WO2015021263A2 - Methylation biomarkers for colorectal cancer - Google Patents

Abstract

The present invention provides compositions and methods for the diagnosis of colorectal cancer in a subject. In one embodiment, the method includes detecting methylation status of one or more genes in a sample to diagnosis the subject. The invention further relates to DNA methylation as a predictor of disease recurrence and patient prognosis, specifically in the field of cancer biology.

Description

TITLE OF THE INVENTION

METHYLATION BIOMARKERS FOR COLORECTAL CANCER

STATEMENT REGARDING FEDERALLYSPONSORED RESEARCH OR

DEVELOPMENT

This invention was made with government support under T32 CA 103652-05 awarded by National Institutes of Health (NIH). The government has certain rights in the invention.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Serial

No. 61/975,323, filed April 4, 2014, and to U.S. Provisional Application Serial No.

61/863,682, filed August 8, 2013, the contents of each of which are incorporated by reference herein in their entireties.

BACKGROUND OF THE INVENTION

Nearly one million people worldwide develop colorectal cancer (CRC) every year (Moghaddam et al, 2007, Cancer Epidemiol Biomarkers Prev 16:2533- 47). CRC results from a combination of environmental and genetic factors that convert normal epithelium into a malignant tumor through a series of stages. An understanding of the early events in tumorigenesis will lead to timely diagnoses and improved outcomes.

Epigenetic changes are early events in CRC and other neoplasias. Epigenetics can include methylation status of a gene. There are numerous genes that have been reported with methylation differences between colorectal tumors and adjacent tissues (Goel and Boland, 2012, Gastroenterology 143: 1442-60; Al-Sohaily et al, 2012, J Gastroenterol Hepatol 27: 1423-31; Li et al, 2013, PLoS ONE

8:e59064, for examples). However, there are limited numbers of studies showing differential methylation in normal colonic mucosa between controls and CRC patients (Shen et al, 2005, J Natl Cancer Inst 97: 1330-38; Milicic et al, 2008, Cancer Res 68:7760-68; Silviera et al, 2012, Cancer Prev Res 5:374-84).

There is, therefore, a pronounced need in the art for novel compositions and methods for detecting and distinguishing CRC. The present invention satisfies this need. SUMMARY OF THE INVENTION

The present invention provides a method of diagnosing colorectal cancer in a subject. In one embodiment, the method comprises: determining the level of methylation of a biomarker in a biological sample of the subject, comparing the level of methylation of the biomarker in the sample of the subject with a comparator control, and diagnosing the subject with colorectal cancer when the level of methylation of the biomarker in the sample of the subject is altered at a statistically significant amount when compared with the level of methylation of the biomarker of the comparator control.

In one embodiment, the biomarker is one or more biomarkers set forth in Tables 6, 10, 1 1, 12, 13, 14, 15, and 16.

In one embodiment, the biomarker is selected from the group consisting ofPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, 5^*55, and any combination thereof.

In one embodiment, the biomarker is selected from the group consisting ofPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, SB5, and any combination thereof.

In one embodiment, when the level of methylation of a biomarker is decreased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting oiPDK4, NR1H4, BCMOl, and any combination thereof.

In one embodiment, when the level of methylation of a biomarker is increased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting of PYCARD, SPRR2A, SPRR1A, AIM2, SB 5 and any combination thereof.

In one embodiment, when the level of methylation of a biomarker is increased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting of INS, LGALS2, ANKRD15, VHL, EDA2R, NMUR1, GRB10, and any combination thereof.

In one embodiment, the level of methylation of the biomarker is measured by detecting the methylation of the biomarker comprising detecting the methylation of CpG sequences in the gene or related regulatory sequence of the biomarker. In one embodiment, the level of methylation of the biomarker is measured by a method selected from the group consisting of PCR, methylation- specific PCR, real-time methylation-specific PCR, PCR assay using a methylation DNA-specific binding protein, quantitative PCR, DNA chip-based assay,

pyrosequencing, and bisulfate sequencing.

In one embodiment, the CpG sequences are located in a region selected from the group consisting of upstream of coding sequences, in the coding regions, in enhancer regions, in intron regions, downstream of coding sequences, and any combination thereof.

In one embodiment, the comparator control is the level of the biomarker in the sample of a healthy subject.

In one embodiment, the comparator control is at least one selected from the group consisting of a positive control, a negative control, a historical control, a historical norm, or the level of a reference molecule in the biological sample.

In one embodiment, the method further comprises the step of treating the subject for the diagnosed colorectal cancer.

In one embodiment, the subject is a human.

The invention also provides a kit for diagnosing colorectal cancer. In one embodiment, the kit comprises a reagent for measuring the level of methylation of a biomarker in a biological sample of the subject wherein the biomarker is selected from the group consisting ofPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, SB5, and any combination thereof.

The invention also provides a method of treating a subject diagnosed with colorectal cancer. In one embodiment, the method comprises diagnosing colorectal cancer in a subject and administering an anti-cancer therapy to the subject in need thereof, wherein diagnosing colorectal cancer in a subject comprises:

determining the level of methylation of a biomarker in a biological sample of the subject, comparing the level of methylation of the biomarker in the sample of the subject with a comparator control, and diagnosing the subject with colorectal cancer when the level of methylation of the biomarker in the sample of the subject is altered at a statistically significant amount when compared with the level of methylation of the biomarker of the comparator control.

BRIEF DESCRIPTION OF THE DRAWINGS For the purpose of illustrating the invention, there are depicted in the drawings certain embodiments of the invention. However, the invention is not limited to the precise arrangements and instrumentalities of the embodiments depicted in the drawings.

Figure 1, comprising Figures 1A through IE, is a series of images demonstrating the effect of diet, Mthfr genotype or strain on expression of five genes in murine normal intestine; human orthologs for these genes are examined in Figures 2-5. Gene names are indicated above the graphs; Pdk4 (Figure 1A), Sprr2A (Figure IB), Nrlh4 (Figure 1C), SprrlA (Figure ID) and Pycard (Figure IE). Bars with black and white backgrounds represent data for C57BL/6 (B6) and BALB/c (C) mice, respectively. Values are means ± SEM. *P < 0.05 and **P < 0.005, diet effect; *P < 0.05, genotype effect; and ^&&&P < 0.001, strain effect (two-factor ANOVA). < 0.05, genotype effect for FD mice; and -P < 0.05, diet effect in Mthfr^+I~ mice

(independent t-tests). For Sprrla in Figure ID, two-factor ANOVA indicated a strain x diet interaction. Post hoc Tukey comparisons indicated a significant diet effect in C57BL/6 mice (-P < 0.001) and a significant strain effect in FD mice < 0.001).

Figure 2, comprising Figures 2A through 2E, is a series of images demonstrating that DNA methylation of five genes in normal colonic mucosa discriminates between controls and CRC subjects. DNA methylation was determined for PDK4 (Figure 2A), SPRR2A (Figure 2B), NR1H4 (Figure 2C), SPRR1A (Figure 2D) and PYCARD (Figure 2E) genes. In this figure and in Figure 3, numbering refers to the NCBI36/hgl 8 version of the UCSC Genome Browser

(http://genome.ucsc.edu/). Individual CpGs assayed in the microarray study (Knock et al, 2008, J Nutr 138:653-58) are boxed. Controls (35 individuals) are shown as black bars, CRC patients (35 subjects) as white bars. Values are means ± SEM. *P < 0.05,

**P < 0.01, ***P < 0.005, ****p < 0.001, independent t-tests.

Figure 3, comprising Figures 3 A and 3B, is a series of images depicting CpG methylation for NR1H4 and PYCARD in normal mucosa of controls without or with polyps. CpG methylation was assessed for controls without (n = 18) or with (n = 17) polyps, for NR1H4 (Figure 3 A) and PYCARD (Figure 3B). Controls without polyps are represented by black bars, controls with polyps as white bars.

Values are means ± SEM. *P < 0.05; borderline significant for PYCARD CpGs 16:31121929, 16:31121927 and 16:31121902 (0.07, 0.09 and 0.08, respectively); independent ?-tests.

Figure 4, comprising Figures 4A through 4E, is an image depicting real-time RT-PCR analysis of transcript levels in normal colon mucosa of individual controls and CRC patients for PDK4 (Figure 4A; 23 control, 22 cancer), SPRR2A (Figure 4B; 13 control, 20 cancer), NR1H4 (Figure 4C; 23 control, 20 cancer), SPRR1A (Figure 4D; 10 control, 20 cancer) and PYCARD (Figure 4E; 23 control, 19 cancer). *P < 0.05, **P < 0.005, ***P < 0.001, independent f-tests.

Figure 5, comprising Figures 5A through 5E, is a series of images depicting the establishment of epigenetic signatures of cancer or polyps based on methylation of specific genes in normal colonic mucosa. (Figure 5 A) DNA

methylation in normal intestine may establish a signature for presence of tumors. Unsupervised hierarchical cluster matrix of PDK4, SPRR2A, NR1H4, SPRR1A, and PYCARD according to their respective levels of DNA methylation at the CpGs indicated in Figure 2. The epigenetic profile of 35 patients with tumors (blue boxes on the right) and 35 controls (orange boxes on the right) was assessed by bisulfite pyrosequencing of DNA extracted from normal intestine mucosa. Data from the 11 CpGs with significance at P < 0.01 in Figure 2 were used for this analysis. The blue and orange dashed lines define the limits of the two major sample clusters, with almost exclusive segregation of CRC patients or control samples, respectively.

(Figure 5B) NR1H4 and PYCARD CpG methylation was analyzed as in panel A, but using only the 35 controls, to distinguish between controls with and without polyps. Controls with polyps are shown on the right as yellow boxes, and controls without polyps are shown in red. The asterisks indicate the two controls with hyperplastic polyps. Yellow and red dashed lines depict two large clusters, comprised mainly of individuals with or without polyps, respectively. Dendograms are shown on the left of heat maps. In the heat maps, dark boxes indicate low levels of CpG methylation, bright boxes represent highly methylated CpGs. Cluster 3.0 and Java TreeView vl. l .5r2 software were used to perform hierarchical clustering and to visualize results. Figure 5C is a series of images depicting pyrosequencing for DNA methylation analysis of 14 CpGs in 4 genes using normal colonic mucosa from controls (black bars, n=30) and from CRC patients (white bars, n=30). Thirteen out of the fourteen tested CpGs for the above 4 genes showed significant differences in methylation in normal colonic mucosa when comparing controls with CRC patients. Figure 5D is a series of images depicting pyrosequencing for DNA methylation analysis of 20 CpGs in 6 genes using peripheral blood from controls (black bars, n=16) and from CRC patients (white bars, n=19). Seventeen out of the twenty tested CpGs in the above 6 genes showed significant differences in methylation in peripheral blood when comparing controls with CRC patients. Figure 5E is an image showing heat map of pyrosequencing-based clustering in peripheral blood of 16 controls and 19 CRC patients. The upper cluster of 24 individuals contains 19 CRC patients (79% specificity for CRC). The lower cluster contains 1 1 individuals, all controls. There are no false negatives for CRC in this clustering

Figure 6 is a schematic of a model at the intersection of retinoid and

PPAR metabolism showing some of the genes that were modulated in preneoplastic intestine. The proteins are: RDH (retinaldehyde dehydrogenase), AKR (aldo-keto reductase), BCDOl (beta-carotene dioxygenase 1), ALDH (aldehyde dehydrogenase) and FXR (fames oid-X-receptor); corresponding genes: Rdhl8, Akrlcl3,

Bcmol/BCMOl, Aldhlal and Nrlh4INRlH4.

Figure 7 is an image showing confirmation of the quality of RNA in mouse microarray experiments by denaturing gel electrophoresis in 1% agarose. CD- 1, CD-2, CD-3, CD-4 depict RNA extracted from BALB/c Mthfr^+/+ mice fed CD. FD- 1, FD-2, FD-3, FD-4 depict RNA extracted from BALB/c Mthfr^+/~ mice fed FD. RNA integrity was also verified using an Agilent 2100 Bioanalyzer. RNA Integrity Numbers (RTNs) were between 9.0 and 9.8 (based on the Agilent RIN software algorithm assigning a 1 to 10 integrity scale, with 10 being totally intact and 1 being totally degraded).

Figure 8A is an image depicting representative pyrograms for PDK4. The deduced percentage methylation is shown in the blue frames directly above the pyrograms, in addition to the dispensed sequence and the chromosomal position of interrogated nucleotides (based on the NCBI36/hgl 8 version of the UCSC Genome Browser; http://genome.ucsc.edu/). The loci that were tested in the methylome profiling study are shown in red. Two examples with very different % methylation are shown, as well as a histogram depicting the expected relative signals, at the bottom. The grey bars highlight the nucleotides involved in the assessed CpG dinucleotides and the yellow bars denote negative controls forjudging bisulfite conversion efficiency. These descriptions also apply to Figures 8B-8F. To ensure that our procedures were optimized for variable methylation levels, several human CRC cell lines, in different culture media, were assessed for generation of the results presented in Figures 8A-8F.

Figure 8B is an image depicting representative pyrograms for SPRR2A Figure 8C is an image depicting representative pyrograms for SPRR2A (different CpGs/regions for SPRR2A as depicted in Figure 8B).

Figure 8D is an image depicting representative pyrograms for SPRR1A. Figure 8E is an image depicting representative pyrograms for NR1H4. Figure 8F is an image depicting representative pyrograms for

PYCARD.

Figure 9, comprising Figures 9A and 9B, is a series of images demonstrating the identification of individual genes and functional gene categories with significant expression changes between BALB/c Mthfr^+/~ FD and BALB/c Mthfr^+/+ CD mice. Figure 9A. Two-dimensional scatter plot depicting the comparison of genes expressed by 4 BALB/c, Mthfr^+/~ FD mice versus four BALB/c Mthfr^+/+ CD mice. Average gene expression is represented by dots. Genes on the identity line (diagonal) denote no changes in expression. Cut-offs for 1.4-fold induction and repression are indicated by the two parallel lines above and below the diagonal, respectively. Figure 9B. Categories of genes that are differentially expressed between BALB/c Mthfr^+/- FD mice and BALB/c Mthfr^+/+ CD mice. The 12 IPA-sorted categories with highest ^-values are shown. This analysis is based on the gene list provided in Table 6. Values after the bars indicate the number of genes in each category.

Figure 10, comprising Figures 10A through 10H, is a series of images demonstrating the effect of diet and Mthfr genotype on expression of eight genes in normal intestine of BALB/c mice. Plain and diamond foregrounds indicate BALB/c mice fed CD or FD, respectively (four mice per group); bars with dashed and solid outlines represent Mthfr^+I+ and Mthfr^+I~ mice, respectively. This convention is valid for subsequent figures. Figures 10A-10H show results for eight genes, indicated above the histograms. Values are means ± SEM. All -values were derived from independent i-tests. * < 0.05, **P < 0.01 and ***P < 0.005, diet effects in Mthfr^+/- mice. ^#P < 0.05 and ^###P < 0.005, genotype effects in FD mice. ^&P < 0.05, difference between Mthfr^+I+ CD mice and Mthfr^+I~ FD mice. For Plscr2, the genotype effect reached borderline significance with FD mice (P = 0.054). Diet difference was borderline significant in Mthfr^+/~ mice for Rdhl8 and Ppmel, with P = 0.058 and 0.060, respectively. Borderline significance was also observed between CD^+/+ and FD^+/" mice for the Pdctrem gene (P = 0.055).

Figure 11 is an image depicting correlation of methylation results between quantitative bisulfite-pyrosequencing and the Infinium methylation array. Shown are scatter plots for the B-values obtained from the Infinium microarray (X- axis) and the methylation values obtained by bisulfite-pyrosequencing (Y-axis) for 6 CpGs sites in 12 individuals (6 controls, 6 CRC patients). These six loci are shown in red in Figures 8A-8F. The mean difference between both methodologies is 0.5% methylation. Spearman r = 0.94; linear regression r² = 0.93; P < 0.001.

Figure 12 is a series of images depicting confirmation of DNA methylation differences in normal colon between control subjects and individuals with CRC, in two different cohorts. The left panel for each figure shows the bisulfite- pyrosequencing data for 29 controls compared to 29 CRC patients for the 6 CpGs that are discussed in Figure 11 ; the right panel for each CpG shows results from 12 controls and 24 CRC patients tested for the same CpGs by methylation microarrays. Data from the 12 individuals that were tested in Figure 1 1 are not included in this figure.

Figure 13, comprising Figures 13A and 13B, is a series of images showing methylation of AIM2a. Figure 13A shows a comparison of normal colon from controls and CRC patients. Figure 13B shows a comparison of biopsies from the left side and right side of the normal colon from CRC patients with left and right tumors respectively. The relevance is that both sides show the same methylation pattern, regardless of where the tumor occurred (suggesting that whole colon is a "field"). AIM2 segments a and b are different CpGs/regions of the same gene.

Figure 14, comprising Figures 14A and 14B, is a series of images showing methylation of AIM2b. Figure 14A shows a comparison of normal colon from controls and CRC patients. Figure 14B shows a comparison of biopsies from the left side and right side of the normal colon from CRC patients with left and right tumors respectively. The relevance is that both sides show the same methylation pattern, regardless of where the tumor occurred (suggesting that whole colon is a "field"). AIM2 segments a and b are different CpGs/regions of the same gene.

Figure 15, comprising Figures 15A and 15B, is a series of images showing methylation oiBCMOl in normal colon from controls and CRC patients. Figure 15A shows percent methylation for BCMOl in controls and patients, from pyrosequencing. Figure 15B shows representative pyrograms for BCMOl.

Figure 16, comprising Figures 16A through 16F, is a series of images related to sidedness of the biopsy from controls or sidedness of the biopsy from patients for the following genes: PDK4 (Figure 16A), NR1H4 (Figure 16B), SPRR2A (Figure 16C and 16D), SPRRIA (Figure 16E), and PYCARD (Figure 16F). The graphs depict a combination of right and left mucosa from controls versus right and left mucosa from patients. The graphs also show normal mucosa from patients with left tumors compared to patients with right tumors. The results demonstrate the ability to detect methylation of the desired gene regardless of the sidedness of the biopsy.

Figure 17A) Performance of 18 CpG SVM in validation population training set of 20 cancer cases and 20 controls, Figure 17B) performance of 18 CpG SVM in validation population test set of 4 cancer cases and 4 controls.

Figure 18. Performance of the optimum 39 CpG SVM in classifying cancer patients and controls in the discovery population of patients from our original study (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84). This SVM has been selected from the 66 CpGs interrogated (13) in the 18 candidate genes described in Table 2.

Figure 19. Correlation between methylation levels in normal colon mucosa (Y-axis) and peripheral blood (X-axis) of 15 of the 24 control patients. Figure 19A) INS cg03366382, Figure 19B) LGALS2 cgl 1081833, Figure 19C) ANKRD15 cgl7694279, Figure 19D) VHL cgl6869108. Trend lines were drawn by "lm" function in R.

Figure 20. Performance of SVM using seven CpGs showing correlation between methylation levels in normal colon and peripheral blood in classifying cancer patients and controls in the validation population.

Figure 21. Performance of SVM using seven CpGs showing correlation between methylation levels in normal colon and peripheral blood in classifying cancer patients and controls in the discovery population (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84).

Figure 22. Performance of 17 CpG SVM from Table 1 1 and Figure 17 in classifying cancer patients and controls in the discovery population (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84). Figure 23. Performance of 38 CpG SVM in classifying cancer patients and controls in the discovery population (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84).

Figure 24 is an image showing eight candidates with the cancer/control methylation level distributions. Y-axis shows beta values (fraction of molecules methylated at indicated Illumina ID CpG) for normal colon mucosa from cancer patients (N=30, "1" on the X-axis) and controls (N=18, "2" on the X-axis).

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to compositions and methods for colorectal cancer (CRC) diagnosis, research and therapy, including but not limited to, colorectal cancer markers. In particular, the present invention relates to methylation levels of genes (e.g., in CG islands of the promoter regions) as diagnostic markers and clinical targets for colorectal cancer.

Accordingly, embodiments of the present invention provide compositions, kits, and methods useful in the detection and screening of colorectal cancer. Experiments conducted during the course of development of embodiments of the present invention identified methylation status of certain genes in colorectal cancer. Some embodiments of the present invention provide compositions and methods for detecting such methylated genes. Identification of aberrantly methylated genes is useful in screening, diagnostic and research uses.

In one embodiment, the present invention provides diagnostic and screening methods that utilize the detection of aberrant methylation of genes or promoters. A non-limiting example of genes or promoters encompassed in the invention include, but is not limited to, PDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2 and any combination thereof. In some instances AIM2, includes two regions, referred to as AIM2a or AIM2b, which are different CpGs/regions of the same gene. In some instances, SPRR2A includes two regions, referred to as SPRR2A and SPRR2A-II, which are different CpGs/regions of the same gene

In some embodiments, methylation is altered in one or more of the described genes in patients with colorectal cancer. For example, in some

embodiments, methylation of genes is increased relative to a control sample from a subject that does not have colorectal cancer (e.g., a population average of samples, a control sample, a prior sample from the same patient, etc.). In other embodiments, methylation of genes is decreased relative to a control sample from a subject that does not have colorectal cancer (e.g., a population average of samples, a control sample, a prior sample from the same patient, etc.). Accordingly, the invention in some instances provides a combination of markers for colorectal cancer, wherein some of the markers include decreased methylation of a gene and other markers include increased methylation of a gene.

Definitions

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although any methods and materials similar or equivalent to those described herein can be used in the practice for testing of the present invention, the preferred materials and methods are described herein. In describing and claiming the present invention, the following terminology will be used.

It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

The articles "a" and "an" are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, "an element" means one element or more than one element.

"About" as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass non-limiting variations of ±40% or ±20% or ±10%, ±5%, ±1%, or ±0.1% from the specified value, as such variations are appropriate.

The term "abnormal" when used in the context of organisms, tissues, cells or components thereof, refers to those organisms, tissues, cells or components thereof that differ in at least one observable or detectable characteristic (e.g., age, treatment, time of day, etc.) from those organisms, tissues, cells or components thereof that display the "normal" (expected) respective characteristic. Characteristics that are normal or expected for one cell or tissue type, might be abnormal for a different cell or tissue type.

The terms "biomarker" and "marker" are used herein interchangeably. They refer to a substance that is a distinctive indicator of a biological process, biological event and/or pathologic condition. The term "bisulfite reagent" refers to a reagent comprising bisulfite, disulfite, hydrogen sulfite or combinations thereof, useful as disclosed herein to distinguish between methylated and unmethylated CpG dinucleotide sequences.

The phrase "body sample" or "biological sample" is used herein in its broadest sense. A sample may be of any biological tissue or fluid from which biomarkers of the present invention may be assayed. Examples of such samples include but are not limited to blood, saliva, buccal smear, feces, lymph, urine, gynecological fluids, biopsies, amniotic fluid and smears. Samples that are liquid in nature are referred to herein as "bodily fluids." Body samples may be obtained from a patient by a variety of techniques including, for example, by scraping or swabbing an area or by using a needle to aspirate bodily fluids. Methods for collecting various body samples are well known in the art. Frequently, a sample will be a "clinical sample," i.e., a sample derived from a patient. Such samples include, but are not limited to, bodily fluids which may or may not contain cells, e.g., blood (e.g., whole blood, serum or plasma), urine, saliva, tissue or fine needle biopsy samples, and archival samples with known diagnosis, treatment and/or outcome history. Biological or body samples may also include sections of tissues such as frozen sections taken for histological purposes. The sample also encompasses any material derived by processing a biological or body sample. Derived materials include, but are not limited to, cells (or their progeny) isolated from the sample, proteins or nucleic acid molecules extracted from the sample. Processing of a biological or body sample may involve one or more of: filtration, distillation, extraction, concentration, inactivation of interfering components, addition of reagents, and the like.

In the context of the present invention, the term "control," when used to characterize a subject, refers, by way of non-limiting examples, to a subject that is healthy, to a patient that otherwise has not been diagnosed with a disease. The term "control sample" refers to one, or more than one, sample that has been obtained from a healthy subject or from a non-disease tissue such as normal colon.

The term "control or reference standard" describes a material comprising none, or a normal, low, or high level of one of more of the marker (or biomarker) expression products of one or more the markers (or biomarkers) of the invention, such that the control or reference standard may serve as a comparator against which a sample can be compared. The term "CpG island" refers to a contiguous region of genomic DNA that satisfies the criteria of a "GC Content">0.5. CpG islands are typically, but not always, between about 0.2 to about 1 kb, or to about 2 kb in length.

"Differentially increased levels" refers to biomarker methylation levels includeding which are at least 1%, 2%, 3%, 4%, 5%, 10% or more, for example, 5%, 10%, 20%, 30%, 40%, or 50%, 60%, 70%, 80%, 90% higher or more, and/or 0.5 fold, 1.1 fold, 1.2 fold, 1.4 fold, 1.6 fold, 1.8 fold higher or more, as compared with a control.

"Differentially decreased levels" refers to biomarker methylation levels which are at least at least 1%, 2%, 3%, 4%, 5%, 10% or more, for example, 5%, 10%, 20%, 30%, 40%, or 50%, 60%, 70%, 80%, 90% lower or less, and/or 0.9 fold, 0.8 fold, 0.6 fold, 0.4 fold, 0.2 fold, 0.1 fold or less, as compared with a control.

A "disease" is a state of health of an animal wherein the animal cannot maintain homeostasis, and wherein if the disease is not ameliorated then the animal's health continues to deteriorate. In contrast, a "disorder" in an animal is a state of health in which the animal is able to maintain homeostasis, but in which the animal's state of health is less favorable than it would be in the absence of the disorder. Left untreated, a disorder does not necessarily cause a further decrease in the animal's state of health.

A disease or disorder is "alleviated" if the severity of a sign or symptom of the disease, or disorder, the frequency with which such a sign or symptom is experienced by a patient, or both, is reduced.

The terms "effective amount" and "pharmaceutically effective amount" refer to a sufficient amount of an agent to provide the desired biological result. That result can be reduction and/or alleviation of a sign, symptom, or cause of a disease or disorder, or any other desired alteration of a biological system. An appropriate effective amount in any individual case may be determined by one of ordinary skill in the art using routine experimentation.

As used herein "endogenous" refers to any material from or produced inside the organism, cell, tissue or system.

"Epigenetic parameters" are, in particular, cytosine methylation.

Further epigenetic parameters include, for example, the acetylation of histones which, however, cannot be directly analysed using the described method but which, in turn, correlate with the DNA methylation. As used herein, the term "exogenous" refers to any material introduced from or produced outside the organism, cell, tissue or system.

The term "expression" as used herein is defined as the transcription and/or translation of a particular nucleotide sequence driven by its promoter.

The term "hypermethylation" refers to the average methylation state corresponding to an increased presence of 5-mCyt at one or a plurality of CpG dinucleotides within a DNA sequence of a test DNA sample, relative to the amount of 5-mCyt found at corresponding CpG dinucleotides within a normal control DNA sample.

The term "hypomethylation" refers to the average methylation state corresponding to a decreased presence of 5-mCyt at one or a plurality of CpG dinucleotides within a DNA sequence of a test DNA sample, relative to the amount of 5-mCyt found at corresponding CpG dinucleotides within a normal control DNA sample.

The "level" of one or more biomarkers means the absolute or relative amount or concentration of the biomarker in the sample. The term "level" also refers to the absolute or relative amount of methylation of the biomarker in the sample.

"Measuring" or "measurement," or alternatively "detecting" or "detection," means assessing the presence, absence, quantity or amount (which can be an effective amount) of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's clinical parameters.

The term "Methylation assay" refers to any assay for determining the methylation state of one or more CpG dinucleotide sequences within a sequence of DNA.

The term "methylation state" or "methylation status" refers to the presence or absence of 5-methylcytosine ("5-mCyt") at one or a plurality of CpG dinucleotides within a DNA sequence. Methylation states at one or more particular CpG methylation sites (each having two antiparallel CpG dinucleotide sequences) within a DNA sequence include "unmethylated," "fully-methylated" and "hemi- methylated."

The terms "methylation-specific restriction enzymes" or "methylation- sensitive restriction enzymes" shall be taken to mean an enzyme that selectively digests a nucleic acid dependant on the methylation state of its recognition site. In the case of such restriction enzymes which specifically cut if the recognition site is not methylated or hemimethylated, the cut will not take place, or with a significantly reduced efficiency, if the recognition site is methylated. In the case of such restriction enzymes which specifically cut if the recognition site is methylated, the cut will not take place, or with a significantly reduced efficiency if the recognition site is not methylated. Preferred are methylation-specific restriction enzymes, the recognition sequence of which contains a CG dinucleotide (for instance cgcg or cccggg). Further preferred for some embodiments are restriction enzymes that do not cut if the cytosine in this dinucleotide is methylated at the carbon atom C5.

"Non-methylation-specific restriction enzymes" or "non-methylation- sensitive restriction enzymes" are restriction enzymes that cut a nucleic acid sequence irrespective of the methylation state with nearly identical efficiency. They are also called "methylation-unspecific restriction enzymes."

"Naturally-occurring" as applied to an object refers to the fact that the object can be found in nature. For example, a polypeptide or polynucleotide sequence that is present in an organism (including viruses) that can be isolated from a source in nature and which has not been intentionally modified by man is a naturally-occurring sequence.

By "nucleic acid" is meant any nucleic acid, whether composed of deoxyribonucleosides or ribonucleosides, and whether composed of phosphodiester linkages or modified linkages such as phosphotriester, phosphoramidate, siloxane, carbonate, carboxymethylester, acetamidate, carbamate, thioether, bridged phosphoramidate, bridged methylene phosphonate, phosphorothioate,

methylphosphonate, phosphorodithioate, bridged phosphorothioate or sulfone linkages, and combinations of such linkages. The term nucleic acid also specifically includes nucleic acids composed of bases other than the five biologically occurring bases (adenine, guanine, thymine, cytosine and uracil). The term "nucleic acid" typically refers to large polynucleotides.

Conventional notation is used herein to describe polynucleotide sequences: the left-hand end of a single-stranded polynucleotide sequence is the 5'- end; the left-hand direction of a double-stranded polynucleotide sequence is referred to as the 5 '-direction. The direction of 5 ' to 3 ' addition of nucleotides to nascent RNA transcripts is referred to as the transcription direction. The DNA strand having the same sequence as an mRNA is referred to as the "coding strand"; sequences on the DNA strand that are located 5' to a reference point on the DNA are referred to as "upstream sequences"; sequences on the DNA strand which are 3' to a reference point on the DNA are referred to as "downstream sequences."

The term "pre-cancerous" or "pre-neoplastic" and equivalents thereof shall be taken to mean any cellular proliferative disorder that is undergoing malignant transformation. Examples of such conditions include, in the context of colorectal cellular proliferative disorders, cellular proliferative disorders with a high degree of dysplasia and the following classes of adenomas: Level 1 : penetration of malignant glands through the muscularis mucosa into the submucosa, within the polyp head; Level 2: the same submucosal invasion, but present at the junction of the head to the stalk; Level 3: invasion of the stalk; and Level 4: invasion of the stalk's base at the connection to the colonic wall. In some instances, pre-neoplastic is used to describe a normal tissue that will form tumors.

As used herein, "predisposition" refers to the property of being susceptible to a cellular proliferative disorder. A subject having a predisposition to a cellular proliferative disorder has no cellular proliferative disorder, but is a subject having an increased likelihood of having a cellular proliferative disorder.

A "polynucleotide" means a single strand or parallel and anti-parallel strands of a nucleic acid. Thus, a polynucleotide may be either a single-stranded or a double-stranded nucleic acid. In the context of the present invention, the following abbreviations for the commonly occurring nucleic acid bases are used. "A" refers to adenosine, "C" refers to cytidine, "G" refers to guanosine, "T" refers to thymidine, and "U" refers to uridine.

The term "oligonucleotide" typically refers to short polynucleotides, generally no greater than about 60 nucleotides. It will be understood that when a nucleotide sequence is represented by a DNA sequence (i.e., A, T, G, C), this also includes an RNA sequence (i.e., A, U, G, C) in which "U" replaces "T."

As used herein, the term "providing a prognosis" refers to providing a prediction of the probable course and outcome of colorectal cancer, including prediction of severity, duration, chances of recovery, etc. The methods can also be used to devise a suitable therapeutic plan, e.g., by indicating whether or not the condition is still at an early stage or if the condition has advanced to a stage where aggressive therapy would be ineffective.

A "reference level" of a biomarker means a level of the biomarker, for example level of methylation of the biomarker that is indicative of a particular disease state, phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or lack thereof. A "positive" reference level of a biomarker means a level that is indicative of a particular disease state or phenotype. A "negative" reference level of a biomarker means a level that is indicative of a lack of a particular disease state or phenotype.

By the term "specifically binds," as used herein, is meant a molecule, such as an antibody, which recognizes and binds to another molecule or feature, but does not substantially recognize or bind other molecules or features in a sample.

"Standard control value" as used herein refers to a predetermined methylation level of a biomarker. The standard control value is suitable for the use of a method of the present invention, in order for comparing the amount of methylation of a biomarker of interest that is present in a sample. An established sample serving as a standard control provides an average amount methylation of a biomarker of interest that is typical for an average, healthy person of reasonably matched background, e.g., gender, age, ethnicity, and medical history. A standard control value may vary depending on the biomarker of interest and the nature of the sample.

As used herein, the term "subject" refers to a human or another mammal (e.g., primate, dog, cat, goat, horse, pig, mouse, rat, rabbit, and the like. In many embodiments of the present invention, the subject is a human being. In such embodiments, the subject is often referred to as an "individual" or a "patient." The terms "individual" and "patient" do not denote a particular age.

Ranges: throughout this disclosure, various aspects of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.

Description

The present invention is based partly on the discovery of a DNA methylation-based signature for human preneoplastic colon. The invention is also based on the identification of human genes that exhibited significant pyrosequencing- based methylation differences, as well as significant expression differences, in normal human colonic mucosa between CRC patients and controls.

Accordingly, the invention provides a colorectal cancer-specific methylation biomarker. In one embodiment, the biomarker is differentially methylated specifically in colorectal cancer cells and can be effectively used for diagnosis of colorectal cancer, as well as the use thereof for providing information for diagnosing colorectal cancer at an early stage.

In one embodiment, the biomarker is one or more biomarkers set forth in tables 6, 10, 1 1, 12, 13, 14, 15, and 16.

In one embodiment, biomarkers of the invention include one or more oiPDK4, PYCARD, NR1H4, SPRR2A, SPRRIA, BCMOl, AIM2, NEK3, and SB5. In one other embodiment, the present invention includes a method for detecting the methylation of one or more ofPDK4, PYCARD, NR1H4, SPRR2A, SPRRIA, BCMOl, AIM2, NEK3, and SB5, which are colorectal cancer-specific methylation biomarkers, and a kit for diagnosing colorectal cancer using the same.

In one embodiment, detection of a decreased level of methylation of a biomarker, wherein the biomarker is selected from the group consisting oiPDK4, NR1H4, BCMOl, and any combination thereof, diagnoses the subject with colorectal cancer.

In another embodiment, detection of an increased level of methylation of a biomarker, wherein the biomarker is selected from the group consisting of PYCARD, SPRR2A, SPRRIA, AIM2, SB5, and any combination thereof, diagnoses the subject with colorectal cancer.

Additional aspects provide novel methods and compositions for determining the relationship between methylation status and other variables including, but not limited to age, sex, tumor location, biopsy location, preneoplastic state, family history, race, country of origin, tumor characteristics (including, tumor type, tumor grade, invasive margin characteristics, lymphocyte infiltration characteristics, direct spread, lymph node spread, venous spread and type of residual adjacent polyp, if present). Biomarker

The present invention provides DNA methylation markers associated with colorectal cancer. Accordingly, a DNA methylation marker associated with colorectal cancer is considered a biomarker in the context of the present invention.

A biomarker is an organic biomolecule which is differentially present in a sample taken from an individual of one phenotypic status (e.g., having a disease) as compared with an individual of another phenotypic status (e.g., not having the disease). A biomarker is differentially present between the two individuals if the mean or median expression level, including methylation level, of the biomarker in the different individuals is calculated to be statistically significant. Biomarkers, alone or in combination, provide measures of relative risk that an individual belongs to one phenotypic status or another. Therefore, they are useful as markers for diagnosis of disease, the severity of disease, therapeutic effectiveness of a drug, and drug toxicity.

Accordingly, the invention provides methods for identifying one or more biomarkers that can be used to aid in the diagnosis, detection, and prediction of gastro-intestinal disease, such as colorectal cancer. The methods of the invention are carried out by obtaining a set of measured values for a plurality of biomarkers from a biological sample derived from a test individual, obtaining a set of measured values for a plurality of biomarkers from a biological sample derived from a control individual, comparing the measured values for each biomarker between the test and control sample, and identifying biomarkers which are significantly different between the test value and the control value, also referred to as a reference value.

The process of comparing a measured value and a reference value can be carried out in any convenient manner appropriate to the type of measured value and reference value for the biomarker of the invention. For example, "measuring" can be performed using quantitative or qualitative measurement techniques, and the mode of comparing a measured value and a reference value can vary depending on the measurement technology employed. For example, when a qualitative colorimetric assay is used to measure biomarker levels, the levels may be compared by visually comparing the intensity of the colored reaction product, or by comparing data from densitometric or spectrometric measurements of the colored reaction product (e.g., comparing numerical data or graphical data, such as bar charts, derived from the measuring device). However, it is expected that the measured values used in the methods of the invention will most commonly be quantitative values (e.g., quantitative measurements of concentration). In other examples, measured values are qualitative. As with qualitative measurements, the comparison can be made by inspecting the numerical data, or by inspecting representations of the data (e.g., inspecting graphical representations such as bar or line graphs).

A measured value is generally considered to be substantially equal to or greater than a reference value if it is at least about 95% of the value of the reference value. A measured value is considered less than a reference value if the measured value is less than about 95% of the reference value. A measured value is considered more than a reference value if the measured value is at least more than about 5% greater than the reference value.

The process of comparing may be manual (such as visual inspection by the practitioner of the method) or it may be automated. For example, an assay device (such as a luminometer for measuring chemiluminescent signals) may include circuitry and software enabling it to compare a measured value with a reference value for a desired biomarker. Alternately, a separate device (e.g., a digital computer) may be used to compare the measured value(s) and the reference value(s). Automated devices for comparison may include stored reference values for the biomarker(s) being measured, or they may compare the measured value(s) with reference values that are derived from contemporaneously measured reference samples.

In one embodiment, the biomarker candidate genes showing the greatest difference in the degree of methylation between normal persons and colorectal cancer patients were screened, and among these genes, PDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and SB 5 (e.g., AIM2a, AIM2b) genes were confirmed for diagnosis of colorectal cancer. An example for screening methylation marker genes according to the present invention comprises the steps of: (a) isolating genomic DNAs from transformed cells and non-transformed cells; (b) reacting the isolated genomic DNAs with a methylated DNA-binding protein, thereby isolating methylated DNAs; and (c) amplifying the methylated DNAs, hybridizing the amplified DNAs to a CpG microarray, and then selecting genes showing the greatest difference in the degree of methylation between the normal cells and the cancer cells, thereby ensuring methylation marker genes. Another method for screening methylation marker genes according to the present invention comprises the use of bisulfite-pyrosequencing after step (a).

The above method for screening biomarker genes can find genes that are differentially methylated in colorectal cancer as well as at various dysplasic stages of the tissue which progresses to colorectal cancer. The screened genes can be used for colorectal cancer screening, risk-assessment, prognosis, disease identification, the diagnosis of disease stages, and the selection of therapeutic targets.

The identification of genes that are methylated in colorectal cancer and abnormalities at various stages of colorectal cancer makes it possible to diagnose colorectal cancer at an early stage in an accurate and effective manner and allows methylation assessment of multiple genes and the identification of new targets for therapeutic intervention. Furthermore, the methylation data according to the present invention may be combined with other non-methylation related biomarker detection methods to obtain a more accurate system for colorectal cancer diagnosis.

According to the method of the present invention, the progression of colorectal cancer at various stages or phases can be diagnosed by determining the methylation stage of one or more nucleic acid biomarkers obtained from a sample. By comparing the methylation stage of a nucleic acid isolated from a sample at each stage of colorectal cancer with the methylation stage of one or more nucleic acids isolated from a sample in which there is no cell proliferative disorder of colorectal tissue, a specific stage of colorectal cancer in the sample can be detected. In one embodiment, the methylation stage may be hypermethylation. In another embodiment, the methylation stage may be hypomethylation.

In another embodiment, methylation of genes is decreased relative to a control sample from a subject that does not have colorectal cancer (e.g., a population average of samples, a control sample, a prior sample from the same patient, etc.). Accordingly, the invention in some instances provides a combination of markers for colorectal cancer, wherein some of the markers include decreased methylation of a gene and other markers include increased methylation of a gene.

In one embodiment of the present invention, nucleic acid may be methylated in the regulatory region of a gene. In another embodiment, a gene which is involved in cell transformation can be diagnosed at an early stage by detecting methylation outside of the regulatory region of the gene, because methylation proceeds inwards from the outside of the gene.

In yet another embodiment of the present invention, cells that are likely to form colorectal cancer can be diagnosed at an early stage using the methylation marker genes. When genes confirmed to be methylated in cancer cells are methylated in cells that appear normal clinically or morphologically, this indicates that the normally appearing cells will progress to cancer. Thus, colorectal cancer can be diagnosed at an early stage by detecting the methylation of colorectal cancer-specific genes in cells that appear normal.

The use of the methylation marker gene of the present invention allows for detection of a cellular proliferative disorder (dysplasia) of colorectal tissue in a sample. The detection method comprises bringing a sample comprising at least one nucleic acid isolated from a subject into contact with at least one agent capable of determining the methylation state of the nucleic acid. The method comprises detecting the methylation of at least one region in at least one nucleic acid, wherein the methylation of the nucleic acid differs from the methylation state of the same region of a nucleic acid present in a sample in which there is no abnormal growth (dysplastic progression) of colorectal cells.

In yet another embodiment of the present invention, the likelihood of progression of tissue to colorectal cancer can be evaluated by examining the methylation of a gene which is specifically methylated in colorectal cancer, and determining the methylation frequency of tissue that is likely to progress to colorectal cancer.

In one aspect, the present invention is based on the discovery of the relationship between colorectal cancer and the methylation status (e.g.,

hypermethylation and/or hypomethylation) of one or more oiPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and SB5 .

In another embodiment of the present invention, a cellular proliferative disorder of colorectal tissue cell can be diagnosed at an early stage by determining the methylation stage of one or more oiPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A,

BCMOl, AIM2, NEK3, and SB 5 from a subject. The methylation stage one or more of PDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and SB 5 may be compared with the methylation state of one or more oiPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and SB 5 from a subject not having a cellular proliferative disorder of colorectal tissue. The nucleic acid is preferably a CpG-containing nucleic acid such as a CpG island.

In another aspect, the present invention provides a method for diagnosing a cellular proliferative disorder of colorectal tissue, the method comprising bringing a sample comprising a nucleic acid into contact with an agent capable of determining the methylation state of the sample, and determining the methylation of at least one region of one or more oiPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and SB5. The methylation of the at least one region in one or more ofPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and 5^*55 differs from the methylation stage of the same region in a nucleic acid present in a subject in which there is no abnormal growth of cells.

Methylation

In the present invention, any nucleic acid sample, in purified or nonpurified form, can be used, provided it contains or is suspected of containing a nucleic acid sequence containing a target locus (e.g., CpG-containing nucleic acid). One nucleic acid region capable of being differentially methylated is a CpG island, a sequence of nucleic acid with an increased density relative to other nucleic acid regions of the dinucleotide CpG. The CpG doublet occurs in vertebrate DNA at only about 20% of the frequency that would be expected from the proportion of G*C base pairs. In certain regions, the density of CpG doublets reaches the predicted value; it is increased by ten- fold relative to the rest of the genome. CpG islands have an average G*C content of about 60%, compared with the 40% average in bulk DNA. The islands take the form of stretches of DNA typically about one to two kilobases long. There are about 45,000 islands in the human genome.

In many genes, the CpG islands begin just upstream of a promoter and extend downstream into the transcribed region. Methylation of a CpG island at a promoter usually suppresses expression of the gene. The islands can also surround the 5' region of the coding region of the gene as well as the 3' region of the coding region. Thus, CpG islands can be found in multiple regions of a nucleic acid sequence including upstream of coding sequences in a regulatory region including a promoter region, in the coding regions (e.g., exons), downstream of coding regions in, for example, enhancer regions, and in introns. Differential methylation can also occur outside of CpG islands. Typically, the CpG-containing nucleic acid is DNA. However, the inventive method may employ, for example, samples that contain DNA, or DNA and RNA containing mRNA, wherein DNA or RNA may be single-stranded or double- stranded, or a DNA-RNA hybrid may be included in the sample.

A mixture of nucleic acids may also be used. The specific nucleic acid sequence to be detected may be a fraction of a larger molecule or can be present initially as a discrete molecule, so that the specific sequence constitutes the entire nucleic acid. It is not necessary that the sequence to be studied be present initially in a pure form; the nucleic acid may be a minor fraction of a complex mixture, such as contained in whole human DNA. Nucleic acids contained in a sample used for detection of methylated CpG islands may be extracted by a variety of techniques such as that described elsewhere herein or procedures known to those of skill in the art.

Nucleic acids isolated from a subject are obtained in a biological sample from the subject. If it is desired to detect colorectal cancer or stages of colorectal cancer progression, the nucleic acid may be isolated from colorectal tissue by scraping or biopsy. Such samples may be obtained by various medical procedures known to those of skill in the art.

In one aspect of the invention, the state of methylation in nucleic acids of the sample obtained from a subject is hypermethylation compared with the same regions of the nucleic acid in a subject not having a cellular proliferative disorder of colorectal tissue. Hypermethylation as used herein refers to the presence or an increase of methylated alleles in one or more nucleic acids. Nucleic acids from a subject not having a cellular proliferative disorder of colorectal tissue contain no detectable or lower levels of methylated alleles when the same nucleic acids are examined.

In another aspect of the invention, the state of methylation in nucleic acids of the sample obtained from a subject is hypomethylation compared with the same regions of the nucleic acid in a subject not having a cellular proliferative disorder of colorectal tissue. Hypomethylation as used herein refers to the absence or diminished level of methylated alleles in one or more nucleic acids. Nucleic acids from a subject not having a cellular proliferative disorder of colorectal tissue contain detectable or higher levels of methylated alleles when the same nucleic acids are examined. Accordingly, the invention in some instances provides a combination of markers for colorectal cancer, wherein some of the markers include decreased methylation of a gene and other markers include increased methylation of a gene. Detection Methods

In one embodiment, the invention provides diagnostic and screening methods that utilize the detection of aberrant methylation of genes or promoters (e.g., including, but not limited to, PDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and SB5. In some embodiments, methylation of a gene is altered (e.g., increased or decreased). That is, in one embodiment, methylation of a gene is decreased relative to a control sample from a subject that does not have colorectal cancer (e.g., a population average of samples, a control sample, a prior sample from the same patient, etc.). In another embodiment, methylation of a gene is increased relative to a control sample from a subject that does not have colorectal cancer (e.g., a population average of samples, a control sample, a prior sample from the same patient, etc.). Accordingly, the invention in some instances provides a combination of markers for colorectal cancer, wherein some of the markers include decreased methylation of a gene and other markers include increased methylation of a gene.

Any patient sample suspected of containing the aberrantly methylated genes or promoters may be tested according to methods of embodiments of the present invention. In some embodiments, the patient sample is subjected to preliminary processing designed to isolate or enrich the sample for the aberently methylated genes or promoters or cells that contain the aberrantly methylated genes or promoters. A variety of techniques known to those of ordinary skill in the art may be used for this purpose, including but not limited to: centrifugation; immunocapture; cell lysis; and, nucleic acid target capture.

In one embodiment, the biomarkers of the invention can be detected using a real-time methylation specific PCR procedure. Real-time methylation-specific PCR is a real-time measurement method modified from the methylation-specific PCR method and comprises treating genomic DNA with bisulfite, designing PCR primers corresponding to the methylated base sequence, and performing real-time PCR using the primers. Methods of detecting the methylation of the genomic DNA include two methods: a method of detection using, for example, a TaqMan™ probe

complementary to the amplified base sequence; and a method of detection using Sybergreen™. Thus, the real-time methylation-specific PCR allows selective quantitative analysis of methylated DNA. A standard curve is plotted using an in vitro methylated DNA sample, and a gene containing no 5'-CpG-3' sequence in the base sequence is also amplified as a negative control group for standardization to quantitatively analyze the degree of methylation.

In one embodiment, the biomarkers of the invention can be detected using a pyrosequencing procedure. The pyrosequencing method is a quantitative realtime sequencing method modified from the bisulfite sequencing method. Similarly to bisulfite sequencing, genomic DNA is converted by bisulfite treatment, and then, PCR primers corresponding to a region containing no 5'-CpG-3' base sequence are constructed. Specifically, the genomic DNA is treated with bisulfite, amplified using the PCR primers, and then subjected to real-time base sequence analysis using a sequencing primer. The degree of methylation is expressed as a methylation index by analyzing the amounts of cytosine and thymine in the 5'-CpG-3' region.

In one embodiment, the biomarkers of the invention can be detected via a PCR using a methylation-specific binding protein or a DNA chip. PCR using a methylation-specific binding protein or a DNA chip assay allows selective isolation of only methylated DNA. Genomic DNA is mixed with a methylation-specific binding protein, and then only methylated DNA was selectively isolated. The isolated DNA is amplified using PCR primers corresponding to the promoter region, and then methylation of the DNA is measured by agarose gel electrophoresis.

In addition, methylation of DNA can also be measured by a quantitative PCR method, and methylated DNA isolated with a methylated DNA- specific binding protein can be labeled with a fluorescent probe and hybridized to a DNA chip containing complementary probes, thereby measuring methylation of the DNA.

In one embodiment, the biomarkers of the invention can be detected by way of using a methylation-sensitive restriction endonuclease. Detection of differential methylation can be accomplished by bringing a nucleic acid sample into contact with a methylation-sensitive restriction endonuclease that cleaves only unmethylated CpG sites. In a separate reaction, the sample is further brought into contact with an isoschizomer of the methylation-sensitive restriction enzyme that cleaves both methylated and unmethylated CpG-sites, thereby cleaving the methylated nucleic acid. Methylation-sensitive restriction endonucleases can be used to detect methylated CpG dinucleotide motifs. Such endonucleases may either preferentially cleave methylated recognition sites relative to non-methylated recognition sites or preferentially cleave non-methylated relative to methylated recognition sites.

Examples of the former are Acc III, Ban I, BstNl, Msp I, and Xma I. Examples of the latter are Acc II, Ava I, BssH II, BstU I, Hpa II, and Not I. Alternatively, chemical reagents can be used which selectively modify either the methylated or non- methylated form of CpG dinucleotide motifs.

Specific primers are added to the nucleic acid sample, and the nucleic acid is amplified by any conventional method. The presence of an amplified product in the sample treated with the methylation-sensitive restriction enzyme but absence of an amplified product in the sample treated with the isoschizomer of the methylation- sensitive restriction enzyme indicates that methylation has occurred at the nucleic acid region assayed. However, the absence of an amplified product in the sample treated with the methylation-sensitive restriction enzyme together with the absence of an amplified product in the sample treated with the isoschizomer of the methylation- sensitive restriction enzyme indicates that no methylation has occurred at the nucleic acid region assayed.

Another method for detecting a methylated CpG-containing nucleic acid comprises the steps of: bringing a nucleic acid-containing sample into contact with an agent that modifies unmethylated cytosine; and amplifying the CpG- containing nucleic acid in the sample using CpG-specific oligonucleotide primers, wherein the oligonucleotide primers distinguish between modified methylated nucleic acid and non-methylated nucleic acid and detect the methylated nucleic acid. The amplification step is optional and desirable, but not essential. The method relies on the PCR reaction to distinguish between modified (e.g., chemically modified) methylated DNA and unmethylated DNA. Such methods are described in U.S. Pat. No. 5,786, 146 relating to bisulfite sequencing for detection of methylated nucleic acid.

In another embodiment, the methylation status of the cancer markers may be detected along with other markers in a multiplex or panel format. Markers are selected for their predictive value alone or in combination with the gene fusions.

The methylation levels of non-amplified or amplified nucleic acids can be detected by any conventional means. In other embodiments, the methods described in U.S. Pat. Nos. 7,611,869, 7,553,627, 7,399,614, and/or 7,794,939, each of which is herein incorporated by reference in its entirety, are utilized. Additional detection methods include, but are not limited to, bisulfate modification followed by any number of detection methods (e.g., probe binding, sequencing, amplification, mass spectrometry, antibody binding, etc.) methylation-sensitive restriction enzymes and physical separation by methylated DNA-binding proteins or antibodies against methylated DNA (See e.g., Levenson, Expert Rev Mol Diagn. 2010 May; 10(4): 481- 488; herein incorporated by reference in its entirety).

In some embodiments, a computer-based analysis program is used to translate the raw data generated by the detection assay (e.g., the presence, absence, or amount of methylation of a given marker or markers) into data of predictive value for a clinician. The clinician can access the predictive data using any suitable means. Thus, in some preferred embodiments, the present invention provides the further benefit that the clinician, who is not likely to be trained in genetics or molecular biology, need not understand the raw data. The data is presented directly to the clinician in its most useful form. The clinician is then able to immediately utilize the information in order to optimize the care of the subject.

The present invention contemplates any method capable of receiving, processing, and transmitting the information to and from laboratories conducting the assays, information provides, medical personal, and subjects. For example, in some embodiments of the present invention, a sample (e.g., a biopsy or a serum or urine or fecal sample) is obtained from a subject and submitted to a profiling service (e.g., clinical lab at a medical facility, genomic profiling business, etc.), located in any part of the world (e.g., in a country different than the country where the subject resides or where the information is ultimately used) to generate raw data. Where the sample comprises a tissue or other biological sample, the subject may visit a medical center to have the sample obtained and sent to the profiling center, or subjects may collect the sample themselves (e.g., a urine sample) and directly send it to a profiling center. Where the sample comprises previously determined biological information, the information may be directly sent to the profiling service by the subject (e.g., an information card containing the information may be scanned by a computer and the data transmitted to a computer of the profiling center using an electronic

communication systems). Once received by the profiling service, the sample is processed and a profile is produced (i.e., methylation data), specific for the diagnostic or prognostic information desired for the subject.

The profile data is then prepared in a format suitable for interpretation by a treating clinician. For example, rather than providing raw expression data, the prepared format may represent a diagnosis or risk assessment (e.g., presence or absence of aberrant methylation) for the subject, along with recommendations for particular treatment options. The data may be displayed to the clinician by any suitable method. For example, in some embodiments, the profiling service generates a report that can be printed for the clinician (e.g., at the point of care) or displayed to the clinician on a computer monitor.

In some embodiments, the information is first analyzed at the point of care or at a regional facility. The raw data is then sent to a central processing facility for further analysis and/or to convert the raw data to information useful for a clinician or patient. The central processing facility provides the advantage of privacy (all data is stored in a central facility with uniform security protocols), speed, and uniformity of data analysis. The central processing facility can then control the fate of the data following treatment of the subject. For example, using an electronic communication system, the central facility can provide data to the clinician, the subject, or researchers.

In some embodiments, the subject is able to directly access the data using the electronic communication system. The subject may chose further intervention or counseling based on the results. In some embodiments, the data is used for research use. For example, the data may be used to further optimize the inclusion or elimination of markers as useful indicators of a particular condition or stage of disease or as a companion diagnostic to determine a treatment course of action.

Compositions for use in the diagnostic methods described herein include, but are not limited to, probes, amplification oligonucleotides, detection reagents, controls and the like. In some embodiments, reagents are provided in the form of an array.

Diagnostic

One aspect of the present invention relates to a method of diagnosing a condition associated with an aberrant methylation of DNA in a sample from a subject by measuring the methylation level of one or more DNA biomarkers from a test sample in comparison to that of a normal or standard sample, wherein the fold difference between the methylation level of the test sample in relation to that of the normal/standard sample indicates the likelihood of the test sample having the condition.

The aberrant methylation is referred as hypermethylation and/or hypomethylation (e.g., demethylation). In a preferred embodiment, the abnormal methylation is hypermethylation. In another preferred embodiment, the abnormal methylation is hypomethylation.

The methylation of DNA often occurs at genome regions known as CpG islands. The CpG islands are susceptible to aberrant methylation (e.g., hypermethylation or hypomethylation) in stage- and tissue-specific manner during the development of a condition or disease (e.g., cancer). Thus the measurement of the level of methylation indicates the likelihood or the stage (e.g., onset, development, or remission stage) of the condition. Accordingly, the invention in some instances provides a combination of markers for colorectal cancer, wherein some of the markers include decreased methylation of a gene and other markers include increased methylation of a gene.

The methylation of DNA can be detected via methods known in the art and those described elsewhere herein. In one embodiment, the level can be measured via a methylated-CpG island recovery assay (MIRA), combined bisulfite-restriction analysis (COBRA) or methylation-specific PCR (MSP). In another preferred embodiment, the methylation levels of a plurality DNA can be measured through MIRA-assisted DNA array.

The biomarkers are fragments of genome DNA that contain a CpG island or CpG islands, or alternatively, are susceptible to aberrant methylation.

Examples of the DNA markers associated with a condition are disclosed elsewhere herein. Specifically, examples of the DNA markers include but are not limited to PDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and SB5.

In another embodiment, the method of present invention is directed to a method of diagnosing a colon cancer in a test subject or a test sample through determining the methylation level of DNA markers from the test subject or test sample in relative to the level of the DNA markers from a normal subject or sample, wherein the DNA markers are one or more genes selected from the group consisting ofPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and SB5. It is contemplated that the biomarkers for altered methylation according to the present invention have the following criteria. An altered methylation status that diagnoses colorectal cancer can include a decreased methylation status relative to a control sample from a subject that does not have colorectal cancer (e.g., a population average of samples, a control sample, a prior sample from the same patient, etc.). In another embodiment, an altered methylation status that diagnoses colorectal cancer can include an increased methylation status relative to a control sample from a subject that does not have colorectal cancer (e.g., a population average of samples, a control sample, a prior sample from the same patient, etc.). Accordingly, the invention in some instances provides a combination of markers for colorectal cancer, wherein some of the markers include decreased methylation of a gene and other markers include increased methylation of a gene.

As apparent from the examples disclosed herein, diagnostic tests that use the biomarkers of the invention exhibit a sensitivity and specificity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% and about 100%. In some instances, screening tools of the present invention exhibit a high sensitivity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% and about 100%.

In one embodiment, analysis of one of the genes or genomic sequence selected from the group consisting oiPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and SB5, and any combination thereof enables for detecting, or detecting and distinguishing colon cell proliferative disorders (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%. Sensitivity is calculated as: (detected neoplasia/all neoplasia; e.g., (detected colon neoplasia/all colon neoplasia); and specificity is calculated as (non-detected negatives/total negatives)).

Preferably, the sensitivity is from about 75% to about 99%, or from about 80% to about 90%, or from about 80% to about 85%. Preferably, the specificity is from about 75% to about 99%, or from about 80% to about 90%, or from about 80% to about 85%.

For certain embodiments, colon neoplasia is herein defined as all colon malignancies and adenomas greater than 1 cm, or subsets thereof. Negatives can be defined as healthy individuals. The present invention enables diagnosis of events that are

disadvantageous to patients or individuals in which important genetic and/or epigenetic parameters within at least one gene or genomic sequence selected from the group consisting ofPDK4, PYCARD, NR1H4, SPRR2A, SPRRIA, BCMOl, AIM2, NEK3, and SB5, and any combination thereof may be used as markers. The parameters obtained by means of the present invention may be compared to another set of genetic and/or epigenetic parameters, the differences serving as the basis for a diagnosis and/or prognosis of events that are disadvantageous to patients or individuals.

In another embodiment, the present invention enables the screening of at-risk populations for the early detection of cancers, for example colorectal carcinomas. Furthermore, in certain aspects, the present invention enables the differentiation of neoplastic (e.g. malignant) from benign (i.e. non-cancerous) cellular proliferative disorders. For example, in certain embodiments, it enables the differentiation of a colorectal carcinoma from small colon adenomas or polyps.

In one embodiment, the present invention provides for diagnostic and classification of colon cancer and/or cancer assays based on measurement of differential methylation status of one or more CpG dinucleotide sequences of at least one gene selected from the group consisting of PDK4, PYCARD, NR1H4, SPRR2A, SPRRIA, BCMOl, AIM2, NEK3, and SB 5, and any combination thereof that comprise such a CpG dinucleotide sequence. Typically, such assays involve obtaining a sample from a subject, performing an assay to measure the methylation state of at least one gene or genomic sequence selected from the group consisting of PDK4, PYCARD, NR1H4, SPRR2A, SPRRIA, BCMOl, AIM2, NEK3, and SB5, and any combination thereof, preferably by determining the methylation status of at least one gene selected from the group consisting ofPDK4, PYCARD, NR1H4, SPRR2A, SPRRIA, BCMOl, AIM2, NEK3, and SB5, and any combination thereof, derived from the sample, relative to a control sample, or a known standard and making a diagnosis based thereon.

Although diagnostic and prognostic accuracy and sensitivity may be achieved by using a combination of markers, such as 2 or more biomarkers of the invention, practical considerations may dictate use of one or more biomarkers and smaller combinations thereof. Any combination of markers for a specific cancer may be used which comprises 1, 2, 3, 4, 5, 6, 7 or more markers. Combinations of 1, 2, 3, 4, 5, 6, 7 or more markers can be readily envisioned given the specific disclosures of individual markers provided herein.

The level of methylation of the differentially methylated GpG islands can provide a variety of information about the disease or cancer. It can be used to diagnose a disease or cancer in the individual. Alternatively, it can be used to predict the course of the disease or cancer in the individual or to predict the susceptibility to disease or cancer or to stage the progression of the disease or cancer in the individual. It can help to predict the likelihood of overall survival or predict the likelihood of reoccurrence of disease or cancer and to determine the effectiveness of a treatment course undergone by the individual. Increase or decrease of methylation levels in comparison with reference level and alterations in the increase/decrease when detected provides useful prognostic and diagnostic value.

The prognostic methods can be used to identify patients with cancer or at risk of cancer. Such patients can be offered additional appropriate therapeutic or preventative options, including endoscopic polypectomy or resection, and when indicated, surgical procedures, chemotherapy, radiation, biological response modifiers, or other therapies. Such patients may also receive recommendations for further diagnostic or monitoring procedures, including but not limited to increased frequency of colonoscopy, virtual colonoscopy, video capsule endoscopy, PET-CT, molecular imaging, or other imaging techniques.

Following the diagnosis of a subject according to the methods of the invention, the subject diagnosed with cancer or at risk for having a proliferative disease, such as cancer can be treated against the disease. Accordingly, the method comprises identifying nucleic acid altered methylation (e.g., hypermethylation and/or hypomethylation) of one or more genes, where nucleic acid altered methylation indicates having or a risk for having a proliferative disease, and administering to the subject a therapeutically effective amount of a therapeutic agent, thereby treating a subject having or at risk for having a proliferative disease.

Anti-cancer drugs that may be used in the various embodiments of the invention, including pharmaceutical compositions and dosage forms and kits of the invention. One type of anti-cancer drug includes cytotoxic agents (i.e., drugs that kill cancer cells in different ways). These include the alkylating agents, antimetabolites, antitumor antibiotics, and plant drugs. Another type of anti-cancer drug includes hormones and hormone antagonists. Some tumors require the presence of hormones to grow. Many of these drugs block the effects of hormones at its tissue receptors or prevent the manufacture of hormones by the body.

Another type of anti-cancer drug includes biological response modifiers. These drugs increase the body's immune system to detect and destroy the cancer.

Non-limiting examples of anti-cancer drugs include but are not limited to: acivicin; aclarubicin; acodazole hydrochloride; acronine; adozelesin; aldesleukin; altretamine; ambomycin; ametantrone acetate; aminoglutethimide; amsacrine;

anastrozole; anthramycin; asparaginase; asperlin; azacitidine; azetepa; azotomycin; batimastat; benzodepa; bicalutamide; bisantrene hydrochloride; bisnafide dimesylate; bizelesin; bleomycin sulfate; brequinar sodium; bropirimine; busulfan; cactinomycin; calusterone; caracemide; carbetimer; carboplatin; carmustine; carubicin

hydrochloride; carzelesin; cedefingol; chlorambucil; cirolemycin; cisplatin;

cladribine; crisnatol mesylate; cyclophosphamide; cytarabine; dacarbazine;

dactinomycin; daunorubicin hydrochloride; decitabine; dexormaplatin; dezaguanine; dezaguanine mesylate; diaziquone; docetaxel; doxorubicin; doxorubicin

hydrochloride; droloxifene; droloxifene citrate; dromostanolone propionate;

duazomycin; edatrexate; eflornithine hydrochloride; elsamitrucin; enloplatin;

enpromate; epipropidine; epirubicin hydrochloride; erbulozole; esorubicin hydrochloride; estramustine; estramustine phosphate sodium; etanidazole; etoposide; etoposide phosphate; etoprine; fadrozole hydrochloride; fazarabine; fenretinide; floxuridine; fludarabine phosphate; fluorouracil; flurocitabine; fosquidone; fostriecin sodium; gemcitabine; gemcitabine hydrochloride; hydroxyurea; idarubicin hydrochloride; ifosfamide; ilmofosine; interleukin II (including recombinant interleukin II, or rIL2), interferon alfa-2a; interferon alfa-2b; interferon alfa-nl ; interferon alfa-n3; interferon beta-I a; interferon gamma-I b; iproplatin; irinotecan hydrochloride; lanreotide acetate; letrozole; leuprolide acetate; liarozole

hydrochloride; lometrexol sodium; lomustine; losoxantrone hydrochloride;

masoprocol; maytansine; mechlorethamine, mechlorethamine oxide hydrochloride rethamine hydrochloride; megestrol acetate; melengestrol acetate; melphalan;

menogaril; mercaptopurine; methotrexate; methotrexate sodium; metoprine;

meturedepa; mitindomide; mitocarcin; mitocromin; mitogillin; mitomalcin; mitomycin; mitosper; mitotane; mitoxantrone hydrochloride; mycophenolic acid; nocodazole; nogalamycin; ormaplatin; oxisuran; paclitaxel; pegaspargase;

peliomycin; pentamustine; peplomycin sulfate; perfosfamide; pipobroman;

piposulfan; piroxantrone hydrochloride; plicamycin; plomestane; porfimer sodium; porfiromycin; prednimustine; procarbazine hydrochloride; puromycin; puromycin hydrochloride; pyrazofurin; riboprine; rogletimide; safingol; safingol hydrochloride; semustine; simtrazene; sparfosate sodium; sparsomycin; spirogermanium

hydrochloride; spiromustine; spiroplatin; streptonigrin; streptozocin; sulofenur;

talisomycin; tecogalan sodium; tegafur; teloxantrone hydrochloride; temoporfin; teniposide; teroxirone; testolactone; thiamiprine; thioguanine; thiotepa; tiazofurin; tirapazamine; toremifene citrate; trestolone acetate; triciribine phosphate;

trimetrexate; trimetrexate glucuronate; triptorelin; tubulozole hydrochloride; uracil mustard; uredepa; vapreotide; verteporfin; vinblastine sulfate; vincristine sulfate; vindesine; vindesine sulfate; vinepidine sulfate; vinglycinate sulfate; vinleurosine sulfate; vinorelbine tartrate; vinrosidine sulfate; vinzolidine sulfate; vorozole;

zeniplatin; zinostatin; zorubicin hydrochloride, improsulfan, benzodepa, carboquone, triethylenemelamine, triethylenephosphoramide, triethylenethiophosphoramide, trimethylolomelamine, chlornaphazine, novembichin, phenesterine, trofosfamide, estermustine, chlorozotocin, gemzar, nimustine, ranimustine, dacarbazine, mannomustine, mitobronitol,aclacinomycins, actinomycin F(l), azaserine, bleomycin, carubicin, carzinophilin, chromomycin, daunorubicin, daunomycin, 6-diazo-5-oxo-l- norleucine, doxorubicin, olivomycin, plicamycin, porfiromycin, puromycin, tubercidin, zorubicin, denopterin, pteropterin, 6-mercaptopurine, ancitabine, 6- azauridine, carmofur, cytarabine, dideoxyuridine, enocitabine, pulmozyme, aceglatone, aldophosphamide glycoside, bestrabucil, defofamide, demecolcine, elfornithine, elliptinium acetate, etoglucid, flutamide, hydroxyurea, lentinan, phenamet, podophyllinic acid, 2-ethylhydrazide, razoxane, spirogermanium, tamoxifen, taxotere, tenuazonic acid, triaziquone, 2,2',2"-trichlorotriethylamine, urethan, vinblastine, vincristine, vindesine and related agents. 20-epi-l,25

dihydroxyvitamin D3; 5-ethynyluracil; abiraterone; aclarubicin; acylfulvene;

adecypenol; adozelesin; aldesleukin; ALL-TK antagonists; altretamine; ambamustine; amidox; amifostine; aminolevulinic acid; amrubicin; amsacrine; anagrelide;

anastrozole; andrographolide; angiogenesis inhibitors; antagonist D; antagonist G; antarelix; anti-dorsalizing morphogenetic protein- 1 ; antiandrogen, prostatic carcinoma; antiestrogen; antineoplaston; antisense oligonucleotides; aphidicolin glycinate; apoptosis gene modulators; apoptosis regulators; apurinic acid; ara-CDP-

DL-PTBA; arginine deaminase; asulacrine; atamestane; atrimustine; axinastatin 1 ; axinastatin 2; axinastatin 3; azasetron; azatoxin; azatyrosine; baccatin III derivatives; balanol; batimastat; BCR/ABL antagonists; benzochlorins; benzoylstaurosporine; beta lactam derivatives; beta-alethine; betaclamycin B; betulinic acid; bFGF inhibitor; bicalutamide; bisantrene; bisaziridinylspermine; bisnafide; bistratene A; bizelesin; breflate; bropirimine; budotitane; buthionine sulfoximine; calcipotriol; calphostin C; camptothecin derivatives; canarypox IL-2; capecitabine; carboxamide-amino-triazole; carboxyamidotriazole; CaRest M3; CARN 700; cartilage derived inhibitor; carzelesin; casein kinase inhibitors (ICOS); castanospermine; cecropin B; cetrorelix; chlorins; chloroquinoxaline sulfonamide; cicaprost; cisporphyrin; cladribine; clomifene analogues; clotrimazole; collismycin A; collismycin B; combretastatin A4;

combretastatin analogue; conagenin; crambescidin 816; crisnatol; cryptophycin 8; cryptophycin A derivatives; curacin A; cyclopentanthraquinones; cycloplatam;

cypemycin; cytarabine ocfosfate; cytolytic factor; cytostatin; dacliximab; decitabine; dehydrodidemnin B; deslorelin; dexamethasone; dexifosfamide; dexrazoxane;

dexverapamil; diaziquone; didemnin B; didox; diethylnorspermine; dihydro-5- azacytidine; dihydrotaxol, 9-; dioxamycin; diphenyl spiromustine; docetaxel;

docosanol; dolasetron; doxifluridine; droloxifene; dronabinol; duocarmycin SA; ebselen; ecomustine; edelfosine; edrecolomab; eflornithine; elemene; emitefur;

epirubicin; epristeride; estramustine analogue; estrogen agonists; estrogen

antagonists; etanidazole; etoposide phosphate; exemestane; fadrozole; fazarabine; fenretinide; filgrastim; finasteride; flavopiridol; flezelastine; fluasterone; fludarabine; fluorodaunorunicin hydrochloride; forfenimex; formestane; fostriecin; fotemustine; gadolinium texaphyrin; gallium nitrate; galocitabine; ganirelix; gelatinase inhibitors; gemcitabine; glutathione inhibitors; hepsulfam; heregulin; hexamethylene

bisacetamide; hypericin; ibandronic acid; idarubicin; idoxifene; idramantone;

ilmofosine; ilomastat; imidazoacridones; imiquimod; immunostimulant peptides; insulin-like growth factor-1 receptor inhibitor; interferon agonists; interferons;

interleukins; iobenguane; iododoxorubicin; ipomeanol, 4-; iroplact; irsogladine;

isobengazole; isohomohalicondrin B; itasetron; jasplakinolide; kahalalide F;

lamellarin-N triacetate; lanreotide; leinamycin; lenograstim; lentinan sulfate;

leptolstatin; letrozole; leukemia inhibiting factor; leukocyte alpha interferon; leuprolide+estrogen+progesterone;leuprorelin; levamisole; liarozole; linear polyamine analogue; lipophilic disaccharide peptide; lipophilic platinum compounds;

lissoclinamide 7; lobaplatin; lombricine; lometrexol; lonidamine; losoxantrone;

lovastatin; loxoribine; lurtotecan; lutetium texaphyrin; lysofylline; lytic peptides; maitansine; mannostatin A; marimastat; masoprocol; maspin; matrilysin inhibitors; matrix metalloproteinase inhibitors; menogaril; merbarone; meterelin; methioninase; metoclopramide; MIF inhibitor; mifepristone; miltefosine; mirimostim; mismatched double stranded R A; mitoguazone; mitolactol; mitomycin analogues; mitonafide; mitotoxin fibroblast growth factor-saporin; mitoxantrone; mofarotene; molgramostim; monoclonal antibody, human chorionic gonadotrophin; monophosphoryl lipid

A+myobacterium cell wall sk; mopidamol; multiple drug resistance gene inhibitor; multiple tumor suppressor 1 -based therapy; mustard anticancer agent; mycaperoxide

B; mycobacterial cell wall extract; myriaporone; N-acetyldinaline; N-substituted benzamides; nafarelin; nagrestip; naloxone+pentazocine; napavin; naphterpin;

nartograstim; nedaplatin; nemorubicin; nemoronic acid; neutral endopeptidase;

nilutamide; nisamycin; nitric oxide modulators; nitroxide antioxidant; nitrullyn; 06- benzylguanine; octreotide; okicenone; oligonucleotides; onapristone; ondansetron; ondansetron; oracin; oral cytokine inducer; ormaplatin; osaterone; oxaliplatin;

oxaunomycin; taxel; taxel analogues; taxel derivatives; palauamine;

palmitoylrhizoxin; pamidronic acid; panaxytriol; panomifene; parabactin;

pazelliptine; pegaspargase; peldesine; pentosan polysulfate sodium; pentostatin; pentrozole; perflubron; perfosfamide; perillyl alcohol; phenazinomycin;

phenylacetate; phosphatase inhibitors; picibanil; pilocarpine hydrochloride;

pirarubicin; piritrexim; placetin A; placetin B; plasminogen activator inhibitor;

platinum complex; platinum compounds; platinum-triamine complex; porfimer sodium; porfiromycin; prednisone; propyl bis-acridone; prostaglandin J2; proteasome inhibitors; protein A-based immune modulator; protein kinase C inhibitor; protein kinase C inhibitors, microalgal; protein tyrosine phosphatase inhibitors; purine nucleoside phosphorylase inhibitors; purpurins; pyrazoloacridine; pyridoxylated hemoglobin polyoxyethylene conjugate; raf antagonists; raltitrexed; ramosetron; ras farnesyl protein transferase inhibitors; ras inhibitors; ras-GAP inhibitor; retelliptine demethylated; rhenium Re 186 etidronate; rhizoxin; ribozymes; RII retinamide;

rogletimide; rohitukine; romurtide; roquinimex; rubiginone Bl ; ruboxyl; safingol; saintopin; SarCNU; sarcophytol A; sargramostim; Sdi 1 mimetics; semustine; senescence derived inhibitor 1 ; sense oligonucleotides; signal transduction inhibitors; signal transduction modulators; single chain antigen binding protein; sizofiran;

sobuzoxane; sodium borocaptate; sodium phenylacetate; solverol; somatomedin binding protein; sonermin; sparfosic acid; spicamycin D; spiromustine; splenopentin; spongistatin 1 ; squalamine; stem cell inhibitor; stem-cell division inhibitors;

stipiamide; stromelysin inhibitors; sulfinosine; superactive vasoactive intestinal peptide antagonist; suradista; suramin; swainsonine; synthetic glycosaminoglycans; tallimustine; tamoxifen methiodide; tauromustine; tazarotene; tecogalan sodium; tegafur; tellurapyrylium; telomerase inhibitors; temoporfin; temozolomide;

teniposide; tetrachlorodecaoxide; tetrazomine; thaliblastine; thiocoraline;

thrombopoietin; thrombopoietin mimetic; thymalfasin; thymopoietin receptor agonist; thymotrinan; thyroid stimulating hormone; tin ethyl etiopurpurin; tirapazamine;

titanocene bichloride; topsentin; toremifene; totipotent stem cell factor; translation inhibitors; tretinoin; triacetyluridine; triciribine; trimetrexate; triptorelin; tropisetron; turosteride; tyrosine kinase inhibitors; tyrphostins; UBC inhibitors; ubenimex;

urogenital sinus-derived growth inhibitory factor; urokinase receptor antagonists; vapreotide; variolin B; vector system, erythrocyte gene therapy; velaresol; veramine; verdins; verteporfin; vinorelbine; vinxaltine; vitaxin; vorozole; zanoterone; zeniplatin; zilascorb; and zinostatin stimalamer. Preferred additional anti-cancer drugs are 5- fluorouracil and leucovorin.

Additional cancer therapeutics include monoclonal antibodies such as rituximab, trastuzumab and cetuximab.

Kits

In one embodiment, the present invention provides a kit comprising: a means for determining methylation of at least one gene or genomic sequence selected from the group consisting ofPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, and SB5 and any combination thereof. In one embodiment, the kit compries instructions for carrying out and evaluating the described method of methylation analysis.

In a further embodiment, said kit may further comprise standard reagents for performing a CpG position-specific methylation analysis.

EXPERIMENTAL EXAMPLES The invention is further described in detail by reference to the following experimental examples. These examples are provided for purposes of illustration only, and are not intended to be limiting unless otherwise specified. Thus, the invention should in no way be construed as being limited to the following examples, but rather, should be construed to encompass any and all variations which become evident as a result of the teaching provided herein.

Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the following illustrative examples, make and utilize the compounds of the present invention and practice the claimed methods. The following working examples therefore, specifically point out the preferred embodiments of the present invention, and are not to be construed as limiting in any way the remainder of the disclosure.

Example 1 : Genes with aberrant expression in murine preneoplastic intestine show epigenetic and expression changes in normal mucosa of colon cancer patients

To identify pro-tumorigenic changes in normal (e.g., pre-neoplastic) human colonic mucosa, candidate genes were identified in a mouse model that had previously been shown to develop intestinal tumors after administration of low folate diets (Knock et al., 2006, Cancer Res 66: 10349-56). Since folates generate the methyl groups for DNA methylation, it was predicted that there would be genetic/epigenetic changes in preneoplastic intestine in the mouse model and that some of these changes might be similar to those seen in human colonic mucosa. Experiments were designed to compare murine candidates to the unrestricted list of preliminary human genes that had shown changes following methylation profiling of normal colonic mucosa in CRC patients and controls (Silviera et al, 2012, Cancer Prev Res (Phila) 5:374-84; and unpublished data).

The mouse model reflects some of the genetic and nutritional factors that have also been reported to affect risk for human CRC. Individuals with low folate intake are more susceptible to CRC than individuals with adequate folate intake (Ma et al, 1997, Cancer Res 57: 1098-102). A polymorphism in methylenetetrahydro folate reductase (MTHFR), which generates methyl groups for S-adenosylmethionine- dependent methylation reactions, can also modulate CRC risk. The association between the MTHFR 677C-^T polymorphism and risk for CRC has been found in several epidemiological studies and the mechanisms are likely to involve epigenetic remodeling through DNA methylation changes (Ma et al, 1997, Cancer Res 57: 1098- 102; Ulvik et al, 2004, Cancer Epidemiol Biomarkers Prev 13:2175-80; Teng et al, 2012, PLoS One 8:e55332).

The impact of folate and MTHFR deficiency on tumorigenesis is apparent in the mouse model for spontaneous intestinal neoplasia in BALB/c mice. BALB/c and C57BL/6 mice were fed folate-deficient (FD) or control diets (CD) for one year. Tumors were only observed in BALB/c mice fed FD (Knock et al., 2006, Cancer Res 66: 10349-56; Knock et al, 2008, J Nutr 138:653-58); a single functional copy of the Mthfr gene increased the number of FD mice with tumors (Knock et al, 2006, Cancer Res 66: 10349-56). Several tumor-predisposing candidate genes involved in cell cycle control, cell survival and DNA repair were identified by comparing expression profiles of tumor tissue with normal tissue (Garcia-Crespo et al, 2009, J Nutr 139:488-94). These observations, in combination with increased DNA damage (Knock et al, 2011, J Nutr Biochem 11 : 1022-9; Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97), decreased expression of tumor suppressors and increased retinoid/PPARA pathway activity in BALB/c normal preneoplastic intestine (Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97), could explain the susceptibility of these mice to intestinal tumorigenesis. Differential expression of Bcmol, Aldhlal and Sprr2a was observed when comparing CD- and FD-fed BALB/c mice. Expression differences were also seen for Bcmol between Mthfr^+I+ and Mthfr^+I~ mice. These changes were consistent with the hypothesis that enhanced RXR/PPAR activity would increase oxidative stress/damage and lead to neoplasia (Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97).

The unique mouse model, without germline mutation or carcinogen induction, provides an opportunity to study early events in intestinal neoplasia. The results presented herein identify specific candidate genes in tumorigenesis.

Microarrays were used to compare BALB/c Mthfr^+I+ CD mice and BALB/c Mthfr^+I~ FD mice, which have relatively lower and higher intestinal tumor susceptibility, respectively (Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97; and references therein). Significant differences in retinoid/PPARA pathway genes between BALB/c mice fed FD and CD were observed. The activation of this pathway is consistent with the findings from a report on gene expression profiling between tumor-susceptible BALB/c and tumor-resistant C57BL/6 mice (Leclerc et al., 2012, Mol Nutr Food Res 57(4):686-97). These murine candidates, obtained from both of the afore- mentioned expression microarrays, were compared to the extensive list of candidate human genes that had been identified through methylation profiling (Silviera et al, 2012, Cancer Prev Res (Phila) 5:374-84; and unpublished data). The inter-species comparison led to the identification of at least 5 human genes (e.g., PDK4, SPRR1A, SPRR2A, NR1H4, and PYCARD) that showed significant pyrosequencing-based methylation differences, in 14 CpG dinucleotides, as well as significant expression differences, in normal human colonic mucosa between CRC patients and controls. In some instances, the inter-species comparison led to the identification of an additional 2 human genes (e.g., AIM2 and BCMOl). The results presented herein suggest that common tumorigenic mechanisms, reflecting an altered metabolic state, are shared by the mouse model and human CRC. Furthermore, these methylation differences contribute to an epigenetics signature for diagnosis of colonic neoplasia.

An understanding of early genetic/epigenetic changes in colorectal cancer (CRC) would aid in diagnosis and prognosis. To identify these changes in human preneoplastic tissue, experiments were designed to study a mouse model in which Mthfr^+/~ BALB/c mice fed folate-deficient (FD) diets develop intestinal tumors in contrast to Mthfr^+/+ BALB/c mice fed control diets (CD). Transcriptome profiling was performed in normal intestine from mice with low or high tumor susceptibility. 12 up-regulated and 51 down-regulated genes in tumor-prone mice were identified. Affected pathways included retinoid acid synthesis, lipid and glucose metabolism, apoptosis and inflammation. Murine candidates from this microarray analysis and murine candidates from an earlier strain-based comparison were compared with a set of human genes that were identified in methylome profiling of normal human colonic mucosa, from CRC patients and controls. From the extensive list of human methylome candidates, five orthologous genes that had shown changes in murine expression profiles (PDK4, SPRR1A, SPRR2A, NR1H4, and PYCARD) were identified. The human orthologs were assayed by bisulfite-pyrosequencing for methylation at 14 CpGs. All CpGs exhibited significant methylation differences in normal mucosa between CRC patients and controls; expression differences for these genes were also observed. PYCARD and NR1H4 methylation differences showed promise as markers for presence of polyps in controls. The results demonstrate that common pathways are disturbed in preneoplastic intestine in the animal model and morphologically normal mucosa of CRC patients, and present an initial version of a DNA methylation-based signature for human preneoplastic colon.

The materials and methods employed in these experiments are now described.

Materials and Methods

Mice and diets

Animal experimentation was approved by the Montreal Children's Hospital Animal Care Committee, in accordance with guidelines of the Canadian

Council on Animal Care. After weaning, BALB/c Mthfr^+I+ and Mthfr^+I~ mice were fed control diets (CD, 2mg folate/kg diet) or folate-deficient diets (FD, 0.3mg folate/kg diet) for one year. Incidence of neoplasia was consistent with our previous reports (Knock et al, 2006, Cancer Res 66: 10349-56; Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97).

Control subjects

Research was approved by the Temple University Office for Human Subjects Protections Institutional Review Board, protocol 1 1910. Biological specimens were collected from subjects undergoing routine screening colonoscopy at Temple University Medical Center to serve as the control arm of the study (clinical data in Table 1). Subjects with a personal or first-degree family history of any cancer were excluded. Subjects with a previous colonoscopic finding of polyps were also excluded. Subjects who were not excluded at this point underwent complete colonoscopic evaluation by a board certified gastroenterologist. If the colonoscope could not be passed to the appendiceal orifice, the subject was excluded. If the complete colon was visualized, two cold forceps biopsies of normal colonic mucosa from the ascending colon (proximal to the hepatic flexure) were pooled.

Table 1 : Description of control subjects

RC987 M 63 YES 4.2

RC923 F 75 NO 4.3

RC985 F 74 NO 4.6

RC949 M 55 NO 4.7

RC968 M 47 YES No information 4.8

RC913 F 60 NO 5.1

RC918 F 60 NO 5.2

RC926 F 72 NO 5.2

RC945 M 78 NO 5.2

RC984 M 55 NO 5.4

RC947 F 86 YES Tubular adenoma w/ LGD 5.5

RC976 F 64 YES No information 5.5

RC963 M 55 NO 5.6

RC971 M 53 NO 5.6

RC983 M 51 NO 6.0

RC986 F 57 NO 6.0

RC977 M 54 YES Tubular adenoma w/ LGD 6.2

RC932 F 56 NO 6.3

RC941 M 62 YES Tubular adenoma w/ LGD 6.3

RC981 M 52 YES No information 6.3

RC911 M 74 NO 6.5

RC912 F 46 NO 6.5

RC930 M 70 YES Tubular adenoma w/ LGD 6.6

RC962 M 51 NO 6.6

RC922 M 60 YES Tubular adenoma w/ LGD 6.8

RC979 M 62 YES Tubular adenoma w/ LGD 6.8

RC953 M 65 YES Tubular adenoma w/ LGD/ 7.6

RC924 M 41 YES Tubular adenoma w/ LGD 7.8

RC935 M 55 NO 8.0

RC917 M 52 YES Tubular adenoma w/ LGD 8.3

RC933 F 59 YES Adenoma? Hyperplastic 8.3

RC914 M 47 YES Tubular adenoma w/ LGD 8.4

RC982 M 61 YES No information 9.5 ^a HPP, hyperplastic polyp; LGD, low-grade dysplasia

b Ordered by increasing methylation, with blue for lower and red for higher % methylation.

Cancer patients

Biological specimens from patients undergoing colon resection for presumed or biopsy-proven colon cancers were also collected. Patients were considered eligible if they had no personal or family history of colon cancer prior to this encounter. Patients with known or clinical features of hereditary cancer syndromes (specifically, hereditary nonpolyposis colorectal cancer or familial adenomatous polyposis syndrome) were excluded. Patients with any personal history of chemotherapy or radiation therapy were also excluded. Patients who remained eligible (described in Table 2) underwent colon resection at a single National Cancer Institute designated Comprehensive Cancer Center (Fox Chase Cancer Center/Temple University). Specimens, determined by a board certified pathologist to be normal appearing colon mucosa, were obtained well away from the lesion in question (-10 cm).

Table 2: Description of cancer patients

N58 M 65 Adenocarcinoma, NOS Sigmoid colon

N59 F 69 Adenocarcinoma, NOS Sigmoid colon

N60 F 42 Adenocarcinoma, NOS Splenic flexure of colon

N61 M 47 Adenocarcinoma, NOS Sigmoid colon

a NOS, not otherwise specified

DNA and RNA isolation from human normal tissue

Morphologically normal colon mucosa specimens were obtained from CRC patients or from controls undergoing screening colonoscopy (Silviera et al, 2012, Cancer Prev Res (Phila) 5:374-84). Samples were treated as previously described (Silviera et al., 2012, Cancer Prev Res (Phila) 5:374-84).

RNA extraction from murine normal preneoplastic intestine

RNA was extracted as described (Leclerc et al, 2012, Mol Nutr Food

Res 57(4):686-97). Eight samples for microarrays were prepared from four BALB/c Mthfr^+/- mice fed FD and four BALB/c Mthfr^+/+ mice fed CD. High quality of RNA was verified (Figure 7). In addition, 16 RNA samples were extracted from BALB/c Mthfr^+I~ and BALB/c Mthfr^+I+ mice fed CD and FD (four mice per group). These samples were used as biological replicates to confirm microarray results by qRT-PCR and verify effects of genotype and diet on expression.

Microarray analysis and quantitative real-time RT-PCR (qRT-PCR) Microarray experiments were performed using Affymetrix Mouse Gene 1.0 ST Array Chips, as previously described (Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97). BALB/c Mthfr ^+/+ mice fed CD were considered as the group with higher tumor resistance and BALB/c Mthfr^+I~ FD group were considered as the tumor-susceptible group. Genes with expression fold changes greater than 1.4 and a / value less than 0.01 after false discovery rate correction were considered significant. Ingenuity Pathways Analysis (IP A) was used to assess biological processes with the most significant changes.

qRT-PCR was performed as described to confirm microarray data (Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97). Primers were designed (Table 3) and amplified fragments of expected sizes (data not shown). A total of 16 mice in four groups (four mice per group) were used; the groups were Mthfr^+I+ CD, Mthfr^+/+ FD, Mthfr^+/- CD and Mthfr^+/~ FD. Table 3: Primer pairs for qRT-PCR for the 13 genes in Figures 1 and 8, and the internal control, Gapdh.

Gene Primer sequences and orientation^a) Amplicon

Symbol size

(S), sense; (A), antisense

RNA extraction, cDNA synthesis and gene-specific Taqman probes (Applied Biosystems) were performed as described (Silviera et al, 2012, Cancer Prev Res (Phila) 5:374-84), to measure steady-state levels oiPDK4, SPRRIA, SPRR2A, NR1H4 and PYCARD in human normal colon mucosa from patients with cancer and controls. Primers and probes are described in Table 4.

Table 4: Taqman primers and probes for qRT-PCR of human genes

a) Taqman assay ID from Applied Biosystems (Life Technologies)

Quantitative CpG methylation analysis by pyrosequencing

Bisulfite pyrosequencing was used to measure methylation of specific CpGs in the 5 ' region of human PDK4, SPRR2A, NR1H4, SPRR1A and PYCARD. CpGs in mouse orthologous regions were also assessed, as described (Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97). Genes and relevant oligonucleotides are presented in Table 5. Representative pyrograms are shown in Figure 8A-8F. Table 5 : Oligonucleotides used for pyrosequencing (PCR and sequencing primers)

5'Biot-CTCTATACCCCATAAACACAAATTAATCAT (A) 48

5 '-AAAAATCTCCCAAAACCTTAAAATT (SEQ) 49

5 '-AAGTTTAGTAGTTGTTTTATGGATAG (S) mouse Pdk4 85 50

5 'Biot-ACAACCCCAACCTAATCCCCAAAA (A) 51

5 '-GGGGAAGGGTTGTAG (SEQ) 52

5 ' -G ATTAGGTTTTA A AT AAGTAGAG AG AATT (S) human BCMOl 78 53

5 'Biot-AACAACTCCAACTCTTAAATTAC (A) 54

5 '-AGAATTTAGTGTTTGTTGTTATG (SEQ) 55

5'Biot- designates a 5'-biotinylated oligonucleotide.

(S), sense PC primer; (A), antisense PCR primer; (SEQ) sequencing primer, same polarity as the non-biotinylated primer. Statistical analysis

Quantitative data are presented as average value of replicates ± SEM. Levene's test was performed to assess equality of variance. Two-factor analysis of variance (ANOVA) was used to evaluate effects of diet and genotype on gene expression; strain and diet were also compared in some cases. As indicated in results, Student's ?-test for independent samples was performed for specific comparisons. Analyses were performed using SPSS for WINDOWS software, version 11.0. P- values < 0.05 were considered significant.

The results of the experiments are now described.

Differences between BALB/c Mthfr^+/" FD and Mthfr^+/+ CD gene expression profiles

The microarray results have been deposited in the Gene Expression Omnibus database (GEO) (Barrett et al., 2013 Nucleic Acids Res 41 :D991-5) (GEO accession no. GSE3401 1). There were 63 genes with significant expression changes (51 increased and 12 decreased; Figure 9A in FD Mthfr^+I~ BALB/c mice compared to CD Mthfr^+/+ BALB/c mice (Table 6).

Table 6: List of probe sets showing significant changes comparing Mth fr^+I~ FD and Mthfr^+I+ CD BALB/c mice

79 3a receptor 1

2 family, polypeptide B36

Nrlh4 NM 0091 10371784 nuclear receptor 1.584618 0.00284734

08 subfamily 1, group H,

member 4

Ppmel NM 0282 10565873 protein phosphatase 1.585815 0.002719474

92 methylesterase 1

Opn3 NM 0100 10360454 opsin 3 1.610279 0.003372821

98

Tmeml40 NM 1979 10537227 transmembrane protein 1.612627 0.001248455

86 140

Lhfpl2 NM 1725 10406676 lipoma HMGIC fusion 1.622328 0.00526471 1

89 partner-like 2

Capg NM 0075 10539135 capping protein (actin 1.680293 0.001066554

99 filament), gelsolin-like

OTTMUSG00 NM 0010 10510215 predicted gene, 1.753379 2.60E-05 000010657 83918 OTTMUSG0000001065

7

Zfp442 BC023805 10488459 zinc finger protein 442 1.754461 0.002914242

Tsku NM 0010 10565727 tsukushin 1.800799 0.007461095

24619

Rdhl8 AY05357 10367050 retinol dehydrogenase 18 1.873362 0.000298723

3

Paqr7 NM 0279 10508992 progestin and adipoQ 1.893568 0.004408808

95 receptor family member

VII

AngptU NM 0205 10450038 angiopoietin-like 4 1.903085 0.003396102

81

Cyp4bl NM 0078 10515201 cytochrome P450, family 1.90774 0.000298723

23 4, subfamily b,

polypeptide 1

Hsd3b3 NM 001 1 10500555 hydroxy-delta-5-steroid 2.002854 0.000766519

61742 dehydrogenase, 3 beta- and steroid delta- isomerase 3

Plscr2 NM 0088 10587799 phospholipid scramblase 2.084744 8.88E-06

80 2

Pdk4 NM 0137 10543017 pyruvate dehydrogenase 2.109608 0.000387017

43 kinase, isoenzyme 4

Ces3 NM 0532 10580635 carboxylesterase 3 2.1421 1 0.002719474

00

Hsd3b2 NM 1531 10500547 hydroxy-delta-5-steroid 2.201288 0.000362313

93 dehydrogenase, 3 beta- and steroid delta- isomerase 2

Tdpoz3 NM 2072 10494003 TD and POZ domain 3.363323 2.60E-05

71 containing 3

These 63 genes were grouped based on their functions using IPA (Figure 9B). The top 3 categories were lipid metabolism, small molecule biochemistry and nucleic acid metabolism. Fatty acid metabolism, LPS/IL-1 mediated inhibition of RXR function and PXR/RXR activation were identified as pathways with the most significant changes.

Evidence for involvement of PPARA in tumorigenesis

Gene expression between the C57BL/6 and BALB/c mouse strains that have different sensitivity to intestinal tumorigenesis was compared. It was showed that the PPARA-oxidation pathway plays a critical role (Leclerc et al., 2012, Mol Nutr Food Res 57(4):686-97). The results presented here are based on diet and genotype comparisons in BALB/c, which confirms the involvement of PPARA. The gene expression profiling identified several genes related to PPARA activation and oxidative stress that are affected by diet and/or Mshjr^' genotype (Table 7), as well as PPARA-responsive genes (Table 8). Responsiveness to PPARG was also reported for some of these genes (references lis ted in Table 8).

Table 7: Relative mRNA levels for four genes related to PPAR activation (Bcmol, Aldhlal), oxidative stress sensitivity (Sprr2a) or oxidative stress response (Ugt2b36). Fou 3ALF^/c^ _/?^^r^nice^

Gene Combined effects of diet and Mthfr genotype Other tested effects¹

Microarray^ q-PCR-based reported fold change Strain Diet Genotype

Fold -vahie effect⁵ effect⁴ effect' change

Bcmol LI .9 0.00005 15.0 ¹ : 34 i/2.1 12.6

(Fifi. 2K\ (Fig. lA) (Fig.lA) (Fig.2A)

Aldhlal Π .4 0.009 | 1.7^! f l .8 | 1.8 no effect

(Fig. 4B) (Fig. 4A) (Fig. 4B) (Fig. 4B)

Sprrla j/1.9 0.005 I SO 5 A n.d.

Um2b36 ^■ \ .(■ 0.006 n.d. †3.1 n.d n.d.

(Table S8) n.d.: not determined

1 Leclerc et al., Mol Nutr Food Res. 2012; doi: 10.1002/mnfr.201200212.

² From Table 6, in the present report.

³ qPCR-based assessment, between C57BL/6 and BALB/c, for Mthfr⁺'^~ mice.

⁴ qPCR-based assessment, between BALB/c Mthfr^+I~ CD and FD mice.

⁵ qPCR-based assessment, between BALB/c Mthfr ⁺ FD and BALB/c Mthfr⁺'- FD mice.

6 Figure IB, in the present report. ⁷ Assessment from microarray data.

Table 8: Relative expression of PPAR responsive genes. Four BALB/c Mthfr^+/~ FD mice were compared to four BALB/c Mthfr^+I+ CD animals for testing the combined effect of diet and Mthfr genotype.

Microarrays¹

Fold -value

change

0.00000001

Cc/3³ 0.001

Lipa³ †1.4 0.002

Cyp4fl5 †1.5 0.007 Akrlcl3⁴ †1.6 0.009

Angptl4³ †1.9 0.003

Hsd3b3⁵ †2.0 0.0008

Pdk4^3>5 †2.1 0.0004

C ¾3³ †2.1 0.003

¹ Table 6.

² Downregulated by PUFA (Grintal et al., 2009 Neurochem Int., 54:535-543).

³ PPARA-responsive genes (Rakhshandehroo et al., 2010 PPAR Res. 10:1-20.)

4 PPARG-responsive gene (Rogue et al., 2011 Toxicol. Appl. Pharmacol., 252: 18-31).

⁵ Induced by PUFA in murine small intestine (van Schothorst et al., 2009 BMC Genomics 10:110).

Confirmed expression changes for eight genes that may promote tumorigenesis in Mthfr^+/ FD mice

The expression of eight genes that may influence tumorigenesis was confirmed by qRT-PCR (Figure 10). These genes are involved in regulation of proliferation (Atfi (Bottone et al, 2005 Mol Cancer Ther 2005 5:693-703)), apoptosis (Plscrl and Plscr2 (Huang et al, 2006 Sheng Li Xue Bao 58:501-10), cell survival (Ppme (Chen et al, 2004 Cancer Cell 5: 127-36; Puustinen et al, 2009 Cancer

Res69:2870-7; Westermarck et al, 2008 Trends Mol Med 14: 152-60)), recognition of aberrant unmethylated DNA (Pdctrem (Takeshita et al, 2008 Curr Opin Immunol 20:383-8)), overexpression or chromosomal rearrangement related to cancer (Lhfpl2 (Rizzatti et al, 2005 Br J Haematol 130:516-26; Garcia-Escudero et al, 2008 Mol Carcinog 47:573-9)) or reduction of retinaldehyde levels (Rdhl8 and Akrlcl3 (Porte et al, 2013 Chem Biol Interact 202: 186-94)).

Five additional murine genes and their human orthologs show changes in expression or methylation in mice and in normal intestinal mucosa of CRC patients

It is hypothesized that some of the mechanisms that induce tumorigenesis in the mice might be shared by human preneoplastic colon. To pinpoint the involved genes, experiments were designed to first look at genes identified in the afore-mentioned murine microarray, i.e. affected by diet or Mthfr genotype (Table 6), that would match human orthologs with demonstrated methylation changes in a recent genome-wide profiling of DNA methylation of normal colonic mucosa in CRC patients and controls. The selection was limited to human genes with methylation changes greater than 2% and for which increased/decreased methylation could correspond to decreased/increased expression in murine mucosa. Genes that were related to the PPAR/oxidative stress pathway were of focus. Only three genes passed this stringent screen: PDK4, SPRR2A and NR1H4. The same filters were applied to the list of mouse genes deduced from our previously strain comparison (Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97); this scan generated only two additional candidates: SPRR1A and PYCARD.

Confirmation of microarray data for these five murine genes was undertaken. Pdk4, a target of PPARA, is a positive regulator of glycolytic metabolism (Jeong et al., 2012, Diabetes Metab J 36:328-35). It is up-regulated by FD in mice with both Mthfr genotypes (Figure 1A). Expression oiPdk4 is also higher for Mthfr^+I~ mice than Mthfr^+I+ mice, for both diets. It was previously reported that Sprr2a is down-regulated in BALB/c compared to C57BL/6 (Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97). This ROS scavenger is likely to play a role in tumorigenic events related to oxidative stress. FD lowered Sprr2a levels in both genotypes (Figure IB). Nrlh4, also known as Fxr, has been shown to up-regulate cell growth and induce PPARA (Goto et al, 201 1, Am J Physiol Endocrinol Metab 301 :E1022-32). FD Mthfr^+I~ mice demonstrated higher expression of r than wild-type mice. Folate deficiency also stimulated its expression in Mthfr^+I~ mice (Figure 1C). Sprrla is a gene with a similar role to Sprr2a and decreased levels in BALB/c mice compared to C57BL/6 (Figure ID) was observed. Surprisingly, a marked elevation by FD was seen in C57BL/6 (Figure ID), whereas such a change was not observed for Sprr2 a (Figure IB). PYCARD down-regulation is well documented in CRC (Riojas et al, 2007, Cancer Biol Ther 6: 1710-6). Pycard showed lower expression in BALB/c compared to C57BL/6, in both diets (Figure IE).

The results presented herein demonstrate that the human orthologs of the five mouse genes presented in Figure 1 showed altered methylation levels in colorectal cancer. These epigenetic changes were confirmed by bisulfite

pyrosequencing-based assays for 6 CpGs in the 5 genes in 6 controls and 6 CRC patients. There was excellent correlation between the two methods when β-values from microarrays were compared to % methylation deduced from pyrosequencing data for the 12 individuals: Spearman r = 0.94, a total of 72 points; linear regression r² = 0.93; P < 0.001 (Figure 1 1). A new cohort of 29 controls and 29 CRC patients, who had not been tested in the original arrays were examined by pyrosequencing (Figure 12, left panel for each marker). They were compared to 12 controls and 24 CRC patients that had been tested for these same 6 CpGs by arrays only (Figure 12, right panel for each marker). There are significant differences between controls and patients, for these independent cohorts of 58 and 36 individuals tested by

pyrosequencing and microarrays, respectively.

The pyrosequencing assessment was expanded to a total of 14 CpGs for these 5 genes (for 6 + 29 = 35 controls and for 6 + 29 = 35 patients) as seen in Figure 2. The additional dinucleotides were in the vicinity of the originally interrogated CpGs. All 14 tested CpGs showed significant differences in methylation in normal mucosa between CRC patients and controls (Figure 2A-2E). CpG dinucleotides showed significantly decreased methylation in the normal colon of CRC patients for PDK4 (Figure 2A) and NR1H4 (Figure 2C). Significantly higher methylation was observed for SPRR2A (Figure 2B), SPRR1A (Figure 2D) and PYCARD (Figure 2E). Experiments were designed to question whether some of the above differences could be used as methylation-based biomarkers for presence or absence of polyps in controls. Approximately half of the controls had been found to have polyps. The average methylation of NR1H4 was lower for subjects with polyps, although the difference did not reach statistical significance (Figure 3A). However, an increased average methylation for the five PYCARD CpGs, with significance for two CpGs (16:31 121937 and 16:31 121918), and borderline significance for three CpGs (16:31 121929, 16:31 121927 and 16:31 121902; Figure 3B) were observed.

Transcript levels of these five genes in normal mucosa of CRC patients and controls were compared, and an increased expression for the five markers in CRC patients (Figure 4A-4E) was observed. No expression differences were seen between controls with polyps and those without polyps.

Although average methylation of the five candidate genes was significantly different between CRC patients and controls (Figure 2A-2E), there is some overlap between the two groups, for each of these five markers. It is hypothesized that the methylation pattern for a collection of these markers could have significant diagnostic power to distinguish normal mucosa between CRC patients and controls. Indeed, hierarchical clustering for classification of 70 individuals led to the identification of 2 major clusters: one cluster comprised predominantly of CRC patients and a second cluster composed exclusively of controls (Figure 5A).

The same principle was applied to evaluate the discriminatory ability for classification of 35 controls with (n = 17) or without (n =18) polyps. Empiric combinatory comparison using different sets of these five biomarkers (data not shown) revealed that NR1H4 and PYCARD aggregated data provide the best combination for a potential methylation signature (Figure 5B). Hierarchical clustering yielded one group almost exclusively composed of controls with polyps (8 out of 9 subjects) and a second group enriched in controls without polyps (17 out of 26 subjects).

Epigenetics in mouse/human preneoplastic intestine

DNA-based biomarkers in normal colonic mucosa would be extremely useful because they have the potential to be diagnostic of colon cancer in the near term or, upon further development, may become prognostic indicators of colon cancer risk. Such biomarkers would provide discriminatory and quantitative biochemical measures to supplement the current endoscopic screening test that is both invasive and subjective.

Environmental factors, such as diet, may be the most important influences on CRC risk. Low dietary folate is one such risk factor. The polymorphism in MTHFR (677C-^T) can also increase cancer risk when folate status is inadequate. The present mouse model, which develops intestinal neoplasia after low dietary folate, is a relevant model for human sporadic CRC because the mice do not have germline mutations and develop tumors over an extended period of time, without carcinogen induction. The use of M//z r-deficient mice allows for the examination of gene-nutrient interactions that have also been observed in human CRC. DNA methylation is altered by MTHFR 677C-^T genotype and folate levels, so that folate- deficient TT individuals show the lowest global DNA methylation and the highest prevalence of cancer history (Friso et al, 2013, Cancer Epidemiol Biomarkers Prev 22:348-55). In this study, experiments were designed to move from investigations in mice to identification of some common mechanisms for tumorigenesis in murine and human intestine.

Another dietary risk factor for CRC is high fat (Sung et al, 201 1, Ann N Y Acad Sci 1229:61-8). Expression profiling in mice revealed significant differences for genes downstream of PPARA, a major regulator of lipid and glucose metabolism. It was hypothesized that a disturbance in folate metabolism can result in activation of the RXR/PPARA pathway that increases fatty acid oxidation, generates oxidative stress/damage and enhances glycolysis, setting the stage for tumorigenesis. Tumors have altered energy metabolism, with a preference for aerobic glycolysis (Warburg effect), instead of metabolism of glucose through the mitochondrial tricarboxylic acid (TCA) cycle (Menendez et al., 2013, Cell Cycle 12: 1 166-79). Without wishing to be bound by any particular theory, given the strong link between high fat diets and development of colon cancer, it is believed that the epigenetic reprogramming of lipid and carbohydrate metabolism probably preceded tumor development and was not programmed by the tumor at distant sites (Silviera et al, 2012, Cancer Prev Res (Phila) 5:374-84). Interestingly, only 25% of BALB/c Mthfr^+/- mice developed tumors (Leclerc et al, 2012, Mol Nutr Food Res 57(4):686-97), and the BALB/c Mthfr^+I~ mice used for confirming mouse expression microarray data by qRT-PCR did not have tumors. These findings directly support the hypothesis that gene expression changes and reprogramming occur before tumor formation.

It is striking that the retinoid pathway and metabolism of lipids and carbohydrates were predominant in murine expression profiling (Figure 9B) and human genome-wide methylation assessment (Silviera et al, 2012, Cancer Prev Res (Phila) 5:374-84). Correlation between vitamin A deficiency and tumor initiation has been confirmed in several studies (Altucci and Gronemeyer, 2001, Nat Rev Cancer 3 : 181-93; and references therein). Retinoic acid regulates gene expression through the retinoic acid receptor and retinoid X receptor (RAR/RXR) heterodimer. In addition, RXR can interact with other nuclear receptors such as PPARs (Figure 6). Retinoids cannot be synthesized directly in humans; they are converted from dietary carotenoids (D'Ambrosio et al, 201 1, Nutrients 3:63-103). Beta-carotene is the major provitamin A carotenoid. Retinaldehyde, the product of BCDOl, can prevent formation of the RXR/PPARA heterodimer. Down-regulation oiBcmol, as observed in Mthfr^+I~ and FD mice, would result in lower retinaldehyde levels and increased PPARA activity. Retinaldehyde can be converted to retinoic acid or to retinol by aldehyde

dehydrogenases or aldo-keto reductases, respectively. Higher Aldhlal expression was observed in FD-fed BALB/c mice (Table 7) and higher expression oiAkrlcli in BALB/c Mthfr^+/~ FD mice (Figure 10); these changes would also contribute to lowering retinaldehyde levels. There was increased expression oiRdhl8 in BALB/c Mthfr^+I~ FD mice; retinol dehydrogenases can also metabolize retinaldehyde, although it is unclear whether the Rdhl8 transcript encodes a functional protein (GenBank accession# AY053573). It has been shown that inhibiting BCMOl expression increases invasion and migration in human CRC cells, and that β-carotene, the BCMOl substrate, up-regulates the gene and reverses these effects. These findings provide a more direct role for BCMOl and retinoid metabolism in human colon cancer and suggest that this gene may also have unsuspected roles in later stages of neoplasia.

To identify early human CRC events, the list of murine candidate genes (Table 6) was compared with genes identified in methylation profiling of normal human colon because disturbances in folate metabolism are known to perturb methylation in humans and in the mouse model (Knock et al, 2008, J Nutr 138:653- 58; Bottiglieri et al, 2012, J Neurosci 32:9173-81). This approach identified five mouse and human orthologous genes that showed, respectively, changes in expression in murine microarrays and changes in human methylation profiling. Bisulfite- pyrosequencing of human DNA was performed in independent samples of normal mucosa of cancer patients and controls to confirm altered methylation in the regulatory regions of these genes, and methylation differences in a total of 14 CpGs were identified (Figure 2). These genes were: NR1H4 which can activate PPARA (Goto et al, 2011, Am J Physiol Endocrinol Metab 301 :E1022-32); PDK4, a target of PPARA that enhances glycolysis (Jeong et al, 2012, Diabetes Metab J 36:328-35); PYCARD, a pro-apoptotic gene (Riojas et al, 2007, Cancer Biol Ther 6: 1710-6); and two different members of the SPRR family, involved in protection against oxidative damage. While it is possible that the different methods and sites of biopsy could result in samples with different cell types, the analysis of methylation levels at more than 27,000 CpGs revealed differences for only 909 (3.3%) between cancer and control specimens at our least stringent statistical threshold (Silviera et al, 2012, Cancer Prev Res (Phila) 5:374-84). To address the issue of tumor sidedness, experiments were designed to determine whether right and left side biopsies give similar results and whether this is independent of tumor location. Experiments were performed to examine the normal colon from a small group of CRC patients with left- or right-sided tumors (n=15 in each group); Figure 16. It was observed that methylation levels are similar in both biopsies, which demonstrates the ability to detect methylation of the desired gene regardless of the sidedness of the biopsy. When the 14 CpGs between groups were compared, there was only one marker (one of the three CpGs in

SPRR2A) that showed a significant difference in methylation. Aging is another factor that can influence DNA methylation and modify CRC risk (Wallace et al, 2010

Cancer Prev Res 3: 1552-64). However, since the age ranges in controls and patients were similar and there was no difference between the mean ages of controls and cancer patients in the original study (Silviera et al., 2012 Cancer Prev Res 5:374-84), from which the five candidates were selected for validation in this study, it is unlikely that age was a major confounder.

Of additional interest was the fact that some of the controls had formed polyps, and one of the above genes, PYCARD, showed significant methylation differences between controls without polyps and controls with polyps (Figure 3). Interestingly, controls included two cases of HPP (hyperplastic polyps), thought not to give rise to colon tumors, and they are at the very low end of the normal PYCARD methylation distribution while other controls with polyps are all in the upper part of the normal PYCARD distribution (Table 1). Furthermore, in the heat map in Figure 5B, these 2 subjects cluster with the controls without polyps.

A major outcome of this study is the potential for using the methylation differences per se as molecular biomarkers for diagnostic purposes. Although some of the methylation differences were relatively small, they were observed in two different cohorts, using 2 different methodologies (Figures 11 and 12), and are therefore reliable. These five genes also exhibited significant expression differences in normal human mucosa between controls and CRC patients, with increased expression in CRC patients (Figure 4). The increased expression was associated with both decreased and increased methylation. While it is true that there is a general (but not absolute) inverse correlation between DNA methylation in the promoter region and transcript levels, the present result is not unusual because methylation within the body of genes and distal to genes is often positively correlated with transcript levels (Hahn et al, 2011 PLoS One 19:el8844. doi:

10.1371/journal.pone.0018844; Jjingo et al, 2012 Oncotarget 3:462-74). In addition, specific mechanisms involving binding efficiency of transcriptional repressor(s) to methylated regions for example, may result in changes in expression (Pipaon et al, 2005 FEBS Lett 579:4610-14).

Increased expression oiPDK4 and NR1H4 in normal mucosa of CRC patients and in Mthfr^+I~ mice fed FD may be highly tumorigenic. As mentioned, the shift away from mitochondrial respiration is a hallmark of tumor metabolism

(Menendez et al, 2013, Cell Cycle 12: 1166-79; Fujiwara et al, 2013, Br J Cancer 108: 170-8). Inhibition of pyruvate dehydrogenase (PDH) through phosphorylation, by pyruvate dehydrogenase kinases (PDKs), results in decreased respiration in tumors (Fujiwara et al, 2013, Br J Cancer 108: 170-8). PDKs are a family of four kinases in humans (Jeong et al, 2012, Diabetes Metab J 36:328-35); siRNA-based knockdown oiPDKl reverses PDH inhibition and the Warburg effect, and can inhibit tumor growth (Fujiwara et al, 2013, Br J Cancer 108: 170-8). PDK4 expression is increased by PPARA, by consumption of high fat diets and in diabetic states (Jeong et al, 2012, Diabetes Metab J 36:328-35); its role in transformation has not been well studied. Decreased methylation oiPDK4 in human CRC mucosa is consistent with the increased expression. Decreased methylation in the 5' region oiPdk4 in Mthfr- deficient mice for both diets was also observed (data not shown).

NR1H4 encodes the farnesoid-X -receptor (FXR). Bile acids, natural ligands for this important entero-hepatic regulator, can induce PPARA expression through a FXR response element in the human PPARA promoter (Goto et al, 2011, Am J Physiol Endocrinol Metab 301:E 1022-32). NR1H4 activation can increase PDK4 expression (Mencarelli et al, 2013, Nutr Metab Cardiovasc Dis 23:94-101). Decreased methylation oiNRlH4 in human CRC mucosa is consistent with the increased expression. In mice, increased Nrlh4 methylation was observed for Mthfr^+I~ FD mice compared to Mthfr^+I+ FD mice, as well as a trend for increased methylation at two CpGs in the 5' region of the gene, in Mthfr^+/~ FD mice compared to Mthfr^+/~ CD mice (data not shown).

Because tumorigenesis is a complex process that involves multiple pathways, there are certainly other genes/pathways identified through microarrays in mice or humans that could contribute to tumor development. For example, in Figure 10, six genes, in addition to Rdhl8 and Akrlcl3 (already mentioned), are shown with confirmed gene expression changes due to folate or Mthfr deficiency. Αίβ can enhance p53- mediated effects by blocking p53 degradation (Wang et al., 2010, J Biol Chem 285: 13201-10), and may contribute to maintenance of genome integrity. Plscrl and Plscr2, two scramblases involved in cell growth and apoptosis, were up-regulated in Mthfr^+/- BALB/c mice fed FD. This result is consistent with PLSCR1

overexpression in CRC patients (Kuo et al, 2011, Mol Med 17:41-7). Another gene involved in cell growth, PPMEl, plays a critical role in maintaining the ERK pathway through inhibition of PP2A (Janssens et al, 2005, Curr Opin Genet Dev 15:34-41). PPMEl activation is correlated with astrocytic glioma progression (Puustinen et al, 2009, Cancer Res 69:2870-7). Pdctrem is involved in the unmethylated DNA- triggered innate immune response (Watarai et al., 2008, Proc Natl Acad Sci U S A 105:2993-8). LhfpU function is not understood but its human ortholog seems to be involved in leukemias (Garcia-Escudero et al, 2008, Mol Carcinog 47:573-9).

Another member of the family (LHFP) is the target of a lipoma-associated translocation (Petit et al, 1999, Genomics 57:438-41).

The cluster analysis of bisulfite pyrosequencing-based DNA methylation data provides an initial epigenetic signature for distinguishing normal mucosa from controls versus normal mucosa in CRC patients (Figure 5A). Additional methylation markers could improve the power of this diagnostic assay, and some of the genes discussed elsewhere herein, as well as other genes identified in our microarrays, could potentially improve the discriminatory power of the diagnostic assay. Clustering of controls without polyps versus those with polyps (Figure 5B) is also of interest, but requires additional markers. Experiments were performed to use a systematic human genome-wide methylation marker discovery study with patients that were not screened for a specific cause of CRC, although none of the subjects had a history of familial cancer, colon polyps or inflammatory bowel disease (Silviera et al, 2012, Cancer Prev Res (Phila) 5:374-84). Candidate genes were intersected with expression profiling data from the mouse model. This original, two-filter approach, in addition to the elucidation of common pathways, provides a solid basis for using these biomarkers as part of an epigenetics signature for normal intestine in colon cancer. Since CRC development proceeds through multiple stages, it would be useful to develop diagnostic tests for effective early intervention. Although much work has been done to assess methylation differences between normal colon and tumors, there are very few genes that have been confirmed by quantitative methods to exhibit methylation differences between controls and CRC patients in normal mucosa (Shen et al, 2005, J Natl Cancer Inst 97: 1330-38; Silviera et al, 2012, Cancer Prev Res (Phila) 5:374-84). The present approach has identified 5 genes, at 14 CpG sites, using the highly quantitative pyrosequencing method which could easily be adapted into a clinical setting. Methylation changes may accumulate over years and could serve as sentinel markers prior to the appearance of polyps or other anomalies. Shedding of colon-derived DNA into stool could allow non-invasive testing (Jain et al, 2013, Expert Rev Mol Diagn 13 :283-94). If some of these differences are systemic, measurements of methylation changes in peripheral blood or saliva would also be extremely useful. The results presented herein provided markers for the establishment of a biochemical measurement of cancer risk that is more objective than routine endoscopy. Example 2: Validation of methylation biomarkers that distinguish normal colon mucosa from cancer patients from normal colon mucosa of patients without cancer

The results presented herein demonstrate that differences in DNA methylation levels of 30 candidate genes reported to discriminate between normal colon mucosa of colon cancer patients and normal colon mucosa of individuals without cancer have been validatee. The results presented herein demonstrate that CpG sites in 16 of the 30 candidate genes show significant differences in mean methylation level in normal colon mucosa in an independent cohort of 24 cancer patients and 24 controls. A support vector machine trained on these data and data for an additional 66 CpGs set yielded an 18-gene signature, composed of 10 of the validated candidate genes plus eight additional candidates. This model exhibited 96% sensitivity and 100% specificity in a 40-sample training set and classified all eight samples in the test set correctly. Moreover, a moderate-strong correlation (Pearson coefficients r=0.253 -0.722) between methylation levels in colon mucosa and methylation levels in peripheral blood for seven of the 18 genes in the support vector model was observed. These seven CpGs, alone, classified 44 of the 48 patients in the validation set correctly and five CpGs selected from only two of the seven genes classified 41 of the 48 patients in the discovery set correctly. These results suggest that methylation biomarkers may be developed that can, at minimum, serve as useful objective and quantitative diagnostic complements to colonoscopy as a cancer screening tool. These data also suggest that it may be possible to monitor biomarker methylation levels in tissues collected much less invasively than by colonoscopy.

The materials and methods employed in these experiments are now described.

Materials and Methods

Description of Cancer Patients and Controls and Tissue collection The "normal" mucosa specimens from patients with colon cancer were collected from colon tissue removed in the operating room. Normal appearing colonic mucosa away from the tumor tissue was sharply removed and the samples were snap frozen prior to DNA isolation. Patients with a known history of FAP (familial adenomatous polyposis) or HNPCC (hereditary non-polyposis colon cancer) were excluded.

Normal colon mucosa control specimens were collected from patients undergoing screening colonoscopy. Each patient was interviewed prior to the procedure. Patients who reported a personal or family history of colon cancer were excluded. Patients with a personal history of colon polyps or inflammatory bowel disease were also excluded. After providing informed consent, each patient underwent complete colonoscopy by a board-certified gastroenterologist. During that procedure, mucosal biopsies were obtained with a radial jaw large capacity biopsy forceps (Boston Scientific). Specimens were placed into RNALater RNA Stabilization Reagent (Ambion, USA), and stored at 4° C prior to DNA isolation. Peripheral blood samples were also collected at this time and DNA was extracted by standard procedures (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84).

Patient data for samples used in this study are presented in Table 9. Patients were matched for sex and as close as possible for age, although cancer patients were, on average, five years older than controls. None of the patients examined in the present study were examined in the previous study (Silviera ML, et al., Cancer Prev Res, 2012, 5(3):374-84). All patient materials were collected with the approval of Temple University IRB protocol 11910 or Fox Chase Cancer Center IRB protocol 1 1-866. Table 9. Description of Controls and Cancer Patients

LGD NOS

Adenoca

rcinoma,

982 M 61 polyp 32 M 70 NOS Sigmoid colon

Mucinou

Tubular s

adenoma w/ adenocar

979 M 62 LGD 31 M 72 cinoma Sigmoid colon

Tubular Adenoca

adenoma w/ rcinoma,

941 M 62 LGD 46 M 72 NOS Sigmoid colon

Adenoca

rcinoma, Descending

987 M 63 HPP 56 M 72 NOS colon

Adenoca

rcinoma, Descending

976 F 64 polyp 44 M 73 NOS colon

Tubular

adenoma w/

LGD/ Adenoca

serrated rcinoma,

953 M 65 ademona 16 M 74 NOS Sigmoid colon

Tubular Adenoca

adenoma w/ rcinoma,

930 M 70 LGD 29 F 74 NOS Sigmoid colon

Adenoca

rcinoma,

923 F 75 normal 15 M 75 NOS Sigmoid colon

Adenoca

rcinoma, Descending

947 F 86 normal 49 F 83 NOS colon

Sample Preparation, Bisulfite Conversion

Tissue samples were rinsed with sterile saline and blotted dry prior to nucleic acid extraction. DNA was extracted using standard phenol-chloroform techniques. The isolated DNA was dissolved in lOmM TrisCl(pH 8.0). Samples were quantified by spectrophotometry and stored at -80°C until ready for use. The EZ DNA Methylation-Gold Kit™ (Zymo Research, USA) was used to convert unmethylated genomic DNA cytosine to uracil. Site-specific CpG methylation was analyzed in the converted DNA template (5μ1 at 50ng^l) using the

Veracode Array Analysis

Site-specific CpG methylation was analyzed in the converted DNA template (5μ1 at 50ng^l) using a custom Veracode Array (Illumina, Inc., San Diego, CA) at the Children's Hospital of Philadelphia Center for Applied Genomics.

Methylation levels (beta- values: fraction of methyl CpG at each site tested) were assessed at 96 CpGs. Thirty of the CpG sites were selected because they fulfilled statistical criteria as significantly different between cancer patients and controls in our original study (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84). The additional 66 CpGs were selected as of interest for other studies (Turan, N., et al, PLoS Genetics, 2010, 6:e 1001033, Turan N, et al, BMC Med Genomics, 2012, 5(1): 10 and Sapienza C, et al, Epigenetics, 201 l,6(l):20-8) but some of these sites also differed significantly between cancer patients and controls in our original study (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84). Statistical Analysis

Because the present study is a validation experiment, in which we have a prior expectation for the direction of the difference between the mean methylation levels of cancer patients versus controls, one-sided, paired t-tests were performed. A -value of 0.05 was considered significant for the validation study.

Binary Classification of Cancer vs. Control Samples

A Support Vector Machine (SVM) with Recursive Feature Elimination (RFE) (Guyon I, et al, Mach Learn, 2002, 46(1-3): 389-422) was used to classify samples. Random, 10-fold, cross-validation was repeated 10 times and a score was calculated for each tested sample at each reduction step. Average accuracy was calculated at each step and all the genes at the point of maximal accuracy were used as initial discriminator. A sub-sequential step to reduce the final discriminator to the minimum number of genes, for which the accuracy remains the same, was applied (Showe MK, et al, Cancer Res., 2009,69(24):9202-10).

Validation of the SVM Classifier

20% of the original dataset was not included in the creation of the discriminator, but we used these samples, together with other samples coming from an independent platform, to test the quality of the signature.

The signature was applied as an equation of the form:

X=a[A] + b[B] + c[C] ... + z[Z] + constant Where A,B,C...Z are the methylation level and a,b,c...z the coefficient associated with each value. If the classification score (X) calculated for each sample is higher than 0, the sample will be declared as cancer, if less than 0 as control. The higher the score, the greater the confidence that the sample is cancer, the lower and more negative the score, the greater the confidence the sample is control (Showe MK, et al, Cancer Res., 2009,69(24):9202-10).

The results of the experiments are now described. Validation of Previously Identified Candidates

Methylation levels across 27,578 CpG sites in DNA extracted from normal colon mucosa of 30 colon cancer patients and 18 controls were profiled.

Significant differences in mean methylation level between cancer patients and controls were identified using three different statistical thresholds (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84): 1 19 CpGs in 1 14 genes differed after

Bonferroni correction for multiple testing; 909 sites in 873 genes differed after applying the Benjamini-Hochberg false discovery rate of 0.05, and; 299 sites in 65 genes differed after applying the ad hoc criterion that genes in which three or more CpGs differed significantly at <0.05 (for a nominal significance of 0.05 X 0.05 X 0.05 = 1.25 X 10^"4). From these gene/CpG lists, 30 CpGs in 30 genes were selected (Table 10) to test in an independent sample of normal colon mucosa from 24 cancer cases and 24 case-matched controls (Table 9). The 30 CpGs were selected from those having the largest magnitude of difference between means in the original study (Tables 1 and 2 in reference (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374- 84)), as well as a selection of CpGs in genes of additional interest that were significantly different in the original study (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84).

Methylation levels at the individual CpG sites in the normal colon mucosa of the 24 cancer patients and 24 matched controls (Table 9) were assayed on bisulfite-converted DNA using a custom-designed Illumina high-throughput

"Veracode" array (see Materials and Methods). Comparison of mean methylation levels between the normal colon mucosa of cancer patients and controls revealed that mean methylation levels of 16 of the 30 CpG sites tested (53%) differed significantly in the independent population (last column, Table 10). The success rate for validation was approximately the same regardless of whether the original statistical threshold was very stringent (Bonferroni; 8/14 candidates validated), moderately stringent (Benjamini-Hochberg; 2/4 candidates validated) or our ad hoc statistical threshold requiring a modest level of significance P<0.05) for at least 3 CpGs in the candidate gene (6/12 candidates validated).

Table 10. The 30 CpGs selected for independent validation whose methylation levels differed significantly between normal colon mucosa of cancer patients versus controls in (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84). Genes/CpGs in bold differed significantly in the validation.

*one-tailed T-test was used because there was an expected direction of difference in this validation. 8/14 Bonferroni candidates, 2/4 Benjamini-Hochberg candidates and 6/12 "3 CpGs<0.05" candidates validated (16/30 validated overall). Support Vector Machine Model

The Illumina Veracode array used for validation contained 66 CpGs in addition to the 30 shown in Table 10. While these CpGs were not selected by using the criteria outlined in the previous study, almost all of the genes containing these additional CpGs were profiled on the Infinium array in the original study and a number of them exhibited statistically significant mean methylation differences between cancer patients and controls.

A support vector machine was trained on the 96 CpG array data using 20 of the 24 cancer patients and 20 of the 24 controls (Table 9). The SVM identified 18 CpGs with optimum performance (96% sensitivity, 100% specificity; Figure 17A) in classifying cancer patients and controls correctly (one cancer patient in the training set was misclassified as a control). These 18 CpGs (Table 11) consist of 10 of the original 16 validated candidates (Table 10) and eight additional candidates. Three of the eight additional candidates (TIMP4, NMUR1 and EDA2R) also exhibited significant methylation differences between cancer patients and controls in the original study (last column Table 1 1). Methylation levels at these 18 CpGs classified all eight patients in the test set correctly (Figure 17B).

Table 11. The top 18 CpGs/genes selected by a support vector machine to classify cancer samples and controls. The 10 CpGs/genes in bold are from the validated class in Table 10.

ITGB4 cgl2146151.CPG.60 0.006 0.451

LGALS2 cgl l081833.CPG.360 0.045 0.994

MGC9712 cg06194808.CPG.60 9 x 10-4 4 x 10-4

NMUR1 cgl0642330.CPG.360 0.017 0.72

RASSF5 cgl7558126.CPG.60 0.022 1.19E-05

SLC16A3 cgl8345635.CPG.60 1.7 x 10-4 7.28E-05

SULT1C2 cgl7966192.CPG.60 0.002 0.016

TIMP4 cg25982743.CPG.60 0.004 0.019

VAV1 cgl3470920.CPG.60 1.7 x 10-4 1.76E-05

VHL cgl6869108.CPG.60 5 x 10-5 5 x 10-4

VMD2 cg09726693.CPG.60 0.001 N/A

Experiments were designed to further test the utility of this 18-gene model by selecting all 65 CpGs interrogating these 18 genes on the original 27K discovery array (on which 30 cancer patients and 18 controls were profiled) from which the 10 validated genes were selected (Table 10). The support vector machine selected 39 CpGs in 16 of the genes (Table 12) as the optimum model (93% sensitivity, 94% specificity; Figure 18). Of note, only four CpGs in the INS gene were interrogated on the array and all four were selected for the SVM model. Multiple CpGs in RASSF5 (five selected of nine interrogated), VHL (five selected of seven interrogated), GRB10 (four selected of 12 interrogated) and CASP8 (three selected of six interrogated) were also selected. In addition, seven of the genes (VA V ,

MGC97112, SULT1C2, SLC16A3, ITGB4, ANKRD15 and ENPEP) were interrogated by only two CpGs on the array and both CpGs in each of these seven genes were selected.

Table 12. Support Vector Machine 39 CpG/16 gene signature selected from the 66 CpGs interrogated in the 18 genes in the original study (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84).

INS cg00613255 0.783478 6 0.000104204

SLC16A3 cgl4417329 0.643913 7 1.77E-05

INS cg25336198 0.609565 8 7.29E-05

ITGB4 cg23913400 0.604783 9 0.101322

ANKRD15 cgl 7694279 0.603043 10 0.196011

RASSF5 cg02589695 0.582609 11 1.97E-05

GRB10 eg 15774495 0.560435 12 0.00100709

TIMP4 cg25982743 0.513913 13 0.0195887

GRB10 cg25915982 0.49913 14 0.26932

ENPEP cgl 7854440 0.487391 15 1.26E-06

NMUR1 cg00143376 0.47913 16 0.0233898

VAV1 cgl 3470920 0.478696 17 1.76E-05

RASSF5 cgl7558126 0.451304 18 1.19E-05

CASP8 cg22898761 0.416087 19 0.950046

VHL cgl6869108 0.408696 20 0.000488308

GRB10 cg26163537 0.408261 21 0.14081

RASSF5 cg22857604 0.408261 22 0.167953

CASP8 cg23410113 0.401739 23 0.987952

CASP8 cg26799474 0.395217 24 7.16E-05

VHL cg03509024 0.376087 25 0.421604

ITGB4 cgl2146151 0.370435 26 0.451674

SLC16A3 cgl 8345635 0.332174 27 7.28E-05

INS cgl 3993218 0.312174 28 0.000309269

VHL cg25539131 0.307826 29 0.533049

GRB10 cgl2903171 0.304348 30 0.0434602

RASSF5 cg24450312 0.302174 31 0.570832

RASSF5 cgl 0167296 0.29913 32 0.588351

MGC9712 cg06194808 0.294348 33 0.000437807

VHL cg20916523 0.293043 34 0.0112254

SUL 1C2 eg 17966192 0.274783 35 0.0160635

ANKRD15 cg26381783 0.26913 36 0.697584

IGFBP5 cgl 9008649 0.261739 37 0.261926

ENPEP cg20773127 0.254783 38 0.0440234

LGALS2 cgl l081833 0.254348 39 0.994267

Correlation Between Methylation Levels in Colon Mucosa and Methylation Levels in Peripheral Blood

The methylation levels of the 96 CpGs were profiled on the Veracode array in DNA extracted from peripheral blood on 15 of the patients without cancer, as well as normal colon mucosa on the same 15 patients. The CpG site-specific methylation levels between the two tissues were compared and a number of genes were identified in which methylation levels between the two tissues were correlated strongly. In fact, 14 of the 96 CpGs showed strong positive correlation (Pearson correlation, r>0.5) between methylation levels in normal colon mucosa and methylation levels in peripheral blood. Of the CpGs selected by the original 18 CpG support vector machine (Figure 17 and Table 11), seven exhibit moderate to strong methylation correlations between tissues (Table 13 and Figure 19) and use of colon methylation levels of these seven seven CpGs, only, results in correct classification of 40 of 48 patients (sensitivity 92%, specificity 87%; Figure 20) profiled on the Veracode array. Use of a seven CpG SVM (data for the identical CpG or nearest CpG interrogated on the Infinium 27K array) to classify patients profiled on the discovery array (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84) results in a sensitivity of 83% and a specificity of 61%. Allowing an SVM to be optimized from all 31 CpGs interrogated in these seven genes resulted in a model with 87% sensitivity and 83% specificity (Figure 21). Table 13. The seven CpGs from the optimum 18 CpG SVM model (Table 11, Figure 17) in which there is a moderate to strong correlation between methylation levels in normal colon mucosa and eri heral blood

Validation of multiple DNA methylation differences

Multiple DNA methylation differences in normal colon mucosa between colon cancer patients and patients without cancer were validated. A significant fraction of these differences (53%) have been confirmed in a sample of 48 patients, none of who were examined in the original discovery study (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84). The fact that approximately half of the CpGs tested exhibit significant differences in mean methylation level in an independent set of samples is a substantial success rate, even for discovery genes judiciously selected (Table 10) from whole-genome profiles. Methylation differences at two additional candidate CpGs, one in IGF2 and one in KCNQ1 (Table 10), approached but did not achieve significance ( =0.06 and =0.08, respectively). Whether the failure to validate additional CpGs reflects a spurious result in the original study or true differences between small sub-populations of colon cancer patients and controls sampled from the overall population in which true differences in means exist cannot be determined from only two independent samples. However, it was observed that the degree of overlap in CpG site methylation level between cancer and control populations was substantial for many loci ((Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84) and data not shown). As a result, it is expected that many statistically significant differences between population means will be difficult to validate in independent populations if they have similar variance, even if there are true differences in population mean.

Given this circumstance, it is likely that multi-gene models like those presented here will be required to classify patients with the level of accuracy required to be useful in the clinic. The number of methylation biomarker genes that will be required will be dependent on the discriminatory power of the markers but clinically useful distinctions for some clinical outcomes are made currently on the basis of measuring transcript levels of only 12-23 genes (http://www.oncotypedx.com/). With respect to methylation biomarkers, there are significant advantages to using DNA, rather than RNA, as a diagnostic molecule (Issa JP, J Clin Oncol, 2012, 30:2566- 2568). In addition to the relative stability of DNA compared with RNA, DNA methylation level, like mRNA level, is a continuous variable. However it exhibits considerably lower variance than population mRNA levels (e.g., Cheung VG, et al, PLoS Biol, 2010,8(9), reviewed in Turan N, et al, Epigenetics, 2010, 5(1): 16-9), in part because DNA methylation levels are constrained between 0 and 1.

When assembling a panel of methylation biomarkers, an additional consideration is whether interrogation of a single or a small number of sites in a larger number of genes (as was the case with early array -based methods

(http://www.illumina.com/technology/infinium_methylation_assay.ilmn), methylation-sensitive restriction endonuclease-based methods (e.g., Herman JG, et al, Curr Protoc Hum Genet., 2001, Chapter 10, Unit 10.6 (PMID: 18428243)) and most bisulfite pyrosequencing assays (Dupont JM, et al, Anal Biochem., 2004, 333(1): 119- 27)) or interrogation of a greater number of sites in a smaller number of genes (larger or custom arrays

(http://www.illumina.com/products/methylation_450_beadchip_kits.ilmn and http://res.illumina.com/documents/products/datasheets/veracode_dna_methylation_pr ofiling.pdf) or multiple bisulfite pyrosequencing assays) would be superior. Although we have observed that it is often the case that methylation levels of different CpG sites within the same CpG island are highly correlated (Silviera ML, et al, Cancer Prev Res, 2012, 5(3):374-84, Turan, N., et al, PLoS Genetics, 2010, 6:el001033 and Katari, S., et al, Hum Mol Genet., 2009, (Epub ahead of print) PMID: 19605411 , PMCID: PMC2748887) and would, therefore, be predicted to add little additional information, it is possible that interrogation of additional CpGs will add predictive power if the additional information is not completely redundant. It is possible to make a preliminary assessment of whether single CpG sites in the 18-gene model perform as well as the 39 CpG sites in 16 of these genes. Figure 22 shows the result of using a single CpG in 17 of the 18 genes (VMD2IBEST1 is not interrogated on the Illumina 27K array used in the original study) in classifying the 30 cancer patients and 18 controls in our original study. Comparing this result with that in Figure 18, it can be observed that the specificity is the same (94% success in classifying controls) but the sensitivity drops from 93% to 83% (five cancer patients misclassified versus two cancer patients misclassified). Thus, for this particular set of candidate genes, additional precision is gained by assessing methylation at more than one site per gene. Even if the number of individual CpGs/individual genes (38 CpGs/38 individual genes, Figure 23 (again, VMD2IBEST1 is not present)) is expanded to the same number used in Figure 18, specificity remains at 94% but specificity rises to only

86%, suggesting that that interrogating multiple CpGs per gene is a superior approach to interrogating individual CpGs in a larger number of genes.

An additional consideration for whether candidate methylation biomarkers such as those identified here are clinically useful is whether they can be assessed in tissues collected less invasively than by colonoscopy. Although there are multiple factors associated with uptake of the test, including education, insurance coverage and ethnicity (Gellad ZF, et al, Gastroenterology, 2010, 138(6) :2177-90), the fact that less than half of those patients recommended to have a screening colonoscopy are compliant

(http://www.screen4coloncancer.org/background_screening.asp) suggests that uptake of a less invasive test, such as might be performed on peripheral blood or saliva, might be substantially greater. There is much interest and some progress in developing such biomarkers (Lange CP, et al, PLoS One, 2012, 7(1 l):e50266, Toth K, et al, PLoS One, 2012, 7(9):e46000 reviewed in Hong L, et al, Genet Test Mol Biomarkers., 2013, 17(5):401-6). As far as whether such biomarkers give a realistic picture of methylation levels in the organ of interest, it is often assumed that methylation levels are tissue-specific but there are many sites for which methylation levels vary between individuals but do not vary substantially between tissues of the same individual (Waterland RA, et al, PLoS genetics, 2010, 6(12):el001252 and http://www.ncbi.nlm.nih.gov/epigenomics). The finding that a significant fraction of candidate biomarkers (seven out of 18, Tables 10 and 13, Figure 19) show a strong correlation between methylation levels in colon mucosa and methylation levels in peripheral blood offers the possibility that methylation levels of candidate biomarkers in tissues collected less invasively could serve as a proxy not only for colon, but for any other tissue or organ that might be difficult or impossible to biopsy.

One clear advantage of cancer screening by colonoscopy is that premalignant lesions that are detected can be removed before they have a chance to progress to cancer. In this study, experiments were designed to define a support vector machine to classify which individuals in the group of controls carried polyps but its performance was mediocre (data not shown): 12 of the controls had polyps (two with hyperplastic polyps, eight with tubular adenomas and two with polyps that were not described histopathologically) and 12 did not (Table 9). A support vector machine using six CpGs selected from the 96 on the array was able to classify 8 of the 12 polyp-carrying individuals correctly. The two individuals with hyperplastic polyps

(which are not thought to give rise to malignancy) were classified as controls but four of the controls were also classified with the polyp group. It seems likely that additional large-scale screens such as that performed by Lange et al. (Lange CP, et al, PLoS One, 2012, 7(1 l):e50266) may be necessary to identify markers of sufficient discriminatory power to make the more subtle distinction between individuals without cancer but with premalignant lesions from individuals who are cancer- free and polyp- free. However, the potential benefits of non-invasive, large-scale screening for relative cancer risk could be highly significant in reducing disease burden. Example 3 : Metabolic genes

Experiments were designed to expand the list of "metabolic gene" candidates that appear to suffer epigenetic changes associated with dietary environmental factors, validate these differences in an independent population and test the efficacy of a panel of such validated markers in diagnosis of cancer. Of the top candidate genes identified in the screen, three "metabolic genes" (APOA1, INS and SLC22A18; Table 14) fulfilled the strict statistical criteria (Bonferroni correction for multiple testing- APOA I; three or more CpGs

differentially methylated at <0.05 - SLC22A18 and INS). Three additional genes from the "metabolic gene" class (LEP, G6PC and CEBPA, Table 14) were identified on the basis of mean transcript level differences between groups, rather than methylation differences. As it turns out, CpGs profiled at these three differentially transcribed loci are also differentially methylated, albeit at lower statistical significance (Table 14). The mean transcript level difference validation oiLEP, CEBPA and G6PC (as well as INS) is all the more powerful because the Realtime RT- PCR estimates of transcript level were obtained on a different set of individuals than those upon which methylation differences were measured or upon which the PCR transcription array was performed. These data suggest that many of the between group methylation differences that do not pass the strict statistical criteria demanded by multiple testing (i.e., three of the six genes in Table 14) are, nevertheless, true methylation differences and are robust enough to predict differences in transcript level.

Table 14 "Metabolic candidate " genes

*The fold-change in transcript level in INS is opposite to that expected for promoter methylation, however, the CpGs profiled in INS are not in a CpG island and methylation of the CpG profiled in CEBPA (in the 3 ' end of the gene) is positively correlated with transcript level. Mean beta values for INS and SLC22A18 are for the CpG with lowest P. Additional significant CpGs were present in both INS and SLC22A18 (14). n.d.: not done

Extending this logic, a more comprehensive list of "metabolic candidates" that could serve as a panel of "dietary risk factor biomarkers" for colon cancer risk was compiled. Given the success of more modest differences in mean methylation level to mirror significant differences in transcript level (Table 14), experiments were designed to reanalyze the Illumina Infinium 27K methylation array data using modified selection criteria: individual CpGs were sorted by absolute magnitude of mean methylation difference (under the assumption that larger differences are more likely to be validated by an independent method) between cancer and control mucosa, followed by -value, then filtered for annotations containing "glucose" or "lipid". In addition to the six genes in Table 14, seventeen additional "metabolic candidates" were identified at which DNA methylation differences are present when normal colon mucosa of cancer patients is compared with normal colon mucosa of controls (Table 15).

All 23 genes appeared in two highly connected networks when subjected to Ingenuity Pathway Analysis (not shown), suggesting that the presence of methylation differences between cancer mucosa and control mucosa reflects underlying metabolic differences, lipid and carbohydrate pathway-wide, between the two groups of individuals. Note that two of these genes (IRS2 and PDPK1, in Table 15) were profiled on the PCR transcription array in a previous study and that, although not validated independently (the PCR array compared pooled cDNA samples from mucosa of only six individuals from each group), transcript level differences appear in both candidates, in the expected direction (assuming an inverse correlation between promoter DNA methylation level and transcript level).

Although the absolute magnitude of DNA methylation differences between-groups is not large (the largest mean difference is 0.12 at LRP6 and ADPN), differences of this magnitude have been demonstrated to be associated with significant differences in transcript level in multiple studies. All six of the

differentially methylated genes in Tables 14 and 15 for which transcript level has been assessed, either by Realtime RTPCR of 20 cancer samples and 20 controls or PCR array analysis using pooled samples, show differences. Moreover, although the distribution of beta- values for cancer and control samples overlaps for all 23 candidates (Tables 14 and 15), it is still possible to classify all of the cancer samples correctly using only a modest number of markers. For example, using only the eight candidates with the cancer/control methylation level distributions shown in Figure 24 and the stringent criterion that any cancer sample that falls within the range of 10% of control samples is mis-classified, all 30 cancer samples fall outside this range for at least two of the eight markers (i.e., all cancer samples appeared outside of 90% of the control group for at least two markers; nominal -value that a cancer sample is misclassified as a control =0.1 X o. l =0.01), and more than 50% of samples are correctly classified for five or more of the eight markers ( <10^~5 that a cancer sample is misclassified as a control)). It is apparent that validation of even half of the candidates in Tables 14 through 16 would lead to a collection of diagnostic and prognostic biomarkers that would be robust enough to be used in a clinical setting.

Table 15. "Metabolic candidate " enes identi ed usin modi ed criteria

Table 16: Validated"metabolic candidate" CpGs in either normal colon mucosa or eri heral blood.

cg22171829' PDK4 1.8236E-05 0.18689655 0.13439024 0.0016413 0.4348 0.35684211 cg25802424 IRS2 0.17543976 0.17666667 0.17051282 3.9459E-08 0.20482759 0.13647059 cg25802424' IRS2 0.01852393 0.02645161 0.04641026 1.5482E-16 0.03103448 0.13588235 cg25802424" IRS2 0.00459765 0.044 0.08297297 8.4793E-21 0.04857143 0.20888889 cg01348757' PUN 0.37223105 0.66090909 0.67627907 0.02243254 0.77137931 0.8945

_Cgl4444710 PDPK1 0.31604243 0.19727273 0.19452381 1.2154E-06 0.21269231 0.19105263 cg21627181 SLC17A4 0.01503802 0.78424242 0.81121951 1.2791E-06 0.84541667 0.94105263 cg20551517 GIP 0.22642646 0.7228125 0.70372093 9.464E-25 0.88655172 0.74210526 cg20551517' GIP 0.00304677 0.759375 0.65047619 3.5501E-40 0.88931034 0.40052632 cg00613255 INS 0.00063262 0.74787879 0.81 0.06289883 0.8962069 0.91947368 cgl7738194 GK2 0.1585336 0.62909091 0.65238095 0.00018403 0.79586207 0.855 cgl7740399 IPF1/PDX1 0.01575371 0.04060606 0.03325581 8.9892E-05 0.02413793 0.01611111 cgl7740399 IPF1/PDX1 0.02309646 0.03666667 0.03046512 0.00216401 0.02724138 0.02055556

The disclosures of each and every patent, patent application, and publication cited herein are hereby incorporated herein by reference in their entirety.

While this invention has been disclosed with reference to specific embodiments, it is apparent that other embodiments and variations of this invention may be devised by

others skilled in the art without departing from the true spirit and scope of the

invention. The appended claims are intended to be construed to include all such

embodiments and equivalent variations.

Claims

CLAIMS What is claimed:

1. A method of diagnosing colorectal cancer in a subject, the method comprising:

a. determining the level of methylation of a biomarker in a

biological sample of the subject,

b. comparing the level of methylation of the biomarker in the sample of the subject with a comparator control, and c. diagnosing the subject with colorectal cancer when the level of methylation of the biomarker in the sample of the subject is altered at a statistically significant amount when compared with the level of methylation of the biomarker of the comparator control.

2. The method of claim 1, wherein the biomarker is selected from the group consisting iPDK4, PYCARD, NR1H4, SPRR2A, SPRRIA, BCMOl, AIM2, NEK3, SB5, and any combination thereof.

3. The method of claim 1, wherein when the level of methylation of a biomarker is decreased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting oiPDK4, NR1H4, BCMOl, and any combination thereof.

4. The method of claim 1, wherein when the level of methylation of a biomarker is increased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting of PYCARD, SPRR2A, SPRRIA, AIM2, SB5 and any combination thereof.

5. The method of claim 1, wherein when the level of methylation of a biomarker is increased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting oilNS, LGALS2, ANKRD15, VHL, EDA2R, NMUR1, GRB10, and any combination thereof.

6. The method of claim 1, wherein the level of methylation of the biomarker is measured by detecting the methylation of the biomarker comprising detecting the methylation of CpG sequences in the gene or related regulatory sequence of the biomarker.

7. The method of claim 1, wherein the level of methylation of the biomarker is measured by a method selected from the group consisting of PCR, methylation-specific PCR, real-time methylation-specific PCR, PCR assay using a methylation DNA-specific binding protein, quantitative PCR, DNA chip-based assay, pyrosequencing, and bisulfate sequencing.

8. The method of claim 5, wherein the CpG sequences are located in a region selected from the group consisting of upstream of coding sequences, in the coding regions, in enhancer regions, in intron regions, downstream of coding sequences, and any combination thereof.

9. The method of claim 1, wherein the comparator control is the level of the biomarker in the sample of a healthy subject.

10. The method of claim 1, wherein the comparator control is at least one selected from the group consisting of a positive control, a negative control, a historical control, a historical norm, or the level of a reference molecule in the biological sample.

1 1. The method of claim 1, comprising the further step of treating the subject for the diagnosed colorectal cancer.

12. The method of claim 1, wherein the subject is a human.

13. A kit for diagnosing colorectal cancer, the kit comprising a reagent for measuring the level of methylation of a biomarker in a biological sample of the subject wherein the biomarker is selected from the group consisting oiPDK4, PYCARD, NR1H4, SPRR2A, SPRR1A, BCMOl, AIM2, NEK3, SB5, and any combination thereof.

14. The kit of claim 13, wherein when the level of methylation of a biomarker is decreased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting oiPDK4, NR1H4, BCMOl, and any combination thereof.

15. The kit of claim 13, wherein when the level of methylation of a biomarker is increased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting of PYCARD, SPRR2A, SPRRIA, AIM2, SB5 and any combination thereof.

16. The kit of claim 13, wherein when the level of methylation of a biomarker is increased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting oilNS, LGALS2, ANKRD15, VHL, EDA2R, NMUR1, GRBIO, and any combination thereof

17. A method of treating a subject diagnosed with colorectal cancer, the method comprising diagnosing colorectal cancer in a subject and administering an anti-cancer therapy to the subject in need thereof, wherein diagnosing colorectal cancer in a subject comprises:

a. determining the level of methylation of a biomarker in a

biological sample of the subject,

b. comparing the level of methylation of the biomarker in the sample of the subject with a comparator control, and c. diagnosing the subject with colorectal cancer when the level of methylation of the biomarker in the sample of the subject is altered at a statistically significant amount when compared with the level of methylation of the biomarker of the comparator control.

18. The method of claim 17, wherein the biomarker is selected from the group consisting ofPDK4, PYCARD, NR1H4, SPRR2A, SPRRIA, BCMOl, AIM2, NEK3, SB5, and any combination thereof.

19. The method of claim 17, wherein when the level of methylation of a biomarker is decreased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting oiPDK4, NR1H4, BCMOl, and any combination thereof.

20. The method of claim 17, wherein when the level of methylation of a biomarker is increased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting of PYCARD, SPRR2A, SPRR1A, AIM2, SB5 and any combination thereof.

21. The method of claim 17, wherein when the level of methylation of a biomarker is increased, the subject is diagnosed with colorectal cancer, wherein the biomarker is selected from the group consisting oilNS, LGALS2, ANKRD15, VHL, EDA2R, NMUR1, GRB10, and any combination thereof.

22. The method of claim 17, wherein the level of methylation of the biomarker is measured by detecting the methylation of the biomarker comprising detecting the methylation of CpG sequences in the gene or related regulatory sequence of the biomarker.

23. The method of claim 17, wherein the level of methylation of the biomarker is measured by a method selected from the group consisting of PCR, methylation-specific PCR, real-time methylation-specific PCR, PCR assay using a methylation DNA-specific binding protein, quantitative PCR, DNA chip-based assay, pyrosequencing, and bisulfite sequencing.

24. The method of claim 22, wherein the CpG sequences are located in a region selected from the group consisting of upstream of coding sequences, in the coding regions, in enhancer regions, in intron regions, downstream of coding sequences and any combination thereof.

25. The method of claim 17, wherein the comparator control is the level of the biomarker in the sample of a healthy subject.

26. The method of claim 17, wherein the comparator control is at least one selected from the group consisting of a positive control, a negative control, a historical control, a historical norm, or the level of a reference molecule in the biological sample.

27. The method of claim 17, wherein the subject is a human.