk

expression is opposite to that of SOCS2, suggesting that its role is to affect primarily the growth

of more mature cells.

Figure 2.3. A look at the expression of relevant genes in CD34(first 3 samples), NBM (next 4 samples), group

4(samples ending in 4) and group 12 (samples ending in 12) in the Valk dataset [1]. The legend represents a

continuum of expression, where white indicates low to negligible expression, grey represents expression, and black

represents high expression.

In order to get further insight into the key players in the SOCS2 signalling pathway, some

genes of interest, such as the JAK/STATs, classifiers of group 4 (PAWR, Meis1, CTNNA1,

CD34, CD7), and normal FLT3 expression were also compared. The immature fraction, bulk

NBM, and groups 4 and 12 of the Valk dataset are displayed in Figure 2.3. The levels of the

JAKs and STATs appear to be consistent, whether it is consistently low or consistently high,

across the samples. The exception to this is STAT5a, which is interestingly involved in the

SOCS2 signalling pathway [71, 72], and FLT3-ITD signalling but not normal FLT3 signalling.

STAT5a shows a similar pattern to SOCS2, being high in immature cells and decreasing with

maturity. The genes that follow a similar pattern of SOCS2, PAWR, and Meis1, are considered qualifiers of this group, as they are also aberrantly low compared to the CD34 cell fraction.

Group 4 Group 12 2500

2000

1500 CD34 SOCS2 Expression 1000

500

0 NBM6 2682.1 2187.1 2685.2 2201.2 1551.2 2665.3 2286.3 2205.3 2753.4 2238.4 3490.5 3301.5 2655.5 2289.5 2259.5 2217.5 2175.5 2679.6 1197.7 3098.7 2268.7 2199.7 2756.8 2223.8 3286.9 2696.9 2254.9 2172.9 CD34-3 1432.11 3096.11 2263.12 2468.12 3278.12 2208.13 2267.13 2756.13 1201.14 2690.14 2237.15 2225.16 2749.16

Patient # 2276.101 2539.101 2666.101

Figure 2.4. An overlay of SOCS2 and CD34 expression across all patients in the Valk dataset, which has been

classified into 16 groups marked by the vertical bars. The first 3 samples are a normal immature CD34 positive

control, and the next 4 are mature normal bone marrow. CD34 expression is indicated in grey and SOCS2

expression in black. Group 4 and 12 have been highlighted as they are groups in which SOCS2 expression appears

to be silenced. In the Valk dataset, expression of 30 is considered absent, 150 to be average, and over 150 to be

high.

Figure 2.4 is an overlay of the expression of CD34 and SOCS2 across the entire Valk dataset of normal controls and AML samples. The gene expression patterns of these two genes were overlay, as I was interested in identifying potentially aberrant patterns of expression that appeared in figure 2.3. As can be seen from the first 7 samples representing the normal controls,

SOCS2 expression is high in normal CD34 sorted cells, and low in bulk normal bone marrow, and CD34 expression follows nicely. In the dataset as a whole, marked variation in the expression of SOCS2 is seen. Group 4 stood out as having aberrant SOCS2 expression, as CD34 expression is high, yet SOCS2 expression is low.

Table 2.1. Characteristics of Valk patients in each of the 16 groups [1].

Valk Group Characteristic

1 EVI1

2 FLT3-ITD

4 cEBPα

6 FLT3-ITD

9 inv(16)

10 EVI1

12 t(15;17)

13 t(8;21)

15 cEBPα

16 11q23

Table 2.1 is a list of characteristics that is found in each of the 16 Valk groups. Group 4 has abnormal cEBPα being either mutated or expressed at very low levels, whereas group 12 harbours the t(15;17). Valk groups in figure 2.4 that also appear to have aberrant SOCS2 expression, based on being high in CD34 and low in SOCS2, are groups 9 (inv(16) patients), 13

(t(8;21) patients), and 15 (cEBPα patients). The group that shows the opposite effect, being low in CD34 and high in SOCS2, is group 16 (11q23 patients).

Valk groups that appear to have an expression pattern similar to group 12 are groups 5, 7, and 11. Interestingly, these patients do not have defined abnormalities. However, having an expression pattern low in both CD34 and SOCS2 is not abnormal, as this is what occurs in the normal bone marrow controls.

Figure 2.5. A) Full length SOCS2 was over-expressed in HEK-293 cells to ensure antibody specificity. B) Protein expression of various Leukemia cell lines using the SOCS2 antibody. β-actin was run as a loading control. C)

SOCS2 mRNA expression compared to GAPDH expression measured by qPCR in various Leukemia cell lines. The first bar represents a normal bone marrow control. Each sample was run in triplicate and error bars represent the standard deviation. D) Methylation of Leukemia cell lines in the CpG region indicated along the x-axis. The legend represents a continuum of methylation, indicating 0% (white), 37.5% (grey) and 75% (black) methylation. The samples were measured in triplicate and an average was taken. The arrow at the bottom of the figure represents the translational start site and translation follows the direction of the arrow. Universally un-methylated and methylated samples were used as a control for the technique and are displayed at the top of the figure for comparison.

Having found extreme variability in primary patient samples in the Valk dataset, I

decided to identify AML samples that had low levels of SOCS2, as this would represent the loss of a negative regulator of cell growth. I first assessed the expression at a RNA and protein level in nine myeloid cell lines (Figure 2.5B,C). Moderate to high levels of SOCS2 expression was seen in the cell lines OCI/AML 1, 2, and 4 and CML blast crisis cell lines K562, Meg01 and

MC3. Very low to absent levels of SOCS2 expression were found in OCI/AML 3 and 5 and the

APL cell line NB4.

A common mechanism for the loss of expression of SOCS family members is that of

promoter hyper-methylation. To determine if methylation was responsible for the loss of SOCS2 expression in the above cell lines, I used bisulphite conversion and EpiTYPER, a mass spectrometry based array, to determine the level of methylation in the upstream region of the

SOCS2 promoter. As shown in Figure 2.5D, a total of 25 distinct sites including 42 CpGs were assessed. As can be seen in this figure, there was almost complete methylation of the promoter in

OCI/AML 3 and 5 and NB4 cells, while the promoters of the expressing cell lines were totally un-methylated (Figure 2.5C). 61

Figure 2.6. A) SOCS2 mRNA expression compared to GAPDH expression measured by qPCR in various Leukemia

patient samples. Included are a normal bone marrow control, a methylated APL cell line, an un-methylted AML and

CML cell line for comparison. Each sample was run in triplicate and error bars represent the standard deviation.

B) Methylation of Leukemia cell lines in the CpG region indicated along the x-axis. The legend represents a

continuum of methylation, indicating 0% (white), 37.5% (grey) and 75% (black) methylation. The samples were

measured in triplicate and an average was taken. The arrow at the bottom of the figure represents the translational

start site and translation follows the direction of the arrow. Universally un-methylated and methylated samples were

used as a control for the technique and are displayed at the top of the figure for comparison.

Having identified hyper-methylation of SOCS2 in AML cell lines, I wanted to determine

if this occurs in primary AML cells, or whether it is an artefact of cell line establishment.

Considering the variable expression of SOCS2 in the AML samples of the Valk dataset (Figure

2.4), I took a random sampling of patients from the Princess Margaret Hospital tissue bank. As is

evident from Figure 2.6, none of the patients showed a hyper-methylated SOCS2 promoter despite having variable SOCS2 expression. I decided to focus on Valk clusters that had very low levels of SOCS2 expression; groups 4 and 12. Group 4 was of interest, as SOCS2 expression is

in contrast to what is seen in normal cells. Group 12 was of interest as the level of SOCS2 was

very low, and this group represents the APL cluster. Since SOCS2 is hyper-methylated in the

APL cell line NB4, I was interested in determining if hyper-methylation of SOCS2 occurs in

primary AML cases as well.

Table 2.2. Group 4 patient information.The patients are ordered according to their WBC.

Patient # Sample Karyotype FAB WBC CD34 CD7 Origin (x109/L) 4623 PB 46, XX, M1 165 0.97 0.76 add(9)(p22), del(11)(q23), add

(12)(p13)

8973 PB normal M1 59.8 0.83 0.95 5648 PB abnormal M1 40.4 0.97 0.98 8278 PB normal M1 24.2 0.33 0.95 5657 PB normal M1 19.8 0.93 0.95 4922 PB abnormal M1 18.6 0.96 0.82

5004 PB abnormal M1 5.2 0.95 0.77 E10E PB unknown M0 0 0.93 0.23 C8E7 PB -9, +21 M1 Unknown 0.38 0.44

To identify potential group 4 cases in the Princess Margaret Hospital Leukemia Tissue

Bank, I took advantage of several unique features or identifiers that distinguished this group of

patients from the others. The patients needed to be either FAB M0 or M1, have high CD34

expression (>30% cells positive) and high CD7 expression (>30% positive). These criteria

yielded 9 samples, shown in table 2.2. To further confirm that these cases were representative of

group 4, I assessed the levels of expression of SOCS2, Prostate Apoptosis Response Gene-4

(PAWR), Meis homeobox 1 (Meis1) and Catenin (cadherin-associated protein)α-1 (CTNNA1)

which are all characteristically low in cluster 4 (Table 2.2 and Figure 2.7). Using these criteria, I

can be fairly certain that these 9 patient samples are similar to the patients in Valk`s group 4. The

relative infrequency of such patients is in keeping with the proportion of these patients in the

Valk set (13 of 285 cases).

Table 2.3. Group 12 patient information. The patients are ordered according to their WBC.

Patient # Sample Origin WBC (x109/L) FLT3 ITD

9418 PB 87.9 yes

8894 PB 80.3 yes

8894b PB 80.3 yes

4630 PB 69.1 yes

4840 PB 54.7 yes

8128 BM 29.9 yes

5057 PB 17.7 yes

3272 PB 14 no

8944 BM 6.7 yes

8943 PB 6.7 yes

3280 BM 1.8 no

8301 BM 1.1 no

9189 BM 0.7 no

8415 BM 0.4 no

C5E5 PB NA no

I also more easily identified 14 APL samples from the Tissue Bank. In 9 cases, the

presenting WBC was greater than 5 x109/L. Of these 9 cases, 8 harboured a FLT3 ITD

(characteristics shown in table 2.3). None of the cases with a WBC less than 5 had this mutation.

A) High WBC (>5) Low WBC (<5)

Figure 2.7. A) Expression of SOCS2, and some group 4 quilifiers; CTNNA, PAWR and FSHD2 in the group 4 patient samples normalized to GAPDH. SOCS2 is represented first, CTNNA1 second, and PAWR thrid. I have included NB4 as an APL cell line control, a normal bone marrow control, AML2 an un-methylated AML cell line control, and K562 a very positive, un-methylated CML control. Each sample was run in triplicate and error bars represent the standard deviation. The vertical bar seperates the patient samples with high WBC from those with low

WBC. B) Expression of SOCS2 in group 12 patient samples normalized to GAPDH. SOCS2 is represented by the bars. The first bar represents a normal bone marrow control. Each sample was run in triplicate and error bars represent the standard deviation. The vertical bar seperates the patient samples with high WBC from those with low

WBC.

I used real time RT-PCR to assess the expression of SOCS2 in the candidate cluster 4 cases (Figure 2.7A) and in the APL cases (Figure 2.7B). In these samples, the level of SOCS2

(and the other G4 qualifiers) expression was low to absent in comparison to K562 cells.

However, I noted that in both groups, cells with higher levels of SOCS2 expression tended to have lower white blood cell counts. When taking into account all of the features necessary to classify a patient into group 4, the patients with high white blood count in group 4 probably are not actually group 4 after all. Patients 8278, E10E, and C8E7 all have either high expression of

SOCS2, CTNNA1, or their CD7 or CD34 expression is not as high as the rest of the group. I kept these patients in for comparison however.

WBC > 5

WBC < 5

p≤ 0.01

WBC > 5

WBC < 5

p≤ 0.01

Figure 2.8. A) Methylation of Leukemia patients in group 4 and B) group 12, in the CpG region indicated along the

x-axis. The legend represents a continuum of methylation, indicating 0% (white), 37.5% (grey) and 75% (black)

methylation. The samples were measured in triplicate and an average was taken. The arrow at the bottom of the

figure represents the translational start site and translation follows the direction of the arrow. Universally un-

methylated and methylated samples were used as a control for the technique and are displayed at the top of the

figure for comparison. The patient samples are listed in order of decreasing WBC from top to bottom. The dividing

line on the right distinguishes between high WBC and low WBC. The grey bars at the bottom of the graph represent

a significant difference between methylation in patients with high WBC compared with low WBC.

To determine if the low level of SOCS2 expression was associated with promoter hyper-

methylation, I assessed the same CpGs as previously. For group 4 (figure 2.8A), 5 of the 9 cases

showed a significant increase in methylation. Interestingly, the cases with less methylation had a

WBC of less than 5, while increasing levels of methylation were seen in the cases with higher

WBC. Expression appeared to be slightly higher in the un-methylated patients (Figure 2.7A). For

group 12 (Figure 2.8B), I again observed hyper-methylation of the SOCS2 promoter. As for

group 4, the increased methylation was confined to the group of patients with a WBC greater

than 5, which were also the group that harboured the FLT3-ITD (Table 2.3).

A between-subjects t-test was performed (p=0.01), using Euclidean distance and average

linkage clustering (done by the multiple array viewer software) to compare the patients with

WBC>5 with the patients with WBC<5, and in group 4, 5/25 regions were significantly different

between the two groups. Group 12 showed 20/25 CpG regions to be significantly different

between high and low WBC.

Table 2.4. WBC and FTL3 in group 12 from Valk [1].

WBC FLT3 ITD FLT3 TKD

0 no no

1 no no

2 no yes

4 no yes

5 no no

23 no yes

28 no no

33 no yes

41 yes no

45 yes no

47 yes no

86 Yes no

103 Yes no

117 yes no

Table 2.3 is adapted from the Valk dataset, in which they found a correlation between the

presence of a FLT3-ITD and a high WBC (WBC >10).

C) 0.9

0.8

0.7 GAPDH

to 0.6

0.5 normalized 0.4 NB4 AML2 0.3 expression 0.2 SOCS2 0.1

0 Control 5‐aza ATRA 5‐aza+ATRA Drug Treatment

Figure 2.9. A) Percent viability of cell lines NB4 and AML2 after treatment with ATRA (1 µm) and decitabine (0.2

µm) for 5 days. .B) Total cell number of cell lines NB4 and AML2 after treatment with ATRA (1 µm) and decitabine

(0.2 µm) for 5 days. C) SOCS2 expression 24 hours after drug treatment.

As promoter methylation was found in many of the cell lines and patient samples, I

wanted to determine whether treating a methylated cell line (NB4) with a demethylating agent

(decitabine) would have an effect on cell growth, viability and SOCS2 expression. Figure 2.9

shows both NB4, a methylated and normally non-expressing SOCS2 cell line, and AML2, a non-

methylated SOCS2 expressing cell line, treated with Decitabine (5-aza) and ATRA for 24 hours.

As shown in figure 2.9A, cell viability in the methylated NB4 cell line dropped by 20% after 24

hours of decitabine treatment, and by about 50% after 72 hours. Decitabine did not have this

affect on the non-methylated AML2 cell line. Both Decitabine and ATRA appeared to be toxic to

both cell lines 5 days after treatment. Also, cell division appeared to slow down in the NB4 cell

line after decitabine treatment. This effect was not seen in AML2 cells or NB4 cells not treated

with decitabine (Figure 2.9B). Transcript levels were examined 24 hours after ATRA treatment.

As evident in figure 2.9C, SOCS2 expression was induced in the methylated cell line NB4 after

decitabine treatment, but not in the ATRA alone treatment, or in the non-methylated cell line

AML2. It is difficult to conclude that the decrease in cell growth and viability seen in part A and

B of this figure is as a direct result of SOCS2 induction, as a multitude of genes are turned on

with decitabine treatment. However, it is a promising result that requires further studies.

0.9

0.8

0.7 GAPDH

0.6

0.5 normalized Control

0.4 5‐aza treatment expression

0.3 SOCS2

0.2

0.1

0 p37p39p40p42p44p46p47p48p49p55p56p59p60 Patient Sample

Figure 2.10. SOCS2 RNA expression of AML patient samples collected before and after treatment with de-

methylating agent decitabine for 8 days. Dose was variable across patients. It appears that decitabine treatment

induces SOCS2 expression in some patients, suggesting a possible use of decitabine in the clinic for a specific subset

of patients. (For patient information from the clinical trial, see Table 2.5).

Dr. Mark Minden previously ran a clinical trial, where AML patients were treated with

varying doses of Decitabine for 8 days. Bone marrow samples were collected at day 0 (Control)

and day 8 (5-aza treatment) for analysis. RNA for these patients was obtained from these time

points. I measured the gene expression of SOCS2 in these patients, and the results are shown in

Figure 2.l0. As can be seen from the graph, SOCS2 expression is induced in some patients by at

least two-fold (patients 44, 49, 56, and 60), while not in others. No APL patients were included

in the study, however patients 47, 48, 49, 59 had high CD7 and CD34 expression, making them

potential group 4 candidates. Furthermore, they had high count disease with blast counts greater than 10 x109/L (Table 2.5). All of these patients showed a slight (patients 47 and 59) to a

substantial (patients 48 and 49) induction of SOCS2 expression after treatment. I predict from

my findings in AML cell lines and patient samples that these patients would show SOCS2

promoter hyper-methylation.

Table 2.5. Patient information for the decitabine clinical trial.

Patient Disease WBC Cytogenetics Group 4? SOCS2

induction?

37 AML M1 1.7 Normal Na Yes

39 AML M1 2.7 Complex Na Na

40 AML 4.6 Normal No No

42 AML 1.4 NA Na Slight

44 AML High Complex No Yes

46 AML 19.8 Complex No Slight

47 AML 11.5 Complex Maybe Slight

48 AML 101 Complex Maybe Yes

49 AML High Normal Maybe Yes

55 AML 1.6 Complex No No

56 AML M4 27 Complex No Yes

59 AML M1 59.8 Normal Maybe Slight

60 AML 172 Normal No Yes

Discussion

My thesis has examined SOCS2 expression, and how it appears to have a disease- dependent role in AML. When 285 AML patients were sorted based on gene expression profiles, it was evident that SOCS2 expression is not constant across groups, but varies remarkably [1].

Expression appears to follow that of the differentiation hierarchy in some groups, like groups 5,

7, 11, and 12 and differs from this in other groups, such as group 4, 9, 13 and 15.

I decided to focus on two of the groups in the Valk dataset that were low in SOCS2 expression. I chose group 4 because it had an immature phenotype, as displayed in the overlay of

CD34 and SOCS2 in figure 2.4, however, SOCS2 expression appears to be aberrantly low when compared to normal CD34 positive cells. Also, as this group had easily definable features, such as high CD7 expression, a normal karyotype, and a FAB M1 classification (with a few M0). In addition to the aberrantly low levels of SOCS2, a number of other genes that are relatively high in the normal CD34+ cells are also low in this group. These include genes such as CTNNA1,

PAWR, Meis1, and Homeobox A9 (HOXA9). This constellation of features suggested a strategy

for me to identify patient samples within the Princess Margaret Hospital leukemia tissue bank

that were representative of the Valk group 4 patients. Using some of these criteria, it was possible for me to identify patient samples that bore a close resemblance to those in group 4, providing support for the classification system proposed by Valk, and allowing us to carry out further studies with this sub-group.

A common mechanism for reducing the expression of a gene is through hyper- methylation of the promoter region. Among the potential group 4 patients I identified, there was an increase in methylation of the SOCS2 promoter. Some patients showed dense methylation of many of the evaluable CpGs, while others showed increased but variegated methylation. It was interesting to note that the cases with little methylation also had presenting white blood counts of less than 5 x109/L.

The second group of patients I studied for SOCS2 expression and methylation were those

with APL, as I had found very low levels of SOCS2 expression and promoter hyper-methylation in the APL cell line NB4. Samples of all 16 APL patients had low levels of SOCS2, and hyper-

methylation was only observed in 11 of the cases. Interestingly, and similar to my observation in

group 4, the patients with white blood counts greater than 5 x109/L had a hyper-methylated

SOCS2 promoter.

A common cause of high WBC in AML patients, particularly those with APL, is a

mutation of FLT3, predominantly by the acquisition of internal tandem duplications in the juxta-

membrane region of the receptor. In all of the high count APL patients, I was able to show the presence of a FLT3-ITD mutation.

FLT3 is a trans-membrane tyrosine kinase that is mutated in 20-30% of cases of AML.

The acquisition of the FLT3-ITD is associated with high white blood cell counts, and reduced

long term survival of patients. The FLT3-ITD protein differs from its normal counterpart by

strongly activating STAT5a [73]. STAT5 and STAT3 are transcription factors that lead to the

expression of survival proteins such as Bcl-xl and Mcl-1. These STAT proteins, however, also

stimulate the expression of negative regulators of cell growth, such as SOCS1 and SOCS2.

Choudhary et al. (2007) used a 32D model system to show that SOCS1 does not have an

inhibitory effect on FLT3-ID signalling [73].

In a recent study, Bullinger et al. (2008) identified patients with a normal karyotype, a

FLT3-ITD, and a high level of SOCS2 expression, which is in keeping with the above noted work of Choudhary et al. (2007) [73]. However, this work is not consistent with my observations of low SOCS2 expression and a hyper-methylated promoter in FLT3-ITD positive patients with

APL or from Valk group 4.

Figure 2.11. SOCS2 expression across Valk groups for patients with a FLT3-ITD. 78

To try to understand these conflicting observations, I returned to the Valk dataset. I extracted all AML patients that had been identified as harbouring a FLT3-ITD. I then compared the level of SOCS2 expression across all of these patients and their Valk groups. As can be seen in figure 2.11, there is relatively high level of SOCS2 RNA expression in normal CD34+ cells, and a much reduced level from total bone marrow, indicating that the level of SOCS2 is down- regulated as cells differentiate. The first 7 samples are FLT3-ITD negative controls; whereas the remaining samples all have a FLT3-ITD mutation. As can readily be appreciated, there is marked variation in the level of SOCS2 expression. Some patients have higher levels than normal

CD34+ cells, while other samples have levels equivalent or lower than bulk normal bone marrow. It is noteworthy that the samples with high levels of SOCS2, mainly groups 2 and 3, had predominantly normal cytogenetics. In contrast to the report by Bullinger et al. (2008), who found high SOCS2 in FLT3-ITD positive patients with normal cytogenetics, Valk patients with these requirements from groups 4, 5 and 6, had low levels of SOCS2, on par with total normal bone marrow [74]. It is not clear why Bullinger did not identify these patients. Upon examination, Valk group 5 patients are predominantly FAB M5, and only 16% of the Bullinger patients fall into this group. Based upon our data and the information gained from the Valk data set, it appears that the presence of a FLT3-ITD is not always associated with high level SOCS2 expression. In some cases, it may in fact be associated with below normal levels of SOCS2 expression that are associated with hyper-methylation of the SOCS2 promoter.

Hyper-methylation of SOCS2 has been reported previously for breast and ovarian cancers

[64] and MPD [6], however not yet for AML. In this manuscript, I found hyper-methylation of the SOCS2 promoter in AML cell lines and primary patient samples. The AML samples that I

focused on were derived from two discrete gene expression groups, and I will therefore discuss

what may be occurring in each group separately.

Valk group 4 AML patients are characterized by having a FAB M1 morphology, normal

cytogenetics, and high expression of CD34 and CD7. The high CD34 expression would suggest

that little differentiation is occurring in this form of AML, and I would therefore expect this

group to have a similar gene expression signature to normal CD34+ cells. In my examination of

the gene expression pattern of these cells, I was struck by a relatively large number of counter- instances of this assumption. Genes such as HOXA2, HOXA5, HOXA9, SOCS2, PAWR,

MEIS1 and KLF4 are all markedly reduced compared to the normal CD34 enriched fraction of

cells. In this manuscript, I have shown that the reduced expression of SOCS2 is due in part to

promoter hyper-methylation. Another feature of group 4 gene expression is the increased

expression of genes associated with T cell development, such as CD7 and T-cell receptor-delta.

The reduced expression of the HOX genes may also be due to silencing by hyper-methylation, or

may reflect the attempted differentiation of these cells towards lymphocytes. Whether the

silencing of SOCS2 in this group is part of this overall mechanism, or arises by another unrelated

mechanism is not clear. It is however noteworthy, that in the patient samples studied here, all of

the cases with a hyper-methylated SOCS2 promoter had an increased white count, and a mutant

FLT3-ITD.

The second group that I found to have hyper-methylation of the SOCS2 promoter was the

APL group of patients with a high white count and a mutant FLT3-ITD. As noted earlier, the expression of SOCS2 is high in the normal CD34+ population of cells and decreases as cells mature. It is therefore not surprising that the expression of SOCS2 is low and its promoter is un- methylated in APL, as the majority of cells have undergone a degree of differentiation. This is in

contrast to the high degree of methylation in the APL cases with a high white blood cell count and mutant FLT3-ITD. What has happened that could account for this change in the promoter of

SOCS2? The most notable difference between the methylated and un-methylated cells is the

acquisition of the FLT3-ITD. As depicted in Figure 2.3 in this thesis, wild type FLT3 is expressed in low count APL cases. However, signalling by wild type and FLT3-ITD are not

equivalent. While both receptors can activate the ERK and AKT pathways, the mutant form of

FLT3 is highly efficient in activating STAT5a. STAT5a drives the expression of growth

promoting genes such as IL2, IL3, IL7, GM-CSF, erythropoietin, thrombopoietin, and different

growth hormones, but also stimulates the expression of genes that reduce cell growth such as

SOCS1 and SOCS2 [73].

In previous studies using the murine cell line 32D, Choudhary et al. (2007) found that the expression of FLT3-ITD led to increased expression of SOCS genes [73]. It was shown that in 32D cells, the FLT3-ITD tyrosine kinase did not engage JAK2. The authors concluded that in these cells, FLT3-ITD signalling was insensitive to the effects of SOCS1. Why then is there a change in the methylation status of the SOCS2 promoter between low and high count APL?

Although not yet tested, I hypothesize that in APL cells with a FLT3-ITD, the complex of molecules that activates STAT5a differs from the complex in the 32D FLT3-ITD cells, and in cases of human AML with a FLT3-ITD and high SOCS2 expression, such as Valk groups 2 &3.

Figure 2.12. A box plot of SOCS2 expression for FLT3-ITD negative (white) and positive (black) patients across all

16 Valk groups of AML patients. C= CD34 positive patients, and N= NBM, and are listed first as a control.

Due to the discrepancy between the Bullinger study and our observations of low SOCS2

in FLT3-ITD positive patients, I wanted to compare SOCS2 expression and FLT3-ITD status

across the entire Valk dataset. This comparison is depicted in the box plot in figure 2.12. As seen

in this graph, group 2 and 3 show results similar to that noted in the Bullinger study, where

SOCS2 expression increases with the presence of a FLT3-ITD. I hypothesize that in this

scenario, STAT5a is activated, inducing SOCS2 expression. However, there is no SOCS2

inhibitable protein the FLT3-ITD complex in these patient groups, therefore SOCS2 expression

is tolerated as it does not inhibit cell growth. In contrast, in APL (group 12) and other subgroups

such as Valk groups 4 and 5, the FLT3-ITD complex includes as an important part of its

signalling, a SOCS2 inhibitable protein such as JAK2. In this setting, in order that the oncogenic

behaviour of the mutant FLT3 is appreciated, there is silencing of SOCS2 through promoter

hyper-methylation. Along the same lines are groups 1, 10 and 16, which have high levels of

SOCS2 in the FLT3-ITD negative cases and low SOCS2 in the FLT3-ITD positive cases. These

latter groups were most likely left out of the Bullinger study as, in general, these cases had

cytogenetic abnormalities or only made up a small subset of the cases e.g. FAB M5. In this

setting (FLT3-ITD and low SOCS2), in order for the oncogenic behaviour of the mutant FLT3 to

be appreciated, there appears to be silencing of SOCS2 through promoter hyper-methylation.

There is still a debate as to whether promoter hyper-methylation occurs as a random acquisition of epigenetic marks conferring a growth advantage, or whether it occurs under the direction of an oncogene that acts through a specific pathway. The study mentioned previously by Gazin et al. (2007) addressed this question by identifying genes that were required for RAS-

mediated epigenetic silencing of the pro-apoptotic FAS gene. They hypothesized that FAS

silencing in K-RAS transformed cells is directed by the constitutively active RAS oncogene.

They carried out a genome wide RNA interference screen in K-RAS transformed NIH 3T3 cells

and found 28 genes that, when knocked down, fail to recruit DNMT1 to the FAS promoter.

Methylation is lost, and FAS expression is restored. At least 9 of these 28 genes were found to

directly associate with the FAS promoter, including the DNA methly-transferase DNMT1 in

transformed NIH 3T3 cells but not in untransformed cells. They suggest that RAS is able to

direct epigenetic silencing through a specific pathway [60].

Although this paper claims to demonstrate that RAS directs methylation, the study had an

important inadequacy that I would like to address. In using transformed NIH 3T3 cells, there is a

selection for cells able to demonstrate a transformed phenotype when transfected with RAS.

These cells may be very different from the cells that do not become transformed, so when

compared to non-transformed NIH 3T3 cells, it is impossible to tease apart whether the differences observed are specific to the cells that were able to be transformed, or whether it is as a result of RAS. Therefore, it is unclear whether RAS is in fact mediating the methylation, or whether the cells that were transformable were already methylated in some fashion.

My observations in this thesis allow me to address the question of whether FLT3-ITD directs methylation of SOCS2, or whether methylation is a random event that allows the outgrowth of a population of cells. In the context of Valk groups 2 and 3, where cells with a

FLT3-ITD have very high levels of SOCS2, it is clear that FLT3-ITD is not driving methylation/silencing of the SOCS2 promoter. In the case of APL, the SOCS2 promoter is not methylated in cells lacking a FLT3-ITD, indicating that it is not a feature of APL cells to have a hyper-methylated SOCS2 promoter. However, almost all of the cases of APL I examined with a

FLT3-ITD do have a hyper-methylated SOCS2 promoter. I suggest that in these cells, there is random hyper-methylation occurring. When SOCS2 becomes silenced by chance, this allows the

FLT3-ITD cells missing the SOCS2 negative regulator to outgrow the other populations of cells.

The cells expressing SOCS2 will not grow quickly, as they are confined by the constraints placed on them.

In summary, I have found that the level of SOCS2 expression varies greatly across the 16

AML sub-classifications in the Valk dataset, as well as in AML cell lines and leukemia patient samples that I have measured. A closer look into two of the easily definable Valk sub- classifications, group 4, an immature group with aberrantly low SOCS2, and group 12, the APL group, has provided insights into the possible role of SOCS2 as a negative regulator in leukemia.

In some AML cell lines, I found that SOCS2 expression is reduced due promoter hyper- methylation. I also examined this in AML samples sorted into Valk groups 4 and 12, and found

that in these examples, patients with a high white count, a FLT3-ITD, and low to absent SOCS2 expression have a hyper-methylated SOCS2 promoter. The clearest case for disease evolution is in APL. I hypothesize that in this situation, SOCS2 is normally low and the promoter not methylated in FLT3-ITD-negative, low count disease. The acquisition of a FLT3-ITD phosphorylates STAT5a, that in turn enhances the expression of SOCS2. This creates a negative feedback loop that limits clonal expansion. However, the random acquisition of methylation of the SOCS2 promoter silences SOCS2 protein expression and the cells carrying the FLT3-ITD can now expand in an uncontrolled manner.

My thesis has shed some light on the potential for SOCS2 as a clinical marker for treatment options in APL patients with high count disease. Due to the preliminary results of my thesis, I suggest that there is a potential benefit for APL patients with high count disease (FLT3-

ITD positive patients) to be treated with a de-methylating agent, such as decitabine, as well as a

FLT3 inhibitor as a treatment option. Since FLT3 inhibitor is toxic, perhaps treating with just a de-methylating agent and chemotherapy could improve outcome.

CHAPTER 3: Summary and Future Work

Significance/Impact on the field

The purpose of my thesis was to study the negative regulator of cytokine signalling,

SOCS2, as little is known about the function of this protein in cancer, including leukemia. To

date, it is unclear as to the functional mechanism of SOCS2 action, such as what and where it

binds, and how it works. My work in this thesis has provided many interesting insights into this question, and provided a basis for exploring it further in the future.

Currently, the exact role of SOCS2 in the JAK/STAT pathway is unknown. Here, I

suggest a means to explore this question, by examining the differences in the protein complex

formed in patients where SOCS2 is expressed and un-methylated, and not expressed and methylated. I predict that SOCS2 is having a negative effect on one complex, but not the other.

Deciphering the difference between proteins that SOCS2 is able to interact with in each situation

should help to inform us about the biology of the disease, and suggest possible new therapies.

Considering the fact that the SOCS2 promoter is methylated in two specific AML

subtypes examined, my findings suggest the potential for SOCS2 as a clinical marker for

treatment options. As it appears that SOCS2 expression is being actively silenced in these

patients, treatment with a de-methylating agent, such as decitabine, may hold promise in the

clinic. Although one could simply use a FLT3 inhibitor, there are a number of possible reasons

for trying to reactivate SOCS2 using a hypo-methylating agent. First, the FLT3 inhibitors are

relatively toxic. Second, in addition to hyper-methylation of SOCS2, there may be hyper-

methylation of other critical genes that affect cell killing. The FLT-3 inhibitor will only

overcome the anti-apoptotic effects of FLT3, but is unlikely to reactivate other genes.

Future work:

I have displayed SOCS2 promoter hyper-methylation in Valk groups with low SOCS2

expression. I focused on these groups because they were both definable by a few key

characteristics that were accessible in routine clinical reports, or easy to measure by RT-PCR.

Using the data that I found relating to these two groups (group 4 being an immature cell type,

and group 12 being a mature cell type), I could then examine characteristics of the other Valk

sub-classes and make predictions about SOCS2 methylation or relevant signalling pathways based on my results. For example, in the groups that contain an activated SOCS2 inducer such as

Stat5, and display a high level SOCS2 expression, I hypothesize that the signalling pathway that is responsible for increasing STAT5a expression is not inhibited by SOCS2, such as what is found in groups 2 and 3. Furthermore, I would expect that these patients would not exhibit

SOCS2 promoter methylation. In contrast, I predict that patients with a FLT3-ITD and low

SOCS2 expression in groups 5 and 11 have a crucial protein inhibited by SOCS2 in the signalling complex driving their malignancy. I would expect these groups to show promoter methylation similar to what I found for group 12. These two models can be tested by taking advantage of gene transfer, siRNA knockdown, small molecules inhibitors, and methylation analysis.

In order to determine whether or not SOCS2 expression does prevent cell growth in cells

that have low SOCS2 expression and promoter methylation, future studies should be focused

towards SOCS2 re-introduction into these cells. The SOCS2 promoter in the cell lines NB4,

AML3, and AML5 were all found to be hyper-methylated, and could therefore serve as

appropriate models for this experiment. K562, AML2, and AML4 were not hyper-methylated,

and would serve as controls. Re-introducing full length SOCS2 into the methylated and un-

methylated cells, and performing colony and viability assays would indicate whether full length

SOCS2 has any effect on methylated cell lines. If SOCS2 is being actively silenced in order for

these cells to grow, I predict a drastic reduction in colony number and viability in the methylated

but not the un-methylated cells. This could also be done using an inducible system, in order to

have an appropriate negative control for SOCS2 negative and SOCS2 positive NB4 cells.

If it is found that SOCS2 inhibits the growth of NB4 cells, it is then possible to begin to identify the signalling pathway that is active in these cells. By taking advantage of an inducible

SOCS2 in these cells, one can induce the expression of SOCS2 and a short time later collect cellular extracts. Using immunoprecipitation or column chromatography with an antibody against SOCS2, protein complexes containing SOCS2 and interacting proteins can be captured.

Using mass spectroscopy, it should be possible to identify the proteins of the complex. This is a

potentially important experiment, as at this time it is not known what signalling molecules are involved in the growth of NB4. As a first approach, I could take a candidate gene approach, and look at known SOCS2 targets, such as IGF-1R. Similar to the experiment described above, it should be possible to determine how SOCS2 can regulate signalling in cells with a FLT3-ITD.

For the studies of FLT3-ITD and SOCS2, a non-methylated cell line would also be required, in an attempt to tease apart the protein complex that exists in a SOCS2 inhibitory state.

A counter experiment would be to knock-down SOCS2 by siRNA in cell lines that are high in SOCS2 expression, to determine whether or not it plays a role in cell growth. This is not an unreasonable experiment, given that SOCS3 was actually found to potentiate cell growth in

MPDs in the presence of the JAK2 V617F mutation. Hookham et al. (2007) found that SOCS3

not only failed to inhibit JAK2 V617F, but potentiated its phosphorylation, expression, and the proliferation of cells. SOCS3 was not degraded in the presence of the JAK mutant, nor was it able to abolish phosphorylation of JAK2 V617F. The mutant JAK was found to enhance SOCS3 phosphorylation in both cell lines and patient samples as well, and lead to an accumulation of phosphorylated SOCS3, stabilizing the SOCS3 protein [22]. Therefore it appears that JAK2

V617F is not negatively regulated by the usual JAK2 inhibitor, SOCS3. Instead, it acts to stabilize the mutant kinase and enhance disease progression. Perhaps in cell lines that have a high level of SOCS2, such as K562, SOCS2 may actually play a positive role in cell signalling.

Furthermore, treating various aspects of the FLT3 signalling pathway, such as FLT3 or

JAK2, with small molecule inhibitors may shed some light on the mechanisms at hand. It is evident from my studies, that the presence of a FLT3-ITD alone does not direct SOCS2 silencing. In both Valk groups 2 and 3, SOCS2 expression increases in the presence of the ITD.

Therefore, I speculate that the Valk groups that express SOCS2 do not contain a SOCS2 inhibitable protein in their FLT3 signalling complex, whereas the Valk groups that have a suppressed SOCS2, do. I suggest treating cells in both of these scenarios with a FLT3 inhibitor over time. This study will confirm the primacy of the FLT3-ITD in maintaining the growth and survival of the cells.

Previous studies have suggested that JAK2 is not important in FLT3-ITD signalling.

However these studies dealt with a small number of cell lines, and the methylation status of

SOCS2 was not explored. It would be interesting to see whether the inhibition of JAK2 has any effect on cell growth and viability in FLT3-ITD negative vs. positive cell lines. I would take advantage of primary patient samples known to have a FLT3-ITD and characterized for the

expression and methylation status of SOCS2. Based on prior data, I would predict that the cases with FLT3-ITD and high SOCS2 would not be affected by the JAK2 inhibitor, while those with a methylated SOCS2 promoter would show reduced growth and survival. Furthermore, the treatment of NB4 cells with the JAK2 inhibitor might begin to shed light on the activated signalling pathway in this cell line, and help direct studies to fully elucidate the relevant signalling cascade.

One of the groups that I focused on in my thesis was Valk group 4. An intriguing observation in this group was the discrepancy between the high level of SOCS2 expression in normal immature CD34 positive cells, and the absence of SOCS2 in the highly CD34 positive leukemic cells of group 4. A comparison between normal CD34 sorted cells and group 4 revealed a further general down regulation of a number of genes involved in cell growth and proliferation such as PAWR, Hoxa5, HoxA9, CTNNA1, and Meis1. I predict that these genes may also be silenced in this group due to promoter hyper-methylation. Preliminary studies in our lab have detected PAWR methylation in AML. To determine whether there is a general methylation profile that distinguishes group 4 patients from other subsets of patients, I will use newly developed array based technologies that allow a genome wide assessment of promoter methylation. For these experiments, I will compare the methylation profile of the methylated

SOCS2 group 4 patients I have identified, to samples that do not have a methylated SOCS2.

In a similar vein, it will be of value to study the changes in methylation in APL samples.

This is a relatively uniform group, identified by the presence of a t(15;17). Within this disease, I have identified groups of patients with a methylated and un-methylated SOCS2 promoter. Again using array technology, it will be possible to see if there are a common set of genes that become

hyper-methylated in the FLT3-ITD positive group of patients. The finding of recurrent

methylation of SOCS2 but apparent random methylation of other genes might indicate a

stochastic model for the development of SOCS2 methylation. However, if a similar set of genes

is differentially hyper-methylated in the FLT3-ITD group, this would suggest a more directed

mechanism for the hyper-methylation.

In order to look at my findings from a broader perspective, methylation is now at the

forefront of many investigations for its potential in the clinic. It has become evident that in various cancers, tumour suppressor genes are often silenced due to promoter methylation.

Therefore, de-methylation and re-activation or re-introduction of these genes is a viable avenue to explore when looking for cancer therapy. SOCS2 is a negative regulator of cytokine

signalling, and appears to be silenced in AML patients in Valk group 4 and group 12 with high

count disease and the presence of a FLT3-ITD. Pre-clinical and clinical trials treating patient samples with FLT3 inhibitors, de-methylating agents, and standard chemotherapy alone and in combination should be conducted, to see if de-methylating therapy holds any promise for the treatment of patients with high count disease and methylated SOCS2.

Summary

To summarize, I have found that the level of SOCS2 expression varies greatly across the

16 AML sub-classifications in the Valk dataset, as well as in AML cell lines and leukemia patient samples that I have measured. A closer look into two of the easily definable Valk sub- classifications, Group 4, an immature group with aberrantly low SOCS2, and Group 12, the APL group with t(15;17) also low for SOCS2, has provided insights into the possible role of SOCS2 as a negative regulator in leukemia. In cell lines, SOCS2 expression correlated well with

promoter hyper-methylation. Cell lines with methylation showed very low SOCS2 expression,

and vice versa. I examined this in AML samples I defined as Valk sub-classifications 4 and 12,

and found that in these examples, SOCS2 expression is low to absent and the SOCS2 promoter is

hyper-methylated in a FLT3-ITD dependent manner.

The clearest case for disease evolution is in APL. I hypothesize that in this situation,

SOCS2 is normally low and the promoter un-methylated in FLT3-ITD-negative, low count

disease. This follows normal differentiation, as SOCS2 is off in mature cell types. The acquisition of a FLT3-ITD phosphorylates STAT5a, which in turn enhances the expression of

SOCS2. This creates a negative feedback loop, which limits clonal expansion. However, the acquisition of SOCS2 promoter methylation silences SOCS2 protein expression, and the cells carrying the FLT3-ITD can now expand in an uncontrolled manner.

My thesis has shed some light on the potential for SOCS2 as a clinical marker for treatment options in FLT3-ITD positive APL patients with high count disease. I suggest that there is a potential benefit for these patients to be treated with a de-methylating agent, such as decitabine, alongside current treatment options (FLT3 inhibitor and chemotherapy), or as a more mild replacement to the toxic effects of the FLT3 inhibitor, and a decitabine and chemotherapy combination could improve treatment outcome.

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CHAPTER 4: Appendices

APPENDIX

FAB classification

In 2004, Valk et al. reported on the gene expression profile of the peripheral blood or bone marrow of 285 patients at diagnosis with AML. An Affymetrix U133A GeneChip was used, containing 13,000 unique genes or signature tags, and unsupervised cluster analyses to reveal 16 distinct types of AML based on patterns of gene expression [1]. Interesting correlations emerged when clustering results were compared to known cytogenetic abnormalities. For example, groups 9, 12, and 13 contained almost exclusively cases that had inv(16), t(15;17) and t(8;21) respectively. Of the 19 cases in group 12, 18 carried the t(15;17) for example. This concordance of a defined chromosomal abnormality with a particular gene expression subgroup validates, in part, the clustering algorithm. Furthermore, the clinical relevance of their methodology was illustrated, in that some clusters were associated with particularly poor outcomes with standard therapy, while improved survival was found in other subgroups.

We therefore asked if there was any relationship between FAB and gene expression

(Figure 4.1). We also asked the converse question; whether a gene expression subgroup was associated with a particular FAB type (Figure 4.2). In figure 4.1, it is readily apparent that, except for the M3/acute promyelocytic leukemia (APL) subtype of AML, the other FAB groups are populated by not one or two gene expression subtypes but often by several. (FAB M6 may be an exception to this, however only a few patients with this FAB form were included in the study and so the homogeneity of this group may be a sample size problem). Viewing the FAB in this way begins to explain the intermediate results of therapy associated with a particular FAB subtype. For example, the FAB M4 group contains significant numbers of patients from gene

expression groups 5 and 9. Group 5 has relatively poor outcome, while group 9 has a much better response to therapy. This view of the FAB also shows the ambiguity of studying a particular

FAB subtype of AML, due to the extreme heterogeneity that can exist in morphologically similar cases. For example, 14 different gene expression groups contribute to the morphologic M2 form of AML. A large proportion of the M2 cases are represented by the gene expression group G13, which have a t(8;21). If one removes G13, there is still high degree of complexity to the M2 morphologic subgroup.

Figure 4.1. The contribution of each gene expression subgroup to an FAB subtype is shown; the height of the column indicates the number of patients with that FAB subtype in the whole dataset.

Each subgroup is represented by a different color.

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In figure 4.2, there are two important messages from this view of the analysis. First, certain

morphologies such as M1, M2, M4 and M5 can be seen across several gene expression subgroups. Second, within morphologic subgroups there may be several individual gene expression subgroups. This suggests that gene expression contributes to the observed morphology in some but not all subgroups.

Figure 4.2. the contribution of each of the FAB subgroups to the individual gene expression subgroups is shown; the height of the column indicates the number of patients with that FAB in the whole dataset. Each subgroup is represented by a different color.

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