WO2012031329A1 - Assay for detection and monitoring of cancer - Google Patents

Abstract

The present disclosure relates generally to an assay for the determination of epigenetic profiles including epigenetic profiles associated with a pathological condition. The present disclosure teaches an assay to detect epigenetic profiles associated with cancer including its form or type, state and minimum residual disease status. The assay enabled herein further identifies and monitors forms and sub-types of leukemia and other hematological malignancies. Kits and assays for medicaments are also taught herein. Methods for screening agents which modulate methylation are also enabled.

Description

ASSAY FOR DETECTION AND MONITORING OF

CANCER

FILING DATA

[0001] This application is associated with and claims priority from Australian Provisional Patent Application No. 2010904085, filed on 10 September 2010, entitled "Assay for detection and monitoring of cancer", the entire contents of which, are incorporated herein by reference.

FIELD

[0002] The present disclosure relates generally to an assay for the determination of epigenetic profiles including epigenetic profiles associated with a pathological condition. The present disclosure teaches an assay to detect epigenetic profiles associated with cancer including its form or type, state and minimum residual disease status. The assay enabled herein further identifies and monitors forms and sub-types of leukemia and other hematological malignancies. Kits and assays for medicaments are also taught herein. Methods for screening agents which modulate methylation are also enabled.

BACKGROUND

[0003] Bibliographic details of the publications referred to by authors in this specification are collected alphabetically at the end of the description.

[0004] Reference to any prior art in this specification is not, and should riot be taken as, an acknowledgment or any form of suggestion that this prior art forms part of the common general knowledge in any country. [0005] Cancer is one of the leading causes of mortality and morbidity across all societies and ethnic groups. Cancer has a complex etiology influenced genetic and environmental pressures. In fact, cancer can be considered a broad spectrum of pathological conditions. [0006] One type of cancer is the hematological form, leukemia. Leukemia is classified on the cell type which becomes malignant. Major types include acute lymphoblastic leukemia (ALL) [also known as acute lymphocytic or lymphatic leukemia], acute myeloid leukemia (AML) [also known as acute myelogenous leukemia], chronic myeloid leukemia (CML) and chronic lymphocytic leukemia (CLL). Within these forms there are sub-types including leukemias which tend to have an onset in an age-dependent manner. For example, some types of leukemia appear more frequently in younger members of a population while others are more frequent in an aging population.

[0007] ALL is the most prevalent of the childhood malignancies in developed countries. Although current treatment protocols give rise to a proportion of 5 -year event free survival outcome, many patients still succumb to the disease, relapse or experience secondary effects of the treatment due to their non-specific nature. It is unclear how leukemia arises and the mechanisms involved in this hematological malignancy. It is also difficult if not impossible to select treatment protocols based on a prediction as to their likely efficacy.

'

[0008] One of the difficulties in the treatment and management of cancers is the sensitivity in determining the presence of malignant cells which constitutes the minimum residual disease (MRD). Considering the pervasiveness of cancer there is a need to be able to identify the MRD at frequencies <10^'5. This will be important for prognostic purposes and for tailored, individual medicine.

[0009] It is apparent that DNA methylation plays a role in the regulation of gene expression in higher organisms. The importance of DNA methylation has been highlighted by its involvement in several human diseases. Methylation of cytosine at the 5' position is a widely known covalent modification of human genomic DNA. In particular, methylation of CpG islands within regulatory regions of the genome appears to be highly tissue specific. It is now apparent that methylation of cytosines outside CpG islands is also important. These regions, within 2kb of CpG islands, have been named "shores" or "island shores" (Irizarry et al., Nature Genetics 47(2,1: 178-186, 2009). Methylation modifications which are also potentially important include the generation of hydroxymethylcytosines and other base methylations as well as RNA methylation.

[0010] Despite the availability of a range of methylation assays (see, for example, Laird, Nature Reviews Genetics 11 :191-203, 2010; Hansen et al., Nature Genetics 43(8):76S-775, 2011 ; Rein et al., Nucleic Acids Res. 25:2255, 1998 and Fraga and Estella, Biotechniques 55:632-649, 2002), selection of regions to amplify and screen is an important aspect of determining an epigenetic profile characteristic of a disease condition. There is a need to identify these crucial regions in the genome and to associate epigenetic profiles to pathological conditions such as cancer. There is also a need to develop better panels of assays to facilitate more precise diagnosis of cancer subtypes, to more accurately predict treatment response and long term outcome, to facilitate the development of patient-specific tailored treatment regimens, and to allow monitoring of disease progression.

SUMMARY

[0011] Throughout this specification, unless the context requires otherwise, the word "comprise" or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or integer or method step or group of elements or integers or method steps but not the exclusion of any element or integer or method step or group of elements or integers or method steps.

[0012] Nucleotide and amino acid sequences are referred to by a sequence identifier number (SEQ ID NO). The SEQ ID NOs correspond numerically to the sequence identifiers <400>1 (SEQ ID .NO:l), <400>2 (SEQ ID NO:2), etc. A summary of the sequence identifiers is provided in Table 1. A sequence listing is provided after the claims.

TABLE 1

Summary of sequence identifiers

SEQUENCE ID NO: DESCRIPTION

1 Oligonucleotide

2 Oligonucleotide

3 Primer sequence 5'3^* - sq_FOXE3_F

4 Primer sequence 5'3' - sq_FOXE3_R

5 Primer sequence 5'3' - sq_TLX3_F

6 Primer sequence 5'3' - sq_TLX3_R

7 Primer sequence 5'3' - sqClorf76_F

8 Primer sequence 5'3' - sqClorf76_R

9 Primer sequence 5'3' - sqFBX039_F

10 Primer sequence 5'3' - sqFBX039_R

1 1 Primer sequence 5 '3' - sqM YOD 1 _F

12 Primer sequence 5'3' - sqMYODl_R

13 Primer sequence 5'3' - sqGUCYlA2_F SEQUENCE ID NO: DESCRIPTION

14 Primer sequence 5'3' - sqGUCYl A2_R

15 Primer sequence 5'3' - sqHRH3_F

16 Primer sequence 5'3' - sqHRH3_R

17 Primer sequence 5'3' - sqPKDREJ_F

18 Primer sequence 5'3' - sqPKDREJ_R

19 Primer sequence 5'3' - sqPNMA2_F

20 Primer sequence 5'3' - sqPNMA2_R

21 Primer sequence 5'3' - sqPPARG F

22 Primer sequence 5^*3' - sqPPARG_R

23 Primer sequence 5 '3' - sqPRLHR_F

24 Primer sequence 5'3' - sqPRLHR R

25 Primer sequence 5'3' - sqPTPRZl_F

26 Primer sequence 5'3' - sqPTPRZl_R

27 Primer sequence 5'3' - sqSCRNlJF

28 Primer sequence 5'3' - sqSCRN 1_R

29 Primer sequence 5'3' - sqSOX17_F

30 Primer sequence 5'3' - sqSOX17_R

31 Primer sequence.5'3' - sqUnknown_F

32 Primer sequence 5'3' - sqUnknown R

[0013] The present disclosure teaches the identification of genomic regions including loci, the extent of epigenetic change to which, is an indicator of a form or type, state or level of cancer or minimal residual disease (MRD) with respect to cancer. The term "epigenetic change" includes distinction of epigenetic sites. Reference to "loci" or "locus" includes genes associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity. Table 2 lists examples of genes in each of these classes. Reference to "cancer" includes hematological malignancies such as leukemias. Reference to "leukemias" include all forms, types and sub-types of leukemias including leukemias which have a high frequency of onset during childhood years (pediatric leukemias) or later onset leukemias (adult leukemias). Hence, the present disclosure is instructional of an assay to detect an epigenetic profile indicative of a form or type (including sub-type), state or MRD status of a cancer including leukemia. Such leukemias include acute lymphatic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), small lymphocytic lymphoma (SLL), chronic lymphocytic leukemia (CLL), acute monocytic leukemia (AMOL), Hodgkin's lymphomas (all types), non-Hodgkin's lymphomas (all types) and other lymphoid and myeloid malignancies.

|0014] Furthermore, the epigenetic profile is also informative as to the spectrum of cancer disease conditions including its various forms, types, sub-types and is useful in monitoring treatment protocols or patients after treatment. Identification of the minimum residual disease (MRD) is important in the decision process in relation to undertaking more or less intensive or toxic therapy and hence is useful for prognosis and tailored therapy on a case by case basis.

[0015] An example of epigenetic change is the extent of change in methylation or change in distinction of methylation sites in one or more regions in one or more of the genetic loci associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity. Examples are listed in Tables 2 and 3. By "one or more" includes from 1 to a number which provides 100% confidence that the change in methylation or other epigenetic marker is associated with a disease condition. In an embodiment this range is from 1 to 16 including 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 different genetic loci or 1 to 16 different regions in any one or more of the listed genetic loci. Greater than 16 loci or regions can nevertheless be measured such as from 16 to 1000.

[0016] Accordingly, enabled herein is a method for identifying an epigenetic profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for the extent of change in epigenetic profile relative to a control or normal/non-cancer tissue within or proximal to a locus selected from one or more loci associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity wherein a change in the epigenetic profile in or proximal to one or more loci is indicative of the presence of a cancerous condition or a propensity to develop same. [0017] Another aspect taught herein is a method for identifying a methylation profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for a change relative to a control or normal/non-cancer tissue in the extent of methylation within or proximal to a locus selected from one or more of the loci associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity wherein a change in the extent of methylation in or proximal to one or more loci is indicative of the presence of a cancerous condition or a propensity to develop same.

[0018] The present specification is further instructional of a method for identifying an epigenetic profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for a change relative to a control or normal/non-cancer tissue in the extent of change in epigenetic profile within or proximal to from 1 to 16 regions in one or more of the loci associated with (i) cell fate commitment;

(ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity wherein a change in the epigenetic profile in or proximal to one or more regions is indicative of the presence of a cancerous condition or a propensity to develop same.

[0019] A still further aspect enabled herein is a method for identifying a methylation profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for a change relative to a control or normal/non- cancer tissue in the extent of methylation within or proximal to from 1 to 16 regions in one or more of the loci associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity wherein a change in the extent of methylation in or proximal to one or more regions is indicative of the presence of a cancerous condition or a propensity to develop same.

[0020] Examples of genes or loci in each class of associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity; are provided in Table 2. Particular loci which are differentially subject to epigenetic change are listed in Table 3. By "proximal" is meant a region up to approximately lOOOkb up- or down-stream from the 5' or 3' end of a locus and includes regulatory elements within this region.

[0021] By "epigenetic change" includes a change in epigenetic profile. By "epigenetic profile" includes epigenetic modifications such as methylation including hypermethylation and hypomethylation, RNA/DNA interactions, expression profiles of non-coding RNA, histone modification, changes in acetylation, obiquitylation, phosphorylation and sumoylation, as well as chromatin altered transcription factor levels and the like leading to activation or deactivation of genetic locus expression. In an embodiment, the extent of methylation, RNA/DNA interaction and non-coding RNA expression are determined as well as any changes therein. In an aspect, the epigenetic modification is an increase or decrease in methylation or an alteration in distribution of methylation sites or other epigenetic sites.

[0022] "Methylation" includes methylation of any base in DNA or RNA including methylation of cytosine, hydroxymethylation of cytosine, 5-methylcytosine and methylation of adenine. The present disclosure teaches a method for detecting methylation in DNA or RNA associated with a spectrum of cancerous conditions including pediatric and adult leukemia or their various types and sub-types. Reference to a "cancerous condition" includes hematological malignancies and tissue cancers, such as leukemias, sarcomas, carcinomas and other tumors. A "leukemia" may, therefore, be of any hematological type or sub-type and extends to pediatric and adult leukemias. [0023] The epigenetic profile is determined in the genome of a cell or sub-population of cells of a subject. Any cell may be tested including but not limited to blood cells, cerebrospinal fluid (CSF) cells, bone marrow cells, buccal cells and cells from rectal swabs or feces. Cells from pre-natal tissues and embryos may also be tested. In addition, cell free DNA or RNA circulating in whole blood, serum or plasma may also be tested. Circulating DNA or RNA may also be tested in other fluids such as urine, pus, respiratory fluid, lymph fluid, feces, bile, saliva, sputum, semen, vaginal flow, cerebral spinal fluid, brain fluid, ascites, milk, secretions from the genitourinary tract and a lavage of a tissue or organ (e.g. a lung).

[0024] In an embodiment, DNA or RNA methylation is detected by chemical conversion or methylation specific or sensitive restriction enzymes.

[0025] Selective chemical conversion, such as using bisulfite treatment [e.g. WO 2005/038051] of unmethylated cytosines to uracil, leaving methylated cytosines

^■If

unchanged. Uracil has the sariie base paring behaviour as thymine. It therefore forms a base pair with adenine. 5-Methylcytosine, on the other hand, base pairs with guanine. Methylated and unmethylated cytosines can therefore be differentiated. Either approach typically employs a PCR step.

[0026] The present disclosure further enables a method for monitoring the treatment of a cancerous condition in which the treatment modulates the epigenetic profile of one or more loci listed in Table 2, the method comprising monitoring for a change relative to a control or a pre- and post-treatment sample in the epigenetic profile within or near the locus or loci. In an embodiment, the extent of change in methylation profile is determined. The sensitivity of the subject assay allows for determination of MRD at a frequency down to about 10^"4 to about 10^"8 (i.e. one cell per 10^ to about 10^'8 cells) which enables better prognostic determinations. Furthermore, identification of malignant cells in treated patients or patients in remission allows for the selection of more intensive or less toxic therapies. [0027] The present disclosure also teaches for the use of an epigenetic profile in one or more loci listed in Table 2 in the manufacture of an assay to identify an epigenetic profile of a cancerous pathological condition. The epigenetic profile includes the determination of extent of epigenetic change such as extent of methylation in any base in DNA or RNA including in cytosine bases. The cytosines include those in CpG and CpNpG islands and shores and in non-CpG and CpNpG islands and shores. By "locus" or "loci" includes coding and non-coding regions (e.g. promoter regions, 5' non-coding regions, exons, introns and 3' non-coding regions). The region encompassed by a locus includes its coding sequence, promoter and up- or down-stream regulatory elements typically within approximately lOOOkb of a transcriptional start site or transcriptional termination signal. Hence, this lOOOkb region is regarded as the region proximal to the locus.

[0028] The assay enabled herein may also be used alone or in combination with assays to detect gene expression transcription of a locus associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity. Particular examples of loci are provided in Tables 2 and 3. The assay taught herein is also useful in epidemiological studies of different ethnic populations with cancer. Accordingly, the present disclosure further provides a method of identifying an epigenetic profile in populations of subjects indicative of a cancerous pathological condition, the method comprising screening for a change relative to a control in a statistically significant number of subjects the extent of epigenetic profile in one or more loci listed in Table 2 or 3 wherein a significant difference in the extent of epigenetic change compared to a control is indicative of the presence of the pathological condition or a propensity to develop same. In accordance with this method, the assay may comprise the further step of determining the extent of expression such as by quantitative reverse transcriptase PCR (qRTPCR), TaqMan, gene expression micro arrays or by Northern or Western blot analysis. In an embodiment, the epigenetic change is extent of methylation or distribution of methylation sites. [0029] The present disclosure further enables a method for screening for an agent which modulates to epigenetic profile of one or more loci listed in Table 2 or 3 in the presence or absence of an agent to be tested, wherein an agent is selected if it induces a change in the epigenetic profile. An example of an agent which modulates methylation includes those which inhibit the DNA methyltransferase enzymes (DNMT1, DNMT2, DNMT3a, DNMT3b). For example 5-aza-C, 5raza-dC (Decitabine) and Zebularine and other known DNA demethylating agents (DMA's) as well as histone deacetylase inhibitors such as valproic acid, SAHA (Vorinostat), Tnchostatin A, and combinations of these agents amongst many other agents.

[0030] A further embodiment taught herein is a kit for use in the above methods comprising primers to amplify a region within a locus or loci listed in Table 2 or 3 for detection of epigenetic change such as methylation profile in DNA. The kit may also be adopted for use in a multiplex assay. In an embodiment, the kit contains components to amplify a genomic region and conduct a methylation assay. [0031] Computer programs to monitor changes in epigenetic profile over time and/or in response to therapeutic intervention are also taught herein.

TABLE 2

Genetic loci listed by function differentially methylated between leukemia and controls

Function Genes

cell fate commitment MYOD1, PPARG, SIXl, ONECUT2, PTFIA, SOX9, TLX3

MYOD1, L3MBTL4, SOX1, EGR4, D RT3, ONECUT2, PPARG, NKX2-8, SOX9, TP73, SX2, SIXl, TFAP2C, transcription factor activity TLX3, FOXE3

sequence-specific DNA MSX2, MYOD1, SOX1, N X2-8, PPARG, SIXl, binding ONECUT2, PTFIA, SOX9, TLX3, FOXE3

MYOD1, SOX1, ZNF454, EGR4, D RT3, N X2-8, ONECUT2, PTFIA, PPARG, SOX9, TP73, SALL3, MSX2, dna-binding SIXl, SOX17, TFAP2C, TLX 3, FOXE3

ACADL, BGLAP, CDH22, CELSR1, CPXM2, CXCL1, CYR61, DHCR24, DMRT3, EGR4, ELOVL4, EPOR, FOXE3, GALR1, GUCY1A2, HS3ST2, KCN 5, KISS1, L3MBTL4, LA A1, LGI2, LPL, LY86, MAG, MMP11, MPST, MS4A7, MSX2, MY03A, MYOD1, NELL1, NINJ1, NKX2-8, NPTX2, OGDHL, ONECUT2, PDE10A, P DREJ, PLD4, PNMA2, PPARG, PRLHR, PRSS12, PTFIA, RBP1, RIMS4, SALL3, SCARF1, SCRN1, SFRP1, SH3GL2, SIXl, SLC18A3, SLC22A3, SLC5A7, SLC8A2, SNAP91, SOX1, SOX17, SOX9, SSTR4, TCL1A, TFAP2C, TLX3, subcellular location TP73, ZNF454,CYP24A1

MYOD1, L3 BTL4, BGLAP, SOX1, EGR4, DMRT3, transcription regulator ONECUT2, PTFIA, PPARG, NKX2-8, SOX9, TP73, MSX2, activity SIXl, TFAP2C, TLX3, FOXE3

TABLE 3

Genetic loci differentially methylated between leukemia and controls

MTNR1A

MTNR1B

MY03A

MYOD1

NELL1

NKX2-8

NPTX2

OGDHL

ONECUT2

OVOL2

PAK7

PDE10A

PGM5

PKDREJ

PLOD2

PNMA2

PPARG

PRSS12

RAB32

RBP1

RBP4

RHCG

SCGN

SCRN1

SFRP1

SH3GL2

SLC12A5

SLC18A3

SLC22A3

SLC5A7

SLC8A2

SLC02A1

SNAP91

SOX1

SOX17

SOX9

TFAP2C

THRB-

TLX3

WWTR1

ZNF365 [0032] Table 2 lists genetic loci classed by function. The class of function is (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity differentially methylated between leukemia and controls. Table 3 lists all the genes identified herein as being subject to differentia] epigenetic regulation. Loci are selected using algorithmic models to separate leukemia bone marrow from normal bone marrow, of which any combination of 16 genes can determine this 100% of the time. Algorithimic models include the Linear Models for Microarray Analysis (LIMMA) model, the Recursive Feature Elimination- Support Vector Machine (RFE-SVM) model and Centroid model. Top candidate loci are those which overlapped all three models. These models could also be used to separate leukemia cases with poor outcome from those with a good outcome. [0033] A list of abbreviations used herein is provided in Table 4.

TABLE 4

Abbreviations

ABBREVIATION DESCRIPTION

A adenine

Ab Antibody

ALL Acute lymphatic leukemia

AML Acute myeloid leukemia

A OL Acute monocytic leukemia

AROC Average area under the receiver operating characteristic curve

AvgLeuk Average Beta value (methylation) of Leukemia Samples

AvgRemFol Average Beta value (methylation) of remission and followup samples

C Cytosine

CLL Chronic lymphocytic leukemia

CML Chronic myeloid leukemia

COMPARE-MS Combination of methylated-DNA precipitation and methylation- sensitive restriction enzymes

CpG Cytosine and guanine separated by phosphate (C-phosphate-G), which links the two nucleotides together in DNA

CpNpG Cytosine and guanine separated by a nucleotide (N) where N is any nucleotide but guanine. The cytosine and N nucleotide are phosphorylated.

DiffBeta Difference between AvgLeuk and AvgRemFol

DNA Deoxyribonuceic acid

G Guanine

LIMMA Linear Models for Microarray Analysis

Lymphatic Lymphoblastic or Lymphocytic

Lymphoblastic Lymphatic

Lymphocyte Lymphatic or Lymphoblastic ABBREVIATION DESCRIPTION

MALDI-TOF Matrix assisted laser desorption ionisation time-of-flight

MLPA Multiplex ligation-dependent probe amplification

MRD Minimal/Minimum residual disease

MS-HRM methylation specific high resolution melting

MS-MCA methylation specific melting curve analysis

Myelogenous Myeloid

Myeloid Myelogenous

PCR Polymerase Chain Reaction

ROC Receiver operating characeristics

RFE Recursive Feature Elimination

SLL Small lymphocytic lymphoma

SVM Support Vector Machine

T Thymine

U uracil

BRIEF DESCRIPTION OF THE FIGURES

[0034] Some figures contain color representations or entities. Color photographs are available from the Patentee upon request or from an appropriate Patent Office. A fee may be imposed if obtained from a Patent Office.

[0035] Figure 1 is a graphical representation showing correlation between methylation values of CpG units from bone marrow samples flash frozen or subject to smearing, staining with Giemsa and mounting with DPX on a microscopic slide.

[0036] Figure 2 is a schematic representation of hierarchical clustering of genes associated with leukemia and non-leukemia cases. This heatmap is provided in black and white. Color prints of the heatmaps are available from the patentee upon request (black represents methylated DNA 50-100% while white represents 0-50% and gradients thereof).

[0037] Figure 3 is a graphical representation showing correlation between methylation of FOXE3 and model cancer cell lines.

[0038] Figure 4 is a graphical representation showing correlation between methylation of TLX3 and cancer cell lines.

[0039] The cancer cell lines referred to in Figures 3 and 4 are defined in Table 5.

[0040] Figure 5 is a diagrammatic representation showing the single allele base extension reaction (SABER):- (A)Primers are initially designed to bisulphite converted DNA (not shown), amplifying a region of interest (approximately 100-300bp). (B) An extension primer is then designed to hybridize within the amplified region. The extension primer is designed up to the CpG site of interest. [0041] The SABER approach differs from normal base extension (C) as only one nucleotide is added in the extension reaction. The primer then extends over the CpG site and terminates following the incorporation of a single nucleotide. [0042] The extended primers are then assayed by Mass spectrometry (D). This method has been shown to be more sensitive than standard base extension when used for SNP interrogation (Ding et ah, J. Invest. Dematol. ]23(3)·Λ70-473, 2004) and could be applied as a multiplex assay to test multiple loci for DNA methylation. [0043] Figure 6 is a representation of an unsupervised clustering heatmap plot of the DNA methylation beta-values from the Illumina Infinium HumanMethylation27 BeadArray of 1 15 ALL-specific probes identified using three supervised learning methods (x-axis). Three distinct clusters comprising the leukemia, remission/non-leukemic and the cell lines are apparent (y-axis). The majority of these probes are hypermethylated in all leukemic samples analyzed. These clusters remained when all 14,876 probes, retained after p- detection cutoff, were taken into account (Figure 9).

[0044] Figure 7 is a representation of heatmap plot of SEQUENOM EpiTYPER DNA methylation results generated from 85 cases of B-Cell ALL with matching leukemic and remission bone marrow samples, controls (DONOR) and cancer cell line (REH). DNA methylation data for a total of 103 CpG sites encompassing 16 probes we selected by Infinium analysis are shown here (x-axis). DNA hypermethylation of 77/85 (91%) leukemic bone marrow samples was observed regardless of ALL subtype confirming the existence of a DNA methylation signature associated with leukemia.

[0045] Figure 8 is a representation showing ALL is associated with an increase in average promoter methylation. Average global promoter DNA methylation levels of bone marrow according to sample group. The overall average beta-value of probes that passed stringent quality control for all samples within a sample group are displayed. Bars represent standard deviation. [0046] Figure 9 shows unsupervised clustering of Infinium beta- values of 14,876 probes accurately delineates disease free tissue from leukemia. Heatmap plot of unsupervised hierarchical clustering of the beta-values of 14,876 probes passing stringent quality control.

[0047] Figure 10 is a representation showing the performance and the average area under the receiver operating characteristics (ROC) curve (AROC) curves of the supervised learning methods applied to the data for DNA methylation profiling, (a) Centroid, (b) RFE- SVM and (c) LIMMA plots are depicted. The average accuracy of classification (accuracy) and the average area under the receiver operating characteristics (ROC) curve (AROC) are plotted against the number of features included in the classification. The error bars represent estimated 95% confidence intervals for the obtained results. Accuracy and AROC reached 100% after 16 features for the Centroid method. Whilst the same accuracy and AROC level was achieved in less than 4 features using RFE-SVM and LIMMA.

[0048] Figure 11 is a representation of unsupervised hierarchical clustering heatmap of SEQUENOM EpiTYPER results. 163 paediatric leukemia cases were analyzed at three loci Corf76, FBX039 and MYOD1 for DNA methylation. Matching leukaemic and remission bone marrow samples were analyzed from each case. Leukemia subtype for each diagnosed case is also depicted and illustrates a hypermethylation signature associated with leukaemic bone marrow.

[0049] Figure 12 is a graphical representation showing results from the modified multiplex methylation MALDI-TOF technique. Methylation is a measure of the of amount of extension primer that has been extended (extension occuring only on the methylated template) over the total extension primer input into each reaction. TABLE 5

Cell Line/Sample Cell Type

CELL LINE DESCRIPTION

MCF7 Breast Cancer

DR75 Lymphoblastoid Cell Line

DG75 Lymphoblastoid Cell Line

DR149 Unknown

40424 Normal Fibroblast

20299 Normal Fibroblast

980342 ^* Normal Fibroblast

23970cyto Normal Lymphoblast

25046cyto Normal Lymphoblast

29390cyto Normal Lymphoblast

GM10926D(N10) Somatic Hybrid Normal

NTERA Embryonal Carcinoma

CHANG HeLa Derivative

K562 Myelogenous Leukemia

HELA Cervical Cancer

PaJu Neuroblastoma Cell Line

CCL-129 IMR32 Spleen Tumor Cell Line

Jurkat Leukemia

GM05837A Somatic Hybrid Normal

DR-10-22 Unknown

REH Non-T, Non-B cell leukaemia

CEM-CCRF Leukkaemia

SW48 Colon Cancer

SKNAS-HOXC4 Neuroblastoma Cell Line

JAM Unknown

NCCIF Germ Cell Tumor (NCCIT) DETAILED DESCRIPTION

[0050] Throughout this specification, unless the context requires otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or integer or method step or group of elements or integers or method steps but not the exclusion of any other element or integer or method step or group of elements or integers or method steps. ,

[0051] The present disclosure teaches a method for identifying an epigenetic profile associated with indicative, instructive or informative of a pathological condition associated with cancer. The epigenetic profile is in loci associated with one or more of (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity. Particular examples are listed by function in Table 2. The pathological condition may also be described as a cancerous condition or cancer such as a hematological or blood born cancer (e.g. leukemia) or solid cancerous tumors or various types and sub-types thereof. Hence, leukemias, sarcomas, carcinomas and the like including hematological malignancies of the blood, bone marrow and lymph nodes are encompassed by a "cancer" or "cancerous condition" as are there various types and subtypes. By "epigenetic change" includes a change in epigenetic profile. By "epigenetic profile" includes epigenetic modifications such as methylation including hypermethylation, hypomethylation and hydroxymethylation, RNA/DNA interactions, expression profiles of non-coding RNA, histone modification, changes in acetylation, obiquitylation, phosphorylation and sumoylation, as well as chromatin altered transcription factor levels and the like leading to activation or deactivation of genetic locus expression. In an embodiment, the extent of methylation, RNA/DNA interaction and non-coding RNA expression are determined as well as any changes therein. In an aspect, the epigenetic modification is an elevation in methylation, an increase or decrease in methylation or an alteration in distribution of methylation sites. An example of types of leukemia include acute lymphatic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML) and chronic lymphocytic leukemia (CLL), small lymphocytic lymphoma (SLL), acute monocytic leukemia (AMOL), Hodgkin's lymphomas (all types) and non- Hodgkin's lymphomas (all types). Sub-types include forms of leukemia having an age- related onset bias such as pediatric leukemias and adult leukemias.

[0052] Hematological malignancies include malignancies of either myeloid or lymphoid lineages including lymphomas, lymphocytic leukemias and myelomas (lymphoid lineage malignancies) and acute and chronic myelogenous leukemia, myelodysplasia syndromes and myeloproliferative diseases (myeloid lineage malignancies).

[0053] The epigenetic profile is in one or more regions in one or more loci listed in Table 2 or 3. These loci are grouped into five classes based on function: i.e. (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity. Reference to a "locus" or "loci" includes promoter, intron, exon and non-encoding 3' and 5' regions proximal to the locus which regions are up to approximately lOOOkb upstream and downstream of the locus as well as known and yet- to-be-defined regulatory elements associated with the gene. The lOOOkb region is referred to herein as being "proximal" to a locus. The epigenetic profile enables determination of minimal residual disease (MRD) which comprises malignant cells at a frequency down to 10^"3 to about 10^"8 including frequencies of 10^"5, 10^"6 and 10^"7 and frequencies inbetween (i.e. one cancer cell per 10³ to 10⁸ cells). The epigenetic profile is determined in the genome of a cell or sub-population of cells of a subject. Any cell may be tested including but not limited to blood cells, cerebrospinal fluid (CSF) cells, bone marrow cells, buccal cells and cells from rectal swabs or feces. Cells from pre-natal tissues and embryos may also be tested. In addition, cell free DNA or RNA circulating in whole blood, serum or plasma may also be tested. Circulating DNA or RNA may also be tested in other fluids such as urine, pus, respiratory fluid, lymph fluid, feces, bile, saliva, sputum, semen, vaginal flow, cerebral spinal fluid, brain fluid, ascites, milk, secretions from the genitourinary tract and a lavage of a tissue or organ (e.g. a lung).

[0054] In an embodiment, the epigenetic profile is extent of methylation or distribution in methylation sites including an increase or decrease in this extent. Methylation includes methylation of any base in DNA or RNA including methylation of cytosine, hydroxymethylation of cytosine and methylation of adenine.

[0055] Accordingly, enabled herein is a method for identifying an epigenetic profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for the extent of change in epigenetic profile relative to a control or normal/non-cancer tissue within or proximal to a locus selected from one or more of the loci associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity wherein a change in epigenetic profile in or proximal to one or more loci is indicative of the presence of a cancerous condition or a propensity to develop same.

[0056] Another aspect taught herein is a method for identifying a methylation profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for a change relative to a control or normal/non-cancer tissue in the extent of methylation within or proximal to a locus selected from one or more of the loci associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity wherein a change in the extent of methylation in or proximal to one or more loci is indicative of the presence of a cancerous condition or a propensity to develop same.

[0057] The present specification is further instructional of a method for identifying an epigenetic profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for a change relative to a control or normal/non-cancer tissue in the extent of change in epigenetic profile within or proximal to a locus selected from 1 to 16 regions in one or more of the loci associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity wherein a change in the epigenetic profile in or proximal to one or more regions is indicative of the presence of a cancerous condition or a propensity to develop same. [0058] A still further aspect enabled herein is a method for identifying a methylation profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for a change relative to a control or normal/non- cancer tissue in the extent of methylation within or proximal to from 1 to 16 regions in one or more of the loci associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity wherein a change in the extent of methylation in or proximal to one or more loci is indicative of the presence of a cancerous condition or a propensity to develop same. [0059] Examples of loci grouped by function are listed in Table 2. All loci identified herein are listed in Table 3.

[0060] Accordingly, enabled herein is a method for identifying an epigenetic profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for the extent of change in epigenetic profile relative to a control or normal/non-cancer tissue within or proximal to a locus selected from one or more of the loci listed in Table 2 or 3 wherein a change in epigenetic profile in or proximal to one or more loci is indicative of the presence of a cancerous condition or a propensity to develop same.

[0061] Another aspect taught herein is a method for identifying a methylation profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for a change relative to a control or normal/non-cancer tissue in the extent of methylation within or proximal to a locus selected from one or more of the loci listed in Table 2 or 3 wherein a change in the extent of methylation in or proximal to one or more loci is indicative of the presence of a cancerous condition or a propensity to develop same.

[0062] The present specification is further instructional of a method for identifying an epigenetic profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for a change relative to a control or normal/non-cancer tissue in the extent of change in epigenetic profile within or proximal to a locus selected from 1 to 16 regions in one or more of the loci listed in Table 2 or 3 wherein a change in the epigenetic profile in or proximal to one or more regions is indicative of the presence of a cancerous condition or a propensity to develop same.

[0063] A still further aspect enabled herein is a method for identifying a methylation profile in the genome of a cell indicative of a cancerous condition or a predisposition thereto, the method comprising screening for a change relative to a control or normal/non- cancer tissue in the extent of methylation within or proximal to from 1 to 16 regions in one or more of the loci listed in Table 2 or 3 wherein a change in the extent of methylation in or proximal to one or more loci is indicative of the presence of a cancerous condition or a propensity to develop same.

[0064] Reference can be made to either Table 2 or Table 3 to identify loci which are subject to differential epigenetic regulation.

[0065] The present disclosure also teaches the use of an epigenetic profile within or proximal to a locus selected from one or more of the list in Table 2. or 3 in the manufacture of an epigenetic assay to provide data which are indicative of the presence of a cancerous condition or a propensity to develop a cancerous condition. In an embodiment, the epigenetic assay determines extent of methylation and or distribution of methylation sites within or proximal to the locus. In an embodiment, from 1 to the number of loci or regions within a locus required to achieve a 100% confidence level that a subject has or does not have cancer are selected. In an embodiment, this number is 16. Hence, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15 or 16 different loci, or 1 to 16 different regions within a single locus, or a combination of both are assayed for any epigenetic change such as change in methylation profile. By function, the loci are associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity. [0066] The epigenetic profile enables the determination of MRD with high sensitivity (down to 10^"3 to 10^"8) in a patient. The identification of MRD facilitates the decision process on whether more intensive therapy is required, such as more toxic therapy, a bone marrow transplant or stem cell therapy or whether an alternative type of therapy is required. Hence, the MRD influences the prognosis of a cancer in a subject.

[0067] Hence, taught herein is a method for determining MRD in a subject, the method comprising determining an epigenetic profile in or proximal to one or more loci listed in Table 2 or 3 wherein an elevation or a decrease in epigenetic change of a specific gene or genes, relative to disease free control tissue is an indication of the MRD.

[0068] The present disclosure further enables a method for determining MRD in a subject, the method comprising determining a change in methylation profile in or proximal to one or more loci or in one or more regions within the one or more loci listed in Table 2 or 3 wherein an elevation or a decrease in extent of methylation is an indication of the MRD. By "methylation profile" means extent of and or distribution of methylation sites within multiple loci, a single locus or a combination of both.

[0069] Further taught herein is a method for determining MRD in a subject, the method comprising determining an epigenetic profile in or proximal to from 1 to 16 loci listed in Table 2 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 wherein an elevation or a decrease in extent of epigenetic change is an indication of the MRD.

[0070] Yet a further aspect enabled herein is a method for determining MRD in a subject, the method comprising determining a methylation profile in or proximal from 1 to 16 loci listed in Table 2 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 wherein an elevation or a decrease in extent of methylation is an indication of the MRD.

[0071] As indicated above, 16 different loci or 16 different regions within one or more loci provides a 100% confidence level of the presence or absence of cancer in a subject. The present disclosure contemplates confidence controls of from about 40% to 100% including 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 and 100%. Any number of loci or regions within one or more loci may be selected to provide from about 40% to about 100% confidence level of the presence or absence of cancer. More than 16 loci or regions may be assayed such as from 16 to 1000 or greater. This may occur when an array is used such as in solid phase amplification using from 1 to 1000 immobilized primers on, for example, a chip. Reference to "1 to 1000" include "16 to 1000" means 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 110, 111, 112, 113, 114, 115, 116, 1 17, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161 , 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191 , 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271 , 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291 , 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 31 1, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321 , 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401 , 402, 403, 404, 405, 406, 407, 408, 409, 410, 41 1, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421 , 422, 423, 424, 425, 426, 427, 428, 429, 430, 431 , 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521 , 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541 , 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 61 1, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631 , 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681 , 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698,699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741 , 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762; 763, 764, 765, 766, 767, 768, 769, 770, 771 , 772, 773, 774, 775, 776, 777, 778, 779, 780, 781 , 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881 , 882, 883, 884, 885, 886, 887, 888, 889, 890, 891 , 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981 , 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999 or 1000. (0072] In accordance with the present disclosure, a method is provided wherein the extent of methylation provides a quantitative or semi-quantitative or qualitative indication of malignant cells at a frequency down to 10^"3 to 10^"8, including 10^"5, 10^"6 and 10^'7 and frequencies inbetween. The method described herein may also be used in conjunction with other assays such as quantitative Reverse transcriptase PCR (qRTPCR), TaqMan, gene expression micro arrays, Northern or Western blot procedures to measure gene expression and transcription. Other assays contemplated herein include quantitative fluorescent PCR (QF-PCR), multiplex fluorescent PCR (MF-PCR), real time PCR (RT-PCR), single cell PCR, restriction fragment length PCR (PCR-RFLP), PCR-RFLP RT-PCR-RFLP, hot start PCR, nested PCR, in situ polonony PCR, in situ rolling circle amplification, bridge PCR, picotiter PCR, emulsion PCR, next generation/massively parallel sequencing and single molecule sequencing. Other amplification methods include selective amplification of target polynucleotide sequences, consensus sequence primed PCR (CP-PCR), arbitrarily primed PCR (AP-PCR), degenerate oligonucleotides-primed PCR (DOP-PCR) and nucleic acid based sequence amplification (NABS A).

[0073] Following amplification, DNA methylation status can be determined in any number of ways such as by electrophoresis which includes capillary, capillary zone, capillary isoelectric focusing and capillary gel electrophoresis as well as capillary electrochromatography, micellar electrokinetic capillary chromatography and transient isotachophoresis by use of arrays, beads, gas chromatography, supercritical fluid chromatography, liquid chromatography (which encompasses partition, adsorption, ion

Γ

exchange, size exclusion, thin-layer and affinity chromatography). Other techniques include comparative genomic hybridization, microarrays, bead arrays, high-throughput genotyping such as with the use of a molecular inversion probe, MALDI-TOF and sequencing by synthesis (SBS).

[0074] Within the meaning of a "pathological condition" or "disease condition" includes an abnormal malignancy as defined by objective or subjective manifestations of cancer diseases. The assay of the present disclosure enables a molecular (i.e. genetic or epigenetic) determination to be made to complement other symptom-based diagnoses such as based on physiological or medical studies or may be made in its own right. The assay may be part of a suite of diagnostic or prognostic genetic assays of embryos, pre- and postnatal subjects. The terms "method", "assay", "system", "test", "determination", "prognostic", "diagnostic", "report" and the like may all be used to describe an epigenetic assay including a methylation assay of selected regions of one or more loci listed in Table 2, the number selected on the basis of the required level of confidence. The methylation assay determines the epigenetic profile or extent of methylation change compared to a control which suggests or indicates or is instructive of a cancerous condition. The present disclosure also teaches epidemiological studies of populations including studies of different ethnic populations with cancer.

[0075] Accordingly, the present disclosure further provides a method of identifying an epigenetic profile in a population of subjects indicative of a cancerous pathological condition the method comprising screening for a change relative to a control in a statistically significant number of subjects of the extent of epigenetic change within or proximal to one or more loci listed in Table 2 or 3 wherein the extent of epigenetic change is indicative of the presence of the pathological condition or a propensity to develop same. [0076] Also taught herein is a method of identifying a methylation profile in a population of subjects indicative of a cancerous pathological condition the method comprising screening for a change relative to a control in a statistically significant number of subjects the extent of methylation within or proximal to one or more loci listed in Table 2 or 3 wherein an increase or decrease in extent of methylation is indicative of the presence of the pathological condition or a propensity to develop same.

[0077] Another aspect of the present disclosure enabled herein is a method of identifying epigenetic profile in a population of subjects indicative of a cancerous pathological condition the method comprising screening for a change relative to a control in a statistically significant number of subjects the extent of epigenetic change within or proximal to from 1 to about 16 loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 wherein an elevation or decrease in extent of epigenetic change is indicative of the presence of the pathological condition or a propensity to develop same. [0078] Yet a further taught herein is a method of identifying a methylation profile in a population of subjects indicative of a cancerous pathological condition the method comprising screening for a change relative to a control in a statistically significant number of subjects the extent of methylation within or proximal to one or more loci listed in Table 2 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 wherein an increase or decrease in extent of methylation is indicative of the presence of the pathological condition or a propensity to develop same.

[0079] As indicated above, the total number of sites selected depends on the level of confidence required for a diagnosis. For example, from about 40% to 100% confidence levels. A total of 16 different loci, or 16 different regions within one or more loci provides a 100% confidence level of the presence or absence of cancer.

[0080] These methods may also be conducted wherein no change or the presence of a change in extent of methylation is indicative of a non-pathological condition or at least an amelioration in the pathogenicity of the condition.

[0081] The term "proximal" includes the region up to approximately lOOOkb upstream or downstream (5' or 3' terminal regions) of a locus. [0082] In an embodiment, a genome wide methylation map has been constructed in accordance with the present disclosure using standard techniques such as and DNA hybridisation microarray and high throughput mass spectrometry of various cells. Any cell type cell may be assayed. These cells include blood cells, CSF cells, bone marrow cells, buccal cells and cells from rectal swabs or faeces. [0083] The present disclosure contemplates that the extent of epigenetic change in within or proximal to one or more loci listed in Table 2 or 3 which corresponds to a healthy condition or a level of disease within the spectrum of cancer. The extent of change in epigenetic profile, as indicated above, permits the sensitive determination of MRD in a subject and its use in prognosis of cancer.

[0084] The cancerous condition includes leukemias and its various types and sub-types such as pediatric and adult leukemias. [0085] Insofar as the epigenetic profile relates to methylation, methylation may occur anywhere within the DNA or RNA of or proximal to a locus including of any cytosine whether in islands or shores or other areas of the nucleic acid. As used herein, the terms "cytosine" or "C", "CpG islands", "CpNpG islands", "island shores" and "shores" all include these basis in a locus or in a region up to approximately lOOOkb in distance upstream or downstream from a locus listed in Table 2 or 3 or more particularly Table 2 or 3 as well as within the locus and upstream and downstream enhancer elements. Multiple loci may be screened or multiple regions within one or more loci screened.

[0086] As used herein, the terms "subject", "case", "patient", "individual", "target" and the like refer to any organism or cell of the organism on which an assay described herein is performed whether for experimental, diagnostic, prophylactic, and/or therapeutic purposes. Typical subjects include both male and female humans but the present disclosure extends to experimental animals such as non-human mammals, (e.g., monkeys, chimpanzees, orangutangs, gorillas, mice, rats, rabbits, sheep, pigs, cows, horses and guinea pigs hamsters). Other test cells may be used such as yeast, microorganisms, single cell organisms to test fundatmental gene function which is conserved throughout phyla. The "subject" may also be referred to as a population since the present disclosure is useful in epidemiological studies or assays of an ethnic population. [0087] The term "genomic DNA" includes all DNA in a cell, group of cells, or in an organelle of a cell and includes exogenous DNA such a transgenes introduced into a cell. [0088] Hence, the present disclosure teaches a method for identifying an epigenetic profile in the genome of a cell indicative of a cancerous pathological condition, the method comprising screening for a change relative to the control in the extent of epigenetic change within or proximal to one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 wherein the extent of epigenetic change is indicative of the presence of the cancerous pathological condition or a propensit to develop same. This method is particularly useful in the screening of a change in extent of methylation or distribution of methylation sites. The change may be indicative of health, remission or cancer.

[0089] As indicated above, the loci may be grouped based on function, i.e. loci associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity.

[0090] Any methylation assay may be employed such as bisulfite sequencing (see, for example, WO 2005/038051), methylation specific melting curve analysis (MS-MCA), high resolution melting (MS-HRM) [Dahl et al, Clin Chem JJ (4:790-793, 2007; Wojdacz et al, Nucleic Acids Res. 35(6)x4l, 2007], MALDI-TOF MS (Tost et al, Nucleic Acids Res 31(9):e5Q, 2003), methylation specific MLPA (Nygren et al, Nucleic Acids Res. 33(14):e\28, 2005); methylated-DNA precipitation/enrichment and methylation-sensitive restriction enzymes (COMPARE-MS) [Yegnasubramaniah et al, Nucleic Acids Res. 34(3)x\9, 2006] or methylation sensitive oligonucleotide microarray (Gitan et al, Genome Res. 72(7;: 158-164, 2002), Infinium (Bibikova et al, Genome Research 16:383-393, 2006) and MethylLight (Trinh et al, 2001 supra; Shivapunkar et al, 2005 supra; WO 00/70090; US Patent No. 6,331,393), via antibodies and protein binding domains targeted to methylated DNA as well as single molecule real time sequencing (Pac Biosciences; Flushberg et al, Nat Methods 7(6,1:461-465, 2010). Multiplex methylation based PCR assays and multiplex kits are also contemplated for use herein. See also Laird 2010 supra; and Hansen et al 2011 supra. The primers used to amplify DNA prior to bisulfite treatment may need to be modified if the bisulfite treated DNA is to be amplified. [0091] Insofar as a methylation assay involves any amplification, an amplification methodology may be employed. Amplification methodologies contemplated herein include the polymerase chain reaction (PCR) such as disclosed in U.S. Patent Nos. 4,683,202 and 4,683,195; the ligase chain reaction (LCR) such as disclosed in European Patent Application No. EP-A-320 308 and gap filling LCR (GLCR) or variations thereof such as disclosed in . International Patent Publication No. WO 90/01069, European Patent Application EP-A-439 182, British Patent No. GB 2,225,1 12A and International Patent Publication No. WO 93/00447. Other amplification techniques include Qp replicase such as described in the literature; Stand Displacement Amplification (SDA) such as described in European Patent Application Nos. EP-A-497 272 and EP-A-500 224; Self-Sustained Sequence Replication (3SR) such as described in Fahy et ah, PCR Methods Appl. 1(1):25- 33, 1991) and Nucleic Acid Sequence-Based Amplification (NASBA) such as described in the literature. ,

[0092] A PCR amplification process is useful in the practice of the assay enabled herein.

[0093] Another aspect of the present disclosure contemplates a method for determining the epigenetic profile within the genome of a eukaryotic cell or group of cells, the method comprising obtaining a sample of genomic DNA or RNA from the cell or group of cells and subjecting the genomic DNA or RNA to bisulfite treatment/conversion followed by primerrspecific amplification within one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 and assaying for extent of change in epigenetic profile relative to a control.

[0094] The present disclosure further provides a method for determining the methylation profile within the genome of a eukaryotic cell or group of cells, the method comprising obtaining a sample of genomic DNA or RNA from the cell or group of cells and subjecting the genomic DNA or RNA to bisulfite treatment/conversion followed by primer-specific amplification within one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 and assaying for extent of change in methylation relative to a control.

[0095] The present disclosure still further enables a method for determining the epigenetic profile within the genome of a eukaryotic cell or group of cells, the method comprising obtaining a sample of genomic DNA or RNA from the cell or group of cells and subjecting the genomic DNA or RNA to bisulfite treatment/conversion followed by primer-specific amplification within from 1 to 16 loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 and assaying for extent of change in epigenetic profile.

[0096] Yet a further aspect taught herein is a method for determining the methylation profile within the genome of a eukaryotic cell or group of cells, the method comprising obtaining a sample of genomic DNA or RNA from the cell or group of cells and subjecting the genomic DNA or RNA to bisulfite treatment/conversion followed by primer-specific amplification within from 1 to 16 loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 and assaying for extent of methylation relative to a control.

[0097] From 1 to 16 different loci may be assayed or from 1 to 16 different sites within from 1 to 16 loci may be assayed.

[0098] A change in epigenetic profile such as a change in extent of methylation or distribution of methylation sites compared to a control (i.e. a subject with known disease status) determines if a subject has cancer, is healthy or is in remission.

[0099] Similarly, genomic DNA or RNA could be subjected to methylation sensitive restriction digestion and amplification for similar assaying for the extent of methylation. Furthermore, the above methods can be used to detect a decrease in extent of methylation. [0100] A "nucleic acid" as used herein, is a covalently linked sequence of nucleotides in which the 3' position of the phosphorylated pentose of one nucleotide is joined by a phosphodiester group to the 5' position of the pentose of the next nucleotide and in which the nucleotide residues are linked in specific sequence; i.e. a linear order of nucleotides. A "polynucleotide" as used herein, is a nucleic acid containing a sequence that is greater than about 100 nucleotides in length. An "oligonucleotide" as used herein, is a short polynucleotide or a portion of a polynucleotide. An oligonucleotide typically contains a sequence of about two to about one hundred bases. The word "oligo" .is sometimes used in place of the word "oligonucleotide". The term "oligo" also includes a particularly useful primer length in the practice of the present invention of up to about 10 nucleotides. [0101] As used herein, the term "primer" refers to an oligonucleotide or polynucleotide that is capable of hybridizing to another nucleic acid of interest under particular stringency conditions. A primer may occur naturally as in a purified restriction digest or be produced synthetically, by recombinant means or by PCR amplification. The terms "probe" and "primers" may be used interchangeably, although to the extent that an oligonucleotide is used in a PCR or other amplification reaction, the term is generally "primer". The ability to hybridize is dependent in part on the degree of complementarity between the nucleotide sequence of the primer and complementary sequence on the target DNA.

[0102] The terms "complementary" or "complementarity" are used in reference to nucleic acids (i.e. a sequence of nucleotides) related by the well-known base-pairing rules that A pairs with T or U and C pairs with G. For example, the sequence 5'-A-G-T-3' is complementary to the sequence 3'-T-C-A-5^* in DNA and 3'-U-C-A-5' in RNA or bisulfite treated genomic DNA. Complementarity can be "partial" in which only some of the nucleotide bases are matched according to the base pairing rules. Gn the other hand, there may be "complete" or "total" complementarity between the nucleic acid strands when all of the bases are matched according to base-pairing rules. The degree of complementarity between nucleic acid strands has significant effects on the efficiency and strength of hybridization between nucleic acid strands as known well in the art. This is of particular importance in detection methods that depend upon binding between nucleic acids, such as those of the invention. The term "substantially complementary" is used to describe any primer that can hybridize to either or both strands of the target nucleic acid sequence under conditions of low stringency as described below or, preferably, in polymerase reaction buffer heated to 95°C and then cooled to room temperature. As used herein, when the primer is referred to as partially or totally complementary to the target nucleic acid, that refers to the 3'-terminal region of the probe (i.e. within about 10 nucleotides of the 3'- terminal nucleotide position).

[0103] The present disclosure teaches, therefore, a methylation profile of the sites within one or more loci listed in Table 2 or 3 in a genome of a eukaryotic cell or group of cells, the methylation profile comprising the extent or level of methylation, the method comprising obtaining a sample of genomic DNA or RNA from the cell or group of cells, subjecting the restriction digested DNA or RNA or bisulfite treated DNA or RNA to an amplification reaction using primers selected to amplify one or more regions in one or more loci listed in Table 2 or 3 or modified primers which can amplify bisulfite treated DNA or RNA and then subjecting the amplified DNA or RNA to methylation detection means to determine relative to control the extent of methylation wherein a change in methylation relative to the control is indicative of a cancerous pathological condition. The control may be a normal subject or a subject with known disease status.

[0104] The present disclosure also contemplates kits for determining the epigenetic profile of one or more nucleotides at one or more sites within the genome of a eukaryotic cell or group of cells. The kits may comprise many different forms but in one embodiment, the kits comprise reagents for the bisulfite methylation assay and primers for amplification.

[0105] A further embodiment taught by the present disclosure is a kit for the use in the above methods comprising primers to amplify a particular site within one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 or which amplify a strand after bisulfite conversion.

[0106] The kit may also comprise instructions for use. [0107] Conveniently, the kits are adapted to contain compartments for two or more of the above-listed components. Furthermore, buffers, nucleotides and/or enzymes may be combined into a single compartment. [0108] As stated above, instructions optionally present in such^Nkits instruct the user on how to use the components of the kit to perform the various methods taught herein. It is contemplated that these instructions include a description of the detection methods enabled herein. [0109] The present disclosure further contemplates kits which contain a primer for a nucleic acid target of interest with the primer being complementary to a predetermined nucleic acid target or a bisulfite treated template. In another embodiment, the kit contains multiple primers or probes, each of which contains a different base at an interrogation position or which is designed to interrogate different target DNA or RNA sequences. In a contemplated embodiment, multiple probes are provided for a set of nucleic acid target sequences that give rise to analytical results which are distinguishable for the various probes. The multiple probes may be in microarray format for ease of use.

[0110] It is contemplated that a kit comprises a vessel containing a purified and isolated enzyme whose activity is to release one or more nucleotides from the 3' terminus of a hybridized nucleic acid probe and a vessel containing pyrophosphate. In one embodiment, these items are combined in a single vessel. It is contemplated that the enzyme is either in solution or provided as a solid (e.g. as a lyophilized powder); the same is true for the pyrophosphate. Preferably, the enzyme is provided in solution. Some contemplated kits contain labeled nucleic acid probes. Other contemplated kits further comprise vessels containing labels and vessels containing reagents for attaching the labels. Microtiter trays are particularly useful and these may comprise from two to 100,000 wells or from about six to about 10,000 wells or from about six to about 1 ,000 wells. [0111] The present disclosure also teaches genome wide screening for epigenetic profiles including methylation profiles. [0112] Another important application is in the high throughput screening of agents which are capable of demethylation genomes. This may be important, for example, in dedifferentiating cells and cancer therapies.

[0113] The present disclosure further enables a method for screening for an agent which modulates epigenetic regulation of one or more loci or a region within one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 the method comprising screening for a change relative to a control in the epigenetic profile within or proximal to a locus in the presence or absence of an agent to be tested, wherein an agent is selected if it induces a change in the extent of epigenetic.

[0114] A method is also provided for screening for an agent which modulates methylation of one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 the method comprising screening for a change relative to a control in the extent of methylation within or proximal to a locus in the presence or absence of an agent to be tested, wherein an agent is selected if it induces a change in the extent of methylation. [0115] A further method is provided for screening for an agent which modulates epigenesis of one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 the method comprising screening for a change relative to a control in the extent of change in the epigenetic profile within or proximal to a locus in the presence or absence of an agent to be tested, wherein an agent is selected if it induces a change in the extent of epigenesis.

[0116] A still further method is provided for screening for an agent which modulates methylation of one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 the method comprising screening for a change relative to a control in the extent of methylation within or proximal to a locus in the presence or absence of an agent to be tested, wherein an agent is selected if it induces a change in the extent of methylation.

[0117] The present disclosure further enables a method for monitoring the treatment of a cancer in which the treatment modulates the epigenetic profile of one or more loci or a region within one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 the method comprising monitoring for a change relative to a control or a pre- and post-treatment sample in the epigenetic profile within or proximal to one or more loci.

[0118] A method for monitoring the treatment of a cancer is provided in which the treatment modulates the methylation of one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 the method comprising monitoring for a change relative to a control or a pre- and post-treatment sample in the extent of methylation within or proximal to one or more loci.

[0119] A further method for monitoring the treatment of a cancer is provided in which the treatment modulates the epigenetic profile of one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 the method comprising monitoring for a change relative to a control or a pre- and post-treatment sample in the extent of the epigenetic profile within or proximal to one or more loci.

[0120] A still further method for monitoring the treatment of a cancer is provided in which the treatment modulates the methylation of one or more loci or a region within one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 the method comprising monitoring for a change relative to a control or a pre- and post- treatment sample in the extent of methylation within or proximal to one or more loci.

[0121] Reference to "one or more loci listed in Table 2 or 3" includes from 1 to 16 loci listed in Table 2 or 3. By function, the 1 to 16 loci may be associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity (see Table 2).

[0122] In another embodiment, the identification of MRD is provided such as a malignant cell at a frequency down to 10^"* to 10^"8 (i.e. one cell in from 10⁴ to 10⁸ cells) which may be instructive in the decision as to whether to apply more intensive therapy such as more toxic therapy, a bone marrow transplant or stem cell therapy.

[0123] In a particular embodiment, methylation is detected in any C such as a C in CpG or CpNpG islands and/or island shores. In another particular embodiment, the cancer is a leukemia or its various types or sub-types.

[0124] In cases where the gene is methylated and silenced in affected individuals or tissues, compounds are screened in high throughput fashion in stable cell lines or individuals to identify drugs that result in demethylation and reactivation of the affected gene. Alternatively, a normal active copy of the affected gene is transfected as a transgene into cells to correct the defect. Such transgenes are introduced with modulating sequences that protect the transgene from methylation and keep it unmethylated and transcriptionally active.

[0125] In cases where the gene is unmethylated and transcriptionally active or transcriptionally over-active in affected individuals or tissues, compounds are screened in high throughput fashion in stable cell lines to identify drugs that result in methylation and silencing of the affected gene. Alternatively, a transgene encoding a double stranded RNA homologous to the affected sequences or homologs thereof, are transfected as a transgene into cells to methylate the gene, silence it and thereby correct the defect. Such double stranded RNA-encoding transgenes are introduced with modulating sequences which protect it from methylation, keep it transcriptionally active and producing double stranded RNA. [0126] The present disclosure further teaches a computer program and hardware which monitors the changing state, if any, of extent of methylation over time or in response to therapeutic and/or behavioral modification. This includes for monitoring MRD. Such a computer program has important utility in monitoring disease progression, response to intervention and may guide modification of therapy or treatment. The computer program is also useful in understanding the association between increasing methylation and disease progression.

[0127] Thus, in accordance with the present disclosure, index values are assigned to levels of methylation in selected loci or proximal thereto which are stored in a machine-readable storage medium, which is capable of processing the data to provide an extent of disease progression or change in methylation for a subject.

[0128] Thus, in another aspect, the disclosure teaches a computer program product for assessing progression of a pathological condition associated with cancer in a subject, the product comprising:

(1) assigning values to extent of methylation or other epigenetic change in one or more regions of one or more loci listed in Table 2 or 3;

(2) means to converting the value to a code; and

(3) means to store the code in a computer readable medium.

[0129] In a related aspect, the present disclosure levels a computer for assessing an association between extent of methylation or other epigenetic change within one or more regions within one or more loci listed in Table 2 or 3 and progression of a cancerous condition wherein the computer comprises:

(1) a machine-readable data storage medium comprising a data storage material encoded with machine-readable data, wherein the machine-readable data comprise values associated with extent of methylation or other epigenetic change in one or more regions of one or more loci listed in Table 2 or 3; (2) means to converting the value to a code; and

(3) means to store the code in a computer readable medium.

[0130] In a related aspect, the present disclosure extends to a computer for assessing an association between extent of methylation or other epigenetic change within one or more loci listed in Table 2 or 3 or in 1 to 16 regions within one or more loci listed in Table 2 or 3 and progression of a cancer disease condition wherein the computer comprises a machine- readable data storage medium comprising a data storage material encoded within machine- readable data, wherein the machine-readable data comprise values associated with the features extent of methylation or other epigenetic change in one or more regions of one or more loci listed in Table 2 or 3.

[0131] The present disclosure also teaches a computer comprising: (1) a working memory for storing instructional codes for. processing the machine-readable data;

(2) a central-processing unit coupled to the working memory and to the machine-readable data storage medium, for processing the machine-readable data to provide data instructional or informative of changing methylation or other epigenetic patterns or disease progression; and

(3) an output hardware coupled to the central processing unit, for receiving the data.

[0132] The computer system herein may also be linked to detection systems such as MALDI-TOF machines.

[0133] Reference to one or more loci herein includes from 1 to 100 such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 and 100. In an embodiment, from 1 to 50 or 1 to 30 or 1 to 20 loci are screened including 1 to 16 such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 and 16. The present disclosure further encompasses from 1 to 100 sites or regions in 1 or more loci such as 16 sites from one or more loci.

[0134] Aspects taught herein are now further described by the following non-limiting Examples.

EXAMPLE 1

Analysis of genetic loci

Methods

[0135] Let *€ R" be a lipd profile (with or without clinical features) and V€ {- 1 ^, 1 ] be the corresponding binary lable (e.g. SU or CD). A linear least-squares support vector machine was used (Bernhard and Smola, Learning with kernels MIT Press, 2002) model of the form ⁼ The coefficients ?— ¾)€ R"are found by minimizing the following functional: arg (Ί )

where λ, is the regularizati on constant.

[0136] After intiial assessment of few different techniques for feature selection, focus was on recursive feature elemination technique (Guyon et al., Machine Learning 6:389-4122, 2002):

1. Let ~ ^{! " " ? n}^be the set of active features.

2. Build an intiial model ^ using all features ' ^{ί e} ^ by minimizing Equation 1. Then irate the following "for" loop.

3. For / = 1, n.

(a) Estimate the least importrant feature ^{:= ar}¾ ^min< 1 Ί,

(b) Discard feature /,, set ^{0t+l = Θ(Λ} ^- (c) Build the t + 1-th model

β«+ 1) _ ^(t- l) _{€ R}n-t-l using the features set with , feature discarded, i.e. by minimizing Equation 1 for the features ^{( < 1 έ 6} ^ ^where *^{t+ 1} = * ^ ^{h ]'} [0137] Feature importance was determined depending on when the feature was removed from the model. The first feature eliminated is assigned the lowest rank; the next feature removed is assigned the next lowest rank, etc. This provides a ranking from the best feature (last removed) to the worst (first removed). The overall ranking of the features across all bootstrap samples is defined as the average rank across all subsampled data sets.

Evaluation

[0138] To estimate the gneralization perforamcne of the models, ^e-0 bootstrap repeased 50 times (Hastie et al., The Elements of Statistical Learning Springer, 2001). One bootstrap sample consists of constructing a training set of the same size as the original data set by sampling the data with replacement. A corresponding independent test set is then formed using all samples not present in the training set.

[0139] Two performance metrics were used. The area under the receiver operating characteristic (AROC) and the rror rate.

(0140) The AROC is the chance of the classifer correctly ranking samples, i.e. the chance that the model would take a random sample from the positive class and a random sample from the negative class and rank the positive class higher than the negative class. This metric is popular in machine learning and biomedical research.

[0141] More formally, given a model f ^{: R}" ^{→ R}*the AROC is defined as the probability of pairs being correctly ordered, or more formally given two pairs of test points with y > y , the AROC is defined as:

A OC(f) £ P{f(x ) > /(f) I y > tf) + = /(f) | y

assuming above that ^' ^ and ^ belong to the space of labeled sample * ^{c ¾}" ^x {-1, 1 }. In particular, given a finite set of test samples X, the AROC can be estimated as:

where ' denotes set caridanlity; denotes the inidcator function, defined as 1 if its argument is true, and O, otherwise; and * := i^- <≡ Λ'} and - := {*!(*. -1)€ ,V]

EXAMPLE 2

Selection of samples

[0142] Leukemia cases were all diagnosed within the Children's Cancer Centre, Royal Children's Hospital, Melbourne, Victoria, Australia. Primary tumor, 28-day post induction and follow up bone marrow smears on microscope slides (6, 12, 24 months post induction) were used to extract genomic DNA for DNA methylation analysis. A subset of primary tumor bone marrow was used to determine if DNA methylation changes occur as a result of the microscope slide process.

EXAMPLE 3

Illumina infinium human methylation 27 DNA microarray

[0143] Thirteen cases of TEL/AML(ETV6/RUNX1) positive leukemia (7 male, 6 female) were chosen for genome-wide DNA methylation analysis. Matched 28-day remission and subsequent follow up bone marrow samples were also analyzed along with non-leukemic bone marrow samples as controls to compare to. The cases from which the samples were analyzed are listed in Table 6 below. TABLE 6

Leukemia samples

Case Age Sex Cytogenetics Leuk Rem Follow ID Dx

(years)

235 5.13 F 46.XX.del(12)(pl3)[3]/46,XX[14] Yes Yes Yes

TEL/AMLl Fusion

088 9.32 M 46.XY,l(21)(ql0)[15]/46,XY[l] No Yes Yes

102 2.97 M 47,XY,+21c Yes No No

084 7.37 M 46,XY Normal FISH No Yes Yes

089 5.37 M 46.XY Normal FISH Yes No No

094 7.24 M 46,X Y,del(7)(q32),- 1 1 ,+r[3]/46,XY[7] Yes No No ^'

1 14 6.96 F 46.XX Normal FISH Yes Yes Yes

123 5.99 F ^'46,XX Normal FISH Yes No No

127 4.76 F 46,XX,del(l I)(q23)[2]/45,X,-X,del(l 1) Yes Yes No

(q23),inc[5]/46,XX [5], TEL/AMLl

positive

133 2.37 M 46.XY, TEL/AML Positive Yes No Yes

143 13.05 F 45 ,X,-X,del(6)(q21 ),add( 12)(p 13),- 13 , Yes No Yes

+16[3]/46,idem,+21 [2]/46,XX[7]

TEL/AMLl positive

175 2.81 M 46,XY Yes Yes No

216 4.21 M 46.XY nuc ish(TELx2),(AMLlx3), Yes Yes Yes

(TEL con AMLlxl)[185/199]/

(MLLxl) [39/198]

344 2.57 F 47,X,- X, ?add(9)(q34), +10,add(12) Yes Yes Yes

(pl?3),+mar[6]/46,XX[9] TEL/AMLl

positive [0144] The genomic DNA samples from the selected cases were processed by the Australian Genome Research Facility (AGRF) for analysis. Results from Infinium analysis were cleaned to remove poor performing probes across all samples analyzed (with a p-detection value of great than 0.05). R was used for statistical analysis (http://www.r- proiect.org') and generation of heatmap plots using the gplots library.

EXAMPLE 4

Bisulphite conversion of genomic DNA [0112] Genomic DNA was subjected to bisulphite conversion using either MethylEasy Xceed (Human Genetic Signatures) or EZ-96 DNA Methylation gold (Zymo Research) kits according to manufacturer's instructions. . Converted DNA was eluted with sufficient volume of elution buffer to give a final sample concentration of 20ng^L. EXAMPLE 5

SEQUENOM MassArray EpiTYPER chemistry

[0113] PCR amplification of bisulphite converted genomic DNA was performed using gene specific primers containing the necessary tags for SEQUENOM analysis (Table 7) and Fast Start PCR master mix (Roche Applied Sciences) according to manufacturer's instructions. Typical PCR cycling conditions performed are: 95°C for 10 minutes denaturation followed by five cycles of 95°C for 10 seconds denaturation, 56°C annealing for 20 seconds, 72°C extension for two minutes and 40 cycles of 95°C for 10 seconds denaturation, 56°C annealing for 20 seconds, 72°C extension for 1.5 minutes. Successful amplicons were then subject to SEQUENOM EpiTYPER chemistry consisting of SAP treatment, RNA transcription and cleavage prior to mass spectrophotometry analysis as outlined in the manufacturer's instructions. Mass spectra were processed using EpiTYPER viewer software vl.0.5 (SEQUENOM Inc.) and cleaned using an in-house R-script to remove poor quality CpG units and samples. Heatmaps of SEQUENOM data were also drawn using heatmap.2 of the gplots library (http://www.r-project.org^'). TABLE 7

Genomic loci selection for SEQUENOMDNA methylation analysis

Gene Name Location of Assay (hgl8) Primer

Length

SYT9_rep01 chrl 1 :722931+7230180 21,23

LAMAl_rep01 chrl8:7107420+7107714 28,31

TLX3rep01 chr5:170668817+17066912 30,24

THEM4_rep01 chrl :150148496+15014884 28,25

WNT2_rep01 chr7:l 16750654+11675092 28,35

FOXE3_rep01 chrl :47654798+47655020 ^• 27,27

CYR61_rep01 chrl :85819176+85819330 28,28

KCNC3_rep01 chrl 9:55523578+55523941 32,24

THRB repOl chr3:24510979+2451 1199 34,31

ACTL6B_rep01 - chr7:00091691 +10009212 24,35

GALRl_rep01 chrl8:73091207+73091513 23,23

EPB41L3_rep01 chrl 8:5533582+5533933 25,23

SH3GL3_rep01 chrl 5:81907023+81907340 27,24

EXTl_rep01 chr8:l 19193240+11919366 32,23

PLOD2_rep01 chr3:147361674+14736192 24,27

L3MBTL4_rep01 chrl 8:6404859+6405148 26,26

SLC18A3_rep01 chrl 0:50488165+50488452 30,27

CELSRl_rep01 chr22:45311 123+45311476 25,25

RIMS4_rep01 chr20:42872176+42872407 23,25

PTFlA_rep01 chrl0:23521471+23521781 24,20

5' Forward Tag AGGAAGAGAG

5' Reverse Tag CAGTAATACGACTCACTATAGGGAGAAGGCT EXAMPLE 6

Sample justification

[0114] DNA methylation was determined using SEQUENOM MassArray EpiTYPER of a select number of gene loci on identical bone marrow samples that were either flash frozen whole or subjected to smearing on a microscope stained with Giemsa and mounted in DPX for microscopy. There was good correlation between the methylation values of CpG units from both samples (R²=0.97, Figure 1) suggesting that the archival process of making the smear slides does not affect DNA methylation. This allowed the performance of further analysis on samples derived from archived bone marrow aspirates.

EXAMPLE 7

Illumina Inflnium analysis [0115] Thirteen TEL/AML positive cases were selected for genome-wide interrogation using Illumina Infinium HumanMethylation27 Bead Chip DNA arrays. DNA methylation of gene promoters of interest were identified by calculating the average beta-value between the two groups leukemia and remission samples and only retaining promoters that were at least 40% differential DNA methylation between the two groups. After data cleaning to applying this rule to on the data, 936 gene promoters were identified as differentially methylated between leukemia and controls (Table 8 lists genes ranked on average methylation difference). Unsupervised hierarchical clustering on these genes was able to separate the samples into two groups consisting of leukemic bone marrow and non- leukemic bone marrow by DNA methylation levels (Figure 2).

[0116] Along with leukemia samples, matched 28-day remission and post-treatment follow up samples were also analyzed on nine out of the 13 cases. The methylation profile of the leukemic sample was distinct from the remission and follow up samples obtained from these cases. (0117] 74 genes showed on average a greater than 80% difference in DNA methylation between leukemic (methylated) and non-leukemic (unmethylated) samples while only 34 genes showed a greater than 40% difference in DNA methylation between leukemic (unmethylated) and non-leukemic (methylated) samples (Table 8).

TABLE 8

Differentially methylated genes in dataset

TFAP2C 0.89 0.85

PKDREJ 0.95 0.85

SLC22A3 0.89 0.85

ACADL 0.87 0.85

UNQ9433 0.91 0.85

SFRP1 0.90 0.85

ARNT2 0.86 0.84

TRIM58 0.87 0.84

MGC52057 0.87 0.84

ADPN 0.89 0.84

SCRN1 0.91 0.84

COL4A3 0.88 0.04 0.84

FU90650 0.88 0.04 0.83

IRX2 0.88 0.05 0.83

NKX2-8 0.89 0.05 0.83

TP73 0.88 0.05 0.83

IRX4 0.88 0.05 0.83

NEF3 0.89 0.06 0.83

ADRB3 0.86 0.02 0.83

PAK7 0.86 0.03 0.83 ITLG 0.86 0.03 0.83

IGSF4 0.88 0.05 0.83

CXCL1 0.87 0.04 0.83

SLC6A2 0.86 0.03 0.83

ACTL6B 0.90 0.07 0.83

SLC16A12 0.86 0.03 0.83

FBX039 0.89 0.07 0.83

BTG4 0.88 0.05 0.83

OGDHL 0.88 0.06 0.83

SOX9 0.91 0.09 0.82

SOX17 0.90 0.07 0.82

AMPH 0.87 0.04 0.82

XKR6 0.85 0.03 0.82

MSX2 0.87 0.05 0.82

CDCP1 0.85 0.03 0.82

O ECUT2 0.91 0.09 0.82

SOX21 0.83 0.01 0.82

MALL 0.85 0.04 0.81

FU21511 0.86 0.05 0.81

RHPN2 0.84 0.03 0.81

DMRT3 0.86 0.05 0.81

WWTR1 0.86 0.06 0.81

ZNF365 0.83 0.03 0.80

FU46831 0.83 0.03 0.80

THRB 0.86 0.05 : . 0.80

SLC02A1 0.84 0.03 0.80

ZNF454 0.87 0.07 0.80

FU46831 0.85 0.05 0.80

NIN31 0.09 0.49 -0.40

CACNA1H 0.24 0.65 -0.41

SFTPB 0.18 0.59 -0.41

SORBS3 0.10 0.51 -0.41

SCT 0.41 0.83 -0.42

MPP7 0.21 0.63 -0.42

GNB3 0.21 0.64 -0.43

DNAI1 0.31 0.75 -0.44

C M 0.26 0.69 -0.44

RAG1 0.20 0.64 -0.44 WMB DiffBeta MPll

-0.44 BZRAP1 -0.45 BLK -0.45 LC -0.45

KAZALDl -0.45

LRP8 -0.45

TBC1D14 -0.46

MAG -0.47

[0118] Genes showing 60-60.99% as shown in Table 9.

TABLE 9

AvgRem DiffBe MethR

SYMBOL ACCESSION AvgLeuk Diff Beta

Fol ta2 atio

OTOP1 NM_177998.1 0.88 0.18 0.70 0.70 4.79

EDN3 NM_000114.2 0.74 0.05 0.70 0.70 16.32

SULT4A1 NM_014351.2 0.72 0.03 0.70 0.70 28.15

POU4F3 NM_002700.1 0.77 0.08 0.70 0.70 10.02

CART1 NM_006982.1 0.77 0.07 0.70 0.70 10.68

FOXQ1 NM_033260.2 0.74 0.05 0.70 0.70 16.00

TFCP2L1 NM_014553.1 0.75 0.06 0.70 0.70 13.61

CYP1B1 NM_000104.2 0.75 0.05 0.69 0.69 13.85

TBX4 NM_018488.2 0.76 0.07 0.69 0.69 11.45

TRPA1 NM_007332.1 0.76 0.07 - 0.69 0.69 11.33

GCM2 NM_004752.1 0.83 0.13 0.69 0.69 6.15

CDC14B IMM_003671.2 0.75 0.06 0.69 0.69 12.91

GULP1 N _016315.2 0.73 0.04 0.69 0.69 19.23

CHAT NM_020986.1 0.73 0.04 0.69 0.69 19.83

CDC42BPB NM_006035.2 0.73 0.04 0.69 0.69 19.18

AP3B2 IMM_004644.3 0.75 0.06 0.69 0.69 13.30

GSCL NM_005315.1 0.73 0.04 0.69 0.69 18.45

ID4 NM_001546.2 0.74 0.05 0.69 0.69 14.31

CHODL NM_024944.2 0.84 0.15 0.69 0.69 5.59

CYP24A1 NM_000782.3 0.83 0.14 0.69 0.69 5.99

KCNJ8 NM_004982.2 0.74 0.05 0.69 0.69 14.47

DCC T_010966.13 0.74 0.06 0.69 0.69 12.79

TMEFF2 NM_016192.2 0.73 0.04 0.68 0.68 17.32

HRH3 NM_007232.1 0.71 0.02 0.68 0.68 32.17

LHX5 NM_022363.2 0.73 0.04 0.68 0.68 16.56

SY 2 NM_133625.2 0.76 0.08 . 0.68 0.68 9.83

PDE10A N _006661.1 0.74 0.06 0.68 0.68 13.37

SYTL4 NM_080737.1 0.83 0.15 0.68 0.68 5.67

KCNB2 NM_004770.2 0.75 0.07 0.68 0.68 10.62

PPP1R3C NM_005398.3 0.72 0.04 0.68 0.68 20.44

SLC5A8 NM_145913.2 0.77 0.09 0.68 0.68 8.42

BMPR1A NM_004329.2 0.71 0.03 0.68 0.68 27.13

STEAP2 N _152999.2 0.70 0.02 0.68 0.68 31.64

GALNT11 NM_022087.1 0.76 0.08 0.68 0.68 9.63

AGTR1 NM_000685.3 0.76 0.08 0.68 0.68 9.00

ABO NM_020469.2 0.72 0.04 0.68 0.68 16.50

INA NM_032727.2 0.71 0.03 0.68 0.68 23.17

TRPA1 NM_007332.1 0.72 0.04 0.68 0.68 16.99

NPR3 NM_000908.2 0.72 0.04 0.68 0.68 18.37

CPXM2 NM_198148.1 0.83 0.16 0.68 0.68 5.33 DiffBe MethR

SYMBOL DiffBeta

tai atio

MGC16291 N _032770.3 0.80 ^"". 0.12 0.68 0.68 6.41

ADCY2 NM_020546.1 0.72 0.04 0.68 0.68 16.23

NAALAD2 NM_005467.2 0.74 0.07 0.68 0.68 11.13

POU4F2 NM_004575.1 0.79 0.12 0.68 0.68 6.81

PTGS2 NT_.004487.18 0.73 0.06 0.67 0.67 13.22

PRLHR NM_004248.1 0.80 0.13 0.67 0.67 6.26

PTF1A N _178161.1 0.80 0.13 0.67 0.67 6.29

GRB14 N _004490.2 0.72 0.04 0.67 0.67 16.09

LRIG3 N _153377.3 0.75 0.08 0.67 0.67 9.46

WNT2 NM_003391.1 0.72 0.04 0.67 0.67 16.81

IRX2 NM_033267.2 0.75 0.08 0.67 0.67 9.49

HTRA1 N _002775.3 0.71 0.04 0.67 0.67 19.59

NR2F2 NM_021005.2 0.73 0.06 0.67 0.67 12.44

SEMA3C NM_006379.2 0.74 0.07 0.67 0.67 11.24

NKX3- 1 NM_006167.2 0.72 0.05 0.67 0.67 15.56

LOC63928 NM_022097.1 0.72 0.05 0.67 0.67 14.32

EVX1 NM_001989.2 0.73 0.06 0.67 0.67 11.84

MGC13040 N _032930.1 0.73 0.06 0.67 0.67 12.12

EVX1 NM_001989.2 0.71 0.05 0.67 0.67 15.81

YAP1 NM_006106.2 0.74 0.08 0.67 0.67 9.49

PDZRN3 NM_015009.1 0.80 0.14 0.67 0.67 5.77

IHH NM_002181.1 0.73 0.06 0.67 0.67 11.83

HOXC10 NM_017409.2 0.69 0.02 0.67 0.67 29.17

VMP NM_080723.2 0.73 0.07 0.66 0.66 10.58

PCDH8 N _032949.1 0.78 0.11 0.66 0.66 7.01

GDA ^' NM_004293.2 0.70 0.04 0.66 0.66 18.82

LOC401498 NM_212558.1 0.72 0.06 0.66 0.66 13.05

SH3GL2 N _003026.1 0.79 0.12 0.66 0.66 6.43

EPHX2 NM_001979.4 0.76 0.10 0.66 0.66 7.53

CDH1 NM_004360.2 0.69 0.03 0.66 0.66 22.37

PCDHAC2 NM_031883.2 0.76 0.09 0.66 0.66 8.04

DCC NM_005215.1 0.71 0.05 0.66 0.66 15.38

DMRT1 NM_021951.2 0.70 0.03 0.66 0.66 20.44

FU46380 NM_207396.1 0.74 0.08 0.66 0.66 9.33

COMP NM_000095.2 0.73 0.07 0.66 0.66 10.32

NKX2-2 NM_002509.2 0.74 0.08 0.66 0.66 9.50

TTC22 NM_017904.1 0.70 0.04 0.66 0.66 18.30

HNT NM_016522.2 0.70 0.04 0.66 0.66 16.21

SIX1 NM_005982.1 0.72 0.06 0.66 0.66 11.89

PRG-3 NM_017753.2 0.73 0.07 0.66 0.66 . 11.04

P2RY1 N _002563.2 0.72 0.06 0.66 0.66 11.77

T EM98 NM_015544.2 0.69 0.03 0.66 0.66 22.19

MYF5 NM_005593.1 0.82 0.16 0.66 ^• 0.66 5.11

FGF10 NM_004465.1 0.72 0.06 0.66 0.66 11.16

WDR17 N _181265.2 0.71 0.05 0.66 0.66 13.35

CTNND2 NM_001332.2 0.70 0.04 0.66 0.66 17.94

EYA4 NT_025741.14 0.72 0.07 0.66 0.66 11.06 Avgftem Meth

SYMBOL ACCESSION AvgLeu DiffBeta DifFBe

• Fol ta2 atio

ZNF300 N _052860.1 0.77 0.12 0.65 0.65 6.50

GP 26 NM_153442.1 0.79 0.14 0.65 0.65 5.67

KCNBl NM_004975.2 0.77 0.12 0.65 0.65 6.64

NPTX2 NM_002523.1 0.87 0.22 0.65 0.65 4.01

ATP6V0A1 NM_005177.2 0.68 0.02 0.65 0.65 27.87

CNTNAP3 NM_033655.2 0.73 0.08 0.65 0.65 9.30

SH3GL3 NM_003027.2 0.69 0.03 0.65 0.65 20.08

C10orf82 NM_144661.2 0.77 0.12 0.65 0.65 . 6.58

GLRB NM_000824.2 0.68 0.03 0.65 0.65 25.96

GPR88 NM_022049.1 0.72 0.07 0.65 0.65 9.98

DK L1 NM_014419.3 0.72 0.07 0.65 0.65 10.15

RASEF N _152573.2 0.69 0.03 0.65 0.65 20.66

SCGN NM_006998.3 0.79 0.14 0.65 0.65 5.75

LPA XM_926329.1 0.83 0.18 0.65 0.65 4.59

ARHGAP8 N _181335.2 0.73 0.08 0.65 0.65 9.59

ADAMTS5 NM_007038.2 0.69 0.04 0.65 0.65 18.08

SNAP25 NM_003081.2 0.71 0.06 0.65 0.65 12.35

CNTNAP2 NM_014141.3 0.75 0.10 0.65 0.65 7.62

CIDEA N _001279.2 0.70 0.05 0.65 0.65 14.18

NEFH N _021076.2 0.76 0.11 0.65 0.65 7.17

SLC18A2 NM_003054.2 0.69 0.04 0.65 0.65 16.45

FA 80A NM_173642.1 0.72 0.07 0.65 0.65 10.25

IGFBP3 NT_007819.16 0.68 0.03 0.65 0.65 20.17

FU21159 NM_024826.1 0.68 0.04 0.65 0.65 18.62

FZD9 N _003508.2 0.88 0.23 0.65 0.65 3.78

GENX-3414 NM_003943.1 0.69 0.05 0.65 0.65 15.26

LRRTM1 NM_178839.3 0.71 0.06 0.65 0.65 11.86

Clorfl88 NM_173795.2 0.76 0.11 0.65 0.65 6.73

NMBR NM_002511.1 0.80 0.16 0.65 0.65 5.07

PITX3 NM_005029.3 0.68 0.04 0.64 0.64 16.91

PRMT8 NM_019854.3 0.67 0.03 0.64 0.64 23.40

ZFP41 NM_173832.3 0.73 0.09 0.64 0.64 8.53

FBXL2 NM_012157.2 0.68 0.04 0.64 0.64 18.89

PTPRO NM_002848.2 0.67 0.02 0.64 0.64 27.60

NR2F2 N _021005.2 0.69 0.05 0.64 0.64 13.84

FU45717 NM_207401.1 0.69 0.05 0.64 0.64 14.85

MTNR1B NM_005959.3 0.89 0.25 0.64 0.64 3.61

ADRA2A N _000681.2 0.69 0.05 0.64 0.64 13.87

EYA2 NMJ305244.3 0.71 0.07 0.64 0.64 10.55

SLC8A2 NM_015063.1 0.85 0.21 0.64 0.64 4.10

FU10916 NM_018271.2 0.70 0.06 0.64 0.64 11.34

EYA4 NM_004100.2 0.68 0.04 0.64 0.64 16.57

DES NM_001927.3 0.68 0.04 0.64 0.64 15.27

WBSCR14 . NM_032951.1 0.70 0.06 0.64 0.64 , 11.17

ACTC N _005159.3 0.72 0.08 0.64 0.64 9.05

CRABP1 NM_004378.1 0.68 0.04 0.64 0.64 15.68

EYA4 NM_004100.2 0.67 0.03 0.64 0.64 23.61 DiffBe MethR

^? -SYMBOL : ACCESSION AvgLeuk DiffBeta

ta2 atio

SCGN N _006998.3 0.69 0.05 0.64 0.64 14.15

GPM6A NM_201592.1 0.69 0.06 0.64 0.64 12.15

KCNK5 NM_003740.2 0.70 0.06 0.64 0.64 11.53

CTNNA2 N _004389.2 0.70 0.06 . 0.64 0.64 11.25

E RFI1 NM_018948.2 0.67 0.04 0.64 0.64 17.39

ADCY2 N _020546.1 0.71 0.07 0.64 0.64 9.53

KIF6 N _145027.3 0.67 0.03 0.64 0.64 21.70

DOCK5 N _024940.4 0.68 0.05 0.63 0.63 14.74

S0X14 NM_004189.2 0.69 0.05 0.63 0.63 13.40

PRRX2 NM_016307.3 0.70 0.06 0.63 0.63 10.91

CLSTN2 NM_022131.1 0.68 0.05 0.63 0.63 14.20

CXCL5 NM_002994.3 0.72 0.09 0.63 0.63 8.02

PAWR N _002583.2 0.67 0.04 0.63 0.63 17.37

GALR1 NM_001480.2 0.73 0.10 0.63 0.63 7.31

LRIG3 NM_153377.3 0.66 0.03 0.63 0.63 21.90

DOK5 NM_018431.3 0.72 0.09 0.63 0.63 8.18

DKK3 NM_015881.5 0.68 0.05 0.63 0.63 14.14

HOXB4 NM_024015.3 0.66 0.03 0.63 0.63 24.69

CART NM_004291.2 0.71 0.08 0.63 0.63 9.12

MARK2 NM_017490.1 0.79 0.16 0.63 0.63 4.85

LYPD5 NM_182573.1 0.72 0.10 0.63 0.63 7.57

DCC NT_010966.13 0.73 0.10 0.63 0.63 7.32

TMEM67 NM_153704.3 0.68 0.05 0.63 0.63 13.15

FAM 19A4 NM_182522.3 0.68 0.05 0.63 0.63 13.57

ACTN2 NM_001103.1 0.75 0.13 0.62 0.62 5.96

MSC N _005098.2 0.79 0.17 0.62 0.62 4.68

ADCY1 NM_021116.1 0.69 0.06 0.62 0.62 10.83

DCC NT_010966.13 0.71 0.09 0.62 0.62 8.16

LANCL3 N _198511.1 0.82 0.20 0.62 0.62 4.16

GPR103 NM_198179.1 0.71 0.09 0.62 0.62 8.01

RELN NM_005045.2 0.69 0.07 0.62 0.62 10.45

SKIP NM_030623.1 0.78 0.16 0.62 0.62 4,93

DRD2 N _016574.2 0.74 0.12 0.62 0.62 6.24

GLRA1 NM_000171.1 0.75 0.13 0.62 0.62 5.81

FU30277 NM_153008.3 0.65 0.03 0.62 0.62 20.49

TCERG1L NM_174937.1 0.72 0.10 0.62 0.62 7.22

ADAMTS1 NM_006988.3 0.66 0.04 0.62 0.62 17.17

UTF1 NM_003577.2 0.69 0.07 0.62 0.62 9.49

GATA6 N _005257.3 0.71 . 0.09 0.62 0.62 7.93

LOC401498 NM_212558.1 0.68 0.07 0.62 0.62 9.99

HOXC13 NM_017410.2 0.67 0.05 0.62 0.62 12.84

MEGF10 NM_032446.1 0.73 0.12 0.62 0.62 6.19

GBX2 NM_001485.2 0.70 0.09 0.62 0.62 8.23

TRHDE NM_013381.1 0.69 0.07 0.62 0.62 9.58

FOXG1 B N _005249.3 0.68 0.07 0.61 0.61 9.84

SLC22A17 NM_020372.2 0.64 0.02 0.61 0.61 ' 28.11

BARHL2 NM 020063.1 0.78 0.17 0.61 0.61 4.64 SYMBOL AvgRem

AvgLeuk DiffBe MethR

DiffBeta

Fol ta2 atio

INADL NM_005799.2 0.68 0.06 0.61 0.61 10.92

ACSL6 NM_015256.2 0.64 0.03 0.61 0.61 22.97

ZNF285 NM_152354.2 0.75 0.13 0.61 0.61 5.61

PDE1C NM_005020.1 0.76 0.15 0.61 0.61 5.08

TSPAN2 NM_005725.3 0.67 0.06 0.61 0.61 11.88

MARVELD3 NM_052858.3 0.66 0.05 0.61 0.61 13.49

RAB32 NM_006834.2 0.66 0.05 0.61 0.61 13.87

G0S2 NM_015714.2 0.66 0.05 0.61 0.61 12.12

DKFZP566N034 NM_030923.2 0.64 0.03 0.61 0.61 21.97

MIPOL1 NM_138731.2 0.64 0.03 0.61 0.61 20.65

RIC3 NM_024557.2 0.65 0.04 0.61 0.61 15.90

HAND2 NM_021973.1 0.71 0.10 0.61 0.61 6.81

MITF NM_000248.2 0.69 0.08 0.61 0.61 8.69

SLC6A3 NM_001044.2 0.72 0.12 0.61 0.61 6.27

PRG-3 N _017753.2 0.65 0.04 0.61 0.61 14.69

TM6SF1 NM_023003.1 0.63 0.03 0.61 0.61 23.28

MINPP1 NM_004897.2 0.63 0.03 0.61 0.61 23.32

CCDC37 NM_182628.1 0.68 0.07 0.61 0.61 9.44

ALDH1L1 NM_012190.2 0.65 0.04 0.61 0.61 16.13

RP11-450P7.3 NM_153270.1 0.85 0.25 0.60 0.60 3.42

LOC348840 NM_182631.1 0.85 0.25 0.60 0.60 3.43

SIX6 NM_007374.1 0.65 0.05 0.60 0.60 12.76

RBP1 NM_002899.2 0.66 0.06 0.60 0.60 11.16

KIF6 NM_145027.3 0.65 0.05 0.60 0.60 14.35

MT1G NM_005950.1 0.65 0.05 0.60 0.60 12.99

GPR39 NM_001508.1 0.65 0.04 0.60 0.60 14.44

GRM7 NM_181874.1 0.71 0.11 0.60 0.60 6.33

SMOC2 NM_022138.1 0.69 0.09 0.60 0.60 7.88

IRS4 NM_003604.1 0.85 0.25 0.60 0.60 3.45

BNC1 IMT_077661.2 0.72 0.12 0.60 0.60 5.98

UNQ9433 NM_207413.1 0.68 0.08 0.60 0.60 8.96

SCARF1 NM_145349.1 0.19 0.79 0.60 -0.60 0.24

APCDD1 NM_153000.3 0.10 0.71 0.60 -0.60 0.14

KCNMB3 NM_014407.3 0.16 0.76 0.61 -0.61 0.20

CRYBB3 NM_004076.3 0.19 0.82 0.62 -0.62 0.24 [0119] Genes showing 70-70.99% as shown in Table

TABLE 10

UTFl NM_003577.2 0.82 0.02 0.80 0.80 35.02

PNMA2 NM_007257.4 0.89 0.09 0.80 0.80 9.42

BNC1 . NT_077661.2 0.85 0.05 0.80 0.80 15.67

SIX1 NM_005982.1 0.85 0.05 0.80 0.80 17.78

TDRD5 NM_173533.2 0.84 0.04 0.80 0.80 18.92

GRM6 NM_000843.2 0.86 0.06 0.80 0.80 14.04

CAMK2B NM_001220.3 0.82 0.03 0.80 0.80 30.92

SOX1 NM_005986.2 0.89 0.09 0.80 0.80 9.82

SLC16A12 NM_213606.1 0.83 0.03 0.79 0.79 25.93

Clorf76 NM_173509.2 0.82 0.02 0.79 0.79 32.80

EFCAB1 NM_024593.2 0.85 0.05 0.79 0.79 16.08

TJP1 NM_003257.2 0.84 0.05 0.79 0.79 18.50

HCN4 NM_005477.1 0.83 0.03 0.79 0.79 23.65

SLC6A2 NM_001043.2 0.84 0.05 0.79 0.79 16.78

SLC34A2 NM_006424.1 0.82 0.03 0.79 0.79 27.02

CYYR1 N _052954.2 0.83 0.04 0.79 0.79 21.42

THRB NM_000461.2 0.83 0.04 0.79 0.79 19.49

PHF21 B NM_138415.1 0.83 0.05 0.79 0.79 17.65

MYOD1 NM_002478.3 0.86 0.07 0.79 0.79 11.62

PRSS12 NM_003619.2 0.87 0.09 0.78 0.78 9.98

RBP4 NM_006744.3 0.82 0.03 0.78 0.78 25.70

N X6-2 NM_177400.1 0.83 0.04 0.78 0.78 19.37

FBN1 NM_000138.2 0.81 0.03 0.78 0.78 26.91

LPL NM_000237.1 0.84 0.06 0.78 0.78 13.43

SFRP1 NM_003012.3 0.82 0.04 0.78 0.78 19.76

SLC1A1 NM_004170.4 0.80 0.03 0.78 0.78 31.82

ADRA1D NM_000678.2 0.82 0.04 0.78 0.78 20.68

SNCAIP NM_005460.2 0.81 0.04 0.78 0.78 21.25

VAX1 NM.199131.1 0.84 0.06 0.78 0.78 13.12

T NM_003181.2 0.84 0.06 0.77 0.77 13.36

RHCG NM_016321.1 0.81 0.04 0.77 0.77 22.53

DHCR24 NM_014762.2 0.85 0.07 0.77 0.77 11.35

ID4 NM_001546.2 0.84 0.06 0.77 0.77 13.38

GPR6 NM_005284.2 0.82 0.04 0.77 0.77 18.89

RAB32 NT_025741.14 0.85 0.08 0.77 0.77 11.08

KLK10 NT_011109.15 0.80 0.03 0.77 0.77 25.92

CPLX2 NM_001008220.1 0.84 0.07 0.77 0.77 11.31

HS3ST2 NM_006043.1 0.86 0.09 0.77 0.77 9.73

ADRA2C N _000683.3 0.87 0.10 0.77 0.77 8.74

GPR78 NM_080819.2 0.82 0.05 0.77 0.77 17.05 SYMBOL ACCESSION AvgLeuk AvgRerri DiffBeta DiffBe MethR

Fifl ta2 atio

NELL1 NM_006157.2 . 0.80 0.03 0.77 0.77 27.03

FU21511 NM_025087.1 0.90 0.13 0.77 0.77 6.90

HAND1 NM_004821.1 0.80 0.03 0.77 0.77 23.91

ANKRD38 NM_181712.2 0.80 0.03 0.76 0.76 24.74

DOCK5 NM_024940.4 0.80 0.04 0.76 0.76 20.92

RASGRF2 NM_006909.1 0.79 0.03 0.76 0.76 27.04

GALR1 NM_001480.2 0.82 0.06 0.76 0.76 14.35

PARVA NM_018222.2 0.80 0.04 0.76 0.76 19.88

GUCY1A2 NM_000855.1 0.80 0.04 0.76 0.76 18.58

MTNR1A NM_005958.3 0.83 0.07 0.76 0.76 12.18

PLAC2 NM_153375.1 0.83 0.07 0.76 0.76 11.89

SPSB4 NM_080862.1 0.80 0.04 0.76 0.76 19.05

WNT7A NM_004625.3 0.81 0.05 0.76 0.76 17.02

MOSC1 NM_022746.2 0.79 0.03 0.76 0.76 23.60

PTHR2 NM_005048.2 0.81 0.05 0.76 0.76 16.64

L3MBTL4 NM_173464.1 0.79 0.03 0.76 0.76 24.86

BRUNOL6 NM_052840.3 0.78 0.02 0.76 0.76 32.00

FA2H NM_024306.2 0.78 0.02 0.76 0.76 38.14

AFAR3 NM_201252.1 0.87 0.12 0.76 0.76 7.47

KCNC1 NM_004976.2 0.92 0.17 0.76 0.76 5.47

TLX3 NM_021025.2 0.82 0.06 0.75 0.75 12.68

FAM19A4 NM_182522.3 0.82 0.06 0.75 0.75 12.96

ARL4 NM_005738.2 0.82 0.07 0.75 0.75 12.08

CNTNAP2 NM_014141.3 0.81 0.06 0.75 0.75 14.20

GRM7 NM_181874.1 0.81 0.06 0.75 0.75 13.65

GPR126 NM_198569.1 0.84 0.08 0.75 0.75 9.94

VGLL2 NM_153453.1 0.82 0.07 0.75 0.75 11.97

HBQ1 NM_005331.3 0.81 0.06 0.75 0.75 14.13

NEUROG1 NT_034772.5 0.80 0.05 0.75 0.75 16.69

EDIL3 NM_005711.3 0.80 0.05 0.75 0.75 15.85

KRT18 NM_000224.2 0.78 0.03 0.75 0.75 29.36

GDNF NM_000514.2 0.82 0.07 0.75 0.75 11.83

GREM1 NM_013372.5 0.80 0.05 0.75 0.75 15.81

RAB32 NM_006834.2 0.80 0.05 0.75 0.75 15.40

PDE4C NM_000923.1 0.86 0.11 0.75 0.75 7.56

SLC24A3 NM_020689.3 0.81 0.06 0.75 0.75 13.77

MOSC2 NM_017898.3 0.79 0.04 0.75 0.75 18.69

SST NM_001048.3 0.83 0.08 0.75 0.75 10.16

PDZRN3 NM_015009.1 0.81 0.07 0.75 0.75 12.32

KCNG1 NM_002237.2 0.81 0.06 0.74 0.74 12.87

CDH22 NM_021248.1 0.85 0.11 0.74 0.74 7.78

G0S2 NM_015714.2 0.79 0.04 0.74 0.74 18.12

SLC27A6 NM_014031.3 0.80 0.06 0.74 0.74 13.55

TRPC1 NM_003304.3 0.82 0.07 0.74 0.74 11.18

NPY5R NM_006174.2 0.86 0.11 0.74 0.74 7.55

SEMA3C NM_006379.2 0.81 0.06 0.74 0.74 12.69

THEM4 NM_053055.3 0.78 0.04 0.74 0.74 20.93 gRem

SYMBOL ACCESSION DiffBeta DiffBe MethR

.. ¥ol ta2 atio

CD164L2 NM_207397.1 0.82 0.08 0.74 0.74 10.04

KALI NM_000216.1 0.78 0.04 0.74 0.74 19.11

ADA 12 NM_003474.3 0.81 0.07 0.7 0.74 12.21

MSC NM_005098.2 0.84 0.11 0.74 0.74 7.98

CYP24A1 NM_000782.3 0.83 0.09 0.74 0.74 8.78

EIF5A2 NM_020390.5 0.78 0.04 0.74 0.74 17.81

GALR1 NM_001480.2 0.80 0.06 0.74 0.74 12.49

SLC5A7 NM_021815.2 0.83 0.09 0.74 0.74 8.93

ADAMTS17 NM_139057.1 0.78 0.04 0.74 0.74 19.90

PKP2 NM_004572.2 0.78 0.04 0.74 0.74 19.30

ARNT2 NM_014862.3 0.79 0.06 0.74 0.74 14.27

CTTNBP2 NM_033427.1 0.79 0.06 0.73 0.73 13.73

KCNN2 NM_021614.2 0.80 0.07 0.73 0.73 11.67

SLIT3 NM_003062.1 0.78 0.05 0.73 0.73 16.81

FU42486 NM_207379.1 0.80 0.07 0.73 0.73 11.48

PRP2 NM_173490.4 0.77 0.04 0.73 0.73 19.02

FU14503 NM_152780.2 0.84 0.11 0.73 0.73 7.62

TRAM 1 LI NM_152402.1 0.88 0.15 0.73 0.73 5.93

S LCI 8 A3 NM_003055.1 0.82 0.08 0.73 0.73 9.66

MLFl NM_022443.2 0.76 0.03 0.73 0.73 25.30

NEFH NM_021076.2 0.88 0.15 0.73 0.73 5.77

DSCAML1 NM_020693.2 0.80 0.07 0.73 0.73 11.24

ASNS - NM_001673.2 0.81 0.08 0.73 0.73 10.40

EIF5A2 NM_020390.5 0.80 0.07 0.73 0.73 11.32

RAB32 NM_006834.2 0.79 0.06 0.73 0.73 13.94

SRD5A2 NM_000348.2 0.80 0.07 0.73 0.73 11.00

OVOL2 NM_021220.2 0.84 0.11 0.73 0.73 7.62

CRYBA2 NM_057093.1 0.75 0.02 0.73 0.73 33.33

KIAA1944 NM_133448.1 0.79 0.06 0.73 0.73 12.74

ATP8A2 NM_016529.3 0.82 0.09 0.73 0.73 8.81

EDIL3 NM_005711.3 0.77 0.04 0.73 0.73 18.72

KCN 2 NM_021614.2 0.79 0.06 0.73 0.73 13.47

RGS7 NM_002924.2 0.79 0.06 0.73 0.73 13.08

DMN NM_145728.1 0.80 0.07 0.73 0.73 10.99

COL14A1 NM_021110.1 0.75 0.03 0.72 0.72 27.04

HLF NM_002126.3 0.78 0.06 0.72 0.72 13.47

GALR1 NT_025004.13 0.85 0.12 0.72 0.72 6.98

NAV2 NM_182964.4 0.78 0.06 0.72 0.72 13.99

LARP6 NM_197958.1 0.77 0.05 0.72 0.72 15.82

ESX1 NM_153448.2 0.88 0.16 0.72 0.72 5.63

KIF17 NM_020816.1 0.74 0.02 0.72 0.72 40.60

GATA4 NT_077531.3 0.82 0.10 0.72 0.72 8.04

CBLN4 NM_080617.4 0.79 0.07 0.72 0.72 11.68

NMNAT3 NM_178177.2 0.78 0.06 0.72 0.72 13.65

FNDC4 NM_022823.1 0.76 0.04 0.72 0.72 18.83

FOXF2 NM_001452.1 0.74 0.03 0.71 0.71 26.90

RASGRF1 NM_002891.3 0.80 0.08 0.71 0.71 9.71

SNX7 NM_015976.2 0.78 0.07 0.71 0.71 11.13

PRIC LEl NM_153026.1 0.74 0.03 0.71 0.71 28.51

EFHA2 NM_181723.1 0.75 0.04 0.71 0.71 20.58

ME1 NM_002395.2 0.75 0.04 0.71 0.71 17.07

GRIA2 NM_000826.2 0.80 0.09 0.71 0.71 9.05

GPC6 NM_005708.2 0.81 0.10 0.71 0.71 7.89

DMRT2 NM_006557.3 0.76 0.05 0.71 0.71 14.90

PLA2G7 NM_005084.2 0.74 0.03 , 0.71 0.71 22.16

CTSL2 NM_001333.2 0.74 0.03 0.71 0.71 22.32

SSTR4 NM_001052.1 0.81 0.10 0.71 0.71 7.78

BMP7 NM_001719.1 0.77 0.06 0.70 0.70 12.05

FOXA2 NM_021784.3 0.77 0.07 0.70 0.70 11.68

SLC12A5 NM_020708.3 0.85 0.15 0.70 0.70 5.74

TSCOT NM_033051.2 0.77 0.07 0.70 0.70 11.74

BNC1 NT_077661.2 0.76 0.06 0.70 0.70 13.18

CACNA1A NM_023035.1 0.78 0.07 0.70 0.70 10.37

DLX5 NT_007933.14 0.79 0.09 0.70 0.70 8.53

ADAMTS14 NM_080722.2 0.73 0.03 0.70 0.70 21.30

PENK NM_006211.2 0.77 0.07 0.70 0.70 11.00

[0120] Genes showing greater than 80% as shown in Table 1 1. Differentially methylated genes in dataset are shown in Table 12.

TABLE 11

AvgRem

. SYMBOL * '¾¾isS¼N "/ DiffBeta DiffBeta MethR

^■ 2 ^' atio

LAMA1 NM_005559.2 0.95 0.02 0.93 0.93 38.69

FOXE3 NM_012186.1 0.92 0.03 0.90 0.90 35.76

GALR1 NM_001480.2 0.93 0.04 0.89 0.89 22.02

CELSR1 NM_014246.1 0.94 0.06 0.88 0.88 15.10

CYR61 NM_001554.3 0.94 0.06 0.88 0.88 16.56

FBX039 NM_153230.1 0.94 0.06 0.88 0.88 15.30

TLX3 NM_021025.2 0.94 0.06 0.88 0.88 15.04

MYOD1 NM_002478.3 0.94 0.07 0.87 0.87 13.50

RBP1 NM_002899.2 0.89 Q.02 0.87 0.87 36.35

S LCI 8 A3 NM_003055.1 0.90 , 0.03 0.87 0.87 30.68

RIMS4 NM_182970.2 0.91 0.04 0.87 0.87 21.77

NELL1 NM_006157.2 0.90 0.03 0.87 0.87 26.31

NPTX2 NM_002523.1 0.90 0.03 0.86 0.86 27.36

LGI2 NM_018176.2 0.88 0.02 0.86 0.86 41.97

ELOVL4 NM_022726.2 0.88 0.02 0.86 0.86 45.54

SALL3 NM_171999.1 0.90 ' 0.04 0.86 0.86 22.10

SH3GL2 NM_003026.1 0.90 0.04 0.86 0.86 21.42

L3MBTL4 NM_173464.1 0.89 0.04 0.86 0.86 24.28

EPB41L3 NM_012307.2 0.88 0.03 0.85 0.85 32.86

PKDREJ NM_006071.1 0.95 0.09 0.85 0.85 10.17

GSH1 NM_145657.1 0.90 0.04 0.85 0.85 19.99

TFAP2C NM_003222.3 0.89 0.04 0.85 0.85 23.58

MY03A NM_017433.3 0.89 . 0.04 0.85 0.85 24.77

SNAP91 NM_014841.1 0.89 0.04 0.85 0.85 23.53

PPARG NM_138712.2 0.92 0.07 0.85 0.85 13.54

SLC22A3 NM_021977.2 0.89 0.04 0.85 0.85 23.62

SFRP1 NM.003012.3 0.90 0.05 0.85 0.85 18.20

ACADL NM_001608.2 0.87 0.02 0.85 0.85 38.21

TRIM 58 NM_015431.2 0.87 0.02 0.84 0.84 39.63

ARNT2 NM_014862.3 0.86 0.02 0.84 0.84 42.85

UNQ9433 NM_207413.1 0.91 0.07 0.84 0.84 13.56

ADPN NM_025225.2 0.89 0.05 0.84 0.84 18.66

MGC52057 NM_194317.2 0.87 0.02 0.84 0.84 34.87

PDEIOA NM_006661.1 0.89 0.05 0.84 0.84 18.65

SCRN1 NM_014766.2 0.91 0.07 0.84 0.84 13.22 CNK5 IMM_003740.2 0.88 0.03 0.84 0.84 25.02

PLOD2 NM_182943.2 0.87 0.03 0.84 0.84 25.56

FU90650 NM_173800.3 0.88 0.04 0.84 ^• 0.84 22.47

TP73 NM_005427.1 0.88 0.04 0.84 0.84 20.99

N X2-8 NM_014360.2 0.89 0.05 0.84 0.84 17.79

COL4A3 NM_000091.2 0.88 0.04 0.84 0.84 21.69

CXCL1 NM_001511.1 0.87 0.03 0.83 0.83 28.02

IRX2 NM_033267.2 0.88 0.05 0.83 0.83 18.27

SOX9 NM 000346.2 0.91 0.08 0.83 0.83 11.92

SLC6A2 NM_001043.2 0.86 0.03 0.83 0.83 27.60

ADRB3 NM_000025.1 0.86 0.02 0.83 0.83 37.45

PAK7 NM_177990.1 0.86 0.03 0.83 0.83 26.83

FBX039 NM_153230.1 0.89 0.06 0.83 0.83 14.44

IRX4 NM_016358.1 0.88 0.05 0.83 0.83 17.76

IGSF4 NM_014333.2 0.88 0.05 0.83 0.83 18.05

ACTL6B NM_016188.3 0.90 0.07 0.83 0.83 12.38

NEF3 NM_005382.1 0.89 0.06 0.83 0.83 15.34

KITLG NM_000899.3 0.86 0.04 0.83 0.83 24.55

S0X17 NM_022454.2 0.90 0.07 0.83 0.83 12.92

SLC16A12 NM_213606.1 0.86 0.03 0.83 0.83 24.69

BTG4 NM_017589.2 0.88 0.05 0.82 0.82 17.23

MSX2 NM_002449.3 0.87 0.04 0.82 0.82 20.35

OGDHL NM_018245.1 0.88 0.06 0.82 0.82 14.85

XKR6 NM_173683.2 0.85 0.03 0.82 0.82 27.53

AMPH NM_001635.2 0.87 0.05 0.82 0.82 19.10

O ECUT2 NM_004852.1 0.91 0.09 0.82 0.82 10.31

CDCP1 NM_178181.1 0.85 0.03 0.82 0.82 27.97

MALL NM_005434.3 0.85 0.04 0.82 0.82 23.42

SOX21 NM_007084.2 0.83 0.01 0.82 0.82 58.10

FU21511 NM_025087.1 0.86 0.05 0.81 0.81 17.09

RHPN2 NM_033103.3 0.84 0.03 0.81 0.81 26.41

FU46831 NMJ207426.1 0.85 0.04 0.81 0.81 19.47

ZNF454 NM_182594.1 0.87 0.06 0.81 0.81 13.54

DMRT3 NM_021240.2 0.86 0.05 .0.81 0.81 16.86

WWTR1 NM_015472.3 0.86 0.06 0.81 0.81 15.51

ZNF365 NM_199451.1 0.83 0.03 0.81 0.81 30.39

FU46831 NM_207426.1 0.83 0.03 0.80 0.80 28.23

THRB NM_000461.2 0.86 0.06 0.80 0.80 15.24

SLC02A1 NM_005630.1 0.84 0.04 0.80 0.80 23.10

MGC52057 NM_194317.2 0.85 0.05 0.80 0.80 18.43

TABLE 12

Example of Differentially methylated genes in dataset

[0121] Tables 13a through 13e show genetic loci with greater than 90% differential methylation between leukemia and controls. Table 13a lists the top 100 genes identified using the centroid model (P ID:8917796) to separate leukemia bone marrow from normal bone marrow, of which any combination of 16 genes can determine this 100% of the time; Table 13b lists the top 100 genes identified using the Linear Models for Microarray Analysis (LIMMA) [De Hertogh et al, BMC Bioinformatics 77:17, 2020 (PMID 20064233)] model to separate leukemia bone marrow from normal bone marrow, of which any combination of 16 genes can determine this 100% of the time; Table 13c lists the top 100 genes identified using the Support Vector Macchine (SVM) [Varewyck and Martens, IEEE Trans. Syusk. Man. Cybern B Cybern 47(2,1:330-340, 201 1 (PMID:20699214)] model to separate leukemia bone marrow from normal bone marrow, of which any combination of 16 genes can determine this 100% of the time; Table 13d lists the top 100 genes identified using SVM to separate leukemia cases that will relapse (poort outcome) to those with a good outcome, of which any combination of four genes can determine this 90% of the time; and Table 13e lists the top 100 genes identified using Centroid to separate leukemia cases that will relapse (poor outcome) to those with a good outcome, of which any combination of four genes can determine this 90% of the time. TABLE 13a

Centroid Top 100

CFS CFSCentroid

TargetID GENE ID SYNONYM ACCESSION SYMBOL Centroid AVG RNK

GR3; GPRIO;

PrRPR;

GC126539;

eg 16196812 GeneID:2834 MGC126541; NM_004248.1 PRLHR 1 19.5 cg05020203 GeneID:401045 NM_207485.1 FU41327 2 20.43

PUM; MYF3;

cg07271264 GeneID:4654 MYOD; NM 002478.3 MYODl 3 21.93 cg24133115 GenelD: 10846 HSPDE10A; NM 006661.1 PDEIOA 4 20.43

NGFIC; NGFI-C;

cgl3481359 GeneID: 1961 PAT133; NM_001965.1 EGR4 5 29.23 cgl7389519 GenelD: 256297 PTFl-p48; NM_178161.1 PTF1A 6 32.63 eg 04534765 GeneID:2587 GALNR; GALNR1; NM_001480.2 GALR1 7 33.8

CNSA2; SH3P4;

EEN-Bl;

SH3D2A;

cgl7977409 GeneID:6456 FU20276; NM_003026.1 SH3GL2 8 32.93

C20orf25;

MGC39564;

cg24101578 GeneID:64405 dJ998H6.1; NM 021248.1 CDH22 9 40.63

Fbx39;

cg20723355 GenelD: 162517 MGC35179; NM_153230.1 FBX039 10 40.3 cg25484904 GeneID:80157 NM 025087.1 FU21511 11 41.87 cq08918749 GeneID:4023 UPD; NM 000237.1 LPL 12 44.93 cgll389172 GeneID:6572 VACHT; NM 003055.1 S LCI 8 A3 13 41.93 cgl7688525 GeneID:91133 HST1031; NM_173464.1 L3MBTL4 14 40.47 cg07846220 GeneID:284217 LAMA; NM_005559.2 LAMA1 15 44.83 cg23037403 GeneID:285676 FU37444; NM_182594.1 ZNF454 16 49.57 cg02919422 GenelD: 64321 FU22252; NM_022454.2 SOX17 17 58.13 cgll377136 GenelD: 10343 NM_006071.1 PKDREJ 18 60.63 cg06675478 GeneID:6656 NMJD05986.2 SOX1 19 58.07 eg 10639440 GeneID:9805 SES1; KIAA0193; NM_014766.2 SCRN1 20 53.37

Fbx39;

cg02613386 GenelD: 162517 MGC35179; NM_153230.1 FBX039 21 61.63

ASMD; FKHL12;

cgl8815943 GeneID:2301 FREAC8; NM_012186.1 FOXE3 22 63.73

CRBP; RBPC;

cgl2497564 GeneID:5947 CRBPl; CRABP-I; NM 002899.2 RBP1 23 61.1

CMD1; SRA1;

cg06391468 GeneID:6662 CMPD1; NM 000346.2 SOX9 24 67.4

ME2; FMI2;

CDHF9; HFMI2;

cg06268694 GeneID:9620 DKFZP434P0729; NM_014246.1 CELSR1 25 75.73

BL2; ST17;

NECL2; TSLCl ;

IGSF4A;

SYNCAM; sTSLC- 1; synCAMl;

cgl0193817 GeneID:23705 DKFZp686F1789; NM 014333.2 IGSF4 26 67.63

Orf4; C7orf8;

C0RTBP2;

KIAA1758;

cg27603796 GeneID:83992 MGC104579; NM_033427.1 CTTNBP2 27 69.77 1 CFS CFSCentroid

TargetID GENE ID SYNONYM ACCESSION SYMBOL Centroid AVG RNK eg17162024 GeneID:389658 NM_207413.1 UNQ9433 28 72.47

FRP; FRP1; FrzA;

cg22418909 GeneID:6422 FRP-1; SARP2; NM_003012.3 SFRP1 29 76.07 eg19064258 GeneID:9956 30ST2; 30ST2; NM 006043.1 HS3ST2 30 68.17

AP180;

cg21688264 GeneID:9892 IAA0656; NM 014841.1 SNAP91 31 71.1

NR1C3; PPARGl;

PPARG2;

cg04632671 GenelD:5468 HUMPPARG; NM_138712.2 PPARG^■ 32 74.37

GROl; GROa;

MGSA; NAP-3;

SCYBl; MGSA-a;

CQ25806808 GeneID:2919 MGSA alpha; NM_001511.1 CXCL1 33 74.7 cgl4795968 GeneID:33 LCAD; ACAD4; NM_001608.2 ACADL 34 84.17

RNX; HOX11L2;

cg25720804 GenelD:30012 MGC29804; NM_021025.2 TLX3 35 82 cgl0019507 GeneID:6754 NMJ301O52.1 SSTR4 36 100.83 cq12420104 GeneID:58524 DMRTA3; NM 021240.2 DM T3 37 86.33

NP2; NARP; NP- cql2799895 GeneID:4885 II; NMJ302523.1 NPTX2 38 96.1 l FPP; MSH; PFM;

CRS2; HOX8;

cg27096144 GenelD:4488 PFM1; NM_002449.3 MSX2 39 95.93

MRT1; BSSP3;

BSSP-3;

MGC12722;

cq07625840 GeneID:8492 MOTOPSIN; NM 003619.2 PRSS12 40 79.43

EMT; EMTH;

cg07237939 GeneID:6581 OCT3; NM 021977.2 SLC22A3 41 91.73

CQ26609631 GeneID:219409 Gsh-1; NM_145657.1 GSH1 42 83.97 cql7371081 GeneID:4745 NRP1; IDH3GL; NM 006157.2 NELL1 43 86.63

MGC125378;

cq2504465i GenelD:206338 MGC125379; NM 173800.3 FU90650 44 92.3

CCNl; GIG1;

cg04453065 GeneID:3491 IGFBP10; NM 001554.3 CYR61 45 94.23

CP24; CYP24;

MGC126273;

MGC126274;

cg02604290 GenelD: 1591 P450-CC24; NM_000782.3 CYP24A1 46 90.8

NKX2H; N X2.8;

eg17685628 GenelD:26257 Nkx2-9; NM_014360.2 NKX2-8 47 84.43

MA2; MM2;

RGAG2;

cq02lS4186 GenelD:10687 KIAA0883; NM_007257.4 PNMA2 48 99.9 cq12839593 GeneID:6495 NM 005982.1 SIX1 49 93.03

OC2; OC-2;

MGC120377;

cq02250594 GenelD:9480 MGC120378; NM 004852.1 ONECUT2 50 99.77 cq03160135 GeneID:245806 VGL2; VITOl; NM_153453.1 VGLL2 51 97.87 cq23771603 GenelD:53904 DFNB30; NM 017433.3 MY03A 52 99.57 cql0063179 GeneID:4543 · MEL-1A-R; NM_005958.3 MTNR1A 53 108

LGIL2; FU10675;

KIAA1916;

MGC126808;

cgl3359415 GenelD:55203 MGC126810; NM_018176.2 LGI2 54 103.8

EMT; EMTH;

cg25313204 GeneID:6581 OCT3; NM_021977.2 SLC22A3 55 88.43 CFS ^• CFSCentroid

TargetID GENE ID SYNONYM ACCESSION SYMBOL Centroid AVG RNK

AD D; STGD2;

cq 13297865 GeneID:6785 STGD3; NM_022726.2 ELOVL4 56 106.47 cq 15191648 GeneID:27164 NM 171999.1 SALL3 57 118.17

NFM; NEFM; NF- cq23290344 GeneID:4741 M; NM 005382.1 NEF3 58 99.2 cg04005707 GeneID:80157 NM_025087.1 FU21511 59 104.93 cg02141570 GenelD: 348840 NM 182631.1 LOC348840 60 139.67 eg 15842276 GeneID:4544 MEL-1B-R; NM _005959.3 MTNR1B 61 114.97

Hxt; eHand;

cgl9721889 GeneID:9421 Thinql; NM_004821.1 HA D1 62 128.83

FRP; FRP1; FrzA;

cql5839448 GeneID:6422 FRP-1; SARP2; NM 003012.3 SFRP1 63 118.37

C6orfl02;

MGC33317;

dJ137F1.4;

dJ188D3.1;

cgl8191162 GenelD: 221458 dJ1043E3.1; NM_145027.3 KIF6 64 99.27 cq25250358 GeneID:5352 LH2; TLH; NM_182943.2 PLOD2 65 107.97

PUM; MYF3;

cql8555440 GenelD: 4654 YOD; NM 002478.3 MYOD1 66 122.53

RIM4;

C20orfl90;

cql9332710 GenelD: 140730 d.781B1.3; NM 182970.2 RIMS4 67 114.83

PC3B;

cg23211240 GenelD: 54766 MGC33003; NM 017589.2 BTG4 68 126.83

CPX2; 921-L;

eg 19885761 GenelD: 10814 CPX-2; NM 001008220.1 CPLX2 69 117.53

BAL; FAP; BSDL;

BSSL; CELL;

FAPP; UPA;

cg03693099 GenelD: 1056 CEase; NM 001807.2 CEL 70 126.47 eg 11530960 GenelD: 10655 NM 006557.3 DMRT2 71 149.73 eg 10862587 GeneID:79411 MGC10771; NM 024506.3 GLB1L 72 130.83 cgl3929328 GenelD: 399823 F0XI2; NM 207426.1 FU46831 73 119.87 cql7252960 GenelD: 3400 NM 001546.2 ID4 74 123.03

KV4; NGK2;

KV3.1;

cq27409364 GenelD: 3746 MGC129855; NM 004976.2 KCNC1 75 153 cq22123464 GenelD: 6543 NCX2; NM 015063.1 SLC8A2 76 130.23 cq 14785449 GenelD: 440 TS11; NM 001673.2 ASNS 77 125.6 cq03963198 GenelD: 50805 NM_016358.1 IRX4 78 138.8 cgl4882700 GenelD: 133060 NM 177998.1 OTOP1 79 129.77

NRXN4; CASPR2;

cq07028533 GeneID:26047 DKFZp781D1846; NM 014141.3 CNTNAP2 80 129.8

PAK5; KIAA1264;

cql2388309 GeneID:57144 MGC26232; NM_177990.1 PAK7 81 137.5 cq 10009830 GeneID:256714 MGC104944; NM 152780.2 FU14503 82 149 cq00546491 GeneID:5137 Hcam3; NM_005020.1 PDE1C 83 125.8

ERF1; TFAP2G;

hAP-2g; AP2- cg02536286 GeneID:7022 GAM A; NM .003222.3 TFAP2C 84 138.7 cq 14889768 GenelD: 10981 NM 006834.2 RAB32 85 147.7 cq22752533 GenelD: 57468 KCC2; KIAA1176; NM 020708.3 SLC12A5 86 164.33

GPCR; GPR170;

cg23759710 GenelD: 165140 TG1019; NM 148962.3 OXER1 87 142.4 CFS CFSCentroid

TargetIO GENEJD SYNONYM ACCESSION SYMBOL Centroid AVG RNK

CASPR3;

CNTNAP3A;

RP11-290L7.1;

eg 13059782 GenelD: 79937 RP11-138L21.1; NM 033655.2 CNTNAP3 88 147.93 cgl5433631 GenelD: 153572 NM 033267.2 IRX2 89 146.13 cg02994956 GeneID:4744 NFH; NM_021O76.2 NEFH 90 156.13 cg09261015 GeneID:80712 ESX1L; ESXR1; NM_153448.2 ESX1 91 156.33 eg 11376198 GenelD: 246181 A R7A4; NM_201252.1 AFAR3 92 165.37

MT75; PRED12;

C21orf68;

cg24130010 GenelD: 140578 FU 12627; NM_024944.2 CHODL 93 13 cgl3334990 GenelD: 2026 NSE; NM 001975.2 EN02 94 141.17

DPDE1;

MGC126222;

eg 13899108 GeneID:5143 PDE4C-791; NM_000923.1 PDE4C 95 133.77 cg00662556 GenelD: 2587 GALNR; GALNRl; NT 025004.13 GALR1 96 153.8 cgl9703610 GenelD: 57453 KIAA1132; NM 020693.2 DSCAML1 97 146.9

MGC126743;

cg00263760 GenelD: 11023 MGC126745; NM 199131.1 VAX1 98 , 155.57

ADRA2L2;

A RARL2;

ADRA2RL2;

cql0235817 GenelD: 152 ALPHA2CAR; NM_000683.3 ADRA2C 99 165.57 cq00565688 GeneID:7161 P73; NM 005427.1 TP73 100 155.53

TABLE 13b

LIMMA Top 100

LIMMA

TargetIO GENEJD SYNONYM ACCESSION SYMBOL LIMMA AV6 RNK cg07846220 GenelD: 284217 LAMA; NM_005559.2 LAMA1 1 2.066667 cg07271264 GeneID:4654 PUM; MYF3; MYOD; NM 002478.3 MYOD1 2 4.033333 cq20723355 GenelD: 162517 Fbx39; MGC35179; NM_153230.1 FBX039 3 5.1 cgll377136 GenelD: 10343 NM_006071.1 PKDREJ 4 7.233333

CMD1; SRA1;

cg0639l468 GenelD: 6662 CMPD1; NM_000346.2 SOX9 5 9.566667

ASMD; FKHL12;

cgl8815943 GenelD: 2301 FREAC8; NM_012186.1 FOXE3 6 10.83333 cg04534765 GenelD: 2587 GALN ; GALNR1; NM 001480.2 GALR1 7 12.86667

ME2; FMI2; CDHF9;

HFMI2;

cg06268694 GenelD: 9620 DKFZP434P0729; NM_014246.1 CELSR1 8 13.6

RNX; HOX11L2;

cg25720804 GeneID:30012 MGC29804; NM_021025.2 TLX 3 9 14.56667

CCN1; GIG1;

cg04453065 GeneID:3491 IGFBP10; NM_001554.3 CYR61 10 16.56667 cg02919422 GenelD: 64321 FU22252; NM_022454.2 SOX 17 11 17.83333 cg25484904 GenelD: 80157 NM 025087.1 FU21511 12 18.03333 cg23037403 GenelD: 285676 FU37444; NM_182594.1 ZNF454 13 20.3 cq 10639440 GenelD: 9805 SES1; KIAA0193; NM_014766.2 SCRN1 14 21.6 cq02613386 GeneID:162517 Fbx39; MGC35179; NM 153230.1 FBX039 15 24.1

FRP; FRPl; FrzA;

cg22418909 GeneID:6422 FRP-1; SARP2; NM_003012.3 SFRP1 16 24.5 cqll389172 GeneID:6572 VACHT; NM 003055.1 SLC18A3 17 27.63333

MGC125378;

cg25044651 GenelD: 206338 MGC125379; NM 173800.3 FU90650 18 27.66667

Orf4; C7orf8;

CORTBP2;

KIAA1758;

cg27603796 GenelD. -83992 MGC104579; NM 033427.1 CTTNBP2 19 28.23333 cq23290344 GenelD :4741 NFM; NEFM; NF-M; NM 005382.1 NEF3 20 32.46667 cg26609631 GenelD: 219409 Gsh-1; NM_145657.1 GSH1 21 33.26667

CNSA2; SH3P4;

EEN-B1; SH3D2A;

eg 17977409 GenelD: 6456 FU20276; NM 003026.1 SH3GL2 22 36.06667

OC2; OC-2;

MGC120377;

cg02250594 GeneID:9480 MGC120378; NM_004852.1 ONECUT2 23 39.03333 cq23211240 GenelD: 54766 PC3B; MGC33003; NM 017589.2 BTG4 24 40.53333

NR1C3; PPARG1;

PPARG2;

cq04632671 GenelD: 5468 HUMPPARG; NM 138712.2 PPARG 25 42.43333 cq00565688 GeneID:7161 P73; NM 005427.1 TP73 26 44.1 cq05020203 GenelD. -401045 NM 207485.1 FU41327 27 45.6

MA2; MM2; RGAG2;

cg02154186 GenelD: 10687 IAA0883; NM_007257.4 PNMA2 28 46.46667 eg 17162024 GeneID:389658 NM _207413.1 UNQ9433 29 46.63333 cgl7371081 GeneID:474S NRP1; IDH3GL; NM .006157.2 NELL1 30 46.66667 cgl5191648 GeneID:27164 NM_171999.1 SALL3 31 46.76667 eg 19064258 GeneID:9956 30ST2; 30ST2; NM 006043.1 HS3ST2 32 47.16667 LIMMA

TargetID GENEJD SYNONYM ACCESSION SYMBOL LIMMA AV6 RNK

LGIL2; FU10675;

KIAA1916;

MGC126808;

cgl3359415 GeneID:55203 MGC126810; NM_018176.2 LGI2 33 47.93333 cg21688264 GeneID:9892 AP180; KIAA0656; NM 014841.1 SNAP91 34 49.8

CRBP; RBPC;

cgl2497564 GeneID:5947 CRBP1; CRABP-I; NM 002899.2 RBP1 35 50.36667

ADMD; STGD2;

cgl3297865 GeneID:6785 STGD3; NM_022726.2 ELOVL4 36 51.2 cq 16232126 GenelD: 60482 CHT; CHT1; hCHT; NM_021815.2 SLC5A7 37 52.56667

RIM4; C20orfl90;

cgl9332710 GenelD: 140730 (JJ781B1.3; NM 182970.2 RIMS4 38 54.5 cgl4795968 GeneID:33 LCAD; ACAD4; NM 001608.2 ACADL 39 54.9 cgl2799895 GeneID:4885 NP2; NARP; NP-II; NM 002523.1 NPTX2 40 54.93333 cg08918749 GeneID:4023 UPD; NM_000237.1 LPL 41 57.8

MRTl; BSSP3;

BSSP-3;

MGC12722;

cg07625840 GenelD: 8492 MOTOPSIN; NM 003619.2 PRSS12 42 57.86667

BL2; ST17; NECL2;

TSLC1; IGSF4A;

SYNCAM; sTSLC-1 ;

synCAMl;

cql0193817 GeneID:23705 DKFZP686F1789; NM 014333.2 IGSF4 43 58.6 cg24133115 GenelD: 10846 HSPDE10A; NM_006661.1 PDE10A 44 58.8 cq07237939 GeneID:6581 EMT; EMTH; OCT3; NM_021977.2 SLC22A3 45 62.33333 cg23771603 GeneID:53904 DFNB30; NM 017433.3 MY03A 46 62.43333

GROl; GROa;

MGSA; NAP-3;

SCYB1; MGSA-a;

cg25806808 GeneID:2919 MGSA alpha; NM_001511.i CXCLl 47 63.7 cg09619146 GeneID: 119587 UNQ676; NM_198148.1 CPXM2 48 63.73333 cq22123464 GeneID:6543 NCX2; NM._015063.1 SLC8A2 49 64.13333

PAK5; KIAA1264;

cgl2388309 GeneID:57144 MGC26232; NM 177990.1 PAK7 50 64.16667 cg06675478 GenelD: 6656 NM 005986.2 SOX1 51 65.2 cq00662556 GeneID:2587 GALNR; GALNR1; NT 025004.13 GALR1 ^' 52 66.7

CASPR3;

CNTNAP3A; RP11- 290L7.1; RP11- cgl3059782 GeneID:79937 138L21.1; NM_033655.2 CNTNAP3 53 67.53333 cq03963198 GenelD: 50805 NM 016358.1 IRX4 54 68.86667 cq03160135 GeneID:245806 VGL2; VITOl; NM_153453.1 VGLL2 55 72.23333 cgl7389519 GeneID:256297 PTF1-P48; NM_178161.1 PTF1A 56 72.66667

PNPLA3; C22orf20;

eg 13184872 GeneID:80339 iPLA(2)epsllon; NM 025225.2 ADPN 57 73.06667 eg 17188046 GeneID:6862 TFT; MGC104817; NM 003181.2 T 58 74.4

FPP; MSH; PFM;

CQ27096144 GenelD: 4488 CRS2; HOX8; PFMl; NM_002449.3 MSX2 59 75.36667 cq 18555440 GeneID:4654 PUM; MYF3; MYOD; NM_002478.3 MYOD1 60 77.73333

FRP; FR^'Pl; FrzA;

eg 15839448 GeneID:6422 FRP-1; SARP2; NM 003012.3 SFRP1 61 79.96667

N X2H; N X2.8;

eg 17685628 GeneID:26257 Nkx2-9; NM_014360.2 N X2-8 62 80.23333

CP24; CYP24;

MGC126273;

MGC126274; P450- cg02604290 GeneID: 1591 CC24; NM_000782.3 CYP24A1 63 85.16667 LIMMA

Target ID GENEJD SYNONYM ACCESSION SYMBOL LIMMA AVG RNK

4.1B; DALl; DAL-1;

cg00027083 GeneID:23136 KIAA0987; NM_012307.2 EPB41L3 64 85.2

RNX; HOX11L2;

cg25942450 GeneID:30012 MGC29804; NM_021025.2 TLX3 65 85.53333

ERFl; TFAP2G; hAP- cg02536286 GeneID:7022 2g; AP2-GAMMA; NM_003222.3 TFAP2C 66 86.56667 eg 10063179 GeneID:4543 MEL-1A-R; NM 005958.3 MTNR1A 67 86.83333

GR3; GPR10;

PrRPR; MGC126539;

cgl6196812 GeneID:2834 MGC126541; NM_004248.1 PRLHR 68 87.5

KV4; NGK2; KV3.1;

cg27409364 GeneID:3746 MGC129855; NM 004976.2 KCNC1 69 89.03333

UAN; IAA0844;

GC41821;

cgl5591678 GeneID:22891 MGC87345; NM_199451.1 ZNF365 70 90.2

XRG6; C8orf7;

eg 10947146 GenelD: 286046 C8orf21; NM_173683.2 XKR6 71 90.33333

FBN; SGS; WMS;

MASS; MFS1;

cgl8671950 GeneID:2200 OCTD; fibrillin; NM 000138.2 FBN1 72 94.6

MGC126743;

cg00263760 GenelD: 11023 MGC126745; NM_199131.1 VAX1 73 95.26667 cgl9721889 GeneID:9421 Hxt; eHand; Thingl; NM 004821.1 HAND1 74 97.8 cg25250358 GeneID:5352 LH2; TLH; NM_182943.2 PLOD2 75 98.06667 cgl7688525 GeneID:91133 HST1031; NM 173464.1 L3MBTL4 76 98.13333 cgl2331389 GenelD: 5950 NM_006744.3 RBP4 77 99.3

SEGN; SECRET;

setagin;

cgl6954341 GenelD: 10590 DJ501N12.8; NM 006998.3 SCGN 78 99.83333 cg062228Sl GeneID:55753 NM 018245.1 OGDHL 79 102.0333 cgl7788682 GeneID:9915 IAA0307; NM_014862.3 ARNT2 80 103.1 cg02844S45 GenelD: 9247 GCMB; hGCMb; NM_004752.1 GCM2 81 103.5333

C20orf25;

MGC39564;

cg24101578 GenelD: 64405 dJ998H6.1; NM 021248.1 CDH22 82 105.1333 cgl2594641 GeneID:130574 NM_194317.2 MGC52057 83 108.3 cgl5433631 GenelD: 153572 NM 033267.2 IRX2 84 109 cgl3929328 GeneID:399823 F0XI2; NM 207426.1 FU46831 85 109.3

BNC; BSN1;

cgl7051321 GeneID:646 HsT19447; NT 077661.2 BNC1 86 111.9

PGT; 0ATP2A1;

eg 19591206 GeneID:6578 SLC21A2; NM 005630.1 SLC02A1 87 112.3667 cg22752533 GenelD: 57468 CC2; IAA1176; NM_020708.3 SLC12A5 88 113.2333 cgl4473924 GenelD: 23024 LNX3; SEMACAP3; NM_015009.1 PDZRN3 89 114.8333 cgl2699371 GenelD: 2587 GALNR; GALNR1; NM 001480.2 GALR1 90 116.0333 cgl7252960 GenelD :3400 NM_001546.2 ID4 91 116.4

RHGK; PDRC2;

CQ10453365 GeneID:51458 C15orf6; NM_016321.1 RHCG 92 116.8667

ADRA2L2;

ADRARL2;

ADRA2RL2;

cgl0235817 GenelD: 152 ALPHA2CAR; NM 000683.3 ADRA2C 93 118.6333 cq17619823 GeneID: 155 BETA3AR; NM 000025.1 ADRB3 94 119.7 IIMMA

TargetID GENEJD SYNONYM ACCESSION SYMBOL LIMMA AVG RNK

GRTH; THRl;

ERBA2; NR1A2;

THRBl; THRB2;

ERBA-BETA;

MGC126109;

cg241208 1 GeneID:7068 MGC126110; NM_000461.2 THRB 95 120.5333 eg 14882700 GenelD: 133060 NM_177998.1 OTOP1 96 123.1

KIAA0018;

SELADIN 1;

Nbla03646; seladin- cg25536676 GeneID: 17l8 1; NM 014762.2 DHCR24 97 123.5333 eg 12839593 GeneID:6495 N M_005982.1 SIX1 98 124 cg05807991 GenelD. -133022 MGC26568; NM 152402.1 TRAM1L1 99 124.4333 cg26721264 GeneID:2587 GALNR; GALNRl; NM 001480.2 GALR1 100 127.9333

TABLE 13c

RFE-SVM Top 100

SVM+

SVM+ TTEST

TargetID GENEJD SYNONYM ACCESSION SYMBOL TTEST AVG RNK cgl2969595 GenelD: 26022 DKFZP564K1964; NM_015544.2 TMEM98 * 1 8.5 cgl2594641 GenelD: 130574 NM_194317.2 MGC52057 2 9.87 cq21801378 GenelD: 60677 CELF6; NM_052840.3 BRUNOL6 3 14.43 cg07846220 GeneID:2842l7 LAMA; NM 005559.2 LAMA1 4 16.03

RILP; FU31627;

cgl6041660 GenelD: 144165 FU31937; NM_153026.1 PR1CKLE1 5 17.57

RPTPrho;

cgl3168820 GenelD: 11122 KIAA0283; NM_133170.2 PTPRT 6 18.13

BIA2;

cg07533148 GeneID:25893 D FZP434C091; NM_015431.2 TRIM58 7 22.6

ADMD; STGD2;

eg 13297865 GeneID:6785 STGD3; NM_022726.2 ELOVL4 8 23.43

CNSA3; EEN-B2;

SH3D2C; SH3P13;

HST19371; EEN- cg25381844 GeneID:6457 2B-L3; NM 003027.2 SH3GL3 9 25.23

PHF4; BHC80L;

cg01775414 GenelD: 112885 FU34161; NM 138415.1 PHF21B 10 28.53 eg 19063972 GenelD: 11166 SOX25; NM 007084.2 SOX21 11 29.83 cq08186362 GenelD: 11255 HH3R; GPCR97; NM_007232.1 HRH3 12 30.6 cg09053680 GeneID:8433 NM_003577.2 UTFl 13 31.03

CQ17688525 GenelD: 91133 HST1031; NM_173464.1 L3MBTL4 14 31.4 cql0227191 GenelD: 3875 K18; CYK18; NM_000224.2 RT18 15 31.67

FH3; NARCl;

NARC-1;

cgl3191808 GenelD: 255738 HCHOLA3; NM 174936.2 PCSK9 16 32 cgl7788682 GeneID:9915 KIAA0307; NM_014862.3 ARNT2 17 32.3

NDSP; MGC16664;

cgl3449778 GenelD: 148753 RP11-12M5.2; NM 173509.2 Clorf76 18 34.67

CASPR3;

CNTNAP3A; RP11- 290L7.1; RP11- cq02090283 GeneID:79937 138L21.1; NM 033655.2 CNTNAP3 19 35.63 cql7619823 GenelD: 155 BETA3AR; NM_000025.1 ADRB3 20 41.8

AL 3; CD292;

cql7776353 GenelD: 657 ACVRLK3; NM_004329.2 BMPR1A 21 42.03

FAAH; FAXDC1;

cq09787254 GeneID:79152 FU25287; NM 024306.2 FA2H 22 42.53 cgl9884262 GeneID:399823 FOXI2; NM 207426.1 FU46831 23 43.7 cq09186006 GenelD .387700 NM 213606.1 SLC16A12 24 .44.73

PTP2; HPTPZ;

PTP18; PTPRZ;

cg00826384 GenelD: 5803 RPTPB; NM_002851.1 PTPR21 25 45.9

PTPU2; GLEPP1;

cq 10646402 GenelD: 5800 PTP-U2; NM_002848.2 PTPRO 26 50.6 cq09688546 GenelD: 2743 NM_000824.2 GLRB 27 52.87 cq20113732 GenelD: 4745 NRP1; IDH3GL; NM 006157.2 NELL1 28 52.9

STMP; IPCA1 ;

IPCA-1; STAMP1;

cg09450020 GenelD: 261729 PCANAP1; NM_152999.2 STEAP2 29 53.7 SVM+

SVM+ TTEST

TargetID GENEJD SYNONYM ACCESSION SYMBOL msT AVG RN

GROl; GROa;

MGSA; NAP-3;

SCYBl; MGSA -a;

cg25806808 GeneID:2919 MGSA alpha; NM_001511.1 CXCL1 30 54.03 cg06572160 GeneID:3748 V3.3; KSHIIID; NM 004977.2 KCNC3 31 55.83

NEF5; NF-66;

TXBP-l;

cg2S764191 GeneID:9118 MGC12702; NM_032727.2 INA 32 64.3 eg 14795968 GeneID:33 LCAD; ACAD4; NM 001608.2 ACADL 33 64.63 al; Stvl; VPPl;

Vphl; ATP6N1;

ATP6N1A;

cg20082357 · GeneID:535 DKFZp781J1951; NM_005177.2 ATP6V0A1 34 65.87 cq09500672 GenelD: 130574 NM 194317.2 MGC52057 35 66.43 cg09775312 GeneID:5507 PPP1R5; NM 00539B.3 PPP1R3C 36 67.93 cg20182358 GeneID:4291 NM 022443.2 MLF1 37 68.9

TASK2; TAS -2;

cg02128567 GenelD:8645 FU11035; NM 003740.2 KCNK5 38 68.97 cq047110S0 GeneID:6505 EAAC1; EAAT3; NM 004170.4 SLC1A1 39 72.97 cg25250358 GeneID:5352 LH2; TLH; NM_182943.2 PLOD2 40 73.6

Hxt; eHand;

cgl9721889 GeneID:9421 Thingl; NM 004821.1 HA D1 41 75.2 cgl2297221 GenelD: 116969 MGC22848; NM_053017.2 ART5 .42 75.47

CEBP; C/EBP- cg02144298 GenelD: 1050 alpha; NM 004364.2 CEBPA 43 77.2 cq23984434 GeneID:2977 GC-SA2; GUC1A2; NM_000855.1 GUCY1A2 44 77.47

FU16103;

FU33655;

cql2128017 GenelD: 284656 MGC43817; NM 173641.1 EPHA10 45 82.83 cg23433607 GeneID: 1114 SCG1; NM 001819.1 CHGB 46 87.37

SPYl; MGC57218;

cg04786857 GeneID:245711 MGC110856; NM 001008779.1 SPDY1 , 47 88.07

PA 5; KIAA1264;

eg 12388309 GenelD: 57144 MGC26232; NM 177990.1 PAK7 48 89

FU90440;

eg 11625946 GenelD: 121227 KIAA3016; NM 153377.3 LRIG3 49 90.27

ASMD; FKHL12;

cgl8815943 GeneID:2301 FREAC8; NM 012186.1 FOXE3 50 90.7 cql7371081 GeneID:4745 NRP1; IDH3GL; NM_006157.2 NELL1 51 91.3

PNPLA3;

C22orf20;

cgl3184872 GeneID:80339 iPLA(2)epsilon; NM_025225.2 ADPN 52 92.97

ARIX; FEOM2;

NCAM2; PMX2A;

CFEOM2;

CQ08876932 GeneID:401 MGC52227; NM 005169.2 PHOX2A 53 94

PPD; KTCN; PPCD;

cg06151165 GeneID:30813 RINX; NM 014588.4 VSX1 54 94.8

HRMT1L3;

cg23739862 GenelD: 56341 HRMT1L4; NM 019854.3 PRMT8 55 94.8

C6orfl02;

MGC33317;

dJ137F1.4;

dJ188D3.1;

cql8191162 GeneID:221458 dJ1043E3.1; NM 145027.3 KIF6 56 95.37

FRP; FRP1; FrzA;

eg 15839448 GeneID:6422 FRP-1; SARP2; NM 003012.3 SFRP1 57 96.3 SVM+

SVM+ TTEST

TargetID GENE ID SYNONYM ACCESSION SYMBOL TTEST AVG RNK eg 10639440 GeneID:9805 SESl; KIAA0193; NM 014766.2 SCRN1 58 96.63

TYP; HVH2; MKP2;

eg 18070061 GeneID: 1846 MKP-2; NM_057158.2 DUSP4 59 97.17 cg01805282 GeneID:2070 C D1J; DFNA10; NT 025741.14 EYA4 60 97.37

CQ24659201 GeneID: 2863 NM_0O1508.1 GPR39 61 98.5

MRCKB;

cg08999486 GenelD: 9578 IAA1124; NM 006035.2 CDC42BPB 62 99.63 cg22734085 GenelD: 152641 NM_153008.3 FU30277 63 101.67 eg 10644361 GenelD: 145282 DKFZp313M2036; NM_138731.2 M1POL1 64 102.2

FU22390; RP11- cg02265318 GeneID:64757 295M18.1; NM 022746.2 MOSC1 65 105.7 cg23196831 GeneID:7373 UND; NM_021110.1 COL14A1 66 106 cg04180868 GenelD: 56952 HHGP; FU11888; NM 020200.5 PRTFDC1 67 106.43

RHGK; PDRC2;

eg 10453365 GenelD: 51458 C15orf6; NM_016321.1 RHCG 68 108.37

DPDE4; PDEIVB;

MGC126529;

cg26963271 GeneID:5142 DKFZp686F2182; NM 002600.2 PDE4B 69 110.3

KIF3X; IF17B;

cql5613048 GenelD: 57576 KIAA1405; NM_020816.1 KIF17 70 111.13

DZIP; DZIPtl;

KIAA0996; RP11- cg00756058 GeneID:22873 23E3.3; NM 198968.1 DZ1P1 71 112.63 cg26831415 GeneID:286097 N .181723.1 EFHA2 72 113.43

PUM; MYF3;

cg07271264 GeneID:4654 MYOD; NM_002478.3 MYOD1 73 113.5

MGC47816; RP11- cgl6534233 GeneID:284716 157D18.1; NM_173642.1 FAM80A 74 113.93

CQ11965370 GenelD .50863 NTM; MGC60329; NM 016522.2 HNT 75 115.47 cg06222851 GeneID:55753 NM_018245.1 OGDHL 76 118.7 cg08900043 GeneID:3226 H0X3I; MGC5259; NM_017409.2 HOXC10 77 118.9 cq26252167 GenelD: 2830 NM_005284.2 GPR6 78 122.1 cg04534765 GeneID:2587 GALNR; GAINR1; NM_001480.2 GALR1 79 123.9

TAFA4; TAFA-4;

cg23967169 GenelD: 151647 FU25161; NM_182522.3 FAM19A4 80 126.07

MGC11138;

cg09892203 GenelD: 27092 MGC24983; NM 014405.2 CACNG4 81 126.1

KIAA0790;

(U323M4.1; RP3- cg09381701 GenelD. -23328 323M4.1; NM 015278.3 SASH1 82 126.6

NACT;

MGC138356;

cq22040627 GeneID:284111 DKFZD686E17257; NM 177550.2 SLC13A5 83 127.67 cq21688264 GenelD: 9892 AP180; KIAA0656; NM 014841.1 SNAP91 84 128.67 eg 11846956 GenelD: 5655 NES1; PRSSL1; NT_011109.15 KLK10 85 130.

E T; EMTH;

C007237939 GenelD: 6581 0CT3; NM 021977.2 SLC22A3 86 130.6

SCS; ACS 3;

BPES2; BPES3;

cg20052718 GeneID:7291 TWIST; NT_007819.16 TWIST1 87 133.57

GDNFR; RET1L;

RETL1; TRNR1;

GDNFRA;

MGC23045; GFR- cq 12087643 GeneID:2674 ALPHA-1; NM 005264.2 GFRA1 88 134.23 cq22594309 GenelD: 127833 Sytll; NM_177402.3 SYT2 89 136.03 SVM+

SVM+ TTEST

TargetID GENEJD SYNONYM ACCESSION SYMBOL TTEST AVG RNK

CYPIN; GUANASE;

MGC9982;

NEDASIN;

C908768421 GeneID:9615 KIAA1258; NM_004293.2 GDA 90 137.97

BOCT; ΒΟΓΤ;

cgO 1557297 GeneID:51310 hBOU; NM 020372.2 SLC22A17 91 139.57

FU11006;

cg23752923 GenelD: 55283 MGC71509; NM 018298.9 MCOLN3 92 142.13

NEOT2; FU10430;

FU14724;

cg02755525 GenelD:81831 FU90456; NM 018092.3 NET02 93 143.37

CED6; GULP; CED- cg06187947 GeneID: 51454 6; NM_016315.2 GULP1 94 143.6 eg 19709625 GenelD: 26577 PCPE2; NM_013363.2 PCOLCE2 95 145 cg08016383 GenelD: 170691 FU32769; NM 139057.1 ADAMTS17 96 145.23

PCDHY; PCDH22;

cg22415432 GenelD: 83259 PCDH11X; NM 032971.1 PCDHl lY 97 145.87

FBN; SGS; WMS;

MASS; MFS1;

cql8671950 GeneID:2200 OCTD; fibrillin; NM 000138.2 FBN1 98 145.97

MAPS; FUTSCH;

cq07380496 GeneID:4131 DKFZp686F1345; NM 032010.1 MAP1B 99 146.03

Orf4; C7orf8;

CORTBP2;

IAA1758;

cg27603796 GeneID:83992 MGC104579; NM 033427.1 CTTNBP2 100 147.6

TABLE 13d

Relapsed Leukaemia RFE-SVM Top 100

Rank RFE Symbol Accession

1 cg22903370 SSR4 NM 006280.1

, 2 cgl 3060154 DAB2IP NT 008470.18

3 cg00915289 CKS1B NM 001826.1

4 cg07643942 LACRT NM 033277.1

5 eg 13494498 TUBB3 NM 006086.2

6 cg04601 137 ADAMTSL5 NM 213604.1

7 cgl7704839 UBL5 NM 024292.2

8 cg04576021 HLA-DOB NM 002120.2

9 eg 19784477 TFPI2 NT 007933.14

10 cg02525396 TOMM40L NM 032174.3

11 cgOOl 12517 PPP1R1B NM 032192.2

12 cg05944800 INSIG2 NM 016133.2

13 cg05781767 PODXL2 NM 015720.1

14 cgl9948393 ANKRD33 NM 182608.2

15 e 15540820 EOMES NM 005442.2

16 cg00231140 ACAA2 NM 00611 1.1

17 cg06585893 CAPN9 NM 006615.2

18 cg07679836 BAK1 NM 001188.2

19 cgl861 1122 LASS2 NM 013384.3

20 cg09276451 SLITL2 NM 138440.1

• 21 cg09352789 XPNPEP1 NM 020383.2

22 cgl 1523020 UPK3A NM 006953.2

23 cg02915544 PSMAL NM 153696.2

24 cg26134665 STX1B2 NM 052874.1

25 cg09907395 KIAA1618 NM 020954.2

26 cg22933847 MRGPRF NM 145015.2

27 cg06836736 ME1 NM 002395.2 Rank RFE Symbol Accession

28 cg24745738 EDNRB NT 024524.13

29 cg24924779 KCNG1 NM 002237.2

30 cg23022999 FLJ45909 NM 198445.1

31 cg06600447 MGC2749 NM 024069.2

32 cg23509869 LST1 NM 205838.1

33 cgOl 172972 ZFYVE9 NM 004799.2

34 eg 16075940 FLJ37396 NM 173671.1

35 cg20903926 Clorfl77 NM 152607.1

36 cg27390220 RIS1 NM 015444.1

37 cg09076012 NOV NM 002514.2

38 eg 12167564 LYST NM 000081.2

39 cgl 5823100 PISD NM 014338.3

40 cg08013810 PHF20 NM 016436.3

41 eg 17983307 D FZp434B1231 NM 178275.3

42 egl 0602543 H19 NT 009237.17

43 cg23850212 ZFP28 NM 020828.1

44 cg00727590 PLA2G3 NM 015715.2

45 cg20849549 PTEN NM 000314.3

46 cg26271255 ARHGAP12 NM 018287.4

47 cgl5198335 MGC4562 NM 133375.2

48 egl 1714502 A 1 NM 000476.1

49 cg06615667 GPR107 NM 020960.2

50 cg0431 1964 LYPD2 NM 205545.1

51 cg02829654 LYST NM 000081.2

52 cg21616088 FBXL4 NM 012160.3

53 cg04794887 LETM1 NM 012318.1

54 egl 3277939 CTAGE5 NM 203357.1

55 cgl2108912 MGCI0993 NM 030577.1

56 cg25488547 CHM NM 000390.1

57 cg23282559 KL NM 004795.2 Rank RFE Symbol Accession

58 cgl0139846 INADL NM 005799.2

59 cg04713352 ATP4A NM 000704.2

60 cg06034822 USF1 NM 007122.3

61 cg04528819 LF14 NM 138693.1

62 cg05473677 OSTalpha NM 152672.3

63 cg07533148 TRIM58 NM 015431.2

64 cgl5819171 FLJ23588 NM 022785.2

65 cg20644981 RPS3A NM 001006.3

66 cg23640903 GFOD1 NM 018988.1

67 cg05225390 ZW10 NM 004724.2

68 cgl4986420 YIPF6 . NM 173834.2

69 cg22578204 TIMP3 NT 01 1520.1 1

70 cgOl-612158 FCRL4 NM 031282.1

71 cg07816439 C1QTNF2 NM 031908.3

72 cg08578641 DNA11 NM 012144.2

73 eg 17687282 A4GNT NM 016161 :1

74 cgO 1929065 CD63 NM 001780.3

75 cg09554443 CD3Z NM 000734.2

76 cg22320000 HNRPU NM 031844.2

77 egl 9154438 CKM NM 001824.2

78 cg06521852 HRIHFB2122 NM 138632.1

79 cg21096399 MCAM NM 006500.2

80 eg 12359315 SLC6A20 NM 020208.2

81 cg2551 1429 NR 1 NM 016588.2

82 egl 5129294 CCL4L2 NM 207007.2

83 cg02579736 FANCD2 NM 033084.3

84 cgl5534366 CDH4 NM 001794.2

85 cg05409038 TEX26I NM 144582.2

86 cg04498679 C2 NM 000063.3

87 cg02489552 FLJ40365 NM 173482.1 Rank RFE Symbol Accession

88 cg26145228 NTNG1 NM 014917.2

89 cg27342122 PHF20L1 NM 024878.1

90 cg07753583 LRRC61 NM 023942.1

91 cgl9722847 IP08 NM 006390.2

92 cg00840516 HYAL2 NM 003773.2

93 cg01816336 MINPP1 NM 004897.2

94 e 14904464 NELL2 NM 006159.1

95 cgl5415507 EPHA2 NM 004431.2

96 cgl3156411 PTHR1 NM 000316.2

97 eg 11402505 FBX027 NM 178820.3

98 cg26135716 GLUL NM 001033056.1

99 cgOl 875775 CHEK2 ^'> NM 007194.3

100 cgl9103609 P N1 NM 002741.3

TABLE 13e

Relapse Leukaemia Centroid Top 100

Rank Centroid Symbol Accession

1 cg00915289 CKS1B NM 001826.1

2 cg22903370 SSR4 NM 006280.1

3 eg 13060154 DAB2IP NT 008470.18

4 eg 19784477 TFPI2 NT 007933.14

5 egl 5540820 EOMES NM 005442.2

6 cg02915544 PSMAL NM 153696.2

7 cg04601137 ADAMTSL5 NM 213604.1

8 cg04576021 HLA-DOB NM 002120.2

9 cgO 1 172972 ZFYVE9 NM 004799.2

10 cg07679836 BAK1 NM 001188.2

11 cg24697031 EVPL NM 001988.1

12 cgl315641 1 PTHR1 NM 000316.2

13 eg 16902509 ITGA8 NM 003638.1

14 eg 18384097 PTPN7 NM 002832.2

15 eg 15823100 PISD NM 014338.3

16 cg25619607 IMPACT NM 018439.1

17 e 13494498 TUBB3 NM 006086.2

18 e 10602543 H19 NT_009237.17

19 cg06912252 C9orfl25 NM 032342.1

20 cg24924779 CNG1 NM_002237.2

21 cg26619317 CNN3 NM 001839.2

22 cg07643942 LACRT NM 033277.1

23 cgl9154438 C M NM 001824.2

24 cg22954818 APOBEC3A NM 145699.2

25 cg23022999 FLJ45909 NM 198445.1

26 cg08602689 KDELC1 NM 024089.1

27 egl 5958424 ACPP NM 001099.2 Rank Centroid Symbol Accession

28 cgl l 812218 GHSR NM 198407.1

29 cgl2167564 LYST NM 000081.2

30 cgl4611174 SIX6 NM 007374.1

31 cg08578641 DNAI1 NM 012144.2

32 cgl2108912 MGC 10993 NM 030577.1

33 eg 12291552 GOLPH4 NM 014498.2

34 eg 13277939 CTAGE5 NM 203357.1

35 cgl5383392 CTTN NM 005231.2

36 egl 1714502 A 1 NM 000476.1

37 cgl5819171 FLJ23588 NM 022785.2

38 egl 5534366 CDH4 NM 001794.2

39 cgl4155416 L3MBTL4 NM 173464.1

40 cg20352798 ZP3 NM 007155.4

41 cg21096399 MCAM NM 006500.2

42 egl 8755783 SPG20 NM 015087.3

43 cg01816336 MlNPPl NM 004897.2

44 cg22231902 EN1 NM 001426.2

45 cg25956985 MAP3K7IP1 NM 153497.2

46 eg 15669092 TUBB3 NM 006086.2

47 cg06585893 CAPN9 NM 006615.2

48 cg08013810 PHF20 NM 016436.3

49 cg07909265 ITGAV NM 002210.2

50 cg27360560 TMEM67 NM 153704.3

51 cgl4904464 NELL2 NM 006159.1

52 egl 1956146 CABYR NM 012189.2

53 cg09085198 ANKRD29 NM 173505.2

54 cg08132931 ADD2 NM 017482.1

55 cgl3365761 SCNN1G NM 001039.2

56 cg00727590 PLA2G3 NM 015715.2

57 cg22584138 SLC6A4 NM 001045.2 Rank Centroid Symbol Accession

58 cg03297731 GDPD3 NM 024307.1

59 cgl 1027570 RBP1 NM 002899.2

60 cg08990057 PLS3 NM 005032.3

61 cg23509869 LST1 NM 205838.1

62 cg09907395 KIAA1618 NM 020954.2

63 cg00243313 IRX4 NM 016358.1

64 e 16652063 SLC13A5 NM 177550.2

65 eg 16422907 CCNA1 NM 003914.2

66 cgl7688525 L3MBTL4 NM 173464.1

67 cg07533148 TRIM58 NM 015431.2

68 cg05781767 PODXL2 NM 015720.1

69 cgl0139846 INADL NM 005799.2

70 cg22917487 CX3CR1 NM 001337.3

71 cg07815386 RNF149 NM 173647.2

72 cg02265318 MOSC1 NM 022746.2

73 cg22040627 SLC13A5 NM 177550.2

74 cg0I401376 EYA4 NT 025741.14

75 cgl7471019 T EM67 NM 153704.3

76 cg02525396 TOMM40L NM 032174.3

77 cg09076012 NOV NM 002514.2

78 cg07908874 TUBGCP2 NM 006659.1

79 cg03626672 CMKOR1 NM 020311.1

80 cg07730329 PCDHGA12 NM 003735.2

81 cg09276451 SLITL2 NM 138440.1

82 cg05944800 INSIG2 NM 016133.2

83 cg05473677 OSTalpha NM 152672.3

84 cg09352789 XPNPEP1 NM 020383.2

85 cg02724472 LRRC17 NM 005824.1

86 cg24745738 EDNRB NT 024524.13

87 cgO 1029592 SOX15 NM 006942.1 Rank Centroid Symbol Accession

88 cg 10006582 BBS5 NM 152384.2

89 cgl 1647681 PCDHGA12 NM 003735.2

90 cg23719124 GSTM5 NM 000851.2

91 cgl 6731240 ZNF577 NM 032679.1

^' 92 cg07136254 HFE2 NM 145277.3

93 cg238502I2 ZFP28 NM 020828.1

94 cg22970435 SPATS 1 NM 145026.2

95 cg23493704 SLC10A3 NM 019848.2

96 cg06615667, GP 107 NM 020960.2

97 cg05938725 NUAK2 NM 030952.1

98 cg06836736 ME1 NM 002395.2

99 cg05368341 SYT6 NM 205848.1

100 eg 10517312 RNF36 NM 080745.2

EXAMPLE 8

Generation of primers

[0145] The nature of bisulphite treatment is that it results in a homogenous mixture of highly converted templates to ones that are not converted at all. To favor amplification of the fully converted templates, primers are chosen which this taken into account.

[0146] The + and - strands are not complementary and thus primers are designed for one strand and it is usually the plus strand as given by the text editor.

[0147] The criteria for primer selection includes:

• Primer sets amplifying no more than a maximum of 450bp.

• At least 30% C within the primer.

· A primer that selectively amplifies bisulfite converted templates.

• An even distribution of bases prior to bisulfite conversion.

• As high GA content as possible.

• A lack of CpG residues and if this can not be avoided then degeneracy at the possibly methylated cytosine residue.

· A long primer (25-30mer) to ensure uniqueness of the primer.

• Have a nested set to do the second round of PCR. Nested or hemi-nested primer is fine.

• T_m calculations to be equivalent for primer pairs. Input primer sequence into Primer Express to check the Tm. Primer express uses nearest neighbour method to calculate Tm. Match primer Tm. If they can not be matched then that can not be helped. Set Tm of the PCR reaction to be 2°C lower than the lowest Tm of the primer set. T_m calculation between Primer Express and NetPrimer are different. EXAMPLE 9

MRD assay development

[0148] Assays for MRD of methylation markers FOXE3, TLX3 (and others) identified by Illumina Infinium Methylation Array (e.g. RIMS4, OGDHL, LVRN, NK2-X2, THRB, MSX2 and GSX1) are developed.

[0149] Robust and sensitive for MRD assays are developed using MS-PCR, Methylight, HRM in addition to SEQUENOM for DNA methylation detection at the promoter regions of these genes.

[0150] Methods are tested for methylation detection which include enrichment strategies for methylated DNA and depletion studies excluding the normal non-methylated DNA. [0151] The limits of detection and sensitivity of these assays are assessed and evaluated.

EXAMPLE 10

TEL/AML subtype [0152] Genes are analyzed by SEQUENOM assay in all TEL/AML(ETV6/RUNX 1 ) samples (tumor and remission) available in the cohort.

[0153] The assays are extended to other subtypes of leukemia using the top candidates found in TEL/AML study, beginning with normal karyotype and hyperdiploidy.

[0154] For prognosis, methylation changes are identified in samples that predict relapse in leukemia and/or poor long term outcome.

.

[0155] Other cohorts of TEL/AML samples are also tested. EXAMPLE 11

Normal baseline controls

[0156] CD34+ bone marrow cells (stem cell lineages) are obtained and DNA Methylation profiles are determined by Illumina Infinium. This is a more appropriate control to use and false positive hits can be removed for DNA methylation associated with normal development.

[0157] In vitro culture methods of the CD34+ cells extracted from donors are also developed.

[0158] Non-leukemia bone marrow samples are sourced to obtain controls to compare against the tumor samples. ^' EXAMPLE 12

Subtypes of leukemia

[0159] Other subtypes of childhood leukemia defined by genetic aberrations including hyperdiploidy, normal karyotype and T-cell leukemias in the cohort are profiled by Infinium. Results are validated by SEQUENOM.

[0160] The Infinium datasets are combined to truly identify pan-leukemia DNA methylation markers that could be used for MRD. [0161] In addition, cases are profiled which had a poor outcome, poor response to therapy, died from disease and/or relapsed to identify prognostic markers of relapse (in addition to MRD) in paediatric leukemia. EXAMPLE 13

Cancer types

[0162] DNA Methylation is profiled by Illumina Infinium from samples from other adult leukemia including AML and MDS and compared back to childhood leukemia dataset.

[0163] Methylation markers identified in TEL/AML dataset across model cancer cell lines of various tissue types including leukemia, colon cancer, breast cancer and the like are screened. Non-cancer cell lines are used as controls. Primary samples from patients with other cancers are then screened.

EXAMPLE 14

Functional Biology [0164] CD34+ cells are cultures in vitro and allowed to differentiate towards the two lineages (T-cell and B-cell), cells are captured at different stages of development and DNA Methylation profiled by Illumina Infinium.

[0165] Cells with gene promoters identified here as being methylated are treated with epigenetic modifying drugs including decitabine, zebularine in conjunction with other drugs currently used as well as those which modulate histone modifications to determine if these drugs can alter the aberrant DNA methylation.

[0166] DNA methylation markers are backtracked to identify markers present in samples taken at birth. EXAMPLE 15

Diagnostic capabilities of DNA methylation

Methylation diagnostics

[0167] To establish the utility of DNA methylation signatures as diagnostic targets, DNA from patient bone marrow aspirates throughout their therapeutic regime are initially bisulfite converted to establish if DNA methylation targets can correctly track disease within these patients. DNA methylation targets have potential in diagnosis and disease monitoring especially within patients, such as those with normal karyotype or which lack PCR amplifiable targets which are patient specific, thus requiring a large amount of work up. TLX3 and FOXE3 are defined and validated to be exclusively hypermethylated in ALL. Methylation of these genes is associated with poor outcome by normal karyotype patients. [0168] Results indicate DNA methylation can diagnose and track disease throughout therapy.

[0169] Completely methylated and unmethylated artificial oligonucleotides are created, designed from the TLX3 and FOXE3 sequences. The SEQUENOM EpiTYPER methylation results for the standards were comparable to the restriction digest and Liquid Chromatography Mass Spec (LC-MS) results from NMI. Gravimetric dilution (accurate dilution method where samples are weighed between each dilution) of the standards highlighted the analytical sensitivity of the EpiTYPER cleavage chemistry (approximately 5%).

[0170] In an attempt to increase the analytical sensitivity of the MALDI-TOF system to make it applicable to disease detection such as MRD detection where current techniques are capable of detecting down to 1 in 10,000 cells, SEQUENOM iPLEX primer extension methodology was investigated. However, the approach was modified, biasing the extension over the methylated template through incorporation of only three nucleotides, absent was the nucleotide required for extension of the unmethylated template (shown in Figure 5). This is known as single allele base extension reaction (SABER), developed for SNP detection.

[0171] Using the SABER appraoch methylation was detected in 0.01% methylated template in an unmethylated background above that of completely unmethylated template representing an analytical sensitivity comparable to current MRD techniques.

EXAMPLE 16

Detection of leukemia in patients

[0172] An unsupervised clustering heatmap plot of the DNA methylation beta-values from the Illumina Infinium HumanMethylation27 BeadArray of 1 15 ALL-specific probes identified using three supervised learning methods (x-axis) is shown in Figure 6. Three distinct clusters comprising the leukemia, remission/non-leukemic and the cell lines are apparent (y-axis). The majority of these probes are hypermethylated in all leukemic samples analyzed. These clusters remained when all 14,876 probes, retained after p- detection cutoff, were taken into account (Figure 7).

[0173] A heatmap plot of SEQUENOM EpiTYPER DNA methylation results generated from 85 cases of B-Cell ALL with matching leukemic and remission bone marrow samples, controls (DONOR) and cancer cell line (REH) is shown in Figure 7. DNA methylation data for a total of 103 CpG sites encompassing 16 probes were selected by Infinium analysis are shown here (x-axis). DNA hypermethylation of 77/85 (91%) leukemic bone marrow samples was observed regardless of ALL subtype confirming the existence of a DNA methylation signature associated with leukemia.

[0174] Figure 8 shows that ALL is associated with an increase in average promoter methylation. Average global promoter DNA methylation levels of bone marrow according to sample group. The overall average b-value of probes that passed stringent quality control for all samples within a sample group are displayed. [0175] Unsupervised clustering of Infinium β-values of 14,876 probes accurately delineating disease free tissue from leukemia is shown in Figure 9. Heatmap plot of unsupervised hierarchical clustering of the beta- values of 14,876 probes passing stringent quality control.

[0176] Figure 10 is a representation showing the performance and the average area under the receiver operating characteristics (ROC) curve (AROC) curves of the supervised learning methods applied to the data for DNA methylation profiling. Centroid,, RFE-SVM and LIMMA plots are depicted (Figures 10a throuh c, respectively). The average accuracy of classification (accuracy) and the average area under the receiver operating characteristics (ROC) curve (AROC) are plotted against the number of features included in the classification. Accuracy and aroc reached 100% after 16 features for the Centroid method. Whilst the same accuracy and AROC level was achieved in less than 4 features using RFE-SVM and LIMMA.

[0177] Unsupervised hierarchical clustering heatmap of SEQUENOM EpiTYPER results is shown in Figure 11. A total of 163 paediatric leukemia cases were analyzed at three loci, Corf 6, FBX039 and MYOD1, for DNA methylation. Matching leukaemic and remission bone marrow samples were analyzed from each case. Leukemia subtype for each diagnosed case is also depicted and illustrates a hypermethylation signature associated with leukaemic bone marrow.

[0178] The results from the modified MALDI-TOF technique is shown in Figure 12. Methylation is a measure of the of amount of extension primer that has been extended (extension occuring on methylated template) over the total extension primer input into each reaction. EXAMPLE 17

Expression association and demethylating agent interrogation

[0179] Bone marrow is processed from ALL diagnosed patients forming a cohort. These cells are stored viably and create the basis for cell line culturing, and subsequent expression interrogation and demethylating agent analysis.

[0180] Those skilled in the art will appreciate that aspects of aspects described herein are susceptible to variations and modifications other than those specifically described. It is to be understood that these aspects include all such variations and modifications. These aspects also include all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of the steps or features.

BIBLIOGRAPHY

Bernhard and Smola, Learning with kernels MIT Press, 2002

Bibikova et al, Genome Research 75:383-393, 2006

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Flushberg et al, Nat Methods 7^:461-465, 2010

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Nygren et al, Nucleic Acids Res. 33(14):e\2S, 2005 Rein et al, Nucleic Acids Res. 26:2255, 1998 Tost et al, Nucleic Acids Res 31(9):e50, 2003 Wojdacz et al, Nucleic Acids Res. 35(6):e4l, 2007

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Claims

CLAIMS:

1. A method for identifying an epigenetic profile in the genome of a cell in a subject indicative of a cancerous condition or a predisposition thereto, said method comprising screening for a change relative to a control in the epigenetic profile within or proximal to a locus selected from one or more loci associated with (i) cell fate commitment; (ii) transcription factor activity; (iii) DNA binding; (iv) subcellular location; and (v) transcription regulator activity or in 1 to 16 regions within one or more of these loci wherein a change in epigenetic profile in or proximal to one or more loci or regions therein is indicative of the presence of a cancerous condition or a propensity to develop same.

2. The method of Claim 1 wherein the loci are selected from those listed in Table 2.

3. The method of Claim 1 wherein the loci are selected from those listed in Table 3.

4. The method of Claim 1 wherein the epigenetic profile is extent of methylation or distribution of methylation sites.

5. The method of Claim 1 wherein the number of loci or regions within loci selected is determined by the percentage level of confidence required to determine which subject has or does not have cancer.

6. The method of Claim 5 wherein if a 100% confidence level is required, the epigenetic profile is determined in 16 different loci or in 16 regions within one or more loci.

7. The method of Claim 1 wherein the cancerous condition is a hematological cancer.

8. The method of Claim 1 wherein the cancerous conditions is a leukemia or sub-type thereof. . ·

9. The method of Claim 8 wherein the leukemia is an early age onset leukemia.

10. The method of Claim 8 wherein the leukemia is selected from acute lymphatic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), small lymphocytic lymphoma (SLL), chronic lymphocytic leukemia (CLL), acute monocytic leukemia (AMOL), Hodgkin's lymphona and non-Hodgkin's lymphona.

1 1. The method of any one of Claims 1 to 10 wherein the epigenetic profile is determined in DNA or RNA from a cell extract.

12. The method of any one of Claims 1 to 10 wherein the epigenetic profile is determined in DNA or RNA present in a sample selected from the list consisting of urine, pus, respiratory fluid, lymph fluid, feces, bile, saliva, sputum, semen, vaginal flow, cerebral spinal fluid, brain fluid, ascites, milk, secretions from the genitourinary tract and a lavage of a tissue or organ (e.g. a lung).

13. The method of any one of Claims 1 to 12 wherein the method identifies a malignant cell at a frequency down from one cell per 10⁸ to one cell per 10⁴ cells.

14. The method of Claim 13 wherein the frequency is the minimal residual disease (MRD) value.

15. The method of any one of Claims 1 to 14 wherein the epigenetic profile is used to monitor treatment of the cancerous condition.

16. A method for determining the epigenetic profile within the genome of a eukaryotic cell or group of cells, the method comprising obtaining a sample of genomic DNA or RNA from the cell or group of cells and subjecting the genomic DNA or RNA to bisulphite treatment and then primer-specific amplification within one or more regions of one or more loci listed in Table 2 or 3 and assaying for extent of methylation relative to a control.

17. The method of Claim 16 wherein the epigenetic profile is extent of methylation or distribution of methylation sites.

18. The method of Claim 16 wherein the epigenetic profile is extent of methylation or distribution of methylation sites.

19. The method of Claim 16 or 17 or 18 wherein the number of loci or regions within loci selected is determined by the percentage level of confidence required to determine which subject has or does not have cancer.

20. The method of Claim 16 wherein the cancerous condition is a hematological cancer.

21. The method of Claim 16 wherein the cancerous condition is a leukemia or sub-type thereof.

22. The method of Claim 21 wherein the leukemia is an early age onset leukemia.

23. The method of Claim 16 wherein the leukemia is ALL, AML, CLL, SLL, CML, AMOL, Hodgkin's lymphona and non-Hodgkin's lymphona.

24. The method of Claim 16 wherein the epigenetic profile is determined in DNA or RNA from a cell extract. _^

25. The method of Claim 16 wherein the epigenetic profile is determined in DNA or RNA present in a sample selected from the list consisting of urine, pus, respiratory fluid, lymph fluid, feces, bile, saliva, sputum, semen, vaginal flow, cerebral spinal fluid, brain fluid, ascites, milk, secretions from the genitourinary tract and a lavage of a tissue or organ (e.g. a lung).

26. The method of Claim 16 wherein the epigenetic profile is used to monitor treatment of the cancerous condition.

27. A kit for the use in the method of any one of Claims 1 to 26 comprising bisulphite treatment of genomic DNA and primers to amplify a site within one or more loci before or after bisulfite treatment listed in Table 2 or 3 for DNA methylation analysis.