Patent Publication Number: US-2020283853-A1

Title: Detecting neoplasm

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application is a continuation of U.S. patent application Ser. No. 15/978,565, filed May 14, 2018 which is a continuation of U.S. patent application Ser. No. 14/775,435, filed Sep. 11, 2015, allowed as U.S. Pat. No. 9,994,911, which is a Section 371 U.S. National Stage entry of International Patent Application No. PCT/US2014/024589, international filing date, Mar. 12, 2014, which claims priority to expired U.S. Provisional Patent Application No. 61/784,429, filed Mar. 14, 2013, the contents of which are incorporated by reference in their entireties. 
    
    
     FIELD OF INVENTION 
     Provided herein is technology relating to detecting neoplasia and particularly, but not exclusively, to methods, compositions, and related uses for detecting premalignant and malignant neoplasms such as pancreatic and colorectal cancer. 
     BACKGROUND 
     In aggregate, gastrointestinal cancers account for more cancer mortality than any other organ system. While colorectal cancers are currently screened, annual US mortality from upper gastrointestinal cancers exceeds 90,000 compared to roughly 50,000 for colorectal cancer. Strikingly, 43,000 men and women are diagnosed each year with pancreatic cancer (PanC), which will cause nearly 37,000 deaths annually (Jemal et al. (2010) “Cancer statistics”  CA Cancer J Clin  60: 277-300). As a result, PanC is the fourth leading cause of cancer deaths (id). Patients who present with symptoms typically already have advanced stage disease and only 15% meet criteria for potentially curative surgery (Ghaneh et al. (2007) “Biology and management of pancreatic cancer”  Gut  56: 1134-52). Despite surgery, 85% will die of recurrent disease (Sohn et al. (2000) “Resected adenocarcinoma of the pancreas-616 patients: results, outcomes, and prognostic indicators”  J Gastrointest Surg  4: 567-79). PanC mortality exceeds 95% and the 5-year survival rate is less than 25% for patients having curative surgery (Cleary et al (2004) “Prognostic factors in resected pancreatic adenocarcinoma: analysis of actual 5-year survivors”  J Am Coll Surg  198: 722-31: Yeo et al (1995) “Pancreaticoduodenectomy for cancer of the head of the pancreas. 201 patients”  Ann Surg  221: 721-33). 
     Among patients with syndromic predisposition to PanC or strong family history, aggressive, invasive screening strategies using computed tomography scans or endoscopic ultrasound have shown a 10% yield for neoplasia (Canto et al. (2006) “Screening for early pancreatic neoplasia in high-risk individuals: a prospective controlled study”  Clin Gastroenterol Hepatol  4: 766-81). This screening strategy is impractical for the general population where most PanC arises without a known pre-disposition (Klein et al. (2001) “Familial pancreatic cancer”  Cancer J  7: 266-73). 
     The nearly uniform lethality of PanC has generated intense interest in understanding pancreatic tumor biology. Precursor lesions have been identified, including pancreatic intraepithelial neoplasm (PanIN, grades I-III) and intraductal papillary mucinous neoplasm (IPMN) (Fernández-del Castillo et al. (2010) “Intraductal papillary mucinous neoplasms of the pancreas”  Gastroenterology  139: 708-13, 713.e1-2; Haugk (2010) “Pancreatic intraepithelial neoplasia—can we detect early pancreatic cancer?”  Histopathology  57: 503-14). Study of both precursors and malignant lesions has identified a number of molecular characteristics at genetic, epigenetic, and proteomic levels that could be exploited for therapy or used as biomarkers for early detection and screening (Kaiser (2008) “Cancer genetics. A detailed genetic portrait of the deadliest human cancers”  Science  321: 1280-1; Omura et al. (2009) “Epigenetics and epigenetic alterations in pancreatic cancer”  Int J Clin Exp Pathol  2: 310-26; Tonack et al. (2009) “Pancreatic cancer: proteomic approaches to a challenging disease”  Pancreatology  9: 567-76). Recent tumor and metastatic lesion mapping studies have shown that there may be a significant latency period between the development of malignant PanC and the development of metastatic disease, suggesting a wide window of opportunity for detection and curative treatment of presymptomatic earliest-stage lesions (Yachida et al. (2010) “Distant metastasis occurs late during the genetic evolution of pancreatic cancer”  Nature  467: 1114-7). 
     PanC sheds (e.g., exfoliates) cells and DNA into local effluent and ultimately into stool. For example, DNA containing a mutant KRAS gene can be identified (e.g., sequenced) from pancreatic juice of patients with pancreatic cancer, PanIN, and IPMN (Yamaguchi et al. (2005) “Pancreatic juice cytology in IPMN of the pancreas”  Pancreatology  5: 416-21). Previously, highly sensitive assays have been used to detect mutant DNA in matched stools of pancreas cancer patients whose excised tumors were known to contain the same sequences (Zou et al (2009) “T2036 Pan-Detection of Gastrointestinal Neoplasms By Stool DNA Testing: Establishment of Feasibility”  Gastroenterology  136: A-625). A limitation of mutation markers relates to the unwieldy process of their detection in conventional assays; typically, each mutational site across multiple genes must be assayed separately to achieve high sensitivity. 
     Methylated DNA has been studied as a potential class of biomarkers in the tissues of most tumor types. In many instances, DNA methyltransferases add a methyl group to DNA at cytosine-phosphate-guanine (CpG) island sites as an epigenetic control of gene expression. In a biologically attractive mechanism, acquired methylation events in promoter regions of tumor suppressor genes are thought to silence expression, thus contributing to oncogenesis. DNA methylation may be a more chemically and biologically stable diagnostic tool than RNA or protein expression (Laird (2010) “Principles and challenges of genome-wide DNA methylation analysis”  Nat Rev Genet  11: 191-203). Furthermore, in other cancers like sporadic colon cancer, methylation markers offer excellent specificity and are more broadly informative and sensitive than are individual DNA mutations (Zou et al (2007) “Highly methylated genes in colorectal neoplasia: implications for screening”  Cancer Epidemiol Biomarkers Prev  16: 2686-96). 
     Analysis of CpG islands has yielded important findings when applied to animal models and human cell lines. For example, Zhang and colleagues found that amplicons from different parts of the same CpG island may have different levels of methylation (Zhang et al. (2009) “DNA methylation analysis of chromosome 21 gene promoters at single base pair and single allele resolution”  PLoS Genet  5: e1000438). Further, methylation levels were distributed bi-modally between highly methylated and unmethylated sequences, further supporting the binary switch-like pattern of DNA methyltransferase activity (Zhang et al. (2009) “DNA methylation analysis of chromosome 21 gene promoters at single base pair and single allele resolution”  PLoS Genet  5: e1000438). Analysis of murine tissues in vivo and cell lines in vitro demonstrated that only about 0.3% of high CpG density promoters (HCP, defined as having &gt;7% CpG sequence within a 300 base pair region) were methylated, whereas areas of low CpG density (LCP, defined as having &lt;5% CpG sequence within a 300 base pair region) tended to be frequently methylated in a dynamic tissue-specific pattern (Meissner et al. (2008) “Genome-scale DNA methylation maps of pluripotent and differentiated cells”  Nature  454: 766-70). HCPs include promoters for ubiquitous housekeeping genes and highly regulated developmental genes. Among the HCP sites methylated at &gt;50% were several established markers such as Wnt 2, NDRG2, SFRP2, and BMP3 (Meissner et al. (2008) “Genome-scale DNA methylation maps of pluripotent and differentiated cells”  Nature  454: 766-70). 
     For pancreatic cancer, PanIN, and IPMN lesions, marker methylation has been studied at the tissue level (Omura et al. (2008) “Genome-wide profiling of methylated promoters in pancreatic adenocarcinoma”  Cancer Biol Ther  7: 1146-56; Sato et al. (2008) “CpG island methylation profile of pancreatic intraepithelial neoplasia”  Mod Pathol  21: 238-44; Hong et al. (2008) “Multiple genes are hypermethylated in intraductal papillary mucinous neoplasms of the pancreas”  Mod Pathol  21: 1499-507). For example, the markers MDFI, ZNF415, CNTNAP2, and ELOVL4 were highly methylated in 96%, 86%, 82%, and 68% of the cancers studied while, comparatively, only 9%, 6%, 3%, and 7% of control (non-cancerous) pancreata, respectively, were highly methylated at these same four loci (Omura et al. (2008) “Genome-wide profiling of methylated promoters in pancreatic adenocarcinoma”  Cancer Biol Ther  7: 1146-56). It was found that measuring the methylation state of both MDFI and CNTNAP2 provided an indicator for pancreatic cancer that had both a high sensitivity (&gt;90%) and a high specificity (&gt;85%) (Omura et al. (2008) “Genome-wide profiling of methylated promoters in pancreatic adenocarcinoma”  Cancer Biol Ther  7: 1146-56). 
     Furthermore, Sato and colleagues found eight genes differentially expressed in pancreatic cancer cell lines before and after treatment with a methyltransferase inhibitor (Sato et al. (2003) “Discovery of novel targets for aberrant methylation in pancreatic carcinoma using high-throughput microarrays”  Cancer Res  63: 3735-42). These markers were subsequently assessed by methylation-specific PCR (MSP) of DNA from Pan-IN lesions. The results showed that SARP-2 (secreted frizzled related protein 1, SFRP1) had 83% sensitivity and could discriminate between Pan-IN 2 and higher grade Pan-IN 3 (Sato et al. (2008) “CpG island methylation profile of pancreatic intraepithelial neoplasia”  Mod Pathol  21: 238-44). Discrimination of a high grade precursor or early stage cancer from a lower grade lesion is important when considering the morbidity of pancreaticoduodenectomy or distal pancreatectomy, the main surgical therapies for PanC. When studying both main-duct and side-branch IPMN precursors, Hong and colleagues showed high sensitivity and specificity for SFRP1 as well, especially in combination with UCHL1 (Hong et al. (2008) “Multiple genes are hypermethylated in intraductal papillary mucinous neoplasms of the pancreas”  Mod Pathol  21: 1499-507). Tissue factor pathway inhibitor 2 (TFPI2) has a well-established tumor suppressor role in GU and GI malignancies, including prostate, cervical, colorectal, gastric, esophageal, and pancreatic cancers (Ma et al. (2011) “MicroRNA-616 induces androgen-independent growth of prostate cancer cells by suppressing expression of tissue factor pathway inhibitor TFPI-2 ” Cancer Res  71: 583-92; Lim et al. (2010) “Cervical dysplasia: assessing methylation status (Methylight) of CCNA1, DAPK1, HS3ST2, PAX1 and TFPI2 to improve diagnostic accuracy”  Gynecol Oncol  119: 225-31; Hibi et al. (2010) “Methylation of TFPI2 gene is frequently detected in advanced well-differentiated colorectal cancer”  Anticancer Res  30: 1205-7; Glockner et al. (2009) “Methylation of TFPI2 in stool DNA: a potential novel biomarker for the detection of colorectal cancer”  Cancer Res  69: 4691-9; Hibi et al. (2010) “Methylation of the TFPI2 gene is frequently detected in advanced gastric carcinoma”  Anticancer Res  30: 4131-3; Tsunoda et al. (2009) “Methylation of CLDN6, FBN2, RBP1, RBP4, TFPI2, and TMEFF2 in esophageal squamous cell carcinoma”  Oncol Rep  21: 1067-73; Tang et al. (2010) “Prognostic significance of tissue factor pathway inhibitor-2 in pancreatic carcinoma and its effect on tumor invasion and metastasis”  Med Oncol  27: 867-75; Brune et al. (2008) “Genetic and epigenetic alterations of familial pancreatic cancers”  Cancer Epidemiol Biomarkers Prev  17: 3536-4). This marker has also been shown to be shed into the GI lumen and was 73% sensitive when assayed from pancreatic juice of cancers and normal subjects (Matsubayashi et al. (2006) “DNA methylation alterations in the pancreatic juice of patients with suspected pancreatic disease”  Cancer Res  66: 1208-17). 
     TFPI2 was among a large number of potential mutation and methylation markers studied in tissue and stool samples as candidates for colorectal neoplasia. In a training-test set analysis of archival stools from almost 700 subjects, a multi-marker methylation panel, including TFPI2, BMP3, NDRG4, and vimentin was shown to have 85% sensitivity in CRC and 64% sensitivity in advanced colorectal adenomas, both at 90% specificity (Ahlquist D et al. (2010) “Next Generation Stool DNA Testing for Detection of Colorectal Neoplasia—Early Marker Evaluation”, presented at  Colorectal Cancer: Biology to Therapy , American Association for Cancer Research). 
     Previous research has tested the performance of colorectal cancer methylation markers in PanC detection. In particular, a case-control study compared DNA from PanC tumor cases to DNA from colonic epithelia using MSP targeting markers previously reported in PanC (e.g., MDFI, SFRP2, UCHL1, CNTNAP2, and TFPI2) and additional discriminant colonic neoplasm markers (e.g., BMP3, EYA4, Vimentin, and NDRG4). A multi-marker regression model showed that EYA4, UCHL1, and MDFI were highly discriminant, with an area under the receiver operating characteristics curve of 0.85. As an individual marker, BMP3 was found to have an area under the receiver operator characteristics curve of 0.90. These four markers and mutant KRAS were subsequently assayed in a larger set of stool samples from PanC subjects in a blinded comparison to matched stools from individuals with a normal colonoscopy. Individually, BMP3 and KRAS were highly specific but poorly sensitive; in combination, sensitivity improved to 65% while maintaining 88% specificity (Kisiel, et al. (2011) “Stool DNA screening for colorectal cancer: opportunities to improve value with next generation tests”  J Clin Gastroenterol  45: 301-8. These results suggested that methylation differences in UCHL1, EYA4, and MDFI at the level of the pancreas were obscured by background colonic methylation in the stool-based comparison. As such, cancer screening is in need of a marker or marker panel for PanC that is broadly informative and exhibits high specificity for PanC at the tissue level when interrogated in samples taken from a subject (e.g., a stool sample). 
     SUMMARY 
     Accordingly, provided herein is technology for pancreatic cancer screening markers and other gastrointestinal cancer screening markers that provide a high signal-to-noise ratio and a low background level when detected from samples taken from a subject (e.g., stool sample). Markers were identified in a case-control study by comparing the methylation state of DNA markers from tumors of subjects with stage I and stage II PanC to the methylation state of the same DNA markers from control subjects (e.g., normal tissue such as normal colon and/or non-neoplastic pancreas) (see, Examples 1 and 11). 
     Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12 133, CLEC11A, ELMO1, EOMES, CLEC 11, SHH, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, and ZNF71) were identified in a case-control study by comparing the methylation state of DNA markers (e.g., from tumors of subjects with stage I and stage II PanC to the methylation state of the same DNA markers from control subjects (e.g., normal tissue such as normal colon and/or non-neoplastic pancreas) (see, Examples 2 and 8). 
     Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from NDRG4, SFRP1, BMP3, HPP1, and/or APC) were identified in case-control studies by comparing the methylation state of DNA markers from esophageal tissue of subjects with Barrett&#39;s esophagus to the methylation state of the same DNA markers from control subjects (see, Examples 4 and 10). 
     Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from ADCY1, PRKCB, KCNK12, C13ORF18, IKZF1, TWIST1, ELMO, 55957, CD1D, CLEC11A, KCNN2, BMP3, and/or NDRG4) were identified in case-control studies by comparing the methylation state of DNA markers from a pancreatic juice sample from subjects with pancreas cancer to the methylation state of the same DNA markers from control subjects (see, Examples 5 and 6). 
     A marker (e.g., a chromosomal region having a CD1D annotation) was identified in a case-control study by comparing the methylation state of a DNA marker (e.g., CD1D) from a stool sample from subjects with pancreas cancer to the methylation state of the same DNA marker from control subjects not having pancreas cancer (see, Example 7). 
     A marker (e.g., miR-1290) was identified in a case-control study by comparing the quantitated amount of a DNA marker (e.g., miR-1290) from a stool sample from subjects with pancreas cancer to the quantitated amount of the same DNA marker from control subjects not having pancreas cancer (see, Example 9). 
     Additional statistical analysis of the results demonstrated that the technology described herein based on these markers specifically and sensitively predicts a tumor site. 
     As described herein, the technology provides a number of methylated DNA markers and subsets thereof (e.g., sets of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more markers) with high discrimination for GI neoplasms overall and/or at individual tumor sites. Experiments applied a selection filter to candidate markers to identify markers that provide a high signal to noise ratio and a low background level to provide high specificity, e.g., when assaying distant media (e.g., stool, blood, urine, metastatic tissue, etc.) for purposes of cancer screening or diagnosis. Further, experiments were performed to demonstrate that the identified methylated DNA markers predict tumor site. As such, the technology provides for specific markers, marker combinations, and algorithms to predict tumor site. 
     In some embodiments, the technology is related to assessing the presence of and methylation state of one or more of the markers identified herein in a biological sample. These markers comprise one or more differentially methylated regions (DMR) as discussed herein, e.g., as provided in Table 1 and/or Table 10. Methylation state is assessed in embodiments of the technology. As such, the technology provided herein is not restricted in the method by which a gene&#39;s methylation state is measured. For example, in some embodiments the methylation state is measured by a genome scanning method. For example, one method involves restriction landmark genomic scanning (Kawai et al. (1994)  Mol. Cell. Biol.  14: 7421-7427) and another example involves methylation-sensitive arbitrarily primed PCR (Gonzalgo et al. (1997)  Cancer Res.  57: 594-599). In some embodiments, changes in methylation patterns at specific CpG sites are monitored by digestion of genomic DNA with methylation-sensitive restriction enzymes followed by Southern analysis of the regions of interest (digestion-Southern method). In some embodiments, analyzing changes in methylation patterns involves a PCR-based process that involves digestion of genomic DNA with methylation-sensitive restriction enzymes prior to PCR amplification (Singer-Sam et al. (1990)  Nucl. Acids Res.  18: 687). In addition, other techniques have been reported that utilize bisulfite treatment of DNA as a starting point for methylation analysis. These include methylation-specific PCR (MSP) (Herman et al. (1992)  Proc. Natl. Acad. Sci. USA  93: 9821-9826) and restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA (Sadri and Hornsby (1996)  Nucl. Acids Res.  24: 5058-5059; and Xiong and Laird (1997)  Nucl. Acids Res.  25: 2532-2534). PCR techniques have been developed for detection of gene mutations (Kuppuswamy et al. (1991)  Proc. Natl. Acad. Sci. USA  88: 1143-1147) and quantification of allelic-specific expression (Szabo and Mann (1995)  Genes Dev.  9: 3097-3108; and Singer-Sam et al. (1992)  PCR Methods Appl.  1: 160-163). Such techniques use internal primers, which anneal to a PCR-generated template and terminate immediately 5′ of the single nucleotide to be assayed. Methods using a “quantitative Ms-SNuPE assay” as described in U.S. Pat. No. 7,037,650 are used in some embodiments. 
     Upon evaluating a methylation state, the methylation state is often expressed as the fraction or percentage of individual strands of DNA that is methylated at a particular site (e.g., at a single nucleotide, at a particular region or locus, at a longer sequence of interest, e.g., up to a ˜100-bp, 200-bp, 500-bp, 1000-bp subsequence of a DNA or longer) relative to the total population of DNA in the sample comprising that particular site. Traditionally, the amount of the unmethylated nucleic acid is determined by PCR using calibrators. Then, a known amount of DNA is bisulfite treated and the resulting methylation-specific sequence is determined using either a real-time PCR or other exponential amplification, e.g., a QuARTS assay (e.g., as provided by U.S. Pat. No. 8,361,720; and U.S. Pat. Appl. Pub. Nos. 2012/0122088 and 2012/0122106, incorporated herein by reference). 
     For example, in some embodiments methods comprise generating a standard curve for the unmethylated target by using external standards. The standard curve is constructed from at least two points and relates the real-time Ct value for unmethylated DNA to known quantitative standards. Then, a second standard curve for the methylated target is constructed from at least two points and external standards. This second standard curve relates the Ct for methylated DNA to known quantitative standards. Next, the test sample Ct values are determined for the methylated and unmethylated populations and the genomic equivalents of DNA are calculated from the standard curves produced by the first two steps. The percentage of methylation at the site of interest is calculated from the amount of methylated DNAs relative to the total amount of DNAs in the population, e.g., (number of methylated DNAs)/(the number of methylated DNAs+ number of unmethylated DNAs)×100. 
     Also provided herein are compositions and kits for practicing the methods. For example, in some embodiments, reagents (e.g., primers, probes) specific for one or more markers are provided alone or in sets (e.g., sets of primers pairs for amplifying a plurality of markers). Additional reagents for conducting a detection assay may also be provided (e.g., enzymes, buffers, positive and negative controls for conducting QuARTS, PCR, sequencing, bisulfite, or other assays). In some embodiments, the kits containing one or more reagent necessary, sufficient, or useful for conducting a method are provided. Also provided are reactions mixtures containing the reagents. Further provided are master mix reagent sets containing a plurality of reagents that may be added to each other and/or to a test sample to complete a reaction mixture. 
     In some embodiments, the technology described herein is associated with a programmable machine designed to perform a sequence of arithmetic or logical operations as provided by the methods described herein. For example, some embodiments of the technology are associated with (e.g., implemented in) computer software and/or computer hardware. In one aspect, the technology relates to a computer comprising a form of memory, an element for performing arithmetic and logical operations, and a processing element (e.g., a microprocessor) for executing a series of instructions (e.g., a method as provided herein) to read, manipulate, and store data. In some embodiments, a microprocessor is part of a system for determining a methylation state (e.g., of one or more DMR, e.g., DMR 1-107 as provided in Table 1, e.g., DMR 1-449 in Table 10); comparing methylation states (e.g., of one or more DMR, e.g., DMR 1-107 as provided in Table 1, e.g., DMR 1-449 in Table 10); generating standard curves; determining a Ct value; calculating a fraction, frequency, or percentage of methylation (e.g., of one or more DMR, e.g., DMR 1-107 as provided in Table 1, e.g., DMR 1-449 in Table 10); identifying a CpG island; determining a specificity and/or sensitivity of an assay or marker; calculating an ROC curve and an associated AUC: sequence analysis; all as described herein or is known in the art. 
     In some embodiments, a microprocessor or computer uses methylation state data in an algorithm to predict a site of a cancer. 
     In some embodiments, a software or hardware component receives the results of multiple assays and determines a single value result to report to a user that indicates a cancer risk based on the results of the multiple assays (e.g., determining the methylation state of multiple DMR, e.g., as provided in Table 1, e.g., as provided in Table 10). Related embodiments calculate a risk factor based on a mathematical combination (e.g., a weighted combination, a linear combination) of the results from multiple assays, e.g., determining the methylation states of multiple markers (such as multiple DMR, e.g., as provided in Table 1, e.g., as provided in Table 10). In some embodiments, the methylation state of a DMR defines a dimension and may have values in a multidimensional space and the coordinate defined by the methylation states of multiple DMR is a result, e.g., to report to a user, e.g., related to a cancer risk. 
     Some embodiments comprise a storage medium and memory components. Memory components (e.g., volatile and/or nonvolatile memory) find use in storing instructions (e.g., an embodiment of a process as provided herein) and/or data (e.g., a work piece such as methylation measurements, sequences, and statistical descriptions associated therewith). Some embodiments relate to systems also comprising one or more of a CPU, a graphics card, and a user interface (e.g., comprising an output device such as display and an input device such as a keyboard). 
     Programmable machines associated with the technology comprise conventional extant technologies and technologies in development or yet to be developed (e.g., a quantum computer, a chemical computer, a DNA computer, an optical computer, a spintronics based computer, etc.). 
     In some embodiments, the technology comprises a wired (e.g., metallic cable, fiber optic) or wireless transmission medium for transmitting data. For example, some embodiments relate to data transmission over a network (e.g., a local area network (LAN), a wide area network (WAN), an ad-hoc network, the internet, etc.). In some embodiments, programmable machines are present on such a network as peers and in some embodiments the programmable machines have a client/server relationship. 
     In some embodiments, data are stored on a computer-readable storage medium such as a hard disk, flash memory, optical media, a floppy disk, etc. 
     In some embodiments, the technology provided herein is associated with a plurality of programmable devices that operate in concert to perform a method as described herein. For example, in some embodiments, a plurality of computers (e.g., connected by a network) may work in parallel to collect and process data, e.g., in an implementation of cluster computing or grid computing or some other distributed computer architecture that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a network (private, public, or the internet) by a conventional network interface, such as Ethernet, fiber optic, or by a wireless network technology. 
     For example, some embodiments provide a computer that includes a computer-readable medium. The embodiment includes a random access memory (RAM) coupled to a processor. The processor executes computer-executable program instructions stored in memory. Such processors may include a microprocessor, an ASIC, a state machine, or other processor, and can be any of a number of computer processors, such as processors from Intel Corporation of Santa Clara, Calif. and Motorola Corporation of Schaumburg, Ill. Such processors include, or may be in communication with, media, for example computer-readable media, which stores instructions that, when executed by the processor, cause the processor to perform the steps described herein. 
     Embodiments of computer-readable media include, but are not limited to, an electronic, optical, magnetic, or other storage or transmission device capable of providing a processor with computer-readable instructions. Other examples of suitable media include, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. The instructions may comprise code from any suitable computer-programming language, including, for example, C, C++, C #, Visual Basic, Java, Python, Perl, and JavaScript. 
     Computers are connected in some embodiments to a network. Computers may also include a number of external or internal devices such as a mouse, a CD-ROM, DVD, a keyboard, a display, or other input or output devices. Examples of computers are personal computers, digital assistants, personal digital assistants, cellular phones, mobile phones, smart phones, pagers, digital tablets, laptop computers, internet appliances, and other processor-based devices. In general, the computers related to aspects of the technology provided herein may be any type of processor-based platform that operates on any operating system, such as Microsoft Windows, Linux, UNIX, Mac OS X, etc., capable of supporting one or more programs comprising the technology provided herein. Some embodiments comprise a personal computer executing other application programs (e.g., applications). The applications can be contained in memory and can include, for example, a word processing application, a spreadsheet application, an email application, an instant messenger application, a presentation application, an Internet browser application, a calendar/organizer application, and any other application capable of being executed by a client device. 
     All such components, computers, and systems described herein as associated with the technology may be logical or virtual. 
     Accordingly, provided herein is technology related to a method of screening for a neoplasm in a sample obtained from a subject, the method comprising assaying a methylation state of a marker in a sample obtained from a subject; and identifying the subject as having a neoplasm when the methylation state of the marker is different than a methylation state of the marker assayed in a subject that does not have a neoplasm, wherein the marker comprises a base in a differentially methylated region (DMR) selected from a group consisting of DMR 1-107 as provided in Table 1 and/or DMR 1-449 in Table 10. In some embodiments, the method further comprises locating the neoplasm site within the subject, wherein the methylation state of the marker indicates the neoplasm site within the subject. The technology is related to identifying and discriminating gastrointestinal cancers, e.g., in some embodiments the neoplasm is a gastrointestinal neoplasm. In some embodiments, the neoplasm is present in the upper gastrointestinal area of the patient and in some embodiments the neoplasm is present in the lower gastrointestinal area of the patient. In particular embodiments, the neoplasm is a pancreas neoplasm, a colorectal neoplasm, a bile duct neoplasm, or an adenoma. The technology also encompasses determining the state or stage of a cancer, e.g., in some embodiments the neoplasm is pre-cancerous. Some embodiments provide methods comprising assaying a plurality of markers, e.g., comprising assaying 2 to 11 markers. 
     The technology is not limited in the methylation state assessed. In some embodiments assessing the methylation state of the marker in the sample comprises determining the methylation state of one base. In some embodiments, assaying the methylation state of the marker in the sample comprises determining the extent of methylation at a plurality of bases. Moreover, in some embodiments the methylation state of the marker comprises an increased methylation of the marker relative to a normal methylation state of the marker. In some embodiments, the methylation state of the marker comprises a decreased methylation of the marker relative to a normal methylation state of the marker. In some embodiments the methylation state of the marker comprises a different pattern of methylation of the marker relative to a normal methylation state of the marker. 
     Furthermore, in some embodiments the marker is a region of 100 or fewer bases, the marker is a region of 500 or fewer bases, the marker is a region of 1000 or fewer bases, the marker is a region of 5000 or fewer bases, or, in some embodiments, the marker is one base. In some embodiments the marker is in a high CpG density promoter. 
     The technology is not limited by sample type. For example, in some embodiments the sample is a stool sample, a tissue sample, a pancreatic juice sample, a pancreatic cyst fluid sample, a blood sample (e.g., plasma, serum, whole blood), an excretion, or a urine sample. 
     Furthermore, the technology is not limited in the method used to determine methylation state. In some embodiments the assaying comprises using methylation specific polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation specific nuclease, mass-based separation, or target capture. In some embodiments, the assaying comprises use of a methylation specific oligonucleotide. In some embodiments, the technology uses massively parallel sequencing (e.g., next-generation sequencing) to determine methylation state, e.g., sequencing-by-synthesis, real-time (e.g., single-molecule) sequencing, bead emulsion sequencing, nanopore sequencing, etc. 
     The technology provides reagents for detecting a DMR, e.g., in some embodiments are provided a set of oligonucleotides comprising the sequences provided by SEQ ID NO: 1-202. In some embodiments are provided an oligonucleotide comprising a sequence complementary to a chromosomal region having a base in a DMR, e.g., an oligonucleotide sensitive to methylation state of a DMR. 
     The technology provides various panels of markers, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12.133, CLEC11A, ELMO1, EOMES, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, NDRG4, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, or ZNF71, and that comprises the marker (see, Tables 1 and 9). In addition, embodiments provide a method of analyzing a DMR from Table 1 that is DMR No. 11, 14, 15, 65, 21, 22, 23, 5, 29, 30, 38, 39, 41, 50, 51, 55, 57, 60, 61, 8, 75, 81, 82, 84, 87, 93, 94, 98, 99, 103, 104, or 107, and/or a DMR corresponding to Chr16:58497395-58497458. Some embodiments provide determining the methylation state of a marker, wherein a chromosomal region having an annotation that is CLEC11A, C13ORF18, KCNN2, ABCB1, SLC38A3, CD1D, IKZF1, ADCY1, CHR12133, RSPO3, MBP3, PRKCB, NDRG4, ELMO, or TWIST1 comprises the marker. In some embodiments, the methods comprise determining the methylation state of two markers, e.g., a pair of markers provided in a row of Table 5. 
     Kit embodiments are provided, e.g., a kit comprising a bisulfite reagent; and a control nucleic acid comprising a sequence from a DMR selected from a group consisting of DMR 1-107 (from Table 1) and/or a DMR selected from a group consisting of DMR 1-449 (from Table 10) and having a methylation state associated with a subject who does not have a cancer. In some embodiments, kits comprise a bisulfite reagent and an oligonucleotide as described herein. In some embodiments, kits comprise a bisulfite reagent; and a control nucleic acid comprising a sequence from a DMR selected from a group consisting of DMR 1-107 (from Table 1) and/or DMR 1-449 (from Table 10) and having a methylation state associated with a subject who has a cancer. Some kit embodiments comprise a sample collector for obtaining a sample from a subject (e.g., a stool sample); reagents for isolating a nucleic acid from the sample; a bisulfite reagent; and an oligonucleotide as described herein. 
     The technology is related to embodiments of compositions (e.g., reaction mixtures). In some embodiments are provided a composition comprising a nucleic acid comprising a DMR and a bisulfite reagent. Some embodiments provide a composition comprising a nucleic acid comprising a DMR and an oligonucleotide as described herein. Some embodiments provide a composition comprising a nucleic acid comprising a DMR and a methylation-sensitive restriction enzyme. Some embodiments provide a composition comprising a nucleic acid comprising a DMR and a polymerase. 
     Additional related method embodiments are provided for screening for a neoplasm in a sample obtained from a subject, e.g., a method comprising determining a methylation state of a marker in the sample comprising a base in a DMR that is one or more of DMR 1-107 (from Table 1) and/or one or more of DMR 1-449 (from Table 10); comparing the methylation state of the marker from the subject sample to a methylation state of the marker from a normal control sample from a subject who does not have a cancer; and determining a confidence interval and/or a p value of the difference in the methylation state of the subject sample and the normal control sample. In some embodiments, the confidence interval is 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% or 99.99% and the p value is 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, or 0.0001. Some embodiments of methods provide steps of reacting a nucleic acid comprising a DMR with a bisulfite reagent to produce a bisulfite-reacted nucleic acid; sequencing the bisulfite-reacted nucleic acid to provide a nucleotide sequence of the bisulfite-reacted nucleic acid; comparing the nucleotide sequence of the bisulfite-reacted nucleic acid with a nucleotide sequence of a nucleic acid comprising the DMR from a subject who does not have a cancer to identify differences in the two sequences; and identifying the subject as having a neoplasm when a difference is present. 
     Systems for screening for a neoplasm in a sample obtained from a subject are provided by the technology. Exemplary embodiments of systems include, e.g., a system for screening for a neoplasm in a sample obtained from a subject, the system comprising an analysis component configured to determine the methylation state of a sample, a software component configured to compare the methylation state of the sample with a control sample or a reference sample methylation state recorded in a database, and an alert component configured to alert a user of a cancer-associated methylation state. An alert is determined in some embodiments by a software component that receives the results from multiple assays (e.g., determining the methylation states of multiple markers, e.g., DMR, e.g., as provided in Table 1, e.g., as provided in Table 10) and calculating a value or result to report based on the multiple results. Some embodiments provide a database of weighted parameters associated with each DMR provided herein for use in calculating a value or result and/or an alert to report to a user (e.g., such as a physician, nurse, clinician, etc.). In some embodiments all results from multiple assays are reported and in some embodiments one or more results are used to provide a score, value, or result based on a composite of one or more results from multiple assays that is indicative of a cancer risk in a subject. 
     In some embodiments of systems, a sample comprises a nucleic acid comprising a DMR. In some embodiments the system further comprises a component for isolating a nucleic acid, a component for collecting a sample such as a component for collecting a stool sample. In some embodiments, the system comprises nucleic acid sequences comprising a DMR. In some embodiments the database comprises nucleic acid sequences from subjects who do not have a cancer. Also provided are nucleic acids, e.g., a set of nucleic acids, each nucleic acid having a sequence comprising a DMR. In some embodiments the set of nucleic acids wherein each nucleic acid has a sequence from a subject who does not have a cancer. Related system embodiments comprise a set of nucleic acids as described and a database of nucleic acid sequences associated with the set of nucleic acids. Some embodiments further comprise a bisulfite reagent. And, some embodiments further comprise a nucleic acid sequencer. 
     Additional embodiments will be apparent to persons skilled in the relevant art based on the teachings contained herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features, aspects, and advantages of the present technology will become better understood with regard to the following drawings: 
         FIG. 1  is a plot showing the marker importance of a subset of methylation markers as measured by Mean Decrease in Accuracy for Site Prediction. 
         FIG. 2  shows marker levels of BMP3 and NDRG4 in brushings (cardia+whole esophagus) in Barrett&#39;s cases and controls as described in Example 8. 
         FIG. 3  shows AUC of stool miR-1290 as described in Example 9. 
     
    
    
     It is to be understood that the figures are not necessarily drawn to scale, nor are the objects in the figures necessarily drawn to scale in relationship to one another. The figures are depictions that are intended to bring clarity and understanding to various embodiments of apparatuses, systems, compositions, and methods disclosed herein. Wherever possible, the same reference numbers are used throughout the drawings to refer to the same or like parts. Moreover, it should be appreciated that the drawings are not intended to limit the scope of the present teachings in any way. 
     DETAILED DESCRIPTION 
     Provided herein is technology relating to detecting neoplasia and particularly, but not exclusively, to methods, compositions, and related uses for detecting premalignant and malignant neoplasms such as pancreatic and colorectal cancer. As the technology is described herein, the section headings used are for organizational purposes only and are not to be construed as limiting the subject matter in any way. 
     In this detailed description of the various embodiments, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of the embodiments disclosed. One skilled in the art will appreciate, however, that these various embodiments may be practiced with or without these specific details. In other instances, structures and devices are shown in block diagram form. Furthermore, one skilled in the art can readily appreciate that the specific sequences in which methods are presented and performed are illustrative and it is contemplated that the sequences can be varied and still remain within the spirit and scope of the various embodiments disclosed herein. 
     All literature and similar materials cited in this application, including but not limited to, patents, patent applications, articles, books, treatises, and internet web pages are expressly incorporated by reference in their entirety for any purpose. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of ordinary skill in the art to which the various embodiments described herein belongs. When definitions of terms in incorporated references appear to differ from the definitions provided in the present teachings, the definition provided in the present teachings shall control. 
     Definitions 
     To facilitate an understanding of the present technology, a number of terms and phrases are defined below. Additional definitions are set forth throughout the detailed description. 
     Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. Furthermore, the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention. 
     In addition, as used herein, the term “or” is an inclusive “or” operator and is equivalent to the term “and/or” unless the context clearly dictates otherwise. The term “based on” is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a”, “an”, and “the” include plural references. The meaning of “in” includes “in” and “on.” 
     As used herein, a “nucleic acid” or “nucleic acid molecule” generally refers to any ribonucleic acid or deoxyribonucleic acid, which may be unmodified or modified DNA or RNA. “Nucleic acids” include, without limitation, single- and double-stranded nucleic acids. As used herein, the term “nucleic acid” also includes DNA as described above that contains one or more modified bases. Thus, DNA with a backbone modified for stability or for other reasons is a “nucleic acid”. The term “nucleic acid” as it is used herein embraces such chemically, enzymatically, or metabolically modified forms of nucleic acids, as well as the chemical forms of DNA characteristic of viruses and cells, including for example, simple and complex cells. 
     The terms “oligonucleotide” or “polynucleotide” or “nucleotide” or “nucleic acid” refer to a molecule having two or more deoxyribonucleotides or ribonucleotides, preferably more than three, and usually more than ten. The exact size will depend on many factors, which in turn depends on the ultimate function or use of the oligonucleotide. The oligonucleotide may be generated in any manner, including chemical synthesis, DNA replication, reverse transcription, or a combination thereof. Typical deoxyribonucleotides for DNA are thymine, adenine, cytosine, and guanine. Typical ribonucleotides for RNA are uracil, adenine, cytosine, and guanine. 
     As used herein, the terms “locus” or “region” of a nucleic acid refer to a subregion of a nucleic acid, e.g., a gene on a chromosome, a single nucleotide, a CpG island, etc. 
     The terms “complementary” and “complementarity” refer to nucleotides (e.g., 1 nucleotide) or polynucleotides (e.g., a sequence of nucleotides) related by the base-pairing rules. For example, the sequence 5′-A-G-T-3′ is complementary to the sequence 3′-T-C-A-5′. Complementarity may be “partial,” in which only some of the nucleic acids&#39; bases are matched according to the base pairing rules. Or, there may be “complete” or “total” complementarity between the nucleic acids. The degree of complementarity between nucleic acid strands effects the efficiency and strength of hybridization between nucleic acid strands. This is of particular importance in amplification reactions and in detection methods that depend upon binding between nucleic acids. 
     The term “gene” refers to a nucleic acid (e.g., DNA or RNA) sequence that comprises coding sequences necessary for the production of an RNA, or of a polypeptide or its precursor. A functional polypeptide can be encoded by a full length coding sequence or by any portion of the coding sequence as long as the desired activity or functional properties (e.g., enzymatic activity, ligand binding, signal transduction, etc.) of the polypeptide are retained. The term “portion” when used in reference to a gene refers to fragments of that gene. The fragments may range in size from a few nucleotides to the entire gene sequence minus one nucleotide. Thus, “a nucleotide comprising at least a portion of a gene” may comprise fragments of the gene or the entire gene. 
     The term “gene” also encompasses the coding regions of a structural gene and includes sequences located adjacent to the coding region on both the 5′ and 3′ ends, e.g., for a distance of about 1 kb on either end, such that the gene corresponds to the length of the full-length mRNA (e.g., comprising coding, regulatory, structural and other sequences). The sequences that are located 5′ of the coding region and that are present on the mRNA are referred to as 5′ non-translated or untranslated sequences. The sequences that are located 3′ or downstream of the coding region and that are present on the mRNA are referred to as 3′ non-translated or 3′ untranslated sequences. The term “gene” encompasses both cDNA and genomic forms of a gene. In some organisms (e.g., eukaryotes), a genomic form or clone of a gene contains the coding region interrupted with non-coding sequences termed “introns” or “intervening regions” or “intervening sequences.” Introns are segments of a gene that are transcribed into nuclear RNA (hnRNA); introns may contain regulatory elements such as enhancers. Introns are removed or “spliced out” from the nuclear or primary transcript; introns therefore are absent in the messenger RNA (mRNA) transcript. The mRNA functions during translation to specify the sequence or order of amino acids in a nascent polypeptide. 
     In addition to containing introns, genomic forms of a gene may also include sequences located on both the 5′ and 3′ ends of the sequences that are present on the RNA transcript. These sequences are referred to as “flanking” sequences or regions (these flanking sequences are located 5′ or 3′ to the non-translated sequences present on the mRNA transcript). The 5′ flanking region may contain regulatory sequences such as promoters and enhancers that control or influence the transcription of the gene. The 3′ flanking region may contain sequences that direct the termination of transcription, posttranscriptional cleavage, and polyadenylation. 
     The term “wild-type” when made in reference to a gene refers to a gene that has the characteristics of a gene isolated from a naturally occurring source. The term “wild-type” when made in reference to a gene product refers to a gene product that has the characteristics of a gene product isolated from a naturally occurring source. The term “naturally-occurring” as applied to an object refers to the fact that an object can be found in nature. For example, a polypeptide or polynucleotide sequence that is present in an organism (including viruses) that can be isolated from a source in nature and which has not been intentionally modified by the hand of a person in the laboratory is naturally-occurring. A wild-type gene is often that gene or allele that is most frequently observed in a population and is thus arbitrarily designated the “normal” or “wild-type” form of the gene. In contrast, the term “modified” or “mutant” when made in reference to a gene or to a gene product refers, respectively, to a gene or to a gene product that displays modifications in sequence and/or functional properties (e.g., altered characteristics) when compared to the wild-type gene or gene product. It is noted that naturally-occurring mutants can be isolated; these are identified by the fact that they have altered characteristics when compared to the wild-type gene or gene product. 
     The term “allele” refers to a variation of a gene; the variations include but are not limited to variants and mutants, polymorphic loci, and single nucleotide polymorphic loci, frameshift, and splice mutations. An allele may occur naturally in a population or it might arise during the lifetime of any particular individual of the population. 
     Thus, the terms “variant” and “mutant” when used in reference to a nucleotide sequence refer to a nucleic acid sequence that differs by one or more nucleotides from another, usually related, nucleotide acid sequence. A “variation” is a difference between two different nucleotide sequences; typically, one sequence is a reference sequence. 
     “Amplification” is a special case of nucleic acid replication involving template specificity. It is to be contrasted with non-specific template replication (e.g., replication that is template-dependent but not dependent on a specific template). Template specificity is here distinguished from fidelity of replication (e.g., synthesis of the proper polynucleotide sequence) and nucleotide (ribo- or deoxyribo-) specificity. Template specificity is frequently described in terms of“target” specificity. Target sequences are “targets” in the sense that they are sought to be sorted out from other nucleic acid. Amplification techniques have been designed primarily for this sorting out. 
     Amplification of nucleic acids generally refers to the production of multiple copies of a polynucleotide, or a portion of the polynucleotide, typically starting from a small amount of the polynucleotide (e.g., a single polynucleotide molecule, 10 to 100 copies of a polynucleotide molecule, which may or may not be exactly the same), where the amplification products or amplicons are generally detectable. Amplification of polynucleotides encompasses a variety of chemical and enzymatic processes. The generation of multiple DNA copies from one or a few copies of a target or template DNA molecule during a polymerase chain reaction (PCR) or a ligase chain reaction (LCR; see, e.g., U.S. Pat. No. 5,494,810; herein incorporated by reference in its entirety) are forms of amplification. Additional types of amplification include, but are not limited to, allele-specific PCR (see, e.g., U.S. Pat. No. 5,639,611; herein incorporated by reference in its entirety), assembly PCR (see, e.g., U.S. Pat. No. 5,965,408; herein incorporated by reference in its entirety), helicase-dependent amplification (see, e.g., U.S. Pat. No. 7,662,594; herein incorporated by reference in its entirety), Hot-start PCR (see, e.g., U.S. Pat. Nos. 5,773,258 and 5,338,671; each herein incorporated by reference in their entireties), intersequence-specfic PCR, inverse PCR (see, e.g., Triglia, et al et al. (1988) Nucleic Acids Res., 16:8186; herein incorporated by reference in its entirety), ligation-mediated PCR (see, e.g., Guilfoyle, R. et al et al., Nucleic Acids Research, 25:1854-1858 (1997); U.S. Pat. No. 5,508,169; each of which are herein incorporated by reference in their entireties), methylation-specific PCR (see, e.g., Herman, et al., (1996) PNAS 93(13) 9821-9826; herein incorporated by reference in its entirety), miniprimer PCR, multiplex ligation-dependent probe amplification (see, e.g., Schouten, et al., (2002) Nucleic Acids Research 30(12): e57; herein incorporated by reference in its entirety), multiplex PCR (see, e.g., Chamberlain, et al., (1988) Nucleic Acids Research 16(23) 11141-11156; Ballabio, et al., (1990) Human Genetics 84(6) 571-573; Hayden, et al., (2008) BMC Genetics 9:80; each of which are herein incorporated by reference in their entireties), nested PCR, overlap-extension PCR (see, e.g., Higuchi, et al., (1988) Nucleic Acids Research 16(15) 7351-7367; herein incorporated by reference in its entirety), real time PCR (see, e.g., Higuchi, et al et al., (1992) Biotechnology 10:413-417; Higuchi, et al., (1993) Biotechnology 11:1026-1030; each of which are herein incorporated by reference in their entireties), reverse transcription PCR (see, e.g., Bustin, S. A. (2000) J. Molecular Endocrinology 25:169-193; herein incorporated by reference in its entirety), solid phase PCR, thermal asymmetric interlaced PCR, and Touchdown PCR (see, e.g., Don, et al., Nucleic Acids Research (1991) 19(14) 4008; Roux, K. (1994) Biotechniques 16(5) 812-814; Hecker, et al., (1996) Biotechniques 20(3) 478-485; each of which are herein incorporated by reference in their entireties). Polynucleotide amplification also can be accomplished using digital PCR (see, e.g., Kalinina, et al., Nucleic Acids Research. 25; 1999-2004, (1997); Vogelstein and Kinzler, Proc Natl Acad Sci USA. 96; 9236-41, (1999); International Patent Publication No. WO05023091A2; US Patent Application Publication No. 20070202525; each of which are incorporated herein by reference in their entireties). 
     The term “polymerase chain reaction” (“PCR”) refers to the method of KB. Mullis U.S. Pat. Nos. 4,683,195, 4,683,202, and 4,965,188, that describe a method for increasing the concentration of a segment of a target sequence in a mixture of genomic DNA without cloning or purification. This process for amplifying the target sequence consists of introducing a large excess of two oligonucleotide primers to the DNA mixture containing the desired target sequence, followed by a precise sequence of thermal cycling in the presence of a DNA polymerase. The two primers are complementary to their respective strands of the double stranded target sequence. To effect amplification, the mixture is denatured and the primers then annealed to their complementary sequences within the target molecule. Following annealing, the primers are extended with a polymerase so as to form a new pair of complementary strands. The steps of denaturation, primer annealing, and polymerase extension can be repeated many times (i.e., denaturation, annealing and extension constitute one “cycle”; there can be numerous “cycles”) to obtain a high concentration of an amplified segment of the desired target sequence. The length of the amplified segment of the desired target sequence is determined by the relative positions of the primers with respect to each other, and therefore, this length is a controllable parameter. By virtue of the repeating aspect of the process, the method is referred to as the “polymerase chain reaction” (“PCR”). Because the desired amplified segments of the target sequence become the predominant sequences (in terms of concentration) in the mixture, they are said to be “PCR amplified” and are “PCR products” or “amplicons.” 
     Template specificity is achieved in most amplification techniques by the choice of enzyme. Amplification enzymes are enzymes that, under conditions they are used, will process only specific sequences of nucleic acid in a heterogeneous mixture of nucleic acid. For example, in the case of Q-beta replicase, MDV-1 RNA is the specific template for the replicase (Kacian et al., Proc. Natl. Acad. Sci. USA, 69:3038 [1972]). Other nucleic acid will not be replicated by this amplification enzyme. Similarly, in the case of T7 RNA polymerase, this amplification enzyme has a stringent specificity for its own promoters (Chamberlin et al, Nature, 228:227 [1970]). In the case of T4 DNA ligase, the enzyme will not ligate the two oligonucleotides or polynucleotides, where there is a mismatch between the oligonucleotide or polynucleotide substrate and the template at the ligation junction (Wu and Wallace (1989) Genomics 4:560). Finally, thermostable template-dependant DNA polymerases (e.g., Taq and Pfu DNA polymerases), by virtue of their ability to function at high temperature, are found to display high specificity for the sequences bounded and thus defined by the primers; the high temperature results in thermodynamic conditions that favor primer hybridization with the target sequences and not hybridization with non-target sequences (H. A. Erlich (ed.), PCR Technology, Stockton Press [1989]). 
     As used herein, the term “nucleic acid detection assay” refers to any method of determining the nucleotide composition of a nucleic acid of interest. Nucleic acid detection assay include but are not limited to, DNA sequencing methods, probe hybridization methods, structure specific cleavage assays (e.g., the INVADER assay, Hologic, Inc.) and are described, e.g., in U.S. Pat. Nos. 5,846,717, 5,985,557, 5,994,069, 6,001,567, 6,090,543, and 6,872,816; Lyamichev et al., Nat. Biotech., 17:292 (1999), Hall et al., PNAS, USA, 97:8272 (2000), and US 2009/0253142, each of which is herein incorporated by reference in its entirety for all purposes); enzyme mismatch cleavage methods (e.g., Variagenics, U.S. Pat. Nos. 6,110,684, 5,958,692, 5,851,770, herein incorporated by reference in their entireties); polymerase chain reaction; branched hybridization methods (e.g., Chiron, U.S. Pat. Nos. 5,849,481, 5,710,264, 5,124,246, and 5,624,802, herein incorporated by reference in their entireties); rolling circle replication (e.g., U.S. Pat. Nos. 6,210,884, 6,183,960 and 6,235,502, herein incorporated by reference in their entireties); NASBA (e.g., U.S. Pat. No. 5,409,818, herein incorporated by reference in its entirety); molecular beacon technology (e.g., U.S. Pat. No. 6,150,097, herein incorporated by reference in its entirety); E-sensor technology (Motorola, U.S. Pat. Nos. 6,248,229, 6,221,583, 6,013,170, and 6,063,573, herein incorporated by reference in their entireties); cycling probe technology (e.g., U.S. Pat. Nos. 5,403,711, 5,011,769, and 5,660,988, herein incorporated by reference in their entireties); Dade Behring signal amplification methods (e.g., U.S. Pat. Nos. 6,121,001, 6,110,677, 5,914,230, 5,882,867, and 5,792,614, herein incorporated by reference in their entireties); ligase chain reaction (e.g., Barnay Proc. Natl. Acad. Sci USA 88, 189-93 (1991)); and sandwich hybridization methods (e.g., U.S. Pat. No. 5,288,609, herein incorporated by reference in its entirety). 
     The term “amplifiable nucleic acid” refers to a nucleic acid that may be amplified by any amplification method. It is contemplated that “amplifiable nucleic acid” will usually comprise “sample template.” 
     The term “sample template” refers to nucleic acid originating from a sample that is analyzed for the presence of “target” (defined below). In contrast, “background template” is used in reference to nucleic acid other than sample template that may or may not be present in a sample. Background template is most often inadvertent. It may be the result of carryover or it may be due to the presence of nucleic acid contaminants sought to be purified away from the sample. For example, nucleic acids from organisms other than those to be detected may be present as background in a test sample. 
     The term “primer” refers to an oligonucleotide, whether occurring naturally as in a purified restriction digest or produced synthetically, that is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product that is complementary to a nucleic acid strand is induced, (e.g., in the presence of nucleotides and an inducing agent such as a DNA polymerase and at a suitable temperature and pH). The primer is preferably single stranded for maximum efficiency in amplification, but may alternatively be double stranded. If double stranded, the primer is first treated to separate its strands before being used to prepare extension products. Preferably, the primer is an oligodeoxyribonucleotide. The primer must be sufficiently long to prime the synthesis of extension products in the presence of the inducing agent. The exact lengths of the primers will depend on many factors, including temperature, source of primer, and the use of the method. 
     The term “probe” refers to an oligonucleotide (e.g., a sequence of nucleotides), whether occurring naturally as in a purified restriction digest or produced synthetically, recombinantly, or by PCR amplification, that is capable of hybridizing to another oligonucleotide of interest. A probe may be single-stranded or double-stranded. Probes are useful in the detection, identification, and isolation of particular gene sequences (e.g., a “capture probe”). It is contemplated that any probe used in the present invention may, in some embodiments, be labeled with any “reporter molecule,” so that is detectable in any detection system, including, but not limited to enzyme (e.g., ELISA, as well as enzyme-based histochemical assays), fluorescent, radioactive, and luminescent systems. It is not intended that the present invention be limited to any particular detection system or label. 
     As used herein, “methylation” refers to cytosine methylation at positions C5 or N4 of cytosine, the N6 position of adenine, or other types of nucleic acid methylation. In vitro amplified DNA is usually unmethylated because typical in vitro DNA amplification methods do not retain the methylation pattern of the amplification template. However, “unmethylated DNA” or “methylated DNA” can also refer to amplified DNA whose original template was unmethylated or methylated, respectively. 
     Accordingly, as used herein a “methylated nucleotide” or a “methylated nucleotide base” refers to the presence of a methyl moiety on a nucleotide base, where the methyl moiety is not present in a recognized typical nucleotide base. For example, cytosine does not contain a methyl moiety on its pyrimidine ring, but 5-methylcytosine contains a methyl moiety at position 5 of its pyrimidine ring. Therefore, cytosine is not a methylated nucleotide and 5-methylcytosine is a methylated nucleotide. In another example, thymine contains a methyl moiety at position 5 of its pyrimidine ring; however, for purposes herein, thymine is not considered a methylated nucleotide when present in DNA since thymine is a typical nucleotide base of DNA. 
     As used herein, a “methylated nucleic acid molecule” refers to a nucleic acid molecule that contains one or more methylated nucleotides. 
     As used herein, a “methylation state”, “methylation profile”, and “methylation status” of a nucleic acid molecule refers to the presence of absence of one or more methylated nucleotide bases in the nucleic acid molecule. For example, a nucleic acid molecule containing a methylated cytosine is considered methylated (e.g., the methylation state of the nucleic acid molecule is methylated). A nucleic acid molecule that does not contain any methylated nucleotides is considered unmethylated. 
     The methylation state of a particular nucleic acid sequence (e.g., a gene marker or DNA region as described herein) can indicate the methylation state of every base in the sequence or can indicate the methylation state of a subset of the bases (e.g., of one or more cytosines) within the sequence, or can indicate information regarding regional methylation density within the sequence with or without providing precise information of the locations within the sequence the methylation occurs. 
     The methylation state of a nucleotide locus in a nucleic acid molecule refers to the presence or absence of a methylated nucleotide at a particular locus in the nucleic acid molecule. For example, the methylation state of a cytosine at the 7th nucleotide in a nucleic acid molecule is methylated when the nucleotide present at the 7th nucleotide in the nucleic acid molecule is 5-methylcytosine. Similarly, the methylation state of a cytosine at the 7th nucleotide in a nucleic acid molecule is unmethylated when the nucleotide present at the 7th nucleotide in the nucleic acid molecule is cytosine (and not 5-methylcytosine). 
     The methylation status can optionally be represented or indicated by a “methylation value” (e.g., representing a methylation frequency, fraction, ratio, percent, etc.) A methylation value can be generated, for example, by quantifying the amount of intact nucleic acid present following restriction digestion with a methylation dependent restriction enzyme or by comparing amplification profiles after bisulfite reaction or by comparing sequences of bisulfite-treated and untreated nucleic acids. Accordingly, a value, e.g., a methylation value, represents the methylation status and can thus be used as a quantitative indicator of methylation status across multiple copies of a locus. This is of particular use when it is desirable to compare the methylation status of a sequence in a sample to a threshold or reference value. 
     As used herein, “methylation frequency” or “methylation percent (%)” refer to the number of instances in which a molecule or locus is methylated relative to the number of instances the molecule or locus is unmethylated. 
     As such, the methylation state describes the state of methylation of a nucleic acid (e.g., a genomic sequence). In addition, the methylation state refers to the characteristics of a nucleic acid segment at a particular genomic locus relevant to methylation. Such characteristics include, but are not limited to, whether any of the cytosine (C) residues within this DNA sequence are methylated, the location of methylated C residue(s), the frequency or percentage of methylated C throughout any particular region of a nucleic acid, and allelic differences in methylation due to, e.g., difference in the origin of the alleles. The terms “methylation state”, “methylation profile”, and “methylation status” also refer to the relative concentration, absolute concentration, or pattern of methylated C or unmethylated C throughout any particular region of a nucleic acid in a biological sample. For example, if the cytosine (C) residue(s) within a nucleic acid sequence are methylated it may be referred to as “hypermethylated” or having “increased methylation”, whereas if the cytosine (C) residue(s) within a DNA sequence are not methylated it may be referred to as “hypomethylated” or having “decreased methylation”. Likewise, if the cytosine (C) residue(s) within a nucleic acid sequence are methylated as compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.) that sequence is considered hypermethylated or having increased methylation compared to the other nucleic acid sequence. Alternatively, if the cytosine (C) residue(s) within a DNA sequence are not methylated as compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.) that sequence is considered hypomethylated or having decreased methylation compared to the other nucleic acid sequence. Additionally, the term “methylation pattern” as used herein refers to the collective sites of methylated and unmethylated nucleotides over a region of a nucleic acid. Two nucleic acids may have the same or similar methylation frequency or methylation percent but have different methylation patterns when the number of methylated and unmethylated nucleotides are the same or similar throughout the region but the locations of methylated and unmethylated nucleotides are different. Sequences are said to be “differentially methylated” or as having a “difference in methylation” or having a “different methylation state” when they differ in the extent (e.g., one has increased or decreased methylation relative to the other), frequency, or pattern of methylation. The term “differential methylation” refers to a difference in the level or pattern of nucleic acid methylation in a cancer positive sample as compared with the level or pattern of nucleic acid methylation in a cancer negative sample. It may also refer to the difference in levels or patterns between patients that have recurrence of cancer after surgery versus patients who not have recurrence. Differential methylation and specific levels or patterns of DNA methylation are prognostic and predictive biomarkers, e.g., once the correct cut-off or predictive characteristics have been defined. 
     Methylation state frequency can be used to describe a population of individuals or a sample from a single individual. For example, a nucleotide locus having a methylation state frequency of 50% is methylated in 50% of instances and unmethylated in 50% of instances. Such a frequency can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a population of individuals or a collection of nucleic acids. Thus, when methylation in a first population or pool of nucleic acid molecules is different from methylation in a second population or pool of nucleic acid molecules, the methylation state frequency of the first population or pool will be different from the methylation state frequency of the second population or pool. Such a frequency also can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a single individual. For example, such a frequency can be used to describe the degree to which a group of cells from a tissue sample are methylated or unmethylated at a nucleotide locus or nucleic acid region. 
     As used herein a “nucleotide locus” refers to the location of a nucleotide in a nucleic acid molecule. A nucleotide locus of a methylated nucleotide refers to the location of a methylated nucleotide in a nucleic acid molecule. 
     Typically, methylation of human DNA occurs on a dinucleotide sequence including an adjacent guanine and cytosine where the cytosine is located 5′ of the guanine (also termed CpG dinucleotide sequences). Most cytosines within the CpG dinucleotides are methylated in the human genome, however some remain unmethylated in specific CpG dinucleotide rich genomic regions, known as CpG islands (see, e.g., Antequera et al. (1990) Cell 62: 503-514). 
     As used herein, a “CpG island” refers to a G:C-rich region of genomic DNA containing an increased number of CpG dinucleotides relative to total genomic DNA. A CpG island can be at least 100, 200, or more base pairs in length, where the G:C content of the region is at least 50% and the ratio of observed CpG frequency over expected frequency is 0.6; in some instances, a CpG island can be at least 500 base pairs in length, where the G:C content of the region is at least 55%) and the ratio of observed CpG frequency over expected frequency is 0.65. The observed CpG frequency over expected frequency can be calculated according to the method provided in Gardiner-Garden et al (1987)  J. Mol. Biol.  196: 261-281. For example, the observed CpG frequency over expected frequency can be calculated according to the formula R=(A×B)/(C×D), where R is the ratio of observed CpG frequency over expected frequency, A is the number of CpG dinucleotides in an analyzed sequence, B is the total number of nucleotides in the analyzed sequence, C is the total number of C nucleotides in the analyzed sequence, and D is the total number of G nucleotides in the analyzed sequence. Methylation state is typically determined in CpG islands, e.g., at promoter regions. It will be appreciated though that other sequences in the human genome are prone to DNA methylation such as CpA and CpT (see Ramsahoye (2000)  Proc. Natl. Acad. Sci. USA  97: 5237-5242; Salmon and Kaye (1970)  Biochim. Biophys. Acta.  204: 340-351; Grafstrom (1985)  Nucleic Acids Res.  13: 2827-2842; Nyce (1986)  Nucleic Acids Res.  14: 4353-4367; Woodcock (1987)  Biochem. Biophys. Res. Commun.  145: 888-894). 
     As used herein, a reagent that modifies a nucleotide of the nucleic acid molecule as a function of the methylation state of the nucleic acid molecule, or a methylation-specific reagent, refers to a compound or composition or other agent that can change the nucleotide sequence of a nucleic acid molecule in a manner that reflects the methylation state of the nucleic acid molecule. Methods of treating a nucleic acid molecule with such a reagent can include contacting the nucleic acid molecule with the reagent, coupled with additional steps, if desired, to accomplish the desired change of nucleotide sequence. Such a change in the nucleic acid molecule&#39;s nucleotide sequence can result in a nucleic acid molecule in which each methylated nucleotide is modified to a different nucleotide. Such a change in the nucleic acid nucleotide sequence can result in a nucleic acid molecule in which each unmethylated nucleotide is modified to a different nucleotide. Such a change in the nucleic acid nucleotide sequence can result in a nucleic acid molecule in which each of a selected nucleotide which is unmethylated (e.g., each unmethylated cytosine) is modified to a different nucleotide. Use of such a reagent to change the nucleic acid nucleotide sequence can result in a nucleic acid molecule in which each nucleotide that is a methylated nucleotide (e.g., each methylated cytosine) is modified to a different nucleotide. As used herein, use of a reagent that modifies a selected nucleotide refers to a reagent that modifies one nucleotide of the four typically occurring nucleotides in a nucleic acid molecule (C, G, T, and A for DNA and C, G, U, and A for RNA), such that the reagent modifies the one nucleotide without modifying the other three nucleotides. In one exemplary embodiment, such a reagent modifies an unmethylated selected nucleotide to produce a different nucleotide. In another exemplary embodiment, such a reagent can deaminate unmethylated cytosine nucleotides. An exemplary reagent is bisulfite. 
     As used herein, the term “bisulfite reagent” refers to a reagent comprising in some embodiments bisulfite, disulfite, hydrogen sulfite, or combinations thereof to distinguish between methylated and unmethylated cytidines, e.g., in CpG dinucleotide sequences. 
     The term “methylation assay” refers to any assay for determining the methylation state of one or more CpG dinucleotide sequences within a sequence of a nucleic acid. 
     The term “MS AP-PCR” (Methylation-Sensitive Arbitrarily-Primed Polymerase Chain Reaction) refers to the art-recognized technology that allows for a global scan of the genome using CG-rich primers to focus on the regions most likely to contain CpG dinucleotides, and described by Gonzalgo et al. (1997)  Cancer Research  57: 594-599. 
     The term “MethyLight™” refers to the art-recognized fluorescence-based real-time PCR technique described by Eads et al. (1999)  Cancer Res.  59: 2302-2306. 
     The term “HeavyMethyl™” refers to an assay wherein methylation specific blocking probes (also referred to herein as blockers) covering CpG positions between, or covered by, the amplification primers enable methylation-specific selective amplification of a nucleic acid sample. 
     The term “HeavyMethyl™ MethyLight™” assay refers to a HeavyMethyl™ MethyLight™ assay, which is a variation of the MethyLight™ assay, wherein the MethyLight™ assay is combined with methylation specific blocking probes covering CpG positions between the amplification primers. 
     The term “Ms-SNuPE” (Methylation-sensitive Single Nucleotide Primer Extension) refers to the art-recognized assay described by Gonzalgo &amp; Jones (1997)  Nucleic Acids Res.  25: 2529-2531. 
     The term “MSP” (Methylation-specific PCR) refers to the art-recognized methylation assay described by Herman et al. (1996)  Proc. Natl. Acad. Sci. USA  93: 9821-9826, and by U.S. Pat. No. 5,786,146. 
     The term “COBRA” (Combined Bisulfite Restriction Analysis) refers to the art-recognized methylation assay described by Xiong &amp; Laird (1997)  Nucleic Acids Res.  25: 2532-2534. 
     The term “MCA” (Methylated CpG Island Amplification) refers to the methylation assay described by Toyota et al. (1999)  Cancer Res.  59: 2307-12, and in WO 00/26401A1. 
     As used herein, a “selected nucleotide” refers to one nucleotide of the four typically occurring nucleotides in a nucleic acid molecule (C, G, T, and A for DNA and C, G, U, and A for RNA), and can include methylated derivatives of the typically occurring nucleotides (e.g., when C is the selected nucleotide, both methylated and unmethylated C are included within the meaning of a selected nucleotide), whereas a methylated selected nucleotide refers specifically to a methylated typically occurring nucleotide and an unmethylated selected nucleotides refers specifically to an unmethylated typically occurring nucleotide. 
     The terms “methylation-specific restriction enzyme” or “methylation-sensitive restriction enzyme” refers to an enzyme that selectively digests a nucleic acid dependent on the methylation state of its recognition site. In the case of a restriction enzyme that specifically cuts if the recognition site is not methylated or is hemimethylated, the cut will not take place or will take place with a significantly reduced efficiency if the recognition site is methylated. In the case of a restriction enzyme that specifically cuts if the recognition site is methylated, the cut will not take place or will take place with a significantly reduced efficiency if the recognition site is not methylated. Preferred are methylation-specific restriction enzymes, the recognition sequence of which contains a CG dinucleotide (for instance a recognition sequence such as CGCG or CCCGGG). Further preferred for some embodiments are restriction enzymes that do not cut if the cytosine in this dinucleotide is methylated at the carbon atom C5. 
     As used herein, a “different nucleotide” refers to a nucleotide that is chemically different from a selected nucleotide, typically such that the different nucleotide has Watson-Crick base-pairing properties that differ from the selected nucleotide, whereby the typically occurring nucleotide that is complementary to the selected nucleotide is not the same as the typically occurring nucleotide that is complementary to the different nucleotide. For example, when C is the selected nucleotide, U or T can be the different nucleotide, which is exemplified by the complementarity of C to G and the complementarity of U or T to A. As used herein, a nucleotide that is complementary to the selected nucleotide or that is complementary to the different nucleotide refers to a nucleotide that base-pairs, under high stringency conditions, with the selected nucleotide or different nucleotide with higher affinity than the complementary nucleotide&#39;s base-paring with three of the four typically occurring nucleotides. An example of complementarity is Watson-Crick base pairing in DNA (e.g., A-T and C-G) and RNA (e.g., A-U and C-G). Thus, for example, G base-pairs, under high stringency conditions, with higher affinity to C than G base-pairs to G, A, or T and, therefore, when C is the selected nucleotide, G is a nucleotide complementary to the selected nucleotide. 
     As used herein, the “sensitivity” of a given marker refers to the percentage of samples that report a DNA methylation value above a threshold value that distinguishes between neoplastic and non-neoplastic samples. In some embodiments, a positive is defined as a histology-confirmed neoplasia that reports a DNA methylation value above a threshold value (e.g., the range associated with disease), and a false negative is defined as a histology-confirmed neoplasia that reports a DNA methylation value below the threshold value (e.g., the range associated with no disease). The value of sensitivity, therefore, reflects the probability that a DNA methylation measurement for a given marker obtained from a known diseased sample will be in the range of disease-associated measurements. As defined here, the clinical relevance of the calculated sensitivity value represents an estimation of the probability that a given marker would detect the presence of a clinical condition when applied to a subject with that condition. 
     As used herein, the “specificity” of a given marker refers to the percentage of non-neoplastic samples that report a DNA methylation value below a threshold value that distinguishes between neoplastic and non-neoplastic samples. In some embodiments, a negative is defined as a histology-confirmed non-neoplastic sample that reports a DNA methylation value below the threshold value (e.g., the range associated with no disease) and a false positive is defined as a histology-confirmed non-neoplastic sample that reports a DNA methylation value above the threshold value (e.g., the range associated with disease). The value of specificity, therefore, reflects the probability that a DNA methylation measurement for a given marker obtained from a known non-neoplastic sample will be in the range of non-disease associated measurements. As defined here, the clinical relevance of the calculated specificity value represents an estimation of the probability that a given marker would detect the absence of a clinical condition when applied to a patient without that condition. 
     The term “AUC” as used herein is an abbreviation for the “area under a curve”. In particular it refers to the area under a Receiver Operating Characteristic (ROC) curve. The ROC curve is a plot of the true positive rate against the false positive rate for the different possible cut points of a diagnostic test. It shows the trade-off between sensitivity and specificity depending on the selected cut point (any increase in sensitivity will be accompanied by a decrease in specificity). The area under an ROC curve (AUC) is a measure for the accuracy of a diagnostic test (the larger the area the better; the optimum is 1: a random test would have a ROC curve lying on the diagonal with an area of 0.5; for reference: J. P. Egan. (1975)  Signal Detection Theory and ROC Analysis , Academic Press, New York). 
     As used herein, the term “neoplasm” refers to “an abnormal mass of tissue, the growth of which exceeds and is uncoordinated with that of the normal tissues” See, e.g., Willis R A, “The Spread of Tumors in the Human Body”, London, Butterworth &amp; Co, 1952. 
     As used herein, the term “adenoma” refers to a benign tumor of glandular origin. Although these growths are benign, over time they may progress to become malignant. 
     The term “pre-cancerous” or “pre-neoplastic” and equivalents thereof refer to any cellular proliferative disorder that is undergoing malignant transformation. 
     A “site” of a neoplasm, adenoma, cancer, etc. is the tissue, organ, cell type, anatomical area, body part, etc. in a subject&#39;s body where the neoplasm, adenoma, cancer, etc. is located. 
     As used herein, a “diagnostic” test application includes the detection or identification of a disease state or condition of a subject, determining the likelihood that a subject will contract a given disease or condition, determining the likelihood that a subject with a disease or condition will respond to therapy, determining the prognosis of a subject with a disease or condition (or its likely progression or regression), and determining the effect of a treatment on a subject with a disease or condition. For example, a diagnostic can be used for detecting the presence or likelihood of a subject contracting a neoplasm or the likelihood that such a subject will respond favorably to a compound (e.g., a pharmaceutical, e.g., a drug) or other treatment. 
     The term “marker”, as used herein, refers to a substance (e.g., a nucleic acid or a region of a nucleic acid) that is able to diagnose a cancer by distinguishing cancerous cells from normal cells, e.g., based its methylation state. 
     The term “isolated” when used in relation to a nucleic acid, as in “an isolated oligonucleotide” refers to a nucleic acid sequence that is identified and separated from at least one contaminant nucleic acid with which it is ordinarily associated in its natural source. Isolated nucleic acid is present in a form or setting that is different from that in which it is found in nature. In contrast, non-isolated nucleic acids, such as DNA and RNA, are found in the state they exist in nature. Examples of non-isolated nucleic acids include: a given DNA sequence (e.g., a gene) found on the host cell chromosome in proximity to neighboring genes; RNA sequences, such as a specific mRNA sequence encoding a specific protein, found in the cell as a mixture with numerous other mRNAs which encode a multitude of proteins. However, isolated nucleic acid encoding a particular protein includes, by way of example, such nucleic acid in cells ordinarily expressing the protein, where the nucleic acid is in a chromosomal location different from that of natural cells, or is otherwise flanked by a different nucleic acid sequence than that found in nature. The isolated nucleic acid or oligonucleotide may be present in single-stranded or double-stranded form. When an isolated nucleic acid or oligonucleotide is to be utilized to express a protein, the oligonucleotide will contain at a minimum the sense or coding strand (i.e., the oligonucleotide may be single-stranded), but may contain both the sense and anti-sense strands (i.e., the oligonucleotide may be double-stranded). An isolated nucleic acid may, after isolation from its natural or typical environment, by be combined with other nucleic acids or molecules. For example, an isolated nucleic acid may be present in a host cell in which into which it has been placed, e.g., for heterologous expression. 
     The term “purified” refers to molecules, either nucleic acid or amino acid sequences that are removed from their natural environment, isolated, or separated. An “isolated nucleic acid sequence” may therefore be a purified nucleic acid sequence. “Substantially purified” molecules are at least 60% free, preferably at least 75% free, and more preferably at least 90% free from other components with which they are naturally associated. As used herein, the terms “purified” or “to purify” also refer to the removal of contaminants from a sample. The removal of contaminating proteins results in an increase in the percent of polypeptide or nucleic acid of interest in the sample. In another example, recombinant polypeptides are expressed in plant, bacterial, yeast, or mammalian host cells and the polypeptides are purified by the removal of host cell proteins; the percent of recombinant polypeptides is thereby increased in the sample. 
     The term “composition comprising” a given polynucleotide sequence or polypeptide refers broadly to any composition containing the given polynucleotide sequence or polypeptide. The composition may comprise an aqueous solution containing salts (e.g., NaCl), detergents (e.g., SDS), and other components (e.g., Denhardt&#39;s solution, dry milk, salmon sperm DNA, etc.). 
     The term “sample” is used in its broadest sense. In one sense it can refer to an animal cell or tissue. In another sense, it is meant to include a specimen or culture obtained from any source, as well as biological and environmental samples. Biological samples may be obtained from plants or animals (including humans) and encompass fluids, solids, tissues, and gases. Environmental samples include environmental material such as surface matter, soil, water, and industrial samples. These examples are not to be construed as limiting the sample types applicable to the present invention. 
     As used herein, a “remote sample” as used in some contexts relates to a sample indirectly collected from a site that is not the cell, tissue, or organ source of the sample. For instance, when sample material originating from the pancreas is assessed in a stool sample (e.g., not from a sample taken directly from a pancreas), the sample is a remote sample. 
     As used herein, the terms “patient” or “subject” refer to organisms to be subject to various tests provided by the technology. The term “subject” includes animals, preferably mammals, including humans. In a preferred embodiment, the subject is a primate. In an even more preferred embodiment, the subject is a human. 
     As used herein, the term “kit” refers to any delivery system for delivering materials. In the context of reaction assays, such delivery systems include systems that allow for the storage, transport, or delivery of reaction reagents (e.g., oligonucleotides, enzymes, etc. in the appropriate containers) and/or supporting materials (e.g., buffers, written instructions for performing the assay etc.) from one location to another. For example, kits include one or more enclosures (e.g., boxes) containing the relevant reaction reagents and/or supporting materials. As used herein, the term “fragmented kit” refers to delivery systems comprising two or more separate containers that each contain a subportion of the total kit components. The containers may be delivered to the intended recipient together or separately. For example, a first container may contain an enzyme for use in an assay, while a second container contains oligonucleotides. The term “fragmented kit” is intended to encompass kits containing Analyte specific reagents (ASR&#39;s) regulated under section 520(e) of the Federal Food, Drug, and Cosmetic Act, but are not limited thereto. Indeed, any delivery system comprising two or more separate containers that each contains a subportion of the total kit components are included in the term “fragmented kit.” In contrast, a “combined kit” refers to a delivery system containing all of the components of a reaction assay in a single container (e.g., in a single box housing each of the desired components). The term “kit” includes both fragmented and combined kits. 
     Embodiments of the Technology 
     Provided herein is technology for pancreatic cancer screening markers and other gastrointestinal cancer screening markers that provide a high signal-to-noise ratio and a low background level when detected from samples taken from a subject (e.g., stool sample). Markers were identified in a case-control study by comparing the methylation state of DNA markers from tumors of subjects with stage I and stage II PanC to the methylation state of the same DNA markers from control subjects (e.g., normal tissue such as normal colon and/or non-neoplastic pancreas) (see, Examples 1 and 11). 
     Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12 133, CLEC11A, ELMO1, EOMES, CLEC 11, SHH, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, and ZNF71) were identified in a case-control study by comparing the methylation state of DNA markers (e.g., from tumors of subjects with stage I and stage II PanC to the methylation state of the same DNA markers from control subjects (e.g., normal tissue such as normal colon and/or non-neoplastic pancreas) (see, Examples 2 and 8). 
     Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from NDRG4, SFRP1, BMP3, HPP1, and/or APC) were identified in case-control studies by comparing the methylation state of DNA markers from esophageal tissue of subjects with Barrett&#39;s esophagus to the methylation state of the same DNA markers from control subjects (see, Examples 4 and 10). 
     Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from ADCY1, PRKCB, KCNK12, C13ORF18, IKZF1, TWIST1, ELMO, 55957, CD1D, CLEC11A, KCNN2, BMP3, and/or NDRG4) were identified in case-control studies by comparing the methylation state of DNA markers from a pancreatic juice sample from subjects with pancreas cancer to the methylation state of the same DNA markers from control subjects (see, Examples 5 and 6). 
     A marker (e.g., a chromosomal region having a CD1D annotation) was identified in a case-control study by comparing the methylation state of a DNA marker (e.g., CD1D) from a stool sample from subjects with pancreas cancer to the methylation state of the same DNA marker from control subjects not having pancreas cancer (see, Example 7). 
     A marker (e.g., miR-1290) was identified in a case-control study by comparing the quantitated amount of a DNA marker (e.g., miR-1290) from a stool sample from subjects with pancreas cancer to the quantitated amount of of the same DNA marker from control subjects not having pancreas cancer (see, Example 9). 
     In addition, the technology provides various panels of markers, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12.133, CLEC11A, ELMO1, EOMES, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, NDRG4, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, or ZNF71, and that comprises the marker (see, Tables 1 and 9). In addition, embodiments provide a method of analyzing a DMR from Table 1 that is DMR No. 11, 14, 15, 65, 21, 22, 23, 5, 29, 30, 38, 39, 41, 50, 51, 55, 57, 60, 61, 8, 75, 81, 82, 84, 87, 93, 94, 98, 99, 103, 104, or 107, and/or a DMR corresponding to Chr16:58497395-58497458. Some embodiments provide determining the methylation state of a marker, wherein a chromosomal region having an annotation that is CLEC11A, C13ORF18, KCNN2, ABCB1, SLC38A3, CD1D, IKZF1, ADCY1, CHR12133, RSPO3, MBP3, PRKCB, NDRG4, ELMO, or TWIST1 comprises the marker. In some embodiments, the methods comprise determining the methylation state of two markers, e.g., a pair of markers provided in a row of Table 5. 
     Although the disclosure herein refers to certain illustrated embodiments, it is to be understood that these embodiments are presented by way of example and not by way of limitation. 
     In particular aspects, the present technology provides compositions and methods for identifying, determining, and/or classifying a cancer such as an upper gastrointestinal cancer (e.g., cancer of the esophagus, pancreas, stomach) or lower gastrointestinal cancer (e.g., adenoma, colorectal cancer). In related aspects, the technology provides compositions and methods for identifying, predicting, and/or detecting the site of a cancer. The methods comprise determining the methylation status of at least one methylation marker in a biological sample isolated from a subject, wherein a change in the methylation state of the marker is indicative of the presence, class, or site of a cancer. Particular embodiments relate to markers comprising a differentially methylated region (DMR, e.g., DMR 1-107, see Table 1, e.g., DMR 1-449, see Table 10) that are used for diagnosis (e.g., screening) of neoplastic cellular proliferative disorders (e.g., cancer), including early detection during the pre-cancerous stages of disease and prediction of a neoplasm site (e.g., by discriminating among cancer types, e.g., upper gastrointestinal cancers and lower gastrointestinal cancers). Furthermore, the markers are used for the differentiation of neoplastic from benign cellular proliferative disorders. In particular aspects, the present technology discloses a method wherein a neoplastic cell proliferative disorder is distinguished from a benign cell proliferative disorder. 
     The markers of the present technology are particularly efficient in detecting or distinguishing between colorectal and pancreatic proliferative disorders, thereby providing improved means for the early detection, classification, and treatment of said disorders. 
     In addition to embodiments wherein the methylation analysis of at least one marker, a region of a marker, or a base of a marker comprising a DMR (e.g., DMR 1-107 from Table 1) (e.g., DMR 1-449 from Table 10) provided herein and listed in Table 1 or 10 is analyzed, the technology also provides panels of markers comprising at least one marker, region of a marker, or base of a marker comprising a DMR with utility for the detection of cancers, in particular colorectal, pancreatic cancer, and other upper and lower GI cancers. 
     Some embodiments of the technology are based upon the analysis of the CpG methylation status of at least one marker, region of a marker, or base of a marker comprising a DMR. 
     In some embodiments, the present technology provides for the use of the bisulfite technique in combination with one or more methylation assays to determine the methylation status of CpG dinucleotide sequences within at least one marker comprising a DMR (e.g., as provided in Table 1 (e.g., DMR 1-107)) (e.g., as provided in Table 10 (e.g., DMR 1-449)). Genomic CpG dinucleotides can be methylated or unmethylated (alternatively known as up- and down-methylated respectively). However the methods of the present invention are suitable for the analysis of biological samples of a heterogeneous nature, e.g., a low concentration of tumor cells, or biological materials therefrom, within a background of a remote sample (e.g., blood, organ effluent, or stool). Accordingly, when analyzing the methylation status of a CpG position within such a sample one may use a quantitative assay for determining the level (e.g., percent, fraction, ratio, proportion, or degree) of methylation at a particular CpG position. 
     According to the present technology, determination of the methylation status of CpG dinucleotide sequences in markers comprising a DMR has utility both in the diagnosis and characterization of cancers such as upper gastrointestinal cancer (e.g., cancer of the esophagus, pancreas, stomach) or lower gastrointestinal cancer (e.g., adenoma, colorectal cancer). 
     Combinations of Markers 
     In some embodiments, the technology relates to assessing the methylation state of combinations of markers comprising a DMR from Table 1 (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 27, 29, 30) or Table 10 (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 27, 29, 30), or more markers comprising a DMR. In some embodiments, assessing the methylation state of more than one marker increases the specificity and/or sensitivity of a screen or diagnostic for identifying a neoplasm in a subject, e.g., an upper gastrointestinal cancer (e.g., esophagus, pancreas, stomach) or a lower gastrointestinal cancer (e.g., adenoma, colorectal). In some embodiments, a marker or a combination of markers discriminates between types and/or locations of a neoplasm. For example, combinations of markers discriminate esophageal neoplasm, stomach neoplasm, pancreatic neoplasm, colorectal neoplasm, and adenomas from each other, from other neoplasms, and/or from normal (e.g., non-cancerous, non-precancerous) tissue. 
     Various cancers are predicted by various combinations of markers, e.g., as identified by statistical techniques related to specificity and sensitivity of prediction. The technology provides methods for identifying predictive combinations and validated predictive combinations for some cancers. 
     In some embodiments, combinations of markers (e.g., comprising a DMR) predict the site of a neoplasm. For example, during the development of the technology described herein, statistical analyses were performed to validate the sensitivity and specificity of marker combinations. For example, marker pairs accurately predicted tumor site in &gt;90% of samples, the top 17 marker pairs accurately predicted tumor site in &gt;80% of samples, and the top 49 marker pairs accurately predicted tumor site in 70% of the samples. 
     Methods for Assaying Methylation State 
     The most frequently used method for analyzing a nucleic acid for the presence of 5-methylcytosine is based upon the bisulfite method described by Frommer, et al. for the detection of 5-methylcytosines in DNA (Frommer et al. (1992)  Proc. Natl. Acad. Sci. USA  89: 1827-31 explicitly incorporated herein by reference in its entirety for all purposes) or variations thereof. The bisulfite method of mapping 5-methylcytosines is based on the observation that cytosine, but not 5-methylcytosine, reacts with hydrogen sulfite ion (also known as bisulfite). The reaction is usually performed according to the following steps: first, cytosine reacts with hydrogen sulfite to form a sulfonated cytosine. Next, spontaneous deamination of the sulfonated reaction intermediate results in a sulfonated uracil. Finally, the sulfonated uricil is desulfonated under alkaline conditions to form uracil. Detection is possible because uracil forms base pairs with adenine (thus behaving like thymine), whereas 5-methylcytosine base pairs with guanine (thus behaving like cytosine). This makes the discrimination of methylated cytosines from non-methylated cytosines possible by, e.g., bisulfite genomic sequencing (Grigg G, &amp; Clark S, Bioessays (1994) 16: 431-36; Grigg G, DNA Seq. (1996) 6: 189-98) or methylation-specific PCR (MSP) as is disclosed, e.g., in U.S. Pat. No. 5,786,146. 
     Some conventional technologies are related to methods comprising enclosing the DNA to be analyzed in an agarose matrix, thereby preventing the diffusion and renaturation of the DNA (bisulfite only reacts with single-stranded DNA), and replacing precipitation and purification steps with a fast dialysis (Olek A, et al. (1996) “A modified and improved method for bisulfite based cytosine methylation analysis”  Nucleic Acids Res.  24: 5064-6). It is thus possible to analyze individual cells for methylation status, illustrating the utility and sensitivity of the method. An overview of conventional methods for detecting 5-methylcytosine is provided by Rein, T., et al. (1998)  Nucleic Acids Res.  26: 2255. 
     The bisulfite technique typically involves amplifying short, specific fragments of a known nucleic acid subsequent to a bisulfite treatment, then either assaying the product by sequencing (Olek &amp; Walter (1997)  Nat. Genet.  17: 275-6) or a primer extension reaction (Gonzalgo &amp; Jones (1997)  Nucleic Acids Res.  25: 2529-31; WO 95/00669; U.S. Pat. No. 6,251,594) to analyze individual cytosine positions. Some methods use enzymatic digestion (Xiong &amp; Laird (1997)  Nucleic Acids Res.  25: 2532-4). Detection by hybridization has also been described in the art (Olek et al., WO 99/28498). Additionally, use of the bisulfite technique for methylation detection with respect to individual genes has been described (Grigg &amp; Clark (1994)  Bioessays  16: 431-6; Zeschnigk et al. (1997)  Hum Mol Genet.  6: 387-95; Feil et al. (1994)  Nucleic Acids Res.  22: 695; Martin et al. (1995)  Gene  157: 261-4; WO 9746705; WO 9515373). 
     Various methylation assay procedures are known in the art and can be used in conjunction with bisulfite treatment according to the present technology. These assays allow for determination of the methylation state of one or a plurality of CpG dinucleotides (e.g., CpG islands) within a nucleic acid sequence. Such assays involve, among other techniques, sequencing of bisulfite-treated nucleic acid, PCR (for sequence-specific amplification), Southern blot analysis, and use of methylation-sensitive restriction enzymes. 
     For example, genomic sequencing has been simplified for analysis of methylation patterns and 5-methylcytosine distributions by using bisulfite treatment (Frommer et al. (1992)  Proc. Natl. Acad. Sci. USA  89: 1827-1831). Additionally, restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA finds use in assessing methylation state, e.g., as described by Sadri &amp; Hornsby (1997)  Nucl. Acids Res.  24: 5058-5059 or as embodied in the method known as COBRA (Combined Bisulfite Restriction Analysis) (Xiong &amp; Laird (1997)  Nucleic Acids Res.  25: 2532-2534). 
     COBRA™ analysis is a quantitative methylation assay useful for determining DNA methylation levels at specific loci in small amounts of genomic DNA (Xiong &amp; Laird, Nucleic Acids Res. 25:2532-2534, 1997). Briefly, restriction enzyme digestion is used to reveal methylation-dependent sequence differences in PCR products of sodium bisulfite-treated DNA. Methylation-dependent sequence differences are first introduced into the genomic DNA by standard bisulfite treatment according to the procedure described by Frommer et al. (Proc. Natl. Acad. Sci. USA 89:1827-1831, 1992). PCR amplification of the bisulfite converted DNA is then performed using primers specific for the CpG islands of interest, followed by restriction endonuclease digestion, gel electrophoresis, and detection using specific, labeled hybridization probes. Methylation levels in the original DNA sample are represented by the relative amounts of digested and undigested PCR product in a linearly quantitative fashion across a wide spectrum of DNA methylation levels. In addition, this technique can be reliably applied to DNA obtained from microdissected paraffin-embedded tissue samples. 
     Typical reagents (e.g., as might be found in a typical COBRA™-based kit) for COBRA™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); restriction enzyme and appropriate buffer; gene-hybridization oligonucleotide; control hybridization oligonucleotide; kinase labeling kit for oligonucleotide probe; and labeled nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery reagents or kits (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components. 
     Preferably, assays such as “MethyLight™” (a fluorescence-based real-time PCR technique) (Eads et al., Cancer Res. 59:2302-2306, 1999), Ms-SNuPE™ (Methylation-sensitive Single Nucleotide Primer Extension) reactions (Gonzalgo &amp; Jones, Nucleic Acids Res. 25:2529-2531, 1997), methylation-specific PCR (“MSP”; Herman et al., Proc. Natl. Acad. Sci. USA 93:9821-9826, 1996; U.S. Pat. No. 5,786,146), and methylated CpG island amplification (“MCA”; Toyota et al., Cancer Res. 59:2307-12, 1999) are used alone or in combination with one or more of these methods. 
     The “HeavyMethyl™” assay, technique is a quantitative method for assessing methylation differences based on methylation-specific amplification of bisulfite-treated DNA. Methylation-specific blocking probes (“blockers”) covering CpG positions between, or covered by, the amplification primers enable methylation-specific selective amplification of a nucleic acid sample. 
     The term “HeavyMethyl™ MethyLight™” assay refers to a HeavyMethyl™ MethyLight™ assay, which is a variation of the MethyLight™ assay, wherein the MethyLight™ assay is combined with methylation specific blocking probes covering CpG positions between the amplification primers. The HeavyMethyl™ assay may also be used in combination with methylation specific amplification primers. 
     Typical reagents (e.g., as might be found in a typical MethyLight™-based kit) for HeavyMethyl™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, or bisulfite treated DNA sequence or CpG island, etc.); blocking oligonucleotides; optimized PCR buffers and deoxynucleotides; and Taq polymerase. 
     MSP (methylation-specific PCR) allows for assessing the methylation status of virtually any group of CpG sites within a CpG island, independent of the use of methylation-sensitive restriction enzymes (Herman et al. Proc. Natl. Acad. Sci. USA 93:9821-9826, 1996; U.S. Pat. No. 5,786,146). Briefly, DNA is modified by sodium bisulfite, which converts unmethylated, but not methylated cytosines, to uracil, and the products are subsequently amplified with primers specific for methylated versus unmethylated DNA. MSP requires only small quantities of DNA, is sensitive to 0.1% methylated alleles of a given CpG island locus, and can be performed on DNA extracted from paraffin-embedded samples. Typical reagents (e.g., as might be found in a typical MSP-based kit) for MSP analysis may include, but are not limited to: methylated and unmethylated PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); optimized PCR buffers and deoxynucleotides, and specific probes. 
     The MethyLight™ assay is a high-throughput quantitative methylation assay that utilizes fluorescence-based real-time PCR (e.g., TaqMan) that requires no further manipulations after the PCR step (Eads et al., Cancer Res. 59:2302-2306, 1999). Briefly, the MethyLight™ process begins with a mixed sample of genomic DNA that is converted, in a sodium bisulfite reaction, to a mixed pool of methylation-dependent sequence differences according to standard procedures (the bisulfite process converts unmethylated cytosine residues to uracil). Fluorescence-based PCR is then performed in a “biased” reaction, e.g., with PCR primers that overlap known CpG dinucleotides. Sequence discrimination occurs both at the level of the amplification process and at the level of the fluorescence detection process. 
     The MethyLight™ assay is used as a quantitative test for methylation patterns in a nucleic acid, e.g., a genomic DNA sample, wherein sequence discrimination occurs at the level of probe hybridization. In a quantitative version, the PCR reaction provides for a methylation specific amplification in the presence of a fluorescent probe that overlaps a particular putative methylation site. An unbiased control for the amount of input DNA is provided by a reaction in which neither the primers, nor the probe, overlie any CpG dinucleotides. Alternatively, a qualitative test for genomic methylation is achieved by probing the biased PCR pool with either control oligonucleotides that do not cover known methylation sites (e.g., a fluorescence-based version of the HeavyMethyl™ and MSP techniques) or with oligonucleotides covering potential methylation sites. 
     The MethyLight™ process is used with any suitable probe (e.g. a “TaqMan®” probe, a Lightcycler® probe, etc.) For example, in some applications double-stranded genomic DNA is treated with sodium bisulfite and subjected to one of two sets of PCR reactions using TaqMan® probes, e.g., with MSP primers and/or HeavyMethyl blocker oligonucleotides and a TaqMan® probe. The TaqMan® probe is dual-labeled with fluorescent “reporter” and “quencher” molecules and is designed to be specific for a relatively high GC content region so that it melts at about a 10° C. higher temperature in the PCR cycle than the forward or reverse primers. This allows the TaqMan® probe to remain fully hybridized during the PCR annealing/extension step. As the Taq polymerase enzymatically synthesizes a new strand during PCR, it will eventually reach the annealed TaqMan® probe. The Taq polymerase 5′ to 3′ endonuclease activity will then displace the TaqMan® probe by digesting it to release the fluorescent reporter molecule for quantitative detection of its now unquenched signal using a real-time fluorescent detection system. 
     Typical reagents (e.g., as might be found in a typical MethyLight™-based kit) for MethyLight™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); TaqMan® or Lightcycler® probes; optimized PCR buffers and deoxynucleotides; and Taq polymerase. 
     The QM™ (quantitative methylation) assay is an alternative quantitative test for methylation patterns in genomic DNA samples, wherein sequence discrimination occurs at the level of probe hybridization. In this quantitative version, the PCR reaction provides for unbiased amplification in the presence of a fluorescent probe that overlaps a particular putative methylation site. An unbiased control for the amount of input DNA is provided by a reaction in which neither the primers, nor the probe, overlie any CpG dinucleotides. Alternatively, a qualitative test for genomic methylation is achieved by probing the biased PCR pool with either control oligonucleotides that do not cover known methylation sites (a fluorescence-based version of the HeavyMethyl™ and MSP techniques) or with oligonucleotides covering potential methylation sites. 
     The QM™ process can by used with any suitable probe, e.g., “TaqMan®” probes, Lightcycler® probes, in the amplification process. For example, double-stranded genomic DNA is treated with sodium bisulfite and subjected to unbiased primers and the TaqMan® probe. The TaqMan® probe is dual-labeled with fluorescent “reporter” and “quencher” molecules, and is designed to be specific for a relatively high GC content region so that it melts out at about a 10° C. higher temperature in the PCR cycle than the forward or reverse primers. This allows the TaqMan® probe to remain fully hybridized during the PCR annealing/extension step. As the Taq polymerase enzymatically synthesizes a new strand during PCR, it will eventually reach the annealed TaqMan® probe. The Taq polymerase 5′ to 3′ endonuclease activity will then displace the TaqMan® probe by digesting it to release the fluorescent reporter molecule for quantitative detection of its now unquenched signal using a real-time fluorescent detection system. Typical reagents (e.g., as might be found in a typical QMT-based kit) for QM™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); TaqMan® or Lightcycler® probes; optimized PCR buffers and deoxynucleotides; and Taq polymerase. 
     The Ms-SNuPE™ technique is a quantitative method for assessing methylation differences at specific CpG sites based on bisulfite treatment of DNA, followed by single-nucleotide primer extension (Gonzalgo &amp; Jones, Nucleic Acids Res. 25:2529-2531, 1997). Briefly, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine to uracil while leaving 5-methylcytosine unchanged. Amplification of the desired target sequence is then performed using PCR primers specific for bisulfite-converted DNA, and the resulting product is isolated and used as a template for methylation analysis at the CpG site of interest. Small amounts of DNA can be analyzed (e.g., microdissected pathology sections) and it avoids utilization of restriction enzymes for determining the methylation status at CpG sites. 
     Typical reagents (e.g., as might be found in a typical Ms-SNuPE™-based kit) for Ms-SNuPE™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); optimized PCR buffers and deoxynucleotides; gel extraction kit; positive control primers; Ms-SNuPE™ primers for specific loci; reaction buffer (for the Ms-SNuPE reaction); and labeled nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery reagents or kit (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components. 
     Reduced Representation Bisulfite Sequencing (RRBS) begins with bisulfite treatment of nucleic acid to convert all unmethylated cytosines to uracil, followed by restriction enzyme digestion (e.g., by an enzyme that recognizes a site including a CG sequence such as MspI) and complete sequencing of fragments after coupling to an adapter ligand. The choice of restriction enzyme enriches the fragments for CpG dense regions, reducing the number of redundant sequences that may map to multiple gene positions during analysis. As such, RRBS reduces the complexity of the nucleic acid sample by selecting a subset (e.g., by size selection using preparative gel electrophoresis) of restriction fragments for sequencing. As opposed to whole-genome bisulfite sequencing, every fragment produced by the restriction enzyme digestion contains DNA methylation information for at least one CpG dinucleotide. As such, RRBS enriches the sample for promoters, CpG islands, and other genomic features with a high frequency of restriction enzyme cut sites in these regions and thus provides an assay to assess the methylation state of one or more genomic loci. 
     A typical protocol for RRBS comprises the steps of digesting a nucleic acid sample with a restriction enzyme such as MspI, filling in overhangs and A-tailing, ligating adaptors, bisulfite conversion, and PCR. See, e.g., et al. (2005) “Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution”  Nat Methods  7: 133-6; Meissner et al. (2005) “Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis”  Nucleic Acids Res.  33: 5868-77. 
     In some embodiments, a quantitative allele-specific real-time target and signal amplification (QuARTS) assay is used to evaluate methylation state. Three reactions sequentially occur in each QuARTS assay, including amplification (reaction 1) and target probe cleavage (reaction 2) in the primary reaction; and FRET cleavage and fluorescent signal generation (reaction 3) in the secondary reaction. When target nucleic acid is amplified with specific primers, a specific detection probe with a flap sequence loosely binds to the amplicon. The presence of the specific invasive oligonucleotide at the target binding site causes cleavase to release the flap sequence by cutting between the detection probe and the flap sequence. The flap sequence is complementary to a nonhairpin portion of a corresponding FRET cassette. Accordingly, the flap sequence functions as an invasive oligonucleotide on the FRET cassette and effects a cleavage between the FRET cassette fluorophore and a quencher, which produces a fluorescent signal. The cleavage reaction can cut multiple probes per target and thus release multiple fluorophore per flap, providing exponential signal amplification. QuARTS can detect multiple targets in a single reaction well by using FRET cassettes with different dyes. See, e.g., in Zou et al. (2010) “Sensitive quantification of methylated markers with a novel methylation specific technology”  Clin Chem  56: A199; U.S. patent application Ser. Nos. 12/946,737, 12/946,745, 12/946,752, and 61/548,639. 
     The term “bisulfite reagent” refers to a reagent comprising bisulfite, disulfite, hydrogen sulfite, or combinations thereof, useful as disclosed herein to distinguish between methylated and unmethylated CpG dinucleotide sequences. Methods of said treatment are known in the art (e.g., PCT/EP2004/011715, which is incorporated by reference in its entirety). It is preferred that the bisulfite treatment is conducted in the presence of denaturing solvents such as but not limited to n-alkylenglycol or diethylene glycol dimethyl ether (DME), or in the presence of dioxane or dioxane derivatives. In some embodiments the denaturing solvents are used in concentrations between 1% and 35% (v/v). In some embodiments, the bisulfite reaction is carried out in the presence of scavengers such as but not limited to chromane derivatives, e.g., 6-hydroxy-2,5,7,8,-tetramethylchromane 2-carboxylic acid or trihydroxybenzone acid and derivates thereof, e.g., Gallic acid (see: PCT/EP2004/011715, which is incorporated by reference in its entirety). The bisulfite conversion is preferably carried out at a reaction temperature between 30° C. and 70° C., whereby the temperature is increased to over 85° C. for short times during the reaction (see: PCT/EP2004/011715, which is incorporated by reference in its entirety). The bisulfite treated DNA is preferably purified prior to the quantification. This may be conducted by any means known in the art, such as but not limited to ultrafiltration, e.g., by means of Microcon™ columns (manufactured by Millipore™). The purification is carried out according to a modified manufacturer&#39;s protocol (see, e.g., PCT/EP2004/011715, which is incorporated by reference in its entirety). 
     In some embodiments, fragments of the treated DNA are amplified using sets of primer oligonucleotides according to the present invention (e.g., see Table 2) and an amplification enzyme. The amplification of several DNA segments can be carried out simultaneously in one and the same reaction vessel. Typically, the amplification is carried out using a polymerase chain reaction (PCR). Amplicons are typically 100 to 2000 base pairs in length. 
     In another embodiment of the method, the methylation status of CpG positions within or near a marker comprising a DMR (e.g., DMR 1-107 as provided in Table 1) (e.g., DMR 1-449 as provided in Table 10) may be detected by use of methylation-specific primer oligonucleotides. This technique (MSP) has been described in U.S. Pat. No. 6,265,171 to Herman. The use of methylation status specific primers for the amplification of bisulfite treated DNA allows the differentiation between methylated and unmethylated nucleic acids. MSP primer pairs contain at least one primer that hybridizes to a bisulfite treated CpG dinucleotide. Therefore, the sequence of said primers comprises at least one CpG dinucleotide. MSP primers specific for non-methylated DNA contain a “T” at the position of the C position in the CpG. 
     The fragments obtained by means of the amplification can carry a directly or indirectly detectable label. In some embodiments, the labels are fluorescent labels, radionuclides, or detachable molecule fragments having a typical mass that can be detected in a mass spectrometer. Where said labels are mass labels, some embodiments provide that the labeled amplicons have a single positive or negative net charge, allowing for better delectability in the mass spectrometer. The detection may be carried out and visualized by means of, e.g., matrix assisted laser desorption/ionization mass spectrometry (MALDI) or using electron spray mass spectrometry (ESI). 
     Methods for isolating DNA suitable for these assay technologies are known in the art. In particular, some embodiments comprise isolation of nucleic acids as described in U.S. patent application Ser. No. 13/470,251 (“Isolation of Nucleic Acids”), incorporated herein by reference in its entirety. 
     Methods 
     In some embodiments the technology, methods are provided that comprise the following steps:
         1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from a body fluids such as a stool sample or pancreatic tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker comprising a DMR (e.g., DMR 1-107, e.g., as provided in Table 1) (e.g., DMR 1-449, e.g., as provided in Table 10) and   2) detecting a neoplasm or proliferative disorder (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%).       

     In some embodiments the technology, methods are provided that comprise the following steps:
         1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from a body fluids such as a stool sample or pancreatic tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting of ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12 133, CLEC11A, ELMO1, EOMES, CLEC 11, SHH, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, and ZNF71, and   2) detecting pancreatic cancer (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%).       

     In some embodiments the technology, methods are provided that comprise the following steps:
         1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from a body fluids such as a stool sample or esophageal tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting of NDRG4, SFRP1, BMP3, HPP1, and APC, and   2) detecting Barrett&#39;s esophagus (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%).       

     In some embodiments the technology, methods are provided that comprise the following steps:
         1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from a body fluids such as a stool sample or pancreatic tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting of ADCY1, PRKCB, KCNK12, C13ORF18, IKZF1, TWIST1, ELMO, 55957, CD1D, CLEC11A, KCNN2, BMP3, and NDRG4, and   2) detecting pancreatic cancer (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%).       

     In some embodiments the technology, methods are provided that comprise the following steps:
         1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from a stool sample) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within a chromosomal region having a CD1D, and   2) detecting pancreatic cancer (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%).
 
Preferably, the sensitivity is from about 70% to about 100%, or from about 80% to about 90%, or from about 80% to about 85%. Preferably, the specificity is from about 70% to about 100%, or from about 80% to about 90%, or from about 80% to about 85%.
       

     Genomic DNA may be isolated by any means, including the use of commercially available kits. Briefly, wherein the DNA of interest is encapsulated in by a cellular membrane the biological sample must be disrupted and lysed by enzymatic, chemical or mechanical means. The DNA solution may then be cleared of proteins and other contaminants, e.g., by digestion with proteinase K. The genomic DNA is then recovered from the solution. This may be carried out by means of a variety of methods including salting out, organic extraction, or binding of the DNA to a solid phase support. The choice of method will be affected by several factors including time, expense, and required quantity of DNA. All clinical sample types comprising neoplastic matter or pre-neoplastic matter are suitable for use in the present method, e.g., cell lines, histological slides, biopsies, paraffin-embedded tissue, body fluids, stool, colonic effluent, urine, blood plasma, blood serum, whole blood, isolated blood cells, cells isolated from the blood, and combinations thereof. 
     The technology is not limited in the methods used to prepare the samples and provide a nucleic acid for testing. For example, in some embodiments, a DNA is isolated from a stool sample or from blood or from a plasma sample using direct gene capture, e.g., as detailed in U.S. Pat. Appl. Ser. No. 61/485,386 or by a related method. 
     The genomic DNA sample is then treated with at least one reagent, or series of reagents, that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker comprising a DMR (e.g., DMR 1-107, e.g., as provided by Table 1) (e.g., DMR 1-449, e.g., as provided by Table 10). 
     In some embodiments, the reagent converts cytosine bases which are unmethylated at the 5′-position to uracil, thymine, or another base which is dissimilar to cytosine in terms of hybridization behavior. However in some embodiments, the reagent may be a methylation sensitive restriction enzyme. 
     In some embodiments, the genomic DNA sample is treated in such a manner that cytosine bases that are unmethylated at the 5′ position are converted to uracil, thymine, or another base that is dissimilar to cytosine in terms of hybridization behavior. In some embodiments, this treatment is carried out with bisulfate (hydrogen sulfite, disulfite) followed byt alkaline hydrolysis. 
     The treated nucleic acid is then analyzed to determine the methylation state of the target gene sequences (at least one gene, genomic sequence, or nucleotide from a marker comprising a DMR, e.g., at least one DMR chosen from DMR 1-107, e.g., as provided in Table 1) (at least one gene, genomic sequence, or nucleotide from a marker comprising a DMR, e.g., at least one DMR chosen from DMR 1-449, e.g., as provided in Table 10). The method of analysis may be selected from those known in the art, including those listed herein, e.g., QuARTS and MSP as described herein. 
     Aberrant methylation, more specifically hypermethylation of a marker comprising a DMR (e.g., DMR 1-107, e.g., as provided by Table 1) (e.g., DMR 1-449, e.g., as provided by Table 10) is associated with a cancer and, in some embodiments, predicts tumor site. 
     The technology relates to the analysis of any sample associated with a cancer of the gastrointestinal system. For example, in some embodiments the sample comprises a tissue and/or biological fluid obtained from a patient. In some embodiments, the sample comprises a secretion. In some embodiments, the sample comprises blood, serum, plasma, gastric secretions, pancreatic juice, a gastrointestinal biopsy sample, microdissected cells from a gastrointestinal biopsy, gastrointestinal cells sloughed into the gastrointestinal lumen, and/or gastrointestinal cells recovered from stool. In some embodiments, the subject is human. These samples may originate from the upper gastrointestinal tract, the lower gastrointestinal tract, or comprise cells, tissues, and/or secretions from both the upper gastrointestinal tract and the lower gastrointestinal tract. The sample may include cells, secretions, or tissues from the liver, bile ducts, pancreas, stomach, colon, rectum, esophagus, small intestine, appendix, duodenum, polyps, gall bladder, anus, and/or peritoneum. In some embodiments, the sample comprises cellular fluid, ascites, urine, feces, pancreatic fluid, fluid obtained during endoscopy, blood, mucus, or saliva. In some embodiments, the sample is a stool sample. 
     Such samples can be obtained by any number of means known in the art, such as will be apparent to the skilled person. For instance, urine and fecal samples are easily attainable, while blood, ascites, serum, or pancreatic fluid samples can be obtained parenterally by using a needle and syringe, for instance. Cell free or substantially cell free samples can be obtained by subjecting the sample to various techniques known to those of skill in the art which include, but are not limited to, centrifugation and filtration. Although it is generally preferred that no invasive techniques are used to obtain the sample, it still may be preferable to obtain samples such as tissue homogenates, tissue sections, and biopsy specimens 
     In some embodiments, the technology relates to a method for treating a patient (e.g., a patient with gastrointestinal cancer, with early stage gastrointestinal cancer, or who may develop gastrointestinal cancer), the method comprising determining the methylation state of one or more DMR as provided herein and administering a treatment to the patient based on the results of determining the methylation state. The treatment may be administration of a pharmaceutical compound, a vaccine, performing a surgery, imaging the patient, performing another test. Preferably, said use is in a method of clinical screening, a method of prognosis assessment, a method of monitoring the results of therapy, a method to identify patients most likely to respond to a particular therapeutic treatment, a method of imaging a patient or subject, and a method for drug screening and development. 
     In some embodiments of the technology, a method for diagnosing a gastrointestinal cancer in a subject is provided. The terms “diagnosing” and “diagnosis” as used herein refer to methods by which the skilled artisan can estimate and even determine whether or not a subject is suffering from a given disease or condition or may develop a given disease or condition in the future. The skilled artisan often makes a diagnosis on the basis of one or more diagnostic indicators, such as for example a biomarker (e.g., a DMR as disclosed herein), the methylation state of which is indicative of the presence, severity, or absence of the condition. 
     Along with diagnosis, clinical cancer prognosis relates to determining the aggressiveness of the cancer and the likelihood of tumor recurrence to plan the most effective therapy. If a more accurate prognosis can be made or even a potential risk for developing the cancer can be assessed, appropriate therapy, and in some instances less severe therapy for the patient can be chosen. Assessment (e.g., determining methylation state) of cancer biomarkers is useful to separate subjects with good prognosis and/or low risk of developing cancer who will need no therapy or limited therapy from those more likely to develop cancer or suffer a recurrence of cancer who might benefit from more intensive treatments. 
     As such, “making a diagnosis” or “diagnosing”, as used herein, is further inclusive of making determining a risk of developing cancer or determining a prognosis, which can provide for predicting a clinical outcome (with or without medical treatment), selecting an appropriate treatment (or whether treatment would be effective), or monitoring a current treatment and potentially changing the treatment, based on the measure of the diagnostic biomarkers (e.g., DMR) disclosed herein. Further, in some embodiments of the presently disclosed subject matter, multiple determination of the biomarkers over time can be made to facilitate diagnosis and/or prognosis. A temporal change in the biomarker can be used to predict a clinical outcome, monitor the progression of gastrointestinal cancer, and/or monitor the efficacy of appropriate therapies directed against the cancer. In such an embodiment for example, one might expect to see a change in the methylation state of one or more biomarkers (e.g., DMR) disclosed herein (and potentially one or more additional biomarker(s), if monitored) in a biological sample over time during the course of an effective therapy. 
     The presently disclosed subject matter further provides in some embodiments a method for determining whether to initiate or continue prophylaxis or treatment of a cancer in a subject. In some embodiments, the method comprises providing a series of biological samples over a time period from the subject; analyzing the series of biological samples to determine a methylation state of at least one biomarker disclosed herein in each of the biological samples; and comparing any measurable change in the methylation states of one or more of the biomarkers in each of the biological samples. Any changes in the methylation states of biomarkers over the time period can be used to predict risk of developing cancer, predict clinical outcome, determine whether to initiate or continue the prophylaxis or therapy of the cancer, and whether a current therapy is effectively treating the cancer. For example, a first time point can be selected prior to initiation of a treatment and a second time point can be selected at some time after initiation of the treatment. Methylation states can be measured in each of the samples taken from different time points and qualitative and/or quantitative differences noted. A change in the methylation states of the biomarker levels from the different samples can be correlated with gastrointestinal cancer risk, prognosis, determining treatment efficacy, and/or progression of the cancer in the subject. 
     In preferred embodiments, the methods and compositions of the invention are for treatment or diagnosis of disease at an early stage, for example, before symptoms of the disease appear. In some embodiments, the methods and compositions of the invention are for treatment or diagnosis of disease at a clinical stage. 
     As noted, in some embodiments, multiple determinations of one or more diagnostic or prognostic biomarkers can be made, and a temporal change in the marker can be used to determine a diagnosis or prognosis. For example, a diagnostic marker can be determined at an initial time, and again at a second time. In such embodiments, an increase in the marker from the initial time to the second time can be diagnostic of a particular type or severity of cancer, or a given prognosis. Likewise, a decrease in the marker from the initial time to the second time can be indicative of a particular type or severity of cancer, or a given prognosis. Furthermore, the degree of change of one or more markers can be related to the severity of the cancer and future adverse events. The skilled artisan will understand that, while in certain embodiments comparative measurements can be made of the same biomarker at multiple time points, one can also measure a given biomarker at one time point, and a second biomarker at a second time point, and a comparison of these markers can provide diagnostic information. 
     As used herein, the phrase “determining the prognosis” refers to methods by which the skilled artisan can predict the course or outcome of a condition in a subject. The term “prognosis” does not refer to the ability to predict the course or outcome of a condition with 100% accuracy, or even that a given course or outcome is predictably more or less likely to occur based on the methylation state of a biomarker (e.g., a DMR). Instead, the skilled artisan will understand that the term “prognosis” refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition. For example, in individuals not exhibiting the condition (e.g., having a normal methylation state of one or more DMR), the chance of a given outcome (e.g., suffering from a gastrointestinal cancer) may be very low. 
     In some embodiments, a statistical analysis associates a prognostic indicator with a predisposition to an adverse outcome. For example, in some embodiments, a methylation state different from that in a normal control sample obtained from a patient who does not have a cancer can signal that a subject is more likely to suffer from a cancer than subjects with a level that is more similar to the methylation state in the control sample, as determined by a level of statistical significance. Additionally, a change in methylation state from a baseline (e.g., “normal”) level can be reflective of subject prognosis, and the degree of change in methylation state can be related to the severity of adverse events. Statistical significance is often determined by comparing two or more populations and determining a confidence interval and/or a p value. See, e.g., Dowdy and Wearden, Statistics for Research, John Wiley &amp; Sons, New York, 1983, incorporated herein by reference in its entirety. Exemplary confidence intervals of the present subject matter are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% and 99.99%, while exemplary p values are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, and 0.0001. 
     In other embodiments, a threshold degree of change in the methylation state of a prognostic or diagnostic biomarker disclosed herein (e.g., a DMR) can be established, and the degree of change in the methylation state of the biamarker in a biological sample is simply compared to the threshold degree of change in the methylation state. A preferred threshold change in the methylation state for biomarkers provided herein is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 50%, about 75%, about 100%, and about 150%. In yet other embodiments, a “nomogram” can be established, by which a methylation state of a prognostic or diagnostic indicator (biomarker or combination of biomarkers) is directly related to an associated disposition towards a given outcome. The skilled artisan is acquainted with the use of such nomograms to relate two numeric values with the understanding that the uncertainty in this measurement is the same as the uncertainty in the marker concentration because individual sample measurements are referenced, not population averages. 
     In some embodiments, a control sample is analyzed concurrently with the biological sample, such that the results obtained from the biological sample can be compared to the results obtained from the control sample. Additionally, it is contemplated that standard curves can be provided, with which assay results for the biological sample may be compared. Such standard curves present methylation states of a biomarker as a function of assay units, e.g., fluorescent signal intensity, if a fluorescent label is used. Using samples taken from multiple donors, standard curves can be provided for control methylation states of the one or more biomarkers in normal tissue, as well as for “at-risk” levels of the one or more biomarkers in tissue taken from donors with metaplasia or from donors with a gastrointestinal cancer. In certain embodiments of the method, a subject is identified as having metaplasia upon identifying an aberrant methylation state of one or more DMR provided herein in a biological sample obtained from the subject. In other embodiments of the method, the detection of an aberrant methylation state of one or more of such biomarkers in a biological sample obtained from the subject results in the subject being identified as having cancer. 
     The analysis of markers can be carried out separately or simultaneously with additional markers within one test sample. For example, several markers can be combined into one test for efficient processing of a multiple of samples and for potentially providing greater diagnostic and/or prognostic accuracy. In addition, one skilled in the art would recognize the value of testing multiple samples (for example, at successive time points) from the same subject. Such testing of serial samples can allow the identification of changes in marker methylation states over time. Changes in methylation state, as well as the absence of change in methylation state, can provide useful information about the disease status that includes, but is not limited to, identifying the approximate time from onset of the event, the presence and amount of salvageable tissue, the appropriateness of drug therapies, the effectiveness of various therapies, and identification of the subject&#39;s outcome, including risk of future events. 
     The analysis of biomarkers can be carried out in a variety of physical formats. For example, the use of microtiter plates or automation can be used to facilitate the processing of large numbers of test samples. Alternatively, single sample formats could be developed to facilitate immediate treatment and diagnosis in a timely fashion, for example, in ambulatory transport or emergency room settings. 
     In some embodiments, the subject is diagnosed as having a gastrointestinal cancer if, when compared to a control methylation state, there is a measurable difference in the methylation state of at least one biomarker in the sample. Conversely, when no change in methylation state is identified in the biological sample, the subject can be identified as not having gastrointestinal cancer, not being at risk for the cancer, or as having a low risk of the cancer. In this regard, subjects having the cancer or risk thereof can be differentiated from subjects having low to substantially no cancer or risk thereof. Those subjects having a risk of developing a gastrointestinal cancer can be placed on a more intensive and/or regular screening schedule, including endoscopic surveillance. On the other hand, those subjects having low to substantially no risk may avoid being subjected to an endoscopy, until such time as a future screening, for example, a screening conducted in accordance with the present technology, indicates that a risk of gastrointestinal cancer has appeared in those subjects. 
     As mentioned above, depending on the embodiment of the method of the present technology, detecting a change in methylation state of the one or more biomarkers can be a qualitative determination or it can be a quantitative determination. As such, the step of diagnosing a subject as having, or at risk of developing, a gastrointestinal cancer indicates that certain threshold measurements are made, e.g., the methylation state of the one or more biomarkers in the biological sample varies from a predetermined control methylation state. In some embodiments of the method, the control methylation state is any detectable methylation state of the biomarker. In other embodiments of the method where a control sample is tested concurrently with the biological sample, the predetermined methylation state is the methylation state in the control sample. In other embodiments of the method, the predetermined methylation state is based upon and/or identified by a standard curve. In other embodiments of the method, the predetermined methylation state is a specifically state or range of state. As such, the predetermined methylation state can be chosen, within acceptable limits that will be apparent to those skilled in the art, based in part on the embodiment of the method being practiced and the desired specificity, etc. 
     Further with respect to diagnostic methods, a preferred subject is a vertebrate subject. A preferred vertebrate is warm-blooded; a preferred warm-blooded vertebrate is a mammal. A preferred mammal is most preferably a human. As used herein, the term “subject’ includes both human and animal subjects. Thus, veterinary therapeutic uses are provided herein. As such, the present technology provides for the diagnosis of mammals such as humans, as well as those mammals of importance due to being endangered, such as Siberian tigers; of economic importance, such as animals raised on farms for consumption by humans; and/or animals of social importance to humans, such as animals kept as pets or in zoos. Examples of such animals include but are not limited to: carnivores such as cats and dogs; swine, including pigs, hogs, and wild boars; ruminants and/or ungulates such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels; and horses. Thus, also provided is the diagnosis and treatment of livestock, including, but not limited to, domesticated swine, ruminants, ungulates, horses (including race horses), and the like. The presently-disclosed subject matter further includes a system for diagnosing a gastrointestinal cancer in a subject. The system can be provided, for example, as a commercial kit that can be used to screen for a risk of gastrointestinal cancer or diagnose a gastrointestinal cancer in a subject from whom a biological sample has been collected. An exemplary system provided in accordance with the present technology includes assessing the methylation state of a DMR as provided in Table 1. 
     EXAMPLES 
     Example 1—Identifying Markers Using RRBS 
     Collectively, gastrointestinal cancers account for more deaths than those from any other organ system, and the aggregate incidence of upper gastrointestinal cancer and that of colorectal cancer (CRC) are comparable. To maximize the efficiency of screening and diagnosis, molecular markers for gastrointestinal cancer are needed that are site-specific when assayed from distant media such as blood or stool. While broadly informative, aberrantly methylated nucleic acid markers are often common to upper gastrointestinal cancers and CRC. 
     During the development of the technology provided herein, data were collected from a case-control study to demonstrate that a genome-wide search strategy identifies novel and informative candidate markers. Preliminary experiments demonstrated that stool assay of a methylated gene marker (BMP3) detects PanC. Then, it was shown that a combined assay of methylated BMP3 and mutant KRAS increased detection over either marker alone. However, markers discriminant in tissue proved to be poor markers in stool due to a high background of methylation, e.g., as detected in control specimens. 
     Study Population, Specimen Acquisition, and Samples 
     The target population was patients with pancreas cancer seen at the Mayo Clinic. The accessible population includes those who have undergone a distal pancreatectomy, a pancreaticoduodenectomy, or a colectomy with an archived resection specimen and a confirmed pathologic diagnosis. Colonic epithelial DNA was previously extracted from micro-dissected specimens by the Biospecimens Accessioning Processing (BAP) lab using a phenol-chloroform protocol. Data on the matching variables for these samples were used by Pancreas SPORE personnel to select tissue registry samples. These were reviewed by an expert pathologist to confirm case and control status and exclude case neoplasms arising from IPMN, which may have different underlying biology. SPORE personnel arranged for BAP lab microdissection and DNA extraction of the pancreatic case and control samples and provided 500 ng of DNA to lab personnel who were blinded to case and control status. Archival nucleic acid samples included 18 pancreatic adenocarcinomas, 18 normal pancreas, and 18 normal colonic epithelia matched on sex, age, and smoking status. 
     The sample types were:
         1) Mayo Clinic Pancreas SPORE registry PanC tissues limited to AJCC stage I and II;   2) control pancreata free from PanC;   3) archived control colonic epithelium free from PanC; and   4) colonic neoplasm from which DNA had been extracted and stored in the BAP lab.
 
Cases and controls were matched by sex, age (in 5-year increments), and smoking status (current or former vs. never).
       

     Main Variables 
     The main variable was the methylation percentage of each individual 101 base-pair amplicon from HCP regions. The methylation percentage in case samples was compared to control samples following RRBS. 
     Methods 
     Libraries were prepared according to previously reported methods (see, e.g., Gu et al (2011) “Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling”  Nature Protocols  6: 468-81) by fragmenting genomic DNA (300 ng) by digestion with 10 units of MspI, a methylation-specific restriction enzyme that recognizes CpG containing motifs. This treatment enriches the samples for CpG content and eliminates redundant areas of the genome. Digested fragments were end-repaired and A-tailed with 5 units of Klenow fragment (3′-5′ exo) and ligated overnight to Illumina adapters containing one of four barcode sequences to link each fragment to its sample ID. Size selection of 160-340 bp fragments (having 40-220 bp inserts) was performed using SPRI beads/buffer (AMPure XP, Beckman Coulter). Buffer cutoffs were from 0.7× to 1.1× of the sample volume of beads/buffer. Samples were eluted in a volume of 22 μl (EB buffer, Qiagen). qPCR was used to gauge ligation efficiency and fragment quality on a small aliquot of sample. Samples then underwent two rounds of bisulfite conversion using a modified EpiTect protocol (Qiagen). qPCR and conventional PCR (Pfu Turbo Cx hotstart, Agilent), followed by Bioanalyzer 2100 (Agilent) assessment on converted sample aliquots, determined the optimal PCR cycle number prior to amplification of the final library. The final PCR was performed in a volume of 50 μl (5 μl of 10×PCR buffer; 1.25 μl of each dNTP at 10 mM; 5 μl of a primer cocktail at approximately 5 μM, 15 μl of template (sample), 1 μl PfuTurbo Cx hotstart, and 22.75 μl water. Thermal cycling began with initial incubations at 95° C. for 5 minutes and at 98° C. for 30 seconds followed by 16 cycles of 98° C. for 10 seconds, 65° C. for 30 seconds, and at 72° C. for 30 seconds. After cycling, the samples were incubated at 72° C. for 5 minutes and kept at 4° C. until further workup and analysis. Samples were combined in equimolar amounts into 4-plex libraries based on a randomization scheme and tested with the bioanalyzer for final size verification. Samples were also tested with qPCR using phiX standards and adaptor-specific primers. 
     For sequencing, samples were loaded onto flow cell lanes according to a randomized lane assignment with additional lanes reserved for internal assay controls. Sequencing was performed by the NGS Core at Mayo&#39;s Medical Genome Facility on the Illumina HiSeq 2000. Reads were unidirectional for 101 cycles. Each flow cell lane generated 100-120 million reads, sufficient for a median coverage of 30× to 50× sequencing depth (based on read number per CpG) for aligned sequences. Standard Illumina pipeline software was used to analyze the reads in combination with RRBSMAP (Xi, et al. (2012) “RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing”  Bioinformatics  28: 430-432) and an in-house pipeline (SAAP-RRBS) developed by Mayo Biomedical and Statistics personnel (Sun et al. (2012) “SAAP-RRBS: streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing”  Bioinformatics  28: 2180-1). The bioinformatic analyses consisted of 1) sequence read assessment and clean-up, 2) alignment to reference genome, 3) methylation status extraction, and 4) CpG reporting and annotation. 
     Statistical Considerations 
     The primary comparison evaluated methylation differences between cases and pancreatic controls at each CpG and/or tiled CpG window. The secondary comparison evaluated methylation differences between between cases and colon controls. Markers were tested for differential methylation by:
         1. Assessing the distributions of methylation percentage for each marker and discarding markers that were more than 1% methylated in 10% of controls;   2. Testing the methylation distribution of the remaining markers between cases and controls using the Wilcoxon rank sum test and ranking markers by p-values; and   3. Using Q-values to estimate false discovery rates (FDR) (Benjamini et al. (1995) “Multiple Testing”  Journal of the Royal Statistical Society. Series B  ( Methodological ) 57: 289-300; Storey et al. (2003) “Statistical significance for genomewide studies”  Proc Natl Acad Sci USA  100: 9440-5). At the discovery-level, an FDR up to 25% is acceptable.       

     Analysis of Data 
     A data analysis pipeline was developed in the R statistical analysis software package (“R: A Language and Environment for Statistical Computing” (2012), R Foundation for Statistical Computing). The workflow comprised the following steps:
     1. Read in all 6,101,049 CpG sites   2. Identify for further analysis only those CpG sites where the total group depth of coverage is 200 reads or more. This cut-off was based on a power assessment to detect a difference of between 20% and 30% methylation between any two groups because anything less than this range has little chance of significance. Group depth of coverage measures the number of reads for all subjects in a group (e.g., if there are 18 subjects per group and each subject as 12 reads then the group depth of coverage is 12×18=216).   3. Estimate the association of disease subtype with the methylation % using variance inflated Poisson regression; the most discriminate CpG sites were determined by comparing the model-fit X 2  to the 95th percentile of all fitted models. Exclude all CpG sites where the variance of the methylation percent across the groups is 0 because these sites are non-informative CpG sites.
 
Applying the filters of 2 and 3 left a total of 1,217,523 CpG sites.
   4. Perform logistic regression on the % methylation (based on the actual counts) using groups defined as Normal Colon, Normal Pancreas, and Cancerous Pancreas. Since the variability in the % methylation between subjects is larger than allowed by the binomial assumption, an over-dispersed logistic regression model was used to account for the increased variance. This dispersion parameter was estimated using the Pearson Chi-square of the fit.   5. From these model fits, calculate an overall F-statistic for the group comparison based on the change in deviance between the models with and without each group as a regressor. This deviance was scaled by the estimated dispersion parameter.   6. Create CpG islands on each chromosome based on the distance between CpG site locations. Roughly, when the distance between two CpG locations exceeds 100 bp, each location is defined as an independent island. Some islands were singletons and were excluded.   7. From the island definition above, the average F statistic is calculated. When the F statistic exceeds 95% (i.e., top 5%) of all CpG sites for the particular chromosome, a figure summary is generated.
 
Further analysis comprised the following selection filters:
   1. ANOVA p-value cutoff &lt;0.01   2. Ratios of % methylation PanC to normal pancreas and normal colon &gt;10   3. % methylation of normals &lt;2%   4. Number of contiguous CpGs meeting criteria ≥3
 
The methylation window was assessed to include additional contiguous CpGs that exhibit significant methylation. Then, the candidates were sorted by gene name for annotated regions and by chromosomal location for nonannotated regions.
   

     Results 
     Roughly 6 million CpGs were mapped at ≥10× coverage. More than 500 CpG islands met significance criteria for differential methylation. After applying the filter criteria above, 107 differentially methylated regions (DMR) were identified (Table 1). 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 DMR 
               
            
           
           
               
               
               
               
               
            
               
                 DMR 
                   
                   
                   
                 region on chromosome 
               
               
                 No. 
                 gene annotation 
                 chromosome 
                 strand 
                 (starting base-ending base) 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 1 
                 none 
                 1 
                 F 
                 35394805-35394875 
               
               
                 2 
                 none 
                 1 
                 F 
                 240161479-240161546 
               
               
                 3 
                 none 
                 1 
                 R 
                 156406057-156406118 
               
               
                 4 
                 AK055957 
                 12 
                 F 
                 133484978-133485738 
               
               
                 5 
                 none 
                 12 
                 R 
                 133484979-133485739 
               
               
                 6 
                 APBA2 
                 15 
                 F 
                 29131299-29131369 
               
               
                 7 
                 none 
                 2 
                 F 
                 71503632-71503860 
               
               
                 8 
                 PCBP3 
                 21 
                 R 
                 47063793-47064177 
               
               
                 9 
                 TMEM200A 
                 6 
                 F 
                 130687223-130687729 
               
               
                 10 
                 none 
                 9 
                 R 
                 120507311-120507354 
               
               
                 11 
                 ABCB1 
                 7 
                 R 
                 87229775-87229856 
               
               
                 12 
                 ADAMTS17 
                 15 
                 R 
                 100881373-100881437 
               
               
                 13 
                 ADAMTS18 
                 16 
                 R 
                 77468655-77468742 
               
               
                 14 
                 ADCY1 
                 7 
                 F 
                 45613877-45614564 
               
               
                 15 
                 ADCY1 
                 7 
                 R 
                 45613878-45614572 
               
               
                 16 
                 AGFG2 
                 7 
                 F 
                 100136884-100137350 
               
               
                 17 
                 ARHGEF7 
                 13 
                 F 
                 111767862-111768355 
               
               
                 18 
                 AUTS2 
                 7 
                 R 
                 69062531-69062585 
               
               
                 19 
                 BTBD11 
                 12 
                 F 
                 107715014-107715095 
               
               
                 20 
                 BVES 
                 6 
                 R 
                 105584524-105584800 
               
               
                 21 
                 c13orf18 
                 13 
                 F 
                 46960770-46961464 
               
               
                 22 
                 c13orf18 
                 13 
                 R 
                 46960910-46961569 
               
               
                 23 
                 CACNA1C 
                 12 
                 F 
                 2800665-2800898 
               
               
                 24 
                 CBLN1 
                 16 
                 R 
                 49315846-49315932 
               
               
                 25 
                 CBS 
                 21 
                 F 
                 44496031-44496378 
               
               
                 26 
                 CBS 
                 21 
                 R 
                 44495926-44496485 
               
               
                 27 
                 CD1D 
                 1 
                 F 
                 158150797-158151142 
               
               
                 28 
                 CELF2 
                 10 
                 F 
                 11059508-11060151 
               
               
                 29 
                 CLEC11A 
                 19 
                 F 
                 51228217-51228703 
               
               
                 30 
                 CLEC11A 
                 19 
                 R 
                 51228325-51228732 
               
               
                 31 
                 CNR1 
                 6 
                 F 
                 88876367-88876445 
               
               
                 32 
                 CNR1 
                 6 
                 R 
                 88875699-88875763 
               
               
                 33 
                 CHRH2 
                 7 
                 F 
                 30721941-30722084 
               
               
                 34 
                 DBNL 
                 7 
                 F 
                 44084171-44084235 
               
               
                 35 
                 DBX1 
                 11 
                 R 
                 20178177-20178304 
               
               
                 36 
                 DHRS12 
                 13 
                 F 
                 52378159-52378202 
               
               
                 37 
                 DLL1 
                 6 
                 F 
                 170598241-170600366 
               
               
                 38 
                 ELMO1 
                 7 
                 F 
                 37487539-37488498 
               
               
                 39 
                 ELMO1 
                 7 
                 R 
                 37487540-37488477 
               
               
                 40 
                 EN1 
                 2 
                 R 
                 119607676-119607765 
               
               
                 41 
                 EOMES 
                 3 
                 F 
                 27763358-27763617 
               
               
                 42 
                 FBLN1 
                 22 
                 R 
                 45898798-45898888 
               
               
                 43 
                 FEM1C 
                 5 
                 F 
                 114880375-114880442 
               
               
                 44 
                 FER1L4 
                 20 
                 R 
                 34189679-34189687 
               
               
                 45 
                 FKBP2 
                 11 
                 F 
                 64008415-64008495 
               
               
                 46 
                 FLT3 
                 13 
                 F 
                 28674451-28674629 
               
               
                 47 
                 FNIP1 
                 5 
                 F 
                 131132146-131132232 
               
               
                 48 
                 FOXP2 
                 7 
                 R 
                 113727624-113727693 
               
               
                 49 
                 GFRA4 
                 20 
                 R 
                 3641457-3641537 
               
               
                 50 
                 GJC1 
                 17 
                 F 
                 42907705-42907798 
               
               
                 51 
                 GJC1 
                 17 
                 R 
                 42907752-42907827 
               
               
                 52 
                 GRIN2D 
                 19 
                 F 
                 48946755-48946912 
               
               
                 53 
                 HECW1 
                 7 
                 R 
                 43152309-43152375 
               
               
                 54 
                 HOXA1 
                 7 
                 R 
                 27136030-27136245 
               
               
                 55 
                 IFIH1 
                 2 
                 R 
                 163174541-163174659 
               
               
                 56 
                 IGF2BP1 
                 17 
                 F 
                 47073394-47073451 
               
               
                 57 
                 IKZF1 
                 7 
                 R 
                 50343848-50343927 
               
               
                 58 
                 INSM1 (region 1) 
                 20 
                 F 
                 20345123-20345150 
               
               
                 59 
                 INSM1 (region 2) 
                 20 
                 F 
                 20350520-20350532 
               
               
                 60 
                 KCNK12 
                 2 
                 F 
                 47797332-47797371 
               
               
                 61 
                 KCNN2 
                 5 
                 F 
                 113696984-113697057 
               
               
                 62 
                 KCTD15 
                 19 
                 R 
                 34287890-34287972 
               
               
                 63 
                 LINGO3 
                 19 
                 F 
                 2290471-2290541 
               
               
                 64 
                 LOC100126784 
                 11 
                 R 
                 19733958-19734013 
               
               
                 65 
                 LOC63930 
                 20 
                 F 
                 61637950-61638000 
               
               
                 66 
                 LOC642345 
                 13 
                 R 
                 88323571-88323647 
               
               
                 67 
                 MLLT1 
                 19 
                 R 
                 6236992-6237089 
               
               
                 68 
                 MPND 
                 19 
                 R 
                 4343896-4242968 
               
               
                 69 
                 MYEF2 
                 15 
                 F 
                 48470117-48470606 
               
               
                 70 
                 NDUFAB1 
                 16 
                 R 
                 23607524-23607650 
               
               
                 71 
                 NFASC 
                 1 
                 F 
                 204797781-204797859 
               
               
                 72 
                 NR5A1 
                 9 
                 F 
                 127266951-127267032 
               
               
                 73 
                 PDE6B 
                 4 
                 F 
                 657586-657665 
               
               
                 74 
                 PLAGL1 
                 6 
                 R 
                 144384503-144385539 
               
               
                 75 
                 PRKCB 
                 16 
                 R 
                 23846964-23848004 
               
               
                 76 
                 PRRT3 
                 3 
                 F 
                 9988302-9988499 
               
               
                 77 
                 PTF1A 
                 10 
                 F 
                 23480864-23480913 
               
               
                 78 
                 RASGRF2 
                 5 
                 R 
                 80256215-80256313 
               
               
                 79 
                 RIMKLA 
                 1 
                 R 
                 42846119-42846174 
               
               
                 80 
                 RNF216 
                 7 
                 F 
                 5821188-5821283 
               
               
                 81 
                 RSPO3 
                 6 
                 F 
                 127440526-127441039 
               
               
                 82 
                 RSPO3 
                 6 
                 R 
                 127440492-127440609 
               
               
                 83 
                 RYBP 
                 3 
                 R 
                 72496092-72496361 
               
               
                 84 
                 SCARF2 
                 22 
                 F 
                 20785373-20785464 
               
               
                 85 
                 SHH 
                 7 
                 F 
                 155597771-155597951 
               
               
                 86 
                 SLC35E3 
                 12 
                 F 
                 69140018-69140202 
               
               
                 87 
                 SLC38A3 
                 3 
                 R 
                 50243467-50243553 
               
               
                 88 
                 SLC6A3 
                 5 
                 R 
                 1445384-1445473 
               
               
                 89 
                 SPSB4 
                 3 
                 F 
                 140770135-140770193 
               
               
                 90 
                 SRCIN1 
                 17 
                 R 
                 36762706-36762763 
               
               
                 91 
                 ST6GAL2 
                 2 
                 F 
                 107502978-107503055 
               
               
                 92 
                 ST6GAL2 
                 2 
                 R 
                 107503155-107503391 
               
               
                 93 
                 ST8SIA1 
                 12 
                 F 
                 22487528-22487827 
               
               
                 94 
                 ST8SIA1 
                 12 
                 R 
                 22487664-22487848 
               
               
                 95 
                 ST8SIA6 
                 10 
                 F 
                 17496177-17496310 
               
               
                 96 
                 SUSD5 
                 3 
                 R 
                 33260338-33260423 
               
               
                 97 
                 TOX2 
                 20 
                 F 
                 42544666-42544874 
               
               
                 98 
                 TWIST1 
                 7 
                 F 
                 19156788-19157093 
               
               
                 99 
                 TWIST1 
                 7 
                 R 
                 19156815-19157227 
               
               
                 100 
                 USP3 
                 15 
                 R 
                 63795435-63795636 
               
               
                 101 
                 USP44 
                 12 
                 R 
                 95942179-95942558 
               
               
                 102 
                 VIM 
                 10 
                 F 
                 17271896-17271994 
               
               
                 103 
                 VWC2 
                 7 
                 R 
                 49813182-49814168 
               
               
                 104 
                 WT1 
                 11 
                 R 
                 32460759-32460800 
               
               
                 105 
                 ZFP30 
                 19 
                 F 
                 38146299-38146397 
               
               
                 106 
                 ZNF570 
                 19 
                 F 
                 37958078-37958134 
               
               
                 107 
                 ZNF71 
                 19 
                 F 
                 57106617-57106852 
               
               
                   
               
            
           
         
       
     
     In Table 1, bases are numbered according to the February 2009 human genome assembly GRCh37/hg19 (see, e.g., Rosenbloom et al. (2012) “ENCODE whole-genome data in the UCSC Genome Browser: update 2012 ” Nucleic Acids Research  40: D912-D917). The marker names BHLHE23 and LOC63930 refer to the same marker. 
     In these candidates, methylation signatures range from 3 neighboring CpGs to 52 CpGs. Some markers exhibit methylation on both strands; others are hemi-methylated. Since strands are not complimentary after bisulfite conversion, forward and reverse regions were counted separately. While Table 1 indicates the strand on which the marker is found, the technology is not limited to detecting methylation on only the indicated strand. The technology encompasses measuring methylation on either forward or reverse strands and/or on both forward and reverse strands; and/or detecting a change in methylation state on either forward or reverse strands and/or on both forward and reverse strands. 
     Methylation levels of the pancreatic cancers rarely exceeded 25% at filtered CpGs, which suggested that the cancer tissues may have high levels of contaminating normal cells and/or stroma. To test this, each of the cancers was sequenced for KRAS mutations to verify allele frequencies for the positive samples. For the 50% that harbored a heterozygous KRAS base change, the frequency of the mutant allele was at least 4 times less than the corresponding wild-type allele, in support of contamination by normal cells and/or stroma. 
     It was found that 7 of the 107 markers are in nonannotated regions and lie in genomic regions without protein coding elements. One marker is adjacent to a ncRNA regulatory sequence (AK055957). Of the remaining 99 candidate markers, approximately 30 have been described as associated with cancer, some of which classify as tumor suppressors. A few examples: 
     
       
         
           
               
               
               
             
               
                   
                   
               
             
            
               
                   
                 ADCY1 
                 Down-regulated in osteosarcoma 
               
               
                   
                 ELMO1 
                 Promotes glioma invasion 
               
               
                   
                 HOXA2 
                 Hyper-methylated in cholangioca 
               
               
                   
                 RSPO3 
                 Wnt signalling regulator 
               
               
                   
                 SUSD5 
                 Mediates bone metastasis in lung cancer 
               
               
                   
                 KCNK12 
                 Hypermethylated in colon cancer 
               
               
                   
                 CLEC11A 
                 Stem cell GF in leukemia 
               
               
                   
                 USP3 
                 Required for S-phase progression 
               
               
                   
                   
               
            
           
         
       
     
     The 69 other candidate markers have a previously identified weak association with cancer (e.g., mutations and/or copy number alterations observed in genome-wide screens) or have no previously identified cancer associations. 
     Example 2—Validating Markers 
     To validate the DMRs as cancer markers, two PCR-based validation studies were performed on expanded sample sets. The first study used samples from patient groups similar to those used in Example 1 (e.g., PanC, normal pancreas, normal colon) and added samples comprising buffy coat-derived DNA from normal patients who had no history of any cancer. The second study used using a selection of pan-GI cancers. 
     For the first validation study, a combination of previously run RRBS samples and newer banked samples were tested to verify technical accuracy and to confirm biological reproducibility, respectively. Methylation specific PCR (MSP) primers were designed for each of the marker regions, excluding only complementary strands in cases of non-strand specific methylation. Computer software (Methprimer) aided semi-manual design of the MSP primers by experienced personnel; assays were tested and optimized by qPCR with SYBR Green dyes on dilutions of universally methylated and unmethylated genomic DNA controls. The MSP primer sequences, each of which include 2-4 CpGs, were designed to provide a quick means of assessing methylation in the samples and were biased to maximize amplification efficiency. Primer sequences and physical parameters are provided in Table 2a and Table 2b: 
                     TABLE 2a                  MSP primers                                             Length       GC Content           SEQ       Name   (nt)   Sequence(5&#39;→ 3&#39;)   (%)   Tm   Ta   ID NO:                                                 abcb1f   21   GAT TTT GTT CGT CGT TAG TGC   42.9   52.3   60.0   1               abcb1r   19   TCT CTA AAC CCG CGA ACG A   52.6   56.0   60.0   2               adamts17f   25   TTC GAA GTT TCG GGA TAG GAA GCG T   48.0   60.0   65.0   3               adamts17r   20   CCT ACC GAC CTT CGA ACG CG   65.0   60.3   65.0   4               adamts18f   21   GGC GGC GCG TAT TTT TTT CGC   57.1   60.6   60.0   5               adamts18r   23   CGC TAC GAT ATA AAC GAC GAC GA   47.8   56.4   60.0   6               adcy1f   19   GGT TCG GTT GTC GTA GCG C   63.2   59.0   65.0   7               adcy1r   20   CCG ACC GTA ATC CTC GAC GA   60.0   58.6   65.0   8               agfg2f   25   TTA GGT CGG GAA TCG TTA TTG TTT C   40.0   55.1   60.0   9               agfg2r   22   GTA AT AAC CCC GCG CTA AAC G   50.0   56.5   60.0   10               arhgef7f   24   TTC GTT TGT TTT TCG GGT CGT AGC   45.8   58.1   60.0   11               arhgef7r   24   ACC ACG TAA CGA TTT ACT CGA CGA   45.8   57.8   60.0   12               auts2f   23   CGT TTT CGG ATT TGA AGT CGT TC   43.5   54.8   65.0   13               auts2r   19   CGC CTC GTC TTC CAA CGA A   57.9   57.7   65.0   14               btbd11f   19   AGG GCG TTC GGT TTT AGT C   52.6   55.1   60.0   15               btbd1r   22   AAC CGA AAA CGA CAA AT CGA T   36.4   53.4   60.0   16               Bvesf   21   TTT GAG CGG CGG TCG TTG ATC   57.1   60.4   60.0   17               Bvesr   22   TCC CCG AT CTA AAC GCT ACG A   50.0   57.8   60.0   18               C13orf18f   25   TTT AGG GAA GTA AAG CGT CGT TTT C   40.0   55.6   60.0   19               C13orf18r   22   AAC GAC GTC TCG ATA CCT ACG A   50.0   57.1   60.0   20               cacna1cf   22   GGA GAG TAT TTC GGT TTT TCG C   45.5   54.2   65.0   21               cacna1cr   24   ACA AAC AAA ATC GAA AAA CAC CCG   37.5   55.2   65.0   22               cbln1f   23   GTT TTC GTT TCG GTC GAG GTT AC   47.8   56.2   60.0   23               cbln1r   25   GCC ATT AAC TCG ATA AAA AAC GCG A   40.0   56.3   60.0   24               Cbsf   25   GAT TTA ATC GTA GAT TCG GGT CGT C   44.0   55.2   65.0   25               Cbsr   22   CCG AAA CGA ACG AAC TCA AAC G   50.0   56.8   65.0   26               cd1df   17   GCG CGT AGC GGC GTT TC   70.6   60.7   60.0   27               cd1dr   19   CCC ATA TCG CCC GAC GTA A   57.9   56.9   60.0   28               celf2f   22   TCG TAT TTG GCG TTC GGT AGT C   50.0   57.0   70.0   29               celf2r   21   CGA AT CCA ACG CCG AAA CGA   52.4   58.4   70.0   30               chr1 156f   24   TTG TCG TTC GTC GAA TTC GAT TTC   41.7   55.8   65.0   31               chr1 156r   23   AAC CCG ACG CTA AAA AAC GAC GA   47.8   59.2   65.0   32               chr1 240f   25   TTG CGT TGG TTA CGT TTT TTT ACG C   40.0   57.3   60.0   33               chr1 240r   23   ACG CCG TAC GAA TAA CGA AAC GA   47.8   58.7   60.0   34               chr1 353f   21   CGT TTT TCG GGT CGG GTT CGC   61.9   61.5   60.0   35               chr1 353r   19   TCC GAC GCT CGA CTC CCG A   68.4   63.1   60.0   36               chr12 133f   22   TCG GCG TAT TTT TCG TAG ACG C   50.0   57.6   60.0   37               chr12 133r   24   CGC AT CTT AAA CGT ACG CTT CGA   45.8   57.7   60.0   38               chr15 291   24   GGT TTA TAA AGA GTT CGG TTT CGC   41.7   54.4   60.0   39       (apba2)f                                       chr15 291   24   AAA ACG CTA AAC TAC CCG AT ACG   41.7   55.3   60.0   40       (apba2)r                                       chr2 715f   19   TGG GCG GGT TTC GTC GTA C   63.2   60.2   65.0   41               chr2 715r   21   GTC CCG AAA CAT CGC AAA CGA   52.4   58.2   65.0   42               chr6 130   20   GCG TTT GGA TTT TGC GTT C   55.0   58.0   60.0   43       (TMEM200A)f                                       chr6 130   20   AAA ATA CGC CGC TAC CGA TA   55.0   60.6   60.0   44       (TMEM200A)r                                       chr9 120f   20   GTT TAG GGA GTC GCG GTT AC   55.0   55.4   60.0   45               chr9 120r   23   CAA ATC CTA CGA ACG AAC GAA CG   47.8   56.2   60.0   46               clec11af   22   AGT TTG GCG TAG TCG GTA GAT C   50.0   56.4   60.0   47               clec11ar   22   GCG CGC AAA TAC CGA ATA AAC G   50.0   57.5   60.0   48               cnr1f   22   TCG GTT TTT AGC GTT CGT TCG C   50.0   58.4   60.0   49               cnr1r   23   AAA CAA CGA AAC GCC AT CCC GA   47.8   59.9   60.0   50               crhr2f   25   TAG TTT TTG GGC GTT ATT TTC GGT C   40.0   56.1   60.0   51               crhr2r   21   GCA ACT CCG TAC ACT CGA CGA   57.1   59.0   60.0   52               Dbnlf   26   TTT TTC GTT TGT TTT TCG GTA TTC GC   34.6   55.5   60.0   53               Dbnlr   22   CGA ATC CTA ACG AAC TAT CCG A   45.5   53.9   60.0   54               dbx1f   25   TTC GGT GGA TTT TCG TAT TGA TTT C   36.0   54.0   60.0   55               dbx1r   24   AAA CGA AAC CGC GAA CTA AAA CGA   41.7   57.6   60.0   56               dhrs12f   22   TTA CGT GAT AGT TCG GGG TTT C   45.5   54.6   60.0   57               dhrs12r   21   ATA AAA CGA CGC GAC GAA ACG   47.6   56.2   60.0   58               elmo1f   24   TTT CGG GTT TTG CGT TTT ATT CGC   41.7   57.2   60.0   59               elmo1r   28   GAA AAA AAA AAA CGC TAA AAA TAC GAC G   28.6   53.3   60.0   60               Eomesf   21   TAG CGC GTA GTG GTC GTA GTC   57.1   58.4   60.0   61               Eomesr   18   CCT CCG CCG CTA CAA CCG   72.2   61.5   60.0   62               fbln1f   22   TCG TTG TTT TAG GAT CGC GTT C   45.5   55.6   60.0   63               fbln1r   22   GAC GAA CGA TAA ACG ACG ACG A   50.0   56.9   60.0   64               fem1cf   21   TTC GGT CGC GTT GTT CGT TAC   52.4   58.0   60.0   65               fem1cr   25   AAA CGA AAA ACA ACT CGA ATA ACG A   32.0   53.8   60.0   66               fer1l4f   18   AGT CGG GGT CGG AGT CGC   72.2   62.3   60.0   67               fer1l4r   23   ATA AT CCC TCC GAA ACC CAC GA   47.8   58.2   60.0   68               fkbp2f   21   TCG GAA GTG ACG TAG GGT AGC   57.1   58.3   60.0   69               fkbp2r   19   CAC ACG CCC GCT AAC ACG A   63.2   60.6   60.0   70               flt3f   21   GCG CGT TCG GGT TTA TAT TGC   52.4   57.2   65.0   71               flt3r   20   GAC CAA CTA CCG CTA CTC GA   55.0   56.1   65.0   72               fnip1f   20   AGG GGA GAA TTT CGC GGT TC   55.0   57.6   65.0   73               fnip1r   24   AAC TAA ATT AAA CCT CAA CCG CCG   41.7   55.9   65.0   74               gfra4f   20   TTA GGA GGC GAG GTT TGC GC   60.0   60.3   65.0   75               gfra4r   28   GAC GAA ACC GTA ACG AAA ATA AAA ACG A   35.7   56.4   65.0   76               gjc1r   24   CGA ACT ATC CGA AAA AAC GAC GAA   41.7   55.6   65.0   77               glc1f   22   GCG ACG CGA GCG TTA ATT TTT C   50.0   57.6   65.0   78               hecw1f   23   TTC GCG TAT ATA TTC GTC GAG TC   43.5   54.2   60.0   79               hecw1r   20   CAC GAC CAC TAT CAC GAC GA   55.0   56.5   60.0   80               hoxa1f   22   GTA CGT CGG TTT AGT TCG TAG C   50.0   55.3   60.0   81               hoxa1r   21   CCG AAA CGC GAT ATC AAC CGA   52.4   57.6   60.0   82               ifih1f   20   CGG GCG GTT AGA GGG TTG TC   65.0   60.4   60.0   83               ifih1r   26   CTC GAA AT TCG TAA AAA CCC TCC GA   42.3   57.4   60.0   84               igf2bp1f   29   CGA GTA GTT TTT TTT TTT ATC GTT TAG AC   27.6   52.1   65.0   85               igf2bp1r   24   CAA AAA ACG ACA CGT AAA CGA TCG   41.7   55.2   65.0   86               ikzf1f   24   GTT TCG TTT TGC GTT TTT TTG CGC   41.7   57.5   65.0   87               ikzflr   19   TCC CGA ATC GCT ACT CCG A   57.9   57.8   65.0   88               insm1 reg1f   17   GCG GTT AGG CGG GTT GC   70.6   60.2   60.0   89               insm1 reg1r   25   ATT ATA TCA ATC CCA AAA ACA CGC G   36.0   54.3   60.0   90               insm1 reg2f   22   TAT TTT TCG AT TCG AGT TCG C   36.4   51.7   60.0   91               insm1 reg2r   22   TCA CCC GAT AAA AAC GAA AAC G   40.9   53.8   60.0   92               kcnk12f   21   GCG TCG TTA GTA GTA CGA AGC   52.4   55.3   60.0   93               kcnk12r   21   GCA CCT CAA CGA AAA CAC CGA   52.4   58.2   60.0   94               kcnn2f   23   TCG AGG CGG TTA ATT TTA TTC GC   43.5   55.8   65.0   95               kcnn2r   23   GCT CTA ACC CAA ATA CGC TAC GA   47.8   56.6   65.0   96               kctd15f   22   TCG GTT TCG AGG TAA GTT TAG C   45.5   54.7   60.0   97               kctd15r   23   CAC TTC GAA ACA AAA TTA CGC GA   39.1   54.3   60.0   98               lingo3f   20   GGA AGC GGA CGT TTT CGT TC   55.0   56.8   65.0   99               lingo3r   22   ACC CAA AT CCG AAA ACG ACG A   45.5   57.3   65.0   100               LOC100126784   19   AGG TTG CGG GCG TGA TTT C   57.9   58.8   65.0   101       (NAV2)f                                       LOC100126784   20   CCA AAA CCA CGC GAA CAC GA   55.0   58.8   65.0   102       (NAV2)r                                       LOC63930   20   GTT CGG AGT GTC GTA GTC GC   60.0   57.7   70.0   103       (bhlhe23)f                                       LOC63930   21   AT CTC GCC TAC GAA ACG ACG   52.4   57.2   70.0   104       (bhlhe23)r                                       LOC642345f   22   GTT TAG GGA CGT TTT CGT TTT C   40.9   52.5   65.0   105               LOC642345r   20   AAC GAA CGC TCG ATA ACC GA   50.0   56.5   65.0   106               mllt1f   20   TTT GGG TCG GGT TAG GTC GC   60.0   59.9   60.0   107               mllt1r   25   GAA ACC AAA AAA ACG CTA ACT CGT A   36.0   54.4   60.0   108               Mpndf   20   CGT TGT TGG AGT TTG GCG TC   55.0   57.1   65.0   109               Mpndr   21   TAC CCG AAC CGC GAT AAA ACG   52.4   57.5   65.0   110               myef2f   25   GGT ATA GTT CGG TTT TTA GTC GTT C   40.0   53.6   65.0   111               myef2r   24   TCT TTT CCT CCG AAA ACC GAA ACG   45.8   57.8   65.0   112               NDUFAB1f   23   GGT TAC GGT TAG TAT TCG GAT TC   43.5   53.0   60.0   113               NDUFAB1r   20   ATA TCA ACC GCC TAC CCG CG   60.0   59.7   60.0   114               NFASCf   24   TTT TGT TTT AT GCG GCG GTT GGC   45.8   59.6   65.0   115               NFASCr   22   TAT CCG AAC TAT CCG CTA CCG A   50.0   56.9   65.0   116               pcbp3f   19   GGT CGC GTC GTT TTC GAT C   57.9   56.6   60.0   117               pcbp3r   17   GCC GCA AAC GCC GAC GA   70.6   62.4   60.0   118               PDE6Bf   21   AT CGG CGG TAG TAC GAG TAC   52.4   56.1   55.0   119               PDE6Br   26   AAA CCA AT CCG TAA CGA TAA TAA CG   34.6   53.9   55.0   120               PLAGL1f   26   GAG TTT TGT TTT CGA AT TAT TTC GC   30.8   52.4   65.0   121               PLAGL1r   18   CCC GAA TTA CCG ACG ACG   61.1   55.7   65.0   122               PRKCBf   21   AGG TTC GGG TTC GAC GAT TTC   52.4   57.3   70.0   123               PRKCBr   21   AAC TCT ACA ACG CCG AAA CCG   52.4   57.7   70.0   124               PRRT3f   23   TTA GTT CGT TTA GCG ATG GCG TC   47.8   57.4   60.0   125               PRRT3r   20   CCG AAA CTA TCC CGC AAC GA   55.0   57.5   60.0   126               PTF1Af   21   TTC GTC GTT TGG GTT ATC GGC   52.4   57.8   60.0   127               PTF1Ar   23   GCC CTA AAA CTA AAA CAA CCG CG   47.8   57.1   60.0   128               RASGRF2f   22   GGT TGT CGT TTT AGT TCG TCG C   50.0   56.6   60.0   129               RASGRF2r   19   GCG AAA ACG CCC GAA CCG A   63.2   61.4   60.0   130               RIMKLAf   22   TCG TTT GGG AGA CGT ATT CGT C   50.0   56.7   60.0   131               RIMKLAr   25   ACT CGA AAA ATT TCC GAA CTA ACG A   36.0   55.0   60.0   132               RNF216f   20   TCG GCG GTT TTC GTT ATC GC   55.0   58.4   60.0   133               RNF216r   21   CCA CGA AAC TCG CAA CTA CGA   52.4   57.4   60.0   134               rspo3f   25   CGT TTA TTT AGC GTA ATC GTT TCG C   40.0   55.0   65.0   135               rspo3r   24   GAA TAA CGA ACG TTC GAC TAC CGA   45.8   56.6   65.0   136               RYBPf   24   CGG ACG AGA TTA GTT TTC GTT AGC   45.8   55.7   60.0   137               RYBPr   24   TCG TCA ATC ACT CGA CGA AAA CGA   45.8   58.4   60.0   138               SCARF2f   22   TCG GTT CGT AGG TAT ACG TGT C   50.0   55.8   60.0   139               SCARF2r   22   GCT ACT ACC AAT ACT TCC GCG A   50.0   56.4   60.0   140               SLC35E3f   21   GTT AGA CGG TTT TAG TTT CGC   42.9   51.8   60.0   141               SLC35E3r   20   AAA AAC CCG ACG ACG ATT CG   50.0   55.8   60.0   142               slc38a3f   21   GTT AGA GTT CGC GTA GCG TAC   52.4   55.3   65.0   143               slc38a3r   25   GAA AAA ACC AAC CGA ACG AAA ACG A   40.0   56.9   65.0   144               slc6a3f   19   CGG GGC GTT TCG ATG TCG C   68.4   62.0   65.0   145               slc6a3r   24   CCG AAC GAC CAA ATA AAA CCA ACG   45.8   57.0   65.0   146               srcin1f   22   CGT TTT ATG TTG GGA GCG TTC G   50.0   56.8   65.0   147               srcin1r   20   GAC CGA ACC GCG TCT AAA CG   60.0   58.5   65.0   148               st6gal2f   21   TAC GTA TCG AGG TTG CGT CGC   57.1   59.3   65.0   149               st6gal21   25   AAA CTC TAA AAC GAA CGA AAC TCG A   36.0   54.9   65.0   150               st8sia1f   21   TCG AGA CGC GTT TTT TGC GTC   52.4   58.7   60.0   151               st8sia1r   20   AAC GAT CCC GAA CCG CCG TA   60.0   61.3   60.0   152               ST8SIA6f   21   CGA GTA GTG CGT TTT TCG GTC   52.4   56.2   60.0   153               ST8SIA6r   22   GAC AAC AAC GAT AAC GAC GAC G   50.0   56.1   60.0   154               SUSD5f   22   AGC GTG CGT TAT TCG GTT TTG C   50.0   59.1   65.0   155               SUSD5r   23   ACC TAC GAT TCG TAA ACC GAA CG   47.8   56.9   65.0   156               TOX2f   23   AGT TCG CGT TTT TTT CGG TCG TC   47.8   58.5   70.0   157               TOX2r   21   AAC CGA CGC ACC GAC TAA CGA   57.1   61.0   70.0   158               twist1f   22   TTG CGT CGT TTG CGT TTT TCG C   50.0   59.9   60.0   159               twist1r   20   CAA CTC GCC AAT CTC GCC GA   60.0   60.2   60.0   160               USP3f   18   TAT TGC GGG GAG GTG TTC   55.6   54.7   60.0   161               USP3r   24   TCA AAA AAT AAT TAA CCG AAC CGA   29.2   51.3   60.0   162               USP44f   24   TTA GTT TTC GAA GTT TTC GTT CGC   37.5   54.4   60.0   163               USP44r   19   TCC GAC CCT ATC CCG ACG A   63.2   59.9   60.0   164               VIMf   27   GAT TAG TTA ATT AAC GAT AAA GTT CGC   29.6   51.0   60.0   165               VIMr   23   CCG AAA ACG CAT AAT ATC CTC GA   43.5   55.0   60.0   166               vwc2f   26   TTG GAG AGT TTT TCG AAT TTT TTC GC   34.6   55.2   65.0   167               vwc2r   19   GAA AAC CAC CCT AAC GCC G   57.9   56.6   65.0   168               wt1f   17   CGC GGG GTT CGT AGG TC   70.6   58.5   65.0   169               wt1r   23   CGA CAA ACA ACA ACG AAA TCG AA   39.1   54.5   65.0   170               zfp30f   22   AGT AGC GGT TAT AGT GGC GTT C   50.0   56.7   65.0   171               zfp30r   22   GCA TTC GCG ACG AAA ACA AAC G   50.0   58.0   65.0   172               ZNF569f   20   GTA TTG AGG TCG GCG TTG TC   55.0   55.9   60.0   173               ZNF569r   19   CCG CCC GAA TAA ACC GCG A   63.2   60.8   60.0   174               ZNF71f   20   CGT AGT TCG GCG TAG TTC GC   60.0   58.2   65.0   175               ZNF71r   21   AAC CCG CCC GAC GAC AT ACG   61.9   62.1   65.0   176                    
In Table 2a, Ta is the optimized annealing temperature and Tm is the melting temperature in ° C. in 50 mM NaCl. Primers celf2f and celf2r; LOC63930 (bhlhe23)f and LOC63930 (bhlhe23)r; PRKCBf and PRKCBr; and TOX2f and TOX2r are used in a 2-step reaction.
 
     Specimens 
     Archived DNA samples from Mayo clinic patients were used for both validations. Cases and controls were blinded and matched by age and sex. The first sample set included DNA from 38 pancreatic adenocarcinomas and controls (20 normal colonic epithelia, 15 normal pancreas, and 10 normal buffy coats). The second sample set included DNA from 38 colorectal neoplasms (20 colorectal adenocarcinomas and 18 adenomas &gt;1 cm), 19 esophageal adenocarcinomas, 10 gastric (stomach) cancers, and 10 cholangiocarcinomas. 
     Methods 
     Archived DNA was re-purified using SPRI beads (AMPure XP-Beckman Coulter) and quantified by absorbance. 1-2 μg of sample DNA was then treated with sodium bisulfite and purified using the EpiTect protocol (Qiagen). Eluted material (10-20 ng) was amplified on a Roche 480 LightCycler using 384-well blocks. Each plate accommodated 4 markers (and standards and controls), thus using a total of 23 plates. The 88 MSP assays had differing optimal amplification profiles and were grouped accordingly. Specific annealing temperatures are provided in Table 2. The 20-μl reactions were run using LightCycler 480 SYBR I Master mix (Roche) and 0.5 μmol of primer for 50 cycles and analyzed, generally, by the 2nd-derivative method included with the LightCycler software. The raw data, expressed in genomic copy number, was normalized to the amount of input DNA, and tabulated. Analysis at the tissue level comprised performing PCA (supplemented with k-fold cross validation), elastic net regression, and constructing box plots of non-zero elastic net markers. In this way, markers were collectively ranked. Of these candidates, because of the importance of minimizing normal cellular background methylation for stool and blood-based assays, the ranking was weighed toward those markers which exhibited the highest fold-change differential between cases and controls. 
     Results 
     Among the 107 methylated DNA markers with proven discrimination for GI cancers, MSP validation was performed on 88 from which subsets were identified for display of more detailed summary data. 
     Detection of Pancreatic Cancer 
     A subset of the methylation markers were particularly discriminant for pancreatic cancer: ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12 133, CLEC11A, ELMO1, EOMES, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, and ZNF71 (see Table 1). Individual AUC values (PanC versus normal pancreas or colon) for these markers were above 0.87, which indicates superior clinical sensitivity and specificity. 
     Initially, the two best stand-alone markers appeared to be CLEC11A and c13orf18, which were 95% and 82% sensitive for pancreatic cancer, respectively, at 95% specificity. Additional experiments designed additional primers to target the most specific CpGs within specified DMRs of selected markers. These additional primers enhanced discrimination further. For example, design of new MSP for the marker PRKCB (initial sensitivity of 68%) dramatically increased discrimination for pancreatic cancer and achieved sensitivity of 100% at 100% specificity. Moreover, the median methylation signal-to-noise ratio for this marker, comparing cancer to normal tissue, was greater than 8000. This provides a metric critical to the detection of cancer markers in samples with high levels of normal cellular heterogeneity. Having base level methylation profiles of the DMRs from the filtered RRBS data allows for the construction of highly sensitive and specific detection assays. These results obtained from the improved MSP designs demonstrate that similar performance specifications can be obtained from the other 106 DMRs with additional design improvements, validation, and testing formats. 
                     TABLE 2b                  MSP primers                                                     GC            SEQ            Length       Content           ID       Name   (nt)   Sequence(5&#39; → 3&#39;)   (%)   Tm   Ta   NO:                                                 dll(sense)r   20   GTC GAG CGC GTT CGT TGT AC   60.0   58.9   65   177               dll(sense)r   22   GAC CCG AAA AAT AAA TCC CGA A   40.9   53.3   65   178               dll(antisense)f   24   GAT TTT TTT AGT TTG TTC GAC GGC   37.5   53.5   65   179               dll(antisense)r   25   AAA ATT ACT AAA CGC GAA ATC GAC G   36.0   54.4   65   180               en1(sense)f   26   TAA TGG GAT GAT AAA TGT ATT CGC GG   38.5   55.2   65   181               en1(sense)r   26   ACC GCC TAA TCC AAC TCG AAC TCG TA   50.0   61.2   65   182               en1(antisense)f   22   GGT GTT TTT AAA GGG TCG TCG T   45.5   55.7   65   183               en1(antisense)r   19   GAC CCG ACT CCT CCA CGT A    63.2   58.4   65   184               foxp2(sense)f   30   GGA AGT TTA TAG TGG TTT CGG CGG GTA GGC   53.3   63.6   60   185               foxp2(sense)r   22   GCG AAA AAC GTT CGA ACC CGC G   59.1   61.9   60   186               grin2d(sense)f   28   TGT CGT CGT CGC GTT ATT TTA GTT GTT C   42.9   59.2   60   187               grin2d(sense)r   22   AAC CGC CGT CCA AAC CAT CGT A   54.6   61.3   60   188               nr5a1(sense)f   25   GAA GAG TTA GGG TTC GGG ACG CGA G   60.0   62.6   65   189               nr5a1(sense)r   25   AAC GAC CAA ATA AAC GCC GAA CCG A   48.0   61.1   65   190               nr5a1(antisense)f    25   CGT AGG AGC GAT TAG GTG GGC GTC G   64.0   64.6   60   191               nr5a1(antisense)r    23   AAA CCA AAA CCC GAA ACG CGA AA   43.5   58.5   60   192               shh(sense)f   26   CGA TTC GGG GGA TGG ATT AGC GTT GT   53.9   62.6   65   193               shh(sense)r   30   CGA AAT CCC CCT AAC GAA AAT CTC CGA AAA   43.3   60.4   65   194               shh(antisense)f   25   CGG GGT TTT TTT AGC GGG GGT TTT C   52.0   61.0   65   195               shh(antisense)r    29   CGC GAT CCG AAA AAT AAA TTA ACG CTA CT   37.9   57.8   65   196               spsb4(sense)f   20   AGC GGT TCG AGT TGG GAC GG   65.0   62.3   65   197               spsb4(sense)r   24   GAA AAA CGC GAT CGC CGA AAA CGC   54.2   61.8   65   198               spsb4(antisense)f   28   GAA GGT TAT TAA TTT AAT AGT CGC GGA A   32.1   53.7   65   199               spsb4(antisense)r   25   AAA AAA AAC GTT CCC GAC GAC CGC G    52.0   62.4   65   200               prkcbf(re-design)   25   AGT TGT TTT ATA TAT CGG CGT TCG G    40.0   55.3   65   201               prkcbr(re-design)   23   GAC TAT ACA CGC TTA ACC GCG AA    47.8   56.9   65   202                    
In Table 2b, Ta is the optimized annealing temperature and Tm is the melting temperature in ° C. in 50 mM NaCl.
 
     Detection of Other GI Neoplasms 
     The markers were then assessed in the 2nd set of samples, which included other GI cancers and precancers as indicated above. The methods, including reaction conditions and platform, were identical to the first validation described above. Data were normalized to the amount of input DNA, allowing copy numbers to be compared between the two validations. Analysis consisted of PCA and k-fold cross-validation, as before. 
     Some methylation sequences that were identified exhibited extraordinary degrees of discrimination, even as stand-alone markers. For example, IKZF1 had 95% sensitivity for adenoma and 80% sensitivity for CRC, with virtually no background methylation in normal samples. The S/N ratios were in excess of 10,000—a degree of discrimination rarely seen with any class of markers. The chr12.133 assay, specific to a completely un-annotated and un-described stretch of methylated DNA, was also adept at detecting all cancers equally well. Several markers (cd1d, chr12.133, clec11a, elmo1, vwc2, zuf71) individually achieved perfect discrimination for gastric cancer, as did twist1 for colorectal cancer (Table 6). 
     Tumor Site Prediction 
     The data collected during the development of embodiments of the technology demonstrate that the methylation states of particular DNA markers accurately predict neoplasm site. In this analysis, a recursive partitioning regression model was used in a decision tree analysis based on combinations of markers with complementary performance to generate a robust site classification. 
     In particular, statistical analyses were performed to validate the sensitivity and specificity of marker combinations. For example, using a “Random Forest” model (see, e.g., Breiman (2001) “Random Forests”  Machine Learning  45: 5-32), tree models were constructed using recursive partitioning tree regression, e.g., as implemented by the rPart package in the R statistical software. Recursive partitioning tree regression is a regression technique which tries to minimize a loss function and thus maximize information content for classification problems. The tree is built by the following process: first the single variable is found that best splits the data into two groups. The data is separated, and then this process is applied separately to each sub-group, and so on recursively until the subgroups either reach a minimum size or until no improvement can be made. The second stage of the procedure consists of using cross-validation to trim back the full tree. A cross validated estimate of risk is computed for a nested set of sub trees and a final model is produced from the sub tree with the lowest estimate of risk. See, e.g., Therneau (2012) “An Introduction to Recursive Partitioning Using RPART Routines”, available at The Comprehensive R Archive Network; Breiman et al. (1983) “Classification and Regression Trees” Wadsworth, Belmont, C A; Clark et al. (1992) “Tree-based models” in J. M. Chambers and T. J. Hastie, eds., Statistical Models in S, chapter 9. Wadsworth and Brooks/Cole, Pacific Grove, Calif.; Therneau (1983) “A short introduction to recursive partitioning” Orion Technical Report 21, Stanford University, Department of Statistics; Therneau et al. (1997) “An introduction to recursive partitioning using the rpart routines” Divsion of Biostatistics 61, Mayo Clinic. 
     As used in this analysis, the classification is Upper GI Lesion vs. Lower GI Lesion vs. Normal Samples. At each node of the regression, all variables are considered for entry but only the variable with the greatest decrease in risk of predicted outcome is entered. Subsequent nodes are added to the tree until there is no change in risk. To avoid overfitting, random forest regression was used. In this approach, 500 prediction trees were generated using bootstrapping of samples and random selection of variables. To determine the importance of the i-th variable, the i-th variable is set aside and the corresponding error rates for the full fit (including all data) vs. the reduced fit (all data except the i-th variable) using all 500 predictions are compared. 
     A forest of 500 trees was constructed to test the predictive power of candidate markers for discriminating among normal tissue, upper gastrointestinal lesions, and lower gastrointestinal lesions. This procedure is done at a very high level of robustness. First, for each tree creation, a bootstrap sample is taken of the dataset to create a training set and all observations not selected are used as a validation set. At each branch in the tree, a random subset of markers is used and evaluated to determine the best marker to use at that particular level of the tree. Consequently, all markers have an equal chance of being selected. The technique provides a rigorous validation and assessment of the relative importance of each marker. Each of the 500 trees is allowed to “vote” on which class a particular sample belongs to with the majority vote winning. The estimated misclassification rate is estimated from all samples not used for a particular tree. 
     To test the relative importance of a given marker, the validation set is again used. Here, once a tree is fit, the validation data is passed down the tree and the correct classification rate is noted. Then, the marker values are randomly permuted within the m-th marker, they are passed down the tree, and the correct classification is again noted. If a marker has high importance, the actual data provides a better classification than the randomly permuted data. Misclassification by the permuted data is referred to as the Mean Decrease in Accuracy. If a marker is not important, the actual data will provide a similar classification as the randomly permuted data.  FIG. 1  is a plot of the marker importance as measured by Mean Decrease in Accuracy. The vertical lines are at 2.5% and 5%. These data indicate that, e.g., for clec11a the estimated Mean Decrease in Accuracy is approximately 12%, indicating that when randomly permuting the results of this marker, the overall accuracy of the prediction decreases by 12%.  FIG. 1  lists the markers in order of importance. 
     The estimated overall misclassification rate of the 500 trees in the forest was 0.0989. The results of the voting process across all 500 trees in the forest is summarized in Table 3 and expanded by subtype in Table 4. In the tables, the tissue sample type is listed in the first column (e.g., non-cancerous (“Normal”), upper gastrointestinal cancer (“Upper”), or lower gastrointestinal cancer (“Lower”) in Table 3; adenoma (“Ad”), normal colon (“Colo Normal”), colorectal cancer (“CRC”), esophageal cancer (“Eso C”), pancreatic cancer (“Pan C”), normal pancreas (“Pan Normal”), and stomach cancer (“Stomach C”) in Table 4). A quantitative classification of the sample by the analysis is provided as a number is columns 1, 2, or 3, for classification as an upper gastrointestinal cancer (column 1), a lower gastrointestinal cancer (column 2), or a normal tissue (column 3), respectively. The numbers provide a measure indicating the success rate of the classifier (e.g., the number of times the classifier classified the sample type in the first column as the type indicated in the first row). 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                   
                 1 
                 2 
                 3 
                 class.error 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 Upper 
                 59.00 
                 1.00 
                 7.00 
                 0.12 
               
               
                 Lower 
                 3.00 
                 33.00 
                 2.00 
                 0.13 
               
               
                 Normal 
                 1.00 
                 0.00 
                 44.00 
                 0.02 
               
               
                   
               
               
                 column 1 = upper GI; column 2 = lower GI; Column 3 = normal 
               
            
           
         
       
     
     
       
         
           
               
               
               
             
               
                   
                 TABLE 4 
               
             
            
               
                   
                   
               
               
                   
                   
                 Predicted by Model 
               
            
           
           
               
               
               
               
               
            
               
                   
                 Sample type 
                 UGIC* 
                 CRN** 
                 Normal 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 UGIC* 
                   
                   
                   
               
               
                   
                 Pancreas Cancer 
                 35 
                 0 
                 3 
               
               
                   
                 Esophagus Cancer 
                 15 
                 0 
                 3 
               
               
                   
                 Stomach Cancer 
                 9 
                 1 
                 0 
               
               
                   
                 CRN** 
                   
                   
                   
               
               
                   
                 Colon Cancer 
                 2 
                 16 
                 2 
               
               
                   
                 Colon Adenoma 
                 1 
                 17 
                 0 
               
               
                   
                 Controls 
                   
                   
                   
               
               
                   
                 Pancreas Normal 
                 0 
                 0 
                 15 
               
               
                   
                 Colon Normal 
                 0 
                 0 
                 20 
               
               
                   
                 Buffy Coat Normal 
                 1 
                 0 
                 9 
               
               
                   
                   
               
               
                   
                 *UGIC = Upper GI Cancer, 
               
               
                   
                 **CRN = CRC + Adenoma ≥ 1 cm 
               
            
           
         
       
     
     Additional analysis demonstrated that a combination of two markers accurately predicted tumor site in &gt;90% of samples, the top 17 two-marker combinations accurately predicted tumor site in &gt;80% of samples, and the top 49 combinations accurately predicted tumor site in 70% of the samples. This observation that multiple combinations of DNA methylation markers accurately predict tumor site demonstrates the robustness of the technology. 
     Using the top two markers in the recursive partition decision tree, all normal tissues were correctly classified as normal, all gastric cancers were correctly classified as upper GI, nearly all esophageal and pancreatic cancers were correctly classified as upper GI, and nearly all colorectal cancers and precancers (adenomas) were correctly classified as lower GI. During the development of embodiments of the technology provided herein, statistical analyses focused on a set of specific markers consisting of clec11a, c13orf18, kcnn2, abcb1, slc38a3, cd1c, ikzf1, adcy1, chr12133, rspo3, and twist1. In particular, statistical analyses described above were directed toward identifying sets of markers (e.g., having two or more markers) that provide increased power to identify cancer and/or discriminate between cancers. Table 5 summarizes the accuracy for each pairwise set of markers, namely clec11a, c13orf18, kcnn2, abcb1, slc38a3, cd1c, ikzf1, adcy1, chr12133, rspo3, and twist1. According to this analysis, the pair of markers consisting of clec11a and twist1 is the most informative, but various other combinations have similar accuracy. 
     
       
         
           
               
             
               
                 TABLE 5 
               
             
            
               
                   
               
               
                 Accuracy for Site Prediction Using  
               
               
                 Various Marker Combinations 
               
            
           
           
               
               
               
            
               
                   
                 accuracy 
                 markers 
               
               
                   
                   
               
            
           
           
               
               
               
            
               
                   
                 90.7 
                 clec11a twist1 
               
               
                   
                 88.7 
                 clec11a chr12.133 
               
               
                   
                 88.7 
                 clec11a rspo3 
               
               
                   
                 88 
                 clec11a ikzf1 
               
               
                   
                 86.7 
                 clec11a adcy1 
               
               
                   
                 84.7 
                 twist1 c13orf18 
               
               
                   
                 84 
                 clec11a cd1d 
               
               
                   
                 83.3 
                 twist1 abcb1 
               
               
                   
                 83.3 
                 c13orf18 chr12.133 
               
               
                   
                 83.3 
                 abcb1 chr12.133 
               
               
                   
                 83.3 
                 abcb1 rspo3 
               
               
                   
                 82 
                 c13orf18 rspo3 
               
               
                   
                 81.3 
                 abcb1 ikzf1 
               
               
                   
                 80.7 
                 abcb1 adcy1 
               
               
                   
                 80 
                 twist1 kcnn2 
               
               
                   
                 80 
                 c13orf18 adcy1 
               
               
                   
                 80 
                 cd1d rspo3 
               
               
                   
                 79.3 
                 c13orf18 cd1d 
               
               
                   
                 79.3 
                 kcnn2 adcy1 
               
               
                   
                 79.3 
                 kcnn2 rspo3 
               
               
                   
                 79.3 
                 cd1d ikzf1 
               
               
                   
                 78.7 
                 c13orf18 ikzf1 
               
               
                   
                 77.3 
                 kcnn2 ikzf1 
               
               
                   
                 77.3 
                 abcb1 cd1d 
               
               
                   
                 76.7 
                 twist1 cd1d 
               
               
                   
                 76.7 
                 kcnn2 chr12.133 
               
               
                   
                 76.7 
                 chr12.133 rspo3 
               
               
                   
                 76 
                 cd1d chr12.133 
               
               
                   
                 75.3 
                 twist1 rspo3 
               
               
                   
                 75.3 
                 kcnn2 cd1d 
               
               
                   
                 74.7 
                 twist1 ikzf1 
               
               
                   
                 74 
                 twist1 slc38a3 
               
               
                   
                 74 
                 slc38a3 ikzf1 
               
               
                   
                 74 
                 slc38a3 chr12.133 
               
               
                   
                 73.3 
                 twist1 chr12.133 
               
               
                   
                 73.3 
                 slc38a3 adcy1 
               
               
                   
                 73.3 
                 adcy1 rspo3 
               
               
                   
                 72.7 
                 slc38a3 rspo3 
               
               
                   
                 72 
                 cd1d adcy1 
               
               
                   
                 72 
                 ikzf1 chr12.133 
               
               
                   
                 72 
                 adcy1 chr12.133 
               
               
                   
                 71.3 
                 ikzf1 adcy1 
               
               
                   
                 70.7 
                 clec11a c13orf18 
               
               
                   
                 70.7 
                 clec11a kcnn2 
               
               
                   
                 70.7 
                 clec11a abcb1 
               
               
                   
                 70.7 
                 clec11a slc38a3 
               
               
                   
                 70.7 
                 ikzf1 rspo3 
               
               
                   
                 70 
                 twist1 adcy1 
               
               
                   
                 70 
                 kcnn2 abcb1 
               
               
                   
                 68 
                 slc38a3 cd1d 
               
               
                   
                 66.7 
                 c13orf18 abcb1 
               
               
                   
                 65.3 
                 c13orf18 kcnn2 
               
               
                   
                 65.3 
                 kcnn2 slc38a3 
               
               
                   
                 64.7 
                 c13orf18 slc38a3 
               
               
                   
                 56 
                 abcb1 slc38a3 
               
               
                   
                   
               
            
           
         
       
     
     Example 3—AUC Analysis of Individual Markers 
     Statistical analysis included principle component analysis to identify uncorrelated linear combinations of the markers whose variance explains the greatest percentage of variability observed in the original data. The analysis determined the relative weights of each marker to discriminate between treatment groups. As a result of this analysis, end-point AUC values were determined for a subset of the markers that measure each marker&#39;s power to discriminate a specific cancer (esophageal, stomach, pancreatic, colorectal, and adenoma) from 1) the other cancer types and from 2) normal samples (e.g., not comprising cancer tissue or not from a patient having cancer or who may develop cancer). These data are provided in Table 6. 
     
       
         
           
               
             
               
                 TABLE 6 
               
               
                   
               
               
                 AUC values for a subset of markers 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 BMP3 
                 NDRG4 
                 abcb1 
                 adcy1 
               
               
                   
               
               
                 Eso C. vs. Other 
                 0.51 
                 0.58 
                 0.67 
                 0.39 
               
               
                 Eso C. vs. Normal 
                 0.82 
                 0.86 
                 0.83 
                 0.63 
               
               
                 Stomach C. vs. Other 
                 0.72 
                 0.70 
                 0.87 
                 0.65 
               
               
                 Stomach C. vs. Normal 
                 0.91 
                 0.95 
                 1.00 
                 0.86 
               
               
                 Pan C. vs. Other 
                 0.59 
                 0.66 
                 0.73 
                 0.69 
               
               
                 Pan C. vs. Normal 
                 0.90 
                 0.90 
                 0.91 
                 0.94 
               
               
                 CRC. vs. Other 
                 0.74 
                 0.59 
                 0.46 
                 0.69 
               
               
                 CRC. vs. Normal 
                 0.91 
                 0.87 
                 0.72 
                 0.86 
               
               
                 Ad. vs. Other 
                 0.74 
                 0.71 
                 0.35 
                 0.71 
               
               
                 Ad. vs. Normal 
                 0.96 
                 0.94 
                 0.61 
                 0.99 
               
               
                   
               
               
                   
                 c13orf18 
                 cacnale 
                 cd1d 
                 chr12.133 
               
               
                   
               
               
                 Eso C. vs. Other 
                 0.60 
                 0.27 
                 0.52 
                 0.52 
               
               
                 Eso C. vs. Normal 
                 0.75 
                 0.42 
                 0.85 
                 0.86 
               
               
                 Stomach C. vs. Other 
                 0.78 
                 0.70 
                 0.75 
                 0.81 
               
               
                 Stomach C. vs. Normal 
                 0.88 
                 0.96 
                 1.00 
                 1.00 
               
               
                 Pan C. vs. Other 
                 0.81 
                 0.85 
                 0.73 
                 0.57 
               
               
                 Pan C. vs. Normal 
                 0.89 
                 0.96 
                 0.94 
                 0.86 
               
               
                 CRC. vs. Other 
                 0.37 
                 0.56 
                 0.67 
                 0.73 
               
               
                 CRC. vs. Normal 
                 0.51 
                 0.75 
                 0.88 
                 0.89 
               
               
                 Ad. vs. Other 
                 0.21 
                 0.42 
                 0.54 
                 0.72 
               
               
                 Ad. vs. Normal 
                 0.35 
                 0.53 
                 0.88 
                 0.99 
               
               
                   
               
               
                   
                 clec11a 
                 ebmol 
                 eomes 
                 glc1 
               
               
                   
               
               
                 Eso C. vs. Other 
                 0.55 
                 0.46 
                 0.37 
                 0.51 
               
               
                 Eso C. vs. Normal 
                 0.81 
                 0.76 
                 0.54 
                 0.69 
               
               
                 Stomach C. vs. Other 
                 0.84 
                 0.76 
                 0.70 
                 0.74 
               
               
                 Stomach C. vs. Normal 
                 1.00 
                 1.00 
                 0.89 
                 0.88 
               
               
                 Pan C. vs. Other 
                 0.89 
                 0.62 
                 0.70 
                 0.54 
               
               
                 Pan C. vs. Normal 
                 0.98 
                 0.93 
                 0.87 
                 0.73 
               
               
                 CRC. vs. Other 
                 0.31 
                 0.71 
                 0.61 
                 0.64 
               
               
                 CRC. vs. Normal 
                 0.56 
                 0.83 
                 0.79 
                 0.80 
               
               
                 Ad. vs. Other 
                 0.35 
                 0.70 
                 0.59 
                 0.65 
               
               
                 Ad. vs. Normal 
                 0.59 
                 0.92 
                 0.77 
                 0.82 
               
               
                   
               
               
                   
                 ibif1 
                 kcnk12 
                 kcnn2 
                 loc63930 
               
               
                   
               
               
                 Eso C. vs. Other 
                 0.11 
                 0.39 
                 0.68 
                 0.40 
               
               
                 Eso C. vs. Normal 
                 0.10 
                 0.66 
                 0.84 
                 0.69 
               
               
                 Stomach C. vs. Other 
                 0.80 
                 0.65 
                 0.76 
                 0.65 
               
               
                 Stomach C. vs. Normal 
                 0.98 
                 0.90 
                 0.91 
                 0.88 
               
               
                 Pan C. vs. Other 
                 0.91 
                 0.71 
                 0.76 
                 0.61 
               
               
                 Pan C. vs. Normal 
                 0.97 
                 0.94 
                 0.91 
                 0.88 
               
               
                 CRC. vs. Other 
                 0.50 
                 0.71 
                 0.46 
                 0.84 
               
               
                 CRC. vs. Normal 
                 0.58 
                 0.93 
                 0.67 
                 0.95 
               
               
                 Ad. vs. Other 
                 0.21 
                 0.67 
                 0.30 
                 0.69 
               
               
                 Ad. vs. Normal 
                 0.22 
                 0.92 
                 0.47 
                 0.93 
               
               
                   
               
               
                   
                 prkcb 
                 rspo3 
                 scarf2 
                 slc38a3 
               
               
                   
               
               
                 Eso C. vs. Other 
                 0.44 
                 0.42 
                 0.13 
                 0.34 
               
               
                 Eso C. vs. Normal 
                 0.62 
                 0.68 
                 0.21 
                 0.50 
               
               
                 Stomach C. vs. Other 
                 0.71 
                 0.64 
                 0.70 
                 0.81 
               
               
                 Stomach C. vs. Normal 
                 0.85 
                 0.86 
                 0.82 
                 0.97 
               
               
                 Pan C. vs. Other 
                 0.74 
                 0.57 
                 0.93 
                 0.83 
               
               
                 Pan C. vs. Normal 
                 0.90 
                 0.93 
                 0.94 
                 0.96 
               
               
                 CRC. vs. Other 
                 0.56 
                 0.80 
                 0.49 
                 0.57 
               
               
                 CRC. vs. Normal 
                 0.71 
                 0.93 
                 0.57 
                 0.73 
               
               
                 Ad. vs. Other 
                 0.46 
                 0.82 
                 0.26 
                 0.32 
               
               
                 Ad. vs. Normal 
                 0.66 
                 1.00 
                 0.34 
                 0.47 
               
               
                   
               
               
                   
                 twist1 
                 vwe2 
                 wt1 
                 znf71 
               
               
                   
               
               
                 Eso C. vs. Other 
                 0.42 
                 0.52 
                 0.35 
                 0.70 
               
               
                 Eso C. vs. Normal 
                 0.74 
                 0.83 
                 0.66 
                 0.90 
               
               
                 Stomach C. vs. Other 
                 0.58 
                 0.78 
                 0.70 
                 0.89 
               
               
                 Stomach C. vs. Normal 
                 0.92 
                 1.00 
                 0.91 
                 1.00 
               
               
                 Pan C. vs. Other 
                 0.67 
                 0.58 
                 0.76 
                 0.50 
               
               
                 Pan C. vs. Normal 
                 0.94 
                 0.92 
                 0.98 
                 0.79 
               
               
                 CRC. vs. Other 
                 0.83 
                 0.72 
                 0.60 
                 0.63 
               
               
                 CRC. vs. Normal 
                 1.00 
                 0.90 
                 0.92 
                 0.91 
               
               
                 Ad. vs. Other 
                 0.70 
                 0.76 
                 0.64 
                 0.64 
               
               
                 Ad. vs. Normal 
                 0.95 
                 0.98 
                 0.89 
                 0.90 
               
               
                   
               
               
                   
                 st8sia1 
                 ikzf1 
                 pcbp3 
                 PCA1 
               
               
                   
               
               
                 Eso C. vs. Other 
                 0.45 
                 0.55 
                 0.54 
                 0.47 
               
               
                 Eso C. vs. Normal 
                 0.64 
                 0.88 
                 0.86 
                 0.79 
               
               
                 Stomach C. vs. Other 
                 0.77 
                 0.74 
                 0.76 
                 0.81 
               
               
                 Stomach C. vs. Normal 
                 0.92 
                 0.97 
                 0.97 
                 0.99 
               
               
                 Pan C. vs. Other 
                 0.65 
                 0.49 
                 0.64 
                 0.72 
               
               
                 Pan C. vs. Normal 
                 0.93 
                 0.85 
                 0.86 
                 0.96 
               
               
                 CRC. vs. Other 
                 0.58 
                 0.81 
                 0.67 
                 0.68 
               
               
                 CRC. vs. Normal 
                 0.74 
                 0.94 
                 0.90 
                 0.96 
               
               
                 Ad. vs. Other 
                 0.67 
                 0.80 
                 0.63 
                 0.62 
               
               
                 Ad. vs. Normal 
                 0.84 
                 0.99 
                 0.86 
                 0.98 
               
               
                   
               
            
           
         
       
     
     Example 4—Barrett&#39;s Esophagus and Esophageal Cancer 
     Development of esophageal cancer is closely linked with Barrett&#39;s epithelial metaplasia and pancreatic adenocarcinoma arises from discrete mucous cell metaplasias. See, e.g., Biankin et al (2003) “Molecular pathogenesis of precursor lesions of pancreatic ductal adenocarcinoma”  Pathology  35:14-24; Cameron et al (1995) “Adenocarcinoma of the esophagogastric junction and Barrett&#39;s esophagus”  Gastroenterology  109: 1541-1546. 
     To meaningfully curb the rising incidence of esophageal adenocarcinoma, effective methods are needed to screen the population for the critical precursor of Barrett&#39;s esophagus (BE). Minimally or non-invasive tools have been proposed for BE screening, but have been hampered by lack of optimally sensitive and specific markers. Desired screening markers discriminate BE from normal esophagogastric mucosa. Certain aberrantly methylated genes are associated as candidate markers for BE (see, e.g.,  Gastroenterology  2011; 140: S-222). 
     Accordingly, during the development of embodiments of the technology experiments were performed to assess the value of selected methylated DNA markers to discriminate BE from adjacent squamous esophagus (SE) and gastric cardia (GC) and from SE and GC in healthy controls. 
     Patients with and without known BE were recruited prior to routine upper endoscopy. BE cases had &gt;1 cm length of circumferential columnar mucosa with histologically confirmed intestinal metaplasia; controls had no BE as determined endoscopically. Biopsies were obtained in cases from BE, GC (1 cm below Z-line), and SE (&gt;2 cm above BE) cases, and in controls from GC (as for BE) and SE (5 cm above Z-line), and promptly frozen. Biopsy samples were processed as a batch, and assayed in blinded fashion. Following DNA extraction and bisulfite treatment, methylation on target genes was assayed by methylation-specific PCR for the markers APC, HPP1, SFRP1, and by QuARTS assay for the markers BMP3 and NDRG4. ß-actin was quantified as a control marker for total human DNA. 
     Among 25 BE cases and 22 controls, the median ages were 67 (range 39-83) and 50 (range 20-78), respectively, and men represented 72% and 46% of the subjects in the BE and control groups, respectively. Median BE length was 6 cm (range 2-14 cm). Except for APC, median levels of methylated markers were significantly and substantially (e.g., 200-1100 times) higher in BE than in adjacent SE and GC or relative to normal SE and GC. Sensitivities for BE at various specificities are shown for each marker (Table 7). Methylated markers were significantly higher in GC adjacent to BE than in GC from normal controls. Methylated APC was higher in BE than SE, but did not distinguish BE from GC. In contrast to methylated markers, ß-actin distributions were similar across tissue groups. Marker levels increased with BE length for NDRG4, SFRP1, BMP3, and HPP1 (p=0.01, 0.01, 0.02, and 0.04, respectively). Factors not significantly affecting marker levels included age, sex, inflammation, and presence of dysplasia (none (8), low grade (6), high grade (11)). 
     As such, these date demonstrate that the selected methylated DNA markers highly discriminate BE from GC and SE, and provide for useful screening applications. 
     
       
         
           
               
               
             
               
                 TABLE 7 
               
             
            
               
                   
               
               
                   
                 Sensitivity for BE, % 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 NDRG4 
                 SFRP1 
                 BMP3 
                 HPP1 
                 APC 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Specificity Cutoff* 
                   
                   
                   
                   
                   
               
               
                 100% 
                 96 
                 96 
                 84 
                 84 
                 0 
               
               
                  95% 
                 96 
                 96 
                 92 
                 88 
                 8 
               
               
                  90% 
                 96 
                 96 
                 92 
                 92 
                 8 
               
               
                   
               
               
                 *Based on combined SE and GC data from normal controls 
               
            
           
         
       
     
     Example 5—Methylated DNA Markers in Pancreatic Juice Discriminate Pancreatic Cancer from Chronic Pancreatitis and Normal Controls 
     Pancreatic juice analysis has been explored as a minimally-invasive approach to early detection of pancreatic cancer (PC). However, cytology and many molecular markers in pancreatic juice have proved insensitive or failed to distinguish PC from chronic pancreatitis (see, e.g.,  J Clin Oncol  2005; 23: 4524). Experiments were performed to verify that assay of aberrantly methylated genes may represent a more accurate approach for PC detection from pancreatic juice (see, e.g., Cancer Res 2006; 66: 1208). In particular, data were collected to assess selected methylated DNA markers assayed from pancreatic juice to discriminate case patients with PC from controls with chronic pancreatitis (CP) or a normal pancreas (NP). 
     A panel of 110 patients (66 PC, 22 CP, 22 NP controls) underwent secretin stimulated pancreatic juice collection during endoscopic ultrasound. Diagnoses were histologically confirmed for PC and radiographically-based for CP and NP. Juice was promptly frozen and stored at −80° C. Assays were performed in blinded fashion on samples thawed in batch. Candidate methylated DNA markers were selected by whole methylome sequencing in a separate tissue study. After DNA was extracted from pancreatic juice and bisulfite treated, gene methylation was determined by methylation-specific PCR for CD1D, CLEC11A, and KCNN2, or by QuARTS for BMP3 and NDRG4. KRAS mutations (7 total) were assayed by QuARTS (presence of any KRAS mutation was considered to be a positive). 8-actin, a marker for human DNA, was also assayed by QuARTS, to provide for control of DNA amount. 
     Respectively for PC, CP, and NP, the median age was 67 (range 43-90), 64 (range 44-86), and 60 (range 35-78); men represented 56, 68, and 21% of these groups respectively. All markers discriminated PC from NP but to a variable extent. The AUC was 0.91 (95% CI, 0.85-0.97), 0.85 (0.77-0.94), 0.85 (0.76-0.94), 0.78 (0.67-0.89), and 0.75 (0.64-0.87) for methylated CD1D, NDRG4, CLEC11A, KCNN2, and BMP3, respectively, and 0.75 (0.64-0.86) for mutant KRAS. Discrimination for PC by CD1D was significantly higher than by KRAS (p=0.01), KCNN2 (p=0.02), or BMP3 (p&lt;0.01). Positively rates in PC and CP are shown for each marker at 95 and 100% normal specificity cutoffs (Table 8); the positively rate in CP (false-positives) was lowest with CD1D and highest with KRAS. Marker levels were not significantly affected by PC site (head, body, tail) or stage (N0 vs. N1). ß-actin levels were similar across patient groups. 
     These data show that methylated DNA markers discriminate PC from CP and NP when assayed from pancreatic juice, e.g., secretin-stimulated pancreatic juice. In particular, methylated CD1D was significantly more sensitive for PC and showed substantially fewer false-positives with CP than did mutant KRAS. 
     
       
         
           
               
               
             
               
                 TABLE 8 
               
             
            
               
                   
               
               
                   
                 Positivity Rates, % 
               
            
           
           
               
               
               
            
               
                   
                 At 95% Specificity* 
                 At 100% Specificity* 
               
            
           
           
               
               
               
               
               
            
               
                   
                 PC 
                 CP 
                 PC 
                 CP 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 Methylation Markers 
                   
                   
                   
                   
               
               
                 CD1D 
                 75 
                 9 
                 63 
                 5 
               
               
                 NDRG4 
                 67 
                 14 
                 56 
                 5 
               
               
                 CLEC11A 
                 56 
                 18 
                 38 
                 5 
               
               
                 KCNN2 
                 33 
                 18 
                 33 
                 18 
               
               
                 BMP3 
                 31 
                 9 
                 23 
                 5 
               
               
                 Mutation Marker 
                   
                   
                   
                   
               
               
                 KRAS 
                 55 
                 41 
                 53 
                 32 
               
               
                   
               
               
                 *Specificity cutoffs based on NP data 
               
            
           
         
       
     
     Example 6—Sensitive DNA Marker Panel for Detection of Pancreatic Cancer by Assay in Pancreatic Juice 
     Pancreatic juice analysis represents a minimally-invasive approach to detection of pancreatic cancer (PC) and precancer. It has been found that specific methylated DNA markers in pancreatic juice discriminate PC from chronic pancreatitis (CP) and normal pancreas (Gastroenterology 2013; 144:S-90), but new markers and marker combinations remain unexplored. 
     Experiments were performed to assess the value of recently discovered methylated DNA markers and mutant KRAS assayed alone and combined in pancreatic juice to discriminate PC from chronic pancreatitis (CP) and reference controls (CON). 
     167 patients (85 PC, 30 CP, 23 premalignant intraductal mucinous neoplasm (IPMN), 29 CON) who had undergone secretin stimulated pancreatic juice collection during EUS were studied. Diagnoses were histologically based for PC, radiographically for CP, and histologically or radiographically for IPMN. Specificity was based on CON, which included patients with risk factors for PC, elevated pancreatic enzymes, or GI symptoms but radiographically-normal pancreas. Juice samples archived at −80° C. were blindly batch assayed. On DNA extracted from 200 μL pancreatic juice, gene methylation was determined after bisulfite treatment by quantitative allele-specific real-time target and signal amplification (QuARTS) for assay of ADCY1, CD1D, BMP3, PRKCB, KCNK12, C13ORF18, IKZF1, CLEC11A, TWIST, NDRG4, ELMO, and 55957 Mutant KRAS mutations (7 total) and 8-actin (a marker for total human DNA) were also assayed by QuARTS. From quantitative data, an algorithm was followed to achieve optimal discrimination by a panel combining all markers. 
     Respectively for PC, CP, IPMN, and CON: median age was 67 (IQR 60-77), 66 (55-77), 66 (60-76) and 70 (62-77); men comprised 52, 53, 49, and 72%. At respective specificity cutoffs of 90% and 95%: the combined marker panel achieved highest PC sensitivities (88% and 77%); ADCY1, the most sensitive single marker, detected 84% and 71%. Other single markers detected PC but to variable extents (table). Overall discrimination by area under ROC curve was higher by panel than by any single marker (p&lt;0.05), except ADCY1 (table). At 90% specificity, panel detected 44% of all IPMNs and 75% (¾) of subset with high grade dysplasia. Positivity rates were substantially lower in CP than in PC for all markers shown in Table 7 (p&lt;0.0001). At 100% specificity, the panel was positive in 58% PC, 17% IPMN, and 13% CP. Accordingly, these results demonstrate that a panel of novel methylated DNA markers and mutant KRAS assayed from pancreatic juice achieves high sensitivity for PC. 
     
       
         
           
               
             
               
                 TABLE 7 
               
             
            
               
                   
               
               
                 Marker positivity rates in pancreatic juice of patients 
               
               
                 with pancreatic cancer (PC), intraductal papillary mucinous 
               
               
                 neoplasm (IPMN), and chronic pancreatitis (CP). 
               
            
           
           
               
               
            
               
                   
                 Positivity Rates, % 
               
            
           
           
               
               
               
               
            
               
                   
                 AUC 
                 At 90% Specificity* 
                 At 95% Specificity* 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                   
                 (PC vs Con) 
                 PC 
                 IPMN 
                 CP 
                 PC 
                 IPMN 
                 CP 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Methylation 
                   
                   
                   
                   
                   
                   
                   
               
               
                 Markers** 
               
               
                 ADCY1 
                 0.89 
                 84 
                 39 
                 30 
                 71 
                 35 
                 17 
               
               
                 C13ORF18 
                 0.82 
                 67 
                 17 
                 13 
                 52 
                 13 
                 3 
               
               
                 PRKCB 
                 0.82 
                 62 
                 9 
                 20 
                 42 
                 4 
                 17 
               
               
                 CD1D 
                 0.82 
                 61 
                 22 
                 10 
                 46 
                 17 
                 10 
               
               
                 KCNK12 
                 0.82 
                 54 
                 17 
                 10 
                 25 
                 13 
                 3 
               
               
                 BMP3 
                 0.81 
                 49 
                 13 
                 7 
                 27 
                 13 
                 0 
               
               
                 IKZF1 
                 0.80 
                 71 
                 22 
                 20 
                 54 
                 22 
                 17 
               
               
                 Mutation 
               
               
                 Marker 
               
               
                 KRAS 
                 0.80 
                 59 
                 22 
                 13 
                 58 
                 17 
                 10 
               
               
                 All Markers 
               
               
                 Panel*** 
                 0.91 
                 88 
                 44 
                 37 
                 77 
                 39 
                 23 
               
               
                   
               
               
                 *Specificity cutoffs based on reference control (CON) data. 
               
               
                 **Top 7 individual methylated DNA markers shown. 
               
               
                 ***Except for ADCY1, the Panel had significantly higher AUC than individual methylated DNA markers (p &lt; 0.05). 
               
            
           
         
       
     
     Example 7: Detecting Pancreatic Cancer within Stool Sample Using CD1D Marker 
     Stool samples from 45 individuals having pancreatic cancer and 45 individuals not having pancreatic cancer were collected and tested for the presence of the CD1D marker. Pancreatic cancer was successfully detected using CD1D marker from stool. 
     Example 8: Novel DNA Methylation Markers Associated with Early-Stage Pancreatic Cancer 
     Study Overview: 
     In independent case-control tissue studies, experiments were performed to identify novel and highly discriminant methylation markers for PanC using RRBS for the discovery phase and methylation-specific PCR (MSP) for the validation phase. 
     Study Population: 
     After approval by the Mayo Clinic Institutional Review Board, tissue samples were identified from existing cancer registries. The accessible population included those who underwent distal pancreatectomy, pancreaticoduodenectomy, colectomy or colon biopsy with a frozen archived specimen. All tissues were reviewed by an expert gastrointestinal pathologist to confirm correct classification. The PanC case samples included pancreatic ductal adenocarcinoma tissues limited to early-stage disease (AJCC stage I and II) (Edge SBB, D. R.; Compton, C. C.; Fritz, A. G.; Greene, F. L.; Trotti, A. (Eds.), editor. AJCC Cancer Staging Manual. 7th ed: Springer, New York; 2010). Neoplasms arising from IPMN lesions were excluded. There were two control groups studied. The first, termed “normal pancreas,” included the histologically normal resection margins of low risk (serous cystadenoma) or focal pancreatic neoplasms (neuroendocrine tumors). The second control group included colonic epithelial tissues from patients confirmed to be free from PanC or colonic neoplasm. Cases and both controls were matched by sex, age (in 5-year increments) and smoking status (current or former vs. never). In a central core laboratory, case and control tissues were microdissected and DNA was extracted using a phenol-chloroform technique, yielding at least 500 ng of DNA. Case identification, matching and DNA extraction were performed by independent personnel to maintain blinding of laboratory personnel to case and control status. 
     Reduced Representation Bieulfite Sequencing: 
     Library preparation (Gu H, Bock C, Mikkelsen T S, Jager N, Smith Z D, Tomazou E, et al. Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution. Nat Methods. 2010; 7:133-6): Genomic DNA (300 ng) was fragmented by digestion with 10 Units of MspI, a methylation-specific restriction enzyme which recognizes CpG containing motifs. This enriches the samples for CpG content and eliminates redundant areas of the genome. Digested fragments were end-repaired and A-tailed with 5 Units of Klenow fragment (3′-5′ exo-), and ligated overnight to methylated TruSeq adapters (Illumina, San Diego Calif.) containing one of four barcode sequences (to link each fragment to its sample ID.) Size selection of 160-340 bp fragments (40-220 bp inserts) was performed using Agencourt AMPure XP SPRI beads/buffer (Beckman Coulter, Brea Calif.). Buffer cutoffs were 0.7× to 1.1× sample volumes of beads/buffer. Final elution volume was 22 uL (EB buffer—Qiagen, Germantown Md.) qPCR was used to gauge ligation efficiency and fragment quality on a small aliquot of sample. Samples then underwent bisulfite conversion (twice) using a modified EpiTect protocol (Qiagen). qPCR and conventional PCR (PfuTurbo Cx hotstart-Agilent, Santa Clara Calif.) followed by Bioanalyzer 2100 (Agilent) assessment on converted sample aliquots determined the optimal PCR cycle number prior to amplification of the final library. Conditions for final PCR: 50 uL r×n: 5 uL of 10× buffer, 1.25 uL of 10 mM each dNTP&#39;s, 5 uL primer cocktail (˜5 uM), 15 uL template (sample), 1 uL PfuTurbo Cx hotstart, 22.75 water. 95C-5 min; 98C-30 sec; 16 cycles of 98C-10 sec, 65C-30 sec, 72C-30 sec; 72C-5 min; 4C. Samples were combined (equimolar) into 4-plex libraries based on the randomization scheme and tested with the bioanalyzer for final size verification, and with qPCR using phiX standards and adaptor-specific primers. 
     Sequencing and Bioinformatics: 
     Samples were loaded onto flow cell lanes according to a randomized lane assignment with additional lanes reserved for internal assay controls. Sequencing was performed by the Next Generation Sequencing Core at the Mayo Clinic Medical Genome Facility on the Illumina HiSeq 2000. Reads were unidirectional for 101 cycles. Each flow cell lane generated 100-120 million reads, sufficient for a median coverage of 30-50 fold sequencing depth (read number per CpG) for aligned sequences. Standard Illumina pipeline software was used for base calling and sequence read generation in the fastq format. As described previously (Sun Z, Baheti S, Middha S, Kanwar R, Zhang Y, Li X, et al. SAAP-RRBS: streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing. Bioinformatics. 2012; 28:2180-1), SAAP-RRBS, a streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing, was used for sequence alignment and methylation extraction. 
     Validation Studies by Methylation-Specific PCR: 
     Overview: Two MSP-based validation studies were performed on expanded sample sets to confirm the accuracy and reproducibility of the observed differentially methylated candidates. The first, an internal validation study, was performed on unmatched, unblinded samples using biological and technical replicates of PanC and normal colon and technical replicates of normal pancreas. This step was performed to ensure that the sites of differential methylation identified by the RRBS data filtration, where % methylation was the unit of analysis, would be reflected in MSP, where the unit of analysis is the absolute genomic copy number of the target sequence, corrected by the concentration of input DNA for each sample. The second, external validation experiment, utilized MSP to test the top candidates in randomly allocated, matched, blinded, independent PanC, benign pancreas and normal colon samples. 
     Primer design: Primers for each marker were designed to target the bisulfite-modified methylated sequences of each target gene (IDT, Coralville Iowa) and a region without cytosine-phosphate-guanine sites in the ß-actin gene, as a reference of bisulfite treatment and DNA input. The design was done by either Methprimer software (University of California, San Francisco Calif.) or by semi-manual methods (by H.Z and W.R.T). Assays were then tested and optimized by running qPCR with SYBR Green (Life Technologies, Grand Island N.Y.) dyes on dilutions of universally methylated and unmethylated genomic DNA controls. 
     Methylation specific PCR: MSP reactions were performed on tissue-extracted DNA as previously described (Kisiel J B, Yab T C, Taylor W R, Chari S T, Petersen G M, Mahoney D W, et al. Stool DNA testing for the detection of pancreatic cancer: assessment of methylation marker candidates. Cancer. 2012; 118:2623-31). Briefly, DNA was bisulfite treated using the EZ DNA Methylation Kit (Zymo Research, Orange, Calif.) and eluted in buffer. One μl bisulfite-treated DNA was used as a template for methylation quantification with a fluorescence-based real-time PCR, performed with SYBR Green master mix (Roche, Mannheim Germany). Reactions were run on Roche 480 LightCyclers (Indianapolis, Ind.), where bisulfite-treated CpGenome Universal Methylated DNA (Millipore, Billerica, Mass.) was used as a positive control, and serially diluted to create standard curves for all plates. Oligonucleotide sequences and annealing temperatures are available upon request. 
     Statistical Analysis 
     RRBS: The primary comparison of interest was the methylation difference between cases and pancreatic controls at each mapped CpG. CpG islands are biochemically defined by an observed to expected CpG ratio exceeding 0.6 (Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. Journal of molecular biology 1987; 196:261-82). However, for this model, tiled units of CpG analysis “differentially methylated region (DMR)” were created based on the distance between CpG site locations for each chromosome. As the distance between any given CpG exceeded the previous or next location by more than 100 bps, a new island identifier was created. Islands with only a single CpG were excluded. The secondary outcome was the same comparison between cases and colon controls. Individual CpG sites were considered for differential analysis only if the total depth of coverage per disease group was ≥200 reads (roughly equating to an average of 10 reads per subject) and the variance of % methylation was greater than zero (non-informative CpG sites with 0 variance were excluded). The criteria for read depth were based on the desired statistical power to detect a difference of 10% in the methylation rate between any two groups in which the sample size of individuals for each group was 18. 
     Statistical significance was determined by logistic regression on the % methylation per DMR (using the actual counts) with the groups defined as PanC, normal pancreas, and normal colon. To account for varying read depths across individual subjects, an over-dispersed logistic regression model was used, where dispersion parameter was estimated using the Pearson Chi-square statistic of the residuals from fitted model. To assess strand specific methylation, forward and reverse regions were analyzed separately. The DMRs were then ranked according to their significance level and were considered as a viable marker region if the methylation rate in the controls was ≤1% but ≥10% in PanC. Each significant DMR was considered as a candidate marker. 
     For the internal validation study, the primary outcome was the area under the receiver operating characteristics curve (AUC) for each marker. This was calculated using logistic regression (JMP version 9.0.1, SAS Institute, Cary N.C.) to model the strength of the concentration-corrected copy number of each marker with PanC in comparison to normal pancreas and normal colon. The markers with the highest AUC values and widest ratio of median genomic copy number between cases and controls were selected for the external validation study. The primary outcome for the external validation experiment was the AUC for each marker plotted against the signal strength of each marker, measured by the log of the ratio of median corrected copy number in cases compared to controls. With eighteen cases there is &gt;80% power to detect an area under the curve of 0.85 or higher from the null hypothesis of 0.5 at a two-sided significance level 0.05. The secondary endpoint was the AUC of two-marker combinations, measured by logistic regression, in which both markers were required to independently associate with PanC cases. 
     RRBS Marker Discovery 
     Matched, blinded, randomly allocated DNA extracts from 18 pancreatic cancer tumors, 18 benign pancreatic control tissues and 18 normal colon epithelial tissues were sequenced by RRBS. Median age was 61 (interquartile range 52-65), 61% were women, and 44% were current or former smokers. A total of 6,101,049 CpG sites were captured in any of the samples with at least 10× coverage. After selecting only CpG sites where group coverage and variance criteria were met, a total of 1,217,523 CpG sites were further considered for analysis. Approximately 500 DMRs met significance criteria for differential methylation. Among these, we identified 107 candidate regions with sufficient methylation signatures for MSP primer design. Methylation signatures ranged from 3 neighboring CpGs to 52 CpGs. Methylation levels of the pancreatic cancers rarely exceeded 25% at filtered CpGs, reflecting high levels of contaminating stromal cells. This was confirmed after sequencing each of the cancers for KRAS mutations to verify allele frequencies for the positive samples; for the 50% of PanC specimens which harbored a heterozygous KRAS base change, the frequency of the mutant allele was at least 4 times less than the corresponding wild-type allele. 
     Internal Validation 
     Based on the number of neighboring CpGs in each candidate gene methylation signature, primers were designed for 87 of the 107 candidate markers. MSP was then used to assay the candidates in sample of DNA from an additional 20 unblinded PanC lesions, 10 additional normal colonic epithelial samples (biologic replicates) as well as, remaining DNA samples from the 18 sequenced PanC lesions, 15 of the sequenced benign pancreatic tissues and 10 of the sequenced normal colon samples (technical replicates). With first-pass primer designs, 74 of 87 markers successfully amplified. With re-design, the remaining 13 primers successfully amplified and were tested in 12 unblinded PanC samples and 10 normal colon samples. ß-actin amplified in all samples. With either first or second-pass MSP, 31 of 87 candidate markers had an AUC&gt;0.85. Based on the magnitude of difference in median genomic copy number between cases and controls for each candidate marker, 23 were selected for external validation in independent samples. These were ABCB1, ADCY1, BMP3, C13ORF18, CACNA1C, CD1D, CHR12:133484978-133485738 (CHR12 133), CLEC11A, ELMO1, FOXP2, GRIN2D, IKZF1, KCNK12, KCNN2, NDRG4, PRKCB, RSPO3, SCARF2, SHH, SLC38A3, TWIST1, VWC3 and WT1. 
     External Validation 
     Matched, blinded, randomly allocated DNA from 18 PanC, 18 benign pancreatic and 36 normal colon epithelial samples were assayed by MSP for the 23 top candidates. The median age of this subset was 60 (interquartile range 54-64). The majority (55%) of samples came from men and 61% were current or former smokers. ß-actin amplified in all samples. 9 of 23 candidates showed excellent association with PanC. The individual AUC values for CACNA1C, CHR12.133, WT1, GRIN2D, ELMO1, TWIST1, C13ORF18, KCNN2, and CLEC11A were 0.95, 0.95, 0.94, 0.94, 0.93, 0.92, 0.91, 0.90 and 0.90, respectively. Good association was seen with 9 other candidates; the AUC values for PRKCB, CD1D, SLC38A3, ABCB1, KCNK12, VWC2, RSPO3, SHH and ADCY1 were 0.89, 0.88, 0.86, 0.86, 0.86, 0.85, 0.85, 0.85 and 0.84 respectively. 
     The log ratio of the median case and control values for each marker was plotted against the AUC. Eight markers, SHH, KCNK12, PRKCB, CLEC11, C13ORF18, TWIST1, ELMO1 and CHR12.133 each had an AUC greater than 0.85, and showed greater than 1.5 log (&gt;30-fold) greater genomic copy number among cases than controls. KCNK12, PRKCB, ELMO1 and CHR12.133 showed greater than 2 log (&gt;100-fold) difference. 
     Complementarity Analysis 
     Among all 231 possible 2-marker combinations, both markers remained highly significant in 30 (13%) pair-wise models of association with PanC. Of those, 18 (8%) showed improvement of the AUC. Noteworthy among several complementary markers, C13ORF8 improved the accuracy of CACNA1C, WT1, GRIND2D, SLC38A3 and SCARF2 with AUCs of 0.99, 0.99, 0.97, 0.96, and 0.95, respectively, for each combination. Though the AUC for SHH as an individual marker was 0.85, it improved the performance of 6 other markers when paired. The AUC of CACNA1C, WT1, SLC38A3, ABCB1, VWC2 and RSPO3improved to 0.96, 0.95, 0.92, 0.98, 0.88 and 0.95, respectively when combined in models with SHH. Of the 18 most robust marker combinations, 9 combinations could be tested in pair-wise comparisons from the internal validation data set. Of these, 7 pairs (78%) remained highly significant in both data sets. 
     Example 9: Highly Discriminant Methylated DNA Markers for Detection of Barrett&#39;s Esophagus 
     To curb the rising incidence of esophageal adenocarcinoma, effective methods are needed to screen the population for the critical precursor—Barrett&#39;s esophagus (BE). Minimally or non-invasive tools have been proposed but hampered by lack of optimally sensitive and specific markers. Experiments were performed and aberrantly methylated BMP3 and NDRG4 were identified as discriminant candidate markers for BE. 
     An aim of such experiments was to prospectively assess the accuracy of methylated BMP3 and NDRG4 to identify BE using endoscopic biopsies (Phase 1) and brushings from the whole esophagus and cardia to simulate non-endoscopic sampling devices (Phase 2). 
     Cases with and controls without BE were recruited prior to endoscopy. BE cases had &gt;1 cm of circumferential columnar mucosa with confirmed intestinal metaplasia; controls had no BE endoscopically. In Phase 1, biopsies were obtained in cases from BE, gastric cardia ((GC); 1 cm below Z-line) and squamous epithelium ((SE); &gt;2 cm above BE) and in controls from GC (as for BE) and SE (5 cm above Z-line); then promptly frozen. Biopsy samples were processed as a batch, and assayed in blinded fashion. In Phase 2, specimens were obtained using a high capacity endoscopic cytology brush (Hobbs Medical, Stafford Springs Conn.); the cardia, BE (in cases), and full esophageal length were brushed to simulate a swallowed sponge sampling device. Following DNA extraction and bisulfite treatment, methylation on target genes was assayed by quantitative allele-specific real-time target and signal amplification. 8-actin was also quantified as a marker for total human DNA. 
     100 subjects were prospectively studied. Phase 1: Among 40 BE cases and 40 controls: median age was 65 (quartiles 55-77) and 54 (37-69) and men comprised 78% and 48%, respectively. Median BE length was 6 cm (range 3-10). Median levels of methylated markers were substantially higher (34-600 times) in BE than in adjacent SE and GC or than in normal SE and GC (Table). In contrast to methylated markers, ß-actin distributions were similar across tissue groups. Both marker levels increased with BE length and age, p&lt;0.001 whereas only NDRG4 increased significantly with presence of dysplasia (none (19), low grade (9), high grade (11); p=0.003). Factors not significantly affecting marker levels included sex and inflammation. Phase 2: Among 10 BE cases and 10 controls, median age was 64 (59-70) and 66 (49, 71) and men comprised 80 and 30% respectively. Median BE length was 2 cm (range 1-4). Discrimination of BE by markers was extraordinary with AUC of 1.0 for NDRG4 and 0.99 for BMP3; levels were &gt;100 times higher in cases than controls ( FIG. 2 ). 
     These experiments demonstrate that selected methylated DNA markers highly discriminate BE from normal GC and SE, both in biopsy and brushed specimens. Table 9 shows the function and cancer biology associations of the selected methylated DNA markers. 
     
       
         
           
               
             
               
                 TABLE 8 
               
             
            
               
                   
               
               
                 Marker levels (copy numbers of markers adjusted for beta  
               
               
                 actin) for BMP3 and NDRG4 biopsies from BE cases (cardia,  
               
               
                 Barrett&#39;s, squamous) and controls (cardia, squamous). 
               
            
           
           
               
               
               
            
               
                   
                 BMP3 
                 NDRG4 
               
            
           
           
               
               
               
               
               
            
               
                   
                 Normal 
                 Barrett&#39;s 
                 Normal 
                 Barrett&#39;s 
               
               
                   
                 controls 
                 cases 
                 controls 
                 cases 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 Squamous 
                 0.8 
                 5.6 
                 1.0 
                 4.9 
               
               
                 Q1, Q3 
                 0.3, 2.2 
                 0.7, 14.8 
                 0.5, 2.7 
                 1.5, 10.9 
               
               
                 P90, P95 
                 7.0, 23.0 
                 25.5, 50.3 
                 5.0, 13.7 
                 32.0, 64.1 
               
               
                 BE 
                   
                 300.2 
                   
                 390.6 
               
               
                 Q1, Q3 
                   
                 137.1, 659.5 
                   
                 146.6, 763.5 
               
               
                 P90, P95 
                   
                 1083.1, 1219.0 
                   
                 921.8, 1006.6 
               
               
                 Cardia 
                 0.5 
                 8.2 
                 2.3 
                 11.5 
               
               
                 Q1, Q3 
                 0.3, 1.9 
                 2.8, 40.3 
                 1.0, 6.3 
                 5.0, 48.3 
               
               
                 P90, P95 
                 10.3, 16.4 
                 190.7, 431.5 
                 13.1, 15.4 
                 116.7, 345.0 
               
               
                 Composite 
                 1.3 
                 131.4 
                 2.3 
                 136.5 
               
               
                 Q1, Q3 
                 0.4, 3.8 
                 67.1, 242.7 
                 1.1, 5.3 
                 68.9, 272.3 
               
               
                 P90, P95 
                 10.0, 15.3 
                 402.9, 417.9 
                 8.1, 12.5 
                 344.0, 383.3 
               
            
           
           
               
               
               
            
               
                 Pvalue 
                 &lt;0.0001 
                 &lt;0.0001 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 9 
               
             
            
               
                   
               
               
                 Function and cancer biology associations of top candidate markers 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                 Reference 
               
               
                   
                   
                   
                   
                   
                 (complete 
               
               
                   
                   
                   
                 Protein 
                 Cancer 
                 reference 
               
               
                 DMR 
                 Symbol 
                 Gene name 
                 Function 
                 association 
                 below table) 
               
               
                   
               
               
                 Chr7: 
                 ABCB1 
                 ATP-binding 
                 Membrane- 
                 Multi-drug 
                 Lee, et al. 
               
               
                 87229775- 
                   
                 cassette, sub- 
                 associated 
                 resistance to 
                 2013 
               
               
                 87229856 
                   
                 family B, 
                 transporter 
                 chemotherapy 
                   
               
               
                   
                   
                 member 1 
                 protein 
                   
                   
               
               
                 Chr7: 
                 ADCY1 
                 Adenylate 
                 Transmembrane 
                 Methylation 
                 Vincent, et al. 
               
               
                 45613877- 
                   
                 cyclase 1 
                 signalling 
                 associated with 
                 2011 
               
               
                 45614564 
                   
                   
                   
                 pancreatic 
                   
               
               
                   
                   
                   
                   
                 cancer 
                   
               
               
                 Chr13: 
                 C13ORF18 
                 KIAA0226-like 
                 Uncharacterized 
                 Methylation 
                 Vincent, et al. 
               
               
                 46960770- 
                   
                   
                   
                 associated with 
                 2011; Yan, et 
               
               
                 46961464 
                   
                   
                   
                 pancreatic 
                 al. 2009 
               
               
                   
                   
                   
                   
                 cancer, cervical 
                   
               
               
                   
                   
                   
                   
                 neoplasia 
                   
               
               
                 Chr12: 
                 CACNA1C 
                 Calcium 
                 Mediates cellular 
                 Methylation 
                 Vincent, et al. 
               
               
                 2800665- 
                   
                 channel, 
                 calcium ion influx 
                 associated with 
                 2011 
               
               
                 2800898 
                   
                 voltage- 
                   
                 pancreatic 
                   
               
               
                   
                   
                 dependent, L 
                   
                 cancer 
                   
               
               
                   
                   
                 type, alpha 1C 
                   
                   
                   
               
               
                   
                   
                 subunit 
                   
                   
                   
               
               
                 Chr1: 
                 CD1D 
                 CD1D molecule 
                 Transmembrane 
                 Target for novel 
                 Liu, et al. 
               
               
                 158150797- 
                   
                   
                 glycoprotein 
                 immunotherapy- 
                   
               
               
                 158151142 
                   
                   
                 mediating 
                 based cancer 
                   
               
               
                   
                   
                   
                 presentation of 
                 treatment; 
                   
               
               
                   
                   
                   
                 antigens to T 
                 expressed by 
                   
               
               
                   
                   
                   
                 cells 
                 medulloblastoma 
                   
               
               
                 Chr19: 
                 CLEC11 
                 C-type lectin-11 
                 C-type lectin 
                 None 
                 — 
               
               
                 51228217- 
                   
                   
                 domain, 
                   
                   
               
               
                 51228703 
                   
                   
                 uncharacterized 
                   
                   
               
               
                 Chr12: 
                 (Chr12-133) 
                 — 
                 Uncharacterized 
                 — 
                 — 
               
               
                 133484978- 
                   
                   
                   
                   
                   
               
               
                 133485738 
                   
                   
                   
                   
                   
               
               
                 Chr7: 
                 ELMO1 
                 Engulfment and 
                 Interaction with 
                 Promotion of 
                 Li, et al. 
               
               
                 37487539- 
                   
                 cell motility 1 
                 cytokinesis 
                 metastatic 
                   
               
               
                 37488498 
                   
                   
                 proteins, 
                 spread 
                   
               
               
                   
                   
                   
                 promotion of cell 
                   
                   
               
               
                   
                   
                   
                 motility and 
                   
                   
               
               
                   
                   
                   
                 phagocytosis 
                   
                   
               
               
                 Chr7: 
                 FOXP2 
                 Forkhead box 
                 Transcription 
                 Expressed in 
                 Stumm, et 
               
               
                 113727624- 
                   
                 P2 
                 factor, expressed 
                 subsets of 
                 al. Campbell, 
               
               
                 113727693 
                   
                   
                 in brain, lung, gut 
                 prostate cancer, 
                 et al. 
               
               
                   
                   
                   
                   
                 lymphoma and 
                   
               
               
                   
                   
                   
                   
                 multiple 
                   
               
               
                   
                   
                   
                   
                 myeloma 
                   
               
               
                 Chr19: 
                 GRIN2D 
                 Glutamate 
                 NMDA receptor, 
                 Methylation 
                 Vincent, et al. 
               
               
                 48946755- 
                   
                 receptor, 
                 neurotransmission 
                 associated with 
                 2011, Jiao, et 
               
               
                 48946912 
                   
                 ionotropic, N- 
                   
                 pancreatic 
                 al. 
               
               
                   
                   
                 methyl D- 
                   
                 cancer, mutant 
                   
               
               
                   
                   
                 aspartate 2 D 
                   
                 in breast cancer 
                   
               
               
                 Chr7: 
                 IKZF1 
                 IKAROS family 
                 DNA binding 
                 Mutant in 
                 Asai, et al. 
               
               
                 50343848- 
                   
                 zinc finger 1 
                 protein 
                 leukemias 
                   
               
               
                 50343927 
                   
                   
                 associated with 
                   
                   
               
               
                   
                   
                   
                 chromatin 
                   
                   
               
               
                   
                   
                   
                 remodeling 
                   
                   
               
               
                 Chr2: 
                 KCNK12 
                 Potassium 
                 Non-functioning 
                 Methylation 
                 Vincent, et al. 
               
               
                 47797332- 
                   
                 channel, sub- 
                 potassium 
                 associated with 
                 2011, Kober, 
               
               
                 47797371 
                   
                 family K, 
                 channel 
                 pancreatic and 
                 et al. 
               
               
                   
                   
                 member 12 
                   
                 colon cancer 
                   
               
               
                 Chr5: 
                 KCNN2 
                 Potassium 
                 Potassium 
                 Overexpressed 
                 Camões, et al. 
               
               
                 113696984- 
                   
                 intermediate/ 
                 channel, voltage- 
                 in prostate 
                   
               
               
                 113697057 
                   
                 small  
                 gated, calcium 
                 cancer 
                   
               
               
                   
                   
                 conductance 
                 activated 
                   
                   
               
               
                   
                   
                 calcium-activated 
                   
                   
                   
               
               
                   
                   
                 channel, 
                   
                   
                   
               
               
                   
                   
                 subfamily N, 
                   
                   
                   
               
               
                   
                   
                 member 2 
                   
                   
                   
               
               
                 Chr16: 
                 NDRG4 
                 N-myc 
                 Cytosolic 
                 Methylated in 
                 Kisiel, et al., 
               
               
                 58497395- 
                   
                 downregulated 
                 signalling protein 
                 pancreatic, 
                 Ahlquist, et al. 
               
               
                 58497458 
                   
                 gene, family 
                 required for cell 
                 colon cancer 
                   
               
               
                   
                   
                 member 4 
                 cycle progression 
                   
                   
               
               
                 Chr16: 
                 PRKCB 
                 Protein kinase 
                 Serine- and 
                 Methylation 
                 Vincent, et al. 
               
               
                 23846964- 
                   
                 C, beta 
                 threonine specific 
                 associated with 
                 2011, Surdez, 
               
               
                 23848004 
                   
                   
                 kinase involved in 
                 pancreatic 
                 et al. 
               
               
                   
                   
                   
                 cell signalling 
                 cancer, 
                   
               
               
                   
                   
                   
                   
                 druggable target 
                   
               
               
                   
                   
                   
                   
                 in Ewing 
                   
               
               
                   
                   
                   
                   
                 sarcoma 
                   
               
               
                 Chr6: 
                 RSPO3 
                 R-spondin, type 
                 Regulatory 
                 Methylation 
                 Vincent, et al. 
               
               
                 127440526- 
                   
                 3 
                 protein in Wnt/β- 
                 associated with 
                 2011, 
               
               
                 127441039 
                   
                   
                 catenin signalling 
                 pancreatic 
                 Seshigiri, et 
               
               
                   
                   
                   
                 pathway 
                 cancer, elevated 
                 al. 
               
               
                   
                   
                   
                   
                 expression in 
                   
               
               
                   
                   
                   
                   
                 colon cancers 
                   
               
               
                 Chr22: 
                 SCARF2 
                 Scavenger 
                 Mediates binding 
                 Methylation 
                 Vincent, et al. 
               
               
                 20785373- 
                   
                 receptor class 
                 and degradation 
                 associated with 
                 2011, Zhao, et 
               
               
                 20785464 
                   
                 F, member 2 
                 of low density 
                 pancreatic 
                 al. 
               
               
                   
                   
                   
                 lipoproteins 
                 cancer, 
                   
               
               
                   
                   
                   
                   
                 methylation and 
                   
               
               
                   
                   
                   
                   
                 reduced 
                   
               
               
                   
                   
                   
                   
                 expression in 
                   
               
               
                   
                   
                   
                   
                 gastric cancer 
                   
               
               
                 Chr7: 
                 SHH 
                 Sonic 
                 Embryogenesis 
                 Methylation 
                 Vincent, et al. 
               
               
                 155597771- 
                   
                 hedgehog 
                   
                 associated with 
                 2011, Gurung, 
               
               
                 155597951 
                   
                   
                   
                 pancreatic 
                 et al. 
               
               
                   
                   
                   
                   
                 cancer, 
                   
               
               
                   
                   
                   
                   
                 epigenetically 
                   
               
               
                   
                   
                   
                   
                 repressed in 
                   
               
               
                   
                   
                   
                   
                 MEN1 
                   
               
               
                   
                   
                   
                   
                 syndrome; 
                   
               
               
                   
                   
                   
                   
                 hedgehog 
                   
               
               
                   
                   
                   
                   
                 signalling 
                   
               
               
                   
                   
                   
                   
                 mediates 
                   
               
               
                   
                   
                   
                   
                 pancreatic 
                   
               
               
                   
                   
                   
                   
                 cancer invasion 
                   
               
               
                 Chr3: 
                 SLC38A3 
                 Solute carrier, 
                 Uncharacterized 
                 Decreased 
                 Person, et al. 
               
               
                 50243467- 
                   
                 family 38, 
                   
                 expression in 
                   
               
               
                 50243553 
                   
                 member 3 
                   
                 lung cancer 
                   
               
               
                 Chr7: 
                 TWIST1 
                 Twist basic 
                 Transcription 
                 Methylation 
                 Vincent, et al. 
               
               
                 19156788- 
                   
                 helix-loop-helix 
                 factor expressed 
                 associated with 
                 2011, Shin, et 
               
               
                 19157093 
                   
                 transcription 
                 in placental and 
                 pancreatic 
                 al. 
               
               
                   
                   
                 factor 1 
                 mesodermal 
                 cancer, biliary 
                   
               
               
                   
                   
                   
                 tissue 
                 cancer, 
                   
               
               
                   
                   
                   
                   
                 urothelial cancer 
                   
               
               
                 Chr7: 
                 VWC2 
                 von Willebrand 
                 Secreted bone 
                 Methylation 
                 Vincent, et al. 
               
               
                 49813182- 
                   
                 factor C 
                 morphogenic 
                 associated with 
                 2011 
               
               
                 49814168 
                   
                 domain 
                 protein antagonist 
                 pancreatic 
                   
               
               
                   
                   
                 containing 2 
                   
                 cancer 
                   
               
               
                 Chrl11: 
                 WT1 
                 Wilms tumor 1 
                 Zinc finger motif 
                 Methylation 
                 Vincent, et al. 
               
               
                 32460759- 
                   
                   
                 transcription 
                 associated with 
                 2011, Jacobs, 
               
               
                 32460800 
                   
                   
                 factor 
                 pancreatic, 
                 et al. 
               
               
                   
                   
                   
                   
                 prostate, ovarian 
                   
               
               
                   
                   
                   
                   
                 and breast 
                   
               
               
                   
                   
                   
                   
                 cancers 
               
               
                   
               
            
           
         
       
     
     Lee W K, Chakraborty P K, Thevenod F. Pituitary homeobox 2 (PITX2) protects renal cancer cell lines against doxorubicin toxicity by transcriptional activation of the multidrug transporter ABCB1. International journal of cancer Journal international du cancer. 2013; 133:556-67. 
     Vincent A, Omura N, Hong S M, Jaffe A, Eshleman J, Goggins M. Genome-wide analysis of promoter methylation associated with gene expression profile in pancreatic adenocarcinoma. Clinical cancer research: an official journal of the American Association for Cancer Research. 2011; 17:4341-54. 
     Yang N, Eijsink J J, Lendvai A, Volders H H, Klip H, Buikema H J, et al. Methylation markers for CCNA1 and C13ORF18 are strongly associated with high-grade cervical intraepithelial neoplasia and cervical cancer in cervical scrapings. Cancer epidemiology, biomarkers &amp; prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2009; 18:3000-7. 
     Liu D, Song L, Brawley V S, Robison N, Wei J, Gao X, et al. Medulloblastoma expresses CD1d and can be targeted for immunotherapy with NKT cells. Clin Immunol. 2013; 149:55-64.Li H, Yang L, Fu H, Yan J, Wang Y, Guo H, et al. Association between Galphai2 and ELMO1/Dock180 connects chemokine signalling with Rac activation and metastasis. Nat Commun. 2013; 4:1706. 
     Stumm L, Burkhardt L, Steurer S, Simon R, Adam M, Becker A, et al. Strong expression of the neuronal transcription factor FOXP2 is linked to an increased risk of early PSA recurrence in ERG fusion-negative cancers. Journal of clinical pathology. 2013; 66:563-8. 
     Campbell A J, Lyne L, Brown P J, Launchbury R J, Bignone P, Chi J, et al. Aberrant expression of the neuronal transcription factor FOXP2 in neoplastic plasma cells. British journal of haematology. 2010; 149:221-30. 
     Jiao X, Wood L D, Lindman M, Jones S, Buckhaults P, Polyak K, et al. Somatic mutations in the Notch, NF-KB, PIK3CA, and Hedgehog pathways in human breast cancers. Genes, chromosomes &amp; cancer. 2012; 51:480-9. 
     Asai D, Imamura T, Suenobu S, Saito A, Hasegawa D, Deguchi T, et al. IKZF1 deletion is associated with a poor outcome in pediatric B-cell precursor acute lymphoblastic leukemia in Japan. Cancer Med. 2013; 2:412-9. 
     Kober P, Bujko M, Oledzki J, Tysarowski A, Siedlecki J A. Methyl-CpG binding column-based identification of nine genes hypermethylated in colorectal cancer. Molecular carcinogenesis. 2011; 50:846-56. 
     Camoes M J, Paulo P, Ribeiro F R, Barros-Silva J D, Almeida M, Costa V L, et al. Potential downstream target genes of aberrant ETS transcription factors are differentially affected in Ewing&#39;s sarcoma and prostate carcinoma. PLoS ONE. 2012; 7:e49819. 
     Kisiel J B, Yab T C, Taylor W R, Chari S T, Petersen G M, Mahoney D W, et al. Stool DNA testing for the detection of pancreatic cancer: assessment of methylation marker candidates. Cancer. 2012; 118:2623-31. 
     Ahlquist D A, Zou H, Domanico M, Mahoney D W, Yab T C, Taylor W R, et al. Next-Generation Stool DNA Test Accurately Detects Colorectal Cancer and Large Adenomas. Gastroenterology. 2012; 142:248-56. 
     Surdez D, Benetkiewicz M, Perrin V, Han Z Y, Pierron G, Ballet S, et al. Targeting the EWSR1-FLI1 oncogene-induced protein kinase PKC-beta abolishes ewing sarcoma growth. Cancer research. 2012; 72:4494-503. 
     Seshagiri S, Stawiski E W, Durinck S, Modrusan Z, Storm E E, Conboy C B, et al. Recurrent R-spondin fusions in colon cancer. Nature. 2012; 488:660-4. 
     Zhao J, Liang Q, Cheung K F, Kang W, Lung R W, Tong J H, et al. Genome-wide identification of Epstein-Barr virus-driven promoter methylation profiles of human genes in gastric cancer cells. Cancer. 2013; 119:304-12. 
     Gurung B, Feng Z, Iwamoto D V, Thiel A, Jin G, Fan C M, et al. Menin epigenetically represses Hedgehog signaling in MEN1 tumor syndrome. Cancer research. 2013; 73:2650-8. 
     Person R J, Tokar E J, Xu Y, Orihuela R, Ngalame N N, Waalkes M P. Chronic cadmium exposure in vitro induces cancer cell characteristics in human lung cells. Toxicol Appl Pharmacol. 2013. 
     Shin S H, Lee K, Kim B H, Cho N Y, Jang J Y, Kim Y T, et al. Bile-based detection of extrahepatic cholangiocarcinoma with quantitative DNA methylation markers and its high sensitivity. The Journal of molecular diagnostics: JMD. 2012; 14:256-63. 
     Jacobs D I, Mao Y, Fu A, Kelly W K, Zhu Y. Dysregulated methylation at imprinted genes in prostate tumor tissue detected by methylation microarray. BMC Urol. 2013; 13:37. 
     Example 9: A Stool-Based microRNA and DNA Marker Panel for the Detection of Pancreatic Cancer 
     Given the extraordinary lethality of pancreatic cancer (PC), practical non-invasive methods for pre-symptomatic screen detection are needed. MicroRNAs (miRNAs) have altered expression in PC. 
     Experiments were performed with having an aim to explore the feasibility of stool miR-1290 for detection of PC. 
     Archival stool samples from 58 PC cases and 64 healthy controls matched on age, gender, and smoking history were analyzed. Detection of miRNA was performed by a stem-loop quantitative reverse transcription polymerase chain reaction (qRT-PCR) approach. Quantitation of miRNA was based on measuring the absolute copies per nanogram of extracted RNA. DNA markers (methylated BMP3, mutant KRAS and ß-actin) were hybrid captured and amplified as described (Cancer 2012, 118:2623). A step-wise logistic regression model, limited to 5 variables, was used to build an optimized marker panel based on miR-1290, DNA markers, and age. The age adjusted areas under the ROC curve (AUCs) for each of the models were compared using the methods of DeLong et al. The association of miR-1290 with clinical factors was assessed using the Wilcoxon Rank Sums test. 
     Distributions of miR-1290 were significantly higher in stools from PC cases than from controls (P=0.0002). Stool miR-1290 levels were not affected by age, sex, tumor site or tumor stage. AUC of stool miR-1290 was 0.74 (95% CI: 0.65-0.82,  FIG. 3 ) for PC detection compared to an AUC of 0.81 (0.73-0.89) by the stool DNA marker panel. The addition of miR-1290 to DNA markers proved incremental (P=0.0007) with an AUC of 0.87 (0.81-0.94). Adding miR-1290 to the DNA panel increased the sensitivity of the test across the entire range of specificities including the critical region of 90-100%. PC sensitivity of the combined marker panel was 64% (50%-76%) at 95% (87%-99%) specificity, and 79% (67%-89%) at 85% (74%-92%) specificity. 
     These experiments identified stool miR-1290 as a marker for PC. 
     Example 11—Identifying Markers Using RRBS 
     During the development of the technology provided herein, data were collected from a case-control study to demonstrate that a genome-wide search strategy identifies novel and informative markers. 
     Study Population, Specimen Acquisition, and Samples 
     The target population was patients with pancreas cancer seen at the Mayo Clinic. The accessible population includes those who have undergone a distal pancreatectomy, a pancreaticoduodenectomy, or a colectomy with an archived resection specimen and a confirmed pathologic diagnosis. Colonic epithelial DNA was previously extracted from micro-dissected specimens by the Biospecimens Accessioning Processing (BAP) lab using a phenol-chloroform protocol. Data on the matching variables for these samples were used by Pancreas SPORE personnel to select tissue registry samples. These were reviewed by an expert pathologist to confirm case and control status and exclude case neoplasms arising from IPMN, which may have different underlying biology. SPORE personnel arranged for BAP lab microdissection and DNA extraction of the pancreatic case and control samples and provided 500 ng of DNA to lab personnel who were blinded to case and control status. Archival nucleic acid samples included 18 pancreatic adenocarcinomas, 18 normal pancreas, and 18 normal colonic epithelia matched on sex, age, and smoking status. 
     The sample types were:
         1) Mayo Clinic Pancreas SPORE registry PanC tissues limited to AJCC stage I and II;   2) control pancreata free from PanC;   3) archived control colonic epithelium free from PanC; and   4) colonic neoplasm from which DNA had been extracted and stored in the BAP lab.
 
Cases and controls were matched by sex, age (in 5-year increments), and smoking status (current or former vs. never).
       

     Methods 
     Libraries were prepared according to previously reported methods (see, e.g., Gu et al (2011) “Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling” Nature Protocols 6: 468-81) by fragmenting genomic DNA (300 ng) by digestion with 10 units of MspI a methylation-specific restriction enzyme that recognizes CpG containing motifs. This treatment enriches the samples for CpG content and eliminates redundant areas of the genome. Digested fragments were end-repaired and A-tailed with 5 units of Klenow fragment (3′-5′ exo) and ligated overnight to Illumina adapters containing one of four barcode sequences to link each fragment to its sample ID. Size selection of 160-340 bp fragments (having 40-220 bp inserts) was performed using SPRI beads/buffer (AMPure XP, Beckman Coulter). Buffer cutoffs were from 0.7× to 1.1× of the sample volume of beads/buffer. Samples were eluted in a volume of 22 μl (EB buffer, Qiagen). qPCR was used to gauge ligation efficiency and fragment quality on a small aliquot of sample. Samples then underwent two rounds of bisulfite conversion using a modified EpiTect protocol (Qiagen). qPCR and conventional PCR (Pfu Turbo Cx hotstart, Agilent), followed by Bioanalyzer 2100 (Agilent) assessment on converted sample aliquots, determined the optimal PCR cycle number prior to amplification of the final library. The final PCR was performed in a volume of 50 μl (5 μl of 10×PCR buffer; 1.25 μl of each dNTP at 10 mM; 5 μl of a primer cocktail at approximately 5 μM, 15 μl of template (sample), 1 μl PfuTurbo Cx hotstart, and 22.75 μl water. Thermal cycling began with initial incubations at 95° C. for 5 minutes and at 98° C. for 30 seconds followed by 16 cycles of 98° C. for 10 seconds, 65° C. for 30 seconds, and at 72° C. for 30 seconds. After cycling, the samples were incubated at 72° C. for 5 minutes and kept at 4° C. until further workup and analysis. Samples were combined in equimolar amounts into 4-plex libraries based on a randomization scheme and tested with the bioanalyzer for final size verification. Samples were also tested with qPCR using phiX standards and adaptor-specific primers. 
     For sequencing, samples were loaded onto flow cell lanes according to a randomized lane assignment with additional lanes reserved for internal assay controls. Sequencing was performed by the NGS Core at Mayo&#39;s Medical Genome Facility on the Illumina HiSeq 2000. Reads were unidirectional for 101 cycles. Each flow cell lane generated 100-120 million reads, sufficient for a median coverage of 30× to 50× sequencing depth (based on read number per CpG) for aligned sequences. Standard Illumina pipeline software was used to analyze the reads in combination with RRBSMAP (Xi, et al. (2012) “RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing”  Bioinformatics  28: 430-432) and an in-house pipeline (SAAP-RRBS) developed by Mayo Biomedical and Statistics personnel (Sun et al. (2012) “SAAP-RRBS: streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing”  Bioinformatics  28: 2180-1). The bioinformatic analyses consisted of 1) sequence read assessment and clean-up, 2) alignment to reference genome, 3) methylation status extraction, and 4) CpG reporting and annotation. 
     Statistical Considerations: 
     The primary comparison of interest is methylation differences between cases and disease controls at each CpG and/or tiled CpG window. The secondary outcome is the same comparison between cases and normal buffy coat and colon controls. Markers were tested for differential methylation by:
         1. Assessing the distributions of methylation percentage for each marker and discarding markers with more than 2.5% methylated background in colon controls and normal buffy coat   2. Testing the distribution of methylation of remaining markers between cases and controls using the Wilcoxon rank sum test and ranking markers by p-values.   3. Using Q-values to estimate the False Discovery Rates (FDR) (Benjamini et al. (1995) “Multiple Testing”  Journal of the Royal Statistical Society. Series B  ( Methodological ) 57: 289-300; Storey et al. (2003) “Statistical significance for genomewide studies”  Proc Natl Acad Sci USA  100: 9440-5). At the discovery-level, an FDR up to 25% is acceptable.       

     Analysis of Data 
     A data analysis pipeline was developed in the R statistical analysis software package (“R: A Language and Environment for Statistical Computing” (2012), R Foundation for Statistical Computing). The workflow comprised the following steps:
         1. Read in all CpG sites   2. Considered only those CpG sites where the total group depth of coverage was 200 reads or more. This is based on the power assessment to detect a difference between 20% and 30% methylation between any two groups; anything less has little chance of significance. So, if there are 18 subjects per group and each subject has 12 reads, the group depth of coverage is 12*18=216.   3. Excluded all the CpG sites where the variance of the % methylation across the groups was 0 (non-informative CpG sites).   4. Performed an over-dispersed logistic regression on the % methylation (using the actual counts) with the groups defined as Normal Colon/Buffy coat, disease specific control, and specific cancer of interest (cases) to determine the statistical significance of the % methylation for the primary and secondary analyses. An over-dispersed logistic model was used since the variability in the % methylation between subjects is larger than what the binomial assumption allows. This dispersion parameter was estimated using the Pearson Chi-square of the fit.   5. Generated area under the Receiver Operating Characteristic curve (ROC) values. Area under the ROC curve is a measure of predictive accuracy of subject specific % methylation and was estimated for the primary analysis (cases vs. disease control) and the secondary analysis (cases vs. normal colon/buffy coat), separately.   6. In a similar fashion to #5, the fold-change (FC, a measure of the separation between cases and controls) for the primary and secondary analysis was also estimated using the ratio of mean % methylation between cases and corresponding control group.   7. 4-6 above was conducted on individual CpG sites as well as methylated CpG regions. These regions were defined for each chromosome as a group of at least 5 CpG sites within roughly 100 base pairs (bps) distance with a mean % methylation &lt;2.5% in normal colon/buffy coat controls   8. CpG regions showing promise for technical and biological validation were identified as having a statistical significant methylation difference, a large FC, and a high AUC for either the primary or secondary analyses.       

     Post-R Analysis: 
     
         
         
           
             1. Sorted individual CpGs and CpG regions by p-value, FC, and AUC. Cut-offs were &lt;0.01, &gt;20, and &gt;0.85 respectively, although these were often adjusted depending on the robustness of the data. For example, highly heterogeneous neoplastic tissue results in lower % methylation values, which in turn affects the filtering. Primary and secondary comparisons can be sorted together or separately depending on the specificity requirements of the application. Normal colonic epithelia are included as a control for uncovering markers suitable for stool assay. If pancreatic juice is being tested, colonic tissue is unnecessary. This can result in a completely different set of markers. 
             2. Ranked marker regions based on assay platform requirements. Currently, methylation-specific PCR (MSP), or similar amplification platforms where discrimination is based on the specificity of primer annealing, is the platform of choice. For this methodology, it is imperative to have 2-5 discriminate CpGs per oligo within an amplifiable stretch of DNA. For stool assays, this requirement is even more stringent in that amplicons must be short (&lt;100 bp). Marker selection, therefore, needs to made on the basis of short contiguous stretches of highly discriminate CpGs. If the platform evolves to a sequence-based technology, the CpG distribution requirements within a region may be entirely different. 
           
         
       
    
     Results 
     Matched, blinded, randomly allocated DNA extracts from 18 pancreatic cancer tumors, 18 benign pancreatic control tissues and 18 normal colonic epithelial tissues were sequenced by RRBS. Median age was 61 (interquartile range 52-65), 61% were women, and 44% were current or former smokers. Roughly 6 million CpGs were mapped at ≥10× coverage. More than 2000 CpG regions met significance criteria for differential methylation. After applying the filter criteria above, 449 differentially methylated regions (DMR) were identified (Table 10). Table 11 presents the identified 449 differentially methylated regions (DMR) ranked by decreasing area under the ROC curve (AUC). 
     In these markers, methylation signatures range from 3 neighboring CpGs to 56 CpGs. Methylation levels of the pancreatic cancers rarely exceeded 25% at filtered CpGs, which suggested that the cancer tissues may have high levels of contaminating normal cells and/or stroma. To test this, each of the cancers was sequenced for KRAS mutations to verify allele frequencies for the positive samples. For the 50% that harbored a heterozygous KRAS base change, the frequency of the mutant allele was at least 4 times less than the corresponding wild-type allele, in support of contamination by normal cells and/or stroma. 
     It was found that 58 of the 449 markers are in nonannotated regions and lie in genomic regions without protein coding elements. Of the remaining 391 candidate markers, approximately 225 have been described as associated with cancer, some of which classify as tumor suppressors. The 166 other candidate markers have a previously identified weak association with cancer (e.g., mutations and/or copy number alterations observed in genome-wide screens) or have no previously identified cancer associations. 
                     TABLE 10                  DMR                             Marker #   Chromosome   Chromosome Coordinates   Annotation                1   chr7   87229775-87229856   ABCB1        2   chr2   207307687-207307794   ADAM23        3   chr15   100881373-100881437   ADAMTS17        4   chr16   77468655-77468742   ADAMTS18        5   chr19   41224781-41225006   ADCK4        6   chr7   45613877-45614572   ADCY1        7   chr2   70994498-70994755   ADD2        8   chr14   105190863-105191031   ADSSL1        9   chr10   116064516-116064600   AFAP1L2        10   chr4   87934353-87934488   AFF1        11   chr2   100720494-100720679   AFF3        12   chr7   100136884-100137350   AGFG2        13   chr9   116151083-116151315   ALAD        14   chr14   103396870-103396920   AMN        15   chr19   10206736-10206757   ANGPTL6        16   chr19   17438929-17438974   ANO8        17   chr15   90358267-90358400   ANPEP        18   chr15   29131299-29131369   APBA2        19   chr19   45430362-45430458   APOC1P1        20   chr13   111767862-111768355   ARHGEF7        21   chr7   98990897-98990989   ARPC1B        22   chr22   51066374-51066431   ARSA        23   chr9   120175665-120176057   ASTN2        24   chr1   203619509-203619829   ATP2B4        25   chr7   69062853-69062972   AUTS2        26   chr8   104152963-104152974   BAALC        27   chr11   64052053-64052132   BAD        28   chr10   121411207-121411375   BAG3        29   chr7   98029116-98029383   BAIAP2L1        30   chr9   135462730-135462765   BARHL1        31   chr10   133795124-133795423   BNIP3        32   chr12   107715014-107715095   BTBD11        33   chr6   105584524-105584800   BVES        34   chr10   21816267-21816490   C10orf140        35   chr12   21680381-21680438   C12orf39        36   chr12   21680681-21680817   C12orf39        37   chr12   117174873-117175030   C12orf49        38   chr13   46960767-46961669   C13orf18        39   chr14   50099743-50099930   C14orf104        40   chr19   16772631-16772712   C19orf42        41   chr20   31061389-31061649   C20orf112        42   chr5   175665232-175665311   C5orf25        43   chr6   42858890-42859092   C6orf226        44   chr9   139735581-139735683   C9orf172        45   chr12   2800756-2800899   CACNA1C        46   chr3   54156904-54156987   CACNA2D3        47   chr11   115373179-115373281   CADM1        48   chr16   89007413-89007432   CBFA2T3        49   chr16   49316205-49316258   CBLN1        50   chr21   44495919-44495933   CBS        51   chr17   77810085-77810206   CBX4        52   chr17   8649567-8649665   CCDC42        53   chr11   64110001-64110069   CCDC88B        54   chr14   91883473-91883674   CCDC88C        55   chr14   99946756-99946806   CCNK        56   chr1   158150797-158151205   CD1D        57   chr5   175969660-175969699   CDHR2        58   chr7   39989959-39990020   CDK13        59   chr16   80837397-80837505   CDYL2        60   chr10   11059508-11060151   CELF2        61   chr22   47130339-47130459   CERK        62   chr2   233389020-233389049   CHRND        63   chr7   73245708-73245798   CLDN4        64   chr19   51228217-51228732   CLEC11A        65   chr3   139654045-139654132   CLSTN2        66   chr7   155302557-155302639   CNPY1        67   chr6   88875699-88875763   CNR1        68   chr6   88876367-88876445   CNR1        69   chr6   88876701-88876726   CNR1        70   chr2   165698520-165698578   COBLL1        71   chr6   75794978-75795024   COL12A1        72   chr12   48398051-48398093   COL2A1        73   chr12   48398306-48398375   COL2A1        74   chr18   449695-449798   COLEC12        75   chr7   30721980-30722020   CRHR2        76   chr16   84875643-84875772   CRISPLD2        77   chr7   151127086-151127195   CRYGN        78   chr10   126812450-126812653   CTBP2        79   chr20   56089440-56089547   CTCFL        80   chr2   219261190-219261327   CTDSP1        81   chr2   80530326-80530374   CTNNA2        82   chr22   43044555-43044737   CYB5R3        83   chr19   1406516-1406625   DAZAP1        84   chr7   44084171-44084235   DBNL        85   chr11   20178177-20178304   DBX1        86   chr4   151000325-151000356   DCLK2        87   chr4   151000358-151000403   DCLK2        88   chr4   183817058-183817157   DCTD        89   chr13   52378159-52378202   DHRS12        90   chr8   13014567-13014682   DLC1        91   chr11   84432067-84432186   DLG2        92   chr6   170598276-170598782   DLL1        93   chr19   39989824-39989852   DLL3        94   chr19   12996198-12996321   DNASE2        95   chr2   230578698-230578802   DNER        96   chr2   225907414-225907537   DOCK10        97   chr18   32073971-32074004   DTNA        98   chr2   233352345-233352605   ECEL1        99   chr7   37487539-37488596   ELMO1       100   chr20   39995010-39995051   EMILIN3       101   chr19   48833763-48833967   EMP3       102   chr2   119607676-119607765   EN1       103   chr3   27763358-27763617   EOMES       104   chr3   27763909-27763981   EOMES       105   chr12   132435207-132435428   EP400       106   chr19   16473958-16474095   EPS15L1       107   chr6   152129293-152129450   ESR1       108   chr3   185825887-185826002   ETV5       109   chr9   140201493-140201583   EXD3       110   chr6   133562127-133562229   EYA4       111   chr1   160983607-160983768   F11R       112   chr20   821836-821871   FAM110A       113   chr22   45898798-45898888   FBLN1       114   chr9   97401449-97401602   FBP1       115   chr16   750679-750715   FBXL16       116   chr5   15500208-15500399   FBXL7       117   chr5   15500663-15500852   FBXL7       118   chr5   114880375-114880442   FEM1C       119   chr20   34189488-34189693   FER1L4       120   chr14   53417493-53417618   FERMT2       121   chr2   219849962-219850042   FEV       122   chr17   7339280-7339492   FGF11       123   chr19   49256413-49256451   FGF21       124   chr10   103538848-103539033   FGF8       125   chr11   64008415-64008495   FKBP2       126   chr11   128564106-128564209   FLI1       127   chr10   102985059-102985130   FLJ41350       128   chr13   28674451-28674629   FLT3       129   chr1   240255240-240255264   FMN2       130   chr5   131132146-131132232   FNIP1       131   chr6   108882636-108882682   FOXO3       132   chr3   71478053-71478206   FOXP1       133   chr7   113724864-113725006   FOXP2       134   chr7   113727624-113727693   FOXP2       135   chr5   160975098-160975142   GABRB2       136   chr12   51786085-51786218   GALNT6       137   chr5   179780839-179780955   GFPT2       138   chr20   3641457-3641537   GFRA4       139   chr17   4462834-4463034   GGT6       140   chr17   4463796-4464037   GGT6       141   chr17   42907549-42907807   GJC1       142   chr8   144358251-144358266   GLI4       143   chr16   4377510-4377615   GLIS2       144   chr12   56881329-56881414   GLS2       145   chr6   24776486-24776667   GMNN       146   chr19   3095019-3095055   GNA11       147   chr22   19710910-19710984   GP1BB       148   chr22   19711364-19711385   GP1BB       149   chr2   131485151-131485219   GPR148       150   chr2   165477564-165477609   GRB14       151   chr2   165477839-165477886   GRB14       152   chr17   73390467-73390597   GRB2       153   chr19   48918266-48918311   GRIN2D       154   chr19   48946755-48946912   GRIN2D       155   chr13   114018369-114018421   GRTP1       156   chr12   13254503-13254606   GSG1       157   chr7   43152309-43152375   HECW1       158   chr7   139440133-139440341   HIPK2       159   chr6   34205664-34206018   HMGA1       160   chr12   121416542-121416670   HNF1A       161   chr20   42984244-42984427   HNF4A       162   chr20   43040031-43040119   HNF4A       163   chr5   177632203-177632260   HNRNPAB       164   chr7   27136030-27136245   HOXA1       165   chr2   176971915-176971968   HOXD11       166   chr19   35540057-35540200   HPN       167   chr2   163174366-163174659   IFIH1       168   chr17   47073421-47073440   IGF2BP1       169   chr11   133797643-133797789   IGSF9B       170   chr7   50343838-50344029   IKZF1       171   chr7   50344414-50344453   IKZF1       172   chr20   20345123-20345150   INSM1       173   chr20   20350520-20350532   INSM1       174   chr15   76632356-76632462   ISL2       175   chr2   182321880-182322022   ITGA4       176   chr2   182322168-182322198   ITGA4       177   chr2   173293542-173293644   ITGA6       178   chr19   2097386-2097437   IZUMO4       179   chr21   27011846-27011964   JAM2       180   chr2   47797260-47797371   KCNK12       181   chr10   79397895-79397945   KCNMA1       182   chr5   113696524-113696682   KCNN2       183   chr5   113696971-113697058   KCNN2       184   chr1   154733071-154733232   KCNN3       185   chr8   99439457-99439482   KCNS2       186   chr19   34287890-34287972   KCTD15       187   chr12   121905558-121905792   KDM2B       188   chr8   136469529-136469873   KHDRBS3       189   chr16   85646495-85646594   KIAA0182       190   chr18   46190841-46190970   KIAA0427       191   chr4   37245694-37245718   KIAA1239       192   chr17   72350351-72350403   KIF19       193   chr2   149633039-149633137   KIF5C       194   chr22   50987245-50987312   KLHDC7B       195   chr12   53298237-53298384   KRT8       196   chr19   54974004-54974086   LENG9       197   chr1   180198528-180198542   LHX4       198   chr19   2290471-2290541   LINGO3       199   chr11   19733958-19734013   LOC100126784       200   chr19   58513829-58513851   LOC100128398       201   chr17   43324999-43325188   LOC100133991       202   chr17   43325784-43325960   LOC100133991       203   chr2   109745715-109745742   LOC100287216       204   chr1   178063099-178063167   LOC100302401       205   chr12   53447992-53448072   LOC283335       206   chr1   45769962-45770141   LOC400752       207   chr20   61637950-61638000   LOC63930       208   chr13   88323571-88323647   LOC642345       209   chr6   111873064-111873162   LOC643749       210   chr5   87956937-87956996   LOC645323       211   chr5   87970260-87970568   LOC645323       212   chr5   87970751-87970850   LOC645323       213   chr12   85430135-85430175   LRRIQ1       214   chr19   497878-497933   MADCAM1       215   chr5   71404528-71404563   MAP1B       216   chr2   39665069-39665282   MAP4K3       217   chr1   156406057-156406118   MAX.chr1.156406057-156406118       218   chr1   23894874-23894919   MAX.chr1.23894874-23894919       219   chr1   240161479-240161546   MAX.chr1.240161479-240161546       220   chr1   244012804-244012986   MAX.chr1.244012804-244012986       221   chr1   35394690-35394876   MAX.chr1.35394690-35394876       222   chr1   35395179-35395201   MAX.chr1.35395179-35395201       223   chr1   39044345-39044354   MAX.chr1.39044345-39044354       224   chr10   101282185-101282257   MAX.chr10.101282185-                   101282257       225   chr10   127033272-127033428   MAX.chr10.127033272-                   127033428       226   chr11   120382450-120382498   MAX.chr11.120382450-                   120382498       227   chr11   47421719-47421776   MAX.chr11.47421719-47421776       228   chr12   133484978-133485066   MAX.chr12.133484978-                   133485066       229   chr12   133485702-133485739   MAX.chr12.133485702-                   133485739       230   chr12   54151078-54151153   MAX.chr12.54151078-54151153       231   chr12   58259413-58259475   MAX.chr12.58259413-58259475       232   chr13   25322044-25322165   MAX.chr13.25322044-25322165       233   chr13   29394692-29394771   MAX.chr13.29394692-29394771       234   chr14   100751586-100751695   MAX.chr14.100751586-                   100751695       235   chr14   61123624-61123707   MAX.chr14.61123624-61123707       236   chr14   89507100-89507162   MAX.chr14.89507100-89507162       237   chr15   40361431-40361644   MAX.chr15.40361431-40361644       238   chr15   89942904-89943197   MAX.chr15.89942904-89943197       239   chr16   25042924-25043187   MAX.chr16.25042924-25043187       240   chr16   85230248-85230405   MAX.chr16.85230248-85230405       241   chr17   1835463-1835690   MAX.chr17.1835463-1835690       242   chr17   60218266-60218449   MAX.chr17.60218266-60218449       243   chr17   76337726-76337824   MAX.chr17.76337726-76337824       244   chr19   11805543-11805639   MAX.chr19.11805543-11805639       245   chr19   22034747-22034887   MAX.chr19.22034747-22034887       246   chr19   32715650-32715707   MAX.chr19.32715650-32715707       247   chr19   5805881-5805968   MAX.chr19.5805881-5805968       248   chr2   127783183-127783233   MAX.chr2.127783183-127783233       249   chr2   232530964-232531124   MAX.chr2.232530964-232531124       250   chr2   239957125-239957163   MAX.chr2.239957125-239957163       251   chr2   43153331-43153424   MAX.chr2.43153331-43153424       252   chr2   71503632-71503860   MAX.chr2.71503632-71503860       253   chr20   43948422-43948484   MAX.chr20.43948422-43948484       254   chr21   47063798-47063877   MAX.chr21.47063798-47063877       255   chr22   17849540-17849622   MAX.chr22.17849540-17849622       256   chr22   38732124-38732211   MAX.chr22.38732124-38732211       257   chr22   42764974-42765049   MAX.chr22.42764974-42765049       258   chr22   46974925-46975007   MAX.chr22.46974925-46975007       259   chr22   50342922-50343232   MAX.chr22.50342922-50343232       260   chr3   132273353-132273532   MAX.chr3.132273353-132273532       261   chr3   193858771-193858843   MAX.chr3.193858771-193858843       262   chr3   24563009-24563117   MAX.chr3.24563009-24563117       263   chr3   75411368-75411473   MAX.chr3.75411368-75411473       264   chr4   26828422-26828522   MAX.chr4.26828422-26828522       265   chr4   8965831-8965868   MAX.chr4.8965831-8965868       266   chr5   142100518-142100780   MAX.chr5.142100518-142100780       267   chr6   169613138-169613249   MAX.chr6.169613138-169613249       268   chr6   64168133-64168268   MAX.chr6.64168133-64168268       269   chr7   129794337-129794536   MAX.chr7.129794337-129794536       270   chr7   1705957-1706065   MAX.chr7.1705957-1706065       271   chr7   28893550-28893569   MAX.chr7.28893550-28893569       272   chr7   47650711-47650882   MAX.chr7.47650711-47650882       273   chr7   64408106-64408135   MAX.chr7.64408106-64408135       274   chr9   108418404-108418453   MAX.chr9.108418404-108418453       275   chr9   120507310-120507354   MAX.chr9.120507310-120507354       276   chr5   89769002-89769411   MBLAC2       277   chr12   51319165-51319319   METTL7A       278   chr2   191272534-191272765   MFSD6       279   chr19   6236947-6237089   MLLT1       280   chr6   168333306-168333467   MLLT4       281   chr8   89339567-89339662   MMP16       282   chr17   2300399-2300476   MNT       283   chr7   156802460-156802490   MNX1       284   chr19   4343896-4242968   MPND       285   chr16   56715756-56716025   MT1X       286   chr15   48470062-48470503   MYEF2       287   chr15   48470606-48470725   MYEF2       288   chr5   16936010-16936058   MYO10       289   chr3   39851068-39851989   MYRIP       290   chr13   33001061-33001251   N4BP2L1       291   chr4   2060477-2060624   NAT8L       292   chr12   125002129-125002192   NCOR2       293   chr16   23607524-23607650   NDUFAB1       294   chr10   105338596-105338843   NEURL       295   chr1   204797773-204797785   NFASC       296   chr2   233877877-233878027   NGEF       297   chr18   31803017-31803114   NOL4       298   chr9   139438534-139438629   NOTCH1       299   chr5   32714270-32714325   NPR3       300   chr9   127266951-127267032   NR5A1       301   chr11   124615979-124616029   NRGN       302   chr11   124616860-124617005   NRGN       303   chr20   327754-327871   NRSN2       304   chr8   99952501-99952533   OSR2       305   chr5   76926598-76926703   OTP       306   chr3   8809858-8809865   OXTR       307   chr19   14172823-14172948   PALM3       308   chr6   52268531-52268702   PAQR8       309   chr20   21686466-21686563   PAX1       310   chr21   47063793-47064177   PCBP3       311   chr7   100203461-100203600   PCOLCE       312   chr4   657555-657666   PDE6B       313   chr7   544848-545022   PDGFA       314   chr2   239194812-239194946   PER2       315   chr19   43979400-43979435   PHLDB3       316   chr6   144384503-144385539   PLAGL1       317   chr2   28844174-28844270   PLB1       318   chr1   242687719-242687746   PLD5       319   chr12   6419210-6419489   PLEKHG6       320   chr22   50745629-50745727   PLXNB2       321   chr2   105471752-105471787   POU3F3       322   chr13   79177868-79177951   POU4F1       323   chr1   203044913-203044929   PPFIA4       324   chr22   50825886-50825981   PPP6R2       325   chr17   74519328-74519457   PRCD       326   chr7   601162-601552   PRKAR1B       327   chr16   23846964-23847339   PRKCB       328   chr16   23847507-23847617   PRKCB       329   chr16   23847825-23848168   PRKCB       330   chr22   18923785-18923823   PRODH       331   chr22   45099093-45099304   PRR5       332   chr3   9988302-9988499   PRRT3       333   chr1   11538685-11538738   PTCHD2       334   chr1   11539396-11539540   PTCHD2       335   chr10   23480864-23480913   PTF1A       336   chr19   5340273-5340743   PTPRS       337   chr2   1747034-1747126   PXDN       338   chr2   1748338-1748444   PXDN       339   chr7   4923056-4923107   RADIL       340   chr19   15568448-15568639   RASAL3       341   chr5   80256215-80256313   RASGRF2       342   chr17   77179784-77179887   RBFOX3       343   chr4   40516823-40516984   RBM47       344   chr4   57775698-57775771   REST       345   chr10   43572798-43572896   RET       346   chr10   121302439-121302501   RGS10       347   chr16   318717-318893   RGS11       348   chr1   241520322-241520334   RGS7       349   chr1   42846119-42846174   RIMKLA       350   chr21   43189031-43189229   RIPK4       351   chr7   5821188-5821283   RNF216       352   chr19   23941063-23941142   RPSAP58       353   chr19   23941384-23941670   RPSAP58       354   chr16   29118636-29118891   RRN3P2       355   chr6   127440492-127441039   RSPO3       356   chr17   42392669-42392701   RUNDC3A       357   chr6   45345446-45345595   RUNX2       358   chr6   45387405-45387456   RUNX2       359   chr3   72496092-72496361   RYBP       360   chr22   20785373-20785464   SCARF2       361   chr8   145561664-145561696   SCRT1       362   chr7   54826636-54826706   SEC61G       363   chr10   38691448-38691521   SEPT7L       364   chr4   154712157-154712232   SFRP2       365   chr7   155597793-155597973   SHH       366   chr4   77610781-77610824   SHROOM3       367   chr21   38120336-38120558   SIM2       368   chr15   68115602-68115675   SKOR1       369   chr17   6949717-6949778   SLC16A11       370   chr11   35441199-35441260   SLC1A2       371   chr19   59025337-59025385   SLC27A5       372   chr2   27486089-27486170   SLC30A3       373   chr12   69140018-69140206   SLC35E3       374   chr12   46661132-46661306   SLC38A1       375   chr3   50243467-50243553   SLC38A3       376   chr7   150760388-150760530   SLC4A2       377   chr5   1445384-1445473   SLC6A3       378   chr2   40679298-40679326   SLC8A1       379   chr5   506178-506343   SLC9A3       380   chr20   61284095-61284194   SLCO4A1       381   chr5   101631546-101631731   SLCO4C1       382   chr10   98945242-98945493   SLIT1       383   chr13   88330094-88330355   SLITRK5       384   chr15   66999854-67000014   SMAD6       385   chr10   112064230-112064280   SMNDC1       386   chr6   84419007-84419072   SNAP91       387   chr17   36508733-36508891   SOCS7       388   chr4   7367687-7367825   SORCS2       389   chr17   70116754-70116823   SOX9       390   chr4   57687746-57687764   SPINK2       391   chr3   140770014-140770193   SPSB4       392   chr17   36762706-36762763   SRCIN1       393   chr6   43141954-43142058   SRF       394   chr7   105029460-105029585   SRPK2       395   chr16   70415312-70415673   ST3GAL2       396   chr2   107502978-107503055   ST6GAL2       397   chr2   107503155-107503391   ST6GAL2       398   chr12   22487528-22487848   ST8SIA1       399   chr10   17496177-17496310   ST8SIA6       400   chr2   242447608-242447724   STK25       401   chr3   120626999-120627116   STXBP5L       402   chr3   33260338-33260423   SUSD5       403   chr16   19179713-19179744   SYT17       404   chr12   115122614-115122632   TBX3       405   chr19   3606372-3606418   TBXA2R       406   chr10   70359250-70359439   TET1       407   chr16   4310204-4310233   TFAP4       408   chr21   32930371-32930409   TIAM1       409   chr4   942190-942382   TMEM175       410   chr6   130686773-130686820   TMEM200a       411   chr6   130687200-130687735   TMEM200a       412   chr3   185215700-185215782   TMEM41A       413   chr20   42544780-42544835   TOX2       414   chr9   140091343-140091644   TPRN       415   chr8   126441476-126441519   TRIB1       416   chr5   14143759-14143880   TRIO       417   chr22   38148620-38148716   TRIOBP       418   chr7   19156788-19157227   TWIST1       419   chr7   19157436-19157533   TWIST1       420   chr4   41259387-41259594   UCHL1       421   chr15   63795401-63795636   USP3       422   chr17   9548120-9548325   USP43       423   chr12   95942077-95942558   USP44       424   chr10   17271896-17271994   VIM       425   chr7   49813135-49814168   VWC2       426   chr7   151078646-151078674   WDR86       427   chr12   49372205-49372274   WNT1       428   chr11   32460759-32460800   WT1       429   chr19   4061206-4061360   ZBTB7A       430   chr8   144623045-144623088   ZC3H3       431   chr2   145274698-145274874   ZEB2       432   chr19   38146299-38146397   ZFP30       433   chr16   88521287-88521377   ZFPM1       434   chr4   2298384-2298498   ZFYVE28       435   chr4   2415252-2415286   ZFYVE28       436   chr20   45986341-45986684   ZMYND8       437   chr22   22862957-22862983   ZNF280B       438   chr6   43336449-43336545   ZNF318       439   chr19   53661819-53662279   ZNF347       440   chr16   88497041-88497148   ZNF469       441   chr19   57019064-57019137   ZNF471       442   chr19   2842178-2842235   ZNF555       443   chr19   37958078-37958134   ZNF570       444   chr8   125985552-125985847   ZNF572       445   chr19   53696101-53696195   ZNF665       446   chr19   53696497-53696704   ZNF665       447   chr19   20149796-20149923   ZNF682       448   chr19   571 06617-571 06967   ZNF71       449   chr7   6655380-6655652   ZNF853                    
In Table 10, bases are numbered according to the February 2009 human genome assembly GRCh37/hg19 (see, e.g., Rosenbloom et al. (2012) “ENCODE whole-genome data in the UCSC Genome Browser: update 2012”  Nucleic Acids Research  40: D912-D917). The marker names BHLHE23 and LOC63930 refer to the same marker.
 
     
       
         
           
               
               
               
               
             
               
                 TABLE 11 
               
               
                   
               
               
                   
                 Chromosome  
                   
                 Area under  
               
               
                 Chromosome 
                 Coordinates 
                 Annotation 
                 the ROC Curve 
               
               
                   
               
             
            
               
                 chr12 
                 53298237-53298384 
                 KRT8 
                 1.00 
               
               
                 chr7 
                 129794337-129794536 
                 MAX.chr7.129794337-129794536 
                 1.00 
               
               
                 chr10 
                 101282185-101282257 
                 MAX.chr10.101282185- 
                   
               
               
                   
                   
                 101282257 
                 0.99 
               
               
                 chr10 
                 126812450-126812653 
                 CTBP2 
                 0.99 
               
               
                 chr9 
                 116151083-116151315 
                 ALAD 
                 0.99 
               
               
                 chr8 
                 13014567-13014682 
                 DLC1 
                 0.99 
               
               
                 chr7 
                 139440133-139440341 
                 HIPK2 
                 0.99 
               
               
                 chr3 
                 39851068-39851989 
                 MYRIP 
                 0.99 
               
               
                 chr19 
                 4061206-4061360 
                 ZBTB7A 
                 0.99 
               
               
                 chr16 
                 84875643-84875772 
                 CRISPLD2 
                 0.99 
               
               
                 chr6 
                 52268531-52268702 
                 PAQR8 
                 0.99 
               
               
                 chr2 
                 239194812-239194946 
                 PER2 
                 0.99 
               
               
                 chr17 
                 1835463-1835690 
                 MAX.chr17.1835463-1835690 
                 0.99 
               
               
                 chr5 
                 506178-506343 
                 SLC9A3 
                 0.99 
               
               
                 chr20 
                 31061389-31061649 
                 C20orf112 
                 0.98 
               
               
                 chr9 
                 139438534-139438629 
                 NOTCH1 
                 0.98 
               
               
                 chr15 
                 48470606-48470725 
                 MYEF2 
                 0.98 
               
               
                 chr12 
                 125002129-125002192 
                 NCOR2 
                 0.98 
               
               
                 chr4 
                 7367687-7367825 
                 SORCS2 
                 0.98 
               
               
                 chr19 
                 6236947-6237089 
                 MLLT1 
                 0.98 
               
               
                 chr7 
                 544848-545022 
                 PDGFA 
                 0.98 
               
               
                 chr7 
                 98029116-98029383 
                 BAIAP2L1 
                 0.98 
               
               
                 chr4 
                 2415252-2415286 
                 ZFYVE28 
                 0.98 
               
               
                 chr12 
                 6419210-6419489 
                 PLEKHG6 
                 0.98 
               
               
                 chr22 
                 50825886-50825981 
                 PPP6R2 
                 0.97 
               
               
                 chr20 
                 45986341-45986684 
                 ZMYND8 
                 0.97 
               
               
                 chr5 
                 142100518-142100780 
                 MAX.chr5.142100518-142100780 
                 0.97 
               
               
                 chr19 
                 16473958-16474095 
                 EPS15L1 
                 0.97 
               
               
                 chr16 
                 29118636-29118891 
                 RRN3P2 
                 0.97 
               
               
                 chr6 
                 75794978-75795024 
                 COL12A1 
                 0.97 
               
               
                 chr9 
                 139735581-139735683 
                 C9orf172 
                 0.97 
               
               
                 chr17 
                 4462834-4463034 
                 GGT6 
                 0.97 
               
               
                 chr17 
                 4463796-4464037 
                 GGT6 
                 0.96 
               
               
                 chr12 
                 95942077-95942558 
                 USP44 
                 0.96 
               
               
                 chr20 
                 42984244-42984427 
                 HNF4A 
                 0.96 
               
               
                 chr7 
                 47650711-47650882 
                 MAX.chr7.47650711-47650882 
                 0.96 
               
               
                 chr4 
                 942190-942382 
                 TMEM175 
                 0.96 
               
               
                 chr7 
                 73245708-73245798 
                 CLDN4 
                 0.96 
               
               
                 chr22 
                 46974925-46975007 
                 MAX.chr22.46974925-46975007 
                 0.96 
               
               
                 chr10 
                 127033272-127033428 
                 MAX.chr10.127033272- 
                   
               
               
                   
                   
                 127033428 
                 0.96 
               
               
                 chr3 
                 132273353-132273532 
                 MAX.chr3.132273353-132273532 
                 0.96 
               
               
                 chr4 
                 26828422-26828522 
                 MAX.chr4.26828422-26828522 
                 0.96 
               
               
                 chr20 
                 61284095-61284194 
                 SLCO4A1 
                 0.96 
               
               
                 chr19 
                 35540057-35540200 
                 HPN 
                 0.96 
               
               
                 chr22 
                 45099093-45099304 
                 PRR5 
                 0.95 
               
               
                 chr17 
                 60218266-60218449 
                 MAX.chr17.60218266-60218449 
                 0.95 
               
               
                 chr6 
                 168333306-168333467 
                 MLLT4 
                 0.95 
               
               
                 chr10 
                 105338596-105338843 
                 NEURL 
                 0.95 
               
               
                 chr9 
                 120175665-120176057 
                 ASTN2 
                 0.95 
               
               
                 chr4 
                 183817058-183817157 
                 DCTD 
                 0.95 
               
               
                 chr6 
                 108882636-108882682 
                 FOXO3 
                 0.95 
               
               
                 chr7 
                 27136030-27136245 
                 HOXA1 
                 0.95 
               
               
                 chr19 
                 14172823-14172948 
                 PALM3 
                 0.95 
               
               
                 chr3 
                 75411368-75411473 
                 MAX.chr3.75411368-75411473 
                 0.94 
               
               
                 chr6 
                 64168133-64168268 
                 MAX.chr6.64168133-64168268 
                 0.94 
               
               
                 chr16 
                 318717-318893 
                 RGS11 
                 0.94 
               
               
                 chr20 
                 43040031-43040119 
                 HNF4A 
                 0.94 
               
               
                 chr7 
                 49813135-49814168 
                 VWC2 
                 0.94 
               
               
                 chr16 
                 85230248-85230405 
                 MAX.chr16.85230248-85230405 
                 0.94 
               
               
                 chr22 
                 38148620-38148716 
                 TRIOBP 
                 0.94 
               
               
                 chr5 
                 89769002-89769411 
                 MBLAC2 
                 0.94 
               
               
                 chr1 
                 158150797-158151205 
                 CD1D 
                 0.93 
               
               
                 chr19 
                 1406516-1406625 
                 DAZAP1 
                 0.93 
               
               
                 chr12 
                 121416542-121416670 
                 HNF1A 
                 0.93 
               
               
                 chr17 
                 76337726-76337824 
                 MAX.chr17.76337726-76337824 
                 0.93 
               
               
                 chr13 
                 88330094-88330355 
                 SLITRK5 
                 0.93 
               
               
                 chr19 
                 54974004-54974086 
                 LENG9 
                 0.93 
               
               
                 chr22 
                 47130339-47130459 
                 CERK 
                 0.92 
               
               
                 chr7 
                 601162-601552 
                 PRKAR1B 
                 0.92 
               
               
                 chr2 
                 70994498-70994755 
                 ADD2 
                 0.92 
               
               
                 chr15 
                 40361431-40361644 
                 MAX.chr15.40361431-40361644 
                 0.92 
               
               
                 chr19 
                 15568448-15568639 
                 RASAL3 
                 0.92 
               
               
                 chr6 
                 24776486-24776667 
                 GMNN 
                 0.92 
               
               
                 chr18 
                 449695-449798 
                 COLEC12 
                 0.92 
               
               
                 chr7 
                 150760388-150760530 
                 SLC4A2 
                 0.92 
               
               
                 chr21 
                 38120336-38120558 
                 SIM2 
                 0.91 
               
               
                 chr15 
                 66999854-67000014 
                 SMAD6 
                 0.91 
               
               
                 chr2 
                 28844174-28844270 
                 PLB1 
                 0.91 
               
               
                 chr11 
                 115373179-115373281 
                 CADM1 
                 0.91 
               
               
                 chr21 
                 47063793-47064177 
                 PCBP3 
                 0.91 
               
               
                 chr2 
                 1748338-1748444 
                 PXDN 
                 0.91 
               
               
                 chr21 
                 47063798-47063877 
                 MAX.chr21.47063798-47063877 
                 0.91 
               
               
                 chr16 
                 56715756-56716025 
                 MT1X 
                 0.90 
               
               
                 chr4 
                 87934353-87934488 
                 AFF1 
                 0.90 
               
               
                 chr9 
                 140091343-140091644 
                 TPRN 
                 0.90 
               
               
                 chr5 
                 15500208-15500399 
                 FBXL7 
                 0.90 
               
               
                 chr19 
                 48833763-48833967 
                 EMP3 
                 0.90 
               
               
                 chr6 
                 43141954-43142058 
                 SRF 
                 0.90 
               
               
                 chr3 
                 185215700-185215782 
                 TMEM41A 
                 0.90 
               
               
                 chr1 
                 160983607-160983768 
                 F11R 
                 0.90 
               
               
                 chr12 
                 58259413-58259475 
                 MAX.chr12.58259413-58259475 
                 0.90 
               
               
                 chr2 
                 47797260-47797371 
                 KCNK12 
                 0.89 
               
               
                 chr16 
                 4377510-4377615 
                 GLIS2 
                 0.89 
               
               
                 chr15 
                 63795401-63795636 
                 USP3 
                 0.89 
               
               
                 chr13 
                 33001061-33001251 
                 N4BP2L1 
                 0.89 
               
               
                 chr3 
                 120626999-120627116 
                 STXBP5L 
                 0.89 
               
               
                 chr7 
                 19156788-19157227 
                 TWIST1 
                 0.89 
               
               
                 chr18 
                 46190841-46190970 
                 KIAA0427 
                 0.89 
               
               
                 chr7 
                 100203461-100203600 
                 PCOLCE 
                 0.88 
               
               
                 chr19 
                 51228217-51228732 
                 CLEC11A 
                 0.88 
               
               
                 chr19 
                 17438929-17438974 
                 ANO8 
                 0.88 
               
               
                 chr12 
                 2800756-2800899 
                 CACNA1C 
                 0.88 
               
               
                 chr6 
                 34205664-34206018 
                 HMGA1 
                 0.88 
               
               
                 chr15 
                 76632356-76632462 
                 ISL2 
                 0.88 
               
               
                 chr6 
                 111873064-111873162 
                 LOC643749 
                 0.88 
               
               
                 chr10 
                 70359250-70359439 
                 TET1 
                 0.88 
               
               
                 chr2 
                 39665069-39665282 
                 MAP4K3 
                 0.88 
               
               
                 chr2 
                 43153331-43153424 
                 MAX.chr2.43153331-43153424 
                 0.87 
               
               
                 chr22 
                 17849540-17849622 
                 MAX.chr22.17849540-17849622 
                 0.87 
               
               
                 chr2 
                 233877877-233878027 
                 NGEF 
                 0.87 
               
               
                 chr8 
                 89339567-89339662 
                 MMP16 
                 0.87 
               
               
                 chr13 
                 46960767-46961669 
                 C13orf18 
                 0.87 
               
               
                 chr6 
                 170598276-170598782 
                 DLL1 
                 0.87 
               
               
                 chr4 
                 40516823-40516984 
                 RBM47 
                 0.87 
               
               
                 chr3 
                 139654045-139654132 
                 CLSTN2 
                 0.87 
               
               
                 chr2 
                 27486089-27486170 
                 SLC30A3 
                 0.87 
               
               
                 chr17 
                 74519328-74519457 
                 PROD 
                 0.86 
               
               
                 chr2 
                 163174366-163174659 
                 IFIH1 
                 0.86 
               
               
                 chr4 
                 41259387-41259594 
                 UCHL1 
                 0.86 
               
               
                 chr7 
                 45613877-45614572 
                 ADCY1 
                 0.86 
               
               
                 chr7 
                 98990897-98990989 
                 ARPC1B 
                 0.86 
               
               
                 chr3 
                 54156904-54156987 
                 CACNA2D3 
                 0.86 
               
               
                 chr16 
                 49316205-49316258 
                 CBLN1 
                 0.86 
               
               
                 chr3 
                 71478053-71478206 
                 FOXP1 
                 0.86 
               
               
                 chr5 
                 87956937-87956996 
                 LOC645323 
                 0.86 
               
               
                 chr21 
                 43189031-43189229 
                 RIPK4 
                 0.86 
               
               
                 chr12 
                 22487528-22487848 
                 ST8SIA1 
                 0.86 
               
               
                 chr20 
                 42544780-42544835 
                 TOX2 
                 0.86 
               
               
                 chr20 
                 821836-821871 
                 FAM110A 
                 0.86 
               
               
                 chr16 
                 4310204-4310233 
                 TFAP4 
                 0.86 
               
               
                 chr11 
                 64110001-64110069 
                 CCDC88B 
                 0.85 
               
               
                 chr8 
                 136469529-136469873 
                 KHDRBS3 
                 0.85 
               
               
                 chr10 
                 102985059-102985130 
                 FLJ41350 
                 0.85 
               
               
                 chr2 
                 176971915-176971968 
                 HOXD11 
                 0.85 
               
               
                 chr12 
                 51319165-51319319 
                 METTL7A 
                 0.85 
               
               
                 chr22 
                 50342922-50343232 
                 MAX.chr22.50342922-50343232 
                 0.85 
               
               
                 chr7 
                 155597793-155597973 
                 SHH 
                 0.85 
               
               
                 chr4 
                 154712157-154712232 
                 SFRP2 
                 0.84 
               
               
                 chr19 
                 57019064-57019137 
                 ZNF471 
                 0.84 
               
               
                 chr5 
                 87970260-87970568 
                 LOC645323 
                 0.84 
               
               
                 chr6 
                 130686773-130686820 
                 TMEM200a 
                 0.84 
               
               
                 chr9 
                 140201493-140201583 
                 EXD3 
                 0.84 
               
               
                 chr12 
                 53447992-53448072 
                 LOC283335 
                 0.84 
               
               
                 chr22 
                 43044555-43044737 
                 CYB5R3 
                 0.84 
               
               
                 chr19 
                 49256413-49256451 
                 FGF21 
                 0.84 
               
               
                 chr17 
                 77810085-77810206 
                 CBX4 
                 0.84 
               
               
                 chr7 
                 156802460-156802490 
                 MNX1 
                 0.84 
               
               
                 chr7 
                 151127086-151127195 
                 CRYGN 
                 0.83 
               
               
                 chr6 
                 169613138-169613249 
                 MAX.chr6.169613138-169613249 
                 0.83 
               
               
                 chr2 
                 71503632-71503860 
                 MAX.chr2.71503632-71503860 
                 0.83 
               
               
                 chr20 
                 21686466-21686563 
                 PAX1 
                 0.83 
               
               
                 chr2 
                 173293542-173293644 
                 ITGA6 
                 0.83 
               
               
                 chr7 
                 87229775-87229856 
                 ABCB1 
                 0.83 
               
               
                 chr2 
                 207307687-207307794 
                 ADAM23 
                 0.83 
               
               
                 chr12 
                 21680381-21680438 
                 C12orf39 
                 0.83 
               
               
                 chr15 
                 89942904-89943197 
                 MAX.chr15.89942904-89943197 
                 0.83 
               
               
                 chr10 
                 43572798-43572896 
                 RET 
                 0.83 
               
               
                 chr19 
                 5805881-5805968 
                 MAX.chr19.5805881-5805968 
                 0.83 
               
               
                 chr19 
                 53661819-53662279 
                 ZNF347 
                 0.83 
               
               
                 chr22 
                 38732124-38732211 
                 MAX.chr22.38732124-38732211 
                 0.83 
               
               
                 chr11 
                 124615979-124616029 
                 NRGN 
                 0.83 
               
               
                 chr2 
                 100720494-100720679 
                 AFF3 
                 0.83 
               
               
                 chr19 
                 497878-497933 
                 MADCAM1 
                 0.82 
               
               
                 chr5 
                 14143759-14143880 
                 TRIO 
                 0.82 
               
               
                 chr18 
                 32073971-32074004 
                 DTNA 
                 0.82 
               
               
                 chr15 
                 48470062-48470503 
                 MYEF2 
                 0.82 
               
               
                 chr3 
                 50243467-50243553 
                 SLC38A3 
                 0.82 
               
               
                 chr16 
                 70415312-70415673 
                 ST3GAL2 
                 0.82 
               
               
                 chr11 
                 35441199-35441260 
                 SLC1A2 
                 0.82 
               
               
                 chr12 
                 51786085-51786218 
                 GALNT6 
                 0.82 
               
               
                 chr2 
                 232530964-232531124 
                 MAX.chr2.232530964-232531124 
                 0.81 
               
               
                 chr22 
                 19710910-19710984 
                 GP1BB 
                 0.81 
               
               
                 chr19 
                 2097386-2097437 
                 IZUMO4 
                 0.81 
               
               
                 chr11 
                 20178177-20178304 
                 DBX1 
                 0.81 
               
               
                 chr7 
                 37487539-37488596 
                 ELMO1 
                 0.81 
               
               
                 chr11 
                 128564106-128564209 
                 FLI1 
                 0.81 
               
               
                 chr7 
                 105029460-105029585 
                 SRPK2 
                 0.81 
               
               
                 chr10 
                 103538848-103539033 
                 FGF8 
                 0.81 
               
               
                 chr11 
                 124616860-124617005 
                 NRGN 
                 0.81 
               
               
                 chr19 
                 57106617-57106967 
                 ZNF71 
                 0.81 
               
               
                 chr9 
                 97401449-97401602 
                 FBP1 
                 0.81 
               
               
                 chr5 
                 113696971-113697058 
                 KCNN2 
                 0.80 
               
               
                 chr19 
                 53696497-53696704 
                 ZNF665 
                 0.80 
               
               
                 chr1 
                 45769962-45770141 
                 LOC400752 
                 0.80 
               
               
                 chr14 
                 91883473-91883674 
                 CCDC88C 
                 0.80 
               
               
                 chr17 
                 43324999-43325188 
                 LOC100133991 
                 0.80 
               
               
                 chr16 
                 23846964-23847339 
                 PRKCB 
                 0.80 
               
               
                 chr19 
                 11805543-11805639 
                 MAX.chr19.11805543-11805639 
                 0.80 
               
               
                 chr12 
                 117174873-117175030 
                 C12orf49 
                 0.80 
               
               
                 chr20 
                 39995010-39995051 
                 EMILIN3 
                 0.80 
               
               
                 chr5 
                 87970751-87970850 
                 LOC645323 
                 0.80 
               
               
                 chr7 
                 4923056-4923107 
                 RADIL 
                 0.80 
               
               
                 chr19 
                 23941063-23941142 
                 RPSAP58 
                 0.80 
               
               
                 chr6 
                 45387405-45387456 
                 RUNX2 
                 0.80 
               
               
                 chr17 
                 6949717-6949778 
                 SLC16A11 
                 0.80 
               
               
                 chr2 
                 165477564-165477609 
                 GRB14 
                 0.80 
               
               
                 chr20 
                 34189488-34189693 
                 FER1L4 
                 0.80 
               
               
                 chr22 
                 50745629-50745727 
                 PLXNB2 
                 0.79 
               
               
                 chr7 
                 155302557-155302639 
                 CNPY1 
                 0.79 
               
               
                 chr7 
                 19157436-19157533 
                 TWIST1 
                 0.79 
               
               
                 chr1 
                 203619509-203619829 
                 ATP2B4 
                 0.79 
               
               
                 chr2 
                 230578698-230578802 
                 DNER 
                 0.79 
               
               
                 chr19 
                 23941384-23941670 
                 RPSAP58 
                 0.79 
               
               
                 chr17 
                 73390467-73390597 
                 GRB2 
                 0.79 
               
               
                 chr15 
                 68115602-68115675 
                 SKOR1 
                 0.79 
               
               
                 chr17 
                 2300399-2300476 
                 MNT 
                 0.79 
               
               
                 chr13 
                 79177868-79177951 
                 POU4F1 
                 0.79 
               
               
                 chr19 
                 59025337-59025385 
                 SLC27A5 
                 0.79 
               
               
                 chr9 
                 135462730-135462765 
                 BARHL1 
                 0.78 
               
               
                 chr8 
                 125985552-125985847 
                 ZNF572 
                 0.78 
               
               
                 chr5 
                 175665232-175665311 
                 C5orf25 
                 0.78 
               
               
                 chr6 
                 42858890-42859092 
                 C6orf226 
                 0.78 
               
               
                 chr12 
                 21680681-21680817 
                 C12orf39 
                 0.78 
               
               
                 chr14 
                 50099743-50099930 
                 C14orf104 
                 0.78 
               
               
                 chr5 
                 175969660-175969699 
                 CDHR2 
                 0.78 
               
               
                 chr16 
                 80837397-80837505 
                 CDYL2 
                 0.78 
               
               
                 chr19 
                 12996198-12996321 
                 DNASE2 
                 0.78 
               
               
                 chr13 
                 28674451-28674629 
                 FLT3 
                 0.78 
               
               
                 chr1 
                 154733071-154733232 
                 KCNN3 
                 0.78 
               
               
                 chr1 
                 35395179-35395201 
                 MAX.chr1.35395179-35395201 
                 0.78 
               
               
                 chr19 
                 5340273-5340743 
                 PTPRS 
                 0.78 
               
               
                 chr3 
                 33260338-33260423 
                 SUSD5 
                 0.78 
               
               
                 chr2 
                 145274698-145274874 
                 ZEB2 
                 0.78 
               
               
                 chr13 
                 25322044-25322165 
                 MAX.chr13.25322044-25322165 
                 0.78 
               
               
                 chr2 
                 80530326-80530374 
                 CTNNA2 
                 0.78 
               
               
                 chr12 
                 56881329-56881414 
                 GLS2 
                 0.78 
               
               
                 chr3 
                 24563009-24563117 
                 MAX.chr3.24563009-24563117 
                 0.78 
               
               
                 chr7 
                 6655380-6655652 
                 ZNF853 
                 0.78 
               
               
                 chr4 
                 2298384-2298498 
                 ZFYVE28 
                 0.77 
               
               
                 chr5 
                 177632203-177632260 
                 HNRNPAB 
                 0.77 
               
               
                 chr22 
                 19711364-19711385 
                 GP1BB 
                 0.77 
               
               
                 chr2 
                 165477839-165477886 
                 GRB14 
                 0.77 
               
               
                 chr13 
                 29394692-29394771 
                 MAX.chr13.29394692-29394771 
                 0.77 
               
               
                 chr14 
                 103396870-103396920 
                 AMN 
                 0.77 
               
               
                 chr12 
                 132435207-132435428 
                 EP400 
                 0.77 
               
               
                 chr8 
                 99439457-99439482 
                 KCNS2 
                 0.77 
               
               
                 chr7 
                 5821188-5821283 
                 RNF216 
                 0.77 
               
               
                 chr17 
                 9548120-9548325 
                 USP43 
                 0.77 
               
               
                 chr3 
                 185825887-185826002 
                 ETV5 
                 0.77 
               
               
                 chr12 
                 121905558-121905792 
                 KDM2B 
                 0.77 
               
               
                 chr3 
                 193858771-193858843 
                 MAX.chr3.193858771-193858843 
                 0.77 
               
               
                 chr19 
                 53696101-53696195 
                 ZNF665 
                 0.77 
               
               
                 chr7 
                 69062853-69062972 
                 AUTS2 
                 0.77 
               
               
                 chr1 
                 242687719-242687746 
                 PLD5 
                 0.76 
               
               
                 chr20 
                 43948422-43948484 
                 MAX.chr20.43948422-43948484 
                 0.76 
               
               
                 chr6 
                 84419007-84419072 
                 SNAP91 
                 0.76 
               
               
                 chr17 
                 43325784-43325960 
                 LOC100133991 
                 0.76 
               
               
                 chr19 
                 41224781-41225006 
                 ADCK4 
                 0.76 
               
               
                 chr5 
                 15500663-15500852 
                 FBXL7 
                 0.76 
               
               
                 chr20 
                 20350520-20350532 
                 INSM1 
                 0.76 
               
               
                 chr1 
                 23894874-23894919 
                 MAX.chr1.23894874-23894919 
                 0.76 
               
               
                 chr1 
                 11538685-11538738 
                 PTCHD2 
                 0.76 
               
               
                 chr14 
                 105190863-105191031 
                 ADSSL1 
                 0.76 
               
               
                 chr22 
                 22862957-22862983 
                 ZNF280B 
                 0.76 
               
               
                 chr17 
                 72350351-72350403 
                 KIF19 
                 0.76 
               
               
                 chr7 
                 50343838-50344029 
                 IKZF1 
                 0.76 
               
               
                 chr2 
                 191272534-191272765 
                 MFSD6 
                 0.76 
               
               
                 chr17 
                 47073421-47073440 
                 IGF2BP1 
                 0.76 
               
               
                 chr10 
                 133795124-133795423 
                 BNIP3 
                 0.75 
               
               
                 chr5 
                 101631546-101631731 
                 SLCO4C1 
                 0.75 
               
               
                 chr12 
                 133485702-133485739 
                 MAX.chr12.133485702- 
                   
               
               
                   
                   
                 133485739 
                 0.75 
               
               
                 chr22 
                 18923785-18923823 
                 PRODH 
                 0.75 
               
               
                 chr20 
                 56089440-56089547 
                 CTCFL 
                 0.75 
               
               
                 chr6 
                 43336449-43336545 
                 ZNF318 
                 0.75 
               
               
                 chr14 
                 61123624-61123707 
                 MAX.chr14.61123624-61123707 
                 0.75 
               
               
                 chr7 
                 30721980-30722020 
                 CRHR2 
                 0.75 
               
               
                 chr17 
                 7339280-7339492 
                 FGF11 
                 0.75 
               
               
                 chr11 
                 84432067-84432186 
                 DLG2 
                 0.75 
               
               
                 chr2 
                 233352345-233352605 
                 ECEL1 
                 0.75 
               
               
                 chr3 
                 27763358-27763617 
                 EOMES 
                 0.75 
               
               
                 chr5 
                 160975098-160975142 
                 GABRB2 
                 0.75 
               
               
                 chr1 
                 244012804-244012986 
                 MAX.chr1.244012804-244012986 
                 0.75 
               
               
                 chr16 
                 25042924-25043187 
                 MAX.chr16.25042924-25043187 
                 0.75 
               
               
                 chr4 
                 57775698-57775771 
                 REST 
                 0.75 
               
               
                 chr6 
                 127440492-127441039 
                 RSPO3 
                 0.75 
               
               
                 chr8 
                 145561664-145561696 
                 SCRT1 
                 0.75 
               
               
                 chr8 
                 144623045-144623088 
                 ZC3H3 
                 0.75 
               
               
                 chr12 
                 48398051-48398093 
                 COL2A1 
                 0.75 
               
               
                 chr2 
                 182321880-182322022 
                 ITGA4 
                 0.75 
               
               
                 chr9 
                 120507310-120507354 
                 MAX.chr9.120507310-120507354 
                 0.74 
               
               
                 chr6 
                 133562127-133562229 
                 EYA4 
                 0.74 
               
               
                 chr2 
                 127783183-127783233 
                 MAX.chr2.127783183-127783233 
                 0.74 
               
               
                 chr11 
                 47421719-47421776 
                 MAX.chr11.47421719-47421776 
                 0.74 
               
               
                 chr19 
                 10206736-10206757 
                 ANGPTL6 
                 0.74 
               
               
                 chr2 
                 225907414-225907537 
                 DOCK10 
                 0.74 
               
               
                 chr1 
                 35394690-35394876 
                 MAX.chr1.35394690-35394876 
                 0.74 
               
               
                 chr4 
                 2060477-2060624 
                 NAT8L 
                 0.74 
               
               
                 chr2 
                 1747034-1747126 
                 PXDN 
                 0.74 
               
               
                 chr6 
                 45345446-45345595 
                 RUNX2 
                 0.74 
               
               
                 chr7 
                 50344414-50344453 
                 IKZF1 
                 0.74 
               
               
                 chr1 
                 180198528-180198542 
                 LHX4 
                 0.74 
               
               
                 chr14 
                 53417493-53417618 
                 FERMT2 
                 0.74 
               
               
                 chr17 
                 77179784-77179887 
                 RBFOX3 
                 0.74 
               
               
                 chr10 
                 98945242-98945493 
                 SLIT1 
                 0.74 
               
               
                 chr2 
                 40679298-40679326 
                 SLC8A1 
                 0.74 
               
               
                 chr12 
                 48398306-48398375 
                 COL2A1 
                 0.74 
               
               
                 chr22 
                 50987245-50987312 
                 KLHDC7B 
                 0.73 
               
               
                 chr12 
                 54151078-54151153 
                 MAX.chr12.54151078-54151153 
                 0.73 
               
               
                 chr7 
                 28893550-28893569 
                 MAX.chr7.28893550-28893569 
                 0.73 
               
               
                 chr10 
                 38691448-38691521 
                 SEPT7L 
                 0.73 
               
               
                 chr1 
                 203044913-203044929 
                 PPFIA4 
                 0.73 
               
               
                 chr22 
                 51066374-51066431 
                 ARSA 
                 0.73 
               
               
                 chr7 
                 113724864-113725006 
                 FOXP2 
                 0.73 
               
               
                 chr12 
                 13254503-13254606 
                 GSG1 
                 0.73 
               
               
                 chr11 
                 19733958-19734013 
                 LOC100126784 
                 0.73 
               
               
                 chr1 
                 39044345-39044354 
                 MAX.chr1.39044345-39044354 
                 0.73 
               
               
                 chr3 
                 9988302-9988499 
                 PRRT3 
                 0.73 
               
               
                 chr22 
                 20785373-20785464 
                 SCARF2 
                 0.73 
               
               
                 chr6 
                 130687200-130687735 
                 TMEM200a 
                 0.73 
               
               
                 chr12 
                 46661132-46661306 
                 SLC38A1 
                 0.73 
               
               
                 chr19 
                 20149796-20149923 
                 ZNF682 
                 0.73 
               
               
                 chr11 
                 133797643-133797789 
                 IGSF9B 
                 0.73 
               
               
                 chr2 
                 105471752-105471787 
                 POU3F3 
                 0.72 
               
               
                 chr5 
                 179780839-179780955 
                 GFPT2 
                 0.72 
               
               
                 chr8 
                 99952501-99952533 
                 OSR2 
                 0.72 
               
               
                 chr19 
                 16772631-16772712 
                 C19orf42 
                 0.72 
               
               
                 chr2 
                 119607676-119607765 
                 EN1 
                 0.72 
               
               
                 chr12 
                 49372205-49372274 
                 WNT1 
                 0.72 
               
               
                 chr5 
                 113696524-113696682 
                 KCNN2 
                 0.72 
               
               
                 chr17 
                 8649567-8649665 
                 CCDC42 
                 0.72 
               
               
                 chr7 
                 1705957-1706065 
                 MAX.chr7.1705957-1706065 
                 0.71 
               
               
                 chr2 
                 149633039-149633137 
                 KIF5C 
                 0.71 
               
               
                 chr19 
                 2842178-2842235 
                 ZNF555 
                 0.71 
               
               
                 chr10 
                 121302439-121302501 
                 RGS10 
                 0.71 
               
               
                 chr21 
                 44495919-44495933 
                 CBS 
                 0.71 
               
               
                 chr10 
                 11059508-11060151 
                 CELF2 
                 0.71 
               
               
                 chr19 
                 48946755-48946912 
                 GRIN2D 
                 0.71 
               
               
                 chr12 
                 133484978-133485066 
                 MAX.chr12.133484978- 
                   
               
               
                   
                   
                 133485066 
                 0.71 
               
               
                 chr5 
                 16936010-16936058 
                 MYO10 
                 0.71 
               
               
                 chr17 
                 42392669-42392701 
                 RUNDC3A 
                 0.71 
               
               
                 chr16 
                 88521287-88521377 
                 ZFPM1 
                 0.71 
               
               
                 chr4 
                 37245694-37245718 
                 KIAA1239 
                 0.71 
               
               
                 chr16 
                 23847507-23847617 
                 PRKCB 
                 0.71 
               
               
                 chr5 
                 76926598-76926703 
                 OTP 
                 0.71 
               
               
                 chr18 
                 31803017-31803114 
                 NOL4 
                 0.71 
               
               
                 chr2 
                 182322168-182322198 
                 ITGA4 
                 0.70 
               
               
                 chr15 
                 90358267-90358400 
                 ANPEP 
                 0.70 
               
               
                 chr12 
                 107715014-107715095 
                 BTBD11 
                 0.70 
               
               
                 chr16 
                 89007413-89007432 
                 CBFA2T3 
                 0.70 
               
               
                 chr4 
                 151000325-151000356 
                 DCLK2 
                 0.70 
               
               
                 chr6 
                 152129293-152129450 
                 ESR1 
                 0.70 
               
               
                 chr19 
                 38146299-38146397 
                 ZFP30 
                 0.70 
               
               
                 chr1 
                 204797773-204797785 
                 NFASC 
                 0.70 
               
               
                 chr22 
                 42764974-42765049 
                 MAX.chr22.42764974-42765049 
                 0.70 
               
               
                 chr2 
                 165698520-165698578 
                 COBLL1 
                 0.70 
               
               
                 chr8 
                 144358251-144358266 
                 GLI4 
                 0.70 
               
               
                 chr2 
                 219261190-219261327 
                 CTDSP1 
                 0.70 
               
               
                 chr2 
                 239957125-239957163 
                 MAX.chr2.239957125-239957163 
                 0.70 
               
               
                 chr10 
                 121411207-121411375 
                 BAG3 
                 0.69 
               
               
                 chr2 
                 233389020-233389049 
                 CHRND 
                 0.69 
               
               
                 chr14 
                 99946756-99946806 
                 CONK 
                 0.69 
               
               
                 chr11 
                 120382450-120382498 
                 MAX.chr11.120382450- 
                 0.69 
               
               
                   
                   
                 120382498 
                   
               
               
                 chr16 
                 750679-750715 
                 FBXL16 
                 0.69 
               
               
                 chr15 
                 100881373-100881437 
                 ADAMTS17 
                 0.69 
               
               
                 chr1 
                 11539396-11539540 
                 PTCHD2 
                 0.69 
               
               
                 chr2 
                 242447608-242447724 
                 STK25 
                 0.69 
               
               
                 chr16 
                 23847825-23848168 
                 PRKCB 
                 0.69 
               
               
                 chr17 
                 42907549-42907807 
                 GJC1 
                 0.69 
               
               
                 chr19 
                 48918266-48918311 
                 GRIN2D 
                 0.69 
               
               
                 chr10 
                 79397895-79397945 
                 KCNMA1 
                 0.69 
               
               
                 chr5 
                 71404528-71404563 
                 MAP1B 
                 0.69 
               
               
                 chr19 
                 43979400-43979435 
                 PHLDB3 
                 0.69 
               
               
                 chr17 
                 70116754-70116823 
                 SOX9 
                 0.69 
               
               
                 chr16 
                 88497041-88497148 
                 ZNF469 
                 0.69 
               
               
                 chr2 
                 131485151-131485219 
                 GPR148 
                 0.69 
               
               
                 chr8 
                 126441476-126441519 
                 TRIB1 
                 0.68 
               
               
                 chr4 
                 151000358-151000403 
                 DCLK2 
                 0.68 
               
               
                 chr19 
                 39989824-39989852 
                 DLL3 
                 0.68 
               
               
                 chr14 
                 89507100-89507162 
                 MAX.chr14.89507100-89507162 
                 0.68 
               
               
                 chr12 
                 115122614-115122632 
                 TBX3 
                 0.68 
               
               
                 chr19 
                 58513829-58513851 
                 LOC100128398 
                 0.68 
               
               
                 chr5 
                 32714270-32714325 
                 NPR3 
                 0.68 
               
               
                 chr3 
                 140770014-140770193 
                 SPSB4 
                 0.68 
               
               
                 chr6 
                 88875699-88875763 
                 CNR1 
                 0.68 
               
               
                 chr4 
                 657555-657666 
                 PDE6B 
                 0.68 
               
               
                 chr16 
                 19179713-19179744 
                 SYT17 
                 0.67 
               
               
                 chr3 
                 8809858-8809865 
                 OXTR 
                 0.67 
               
               
                 chr10 
                 116064516-116064600 
                 AFAP1L2 
                 0.67 
               
               
                 chr4 
                 77610781-77610824 
                 SHROOM3 
                 0.67 
               
               
                 chr6 
                 88876367-88876445 
                 CNR1 
                 0.67 
               
               
                 chr7 
                 151078646-151078674 
                 WDR86 
                 0.67 
               
               
                 chr2 
                 109745715-109745742 
                 LOC100287216 
                 0.67 
               
               
                 chr14 
                 100751586-100751695 
                 MAX.chr14.100751586- 
                 0.67 
               
               
                   
                   
                 100751695 
                   
               
               
                 chr21 
                 32930371-32930409 
                 TIAM1 
                 0.67 
               
               
                 chr4 
                 57687746-57687764 
                 SPINK2 
                 0.67 
               
               
                 chr2 
                 219849962-219850042 
                 FEV 
                 0.66 
               
               
                 chr20 
                 327754-327871 
                 NRSN2 
                 0.66 
               
               
                 chr1 
                 178063099-178063167 
                 LOC100302401 
                 0.66 
               
               
                 chr19 
                 45430362-45430458 
                 APOC1P1 
                 0.66 
               
               
                 chr13 
                 111767862-111768355 
                 ARHGEF7 
                 0.66 
               
               
                 chr19 
                 37958078-37958134 
                 ZNF570 
                 0.66 
               
               
                 chr19 
                 32715650-32715707 
                 MAX.chr19.32715650-32715707 
                 0.66 
               
               
                 chr8 
                 104152963-104152974 
                 BAALC 
                 0.66 
               
               
                 chr19 
                 3095019-3095055 
                 GNA11 
                 0.66 
               
               
                 chr19 
                 3606372-3606418 
                 TBXA2R 
                 0.66 
               
               
                 chr12 
                 69140018-69140206 
                 SLC35E3 
                 0.66 
               
               
                 chr4 
                 8965831-8965868 
                 MAX.chr4.8965831-8965868 
                 0.66 
               
               
                 chr17 
                 36508733-36508891 
                 SOCS7 
                 0.66 
               
               
                 chr16 
                 85646495-85646594 
                 KIAA0182 
                 0.65 
               
               
                 chr7 
                 54826636-54826706 
                 SEC61G 
                 0.65 
               
               
                 chr9 
                 108418404-108418453 
                 MAX.chr9.108418404-108418453 
                 0.65 
               
               
                 chr7 
                 64408106-64408135 
                 MAX.chr7.64408106-64408135 
                 0.65 
               
               
                 chr10 
                 21816267-21816490 
                 C10orf140 
                 0.65 
               
               
                 chr7 
                 39989959-39990020 
                 CDK13 
                 0.65 
               
               
                 chr1 
                 240255240-240255264 
                 FMN2 
                 0.65 
               
               
                 chr13 
                 114018369-114018421 
                 GRTP1 
                 0.65 
               
               
                 chr13 
                 88323571-88323647 
                 LOC642345 
                 0.65 
               
               
                 chr5 
                 80256215-80256313 
                 RASGRF2 
                 0.65 
               
               
                 chr10 
                 112064230-112064280 
                 SMNDC1 
                 0.65 
               
               
                 chr12 
                 85430135-85430175 
                 LRRIQ1 
                 0.65 
               
               
                 chr1 
                 241520322-241520334 
                 RGS7 
                 0.65 
               
               
                 chr19 
                 22034747-22034887 
                 MAX.chr19.22034747-22034887 
                 0.65 
               
               
                 chr21 
                 27011846-27011964 
                 JAM2 
                 0.65 
               
               
                 chr11 
                 64052053-64052132 
                 BAD 
                 0.65 
               
               
                 chr1 
                 42846119-42846174 
                 RIMKLA 
                 0.64 
               
               
                 chr10 
                 17271896-17271994 
                 VIM 
                 0.64 
               
               
                 chr13 
                 52378159-52378202 
                 DHRS12 
                 0.63 
               
               
                 chr3 
                 27763909-27763981 
                 EOMES 
                 0.63 
               
               
                 chr7 
                 100136884-100137350 
                 AGFG2 
                 0.62 
               
               
                 chr6 
                 88876701-88876726 
                 CNR1 
                 0.62 
               
               
                 chr19 
                 2290471-2290541 
                 LINGO3 
                 0.62 
               
               
                 chr6 
                 105584524-105584800 
                 BVES 
                 0.61 
               
               
                 chr16 
                 23607524-23607650 
                 NDUFAB1 
                 0.61 
               
               
                 chr11 
                 64008415-64008495 
                 FKBP2 
                 0.60 
               
               
                 chr20 
                 3641457-3641537 
                 GFRA4 
                 0.59 
               
               
                 chr19 
                 4343896-4242968 
                 MPND 
                 0.59 
               
               
                 chr2 
                 107503155-107503391 
                 ST6GAL2 
                 0.59 
               
               
                 chr1 
                 240161479-240161546 
                 MAX.chr1.240161479-240161546 
                 0.57 
               
               
                 chr6 
                 144384503-144385539 
                 PLAGL1 
                 0.57 
               
               
                 chr3 
                 72496092-72496361 
                 RYBP 
                 0.57 
               
               
                 chr5 
                 131132146-131132232 
                 FNIP1 
                 0.55 
               
               
                 chr17 
                 36762706-36762763 
                 SRCIN1 
                 0.55 
               
               
                 chr11 
                 32460759-32460800 
                 WT1 
                 0.55 
               
               
                 chr9 
                 127266951-127267032 
                 NR5A1 
                 0.53 
               
               
                 chr7 
                 44084171-44084235 
                 DBNL 
                 0.46 
               
               
                 chr15 
                 29131299-29131369 
                 APBA2 
                 0.44 
               
               
                 chr5 
                 114880375-114880442 
                 FEM1C 
                 0.44 
               
               
                 chr19 
                 34287890-34287972 
                 KCTD15 
                 0.44 
               
               
                 chr16 
                 77468655-77468742 
                 ADAMTS18 
                   
               
               
                 chr22 
                 45898798-45898888 
                 FBLN1 
                   
               
               
                 chr7 
                 113727624-113727693 
                 FOXP2 
                   
               
               
                 chr7 
                 43152309-43152375 
                 HECW1 
                   
               
               
                 chr20 
                 20345123-20345150 
                 INSM1 
                   
               
               
                 chr20 
                 61637950-61638000 
                 LOC63930 
                   
               
               
                 chr1 
                 156406057-156406118 
                 MAX.chr1.156406057-156406118 
                   
               
               
                 chr10 
                 23480864-23480913 
                 PTF1A 
                   
               
               
                 chr5 
                 1445384-1445473 
                 SLC6A3 
                   
               
               
                 chr2 
                 107502978-107503055 
                 ST6GAL2 
                   
               
               
                 chr10 
                 17496177-17496310 
                 ST8SIA6 
               
               
                   
               
            
           
         
       
     
     All publications and patents mentioned in the above specification are herein incorporated by reference in their entirety for all purposes. Various modifications and variations of the described compositions, methods, and uses of the technology will be apparent to those skilled in the art without departing from the scope and spirit of the technology as described. Although the technology has been described in connection with specific exemplary embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in pharmacology, biochemistry, medical science, or related fields are intended to be within the scope of the following claims.