# Patent

## Title
Methods for predicting esophageal adenocarcinoma (EAC)

## Abstract

This invention relates, e.g., to methods for predicting a subject's risk for developing esophageal adenocarcinoma (EAC) or high-grade dysplasia (HGD), comprising determining in a sample from the subject the methylation levels of transcriptional promoter regions of various combinations of, among other genes, (a) cadherin 13, H-cadherin (heart) (CDH13); (b) tachykinin-1 (TAC1); (c) nel-like 1 (NELL1); (d) A-kinase anchoring protein 12 (AKAP12); (e) somatostatin (SST); (f) transmembrane protein with EGF-like and two follistatin-like domains (HPP1); (g) CDKN2a, cyclin-dependent kinase inhibitor 2a (p16); or (h) runt-related transcription factor 3 (RUNX3).

## Background
This application is the U.S. National Phase Application, pursuant to 35 U.S.C. §371, of PCT International Application Ser. No. PCT/US2009/034508, filed Feb. 19, 2009, designating the U.S. and published in English on Aug. 27, 2009, as publication WO 2009/105533 A2, which claims priority to U.S. Provisional Application Nos. 61/066,281, filed Feb. 19, 2008; 61/131,748, filed Jun. 11, 2008; and 61/132,418, filed Jun. 18, 2008, the disclosures of which are hereby incorporated by reference in their entireties.


This application was made with U.S. government support, including NIH/NCI CA 085069. The U.S. government thus has certain rights in the invention.


BACKGROUND INFORMATION
Barrett's esophagus (BE), a sequela of chronic gastroesophageal reflux disease (GERD), is a highly premalignant condition that increases an individual's chance of developing esophageal adenocarcinoma (EAC) by 30- to 125-fold. Therefore, subjects with BE are usually enrolled in surveillance programs in which they undergo endoscopy at regular intervals for the rest of their lives. However, the incidence of EAC in BE patients under surveillance is only 1/200 patient-years. Conversely, cancers or advanced high-grade dysplasias (HGDs) may develop during the interim and are sometimes missed if surveillance is performed at long intervals. In addition, the current marker of EAC risk in BE, dysplasia, is plagued by high inter-observer variability and limited predictive accuracy. Because neoplastic progression is infrequent in BE, the merits of and appropriate interval for endoscopic surveillance in BE have led to frequent debate. Thus, a means of stratifying patients into groups at high, intermediate, and low risk of neoplastic progression would be highly useful. This process would benefit greatly from effective biomarkers to stratify patients according to their level of neoplastic progression risk.
Methylation constitutes the epigenetic modification of DNA by the addition of methyl groups, usually on cytosines at the sequence 5′-CpG-3′. This event is most relevant when it occurs within CpG islands, which are CpG-rich regions in 5′ gene regions of about half of all genes, often involving promoter regions. These islands are normally unmethylated but are vulnerable to de novo methylation, which can silence gene expression. See, e.g., Gardiner-Garden et al. (1987) J Mol Biol 196, 261-282 or Takai et al. (2002) Proc Natl Acad Sci USA 99, 3740-3745 for discussions of CgG islands. It has been reported that promoter hypermethylation of several tumor suppressor genes is correlated with the incidence of several cancers.
The inventors and their colleagues previously reported that hypermethylation of promoter regions of three genes—cyclin-dependent kinase inhibitor 2a (CDKN2a, or p16), runt-related transcription factor 3 (RUNX3), and transmembrane protein with EGF-like and two follistatin-like domains (HPP1)—occurs early in (BE)-associated neoplastic progression and appears to represent independent risk factors for the progression of Barrett's esophagus (BE) to high-grade dysplasias (HGD) or esophageal adenocarcinoma (EAC). See, e.g., Schulmann et al. (2005) Oncogene 24, 4138-4148. Later, the inventors and colleagues developed a tiered risk stratification model to predict progression in BE using epigenetic and clinical features, validating the use of a panel of the three markers (see, e.g., Sato et al. (2008) PLoS ONE 3, e1890).
Hypermethylation of these and additional promoter regions might serve as useful biomarkers for stratifying subjects according to their risk for development of EAC or HGD.



DESCRIPTION OF THE DRAWINGS
FIG. 1 shows the genomic sequence of a promoter region of p16, from 1000 nt upstream of the transcriptional start site into exon 1, indicating the start and end sequences of the CpG Island and the transcriptional start site (TSS) (FIG. 1A), and the bisulfite methyl sequence, indicating the locations of the Cg sequences, the TSS, and the locations of the forward and reverse primers and of the probe used in qMSP (FIG. 1B).
FIG. 2 shows the genomic sequence of a promoter region of AKAP12, from 1000 nt upstream of the transcriptional start site into exon 1, indicating the start and end sequences of the CpG Island and the transcriptional start site (TSS) (FIG. 2A), and the bisulfite methyl sequence, indicating the locations of the Cg sequences, the TSS, and the locations of the forward and reverse primers and of the probe used in qMSP (FIG. 2B).
FIG. 3 shows the genomic sequence of a promoter region of CDH13, from 1000 nt upstream of the transcriptional start site into exon 1, indicating the start and end sequences of the CpG Island and the transcriptional start site (TSS) (FIG. 3A), and the bisulfite methyl sequence, indicating the locations of the Cg sequences, the TSS, and the locations of the forward and reverse primers and of the probe used in qMSP (FIG. 3B).
FIG. 4 shows the genomic sequence of a promoter region of HPP1, from 1000 nt upstream of the transcriptional start site into exon 1, indicating the start and end sequences of the CpG Island and the transcriptional start site (TSS) (FIG. 4A), and the bisulfite methyl sequence, indicating the locations of the Cg sequences, the TSS, and the locations of the forward and reverse primers and of the probe used in qMSP (FIG. 4B).
FIG. 5 shows the genomic sequence of a promoter region of NELL1, from 1000 nt upstream of the transcriptional start site into exon 1, indicating the start and end sequences of the CpG Island and the transcriptional start site (TSS) (FIG. 5A), and the bisulfite methyl sequence, indicating the locations of the Cg sequences, the TSS, and the locations of the forward and reverse primers and of the probe used in qMSP (FIG. 5B).
FIG. 6 shows the genomic sequence of a promoter region of RUNX3, from 1000 nt upstream of the transcriptional start site into exon 1, indicating the start and end sequences of the CpG Island and the transcriptional start site (TSS) (FIG. 6A), and the bisulfite methyl sequence, indicating the locations of the Cg sequences, the TSS, and the locations of the forward and reverse primers and of the probe used in qMSP (FIG. 6B).
FIG. 7 shows the genomic sequence of a promoter region of SST, from 1000 nt upstream of the transcriptional start site into exon 1, indicating the start and end sequences of the CpG Island and the transcriptional start site (TSS) (FIG. 7A), and the bisulfite methyl sequence, indicating the locations of the Cg sequences, the TSS, and the locations of the forward and reverse primers and of the probe used in qMSP (FIG. 7B).
FIG. 8 shows the genomic sequence of a promoter region of TAC1, from 1000 nt upstream of the transcriptional start site into exon 1, indicating the start and end sequences of the CpG Island and the transcriptional start site (TSS) (FIG. 8A), and the bisulfite methyl sequence, indicating the locations of the Cg sequences, the TSS, and the locations of the forward and reverse primers and of the probe used in qMSP (FIG. 8B).
FIG. 9 shows receiver-operator characteristic (ROC) curve analysis of normalized methylation value (NMV). ROC curve analysis of CDH13 NMVs in esophageal adenocarcinoma (EAC) vs. normal esophagus (NE) (FIG. 9A), esophageal squamous cell carcinoma (ESCC) vs. NE (FIG. 9B), and EAC vs. ESCC (FIG. 9C). The high area under the ROC curve (AUROC) conveys the accuracy of this biomarker in distinguishing EAC from NE and from ESCC in terms of its sensitivity and specificity.
FIG. 10 shows a correlation between Barrett's segment length and CDH13 hypermethylation. FIG. 10A shows that the normalized methylation value (NMV) of CDH13 was significantly higher in long-segment BE (LSBE, mean=0.4071) than in short-segment BE (SSBE, mean=0.131; p=0.0032, Student's t-test). FIG. 10B shows that positive CDH13 hypermethylation status was significantly correlated with BE segment length (p=0.0005, Student's t-test).
FIG. 11 shows CDH13 methylation level and mRNA expression in esophageal cancer cell lines after treatment with the demethylating agent 5-aza-2′-deoxycytidine (5-Aza-dC). KYSE220 and OE33 EAC cells were subjected to 5-Aza-dC treatment. In both cell lines, after 5-Aza-dC treatment, the NMV of CDH13 was diminished, while the normalized mRNA value (NRV) of CDH13 was increased.
FIG. 12 shows receiver-operator characteristic (ROC) curves for the uncorrected 8-marker and 8-marker-plus-age panels, overfitting-corrected ROC curves for the 8-marker and 8-marker-plus-age panels, and ROC curves for age alone in the 2-, 4-year, and combined prediction models. FIG. 12A shows an uncorrected ROC curve (AUC=0.843) and an overfitting-corrected ROC curve (AUC=0.745) for the 8-marker panel in the 2-year prediction model; shrinkage due to overfitting correction minimal, at 0.098. FIG. 12B shows an uncorrected ROC curve (AUC=0.829) and an overfitting-corrected ROC curve (AUC=0.720) for the 8-marker panel in the 4-year prediction model; shrinkage minimal, at 0.109. FIG. 12C shows an uncorrected ROC curve (AUC=0.840) and an overfitting-corrected ROC curve (AUC=0.732) for the 8-marker panel in the combined prediction model; shrinkage minimal, at 0.108. FIG. 12D shows an uncorrected ROC curve for the 8-marker-plus-age panel (AUC=0.858), overfitting-corrected ROC curve (AUC=0.756), and a ROC curve for age alone (0.604) in the 2-year prediction model; increment over age alone substantial, at 0.152. FIG. 12E shows an uncorrected ROC curve for the 8-marker-plus-age panel (AUC=0.850), an overfitting-corrected ROC curve (AUC=0.744), and a ROC curve for age alone (0.630) in the 4-year prediction model; increment over age alone substantial, at 0.114. FIG. 12F shows an uncorrected ROC curve for the 8-marker-plus-age panel (AUC=0.855), an overfitting-corrected ROC curve (AUC=0.753), and a ROC curve for age alone (0.635) in the combined prediction model; increment over age alone substantial, at 0.118.
FIG. 13 shows risk stratification of BE patients by predictiveness curves of the 8-marker panel, age alone, and the 8-marker-plus-age panel in the combined, 2-year and 4-year prediction models. FIG. 13A shows a predictiveness curve of the 8-marker panel in the combined model. After rigorous overfitting correction, at risk=0.1 and =0.5, 45% and 4% of subjects had estimated risks below 0.1 (LR group) and above 0.5 (HR group), respectively; while the remaining 51% had estimated risks between 0.1 and 0.5 (IR group). FIG. 13B shows predictiveness curves of age alone and of the 8-marker-plus-age panel in the combined model. After rigorous overfitting correction, BE patients were stratified into LR (15%) and IR (85%) groups by age alone. BE patients were stratified into LR (51%), IR (44%) and HR (5%) groups by the 8-marker-plus-age panel. FIG. 13C shows a predictiveness curve of the 8-marker panel in the 2-year model. After rigorous overfitting correction, BE patients were stratified into LR (45%), IR (51%) and HR (4%) groups. FIG. 13D shows predictiveness curves of age alone and of the 8-marker-plus-age panel in the 2-year model. After rigorous overfitting correction, BE patients were stratified into LR (11%) and IR (89%) groups by age alone. BE patients were stratified into LR (52%), IR (44%) and HR (4%) groups by the 8-marker-plus-age panel. FIG. 13E shows a predictiveness curve of the 8-marker panel alone in the 4-year model. After rigorous overfitting correction, BE patients were stratified into LR (44%), IR (51%) and HR (5%) groups. FIG. 13F shows predictiveness curves of age alone and of the 8-marker-plus-age panel in the 4-year model. After rigorous overfitting correction, BE patients were stratified into LR (15%) and IR (85%) groups by age alone. BE patients were stratified into LR (51%), IR (44%) and HR (5%) groups by the 8-marker-plus-age panel. LR: low-risk; IR, intermediate-risk; HR: high-risk; OC: overfitting correction.
FIG. 14 shows statistical considerations, including cut-off points for Low and High risk.



DESCRIPTION OF THE INVENTION
The inventors extend herein their previous studies identifying hypermethylation of promoter regions of HPP1, p16 and RUNX3 as markers for the development of esophageal adenocarcinoma (EAC) or high-grade dysplasia (HGD), by identifying about 55 additional markers that can be used to predict a subject's risk for developing EAC or HGD. These additional markers include, e.g., hypermethylated promoter regions of nel-like 1 (NELL1), tachykinin-1 (TAC1), somatostatin (SST), A-kinase anchoring protein 12 (AKAP12), cadherin 13, H-cadherin (heart) (CDH13), and of the 50 genes shown in Table 11.
This invention relates, e.g., to a method for predicting a subject's risk for developing (e.g., progressing from BE to) EAC or HGD, comprising (a) determining in a sample from the subject the methylation levels of transcriptional promoter regions of at least two or three of the above-mentioned genes, in various combinations that are elucidated elsewhere herein, and (b) calculating a methylation value (e.g., a methylation index or a linear regression score) that takes into account the methylation levels of the measured transcriptional promoter regions, wherein a methylation value that is below a first predetermined threshold value is indicative that the subject has a low risk of developing EAC or HGD, and a methylation value that is above a second predetermined threshold value is indicative that the subject has a high risk of developing EAC or HGD.
As used herein, a subject that is at “high risk” (or at an “increased risk”) for developing EAC or HGD has a greater than 90% likelihood of developing EAC or HGD, within 2 years, 4 years, or ever at all, depending on which model as discussed herein is used. “Low risk” (or a “decreased risk”) is defined is defined as a greater than 95% chance that the patient will NOT develop HGD or EAC—within 2 years, 4 years, or ever at all, depending on which model as discussed herein is used.
Advantages of a method of the invention include, e.g., that it is rapid, accurate and inexpensive, and that it can be easily adapted to high throughput format, using automated (e.g., robotic) systems, which allow many measurements to be carried out simultaneously. Furthermore, the methods can be miniaturized. A stratification method of the invention can benefit BE patients in two ways: 1) by decreasing the frequency at which low-risk individuals undergo surveillance endoscopy, thus eliminating unnecessary anxiety, expense, and diminishing procedure-related complications; and 2) by identifying the small group of truly high-risk BE patients for more frequent, intensive surveillance, resulting in earlier and more accurate detection of HGDs and EACs.
One aspect of the invention is a first method for predicting a subject's risk for developing esophageal adenocarcinoma (EAC) or high-grade dysplasia (HGD), comprising

    
    
        a) determining in a sample from the subject the methylation levels of transcriptional promoter regions of three or more of the following genes:
        
            i) CDH13,
            ii) TAC1,
            iii) NELL1,
            iv) AKAP12) or
            v) SST,

and

        
        
        b) calculating a methylation value (e.g., a methylation index or a linear regression score) that takes into account the methylation levels of the measured transcriptional promoter regions,
        wherein a methylation value that is below a first predetermined threshold is indicative that the subject has a low risk of developing EAC or HGD, and a methylation value that is above a second predetermined threshold is indicative that the subject has a high risk of developing EAC or HGD.
    
    


This first method can further comprise

    
    
        a) determining the methylation levels of promoter regions of one or more of
        
            vi) HPP1,
            vii) p16, or
            viii) RUNX3, and
        
        
        b) calculating a methylation value that takes into account the methylation levels of the measured transcriptional promoter regions.
    
    


In embodiments of this first method, the methylation levels can be determined for promoter regions of a total of 3, 4, 5, 6, 7 or all 8 of the genes. In another embodiment of this first method, methylation levels for the promoter regions of one or more of the 50 genes listed in Table 11 can also be determined and included in the calculation of the methylation value.
Another aspect of the invention is a second method for predicting a subjects risk for developing esophageal adenocarcinoma (EAC) or high-grade dysplasia (HGD), comprising

    
    
        a) determining in a sample from the subject
        
            the methylation level of a transcriptional promoter region of CDH13, and
            the methylation levels of transcriptional promoter regions of at least two of the following genes:
            
                i) HPP1
                ii) p16
                iii) RUNX3
                iv) TAC1
                v) NELL1
                vi) AKAP12, or
                vii) SST, and
            
            
        
        
        b) calculating a methylation value (e.g. a linear regression score or a methylation index) that takes into account the methylation levels of the measured transcriptional promoter regions,
        wherein a methylation value that is below a first predetermined threshold is indicative that the subject has a low risk of developing EAC or HGD, and a methylation value that is above a second predetermined threshold is indicative that the subject has a high risk of developing EAC or HGD.
    
    


In embodiments of the this second method, the methylation levels can be determined for promoter regions of a total of 3, 4, 5, 6, 7 or all 8 of the genes; in another embodiment, the methylation levels are measured for CDH13, HPP1, p16 and RUNX3. In another embodiment of this second method, methylation levels for the promoter regions of one or more of the 50 genes listed in Table 11 can also be determined and included in the calculation of the methylation value.
Another aspect of the invention is a third method for predicting a subject's risk for developing esophageal adenocarcinoma (EAC) or high-grade dysplasia (HGD), comprising

    
    
        a) determining in a sample from the subject the methylation levels of transcriptional promoter regions of at least two of the genes listed in Table 11, and
        b) calculating a methylation value that takes into account the methylation levels of the measured transcriptional promoter regions,
        wherein a methylation value (e.g. a linear regression score or a methylation index) that is below a first predetermined threshold is indicative that the subject has a low risk of developing EAC or HGD, and a methylation value that is above a second predetermined threshold is indicative that the subject has a high risk of developing EAC or HGD. Any combination of two or more of the genes listed in Table 11 can be used.
    
    


Another aspect of the invention is a method for predicting a subject's risk for developing EAC or HGD, comprising

    
    
        a) determining in a sample from the subject the methylation levels of transcriptional promoter regions of CDH13, TAC1, NELL1, AKAP12, SST, HPP1, p16, and RUNX3, and
        b) calculating a linear regression score for the 8 methylation levels,
        wherein the methylation levels are determined by qMSP, and
        wherein a linear regression score that is no more than 0.13 is indicative that the subject has a low risk of developing EAC or HGD within 4 years, and a linear regression score that is equal to or above 0.39 is indicative that the subject has an increased risk (e.g., a high risk) of developing EAC or HGD in 4 years.
    
    


Another aspect of the invention is a method for predicting a subject's risk for developing EAC or HGD, comprising

    
    
        a) determining in a sample from the subject the methylation levels of transcriptional promoter regions of CDH13, TAC1, NELL1, AKAP12, SST, HPP1, p16, and RUNX3, and
        b) calculating a methylation index for the 8 methylation levels,
        wherein the methylation levels are determined by qMSP, and
        wherein a methylation index that is no more than 2 is indicative that the subject has a decreased risk (e.g., a low risk) of developing EAC or HGD in 4 years, and a methylation index that is equal to or above 3 is indicative that the subject has an increased risk (e.g., a high risk) of developing EAC or HGD in 4 years.
    
    


In any of the preceding methods, the subject can have, or be suspected of having, BE; the subject can be human; and/or the sample can be a biopsy tissue. Any of a variety of methods can be used to determine the methylation levels, including the quantitative methods: real-time quantitative methylation-specific PCR (qMSP), pyrosequencing, or methylation arrays.
A method of the invention can be performed in conjunction with other assays for EAC, including, e.g., performing conventional histological analysis of the sample (wherein the detection of the presence and degree of dysplasia is further indicative that the subject is at increased risk for developing EAC); determining the age of the subject (wherein if a human subject is more than about 60 years old, this is further indicative that the subject is at risk for developing EAC, and wherein the greater the age of the patient, the higher his or her risk); or determining the BE segment length (wherein a length of 3 cm or greater is further indicative that the subject has a higher risk of developing EAC).
Another aspect of the invention is a method for determining a treatment strategy for a subject (or a method for treating a subject), comprising predicting the subject's risk for developing EAC or HGD and, depending on the assessed risk, deciding how to treat or monitor the subject (or treating or monitoring the subject). For example, if the subject is predicted to be at a decreased risk (e.g. at a low risk) for developing EAC or HGD, but has BE, a decision is made to treat the subject by endoscopic monitoring with endoscopy and biopsies every 2-3 years (e.g., every 3 years); whereas if the subject is predicted to be at an increased risk (e.g. at a high risk) for developing EAC or HGD but has BE, a decision is made to treat the subject by monitoring with endoscopy and biopsies every one year or less.
Another aspect of the invention is a method for following the course of development of EAC in a subject, comprising (a) determining in a sample from the subject, at least two time points, the methylation levels of transcriptional promoter regions of a set of genes as described above, and (b) calculating a methylation value that takes into account the methylation levels of the measured transcriptional promoter regions, wherein an increase in the methylation value between the at least two time points indicates that the EAC has progressed. Any set of time points can be used, e.g., yearly, every other year, etc.
Another aspect of the invention is a method for evaluating a therapeutic method (including, e.g., monitoring the effect of a candidate therapeutic agent), comprising, before and after initiation of the therapy, (a) determining in a sample from the subject the methylation levels of transcriptional promoter regions of a set of genes as described above, and (b) calculating a methylation value that takes into account the methylation levels of the measured transcriptional promoter regions, wherein an decrease in the methylation value following the therapeutic treatment indicates that the treatment has been effective.
Another aspect of the invention is a kit for predicting a subject's risk for developing esophageal adenocarcinoma (EAC) or high-grade dysplasia (HGD), comprising reagents (e.g., suitable primers and probes for determining in a sample from the subject the methylation level of a transcriptional promoter region from the genes or combinations of genes discussed herein, using qMSP; and, optionally, directions for carrying out a method of the invention, containers or packaging materials, and/or a computer program (e.g., a program that calculates a methylation value in a sample based on the determined methylation levels).
In general, a subject to be evaluated by a method of the invention has, or is suspected of having BE. Human patients suspected of having BE often present with heartburn and are subjected to an endoscopy to definitively determine by endoscopy and biopsies for histology whether they have BE and/or dysplasia. Subjects who evince BE, with or without dysplasia, are generally monitored endoscopically periodically, even if symptoms later disappear.
Subjects that can be evaluated by a method of the invention include any of a variety of vertebrates, including, e.g., laboratory animals (e.g., mouse, rat, rabbit, monkey, or guinea pig, in particular mouse or rat models for EAC), farm animals (e.g., cattle, horses, pigs, sheep, goats, etc.), and domestic animals or pets (e.g., cats or dogs). Non-human primates and, preferably, humans, are included.
Suitable samples (e.g., test samples, or control samples) that can be tested by a method of the invention include, e.g., biopsies of esophageal epithelium. For example, the sample can be from grossly apparent BE epithelium or from mass lesions in patients manifesting these changes at endoscopic examination. Methods for obtaining samples and preparing them for analysis are conventional and well-known in the art.
A transcriptional “promoter region,” as used herein, refers to a sequence that is at or near the 5′ end of an mRNA transcribed from a gene. The region can be, or can be a portion of, a sequence of the genomic DNA comprising

    
    
        (a) at least about 500 (and in some cases, as many as about 1,000 or 2,000) contiguous base pairs (bp) extending upstream of a transcription initiation site, wherein the sequence comprises transcriptional regulatory sequences, such as promoter sequences, and at least a portion of a CpG island; or
        (b) the transcription initiation site;
        (c) at least about 200 bp of the 5′ coding sequence of the first exon of a gene, or the entire first exon when the sequence is included in a CpG island; or
        (d) overlapping sequences thereof.
    
    


Although much of the discussion herein is directed to promoter regions that extend no further downstream into a gene than the first exon, recent studies have suggested that hypermethylation of CpG islands that are located much further downstream in a gene, or in intergenic regions, can also silence gene expression. Furthermore, hypermethylation of CpG “shores” that extend as far as 3,000 bp upstream of a transcriptional start site, have also been reported to silence gene expression. See, e.g., Irizarry et al. (2009) Nat Genet 41, 178-186. As such more distant CpG islands become identified for the genes identified herein, methylation of those regions can also be assayed by a method of the invention to predict a subjects risk for developing EAC or HGD.
In some embodiments of the invention, a promoter region is selected for analysis which consists of a smaller portion of a larger promoter region as discussed above. For example, if qMSP PCR is used to determine the methylation level, the MSP PCR products are generally designed to be about 80-100 bp long. The actual sequences assayed to determine methylation levels can be the primers (about 18-24 nt) and/or probe (about 18-24 nt). Within each of these about 20-base pieces, there are generally between 2 and 7 CpGs. Annealing of the primers and probes must be “specific,” and depends on a complete or near-complete match with the test DNA, with greater fidelity being required at or near the 3′ end than at or near the 5′ end of the primer or probe. Technically, the smallest region assayable is about 20 nt. A typical CpG island is about 200 bp long and contains at least 60% of its expected quota of CpGs, which would be 7 CpGs for a 200 bp island. A transcriptional promoter region that is assayed by a method of invention should contain one or more sites associated with methylation, such as CpG dinucleotides, and suitable for quantitative measurement, such as by qMSP.
A skilled worker would know how to select a suitable promoter region to analyze for a gene of interest. For example, sequences can be selected from the large promoter regions that are shown in FIGS. 1-8 for genes HPP1, p16, RUNX3, NELL1, TAC1, SST, AKAP12, and CDH13, and from the large promoter regions of SEQ ID NOs: 50-121. Suitable promoter regions can be selected on the basis of sequences found in searchable databases, such as GenBank or Entrez Gene (NCBI), and the annotations in the records therein regarding the positions of for example, the transcription initiation sites. Such information can also be obtained from a variety of other sources that will be evident to a skilled worker. See, for example, the discussion in Example IV.
Sequences of promoter regions shown herein and in searchable databases are sometimes presented as just one strand of a DNA double helix. Because the PCR-amplified DNA in a test sample is double-stranded, a probe of the invention can bind to one of the two DNA strands of the double-stranded DNA, and is completely complementary to the other strand. Which of the two strands is being described is not always indicated in the discussion herein; but it will be evident to a skilled worker whether a given probe binds to one of the DNA strands of an amplicon, or to its complete complement.
A “methylation level,” as used herein, is a function of the method used to measure this value. In the experiments shown herein, the measurement is accomplished with qMSP. Using this method, a methylation level is the quantitative measurement of methylated DNA for a gene promoter region, defined by the percentage of DNA in the sample that is methylated at each MSP primer/promoter location. This can range from 0 to 100%. In general, but not invariably, most of the CpGs in a given CpG island are methylated. If other methods are used to measure the methylation level, such as pyrosequencing or bisulfite sequencing, the measurement can reflect a quantitative measurement of the number of methylated cytosines in CpG islands (or CpG dinucleotides) in a promoter region from a gene of interest.
It is generally desirable to measure a methylation level in relation to a normalization standard or a reference value. In Example II herein, actin is used as an internal control to determine how much DNA is methylated, and the methylation levels are referred to as NMVs (normalized methylation values). The amount of actin is measured by PCR rather than MSP, because the primers/probes for actin contain no methylatable sequences (i.e., there are no CpGs in the sequences). Other suitable normalization controls, including other constitutive genes or genes whose sequences which are methylated to a known degree, will be evident to a skilled worker.
In establishing that the eight markers for which qMSP studies are reported herein are strongly predictive, the inventors compared the methylation levels of the markers to baseline values in normal tissues. ROC curves for normal tissue vs. Barrett's and normal tissue vs. tumor are shown in the Examples herein; these ROC curves had very large areas (AUROCs) under them, indicating values ranging from 0.622 to 0.845.
These eight markers were selected for the panel, at least in part, because they are not methylated in normal tissues of normal subjects. That is, the mean methylation level for one of these markers in a normal population would be zero. Therefore, an assay using these eight markers does not require a comparison to normal subjects. This is an advantage of using these eight markers. A “normal” subject, as used herein, is one who does not have detectable neoplasia or metaplasia of the esophagus and therefore is not expected to develop BE or EAC. In general, a level associated with a “normal” subject includes a statistically obtained value associated with a population of normal subjects. For example, a methylation level in a normal subject includes the mean or average methylation level in a substantial population of subjects.
For other markers, such as the promoter regions of some of the 50 genes listed in Table 11, there may be some degree of methylation of the markers in normal subjects. In those cases, the methylation level is compared to the level in a comparable region from a normal subject or population of subjects, such as from the same promoter region from a matched tissue. Suitable comparisons can be made to levels in normal white blood cells (WBCs) and/or normal esophagus, from normal and/or diseased subjects.
In some embodiments, it is desirable to express the results of an assay in terms of a statistically significant increase in a value compared to a baseline value. A “significant” increase or decrease in a value, as used herein, can refer to a difference which is reproducible or statistically significant, as determined using statistical methods that are appropriate and well-known in the art, generally with a probability value of less than five percent chance of the change being due to random variation. Some such statistical tests are discussed herein; others will be evident to a skilled worker.
It will be appreciated by those of skill in the art that a baseline or normal level need not be established for each assay as the assay is performed but rather, baseline or normal levels can be established by referring to a form of stored information regarding a previously determined baseline methylation levels for a given gene or panel of genes, such as a baseline level established by any of the above-described methods. Such a form of stored information can include, for example, a reference chart, listing or electronic file of population or individual data regarding “normal levels” (negative control) or polyp positive (including staged tumors) levels; a medical chart for the patient recording data from previous evaluations; a receiver-operator characteristic (ROC) curve; or any other source of data regarding baseline methylation levels that is useful for the patient to be diagnosed.
A “methylation value,” as used herein, is a quantitative value that takes into account the methylation levels of all of the markers tested in a particular assay, and weighs the contributions of the levels appropriately. A methylation value can be calculated in several ways, which will be evident to a skilled worker. These include, e.g., a linear regression score from the linear regression of all of the markers in a panel (e.g., the panel of eight markers described in Example III), or a “methylation index.”
In one embodiment of the invention, the methylation value is expressed as a linear regression score, as described, e.g., in Irwin, in Neter, Kutner, Nachtsteim, Wasserman (1996) Applied Linear Statistical Models, 4th edition, page 295. See also the Examples herein. Typical regression scores are indicated in Table 1, for both an 8-marker panel (using all 8 of the markers discussed herein) and a 3-marker panel with the promoter regions of HPP1, p16 and RUNX3.







TABLE 1







8-marker panel:










Specificity (95% CI) @ sensitivity
Sensitivity (95% CI) @ specificity













0.95
0.9
0.8
0.9
0.8





Combined model







Age
0.219
0.260
0.390
0.221
0.371



(0.125, 0.370)
(0.162, 0.425)
(0.240, 0.508)
(0.054, 0.448)
(0.204, 0.532)


Marker panel
0.527
0.567
0.724
0.443
0.629



(0.292, 0.730)
(0.413, 0.849)
(0.574, 0.914)
(0.350, 0.838)
(0.527, 0.941)


Marker panel +
0.515
0.576
0.781
0.457
0.757


age
(0.352, 0.779)
(0.484, 0.867)
(0.647, 0.944)
(0.372, 0.869)
(0.598, 0.964)


2-year model







Age
0.205
0.205
0.351
0.176
0.354



(0.106, 0.324)
(0.138, 0.389)
(0.197, 0.484)
(0.021, 0.426)
(0.172, 0.538)


Marker panel
0.383
0.547
0.757
0.607
0.721



(0.288, 0.794)
(0.436, 0.873)
(0.595, 0.935)
(0.393, 0.870)
(0.593, 0.969)


Marker panel +
0.454
0.615
0.786
0.536
0.786


age
(0.334, 0.833)
(0.474, 0.918)
(0.652, 0.956)
(0.400, 0.934)
(0.600, 0.987)


4-year model







Age
0.217
0.249
0.384
0.232
0.382



(0.120, 0.366)
(0.158, 0.430)
(0.229, 0.506)
(0.038, 0.467)
(0.214, 0.541)


Marker panel
0.494
0.523
0.704
0.465
0.606



(0.273, 0.746)
(0.426, 0.835)
(0.579, 0.909)
(0.346, 0.814)
(0.545, 0.941)


Marker panel +
0.507
0.574
0.757
0.450
0.724


age
(0.359, 0.780)
(0.488, 0.864)
(0.649, 0.940)
(0.385, 0.885)
(0.600, 0.963)














Linear score @ sensitivity
Linear score @ specificity
















0.95
0.9
0.8
0.9
0.8






Combined model








Age
0.135
0.154
0.201
0.365
0.319



Marker panel
0.147
0.159
0.224
0.395
0.253



Marker panel +
0.112
0.136
0.276
0.443
0.297



age








2-year model








Age
0.122
0.125
0.166
0.280
0.250



Marker panel
0.073
0.110
0.178
0.307
0.202



Marker panel +
0.063
0.107
0.220
0.356
0.232



age








4-year model








Age
0.133
0.148
0.195
0.352
0.308



Marker panel
0.130
0.141
0.209
0.391
0.254



Marker panel +
0.105
0.131
0.235
0.431
0.290



age










3-marker panel:










Specificity (95% Cl) @ sensitivity
Sensitivity (95% Cl) @ specificity













0.95
0.9
0.8
0.9
0.8





Combined model







Age
0.233
0.353
0.463
0.340
0.400



(0.146, 0.417)
(0.194, 0.489)
(0.342, 0.598)
(0.130, 0.498)
(0.261, 0.570)


Marker panel
0.076
0.252
0.408
0.289
0.358



   (0, 0.352)
(0.006, 0.464)
(0.175, 0.604)
(0.156, 0.482)
(0.286, 0.630)


Marker panel +
0.213
0.410
0.482
0.378
0.578


age
(0.111, 0.469)
(0.169, 0.566)
(0.356, 0.715)
(0.222, 0.596)
(0.373, 0.741)


2-year model







Age
0.213
0.248
0.401
0.326
0.412



(0.122, 0.351)
(0.164, 0.445)
(0.242, 0.573)
(0.121, 0.524)
(0.250, 0.588)


Marker panel
0.028
0.214
0.403
0.265
0.476



(0.001, 0.316)
(0.022, 0.461)
(0.148, 0.641)
(0.132, 0.500)
(0.265, 0.676)


Marker panel +
0.166
0.267
0.432
0.382
0.606


age
(0.072, 0.416)
(0.125, 0.555)
(0.241, 0.753)
(0.212, 0.620)
(0.344, 0.772)


4-year model







Age
0.227
0.323
0.440
0.377
0.429



(0.142, 0.389)
(0.189, 0.466)
(0.303, 0.573)
(0.153, 0.524)
(0.286, 0.582)


Marker panel
0.147
0.265
0.419
0.310
0.381



   (0, 0.374)
(0.027, 0.476)
(0.225, 0.606)
(0.162, 0.485)
(0.286, 0.625)


Marker panel +
0.188
0.368
0.449
0.379
0.619


age
(0.098, 0.440)
(0.150, 0.534)
(0.315, 0.741)
(0.237, 0.591)
(0.389, 0.756)














Linear score @ sensitivity
Linear score @ specificity
















0.95
0.9
0.8
0.9
0.8






Combined model








Age
0.097
0.149
0.207
0.386
0.335



Marker panel
0.174
0.182
0.192
0.309
0.271



Marker panel +
0.088
0.166
0.197
0.374
0.317



age








2-year model








Age
0.081
0.093
0.142
0.294
0.252



Marker panel
0.126
0.139
0.148
0.232
0.198



Marker panel +
0.064
0.095
0.143
0.277
0.239



age








4-year model








Age
0.092
0.131
0.183
0.356
0.309



Marker panel
0.156
0.163
0.174
0.301
0.252



Marker panel +
0.072
0.141
0.171
0.353
0.298



age









When the methylation value is expressed in this manner, the cut-off values to determine that a subject is at low risk or at high risk for developing EAC or HGD can be determined from the linear scores in Table 1. For the low-risk cut-off, a very high sensitivity (e.g., 95%) is desirable, so the cut-off value (threshold value) for the 8-marker panel is about 0.147 for the combined model, about 0.073 for the 2-year model, or about 0.130 for the 4-year model. For the high-risk cut-off, a high specificity (e.g., 90%) is desirable, so that the cutoff (score) is about 0.395 for the combined model, about 0.307 for the 2-year panel, and about 0.391 for the 4-year model. The values for a panel of fewer markers would vary accordingly. See, e.g, the values in Table 1 for the 3-marker panel. The cut-off values would likely differ if different genes were integrated into the model; suitable cut-off values can be determined without undue experimentation by a skilled worker. The term “about” a number, as used herein, refers to any value within 20% of the number.
In another embodiment of the invention, the methylation value is expressed as a “methylation index.” A methylation index (MI) is defined as the number of genes which demonstrated an altered methylation level (i.e., which exceed or fall below a previously determined methylation level cutoff) within a defined set of genes. For example, if there are four genes in a defined gene set and none of these four genes is methylated, the MI equals 0; if any one of the four are methylated, the MI equals 1; if any two of the four are methylated, the MI equals 2; if any three of the four are methylated, the MI equals 3; and if all four of these four genes are methylated, the MI equals 4 (i.e., the maximum possible MI for this gene set).
When the methylation value is expressed in terms of a MI, the cut-off values to determine that a subject is at low risk or at high risk for developing EAC or HGD can be the optimal point (the value closest to the origin) on the ROC curve of normal vs. EAC. In one embodiment of the invention, in which the 8-marker panel is used, a methylation index of at most about two is indicative that the subject has a low risk of developing EAC or HGD, and a methylation index of equal to or greater than about three is indicative that the subject is at high risk for developing EAC or HGD. The cut-off values will be a function of the number of markers in a panel. For example, for a panel or 50 markers, the cut-off values would be less than 12 or equal to or greater than 13. The actual cut-off values for any given panel of markers can be determined readily by a skilled worker, using routine methods, e.g., as described herein.
The difference between the methylation level of a test subject and normal methylation levels may be a relative or absolute quantity. Thus, “methylation level” is used to denote any measure of the quantity of methylation of the gene or panel of genes. The level of methylation may be either abnormally high, or abnormally low, relative to a defined high or low threshold value determined to be normal for a particular group of subjects. The difference in level of methylation between a subject and the reference methylation level may be equal to zero, indicating that the subject is or may be normal, or that there has been no change in levels of methylation since the previous assay.
The methylation levels and any differences that can be detected may simply be, for example, a measured fluorescent value, radiometric value, densitometric value, mass value etc., without any additional measurements or manipulations. Alternatively, the levels or differences may be expressed as a percentage or ratio of the measured value of the methylation levels to a measured value of another compound including, but not limited to, a standard or internal DNA standard, such as beta-actin. This percentage or ratio may be abnormally low, i.e., falling below a previously defined normal threshold methylation level; or this percentage or ratio may be abnormally high, i.e., exceeding a previously defined normal threshold methylation level. For example, this can be the optimal point on the ROC curve. The difference may be negative, indicating a decrease in the amount of measured levels over normal value or from a previous measurement, and the difference may be positive, indicating an increase in the amount of measured methylation levels over normal values or from a previous measurement. The difference may also be expressed as a difference or ratio of the methylation levels to itself, measured at a different point in time. The difference may also be determined using in an algorithm, wherein the raw data are manipulated.
The following paragraphs describe some of the considerations concerning refining suitable cut-off points, using a proposed, even larger study than the one presented herein:
For the 2- or 4-year models, sensitivity connotes the fraction of subjects testing positive among these who progressed to HGD or EAC within 2 or 4 years of marker measurement; while for the combined model, it is the fraction of subjects testing positive among those who ever progressed to HGD or EAC within the observation window. Similarly, specificity is the fraction of subjects testing negative among those who did not progress to HGD or EAC within 2 or 4 years (for the 2- or 4-year models). The following null (H0) and alternative (H1) hypotheses will be tested in choosing 2 threshold values to define the 3 risk groups (i.e., high-, intermediate-, and low-risk groups) from a single ROC curve.

    
    
        High-risk Cutoff:
        
            H0: Sensitivity0<=0.30 (at Specificity0=0.90)
            H1: Sensitivity1>0.30 (=0.45) (at Specificity1=0.90)
            (corresponding to PPV0=0.32 vs. PPV1=0.41 with progression rate 0.135, or
            (corresponding to PPV0=0.20 vs. PPV1=0.27 with progression rate 0.075)
        
        
    
    


That is, we maximize specificity at the expense of sensitivity in choosing the high-risk cutoff value, reasoning that the FPC or false-positive cost (unnecessary endoscopies in patients who would not otherwise have been endoscoped) outweighs the FNC or false-negative cost (failure to diagnose/predict 60% of cases in a low-prevalence population [13.5% over the duration of the study]). A sensitivity of 0.30 at a specificity of 0.90 is considered minimally clinically acceptable, and we anticipate that our markers will have sensitivity of 0.45 at this specificity value. Our PPV at alternative of 0.41 (alternative hypothesis) greatly exceeds this population prevalence. Thus, any true positives detected (predicted) will represent gains in early diagnosis and can be considered a significant gain, whereas missed diagnoses (predictions) would have been missed anyway under the current standard endoscopic surveillance interval of 2-3 years.

    
    
        Low-risk Cutoff:
        
            H0: Specificity0<=0.30 (@ Sensitivity0=0.95)
            H1: Specificity1>0.30 (=0.50) (@ Sensitivity1=0.95)
            (corresponding to NPV0=0.98 vs. NPV1=0.99 with progression rate 0.135)
            (corresponding to NPV0=0.987 vs. NPV1=0.992 with progression rate 0.075)
        
        
    
    


For the low-risk cutoff, we must minimize FNC (failure to diagnose HGD or cancer) because these cases would have been diagnosed under the current standard surveillance interval of 2-3 years, thus if we lengthen it to 4-6 years, we must be certain that we fail to predict as few progressors as possible. That is, a high FNC is not acceptable. Conversely, a high FPC is acceptable for the low-risk group cutoff value, because in current clinical practice, 100% of this group would have been endoscoped at 2-3-year intervals. Thus, any reduction below 100% in this group can be considered a significant gain because it represents a savings of unnecessary surveillance endoscopies. In this case we consider a specificity at 0.30 given a sensitivity of 0.95 is minimally clinically acceptable, and we anticipate that our markers will have a specificity of 0.5 at this sensitivity.
Any of a variety of methods can be used to determine (measure) methylation levels. In one embodiment of the invention, quantitative methods are used, such as, e.g., real-time quantitative methylation-specific PCR (qMSP), pyrosequencing, or methylation microarrays (using, e.g., the Human CpG Island Microarrays and/or methylation system sold by Agilent Technologies, Santa Clara, Calif. and/or methods as described in Beier et al. (2007) Adv Biochem Eng Biotechnol 104, 1-11). A “methylation array,” as used herein, refers to an array of probes that can be used to distinguish between methylated and unmethylated DNA (e.g., between cytosines that are, or are not, methylated).
In other embodiments of the invention (e.g., to determine if a subject has BE or EAC, but not necessarily to determine the course of development of the condition or to stratify subjects into different risk groups), non-quantitative measurement methods can be used. These include, e.g., assays based on methylated DNA immunoprecipitation, using a monoclonal antibody against 5-methylcytosine (see, e.g., Weber et al. (2005) Nat Genet 37, 853-862); Southern blotting analysis using a methylation-sensitive restriction enzyme; single nucletotide primer extension (SNuPE—Gonzalgo et al. (1997) Nuc Acids Res 25, 2532-2534); restriction landmark genomic scanning for methylation (RLGS-M); or combined bisulfite restriction analysis (COBRA—see, e.g., Xiong et al. (1997) Nuc Acids Res 25, 2532-2534).
Real-time polymerase chain reactions (PCR), such as quantitative real-time PCR, can be employed in many of the assays described herein. Such assays are well-known in the art and can be practiced generally according to the known methods. See for example, Heid et al. (1996) Genome Res. 6, 986-994. Briefly, a sequence of interest (e.g., from a promoter region of the invention) is PCR amplified, using a forward PCR primer and a reverse PCR primer, in the presence of a fluorogenic probe that can distinguish between a methylated and a non-methylated version of the amplified sequence.
For quantitative methylation-specific real-time PCR (qMSP), the target DNA is pretreated before PCR amplification with an agent, such as bisulfite, that converts methylated cytosines (C's) to uracils (U's). The fluorogenic probe is designed to recognize a sequence in which methylated C's have been converted to U's by this procedure (or, if a control is required, to recognize a sequence in which C's are present in those positions). The qMSP assay is designed such that the labeling moieties on the 5′ and/or 3′ ends of the fluorogenic probe do not fluoresce unless PCR amplification of the sequence to which the probe binds has occurred, followed by hybridization of the probe to the amplified sequences, in which case fluorescence of the probe can be seen. The labeling moieties on both ends of a probe are fluorescent molecules, which quench one another. For simplicity, the labeling moiety on one end (e.g., the 5′ end) is sometimes referred to as a “fluorophore,” and the labeling moiety on the other end (e.g., the 3′ end) as a “quencher.” When a single stranded probe is not hybridized to a target and is free in solution, the probe molecule is flexible and folds back partially on itself, so that the quencher and the fluorophore are close together; the quencher thus prevents the probe from fluorescing. Furthermore, when a probe of the invention is hybridized to a single-stranded target, the two labeling moieties are close enough to one another to quench each other. However, without wishing to be bound by any particular mechanism, it is suggested that when the probe is hybridized to its target to form a perfect double stranded DNA molecule, a 5′ to 3′ exonuclease which recognizes perfect hybrids, and which is an activity of the enzyme used for PCR, cleaves the duplex, releasing the fluorophore. The fluorophore is thus separated from the quencher, and will fluoresce. The amount of detected fluorescence is proportional to the amount of amplified DNA.
In a real time PCR, the released fluorescent emission is measured continuously during the exponential phase of the PCR amplification reaction. Since the exponential accumulation of the fluorescent signal directly reflects the exponential accumulation of the PCR amplification product, this reaction is monitored in real time (“real time PCR”). Oligonucleotides used as amplification primers (e.g., DNA, RNA, PNA, LNA, or derivatives thereof) preferably do not have self-complementary sequences or have complementary sequences at their 3′ end (to prevent primer-dimer formation). Preferably, the primers have a GC content of about 50% and may contain restriction sites to facilitate cloning Amplification primers can be between about 10 and about 100 nt in length. They are generally at least about 15 nucleotides (e.g., at least about 15, 20, or 25 nt), but may range from about 10 to a full-length sequence, and not longer than 50 nt. In some circumstances and conditions, shorter or longer lengths can be used Amplification primers can be purchased commercially from a variety of sources, or can be chemically synthesized, using conventional procedures. Suitable primers can be readily designed by conventional methods, such as inspection of known sequences of promoter regions of interest or by use of computer programs such as ClustalW from the European Bioinformatics Institute (world wide web site ebi.ac.uk/clustalw.htm). Some exemplary PCR primers are described in the Examples.
In one embodiment of the invention, rather than, or in addition to, using a fluorogenic probe that can distinguish between methylated and unmethylated cytosines, one of both of the PCR primers are designed so as to distinguish between such sequences.
Probes and conditions can be selected, using routine conventional procedures, to insure that hybridization of a probe to a sequence of interest is specific. A probe that is “specific for” a nucleic acid sequence (e.g., in a DNA molecule) contains sequences that are substantially similar to (e.g., hybridize under conditions of high stringency to) sequences in one of the strands of the nucleic acid. By hybridizing “specifically” is meant herein that the two components (the target DNA and the probe) bind selectively to each other and not generally to other components unintended for binding to the subject components. The parameters required to achieve specific binding can be determined routinely, using conventional methods in the art. A probe that binds (hybridizes) specifically to a target of interest does not necessarily have to be completely complementary to it. For example, a probe can be at least about 95% identical to the target, provided that the probe binds specifically to the target under defined hybridization conditions, such a conditions of high stringency.
As used herein, “conditions of high stringency” or “high stringent hybridization conditions” means any conditions in which hybridization will occur when there is at least about 85%, e.g., 90%, 95%, or 97 to 100%, nucleotide complementarity (identity) between a nucleic acid of interest and a probe. Generally, high stringency conditions are selected to be about 5° C. to 20° C. lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength and pH. Hybridization as used according to the present invention, refers to hybridization under standard conditions used for real-time PCR to achieve amplification.
The size of a probe used to detect the methylation level of a promoter region will vary according to a variety of factors, including, e.g., the ease of preparing (e.g., synthesizing) the probe, the assay method employed to determine the methylation level, etc.
Methods for labeling probes with fluorophores are conventional and well-known. Suitable fluorescer-quencher dye sets will be evident to the skilled worker. Some examples are described, e.g., in Holland et al. (1991) Proc. Natl. Acad. Sci. 88, 7276-7280; WO 95/21266; Lee et al. (1993) Nucleic Acids Research 21, 3761-3766; Livak et al. (1995), supra; U.S. Pat. No. 4,855,225 (Fung et al); U.S. Pat. No. 5,188,934 (Menchen et al.); PCT/US90/05565 (Bergot et al.), and others.
The fluorogenic probes described in the Examples herein function by means of FRET (fluorescence resonance energy transfer). The FRET technique utilizes molecules having a combination of fluorescent labels which, when in proximity to one another, allows for the transfer of energy between labels. See, e.g., the Examples herein or “iQ5 Real Time PCR Detection System” Manual (Bio-Rad, Hercules, Calif.). Other well-known methods for the detection of real-time PCR will be evident to a skilled worker. For example, molecular beacons can be used.
Methods of PCR amplification (including qMSP), and reagents used therein, as well as methods for detecting emission spectra, are conventional. For guidance concerning PCR reactions, see, e.g., PCR Protocols: A Guide to Methods and Applications (Innis et al. eds, Academic Press Inc. San Diego, Calif. (1990)). These and other molecular biology methods used in methods of the invention are well-known in the art and are described, e.g., in Sambrook et al., Molecular Cloning: A Laboratory Manual, current edition, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y., and Ausubel et al., Current Protocols in Molecular Biology, John Wiley & sons, New York, N.Y.
An assay of the invention can be performed in conjunction with one or more other diagnostic (predictive) methods, based, for example, on genomic information, proteomic information, histological analysis of tissue sample, or other methods known in the art. Suitable methods include, e.g., detecting low-grade or high-grade dysplasia (wherein the presence of high-grade dysplasia is further indicative that the subject has an increased risk of developing EAC); taking into consideration the age of the subject (wherein a human subject that is more than 60 years old has an increased risk of developing EAC, and wherein the older the subject, the greater the risk); or determining BE segment length (wherein a length of at least 3 cm in length is further indicative that the subject has a high risk of developing EAC, and wherein the longer the BE segment, the greater the risk).
One aspect of the invention is a kit for predicting a subjects risk for developing EAC or HGD. A skilled worker will recognize components of kits suitable for carrying out a method of the invention. The agents in the kit can encompass reagents for carrying out a method of the invention (e.g., primers and probes for qMSP of sets of promoter regions of the invention). The kit may also include additional agents suitable for detecting, measuring and/or quantitating the amount of PCR amplification.
Optionally, a kit of the invention may comprise instructions for performing the method. Optional elements of a kit of the invention include suitable buffers, control reagents, containers, or packaging materials. The reagents of the kit may be in containers in which the reagents are stable, e.g., in lyophilized form or stabilized liquids. The reagents may also be in single use form, e.g., for the performance of an assay for a single subject.
Kits of the invention may comprise one or more computer programs that may be used in practicing the methods of the invention. For example, a computer program may be provided that calculates a methylation value in a sample from results of the determining methylation levels. Such a computer program may be compatible with commercially available equipment, for example, with commercially available microarray or real-time PCR. Programs of the invention may take the output from microplate reader or realtime-PCR gels or readouts and prepare a calibration curve from the optical density observed in the wells, capillaries, or gels and compare these densitometric or other quantitative readings to the optical density or other quantitative readings in wells, capillaries, or gels with test samples.
In addition to the clinical uses discussed herein, kits of the invention can be used for experimental applications.
In the foregoing and in the following examples, all temperatures are set forth in uncorrected degrees Celsius; and, unless otherwise indicated, all parts and percentages are by weight.
EXAMPLES
Example I
Studies of Methylation Levels and Frequencies of Nel-like 1 (NELL1), Tachykinin-1 (TAC1), Somatostatin (SST), and A-kinase Anchoring Protein 12 (AKAP12)
Methylation levels and frequencies of the above genes were studied, using real-time quantitative methylation-specific PCR (qMSP), in 259 endoscopic esophageal biopsy specimens of differing histologies. Prevalences were determined in each of the following histological categories: NE, BE, LGD, HGD or EAC. The methods used in these studies are, in general, similar or identical to those described in Example II. For details of these studies, see, e.g., the following references, all of which are incorporated by reference herein in their entirety: Jin et al. (2007) Oncogene 26, 6332-6340; Jin et al. (2007) Clin Cancer Res 13, 6293-6300; Jin et al. (2008) Cancer 112, 43-49; Jin et al. (2008) Cancer 112, 43-49; and Jin et al. (2008) Cancer Epidemiol Biomarkers Prey 17, 111-117. Among 10 genes evaluated, the five genes noted above were methylated early and often in BE-associated neoplastic progression.
Example II
Promoter Hypermethylation of CDH13 is a Common, Early Event in Human Esophageal Adenocarcinogenesis and Correlates with Clinical Risk Factors
CDH13 (also known as H-cadherin and T-cadherin), a member of the cadherin gene superfamily, was isolated and has been mapped to 16q24, a locus that frequently undergoes deletion in human cancers, including esophageal carcinoma. In contrast to other known cadherins such as E-cadherin, N-cadherin, and P-cadherin, which are transmembrane proteins, CDH13 lacks conventional transmembrane and cytoplasmic domains and is attached to the plasma membrane through a glycosyl phosphatidyl inositol anchor. Several studies have suggested that CDH13 functions as a tumor suppressor gene and possesses potent antitumor activity in several human cancers both in vitro and in vivo. Hypermethylation of CDH13 has been described in many human cancers, including ESCC. Prior to the present studies, however, hypermethylation of CDH13 in precancerous lesions such as Barrett's metaplasia (BE), as well as in BE-associated EAC, is an area that had not been explored.
This Example describes studies of hypermethylation of the promoter region of CDH13 in Barrett's-associated esophageal adenocarcinogenesis. 259 human esophageal tissues were examined for CDH13 promoter hypermethylation by real-time methylation-specific PCR. CDH13 hypermethylation showed discriminative receiver-operator characteristic curve profiles, sharply demarcating esophageal adenocarcinoma (EAC) from esophageal squamous cell carcinoma (ESCC) and normal esophagus (NE) (p<0.0001). CDH13 normalized methylation values (NMV) were significantly higher in Barrett's esophagus (BE), dysplastic BE (D), and EAC than in NE (p<0.0000001). CDH13 hypermethylation frequency was 0% in NE but increased early during neoplastic progression, rising to 70% in BE, 77.5% in D, and 76.1% in EAC. Both CDH13 hypermethylation frequency and its mean NMV were significantly higher in BE with than without accompanying EAC. In contrast, only five (19.2%) of 26 ESCCs exhibited CDH13 hypermethylation. Furthermore, both CDH13 hypermethylation frequency and its mean NMV were significantly higher in EAC than in ESCC, as well as in BE or D vs. ESCC. Interestingly, mean CDH13 NMV was significantly lower in short-segment than in long-segment BE, a known clinical risk factor for neoplastic progression. Similarly, BE segment length was significantly lower in specimens with unmethylated than with methylated CDH13 promoters. 5-aza-2′-deoxycytidine treatment of OE33 EAC and KYSE220 ESCC cells reduced CDH13 methylation and increased CDH13 mRNA expression. Our results reveal that promoter hypermethylation of CDH13 is a common event in EAC but not in ESCC and occurs early during BE-associated esophageal neoplastic progression, correlating with clinical criteria associated with neoplastic progression risk.
A. Materials and Methods
(1) Tissue samples. The 259 specimens examined in the current study comprised 66 from normal esophagus (NE), 60 of non-dysplastic Barrett's metaplasia {BE, including 36 obtained from patients with BE alone (Ba) and 24 from patients with BE accompanied by EAC (Bt)}, 40 from dysplastic BE {D, including 19 low-grade (LGD) and 21 high-grade (HGD)}, 67 EACs, and 26 ESCCs. All patients provided prior written informed consent under a protocol approved by the Institutional Review Boards at the University of Maryland School of Medicine, the Baltimore Veterans Affairs Medical Center, and the Johns Hopkins University School of Medicine. Biopsies were obtained using a standardized biopsy protocol as previously described (Schulmann et al. (2005) Oncogene 24, 4138-4148). Research tissues were taken from grossly apparent BE epithelium or from mass lesions in patients manifesting these changes at endoscopic examination, and histology was confirmed using parallel aliquots culled from identical locations at endoscopy. All research biopsy specimens were stored in liquid nitrogen prior to DNA extraction. Clinicopathologic characteristics are summarized in Table 2.







TABLE 2







Clinicopathologic characteristics and methylation status of CDH13 in human esophageal tissues












Number
Age (year)
NMV2
Methylation Status (cutoff 0.06)3















Clinical characteristics1
of samples
mean
mean
p
Frequency
UM
M
p


















Histology










Normal esophagus
66
64.3
0.0054

  0%
66
0


BE
60
63.7
0.3122
$<0.00001*/#
  70%
18
42


Ba
36
62.5
0.2623

58.3%
15
21
<0.05†


Bt
24
65.5
0.3871
$<0.05
87.5%
3
21


Dysplasia in Barrett's esophagus
40
65.3
0.3383
$<0.00001*/#
77.5%
9
31


Low-grade dysplasia
19
65.3
0.2833
$<0.000001*
78.9%
4
15
NS†


High-grade dysplasia
21
65.2
0.388
$<0.000001*
76.2%
5
16


EAC
67
65.1
0.2392
$<0.000001*/#
76.1%
16
51
<0.0001‡


ESCC
26
62.5
0.0458
$<0.001*
19.2%
21
5


Barrett's segment of Ba


Short-segment (<3 cm)
14
62.3
0.131
$<0.01
28.6%
10
4
<0.01†


Long-segment (>=3 cm)
16
62.8
0.4071

87.5%
2
14


Stage of EAC patients


I
7
63
0.3081
¶NS
85.7%
1
6
NS†


II
15
65.2
0.2408

73.3%
4
11


III
25
64.6
0.2111

  72%
7
18


IV
7
66.3
0.2921

 100%
0
7


Lymph node metastasis in EAC


patients


Negative
25
64.9
0.2751
$NS
  75%
5
20
NS‡


Positive
25
64.6
0.2277

  76%
6
19


Smoking status of EAC patients


Never
6
58.5
0.2984
¶NS
 100%
0
6
NS†


Former
24
68.5
0.2143

79.2%
5
19


Current
13
60.8
0.2561

76.9%
3
10


Alcohol drinking status of EAC


patients


Never
16
65.3
0.2209
¶NS
  75%
4
12
NS†


Former
15
63
0.2524

86.7%
2
13


Current
10
65.7
0.2427

  80%
2
8





1BE, Barrett's metaplasia; Ba, BE from patients with Barrett's alone; Bt, BE from patients with Barrett's accompanied by EAC; EAC, esophageal adenocarcinoma; ESCC, esophageal squamous cell carcinoma.


2NMV: normalized methylation value; $, Mann-Whitney U test; *, comparisons made to normal esophagus; #, comparisons made to ESCC; ¶, Kruskal-Wallis test.


3UM, unmethylated; M, methylated; †, Fisher's exact test; ‡, Chi-square for independence test.


NS, not significant.






(2) Cell lines. OE33 EAC and KYSE220 ESCC cells were cultured in 47.5% RPMI 1640, 47.5% F-12 supplemented with 5% fetal bovine serum.
(3) DNA and RNA Extraction
Genomic DNA was extracted from biopsies and cultured cells using a DNeasy Tissue Kit (Qiagen, Valencia, Calif.). Total RNA was isolated from cultured cells using TRIzol reagent (Invitrogen, Carlsbad, Calif.). DNAs and RNAs were stored at −80° C. prior to analysis.
(4) Bisulfite Treatment and Real-time Methylation-specific PCR
One ug DNA was treated with bisulfate to convert unmethylated cytosines to uracils prior to MSP using an EpiTect Bisulfite Kit (Qiagen, Valencia, Calif.). Promoter methylation levels of CDH13 were determined by real-time quantitative MSP with an ABI 7900 Sequence Detection System (Applied Biosystems, Foster City, Calif.), using primers and probes as follows: CDH13-forward: 5′-TTTGGGAAGTTGGTTGGTTGGC-3′ (SEQ ID NO:17); CDH13-reverse: 5′-ACTAAAAACGCCCGACGACG-3′ (SEQ ID NO:18) and probe: 5′-TATGTTTAGTGTAGTCGCGTGTATGAATGAA-3′ (SEQ ID NO:19). β-actin was used for normalization of data. Primers and probe for β-actin were the same as previously reported (Jin et al. (2007) Oncogene 26, 6332-6340). A standard curve was generated using serial dilutions of CpGenome Universal Methylated DNA (CHEMICON, Temecula, Calif.). Normalized methylation value (NMV) was defined as follows: NMV=(CDH13-S/CDH13-FM)/(ACTB-S/ACTB-FM), where CDH13-S and CDH13-FM represent CDH13 methylation levels (derived from the standard curve) in sample and fully methylated DNAs, respectively, while ACTB-S and ACTB-FM correspond to β-actin in sample and fully methylated DNAs, respectively.
(5) Real-time Quantitiative RT-PCR
To determine CDH13 mRNA levels, one-step real-time quantitative RT-PCR was performed using a Qiagen QuantiTect Probe RT-PCR Kit (Qiagen, Hilden, Germany) and an ABI 7900 Sequence Detection System (Applied Biosystems, Foster City, Calif.). Primers and probe for CDH13 were as follows: CDH13-forward: 5′ATGTTGGCAAGGTAGTCGATAGTG-3′ (SEQ ID NO:20); CDH13-reverse: 5′-ACGCTCCCTGTGTTCTCATTG-3′ (SEQ ID NO:21) and probe: 5′-CCAGAAAGGTCCAAGTTCCGGCTCACT-3 (SEQ ID NO:22). β-actin was used for normalization of data. Primers and probe for β-actin were the same as previously reported (Jin et al. (2007) Oncogene 26, 6332-6340). A standard curve was generated using serial dilutions of qPCR Reference Total RNA (Clontech, Mountainview, Calif.). Normalized mRNA value (NRV) was calculated according to the following formula for relative expression of target mRNA: NRV=(TarS/TarC)/(ACTB-S/ACTB-C, where TarS and TarC represent levels of target gene mRNA expression (derived from the standard curve) in sample and control mRNAs, respectively, while ACTB-S and ACTB-C correspond to amplified ACTB levels in sample and control mRNAs, respectively.
(6) 5-Aza-dC Treatment of Esophageal Cancer Cell Lines
To determine whether CDH13 inactivation was due to promoter hypermethylation in esophageal cancer, 2 esophageal cancer cell lines (KYSE220 and OE33) were subjected to 5-Aza-dC (Sigma, St. Louis, Mo.) treatment as previously described (Bender et al. (1999) Mol Cell Biol 19, 6690-6698; Shibata et al. (2002) Cancer Res 62, 5637-5640). Briefly, 1×105 cells/ml were seeded onto a 100 mm dish and grown for 24 h. Then, 1 μl of 5 mM 5-Aza-dC per ml of cells was added every 24 hours for 4 days. DNAs and RNAs were harvested on day 4.
(7) Data Analysis and Statistics
Receiver-operator characteristic (ROC) curve analysis (Hanley et al. (1982) Radiology 143, 29-36) was performed using NMVs for the 67 EAC, 26 ESCC and 66 NE specimens by Analyse-It© software (Version 1.71, Analyse-it Software, Leeds, UK). Using this approach, the area under the ROC curve (AUROC) identified optimal sensitivity and specificity levels at which to distinguish normal from malignant esophageal tissues (NE vs. EAC), yielding a corresponding NMV threshold with which to dichotomize the methylation status of CDH13. The threshold NMV value determined from this ROC curve was applied to determine the status of CDH13 methylation in all tissue types included in the study. For all other statistical tests, Statistica (version 6.1; StatSoft, Inc., Tulsa, Okla.) was employed. Differences with p<0.05 were considered significant.
B. Results
(1) CDH13 Promoter Hypermethylation in Esophageal Tissues
Promoter hypermethylation of CDH13 was analyzed in 66 NE, 60 BE (including 36 Ba and 24 Bt), 40 D (including 19 LGD and 21 HGD), 67 EAC, and 26 ESCC. CDH13 promoter hypermethylation showed highly discriminative ROC curve profiles and AUROCs, clearly distinguishing both EAC and ESCC from NE (FIGS. 9A and 9B), as well as EAC from ESCC (FIG. 9C).
The cutoff NMV for CDH13 (0.06) was identified from the ROC curve (EAC vs. NE) to achieve the highest possible sensitivity while maintaining 100% specificity. Mean NMV and frequency of CDH13 hypermethylation for each tissue type are shown in Table 2.
NMVs of CDH13 were significantly higher in ESCC, EAC, D, HGD, LGD, BE, Ba and Bt than in NE (p<0.001, Mann-Whitney U test). The frequency of CDH13 hypermethylation was significantly higher in BE (70%), D (77.5%), and EAC (76.1%) than in N (0%; p<0.0001, p<0.0001 and p<0.0001, respectively; Fisher's exact test). Interestingly, both CDH13 hypermethylation frequency and mean NMV were significantly higher in Bt than in Ba (87.5% vs. 58.3%, p=0.021 and 0.3871 vs. 0.2623, p=0.045, respectively). The mean CDH13 NMV in EAC (0.2722) was significantly higher than that in matching NE (0.0034) for 27 cases in which matching NE and EAC were available (p<0.00001, Wilcoxon matched pairs test). In contrast to EAC, only five (19.2%) of 26 ESCCs manifested hypermethylation of CDH13. There was no significant difference in mean CDH13 NMV between tumor and normal tissue in 13 cases for which matching ESCC (0.0337) and NE (0.0131; p=0.6, Wilcoxon matched pairs test) were available. Both CDH13 hypermethylation frequency and mean NMV were significantly higher in EAC than in ESCC (76.1% vs. 19.2%, p<0.0001 and 0.2392 vs. 0.0458, p<0.0001, respectively), as well as in D vs. ESCC (77.5% vs. 19.2%, p<0.0001 and 0.3383 vs. 0.0458, p<0.0001, respectively) and in BE vs. ESCC (70% vs. 19.2%, p<0.0001 and 0.3122 vs. 0.0458, p<0.0001; Table 2).
According to generally accepted criteria, BE was defined as long-segment (LSBE) if it was equal to or greater than 3 cm in length, or short-segment (SSBE) if less than 3 cm. The mean NMV of CDH13 was significantly higher in LSBE than in SSBE (0.4071 vs. 0.131; p<0.01, Student's t-test, Table 2 and FIG. 10A). Similarly, segment lengths of BEs with methylated CDH13 promoters (mean=5.83 cm) were significantly longer than segment lengths of BEs with unmethylated CDH13 promoters (mean=1.83 cm; p<0.001, Student's t-test; FIG. 10B), and the frequency of CDH13 hypermethylation was significantly higher in LSBE than in SSBE (87.5% vs. 28.6%; p<0.01, Fisher's exact test; Table 2).
No significant associations were observed between CDH13 promoter hypermethylation and patient age (data not shown), survival (log-rank test, data not shown), tumor stage, lymph node metastasis, smoking, or alcohol consumption (Table 2).
(2) CDH13 Methylation and mRNA Levels in Esophageal Cancer Cell Lines Pre- and Post-5-Aza-dC Treatment
KYSE220 ESCC and OE33 EAC cells were subjected to 5-Aza-dC treatment. After 5-Aza-dC treatment, the NMV of CDH13 was diminished and the mRNA level of CDH13 was increased in both KYSE220 and OE33 cells (FIG. 11).
C. Discussion
In this Example, we systematically investigated hypermethylation of the CDH13 gene promoter in cell lines and primary human esophageal lesions of contrasting histological types and grades by qMSP. Our results demonstrate that CDH13 promoter hypermethylation occurs frequently in human EAC, but not in ESCC. In addition, our data show that CDH13 hypermethylation increases early during esophageal adenocarcinogenesis, from 0% in NE to 58.3% in BE, 77.5% in D, and 76.1% in EAC. These results imply that hypermethylation of CDH13 occurs early in most subjects, that its frequency increases during adenocarcinogenesis, and that it is tissue-specific (i.e., common in EAC but rare in ESCC). Further evidence supporting this tissue specificity is provided by ROC curves, which clearly distinguished EAC from ESCC. Similarly, support for tissue specificity is evident from the finding that both CDH13 hypermethylation frequency and mean CDH13 NMV were significantly higher in EAC than in ESCC. In addition, the low frequency (19.2%) of CDH13 hypermethylation in ESCC, as determined in the current study, is consistent with previous findings by other groups. Thus, CDH13 hypermethylation appears to constitute a critical event unique to human EAC.
Several studies have suggested that methylation of certain genes may occur as a field change and may be associated with an increased risk of malignant progression. CDKN2A, ESR1, and MYOD1 were methylated only in BE from patients who possessed dysplasia or cancer in other regions of their esophagus, but not in patients with no evidence of progression beyond BE, while CALCA, MGMT, and TIMP3 were methylated more frequently in normal stomach, normal esophageal mucosa and intestinal metaplasia from patients with distant dysplasia or esophageal cancer than from patients without dysplasia or cancer. Previously, we demonstrated that hypermethylation of p16, RUNX3, and HPP1 in BE or LGD may represent independent risk factors for the progression of BE to HGD or EAC (Schumann et al. (2005) Oncogene 24, 4138-4148). Example I herein shows that both hypermethylation frequency and NMV of the nel-like 1, tachykinin-1, somatostatin and AKAP12 genes were higher in BE with accompanying EAC than in BE without accompanying EAC. Interestingly, both CDH13 hypermethylation frequency and level were significantly higher in BE with than without accompanying EAC in the current study, suggesting that CDH13 is a biomarker of more ominous disease lurking nearby.
In the present Example, we also correlated CDH13 methylation with clinicopathologic features. Despite some degree of controversy regarding the length of the BE segment as a predictive factor in BE progression, it is likely that this clinical parameter is an important predictor of neoplastic progression. In the Seattle Barrett's Esophagus Project, BE segment length was not related to cancer risk in a prospective cohort study of 309 Barrett's patients (p>0.2); however, when patients with HGD at entrance were excluded, a strong trend was observed, with a 5 cm difference in length associated with a 1.7-fold increase in cancer risk (95% CI, 0.8-3.8-fold). Significant differences in the frequency of both dysplasia and EAC were observed between SSBE and LSBE, at 8.1% vs. 24.4% for dysplasia (p<0.0001) and 0% vs. 15.4% for EAC (p<0.0005). In a comprehensive prospective study of 889 consecutive patients, the prevalence of dysplasia and cancer differed significantly in patients with SSBE vs. LSBE. More recently, a significantly increased risk of progression to HGD or EAC with LSBE after a mean follow-up of 12.7 years was reported. In the studies described in Example I, the nel-like 1, tachykinin-1, somatostatin and AKAP12 genes were significantly more hypermethylated in LSBE than in SSBE. Notably, in the current study, CDH13 methylation also showed a strong relationship to BE segment length. The mean NMV of CDH13 was significantly higher in LSBE than in SSBE. Similarly, the length of the BE segment was significantly greater in specimens with methylated than with unmethylated CDH13 promoters. Thus, CDH13 hypermethylation may constitute a molecular correlate of BE segment length, as well as a harbinger of nearby neoplastic disease. These results also suggest that epigenetic alterations, which may account for some of the biologic behavior of BE, clearly differ between LSBE and SSBE, suggesting a need for further large-scale studies.
In accordance with previous findings in other primary cancer cell types, we observed that methylation of CDH13 in EAC and ESCC cancer cell lines was associated with silenced or reduced expression of CDH13 mRNA. Treatment with 5-Aza-dC restored mRNA expression and reversed CDH13 methylation in these cells. Restoration of CDH13 mRNA expression by demethylating agent treatment implies that DNA hypermethylation was responsible for silencing of CDH13.
In summary, findings of this Example suggest that hypermethylation of the CDH13 promoter is a common event in human esophageal adenocarcinogenesis, occurs early during Barrett's-associated esophageal carcinogenesis, and is associated with clinical risk factors of progression. In addition, CDH13 hypermethylation is uncommon in human ESCC, thus making it a potential cell type-specific biomarker for EAC.
Example III
A Multicenter, Double-blinded Validation Study of Methylation Biomarkers for Progression Prediction in Barrett's Esophagus
Esophageal adenocarcinoma risk in Barrett's esophagus (BE) is increased 30- to 125-fold versus the general population, yet neoplastic progression occurs rarely in BE. Molecular biomarkers would stratify patients for more efficient surveillance endoscopy and improve early detection of progression. We therefore performed a retrospective, multicenter, double-blinded validation study of 8 BE progression prediction methylation biomarkers. Progression or nonprogression were determined at 2 years (tier 1) and 4 years (tier 2). Methylation was assayed in 145 nonprogressors (NPs) and 50 progressors (Ps) using real-time quantitative methylation-specific PCR. Ps were significantly older than NPs (70.6 vs. 62.5 years, p<0.001). We evaluated a linear combination of the 8 markers, using coefficients from a multivariate logistic regression analysis. Areas under the ROC curve (AUCs) were high in the 2-, 4-year and combined data models (0.843, 0.829 and 0.840; p<0.001, p<0.001 and p<0.001, respectively). In addition, even after rigorous overfitting correction, the incremental AUCs contributed by panels based on the 8 markers plus age vs. age alone were substantial (Δ-AUC=0.152, 0.114 and 0.118, respectively) in all three models. A methylation biomarker-based panel to predict neoplastic progression in BE has clinical value in improving both the efficiency of surveillance endoscopy and the early detection of neoplasia.
A. Materials and Methods
(1) Definition of Barrett's esophagus progressor and nonprogressor patients and sample collection. Progressors (Ps) and nonprogressors (NPs) were defined as described previously (Montgomery et al. (2001) Hum Pathol 32, 379-388). Ps were considered both as a single combined group, and in two tiers: progression within 2 years (tier 1) or 4 years (tier 2). 195 BE biopsies (145 NPs and 50 Ps) were obtained from 5 participating centers: the Mayo Clinic at Rochester/Jacksonville, the University of Arizona, the University of North Carolina, and Johns Hopkins University. All patients provided written informed consent under a protocol approved by Institutional Review Boards at their institutions. Biopsies were taken using a standardized biopsy protocol (Corley et al. (2002) Gastroenterology 122, 633-640; Montgomery et al. (2001, supra). Clinicopathologic features are summarized in Table 3.







TABLE 3







Clinicopathologic characteristics in 50


progressors and 145 non-progressors











Progressor
Non-Progressor




(N = 50)
(N = 145)
p value





Age (years, mean ± SD)*
70.6 ± 9.1
62.5 ± 12.3
<0.001  


Gender





Male
46 (92%)
113 (78%) 
NS


Female
4 (8%)
32 (22%)



Race





White
 50 (100%)
144 (99%) 
NS


Unknown
0 (0%)
1 (1%)



Index of Histology





BE
26 (52%)
87 (60%)
NS


LGD
22 (44%)
58 (40%)



Unknown
2 (4%)
0 (0%)



Barrett's length
 6.0 ± 3.3
5.4 ± 3.6
NS


(cm, mean ± SD)





Body mass index (kg/m2)





Normal weight (18.5-24.9)
10 (20%)
27 (19%)
NS


Overweight (25-29.9)
15 (30%)
53 (37%)



Obese (30+)
20 (40%)
53 (37%)



Unknown
5 (10%)
12 (8%) 



Smoking status*





Yes
4 (8%)
15 (10%)
NS


No
46 (92%)
124 (86%) 



Unknown
0 (0%)
6 (4%)



Alcohol Use*





Yes
15 (30%)
43 (30%)
NS


No
35 (70%)
94 (65%)



Unknown
0 (0%)
8 (6%)



ASA/NSAID use*





Yes
17 (34%)
61 (42%)
0.039**


No
33 (66%)
81 (56%)



Unknown
0 (0%)
3 (2%)



PPI or H2 Blocker*





Yes
40 (80%)
123 (85%) 
NS


No
10 (20%)
21 (14%)



Unknown
0 (0%)
1 (1%)



Family History of BE,





LGD, HGD or EAC





Yes
3 (6%)
11 (8%) 
NS


No
47 (94%)
126 (87%) 



Unknown
0 (0%)
8 (6%)





SD, standard deviation; BE, Barrett's metaplasia; LGD, low-grade dysplasia; HGD, high-grade dysplasia; EAC, esophageal adenocarcinoma; ASA, acetylsalicylic acid; NSAID, non-steroidal anti-inflammatory drug; PPI, proton pump inhibitor; NS, not significant.


*at time of procedure;


**p-value is adjusted for age.






(2) Bisulfite Treatment and Real-Time Quantitative Methylation-Specific PCR (qMSP)
Bisulfite treatment was performed and promoter methylation levels of 8 genes (p16, HPP1, RUNX3, CDH13, TAC1, NELL1, AKAP12 and SST) were determined by qMSP on an ABI 7900 Sequence Detection (Taqman) System, as described in Montgomery et al. (2001, supra). β-actin was used for normalization. Primers and probes for qMSP are described in Table 4. In this table, the forward primer sequences, reading from top to bottom, are SEQ ID NOs: 23-31; the reverse primer sequences, reading from top to bottom, are SEQ ID NOs: 32-40; and the TaqMan probe sequences, reading from top to bottom, are SEQ ID NOs: 41-49.
A standard curve was generated using serial dilutions of CpGenome Universal Methylated DNA (CHEMICON, Temecula, Calif.). A normalized methylation value (NMV) for each gene of interest was defined as described in Montgomery et al. (2001, supra). Wetlab analysts and all SJM laboratory personnel were blinded to specimen P or NP status.
(3) Data Analysis and Statistics
Associations between progression status and patient characteristics were tested using Student's t-test or Chi-squared testing. Relationships between biomarkers and patient progression status were examined using Wilcoxon rank-sum testing.
To evaluate the predictive utility of the markers, we constructed receiver operating characteristic (ROC) curves. ROC curve analyses were first conducted on individual markers, then in combination to determine whether a panel performed better than any single marker. Our algorithm rendered a single composite score, using the linear predictor from a binary regression model justified under the linearity assumption (Jin et al. 2007) Clin Cancer Res 13, 6293-6300). The predictive accuracy of composite scores was evaluated based on a resampling algorithm: we randomly split data into a learning set containing 2⁄3 and a test set including 1⁄3 of observations. The combination rule derived from the learning set produced two ROC curves, from the learning and test sets, respectively. Vertical differences between these two ROC curves yielded the overestimation of sensitivities at given specificities. This procedure was repeated 200 times, and these 200 differences were averaged to estimate the expected overfitting.
We also utilized predictiveness curves (Jin et al. (2008) Cancer 112, 43-49) to display risk distribution as a function of the combined marker in the population. This curve represents a plot of risk associated with the vth quantile of the marker, P{D=1|Y=F−1(v)} vs. v, with F(•) the cumulative distribution of the marker. These plots display population proportions at different risk levels more clearly than do other metrics (like ROC curves). Since a case-control sample was studied, we used an external progression prevalence rate to calculate risk in the targeted screening population. To calibrate for future samples, a shrinkage coefficient estimated from the logistic regression model was applied to the linear predictors from which risk was calculated (Jin et al. (2008) Cancer Epidemiol Biomarkers Prev 17, 111-117).
All analyses were performed in R (see the world wide web site www.r-project.org). Statistical data analysts were blinded to the identities of the 8 biomarkers.
B. Results
(1) Clinical Characteristics
Ps vs. NPs did not differ significantly by gender, body mass index, BE segment length, LGD patient percentage, family history of BE, LGD, HGD or EAC, cigarette smoking, or alcohol use; however, Ps were significantly older than NPs (70.6 vs. 62.5 years; p<0.001, Student's t test; Table 3). Samples consisted of one biopsy from each of 50 Ps and 145 NPs (195 patients) in the combined model. In the 2-year model, we redefined progressors whose interval from index to final procedure exceeded 2 years as nonprogressors, yielding 36 Ps and 159 NPs. In the 4-year model, we redefined progressors whose interval from index to final procedure exceeded 4 years as nonprogressors, yielding 47 Ps and 148 NPs.
(2) Univariate Analyses
NMVs of HPP1, p16 and RUNX3 were significantly higher in Ps vs. NPs by Wilcoxon test (0.456, 0.138, and 0.104 vs. 0.273, 0.069 and 0.063; p=0.0025, 0.0066 and 0.0002, respectively). The remaining 5 markers did not differ significantly in Ps vs. NPs (Table 5).







TABLE 5







Univariate analysis for 8 methylation biomarkers


in 50 progressors and 145 non-progressors













p value




NMV (mean ± SD)
(Wilcoxon rank










Biomarker
Non-progressor
Progressor
sum test)





HPP1
0.273 ± 0.326
0.456 = 0.432
0.0025


p16
0.069 ± 0.209
0.138 ± 0.249
0.0066


RUNX3
0.063 ± 0.179
0.104 ± 0.168
0.0002


CDH13
0.169 ± 0.263
0.201 ± 0.373
0.7682


TAC1
0.193 ± 0.231
0.231 = 0.228
0.1686


NELL1
0.148 ± 0.247
0.113 ± 0.150
0.7644


AKAP12
0.163 ± 0.244
0.280 ± 0.403
0.2484


SST
0.455 ± 0.378
0.482 = 0.486
0.9143





NMV, normalized methylation value; SD, standard deviation






We further assessed the classification accuracy of single markers using ROC curve analyses. Areas under ROC curve (AUCs) for HPP1, p16 and RUNX3 were all significantly greater than 0.50, (Table 6).







TABLE 6







Receiver-operator characteristic curve analysis for 8 methylation


biomarkers in 50 progressors and 145 non-progressors










Biomarker
AUC (95% confidence interval)






HPP1
0.647 (0.556, 0.739)



p16
0.628 (0.534, 0.722)



RUNX3
0.671 (0.586, 0.756)



CDH13
0.515 (0.409, 0.622)



TAC1
0.571 (0.470, 0.673)



NELL1
0.516 (0.420, 0.611)



AKAP12
0.561 (0.451, 0.672)



SST
0.506 (0.401, 0.611)





AUC, area under the receiver-operator characteristic curve






(3) Logistic Regression Analyses of the 8-marker Panel

We then combined all 8 markers by performing logistic regression and treating them as linear predictors (Table 7, FIG. 12)
All models exhibited high AUCs (0.843, 0.829 and 0.840, respectively; Table 7, FIGS. 12A-12C. We performed overfitting correction based on 3-fold cross-validation and 200 bootstraps. The overfitting-corrected AUCs remained high (0.745, 0.720 and 0.732, respectively), while shrinkages from overfitting correction were modest (0.098, 0.109 and 0.108, respectively) in the three models (Table 7, FIGS. 12A-12C).
We also explored the incremental AUC value contributed by an 8-marker-plus-age panel to that of age alone (Table 8, FIG. 12). The AUCs of the 8-marker-plus-age panels in the three models (0.858, 0.850 and 0.855, respectively) were higher than those of age alone (0.604, 0.630 and 0.635, respectively; Table 8, FIGS. 12D-12F).
Overfitting-corrected AUCs remained high (0.756, 0.744 and 0.753, respectively), and increments contributed by the age-plus-biomarker panel vs. age were substantial (0.152, 0.114 and 0.118, respectively) in the three models (Table 8, FIGS. 12D-12F).
(4) Sensitivity and Specificity of the 8-marker Panel
While maintaining high specificity to minimize false-positive results, our model still predicted a number of new early diagnoses, i.e., diagnoses that would not have occurred earlier without the panel (Table 9).
While maintaining specificity at 0.9 or 0.8, sensitivities (0.443 and 0.629 for the combined model, 0.607 and 0.721 for the 2-year model, and 0.465 and 0.606 for the 4-year model, respectively) were above or approached 50% in all three models based on the 8-marker panel alone. Furthermore, at 0.9 or 0.8 specificities, sensitivities (0.457 and 0.757 for the combined model, 0.536 and 0.786 for the 2-year model, and 0.450 and 0.724 for the 4-year model, respectively) exceeded or approached 50% in all models based on the 8-marker-plus-age panel.
From the foregoing description, one skilled in the art can easily ascertain the essential characteristics of this invention, and without departing from the spirit and scope thereof, can make changes and modifications of the invention to adapt it to various usage and conditions and to utilize the present invention to its fullest extent. The preceding preferred specific embodiments are to be construed as merely illustrative, and not limiting of the scope of the invention in any way whatsoever. The entire disclosure of all applications, patents, and publications (including provisional patent applications 61/066,281, filed Feb. 19, 2008; 61/131,748, filed Jun. 11, 2008; and 61/132,418, filed Jun. 18, 2008) cited above and in the figures are hereby incorporated in their entirety by reference.







TABLE 4







Supplementary Table 4. Primer and probe sequences











Forward primer
Reverse primer



Gene
sequence (5′-3′)
sequence (5′-3′)
TaqMan probe sequence (5′-3′)





HPP1
GTTATCGTCGTCGTTTTTGTTGTC
GACTTCCGAAAAACACAAAATCG
CCGAACAACGAACTACTAAACATCCCGCG


 


p16
TGGAATTTTCGGTTGATTGGTT
AACAACGTCCGCACCTCCT
ACCCGACCCCGAACCGCG


 


RUNX3
GGGTTTTGGCGAGTAGTGGTC
ACGACCGACGCGAACG
CGTTTTGAGGTTCGGGTTTCGTCGTT


 


CDH13
TTTGGGAAGTTGGTTGGTTGGC
ACTAAAAACGCCCGACGACG
TATGTTTAGTGTAGTCGCGTGTATGAATGAA


 


Tachykinin-1
GGCGGTTAATTAAATATTGAGTAGAAA
AAATCCGAACGCGCTCTTTCG
AGAATGTTACGTGGGTTTGGAGGTTTAAGGAG



GTCGC




 


Nel-like 1
GGGTTTTTAGACGAACGTAGTCGTAGC
CCACCGCTAACGCGACGA
ACGACGTAAAACTCGAAACCCGACCC


 


AKAP12
GTTTTGTTAGAAACGGGAGGCGTTC
GAAACCAAAAACGCTACGACGCG
TCGGGTGGGCGGTTGTTTGGATT


 


Somatostatin
GGGGCGTTTTTTAGTTTGACGT
AACAACGATAACTCCGAACCTCG
AACGACTTATATACTCTCAACCGTCTCCCTCTA


 


β-actin
TGGTGATGGAGGAGGTTTAGTAAGT
AACCAATAAAACCTACTCCTCCCTTAA
ACCACCACCCAACACACAATAACAAACACA
















TABLE 7







Logistic regression and overfitting correction for the 2-year, 4-year and combined models













AUC1 (95% CI)
p-value of AUC1
AUC2 (95% CI)
p-value of AUC2
Shrinkage (AUC1 − AUC2)





2-year model
0.843 (0.763, 0.924)
<0.001
0.745 (0.685, 0.875)
0.001
0.098


4-year model
0.829 (0.773, 0.907)
<0.001
0.720 (0.694, 0.856)
0.004
0.109


combined model
0.840 (0.773, 0.907)
<0.001
0.732 (0.697, 0.868)
0.002
0.108





AUC, area under the receiver-operator characteristic curve; AUC1, original AUC; AUC2, overfitting corrected AUC; CI, confidence interval.













TABLE 8







Incremental values above age alone for the 2-year, 4-year and combined models












AUC1 (95% CI)
AUC2 (95% CI)
AUC3 (95% CI)
Increment over age (AUC3 − AUC1)





2-year model
0.604 (0.491, 0.718)
0.858 (0.783, 0.932)
0.756 (0.699, 0.884)
0.152


4-year model
0.630 (0.526, 0.735)
0.850 (0.784, 0.917)
0.744 (0.719, 0.871)
0.114


combined model
0.635 (0.532, 0.737)
0.855 (0.790, 0.919)
0.753 (0.729, 0.878)
0.118





AUC, area under the receiver-operator characteristic curve; AUC1, AUC of age alone; AUC2, AUC of age plus markers; AUC3, overfitting corrected AUC of age plus markers; CI, confidence interval.













TABLE 9







Specificity and sensitivity for 2-year, 4-year and combined models










Specificity (95% CI) at sensitivity =
Sensitivity (95% CI) at specificity =












0.9
0.8
0.9
0.8





Combined model






Age
0.260 (0.162, 0.425)
0.390 (0.240, 0.508)
0.221 (0.054, 0.448)
0.371 (0.204, 0.532)


Marker combination
0.567 (0.413, 0.849)
0.724 (0.574, 0.914)
0.443 (0.350, 0.838)
0.629 (0.527, 0.941)


Age + marker combination
0.576 (0.484, 0.867)
0.781 (0.647, 0.944)
0.457 (0.372, 0.869)
0.757 (0.598, 0.964)


2-year model






Age
0.205 (0.138, 0.389)
0.351 (0.197, 0.484)
0.176 (0.021, 0.426)
0.354 (0.172, 0.538)


Marker combination
0.547 (0.436, 0.873)
0.757 (0.595, 0.935)
0.607 (0.393, 0.870)
0.721 (0.593, 0.969)


Age + marker combination
0.615 (0.474, 0.918)
0.786 (0.652, 0.956)
0.536 (0.400, 0.934)
0.786 (0.600, 0.987)


4-year model






Age
0.249 (0.158, 0.430)
0.384 (0.229, 0.506)
0.232 (0.038, 0.467)
0.382 (0.214, 0.541)


Marker combination
0.523 (0.426, 0.835)
0.704 (0.579, 0.909)
0.465 (0.346, 0.814)
0.606 (0.545, 0.941)


Age + marker combination
0.574 (0.488, 0.864)
0.757 (0.649, 0.940)
0.450 (0.385, 0.885)
0.724 (0.600, 0.963)





CI, confidence interval






(5) Risk Stratification of BE Patients

ROC curves derived from these marker-based models were used to establish thresholds to stratify patients into risk categories. This procedure was performed to identify high-risk (HR) individuals for more frequent endoscopic screening. The threshold above which patients were classified as HR was chosen at specificity=90%, to minimize false-positive, unnecessary endoscopies (type II error). A second threshold was established to identify low-risk (LR) individuals for less frequent endoscopic screening. The threshold below which patients were classified as LR was chosen at sensitivity=90%, to minimize false-negative, missed HR individuals (type I error). Based on the combined P and NP classification, we classified patients as LR with a threshold that corresponded to 90% true-positives and 43% false-positives; the HR group was defined using a threshold that yielded 43% true-positives and 10% false-positives. Assuming progression to HGD and/or EAC at 13.5% over 5 years (Jin et al. (2008) International Journal of Cancer 123, 2331-2336), the corresponding negative predictive value relating to our LR threshold was 97% (i.e., progression risk in the LR group was 3%) and the positive predictive value relating to HR was 40% (i.e., progression risk in the HR group was 40%).
(6) Predictiveness Curve Analyses
We used predictiveness curves (also known as risk plots) to assess the clinical utility of the combined classification rules in stratifying patients according to risk levels in the target population. To create predictiveness curves, we ordered and plotted risks from lowest to highest value. A progression rate to HGD and/or EAC of 13.5% over 5 years was assumed in adjusting estimates from the case-control sample to reflect population risk and its distribution. Results are shown in Table 10, FIG. 13.







TABLE 10







Overfitting-corrected predictiveness curve analyses


in 2-year, 4-year and combined models









Risk probability











<0.1 (LR)
0.1-0.5 (IR)
>0.5 (HR)





Combined model





8-marker panel
45%
51%
4%


age alone
15%
85%
0%


age plus 8-marker panel
51%
44%
5%


2-year model





8-marker panel
45%
51%
4%


age alone
11%
89%
0%


age plus 8-marker panel
52%
44%
4%


4-year model





8-marker panel
44%
51%
5%


age alone
15%
85%
0%


age plus 8-marker panel
51%
44%
5%





LR, low-risk; IR, intermediate-risk; HR, high-risk.






After overfitting correction, by age alone, nearly 90% of BE patients were classified as intermediate-risk (IR), whereas patients were well-stratified into low-risk (LR), IR, or high-risk (HR) categories by both the 8-marker alone and age plus 8-marker panels in all three models (Table 10, FIG. 13).

(7) Discussion

In the current study, with specificity at 0.9, sensitivities of progression prediction approached 50% based on both the 8-marker panel alone and 8-marker-plus-age panel in all three models. These findings indicate that even while performing at high specificity, these biomarker models predicted half of progressors to HGD and EAC that would not have been diagnosed earlier without using these biomarkers.
Based on age alone, with specificity at 90%, only 17.6%, 23.2% and 22.1% of progressors were predicted in the three models. However, with panels based on age plus biomarkers or on biomarkers alone, approximately 60%, 50% and 50% of progressors were accurately predicted in these models. Predicted progressors represent patients in whom we can intercede earlier, resulting in higher cure rates. Finally, our combined risk model outperformed known risk in the general BE population (13.5% progression risk over 5 years), both in negative predictive value (3% progression risk over 5 years for the LR group) and positive predictive value (40% progression risk over 5 years for the HR group).
Age is a common risk factor for many cancers, including EAC. In the current study, Ps were significantly older than NPs, and the AUCs of age alone were 0.604, 0.630 and 0.635, respectively in the three models, suggesting that age per se predicts neoplastic progression in BE. However, the incremental prediction accuracy (above age) contributed by the 8-marker panel was substantial in all three models.
Thus, the current findings suggest that this 8-marker panel is more objective and quantifiable and possesses higher predictive sensitivity and specificity than do clinical features, including age. Furthermore, although age was a good classifier for disease progression, predictiveness curves revealed that age did not successfully stratify BE patients according to their progression risk. Moreover, age per se is not an accepted risk marker on which to base clinical decisions regarding surveillance interval or neoplastic progression risk in BE. In contrast, models based on both the 8-marker panel and the age-plus-8-marker panel provided estimated progression risks either close to 0 (i.e., LR) or between 0.1 and 0.5 (i.e., IR) in the majority of individuals, suggesting that these markers exerted a substantial impact on risk category. This finding also suggests that in clinical practice, separate thresholds can be chosen to define high, intermediate, and low risk, based on predictiveness curves.
In conclusion, we have developed a risk stratification strategy to predict neoplastic progression in BE patients based on an 8-marker tissue methylation panel. At high specificity levels, this model accurately predicted approximately half of HGDs and EACs that would not have otherwise been predicted. This model is expected to reduce endoscopic procedures performed in BE surveillance while simultaneously increasing detection at earlier stages. Thus, these findings suggest that a methylation biomarker panel offers a clinically useful tool in the risk stratification of BE patients.
Example IV
Identification of about 50 Additional Markers for Predicting the Development of EAC or HGD
Agilent methylation array analyses were performed on 5 BE progressor patients (BE biopsy tissue samples from subjects who later developed HGD or EAC) and 4 BE nonprogressor patients (BE biopsy tissue samples from subjects who did not later develop HGD or EAC). Bioinformatic calculations and gene filtering criteria were applied to the data generated. Based on these procedures, a list of 50 methylation targets strongly associated with progression was derived. The screen included Student's t-testing for significance of associations, selection of loci that were hypermethylated in progressors vs. nonprogressors, and gene ontologic criteria for relevance of loci to cancer. “T-test (unlogged”) connotes the t-test performed on methylation values that were not log-transformed. “Fold change” denotes the ratio for each gene or locus of methylation level in progressors to methylation level in nonprogressors.
Table 11 lists about 50 of the most promising markers identified by this study. Column 1 indicates the gene symbol of each gene, column 2 the gene ID number, and column 3 the official name of the gene. Having this information, a skilled worker can readily determine genomic sequences comprising coding sequences for each of these genes (e.g., for part or all of exon 1), and upstream regulatory regions, by accessing, for example, GenBank and/or the Entrez Gene searchable database (NCBI). The position of the transcription start sites can be found on any of these web sites, or elsewhere. Table 12 provides the start position and the end position, relative to the transcriptional initiation site, for the probes from the Agilent Human CpG Island microarray which was used to identify each of these targets. A skilled worker can readily determine the sequence of each of these probes, by matching the start and end positions relative to the relevant genomic sequence. Sequences of promoter regions of these about 50 genes, extending from −500 to +100 nucleotides relative to the transcriptional start sites, are provided in the Sequence Listing attached hereto, as SEQ ID NOs: 50-121. In some cases, two or more promoter sequences are listed for each gene, so multiple promoter regions are provided.
Table 12 also provides the results of an unlogged T-test, the test of significance of association of progression with methylation level, performed on non-log-transformed methylation values. Values of less than 0.05 indicate statistically significant results. Also included in Table 2 are the fold changes, which further clarifies the significance of the results.
Table 13 provides suitable PCR primers and probes for qMSP analysis for a number of gene promoter regions that can be analyzed by a method of the invention. The forward primers sequences have SEQ ID NOs: 122 to 199, starting at the top of the table (AKAP 12) and ending at the bottom of the table (TDE/TMS/SERINC3); the reverse primers have SEQ ID NOs: 200-277, starting at the top of the table (AKAP 12) and ending at the bottom of the table (TDE/TMS/SERINC3); and the TaqMan probe sequences have SEQ ID NOs: 278 to 295, starting at the top of the table (AKAP 12) and ending at the bottom of the table (TDE/TMS/SERINC3).









TABLE 11





Gene Symbol
Gene ID
Name

















WIT1
51352
Wilms tumor upstream neighbor 1


CNTNAP5
129684
contactin associated protein-like 5


KCNG2
26251
potassium voltage-gated channel, subfamily G, member 2


ACTRT2
140625
actin-related protein T2


PHC2
1912
polyhomeotic homolog 2 (Drosophila)


OTOP1
133060
otopetrin 1


TUSC3
7991
tumor suppressor candidate 3


CEBPD
1052
CCAAT/enhancer binding protein (C/EBP), delta


WDR5
11091
WD repeat domain 5


CALML3
810
calmodulin-like 3


CIDEA
1149
cell death-inducing DFFA-like effector a


CAMK2N2
94032
calcium/calmodulin-dependent protein kinase II inhibitor 2


STX1A
6804
syntaxin 1A (brain)


SMARCD3
6604
SWI/SNF related, matrix associated, actin dependent




regulator of chromatin, subfamily d, member 3


LOC728489
728489
DNLZ DNL-type zinc finger [Homo sapiens]


CALCA
796
calcitonin-related polypeptide alpha


COL2A1
1280
collagen, type II, alpha 1


DPY19L2
283417
dpy-19-like 2 (C. elegans)


NULP1
22980
transcription factor 25 (basic helix-loop-helix)


TIMP2
7077
TIMP metallopeptidase inhibitor 2


TPTE
7179
transmembrane phosphatase with tensin homology


SNX7
51375
sorting nexin 7


KIAA0746
23231
KIAA0746 protein [Homo sapiens]


NQO2
4835
NAD(P)H dehydrogenase, quinone 2


FKBP5
2289
FK506 binding protein 5


ABCA1
19
ATP-binding cassette, sub-family A (ABC1), member 1


FAM86A
196483
family with sequence similarity 86, member A


ATXN2L
11273
ataxin 2-like


CBFB
865
core-binding factor, beta subunit


RAX
30062
retina and anterior neural fold homeobox


ZADH2-TSHZ1
284273
zinc binding alcohol dehydrogenase domain containing 2


ECAT8-TDRD12
91646
tudor domain containing 12


PROKR2
128674
prokineticin receptor 2


NCAM2
4685
neural cell adhesion molecule 2


SPEG
10290
SPEG complex locus


VWC2
375567
von Willebrand factor C domain containing 2


FOXR1
283150
forkhead box R1


TARBP2
6895
TAR (HIV-1) RNA binding protein 2 [Homo sapiens]


STXBP6
29091
syntaxin binding protein 6 (amisyn)


PRSS21
10942
protease, serine, 21 (testisin)


LOC388335
388335
transmembrane protein 220


MORG1-C19orf56
84292
MORG1 mitogen-activated protein kinase organizer 1 [Homo sapiens]


DUSP4
1846
dual specificity phosphatase 4


BARHL1
56751
BarH-like homeobox 1


ACOT4
122970
acyl-CoA thioesterase 4


SOX13
9580
SRY (sex determining region Y)-box 13


FAM89A
375061
family with sequence similarity 89, member A


LOC389151
389151
Hypothetical gene supported by AK 127998 [Homo sapiens]


H2AFY
9555
H2A histone family, member Y


RND2-VAT1
8153
Rho family GTPase 2




















TABLE 12





Summarized_Annotation






(see footnote)
T-test (Unlogged)
Fold Change
1st_Start_DIST
1st_End_DIST



















WIT1
0.054748469
1.529924366
46
−1177



0.127112854
2.680685148
−374
−243



0.03270418
5.823186847
−243
685



0.195202702
3.892611484
685
1172



0.373029027
1.094911093
1172



CNTNAP5
0.090991109
2.9328864
−739
−10



0.006896637
3.682532282
−10
1194


KCNG2
0.032172541
6.600879249
−1059
20


ACTRT2
0.021427804
3.514396256
68
1024


PHC2
0.194287204
0.803905338
407
227



0.009505882
1.556061912
−164
−1975


OTOP1
0.00873557
1.996740331
−507
−1015



0.013063838
2.533766322
−1015
−1298


TUSC3
0.024414219
3.194999649
−778
71



0.058508408
0.796560389
71
217


CEBPD
0.44804791
0.922768031

579



0.145192223
0.892451088
579
379



0.110294815
0.766167725
379
−79



0.006027516
1.857346861
−170
−248



0.01066883
0.768934183
−248
−658



0.014391975
0.505325798
−658
−845



0.153326986
0.850129335
−845
−1415


WDR5
0.582229852
1.067809738
−744
−567



0.000380972
1.835084417
−466
−384



0.031893346
1.282774124
−384
133


CALML3
0.022287889
3.484184157
−86
625


CIDEA
0.420819368
1.388727137
−200
75



0.04871235
1.689889128
75
765


CAMK2N2
0.731511241
1.169463398
592
533



0.249109573
0.824518187
479
362



0.07095243
0.577021958
362
10



0.010900226
2.181112613
10
−201


STX1A
0.038066027
1.128017573
2090
25



0.022054517
1.590440051
−101
−211


SMARCD3
0.556178898
0.962808769
3002
925



0.982445133
1.004136503
925
−156



0.00760048
1.53837593
−156
−558


LOC728489
0.045428198
1.735817023

1355



0.021707862
1.342200045
507
133



0.430944223
0.913204715
133
−452



0.006971866
2.449688053
−452
−1335


CALCA
0.008763467
4.160015621
−834
−1625



0.377684202
0.598178582
−1625
−1735



0.473608532
0.590618877
−1735



COL2A1
0.481669237
1.454062319
382
152



0.473259273
1.0793924
152
−236



0.007187549
1.861014944
−236
−892


DPY19L2
0.219961557
1.302006679
351
269



0.044214645
4.443192912
269
−375


NULP1
0.01524316
3.066812302
191
258


TIMP2
0.763775082
1.072275953
−268
−393



0.046551728
1.873116751
−393
−1342


TPTE
0.007476816
1.810273253
−290
−1031


SNX7
0.003638667
2.017852438
−2221
−87



0.504284738
0.901250456
−87
−59



0.146970194
0.165576139
144
183



0.144854808
0.773781767
221
682


KIAA0746
0.059431121
0.721896309
483
452



0.714018784
0.960731458
337
231



0.093368773
1.502582138
231
−127



0.00346599
1.501911337
−127
−178



0.127399728
0.818064653
−213
−1003


NQO2
0.000793319
1.901274447
−432
100



0.408643661
0.787686378
100
369



0.012951748
0.806950123
369
798


FKBP5
0.057083677
0.645046871
504
186



0.043848737
2.617932515
186
−4



0.171069191
0.68483178
−66
−1989


ABCA1
0.438097496
0.904037936
729
523



0.051536955
0.554789461
523
498



0.047002972
0.786989409
484
65



0.444393657
0.927728865
65
−284



0.040354485
3.263976188
−284
−1836


FAM86A
0.00109257
1.744368579
−76
−2493


ATXN2L
0.671694023
0.961787919

−136



0.013387441
1.63833225
−136
248



0.007404224
0.424910149
311
637



0.83561023
1.026890115
637
1086



0.027453829
0.821493784
1103
2606


CBFB
0.006047531
1.578011376
−196
389



0.022462991
0.261319251
389
421



0.052820796
0.810642249
421
680



0.232791679
0.452826487
680
3100


RAX
0.003424917
0.341136147
811
765



0.53919907
0.894047131
765
672



0.186308784
0.619095764
672
362



0.356543793
0.471450723
362
264



0.785675801
0.863480245
264
59



0.006778448
2.563190404
59
−975


ZADH2-TSHZ1
0.004452151
1.544560109
−114
−393



0.671630772
1.063818306
−524
361


ECAT8-TDRD12
0.010646042
2.759409695
−657
109


PROKR2
0.024442126
1.631635557
−241
−1531



0.700170469
1.196057816
−1531
−1580



0.19159503
2.281446772
−1580



NCAM2
0.022725316
3.103743563
−1156
−115


SPEG
0.021002785
1.833326378
−2197
10


VWC2
0.001876629
2.670765671
−1114
−134



0.091852037
0.241146069
−134
140



0.119177396
0.579919302
140
378



0.37416275
1.317443723
378
907



0.870858584
1.039078988
907
1012



0.127291137
2.826070887
1012
1597



0.019402038
1.112421128
1703
1938


FOXR1
0.030320332
1.965183625
−579
202



0.110729938
0.766150405
352
389



0.352099558
1.092009037
389
1005


TARBP2
0.327543863
1.097363993
−1372
−1227



0.023478606
1.5884659
−1227
267


STXBP6
0.5682681
0.868067081
833
65



0.040075285
0.819455589
65
−54



0.012287693
0.460631262
−54
−106



0.161895799
0.754554476
−106
−338



0.004737596
1.650593326
−338
−470


PRSS21
0.508214824
0.944542508
50
399



0.014750037
3.52441924
399
1173


LOC388335
0.000482273
2.337771731
326
−211


MAN2B1-MORG1
0.171273945
0.827495783
2235
37


MORG1-C19orf56
0.016353821
1.881692924
37
562



0.569957301
0.895717416
562
362



0.759414503
1.04610352
362
1547


DUSP4
0.108672971
0.499937774
841
660



0.465309715
1.062043875
660
114



0.004659006
1.567619335
114
−431



0.013287136
0.802388482
−431
−717



0.195808583
0.744923471
−717
343



0.200661958
0.796484593
343
−60



0.941277939
1.011183854
−60
−1544



0.504776305
0.931523396
−1544
−1647


BARHL1
0.101486268
2.168126904
−1699
−273


ACOT4
0.076392378
2.797523952
−952
21


SOX13
0.329456819
0.834384307
−626
78



0.030313663
1.559392038
78
353


FAM89A
0.154218745
0.720655055
891
373



0.137713712
0.744874868
373
254



0.339750588
0.901015567
237
55



0.176723265
0.578928107
−122
−168



0.00703254
1.552683921
−168
−975


LOC389151
0.008145019
1.811889362
106
−646


H2AFY
0.161579429
0.733802734
916
672



0.208703051
0.813403124
672
582



0.042030525
0.632721188
582
249



0.006375904
1.528332169
249
−106



0.212726359
0.908201238
−106
−192



0.392803941
0.89368228
−192
−120


RND2-VAT1
0.012188821
1.531737049
−957
−29



0.479293572
0.726740894
63
203



0.344596564
0.89104188
203
579























TABLE 13








Forward
Reverse
TaqMan







Primer
Primer
Probe
Annealing
Length


Target gene
Target gene
Amplicon
Sequence
Sequence 
Sequence 
tem-
of


symbol
name
Name
(5′ > 3′)
(5′ > 3′)
(5′ > 3′)
perature
Amplicon






















AKAP12
A kinase
AKAP12-M1
gtttTGTTAG
GAAACCAAA
TCGGGTGG
60
84



(PRKA) anchor

AAACGGG
AACGCTAC
GCGGTTGTT





protein (gravin)

AGGCGttC
GACGCG
TGGATT





12








 


ATF3
Activating
ATF3-M1
TGGGtTGG
tCCAAACAT

62.5
125



transcription

GGtCGGGA
AACCAATAA






factor 3

ttC
TACCCAAAC









CAACG





 


ATF3
Activating
ATF3-M2
 
CTCAAACTA

60
100



transcription

 
ACGACGCG






factor 3

 
ATCG





 


ACY1
Aminoacylase 1
ACY1-M1
CGGGAtCG
CCGAACTAA

60
96





TttTGAGtTtt
CCCTACTCT








CGGC
AACAAACTCG





 


ACY1
Aminoacylase 1
ACY1-M2
GGGCGGTt
TCCTATCGC

60
105





tTGAGttTG
TAAACTCAC








CGAttTC
GCTCG





 


ATP1B2
ATPase,
ATP1B2-M2
GGGTTTAG
AAAACACGA
 
57




Na+/K+

GATCGGT
AAAACGAAA
 





transporting,

GTATTTTT
ACGACGCG
 





beta 2

CGTC







polypeptide








 


ATP1B2
ATPase,
ATP1B2-M1
TGGTTGTT
AAAACGAAA
 
60




Na+/K+

TTTTAGTT
CTCAAAACT
 





transporting,

GCGCGTTT
CGCTCCG
GTGtAGTGGT





beta 2

TC







polypeptide








 


CAV1
Caveolin 1,
CAV1-M1
 
ATACTTTTA
AAACTTAAA
57
110



caveolae

TTTGGGAG
ATAACACTC
CAAACATAC





protein, 22 kDa

AtATTTtAG
GTTTACATC
AAAATTTAA







 
TAATCG
CATTTCCCA









TC




 


CTBP1
C-terminal
CTBP1-M1
 
CCGAAACG
NA
60
113



binding protein

 
CGACTACG






1

 
AAACG





 


CARS
Cysteinyl-tRNA
CARS-M1
CGCGATG
TTCCGAAAC
NA
60.5




synthetase

TTTCGGAG
ACGCCCGA








CGC
TCG





 


SMAD4/ELAC1
ElaC homolog
SMAD4-M1
AGGtttAGG
CCTCCCGC
NA
60
93



1 (E. coli)

TttAGATTtA
TCCGAATAA








 
CG








 






 


ENG
Endoglin
ENG-M1
AGGGTTTT
CTAACTCAT
 
60




(Osler-Rendu-

TTATTTAG
CCAACCCG
 





Weber

TGATAAAG
ACCG
GtttAGt





syndrome 1)

TTCGTGG






 


FDXR
Ferredoxin
FDXR-M1
AGTAttATA
CTCAAATCC

57
126



reductase

 
CCGTCTTTA








 
CCG








 






 


FDXR
Ferredoxin
FDXR-M2
TTTCGTTT
ACCAACGC

57
116



reductase

GTGGGCG
CAACAACG








GGTTC
CG





 


GPS2
G protein
GPS2-M1
 
ACTTTACGA

60
125



pathway

 
CAACGACA






suppressor 2

GAGttTGtA
CCGACG








 






 


GPS2
G protein
GPS2-M2
 
CGCTAACAC

60
80



pathway

 
CGAAACAAC






suppressor 2

 
GCG








 






 


GSTM4
Glutathione S-
GSTM4-M1
AGAGttTGT
CCCAAAAAC

60
126



transferase M4

 
TCGACTATA








 
CGCGT





 


HES2
Hairy and
HES2-M1
 
ACGAACGA
NA
60
118



enhancer of

 
AAAACTCGA






split 2

 
ACGAAAACT






(Drosophila)


ACG





 


HINT1
Histidine triad
HINT1-M1
AGtttAGGT
CCGCAAAA

60
101



nucleotide

 
CGTACGAC






binding protein

 
GCG






1








 


TGFBR2
In multiple
TGFBR2-
AGAGAGtT
TCACTCAAC
NA
62.5
120



clusters
M1
AGGGGtTG
TTCAACTCA








 
ACGCTACG





 


ID3
Inhibitor of
ID3-M1
ATTTTTTG
ACCCTAAAC
NA
60
91



DNA binding 3,

AATTCGCG
GTTCACAAC






dominant

GTTTCGC
CCG






negative helix-









loop-helix









protein








 


ITGA3
Integrin, alpha
ITGA3-M1
GATAGGT
CCGAACTAC

57
131



3 (antigen

GTTTGGG
GCTTTCCTT






CD49C, alpha

GGAGAAG
CCG






3 subunit of

GC







VLA-3









receptor)








 


ITGA3
Integrin, alpha
ITGA3-M2
TGGGGGtA
ACCTATTCA

60
88



3 (antigen

 
CCTACTCCC






CD49C, alpha

 
CGCG






3 subunit of

 







VLA-3









receptor)








 


JAK1
Janus kinase 1
JAK1-M1re
GGTAGTTT
CCGAAAAAC
NA
60.5




(a protein

CGCGAGC
GAAACGAAA






tyrosine

GAAGTC
TCGCG






kinase)








 


JUND
Jun D proto-
JUND-M2
 
TAACGACGA

60
105



oncogene

 
CTCCTACGC








 
CG








 






 


JUND
Jun D proto-
JUND-M1
 
AAACGAAAA

60
111



oncogene

GGAGGtttT
CGATACCG








 
ACCTCCG





 


MST1R
Macrophage
MST1R-M1
 
GCCGCTATA
NA
60
83



stimulating 1

 
CACTAACGC






receptor (c-

 
TTAACG






met-related









tyrosine









kinase)








 


MLH1
mutL homolog
MLH1-M
CTATCGCC
CGTTATATA
CGCGACGT
60
87



1, colon

GCCTCATC
TCGTTCGTA
CAAACGCC





cancer,

GT
GTATTCGTG
ACTACG





nonpolyposis


TTT






type 2








 


MEF2A
MADS box
MEF2A-M1
 
GCCACCAA
NA
60
67



transcription

 
CGCTTCGCG






enhancer

 







factor 2,









polypeptide A









(myocyte









enhancer









factor 2A)








 


MAL
Mal, T-cell
MAL-M1
AGTTTTTA
CGAAAAACC
 
57




differentiation

GTTTTGGA
CGACCCGA
 





protein

CGTTCGTA
ACG
 







GCG






 


MAP2K7
Mitogen-
MAP2K7-
 
TCCGCGCG
NA
60
124



activated
M1
 
TACGTACTT






protein kinase

 
CG






kinase 7








 


MCL1
Myeloid cell
MCL1-M1
 
TACCCGAC
NA
60
126



leukemia

 
CGAACCGA






sequence 1

 
AACG






(BCL2-related)








 


NELL1
NEL-like 1
NELL1-M1
GGGTTTTT
CCACCGCT
 
60.5




(chicken)

AGACGAA
AACGCGAC
 







CGTAGTC
GA
 







GTAGC






 


NBL1
Neuroblastoma,
NBL1-M2
GGGAGTT
GACGAAAC
NA
60.5




suppression

ATTTTGAC
GACCGACA






of

GTCGGAG
AAACG






tumorigenicity 1

TC






 


NBL1
Neuroblastoma,
NBL1-M1
GGTTGTTT
CCCGAACTA
NA
60.5




suppression

TGTTTTTC
AACTTCGAC






of

GGGCGTC
GCG






tumorigenicity 1








 


NME6
Non-metastatic
NME6-M1
ATTTTATTT
ACAAAACCC
NA
60.5




cells 6, protein

TACGTCGC
GACCACCA






expressed in

GGCGTAAT
AACC






(nucleoside-









diphosphate









kinase)








 


NOTCH2
Notch homolog
NOTCH2-
tTTtTGtAtT
TACGAATCA

60
106



2 (Drosophila)
M1
GGTtAAGtt
CTAAACCCG








 
TCCG








 






 


N33/TUSC3
Tumor
N33-M



60




suppressor









candidate 3








 


PTMS
Parathymosin
PTMS-M1
 
CGCAAAACT

60
90





 
ACGACCGT








 
CCG





 


PLAGL1
Pleiomorphic
PLAGL1-M1
TTTATCGG
AAAACCCCT
 
60.5




adenoma

TGATTCGG
AACGAAAAC
 





gene-like 1

TTCGTAGG
GTCACG
 







AC






 


PVRL3
Poliovirus
PVRL3-M2
TTCGTTTT
GCTACCGA
NA
60.5




receptor-

AGTTTCGG
CTAACACTT






related 3

TAGTGGC
AACCGAACG








GTC






 


PVRL3
Poliovirus
PVRL3-M1
TTTTTTCG
ACGAAAACT
NA
60.5




receptor-

TTTTAGGT
ACGAAACAA






related 3

TTTCGTTA
TCACGCTCG








TAGGG






 


PFDN5
Prefoldin
PFDN5-M1
GGAGGAT
CTCGACGA
NA
60.5




subunit 5

TATAGAGT
ACAACGCTC








TGTTTGGC
TAACCG








GTAGC






 


PSEN1
Presenilin 1
PSEN1-M1
 
CCGCTATTT
NA
60
119



(Alzheimer

 
TATTTCCGA






disease 3)

 
TATAAAACC









GCG





 


PSMC3
Proteasome
PSMC3-M1
AAGAtTAtA
CGTCCTATT
NA
60
135



(prosome,

TTTtttAAAG
TTTACCGAA






macropain)

 
CGCG






26S subunit,

GA







ATPase, 3








 


PTPRO
protein tyrosine
PTPRO-M
AGCGGTG
GCGAAAAC
CGACAAAC
60
81



phosphatase,

CGTTTTAG
AACAAAACG
GCTTCCCG





receptor type, O

GGTAC
TACG
CGACTAAA




 


PSMD2
Proteasome
PSMD2-M1
GCGTTTCG
CCTTTTTCC
 
60.5




(prosome,

GTTCGTTT
CAAACCAAC
 





macropain)

CGGC
TCGCG
TGGAG





26S subunit,









non-ATPase, 2








 


PRKRA
Protein kinase,
PRKRA-M1
TGttTGTGttt
GAAACCTTA
NA
60
142



interferon-

 
ATACCGTAA






inducible

 
CTCCGACTA






double


CG






stranded RNA









dependent









activator








 


RAB32
RAB32,
RAB32M
CGGTAGA
ATCTCGAAC
CGCTACGA
60
129



member RAS

GCGCGAG
GCTAAAACG
CTATCGAAC





oncogene

GTC
ACG
GCGCCTC





family








 


RHOB
Ras homolog
RHOB-M1
AGAtAGttA
CGCTACGA
NA
60
78



gene family,

 
AACTCTAAC






member B

 
GATACCCG





 


RRAD
Ras-related
RRAD-M1
GCGGTTTT
ACGCACTC
 
60.5




associated with

GGAGTTA
GTAATCCGA
GtTGtAGtAGt





diabetes

GAGTTTAG
ACTCG
 







TGC






 


REST
RE1-silencing
REST-M1
 
GCGTAACC
NA
60
105



transcription

 
GCTAAACG






factor

 
CG





 


RGS12
Regulator of G-
RGS12-M1
AATTAGTT
CGACGACG

60
81



protein

 
CTAATACGC






signalling 12

 
ACG








 






 


STK4
Serine/threonine
STK4-M1
GCGTTATT
CGACCCTAA
NA
60.5




kinase 4

GATATTTT
TCACGTAAT








AGAGATTA
TCGAACG








GCGGGTA









TC






 


SMAD7
SMAD,
SMAD7-M1
TTttATGTA
CCGCTTCC
NA
60
112



mothers

 
CTAAAAACG






against DPP

 
ACCG






homolog 7









(Drosophila)








 


SLC39A7
Solute carrier
SLC39A7-
ACGTTTAA
TCCGCCCT
NA
60.5




family 39 (zinc
M1
AGTTTTGA
CCTAACTAT






transporter),

GGTTTAAG
AACCG






member 7

AGGAATC






 


SLC39A7
Solute carrier
SLC39A7-
CGTGTTAG
CCGCCTTTA
NA
60.5




family 39 (zinc
M2
TGTAGGAT
AATCCCGC






transporter),

GGATTCGTC
GA






member 7








 


SST
Somatostatin
SST-M1
GGGGCGT
AACAACGAT
 
60.5






TTTTTAGT
AACTCCGAA
TATACTCTC







TTGACGT
CCTCG
 









CCTCTA




 


SFRS10
Splicing factor,
SFRS10-M1
GAGttTGGt
ACCGCTCAA

60
85



arginine/serine-

TAAGGAG
AACCGAAAT






rich 10

 
ACTCCG






(transformer 2









homolog,









Drosophila)








 


SRPX
Sushi-repeat-
SRPX-M1
 
CGATATACG

60
75



containing

 
AAACTCCCC






protein, X-

 
ATAACGAACG






linked








 


HLTF/
SWI/SNF
SMARCA3-
GGGGTTT
CCCGCTAC
AAATAATTC
60.5



SMARCA3
related, matrix
M1
CGTGGTTT
CATTCAAAA
 





associated,

TTTCGC
ACGACG
 





actin



CC





dependent









regulator of









chromatin,









subfamily a,









member 3








 


TAC1
Tachykinin,
TAC1-M1
GGCGGTT
AAATCCGAA
AGAATGTtA
60.5




precursor 1

AATTAAAT
CGCGCTCTT
 





(substance K,

ATTGAGTA
TCG
GGAGGtTtAA





substance P,

GAAAGTC

GGAG





neurokinin 1,

GC







neurokinin 2,









neuromedin L,









neurokinin









alpha,









neuropeptide









K,









neuropeptide









gamma)








 


TAX1BP3
Tax1 (human
TAX1BP3-
 
CCGAACCT
NA
60.5
85



T-cell leukemia
M1
AGAAGATG
CGATATCAA






virus type I)

 
CGCTATCG






binding protein

 







3








 


TAX1BP3
Tax1 (human
TAX1BP3-
 
CTTTCGAAA
NA
60
111



T-cell leukemia
M2
 
ATCCCTACG






virus type I)

 
CGTACG






binding protein









3








 


TRAF2
TNF receptor-
TRAF2-M1
 
CTCACCCAA
NA
60
108



associated

 
CGATCGCA






factor 2

 
ACG





 


TP53AP1
TP53 activated
TP53AP1-
 
ACCCAAAAT

60
105



protein 1
M1
 
AAAACCGAA








 
ACCCAAAACG





 


TP53I3
Tumor protein
TP53I3-M1
AGTTTGTT
GAAACCCAA
 
fail




p53 inducible

TATTTATG
CCTCTTAAC
 





protein 3

TTTAAGAT
GAACG
GTG







GGGCGG






 


TP53I3
Tumor protein
TP53I3-M2
TTTTCGGT
AATAATTCT
NA
60.5




p53 inducible

TATTTTAG
AACTCCTAC






protein 3

GTTTAGTC
GAATCCCG








GTTTATTTC






 


UBE1
Ubiquitin-
UBE1-M1
AGTCGTAT
TCCCTACTC
NA
57




activating

TTTATGAG
GATTACGAT






enzyme E1

GCGTGG
TCATTCG






(A1S9T and









BN75









temperature









sensitivity









complementing)








 


DSIPI/
SC22 domain
DSIPI-M1
 
ACATCCACG

60
101


TSC22D3
family, member 3

GAGttTGAA
CTACCGCTCG








 









 






 


MO25/CAB39
Calcium
MO25-M1
 
CCGAACCC
NA
60
114



binding protein

 
GACTTTAAC






39

 
GTACG





 


IHPKI
Inositol
IHPKI-M1
GAGGttAA
CAAAACCTC
NA
60
128



hexaphosphate

 
TACCGAAAA






kinase 1

 
CGTAAAACA









CG





 


RBMS1
RNA binding
RBMS1-M1
GATTTATA
CGAAAACG
NA
60.5




motif, single

GGGTTTTC
AAACCTAAA






stranded

GTTCGTTT
CGCCG






interacting

AATCGC







protein 1








 


TGFBR2
Transforming
TGFBR2-
 
ACTCACCC

60
133



growth factor,
M2
 
GACTTCTAA






beta receptor II

 
ACGTACG






(70/80 kDa)








 


MTM/MTM1
Myotubularin
MTM-M1
 
ACCAAACCA

60
78





 
ACCGTAAAC








GttTGGtAA
GCG








 






 


BTG1
B-cell
BTG1-M1re
GTAGGTTT
GAAACCCG
 
55




translocation

TTAGTATT
AAACCGCTC
 





gene 1, anti-

GACGATA
CG
AGGG





proliferative

GCGAGC






 


EGR1
Early growth
EGR1-M1
GTTACGAC
CGACTCCC
NA
60.5




response 1

GGAGGCG
CAAATTCTA








GATTC
CGCG





 


MEN1
Multiple
MEN1-M1
GttTGAAGG
AAATAATAA
NA
60
108



endocrine

GAAGGGtt
CGAACCGA






neoplasia I

AATtttTGAG
ACCGCTACG








 






 


TDE1/TMS1/
Serine
TDE1-M1
 
AAACATAAC

60
94


SERINC3
incorporator 3

 
GATTTCTCA








 
AACCGAAAA









CG

## Claims

1. A method comprising
measuring the methylation levels of transcriptional promoter regions of the following genes:
i) cadherin 13, H-cadherin (heart) (CDH13),
ii) transmembrane protein with EGF-like and two follistatin-like domains (HPP1),
iii) cyclin-dependent kinase inhibitor 2a (p16), and
iv) runt-related transcription factor 3 (RUNX3),


by performing real-time quantitative methylation-specific PCR (qMSP), pyrosequencing or using a methylation array with an esophageal epithelial sample collected from a human subject.

2. The method of claim 1, further comprising measuring the methylation level of a transcriptional promoter region of one or more of the following genes:
v) tachykinin-1 (TAC1),
vi) nel-like 1 (NELL1),
vii) A-kinase anchoring protein 12 (AKAP12), and
viii) somatostatin (SST).

3. The method of claim 2, wherein the methylation levels are measured for the promoter regions of a total of 5 genes.

4. The method of claim 2, wherein the methylation levels are measured for the promoter regions of a total of 6 genes.

5. The method of claim 2, wherein the methylation levels are measured for the promoter regions of a total of 7 genes.

6. The method of claim 2, wherein the methylation levels are measured for the promoter regions of a total of 8 genes.

7. The method of claim 1, further comprising measuring the methylation level of a transcriptional promoter region of one or more of the genes in Table 11.

8. The method of claim 2, further comprising measuring the methylation levels of a transcriptional promoter region of one or more of the genes in Table 11.

9. The method of any of claim 1-2, 3-6, and 7-8, wherein the methylation level is determined by qMSP, and
a) at least one primer used in the qMSP reaction is capable of distinguishing between methylated and unmethylated nucleic acid, or
b) a probe used in the qMSP reaction is capable of distinguishing between methylated and unmethylated nucleic acid, or
c) both primers and a probe used in the qMSP reaction are capable of distinguishing between methylated and unmethylated nucleic acid.

10. The method of claim 1, wherein the human subject has or is suspected of having Barrett's esophagus (BE).

11. The method of claim 1, wherein the human subject has or is suspected of having esophageal adenocarcinoma (EAC) or high grade dysplasia (HGD).

12. The method of claim 1, wherein the methylation level is determined by qMSP and the primers and probes comprise SEQ ID NOS:23-26, 32-35 and 41-44.

13. The method of claim 2, wherein the methylation level is determined by qMSP and the primers and probes comprise SEQ ID NOS:27-30, 36-39, and 45-48.