Detection of ovarian cancer

Abstract

This invention provides a method for detecting the presence of ovarian cancer and its subtypes. [Solution] A method comprising measuring the methylation level of one or more genes in a biological sample of a human individual by treating the genomic DNA in the biological sample with a reagent that modifies the DNA in a methylation-specific manner; amplifying the treated genomic DNA using a set of primers for the selected one or more genes; and determining the methylation level of the one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture, wherein the one or more genes are selected from FAIM2, CAPN2, and SIM2.

Description

Detailed Description of the Invention

[0001] [Technical Field] Cross - Reference to Related Applications This application claims the benefit of U.S. Provisional Application No. 62 / 928,888, filed on October 31, 2019, and U.S. Provisional Application No. 63 / 065,081, filed on August 13, 2020, which are hereby incorporated by reference in their entirety.

[0002] Provided herein are technologies for ovarian cancer screening, and in particular, but not limited to, methods, compositions, and related uses for detecting the presence of ovarian cancer and subtypes of ovarian cancer (e.g., clear cell ovarian cancer, endometrioid ovarian cancer, mucinous ovarian cancer, serous ovarian cancer). [Background Art] Ovarian cancer is one of the most lethal gynecological malignancies in developed countries. In the United States, approximately 23,000 women are diagnosed with this disease each year, and nearly 14,000 women die from it. There are three main types of ovarian cancer: epithelial, germ cell, and sex cord - stromal. Approximately 90% of ovarian cancers originate from epithelial tissue, which is the inner lining outside the ovaries. This type of ovarian cancer is divided into serous, mucinous, endometrioid, clear cell, transitional, and undifferentiated types. The risk of epithelial ovarian cancer increases with age, especially after the age of 50. Germ cell tumors account for about 5% of ovarian cancers. This starts from the cells that give rise to eggs. This type of ovarian cancer can occur in women of all ages, but about 80% are found in women under 30 years old. The main subtypes are teratoma, undifferentiated germ cell tumor, endodermal sinus tumor, and choriocarcinoma. Sex cord - stromal tumors, which account for about 5% of ovarian cancers, grow in the connective tissue that connects the ovaries and produces estrogen and progesterone. Many are found in older women.

[0003] Despite the progress of cancer therapies, the mortality rate due to ovarian cancer has not substantially changed over the past 20 years. Considering that the slope of the survival rate varies significantly depending on the stage at which the disease is diagnosed, early detection remains the most important factor in improving the long - term survival of ovarian cancer patients.

[0004] Improved methods are needed to detect ovarian cancer and its various subtypes (e.g., clear cell ovarian cancer, endometrioid ovarian cancer, mucinous ovarian cancer, and serous ovarian cancer).

[0005] This invention addresses these requirements. [Overview of the prefecture] As mentioned above, ovarian cancer (OC) is the leading cause of gynecological cancer death and is one of the most frequent causes of fatal malignancies in women overall (see Ozor RF, et al., Epithelial ovarian cancer. In: Hoskin W.J., Perez C.A., Young R.C., editors. Principles and Practice of Gynecologic Oncology. Lippincott Williams & Wilkins; Philadelphia, PA, USA: 2000. pp.981-1057). Symptoms are often nonspecific, making early detection difficult, and the majority of patients present with advanced-stage disease.

[0006] In recent years, histopathological, molecular, and genetic studies have revealed the characteristics of several subtypes of ovarian carcinoma (OC). The main histological types are epithelial in origin and include serous ovarian carcinoma (serous OC), clear cell carcinoma (clear cell OC), endometrioid carcinoma (endometrioid OC), and mucinous carcinoma (mucinous OC). Serous OC is the most common malignant form of ovarian cancer, accounting for up to 70% of all ovarian cancer cases. Clear cell OC is the second most common histological type, accounting for approximately 10-13% of women diagnosed with ovarian cancer. Endometrioid OC is the third most common histological type of ovarian cancer and, like clear cell carcinoma, is thought to arise from endometriosis. Mucinous OC accounts for 4% of ovarian cancers and is generally diagnosed at an early stage.

[0007] An effective screening approach is urgently needed to reduce the significant losses caused by occlusive carcinoma (OC) and its various subtypes (e.g., clear cell OC, serous OC, endometrioid OC, mucinous OC). Innovation is essential to provide accurate, affordable, and safe screening tools for pre-symptomatic detection of early-stage cancer and advanced precancerous conditions.

[0008] The present invention addresses such requirements. In fact, the present invention provides novel methylated DNA markers for identifying cases of occlusive dressing (OC) and its various subtypes (e.g., clear cell OC, serous OC, endometrial OC, mucinous OC).

[0009] Methylated DNA has been studied as a promising class of biomarkers in tissues of most tumor types. Often, DNA methyltransferases add methyl groups to DNA at cytosine-phosphate-guanine (CpG) island sites as an epigenetic regulation of gene expression. In a biologically intriguing mechanism, acquired methylation events in the promoter regions of tumor suppressor genes are thought to silence expression, thereby contributing to carcinogenesis. DNA methylation may be a more chemically and biologically stable diagnostic tool than RNA or protein expression (Laird (2010) Nat Rev Genet 11:191-203). Furthermore, in other cancers such as sporadic colorectal cancer, methylation markers offer superior specificity, providing broader information and higher sensitivity than individual DNA mutations (Zou et al (2007) Cancer Epidemiol Biomarkers Prev 16:2686-96).

[0010] Analysis of CpG islands has yielded important insights applicable 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) PLoS Genet 5:e1000438). Furthermore, the methylation levels were bimodally distributed between highly methylated and unmethylated sequences, further supporting a binary-switch-like pattern of DNA methyltransferase activity (Zhang et al. (2009) PLoS Genet 5:e1000438). Analysis of in vivo mouse tissues and in vitro cell lines demonstrated that only about 0.3% of high-CpG density promoters (HCPs, defined as having more than 7% CpG sequences within a 300-base pair region) were methylated, while low-CpG density regions (LCPs, defined as having less than 5% CpG sequences within a 300-base pair region) tended to be methylated frequently in dynamic tissue-specific patterns (Meissner et al. (2008) Nature 454:766-70). HCPs include promoters for ubiquitous housekeeping genes and highly regulated developmental genes. Several established markers, such as Wnt2, NDRG2, SFRP2, and BMP3, were found among HCP sites methylated more than 50% (Meissner et al. (2008) Nature 454:766-70).

[0011] Epigenetic methylation of DNA at cytosine-phosphate-guanine (CpG) island sites by DNA methyltransferases has been studied as a promising class of biomarkers in tissues of most tumor types. In a biologically intriguing mechanism, acquired methylation events in the promoter regions of tumor suppressor genes are thought to silence expression and contribute to carcinogenesis. DNA methylation may be a more chemically and biologically stable diagnostic tool than RNA or protein expression. Furthermore, in other cancers such as sporadic colorectal cancer, abnormal methylation markers offer broader information, higher sensitivity, and superior specificity than individual DNA mutations.

[0012] Several methods are available for searching for novel methylation markers. Microarray-based matching of CpG methylation is a reasonable and high-throughput approach, but this strategy is biased towards known target regions, mainly established tumor suppressor promoters. Alternative methods to genome-wide analysis of DNA methylation have been developed over the last decade. Three basic approaches exist. The first uses restriction enzyme digestion of DNA that recognizes specific methylation sites, followed by several possible analytical techniques that provide methylation data limited to the enzyme-recognized sites or primers used to amplify the DNA in a quantification step (such as methylation-specific PCR (MSP)). The second approach enriches the methylation fraction of genomic DNA using antibodies targeting methyl-cytosine or other methylation-specific binding domains, followed by microarray analysis or sequencing to map the fragments to a reference genome. This approach does not provide single-nucleotide resolution for all methylation sites within the fragments. The third approach begins with bisulfite treatment of the DNA to convert all unmethylated cytosines to uracil, followed by restriction enzyme digestion and full sequencing of all fragments after binding to adapter ligands. Selection of restriction enzymes allows for enrichment of fragments in CpG-high-density regions, reducing the number of redundant sequences that may map to multiple gene locations during analysis.

[0013] RRBS provides single-nucleotide resolution CpG methylation status data for 80–90% of all CpG islands and most tumor suppressor promoters with moderate to high read coverage. In cancer case-control studies, analysis of these reads identifies variable methylation regions (DMRs). Previous RRBS analyses of pancreatic cancer specimens have revealed hundreds of DMRs, many of which were not associated with carcinogenesis and many were not annotated. Further validation studies on independent tissue sample sets confirmed that the marker CpG demonstrated 100% sensitivity and specificity in terms of performance.

[0014] Provided herein are, but are not limited to, techniques for screening OCs and various OC subtypes (e.g., clear cell OCs, endometrioid OCs, mucinous OCs, serous OCs), methods, compositions, and related applications for detecting the presence of OCs and various OC subtypes (e.g., clear cell OCs, endometrioid OCs, mucinous OCs, serous OCs).

[0015] In fact, as described in Examples I and II, experiments conducted during the process of clarifying embodiments of the present invention identified a novel set of variable methylation regions (DMRs) for distinguishing between 1) ovarian cancer-derived DNA and non-tumor control DNA, 2) clear cell OC tissue-derived DNA and non-tumor control DNA, 3) endometrioid OC tissue-derived DNA and non-tumor control DNA, 4) myxoid OC tissue-derived DNA and non-tumor control DNA, and 5) serous OC tissue-derived DNA and non-tumor control DNA.

[0016] Such experiments have shown that OC tissue and benign tissue (see Tables 1A, 1B, 3, 4A, 6A, and 8A; see Examples I and II), clear cell OC tissue and benign tissue (see Tables 1A, 1B, 2A, 4B, 5B, 6A, and 8B; see Examples I and II), endometrioid OC tissue and benign tissue (see Tables 1A, 1B, 2B, 4C, 5C, 6A, and 8C; see Examples I and II), and mucinous OC tissue and benign tissue (see Tables 1A, 1B, 2B, 4C, 5C, 6A, and 8C; see Examples I and II). Tables B, 2C, 4D, 5D, 6A, and 8D; see Examples I and II), distinguish between serous OC tissue and benign tissue (see Tables 1A, 1B, 2D, 4E, 5A, 6A, and 8E; see Examples I and II), and detect OC (e.g., OC, clear cell OC, intometrioid OC, mucinous OC, serous OC) in blood samples (see Table 9; see Example III), listing and describing 560 novel DNA methylation markers.

[0017] From these 560 novel DNA methylation markers, further experiments identified the following panel of markers and / or markers that can distinguish ovarian cancer tissue from benign tissue: ·AGRN_A、ATP10A_A、ATP10A_B、ATP10A_C、ATP10A_D、BCAT1、CCND2_D、CMTM3_A、ELMO1_A、ELMO1_B、ELMO1_C、EMX1、EPS8L2_A、EP S8L2_B, EPS8L2_C, EPS8L2_D, FAIM2_A, FLJ34208_A, GPRIN1, GYPC_A, INA_A, ITGA4_B, KCNA3_A, KCNA3_C, LBH, LIME1_A, LIME1_ B LOC646278 LRRC4 LRRC41_A MAX.chr1.110626771-110626832 MAX.chr1.147790358-147790381 MAX.chr1.161591532-16 1591608. MAX.chr15.28351937-28352173. MAX.chr15.28352203-28352671. MAX.chr15.29131258-29131734 95-8860062, MAX.chr5.42952182-42952292, MDFI, NCOR2, NKX2-6, OPLAH_A, PARP15, PDE10A, PPP1R16B, RASSF1_B, SEPTIN9, SK I, SLC12A8, SRC_A, SSBP4_B, ST8SIA1, TACC2_A, TSHZ3, UBTF, VI M, VIPR2_A, ZBED4, ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZNF382_A, ZNF4 69_B, ATP6V1B1_A, BZRAP1, GDF6, IFFO1_A, IFFO1_B, KCNAB2, LIMD2, MAML3_B, MAX.chr14.102172350-102172770, MAX.chr16.8 5482307-85482494 MAX.chr17.76254728-76254841 MAX.chr5 .42993898-42994179 AND RASAL3(FILE1A, FIGURE1B, FIGURE 6A; ·MAX.chr16.85482307-85482494, GDF6, IFFO_A, MAX.chr5.42993898-42994179, MAX.chr17.76254728-76254841, MAX.chr14.102172350-102172770, RASAL3, BZRAP1, and LIMD2 (see Table 3; Example I); ·PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAML3_A, SKI, DNMT3A_A, and C2CD4D (see Table 4A; Example I); and BCAT1_6015, SKI, SIM2_B, DNMT3A_A, CDO1_A, and DSCR6 (see Table 8A; Example II).

[0018] From these 560 novel DNA methylation markers, further experiments identified the following panel of markers and / or markers for detecting ovarian cancer (e.g., OC, clear cell OC, endometrioid OC, mucinous OC, serous OC) in blood samples (e.g., plasma samples, whole blood samples, leukocyte samples, serum samples): · GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), SRC (e.g., SRC_A, SRC_B), SIM2 (e.g., SIM2_A, SIM2_B), AGRN (e.g., AGRN_A, AGRN_B, AGRN_C, AGRN_8794), FAIM2 (e.g., FAIM2_A, FAIM2_B), CELF2 (e.g., CELF2_A, CELF2_B), DSCR6, GYPC (e.g., GYPC_A, GYPC_B, GYPC_C), CAPN2 (e.g., CAPN2_A, CAPN2_B), and BCAT1 (see Table 9; Example III); and ·ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, G PRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, SIM2_A, AGRN_8794, BCAT1_6015, KCNA3_7518, KCNA3_7320, LOC10013136, GYPC_C, SRC (e.g., SRC_A, SRC_B), NR2F6, TSHZ3, CELF2 (e.g., CELF2_A, CELF2_B), TACC2 (e.g., TACC2_A, TACC2_B), VIPR2 (e.g., VIPR2_A, VIPR2_B), and SPOCK2_74333 (see Table 10, Example III).

[0019] From these 560 novel DNA methylation markers, further experiments identified the following panel of markers and / or markers for detecting ovarian cancer (e.g., OC, clear cell OC, endometrioid OC, mucinous OC, serous OC) in blood samples (e.g., plasma samples, whole blood samples, leukocyte samples, serum samples) in combination with elevated levels of cancer antigen 125 (CA-125): CA-125 and ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LB H, CMTM3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, and SIM2_A (see Tables 11, 12 and 13, and Example III).

[0020] From these 560 novel DNA methylation markers, further experiments identified the following panel of markers and / or markers that can distinguish clear cell OC tissue from ovarian tissue: TACC2_A, LRRC41_A, EPS8L2, LBH, LIME1_B, MDFI, FAIM2_A, GYPC_A, AGRN_B, and ZBED4 (see Table 2A; Example I); ·MT1A_A, CELF2_A, KCNA3_A, MDFI, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790 381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML 3_A, MAX.chr14.105512178-105512224, EPS8L2_E, SKI, GPRIN1_A, MAX.chr8.142215938-142216298, CDO1_A, DNMT3A_A, SIM2_A, SKI, MT1A_B, GYPC_A, BCL2L11, PISD, and C2CD4D (see Table 4B; Example I); ·NCOR2, MT1A_B, CELF2_A, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, SKI, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, MT1A_B, GYPC_A, BCL2L11, GDF6, and C2CD4D (see Table 5B; Example I); and ·AGRN_8794, BHLHE23_8339, EPS8L2_F, RASSF1_8293, MDFI_6321, SKI, GYPC_C, NKX2-6_4159, LOC100131366, FAIM2_B, GPRIN1_B, LRRC41_B, TACC2_B, LBH, SIM2_B, CDO1_A, and DSCR6 (see Table 8B; Example II).

[0021] From these 560 novel DNA methylation markers, further experiments identified the following panel of markers and / or markers that can distinguish between intometabolic OC tissue and benign tissue: PARP15, GPRIN1_A, GYPC1_A, FLJ34208, MAX.chr1.147790358-147790381, FAIM2_A, SH2B3, KCNQ5, IRF4, and BCAT1 (see Table 2B; Example I); NCOR2, CELF2_A, PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, MAML3_A, SKI, GPRIN1_A, SKI, BCL2L11, and C2CD4D (see Table 4C; Example I); · NCOR2, PALLD, PRDM14, MAX.chr1.147790358 - 147790381, MAX.chr11.14926602 - 14926671, DSCR6, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, and C2CD4D (see Table 5C; see Example I); and · BCAT1_6015, EPS8L2_F, SKI, NKX2 - 6_4159, C1QL3_B, GPRIN1_B, PARP15, OXT_C, SIM2_B, DNMT3A_A, and CELF2_A (see Table 8C; see Example II).

[0022] From these 560 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers that can distinguish mucinous OC tissue from benign tissue: · CMTM3_A, ATP10A_C, TSHZ3, ZMIZ1_B, ATP10A_B, ELMO1_B, TACC2_A, LRRC4, VIM, and ZNF382_A (see Table 2C; see Example I); · NCOR2, MT1A_A, KCNA3_A, ZMIZ1_C, TACC2_A, MAX.chr1.147790358 - 147790, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, BCL2L11, and GATA2 (see Table 4D; see Example I); · NCOR2, PALLD, TACC2_A, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, and BCL2L11 (see Table 5D; see Example I); and · BCAT1_6015, ELMO1_9100, KCNA3_7518, KCNA3_7320, MDFI_6321, SKI, VIPR_B, ZNF382_B, ATP10A_E, CMTM3_B, ZMIZ1_D, SRC_B, HDGFRP3, TACC2_B, TSHZ3, LBH, DNMT3A_A (see Table 8D; see Example II).

[0023] From these 560 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers that can distinguish serous OC tissue from benign tissue: · MAX.chr1.147790358-147790381, MAML3, NR2F6, DNMT3A_A, SKI, SOBP, UBTF, AGRN_C, MAX.chr12.30975740-30975780, and CAPN2_A (see Table 2D; refer to Example I); · PALLD, PRDM14, MAX.chr1.147790358-147790381, CAPN2_A, MAX.chr6.10382190-10382225, SKI, NR2F6, IFFO1_A, MT1A_B, IFFO1_B, GDF6, and C2CD4D (see Table 4E; refer to Example I); · NCOR2, MAX.chr1.147790358-147790381, MAX.chr6.10382190-10382225, IFFO1_A, GDF6, and C2CD4D (see Table 5A; refer to Example I); and · SKI, PEAR1_B, CAPN2_B, SIM2_B, DNMT3A_A, CDO1_A, and NR2F6 (see Table 8E; refer to Example II).

[0024] 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, or 8 markers) that highly discriminate between overall ovarian cancer and various types of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC). The 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 for ovarian cancer screening or diagnosis purposes.

[0025] In some embodiments, the technique relates to evaluating the presence and methylation status of one or more of the markers identified herein in a biological sample (e.g., ovarian tissue, plasma sample). These markers include one or more variable methylation regions (DMRs) as considered herein, for example, as shown in Table 1A and Table 6A. The methylation status is evaluated in embodiments of the technique. Therefore, the techniques provided herein are not limited in the method by which the methylation status of a gene is measured. For example, in some embodiments, the methylation status is measured by genome scanning methods. For example, one method includes restriction enzyme landmark genome scanning (Kawai et al. (1994) Mol. Cell. Biol. 14:7421-7427), and another example includes methylation-sensitive arbitrary prime 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 spectroscopy (digestion-Southern assay) of the region of interest. In some embodiments, analyzing changes in methylation patterns involves a PCR-based process including digestion of genomic DNA with methylation-sensitive or methylation-dependent restriction enzymes before PCR amplification (Singer-Sam et al. (1990) Nucl. Acids Res. 18:687). Furthermore, other techniques utilizing bisulfite treatment of DNA as a starting point for methylation analysis have been reported. 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 technology is used for the detection of gene mutations (Kuppuswamy et al.). al. (1991) Proc. Natl. Acad. Sci. USA 88:1143-1147), and quantification of allele-specific expression (Szabo and Mann (1995) Genes Dev. 9:3097-3108, and Singer-Sam This technique has been developed for et al. (1992) PCR Methods Appl. 1:160-163). Such techniques use internal primers that anneal to a template generated by PCR and terminate immediately next to the 5' of the single nucleotide being assayed. A method using the “Quantitative Ms-SNuPE Assay” described in U.S. Patent No. 7,037,650 is used in some embodiments.

[0026] When evaluating methylation status, it is often expressed as the percentage of individual DNA strands that are methylated at a specific site (e.g., in a single nucleotide, at a specific region or locus, in a relatively long target sequence, e.g., over a portion of DNA up to approximately 100 bp, 200 bp, 500 bp, 1000 bp or more) compared to the DNA population in the sample containing that specific site. Traditionally, the amount of unmethylated nucleic acid is determined by PCR using a calibrator. A known amount of DNA is then treated with bisulfite, and the resulting methylation-specific sequences are determined using either real-time PCR or other exponential amplification methods, such as the QuARTS assay (e.g., provided by U.S. Patent No. 8,361,720, incorporated herein by reference, and U.S. Patent Application Publications 2012 / 0122088 and 2012 / 0122106).

[0027] For example, in some embodiments, the method includes generating a standard curve for a non-methylated target by using an external standard. The standard curve consists of at least two points and relates to the real-time Ct value of non-methylated DNA relative to a known quantitative standard. Then, a second standard curve for a methylated target consists of at least two points and an external standard. This second standard curve relates to the Ct of methylated DNA relative to a known quantitative standard. Next, the Ct values ​​of the test sample are determined for the methylated and non-methylated populations, and the genomic equivalent of the DNA is calculated from the standard curves generated by the first two steps. The percentage of methylation at the target site is calculated from the amount of methylated DNA relative to the total amount of DNA in the population, for example, (number of methylated DNAs) / (number of methylated DNAs + number of non-methylated DNAs) × 100.

[0028] This specification also provides compositions and kits for carrying out methods. For example, in some embodiments, reagents specific to one or more markers (e.g., primers, probes) are provided individually or in sets (e.g., sets of primer pairs for amplifying multiple markers). Further reagents for carrying out detection assays (e.g., QuARTS, PCR, sequencing, bisulfite, or enzymes, buffers, positive and negative controls for carrying out other assays) may also be provided. In some embodiments, kits contain reagents that can modify DNA in a methylation-specific manner (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents). In some embodiments, kits are provided that contain one or more reagents necessary, sufficient, or useful for carrying out a method. Reaction mixtures containing reagents are also provided. Further provided are master mix reagent sets containing multiple reagents that can be added to each other and / or to a test sample to complete a reaction reaction.

[0029] In some embodiments, the techniques described herein relate to a programmable machine designed to perform a set of arithmetic or logical operations provided by the methods described herein. For example, some embodiments of the techniques relate to computer software and / or computer hardware (e.g., implemented therein). In one aspect, the techniques relate to a computer including a form of memory, elements for performing arithmetic and logical operations, and processing elements (e.g., a microprocessor) for executing a set of instructions (e.g., the methods provided herein) for reading, manipulating, and storing data. In some embodiments, the microprocessor is part of a system, or is known in the art, for determining the methylation status of (e.g., one or more DMRs, e.g., DMR1-560 as shown in Tables 1A and 6A); comparing the methylation status of (e.g., one or more DMRs, e.g., DMR1-560 as shown in Tables 1A and 6A); generating standard curves; determining Ct values; calculating the rate, frequency, or percentage of methylation of (e.g., one or more DMRs, e.g., DMR1-560 as shown in Tables 1A and 6A); identifying CpG islands; determining the specificity and / or sensitivity of assays or markers; calculating ROC curves and associated AUCs; and sequence analysis, all as described herein.

[0030] In some embodiments, a microprocessor or computer uses methylation status data in an algorithm for predicting the location of cancer.

[0031] In some embodiments, a software or hardware component receives the results of multiple assays, determines a single value result indicating cancer risk based on the results of the multiple assays, and reports it to the user (e.g., determining the methylation status of multiple DMRs (e.g., as shown in Tables 1A and 6A)). In related embodiments, a risk factor is calculated based on a mathematical combination (e.g., a weighted combination, a linear combination) of results from multiple assays, for example, determining the methylation status of multiple markers (e.g., multiple DMRs as shown in Tables 1A and 6A). In some embodiments, the methylation status of DMRs may define a dimension and have a value in a multidimensional space, and the coordinates defined by the methylation status of multiple DMRs are, for example, results related to cancer risk, to be reported to the user.

[0032] Some embodiments include storage media and memory components. Memory components (e.g., volatile and / or non-volatile memory) are used to store instructions (e.g., embodiments of processes provided herein) and / or data (e.g., workpieces such as methylation measurements, sequences, and associated statistical descriptions). Some embodiments also relate to systems including one or more of a CPU, a graphics card, and a user interface (e.g., including output devices such as a display and input devices such as a keyboard).

[0033] Programmable machines related to this technology include conventional existing technologies and technologies under development or not yet developed (e.g., quantum computers, chemical computers, DNA computers, optical computers, spintronics-based computers, etc.).

[0034] In some embodiments, the technology includes wired (e.g., metal cables, optical fibers) or wireless transmission media 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 reside on the network, such as peers, and in some embodiments, the programmable machines have a client / server relationship.

[0035] In some embodiments, data is stored on a computer-readable storage medium such as a hard disk, flash memory, optical media, or floppy disk.

[0036] In some embodiments, the techniques provided herein relate to multiple programmable devices that work together to implement the methods described herein. For example, in some embodiments, multiple computers (e.g., connected by a network) can operate in parallel, collect and process data in an implementation of cluster computing or grid computing or some other distributed computing architecture that relies on a complete computer (equipped with an onboard CPU, storage, power supply, network interface, etc.) connected to a network (private, public, or the Internet) by conventional network interfaces such as Ethernet, optical fiber, or by wireless network technology.

[0037] For example, some embodiments provide a computer including a computer-readable medium. The embodiments include random access memory (RAM) linked to a processor. The processor executes computer-executable program instructions stored in the memory. Such a processor may include a microprocessor, an ASIC, a state machine, or other processor, and may be one of a number of computer processors, such as those from Intel Corporation in Santa Clara, California, and Motorola Corporation in Schaumburg, Illinois. Such a processor may include or communicate with a medium, for example, a computer-readable medium, which, when executed by the processor, stores instructions that cause the processor to perform the steps described herein.

[0038] Embodiments of a computer-readable medium include, but are not limited to, electronic, optical, magnetic, or other storage or transmission devices capable of providing computer-readable instructions to a processor. Other examples of suitable media include, but are not limited to, floppy disks, CD-ROMs, DVDs, magnetic disks, memory chips, ROMs, RAMs, ASICs, configured processors, all optical media, all magnetic tapes or other magnetic media, or any other media from which a computer processor can read instructions. Furthermore, various other forms of computer-readable media can transmit or communicate instructions to a computer and include both wired and wireless routers, private or public networks, or other transmission devices or channels. Instructions may include code from any suitable computer programming language, such as C, C++, C#, Visual Basic, Java, Python, Perl, and JavaScript.

[0039] In some embodiments, the computer is connected to a network. The computer may also include several external or internal devices, such as a mouse, CD-ROM, DVD, keyboard, display, or other input or output devices. Examples of computers include personal computers, digital assistants, personal digital assistants, mobile phones, and mobile phones. These include phones, smartphones, pagers, digital tablets, laptop computers, internet appliances, and other processor-based devices. Generally, the computers relating to the embodiments of the technology provided herein may be any type of processor-based platform running on any operating system capable of supporting one or more programs including the technology provided herein, such as Microsoft Windows, Linux, UNIX, Mac OS X, etc. Some embodiments include a personal computer running other application programs (e.g., applications). Applications can be stored in memory and include, for example, word processing applications, spreadsheet applications, email applications, instant messenger applications, presentation applications, internet browser applications, calendar / organizer applications, and any other applications that can be run by the client device.

[0040] All such components, computers, and systems described herein in relation to this technology may be logical or virtual.

[0041] Accordingly, provided herein is a technique for screening for ovarian cancer and / or various forms of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC) in a sample obtained from a subject, the method comprising assaying the methylation status of a marker in a sample obtained from a subject (e.g., ovarian tissue) (e.g., plasma sample), and identifying the subject as having OC and / or a particular form of OC (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC) if the methylation status of the marker differs from the methylation status of the marker assayed in a subject without such cancer, wherein the marker comprises a base in a variable methylation region (DMR) selected from the group consisting of DMRs 1 to 560 as shown in Tables 1A and 6A.

[0042] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has ovarian cancer: AGRN_A, ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, BCAT1, CCND2_D, CMTM3_A, ELMO1_A, ELMO1_B, ELMO1_C, EMX1, EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2 _D, FAIM2_A, FLJ34208_A, GPRIN1, GYPC_A, INA_A, ITGA4_B, KCNA3_A, KCNA3_C, LBH, LIME1_A, LIME1_B, LOC646278, LRRC4, LRRC41_A, MAX.chr1. 110626771-110626832, MAX.chr1.147790358-147790381, MAX.chr1.161591532-161591608, MAX.chr15.28351937-28352173, MAX.chr15.2835 2203-28352671, MAX.chr15.29131258-29131734, MAX.chr4.8859995-8860062, MAX.chr5.42952182-42952292, MDFI, NCOR2, NKX2-6, OPLAH_A, PARP15, PDE10A, PPP1R16B, RASSF1_B, SEPTIN9, SKI, SLC12A8, SRC_A, SSBP4_B, ST8SIA1, TACC2_A, TSHZ3, UBTF, VIM, VIPR2_A, ZBED4, ZMIZ1_A, Z MIZ1_B, ZMIZ1_C, ZNF382_A, ZNF469_B, ATP6V1B1_A, BZRAP1, GDF6, IFFO1_A, IFFO1_B, KCNAB2, LIMD2, MAML3_B, MAX.chr14.102172350-102172770, MAX.chr16.85482307-85482494, MAX.chr17.76254728-76254841, MAX.chr5.42993898-42994179, and RASAL3 (see Tables 1A, 1B, and 6A; see Example I).

[0043] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has ovarian cancer: MAX.chr16.85482307-85482494, GDF6, IFFO_A, MAX.chr5.42993898-42994179, MAX.chr17.76254728-76254841, MAX.chr14.102172350-102172770, RASAL3, BZRAP1, and LIMD2 (see Table 3; Example I).

[0044] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has ovarian cancer: PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAML3_A, SKI, DNMT3A_A, and C2CD4D (see Table 4A; Example I).

[0045] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has ovarian cancer: BCAT1_6015, SKI, SIM2_B, DNMT3A_A, CDO1_A, and DSCR6 (see Table 8A; Example II).

[0046] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has clear cell ovarian cancer: TACC2_A, LRRC41_A, EPS8L2, LBH, LIME1_B, MDFI, FAIM2_A, GYPC_A, AGRN_B, and ZBED4 (see Table 2A; Example I).

[0047] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has clear cell ovarian cancer: MT1A_A, CELF2_A, KCNA3_A, MDFI, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926 602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, MAX.chr14.105512178-105512224, EPS8L2_E, SKI, GPRIN1_A, MAX.chr8.142215938-142216298, CDO1_A, DNMT3A_A, SIM2_A, SKI, MT1A_B, GYPC_A, BCL2L11, PISD, and C2CD4D (see Table 4B; Example I).

[0048] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has clear cell ovarian cancer: NCOR2, MT1A_B, CELF2_A, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.14779035 8-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, SKI, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, MT1A_B, GYPC_A, BCL2L11, GDF6, and C2CD4D (see Table 5B; Example I).

[0049] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has clear cell ovarian cancer: AGRN_8794, BHLHE23_8339, EPS8L2_F, RASSF1_8293, MDFI_6321, SKI, GYPC_C, NKX2-6_4159, LOC100131366, FAIM2_B, GPRIN1_B, LRRC41_B, TACC2_B, LBH, SIM2_B, CDO1_A, and DSCR6 (see Table 8B; Example II).

[0050] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has endometrioid ovarian cancer: PARP15, GPRIN1_A, GYPC1_A, FLJ34208, MAX.chr1.147790358-147790381, FAIM2_A, SH2B3, KCNQ5, IRF4, and BCAT1 (see Table 2B; Example I).

[0051] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has endometrioid ovarian cancer: NCOR2, CELF2_A, PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, MAML3_A, SKI, GPRIN1_A, SKI, BCL2L11, and C2CD4D (see Table 4C; Example I).

[0052] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has endometrioid ovarian cancer: NCOR2, PALLD, PRDM14, MAX.chr1.147790358-147790381, MAX.chr11.14926602-14926671, DSCR6, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, and C2CD4D (see Table 5C; Example I).

[0053] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has endometrioid ovarian cancer: BCAT1_6015, EPS8L2_F, SKI, NKX2-6_4159, C1QL3_B, GPRIN1_B, PARP15, OXT_C, SIM2_B, DNMT3A_A, and CELF2_A (see Table 8C; Example II).

[0054] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has mucinous ovarian cancer: CMTM3_A, ATP10A_C, TSHZ3, ZMIZ1_B, ATP10A_B, ELMO1_B, TACC2_A, LRRC4, VIM, and ZNF382_A (see Table 2C; Example I).

[0055] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has mucinous ovarian cancer: NCOR2, MT1A_A, KCNA3_A, ZMIZ1_C, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, BCL2L11, and GATA2 (see Table 4D; Example I).

[0056] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has mucinous ovarian cancer: NCOR2, PALLD, TACC2_A, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, and BCL2L11 (see Table 5D; Example I).

[0057] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has mucinous ovarian cancer: BCAT1_6015, ELMO1_9100, KCNA3_7518, KCNA3_7320, MDFI_6321, SKI, VIPR_B, ZNF382_B, ATP10A_E, CMTM3_B, ZMIZ1_D, SRC_B, HDGFRP3, TACC2_B, TSHZ3, LBH, DNMT3A_A (see Table 8D; Example II).

[0058] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has serous ovarian cancer: MAX.chr1.147790358-147790381, MAML3, NR2F6, DNMT3A_A, SKI, SOBP, UBTF, AGRN_C, MAX.chr12.30975740-30975780, and CAPN2_A (see Table 2D; Example I).

[0059] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has serous ovarian cancer: PALLD, PRDM14, MAX.chr1.147790358-147790381, CAPN2_A, MAX.chr6.10382190-10382225, SKI, NR2F6, IFFO1_A, MT1A_B, IFFO1_B, GDF6, and C2CD4D (see Table 4E; Example I).

[0060] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has serous ovarian cancer: NCOR2, MAX.chr1.147790358-147790381, MAX.chr6.10382190-10382225, IFFO1_A, GDF6, and C2CD4D (see Table 5A; Example I).

[0061] In some embodiments, where the sample obtained from the subject is ovarian tissue and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in subjects without ovarian cancer, it indicates that the subject has serous ovarian cancer: SKI, PEAR1_B, CAPN2_B, SIM2_B, DNMT3A_A, CDO1_A, and NR2F6 (see Table 8E; Example II).

[0062] In some embodiments, where the sample obtained from the subject is a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample) and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in a subject that does not have OC, it is indicated that the subject has OC: GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), SRC (e.g., SRC_A, SRC_B). , SIM2 (e.g., SIM2_A, SIM2_B), AGRN (e.g., AGRN_A, AGRN_B, AGRN_C, AGRN_8794), FAIM2 (e.g., FAIM2_A, FAIM2_B), CELF2 (e.g., CELF2_A, CELF2_B), DSCR6, GYPC (e.g., GYPC_A, GYPC_B, GYPC_C), CAPN2 (e.g., CAPN2_A, CAPN2_B), and BCAT1 (see Table 9; Example III).

[0063] In some embodiments, where the sample obtained from the subject is a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample) and the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in a subject that does not have OC, it is indicated that the subject has OC: ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CMTM3_ B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, SIM2_A, AGRN_8794, BCAT1_6015, KCNA3_7518, KCNA3_7320, L OC10013136, GYPC_C, SRC (e.g., SRC_A, SRC_B), NR2F6, TSHZ3, CELF2 (e.g., CELF2_A, CELF2_B), TACC2 (e.g., TACC2_A, TACC2_B), VIPR2 (e.g., VIPR2_A, VIPR2_B), and SPOCK2_74333 (see Table 10, Example III).

[0064] In some embodiments, the subject has OC if the sample obtained from the subject is a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample), 1) an elevated level of CA-125 is detected, and 2) the methylation status of one or more of the following markers differs from the methylation status of one or more markers assayed in a subject that does not have OC: ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L 2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, and SIM2_A (see Tables 11-13 and Example III).

[0065] This technology relates to the identification and differentiation of ovarian cancer and / or various forms of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC). Some embodiments include methods for assaying multiple markers, e.g., 1, 2, 3, 2-11-100 or 120 or 375 or 560 (e.g., 1-4, 1-6, 1-7, 1-8, 1-9, 1-10, 1-11, 1-12, 1-13, 1-14, 1-15, 1-16, 1-17, 1-18, 1-19, 1-20, 1-25, 1-50, 1-75, 1-100, 1-200, 1-300, 1-400). , 1-500 pieces, 1-560 pieces) (For example, 2-4 pieces, 2-6 pieces, 2-7 pieces, 2-8 pieces, 2-9 pieces, 2-10 pieces, 2-11 pieces, 2-12 pieces, 2-13 pieces, 2-14 pieces, 2-15 pieces, 2-16 pieces, 2-17 pieces, 2-18 pieces, 2-19 pieces, 2-20 pieces, 2-25 pieces, 2-50 pieces, 2-75 pieces, 2-100 pieces, 2-200 pieces, 2-300 pieces, 2-400 pieces, 2-500 pieces, 2-560 pieces) (For example, 3-4 pieces, 3-6 pieces, 3-7 pieces, 3-8 pieces, 3-9 pieces, 3-10 pieces, 3-11 pieces, 3-12 pieces, 3 ~13 pieces, 3~14 pieces, 3~15 pieces, 3~16 pieces, 3~17 pieces, 3~18 pieces, 3~19 pieces, 3~20 pieces, 3~25 pieces, 3~50 pieces, 3~75 pieces, 3~100 pieces, 3~200 pieces, 3~300 pieces, 3~400 pieces, 3~500 pieces, 3~560 pieces) (For example, 4~5 pieces, 4~6 pieces, 4~7 pieces, 4~8 pieces, 4~9 pieces, 4~10 pieces, 4~11 pieces, 4~12 pieces, 4~13 pieces, 4~14 pieces, 4~15 pieces, 4~16 pieces, 4~17 pieces, 4~18 pieces, 4~19 pieces, 4~20 pieces, 4~25 pieces, 4~50 pieces, 4~75 The present invention provides a method for assaying markers in quantities of 1, 4-100, 4-200, 4-300, 4-400, 4-500, and 4-560 (for example, 5-6, 5-7, 5-8, 5-9, 5-10, 5-11, 5-12, 5-13, 5-14, 5-15, 5-16, 5-17, 5-18, 5-19, 5-20, 5-25, 5-50, 5-75, 5-100, 5-200, 5-300, 5-400, 5-500, and 5-560).

[0066] This technique is not limited to the methylation state being evaluated. In some embodiments, evaluating the methylation state of a marker in a sample includes determining the methylation state of a single base. In some embodiments, assaying the methylation state of a marker in a sample includes determining the degree of methylation at multiple bases. Furthermore, in some embodiments, the methylation state of a marker includes an increase in methylation of the marker compared to the normal methylation state of the marker. In some embodiments, the methylation state of a marker includes a decrease in methylation of the marker compared to the normal methylation state of the marker. In some embodiments, the methylation state of a marker includes different patterns of methylation of the marker compared to the normal methylation state of the marker.

[0067] Furthermore, in some embodiments, the marker is a region of 100 or fewer bases, a region of 500 or fewer bases, a region of 1000 or fewer bases, a region of 5000 or fewer bases, or in some embodiments, the marker is a single base. In some embodiments, the marker is present in a high CpG density promoter.

[0068] This technology is not limited by the type of sample. For example, in some embodiments, the sample may be a fecal sample, a tissue sample (e.g., ovarian tissue sample), a blood sample (e.g., plasma, serum, whole blood), excrement, or a urine sample.

[0069] Furthermore, this technology is not limited to the methods used to determine the methylation state. In some embodiments, assaying involves using methylation-specific polymerase chain reactions, nucleic acid sequencing, mass spectrometry, methylation-specific nucleases, mass-based separation, or targeted capture. In some embodiments, assaying involves using methylation-specific oligonucleotides. In some embodiments, this technology determines the methylation state using ultra-parallel sequencing (e.g., next-generation sequencing), such as sequencing-by-synthesis, real-time (e.g., single-molecule) sequencing, bead emulsion sequencing, or nanopore sequencing.

[0070] This technology provides reagents for detecting DMRs, and in some embodiments, for example, a set of oligonucleotides containing sequences represented by SEQ ID NOs: 1 to 283 (see Tables 1C and 6B). In some embodiments, oligonucleotides containing sequences complementary to chromosomal regions having bases in DMRs are provided, for example, oligonucleotides with high sensitivity to the methylation status of DMRs.

[0071] This technology provides a panel of various markers used to identify ovarian cancer, for example, in some embodiments the markers are AGRN_A, ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, BCAT1, CCND2_D, CMTM3_A, ELMO1_A, ELMO1_B, ELMO1_C, EMX1, EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D, FAIM2_A, FLJ34208_A, GPRIN1, GYPC_A, INA_A, ITGA4 _B, KCNA3_A, KCNA3_C, LBH, LIME1_A, LIME1_B, LOC646278, LRRC4, LRRC41_A, MAX.chr1.110626771-110626832, MAX.chr1.147790358-1477 90381, MAX.chr1.161591532-161591608, MAX.chr15.28351937-28352173, MAX.chr15.28352203-28352671, MAX.chr15.29131258-2913173 4, MAX.chr4.8859995-8860062, MAX.chr5.42952182-42952292, MDFI, NCOR2, NKX2-6, OPLAH_A, PARP15, PDE10A, PPP1R16B, RASSF1_B, SEPT IN9, SKI, SLC12A8, SRC_A, SSBP4_B, ST8SIA1, TACC2_A, TSHZ3, UBTF, VIM, VIPR2_A, ZBED4, ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZNF382_A, ZNF469_B, This includes chromosomal regions having annotations such as ATP6V1B1_A, BZRAP1, GDF6, IFFO1_A, IFFO1_B, KCNAB2, LIMD2, MAML3_B, MAX.chr14.102172350-102172770, MAX.chr16.85482307-85482494, MAX.chr17.76254728-76254841, MAX.chr5.42993898-42994179, and RASAL3 (see Tables 1A, 1B, 6A, and 6B; see Example I).

[0072] This technology provides a panel of various markers used to identify ovarian cancer, and in some embodiments, for example, the markers include chromosomal regions with annotations such as MAX.chr16.85482307-85482494, GDF6, IFFO_A, MAX.chr5.42993898-42994179, MAX.chr17.76254728-76254841, MAX.chr14.102172350-102172770, RASAL3, BZRAP1, and LIMD2 (see Table 3; Example I).

[0073] This technology provides a panel of various markers used to identify ovarian cancer, and in some embodiments, for example, the markers include chromosomal regions having annotations such as PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAML3_A, SKI, DNMT3A_A, and C2CD4D (see Table 4A; Example I).

[0074] This technology provides a panel of various markers used to identify ovarian cancer, and in some embodiments, for example, the markers include chromosomal regions having annotations such as BCAT1_6015, SKI, SIM2_B, DNMT3A_A, CDO1_A, and DSCR6 (see Table 8A; Example II).

[0075] This technology provides a panel of various markers used to identify clear cell ovarian cancer, for example, in some embodiments the markers include chromosomal regions having annotations such as TACC2_A, LRRC41_A, EPS8L2, LBH, LIME1_B, MDFI, FAIM2_A, GYPC_A, AGRN_B, and ZBED4 (see Table 2A; Example I).

[0076] This technology provides a panel of various markers used to identify clear cell ovarian cancer, for example, in some embodiments the markers are MT1A_A, CELF2_A, KCNA3_A, MDFI, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190- This includes chromosomal regions with the annotations 10382225, DSCR6, MAML3_A, MAX.chr14.105512178-105512224, EPS8L2_E, SKI, GPRIN1_A, MAX.chr8.142215938-142216298, CDO1_A, DNMT3A_A, SIM2_A, SKI, MT1A_B, GYPC_A, BCL2L11, PISD, and C2CD4D (see Table 4B; Example I).

[0077] This technology provides a panel of various markers used to identify clear cell ovarian cancer, and in some embodiments, for example, the markers include chromosomal regions having annotations such as NCOR2, MT1A_B, CELF2_A, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, SKI, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, MT1A_B, GYPC_A, BCL2L11, GDF6, and C2CD4D (see Table 5B; Example I).

[0078] This technology provides a panel of various markers used to identify clear cell ovarian cancer, for example, in some embodiments the markers include chromosomal regions having annotations such as AGRN_8794, BHLHE23_8339, EPS8L2_F, RASSF1_8293, MDFI_6321, SKI, GYPC_C, NKX2-6_4159, LOC100131366, FAIM2_B, GPRIN1_B, LRRC41_B, TACC2_B, LBH, SIM2_B, CDO1_A, and DSCR6 (see Table 8B; Example II).

[0079] This technology provides a panel of various markers used to identify endometrioid ovarian carcinoma, for example, in some embodiments the markers include chromosomal regions with annotations such as PARP15, GPRIN1_A, GYPC1_A, FLJ34208, MAX.chr1.147790358-147790381, FAIM2_A, SH2B3, KCNQ5, IRF4, and BCAT1 (see Table 2B; Example I).

[0080] This technology provides a panel of various markers used to identify endometrioid ovarian cancer, for example, in some embodiments the markers include chromosomal regions having annotations such as NCOR2, CELF2_A, PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, MAML3_A, SKI, GPRIN1_A, SKI, BCL2L11, and C2CD4D (see Table 4C; Example I).

[0081] This technology provides a panel of various markers used to identify endometrioid ovarian carcinoma, for example, in some embodiments the markers include chromosomal regions having annotations such as NCOR2, PALLD, PRDM14, MAX.chr1.147790358-147790381, MAX.chr11.14926602-14926671, DSCR6, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, and C2CD4D (see Table 5C; Example I).

[0082] This technology provides a panel of various markers used to identify endometrioid ovarian carcinoma, and in some embodiments, for example, the markers include chromosomal regions having annotations such as BCAT1_6015, EPS8L2_F, SKI, NKX2-6_4159, C1QL3_B, GPRIN1_B, PARP15, OXT_C, SIM2_B, DNMT3A_A, and CELF2_A (see Table 8C; Example II).

[0083] This technology provides a panel of various markers used to identify mucinous ovarian cancer, for example, in some embodiments the markers include chromosomal regions having annotations such as CMTM3_A, ATP10A_C, TSHZ3, ZMIZ1_B, ATP10A_B, ELMO1_B, TACC2_A, LRRC4, VIM, and ZNF382_A (see Table 2C; Example I).

[0084] This technology provides a panel of various markers used to identify mucinous ovarian cancer, for example, in some embodiments the markers include chromosomal regions having annotations such as NCOR2, MT1A_A, KCNA3_A, ZMIZ1_C, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, BCL2L11, and GATA2 (see Table 4D; Example I).

[0085] This technology provides a panel of various markers used to identify mucinous ovarian cancer, for example, in some embodiments the markers include chromosomal regions having annotations such as NCOR2, PALLD, TACC2_A, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, and BCL2L11 (see Table 5D; Example I).

[0086] This technology provides a panel of various markers used to identify mucinous ovarian cancer, for example, in some embodiments the markers include chromosomal regions having annotations such as BCAT1_6015, ELMO1_9100, KCNA3_7518, KCNA3_7320, MDFI_6321, SKI, VIPR_B, ZNF382_B, ATP10A_E, CMTM3_B, ZMIZ1_D, SRC_B, HDGFRP3, TACC2_B, TSHZ3, LBH, and DNMT3A_A (see Table 8D; Example II).

[0087] This technology provides a panel of various markers used to identify serous ovarian cancer, and in some embodiments, for example, the markers include chromosomal regions having annotations such as MAX.chr1.147790358-147790381, MAML3, NR2F6, DNMT3A_A, SKI, SOBP, UBTF, AGRN_C, MAX.chr12.30975740-30975780, and CAPN2_A (see Table 2D; Example I).

[0088] This technology provides a panel of various markers used to identify serous ovarian cancer, and in some embodiments, for example, the markers include chromosomal regions having annotations such as PALLD, PRDM14, MAX.chr1.147790358-147790381, CAPN2_A, MAX.chr6.10382190-10382225, SKI, NR2F6, IFFO1_A, MT1A_B, IFFO1_B, GDF6, and C2CD4D (see Table 4E; Example I).

[0089] This technology provides a panel of various markers used to identify serous ovarian cancer, for example, in some embodiments the markers include chromosomal regions having annotations such as NCOR2, MAX.chr1.147790358-147790381, MAX.chr6.10382190-10382225, IFFO1_A, GDF6, and C2CD4D (see Table 5A; Example I).

[0090] This technology provides a panel of various markers used to identify serous ovarian cancer, for example, in some embodiments the markers include chromosomal regions having annotations such as SKI, PEAR1_B, CAPN2_B, SIM2_B, DNMT3A_A, CDO1_A, and NR2F6 (see Table 8E; Example II).

[0091] This technology provides a panel of various markers used to identify ovarian cancer, and in some embodiments, for example, the markers include chromosomal regions having annotations such as GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), SRC (e.g., SRC_A, SRC_B), SIM2 (e.g., SIM2_A, SIM2_B), AGRN (e.g., AGRN_A, AGRN_B, AGRN_C, AGRN_8794), FAIM2 (e.g., FAIM2_A, FAIM2_B), CELF2 (e.g., CELF2_A, CELF2_B), DSCR6, GYPC (e.g., GYPC_A, GYPC_B, GYPC_C), CAPN2 (e.g., CAPN2_A, CAPN2_B), and BCAT1 (see Table 9; Example III).

[0092] This technology provides a panel of various markers used to identify ovarian cancer, for example, in some embodiments the markers are ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPR The region includes chromosomal regions having annotations such as IN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, SIM2_A, AGRN_8794, BCAT1_6015, KCNA3_7518, KCNA3_7320, LOC10013136, GYPC_C, SRC (e.g., SRC_A, SRC_B), NR2F6, TSHZ3, CELF2 (e.g., CELF2_A, CELF2_B), TACC2 (e.g., TACC2_A, TACC2_B), VIPR2 (e.g., VIPR2_A, VIPR2_B), and SPOCK2_74333 (see Table 10, Example III).

[0093] This technology provides a panel of various markers used to identify ovarian cancer, for example, in some embodiments the markers are ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPR The region includes chromosomal regions having annotations such as IN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, SIM2_A, AGRN_8794, BCAT1_6015, KCNA3_7518, KCNA3_7320, LOC10013136, GYPC_C, SRC (e.g., SRC_A, SRC_B), NR2F6, TSHZ3, CELF2 (e.g., CELF2_A, CELF2_B), TACC2 (e.g., TACC2_A, TACC2_B), VIPR2 (e.g., VIPR2_A, VIPR2_B), and SPOCK2_74333 (see Table 10, Example III).

[0094] Embodiments of the kit are provided, for example, a kit comprising a reagent capable of modifying DNA in a methylation-specific manner (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents) and a control nucleic acid having a methylation state associated with a subject that does not have ovarian cancer or a subtype of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC). In some embodiments, the kit comprises a bisulfite reagent and oligonucleotides described herein. In some embodiments, the kit comprises a reagent capable of modifying DNA in a methylation-specific manner (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents) and a control nucleic acid having a methylation state associated with a subject that includes a DMR sequence selected from the group consisting of DMR1 to 560 (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC). Some embodiments of the kit include a sample collector for obtaining a sample from a subject (e.g., fecal sample, ovarian tissue sample, plasma sample, serum sample, whole blood sample), reagents capable of modifying DNA in a methylation-specific manner (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents), and oligonucleotides as described herein.

[0095] This technology relates to embodiments of compositions (e.g., reaction mixtures). In some embodiments, compositions are provided comprising nucleic acids containing DMRs and reagents capable of modifying DNA in a methylation-specific manner (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents). Some embodiments provide compositions comprising nucleic acids containing DMRs and oligonucleotides described herein. Some embodiments provide compositions comprising nucleic acids containing DMRs and methylation-sensitive restriction enzymes. Some embodiments provide compositions comprising nucleic acids containing DMRs and polymerases.

[0096] Embodiments of additional related methods are provided for screening ovarian cancer and / or various forms of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC) in samples obtained from subjects (e.g., ovarian tissue samples, plasma samples, fecal samples), for example, the method includes determining the methylation status of a marker in a sample containing a base in a DMR that is one or more of the DMR1-506 (Tables 1A and 6A), comparing the methylation status of the marker in the subject sample with the methylation status of the marker in a normal control sample from a subject that does not have ovarian cancer (e.g., ovarian cancer and / or forms of ovarian cancer: clear cell OC, endometrioid OC, mucinous OC, serous OC), and determining the confidence interval and / or p-value of the difference in methylation status between the subject sample and the normal control sample. In some embodiments, the confidence intervals are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9%, or 99.99%, and the p-values ​​are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, or 0.0001. Some embodiments of the method provide steps of: reacting a nucleic acid containing a DMR with a reagent capable of modifying the nucleic acid in a methylation-specific manner (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents) to produce, for example, a methylation-specific modified nucleic acid; sequencing the methylation-specific modified nucleic acid to obtain the nucleotide sequence of the methylation-specific modified nucleic acid; comparing the nucleotide sequence of the methylation-specific modified nucleic acid with the nucleotide sequence of a DMR-containing nucleic acid from a subject that does not have ovarian cancer and / or ovarian cancer morphology to identify differences between the two sequences; and, if differences exist, identifying the subject as having ovarian cancer and / or ovarian cancer morphology (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC).

[0097] The present invention provides a system for screening for ovarian cancer in samples obtained from subjects. Exemplary embodiments of the system include, for example, a system for screening for ovarian cancer and / or types of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC) in samples obtained from subjects (e.g., ovarian tissue samples, plasma samples, fecal samples), the system including an analytical component configured to determine the methylation status of the sample, a software component configured to compare the methylation status of the sample with the methylation status of a control or reference sample recorded in a database, and a warning component configured to alert the user to ovarian cancer-related methylation status. In some embodiments, the warning is determined by a software component that receives results from multiple assays (e.g., multiple markers, e.g., as shown in Tables 1A and 6A, e.g., determining the methylation status of DMRs), calculates values ​​or results, and reports based on the multiple results. Some embodiments provide a database of weighting parameters associated with each DMR provided herein for use in calculating values ​​or results and / or reporting warnings to the user (e.g., 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 indicating cancer risk in a subject.

[0098] In some embodiments of the system, the sample comprises nucleic acids containing DMRs. In some embodiments, the system further comprises components for isolating nucleic acids, components for collecting samples, such as components for collecting fecal samples. In some embodiments, the system comprises nucleic acid sequences containing DMRs. In some embodiments, the database comprises nucleic acid sequences from subjects that do not have ovarian cancer and / or a particular type of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC). Further provided are nucleic acids, e.g., sets of nucleic acids, each having a sequence containing a DMR. In some embodiments, a set of nucleic acids, each having a sequence from subjects that do not have ovarian cancer and / or a particular type of ovarian cancer. Embodiments of the related system include a database of nucleic acid sequences and nucleic acid sequences associated with sets of nucleic acids as described. Some embodiments further comprise reagents that can modify DNA in a methylation-specific manner (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents). Also, some embodiments further comprise a nucleic acid sequencer.

[0099] In certain embodiments, methods are provided for characterizing samples from human patients (e.g., ovarian tissue samples, plasma samples, whole blood samples, serum samples, fecal samples). For example, in some embodiments, such embodiments include obtaining DNA from a human patient sample, assaying the methylation status of DNA methylation markers containing bases in DMRs selected from the group consisting of variable methylation regions (DMRs) 1 to 560 in Tables 1A and 6A, and comparing the assayed methylation status of one or more DNA methylation markers to a reference methylation level of one or more DNA methylation markers in human patients who do not have ovarian cancer and / or a particular type of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC).

[0100] Such methods are not limited to specific types of samples from human patients. In some embodiments, the sample is an ovarian tissue sample. In some embodiments, the sample is a plasma sample. In some embodiments, the sample is a fecal sample, a tissue sample, an ovarian tissue sample, a blood sample (e.g., a plasma sample, a whole blood sample, a serum sample), or a urine sample.

[0101] In some embodiments, such methods can be used with multiple (e.g., 1-4, 1-6, 1-7, 1-8, 1-9, 1-10, 1-11, 1-12, 1-13, 1-14, 1-15, 1-16, 1-17, 1-18, 1-19, 1-20, 1-25, 1-50, 1-75, 1-100, 1-200, 1-300, 1-400, 1-500, 1-560) (e.g., 2-4, 2-6, 2-7, 2-8, 2 ~9 pieces, 2~10 pieces, 2~11 pieces, 2~12 pieces, 2~13 pieces, 2~14 pieces, 2~15 pieces, 2~16 pieces, 2~17 pieces, 2~18 pieces, 2~19 pieces, 2~20 pieces, 2~25 pieces, 2~50 pieces, 2~75 pieces, 2~100 pieces, 2~200 pieces, 2~300 pieces, 2~400 pieces, 2~500 pieces, 2~560 pieces) (For example, 3~4 pieces, 3~6 pieces, 3~7 pieces, 3~8 pieces, 3~9 pieces, 3~10 pieces, 3~11 pieces, 3~12 pieces, 3~13 pieces, 3~14 pieces, 3~15 pieces, 3~16 pieces, 3~17 1, 3-18, 3-19, 3-20, 3-25, 3-50, 3-75, 3-100, 3-200, 3-300, 3-400, 3-500, 3-560) (For example, 4-5, 4-6, 4-7, 4-8, 4-9, 4-10, 4-11, 4-12, 4-13, 4-14, 4-15, 4-16, 4-17, 4-18, 4-19, 4-20, 4-25, 4-50, 4-75, 4-100, 4-200 The method includes assaying 1, 4-300, 4-400, 4-500, and 4-560 DNA methylation markers. In some embodiments, such a method involves assaying 2 to 375 DNA methylation markers.In some embodiments, such a method includes assaying the methylation status of one or more DNA methylation markers in a sample, and including determining the methylation status of a single base. In some embodiments, such a method includes assaying the methylation status of one or more DNA methylation markers in a sample, and including determining the degree of methylation at multiple bases. In some embodiments, such a method includes assaying the methylation status of the forward strand or assaying the methylation status of the reverse strand.

[0102] In some embodiments, the DNA methylation marker is a region of 100 or fewer bases. In some embodiments, the DNA methylation marker is a region of 500 or fewer bases. In some embodiments, the DNA methylation marker is a region of 1000 or fewer bases. In some embodiments, the DNA methylation marker is a region of 5000 or fewer bases. In some embodiments, the DNA methylation marker is a single base. In some embodiments, the DNA methylation marker is present in a high CpG density promoter.

[0103] In some embodiments, assaying involves using methylation-specific polymerase chain reactions, nucleic acid sequencing, mass spectrometry, methylation-specific nucleases, mass-based separation, or targeted capture.

[0104] In some embodiments, the assay involves the use of methylation-specific oligonucleotides. In some embodiments, methylation-specific oligonucleotides are selected from the group consisting of SEQ ID NOs: 1 to 283 (Table 1C, Table 6B).

[0105] In some embodiments, AGRN_A, ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, BCAT1, CCND2_D, CMTM3_A, ELMO1_A, ELMO1_B, ELMO1_C, EMX1, EPS8L2_ A, EPS8L2_B, EPS8L2_C, EPS8L2_D, FAIM2_A, FLJ34208_A, GPRIN1, GYPC_A, INA_A, ITGA4_B, KCNA3_A, KCNA3_C, LBH, LIME1_A, LIME1_B, L OC646278, LRRC4, LRRC41_A, MAX.chr1.110626771-110626832, MAX.chr1.147790358-147790381, MAX.chr1.161591532-161591608, MA X.chr15.28351937-28352173, MAX.chr15.28352203-28352671, MAX.chr15.29131258-29131734, MAX.chr4.8859995-8860062, MAX.chr 5.42952182-42952292, MDFI, NCOR2, NKX2-6, OPLAH_A, PARP15, PDE10A, PPP1R16B, RASSF1_B, SEPTIN9, SKI, SLC12A8, SRC_A, SSBP4_B, S T8SIA1, TACC2_A, TSHZ3, UBTF, VIM, VIPR2_A, ZBED4, ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZNF382_A, ZNF469_B, ATP6V1B1_A, BZRAP1, GDF6, IFFO1 Chromosomal regions having annotations selected from the group consisting of _A, IFFO1_B, KCNAB2, LIMD2, MAML3_B, MAX.chr14.102172350-102172770, MAX.chr16.85482307-85482494, MAX.chr17.76254728-76254841, MAX.chr5.42993898-42994179, and RASAL3 (Tables 1A, 1B, 6A, and 6B; see Example I) contain DNA methylation markers.

[0106] In some embodiments, chromosomal regions having annotations selected from the group consisting of MAX.chr16.85482307-85482494, GDF6, IFFO_A, MAX.chr5.42993898-42994179, MAX.chr17.76254728-76254841, MAX.chr14.102172350-102172770, RASAL3, BZRAP1, and LIMD2 (see Table 3; Example I) contain DNA methylation markers.

[0107] In some embodiments, chromosomal regions having annotations selected from the group consisting of PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAML3_A, SKI, DNMT3A_A, and C2CD4D (see Table 4A; Example I) contain DNA methylation markers.

[0108] In some embodiments, chromosomal regions having annotations selected from the group consisting of BCAT1_6015, SKI, SIM2_B, DNMT3A_A, CDO1_A, and DSCR6 (see Table 8A; Example II) contain DNA methylation markers.

[0109] In some embodiments, chromosomal regions having annotations selected from the group consisting of TACC2_A, LRRC41_A, EPS8L2, LBH, LIME1_B, MDFI, FAIM2_A, GYPC_A, AGRN_B, and ZBED4 (see Table 2A; Example I) contain DNA methylation markers.

[0110] In some embodiments, the names MT1A_A, CELF2_A, KCNA3_A, MDFI, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, and MAX.chr14.105 are used. Chromosomal regions having annotations selected from the group consisting of 512178-105512224, EPS8L2_E, SKI, GPRIN1_A, MAX.chr8.142215938-142216298, CDO1_A, DNMT3A_A, SIM2_A, SKI, MT1A_B, GYPC_A, BCL2L11, PISD, and C2CD4D (see Table 4B; Example I) contain DNA methylation markers.

[0111] In some embodiments, chromosomal regions having annotations selected from the group consisting of NCOR2, MT1A_B, CELF2_A, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, SKI, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, MT1A_B, GYPC_A, BCL2L11, GDF6, and C2CD4D (see Table 5B; Example I) contain DNA methylation markers.

[0112] In some embodiments, chromosomal regions having annotations selected from the group consisting of AGRN_8794, BHLHE23_8339, EPS8L2_F, RASSF1_8293, MDFI_6321, SKI, GYPC_C, NKX2-6_4159, LOC100131366, FAIM2_B, GPRIN1_B, LRRC41_B, TACC2_B, LBH, SIM2_B, CDO1_A, and DSCR6 (see Table 8B; Example II) contain DNA methylation markers.

[0113] In some embodiments, chromosomal regions having annotations selected from the group consisting of PARP15, GPRIN1_A, GYPC1_A, FLJ34208, MAX.chr1.147790358-147790381, FAIM2_A, SH2B3, KCNQ5, IRF4, and BCAT1 (see Table 2B; Example I) contain DNA methylation markers.

[0114] In some embodiments, chromosomal regions having annotations selected from the group consisting of NCOR2, CELF2_A, PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, MAML3_A, SKI, GPRIN1_A, SKI, BCL2L11, and C2CD4D (see Table 4C; Example I) contain DNA methylation markers.

[0115] In some embodiments, chromosomal regions having annotations selected from the group consisting of NCOR2, PALLD, PRDM14, MAX.chr1.147790358-147790381, MAX.chr11.14926602-14926671, DSCR6, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, and C2CD4D (see Table 5C; Example I) contain DNA methylation markers.

[0116] In some embodiments, chromosomal regions having annotations selected from the group consisting of BCAT1_6015, EPS8L2_F, SKI, NKX2-6_4159, C1QL3_B, GPRIN1_B, PARP15, OXT_C, SIM2_B, DNMT3A_A, and CELF2_A (see Table 8C; Example II) contain DNA methylation markers.

[0117] In some embodiments, chromosomal regions having annotations selected from the group consisting of CMTM3_A, ATP10A_C, TSHZ3, ZMIZ1_B, ATP10A_B, ELMO1_B, TACC2_A, LRRC4, VIM, and ZNF382_A (see Table 2C; Example I) contain DNA methylation markers.

[0118] In some embodiments, chromosomal regions having annotations selected from the group consisting of NCOR2, MT1A_A, KCNA3_A, ZMIZ1_C, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, BCL2L11, and GATA2 (see Table 4D; Example I) contain DNA methylation markers.

[0119] In some embodiments, chromosomal regions having annotations selected from the group consisting of NCOR2, PALLD, TACC2_A, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, and BCL2L11 (see Table 5D; Example I) contain DNA methylation markers.

[0120] In some embodiments, chromosomal regions having annotations selected from the group consisting of BCAT1_6015, ELMO1_9100, KCNA3_7518, KCNA3_7320, MDFI_6321, SKI, VIPR_B, ZNF382_B, ATP10A_E, CMTM3_B, ZMIZ1_D, SRC_B, HDGFRP3, TACC2_B, TSHZ3, LBH, DNMT3A_A (see Table 8D; Example II) contain DNA methylation markers.

[0121] In some embodiments, chromosomal regions having annotations selected from the group consisting of MAX.chr1.147790358-147790381, MAML3, NR2F6, DNMT3A_A, SKI, SOBP, UBTF, AGRN_C, MAX.chr12.30975740-30975780, and CAPN2_A (see Table 2D; Example I) contain DNA methylation markers.

[0122] In some embodiments, chromosomal regions having annotations selected from the group consisting of PALLD, PRDM14, MAX.chr1.147790358-147790381, CAPN2_A, MAX.chr6.10382190-10382225, SKI, NR2F6, IFFO1_A, MT1A_B, IFFO1_B, GDF6, and C2CD4D (see Table 4E; Example I) contain DNA methylation markers.

[0123] In some embodiments, chromosomal regions having annotations selected from the group consisting of NCOR2, MAX.chr1.147790358-147790381, MAX.chr6.10382190-10382225, IFFO1_A, GDF6, and C2CD4D (see Table 5A; Example I) contain DNA methylation markers.

[0124] In some embodiments, chromosomal regions having annotations selected from the group consisting of SKI, PEAR1_B, CAPN2_B, SIM2_B, DNMT3A_A, CDO1_A, and NR2F6 (see Table 8E; Example II) contain DNA methylation markers.

[0125] In some embodiments, chromosomal regions having annotations selected from the group consisting of GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), SRC (e.g., SRC_A, SRC_B), SIM2 (e.g., SIM2_A, SIM2_B), AGRN (e.g., AGRN_A, AGRN_B, AGRN_C, AGRN_8794), FAIM2 (e.g., FAIM2_A, FAIM2_B), CELF2 (e.g., CELF2_A, CELF2_B), DSCR6, GYPC (e.g., GYPC_A, GYPC_B, GYPC_C), CAPN2 (e.g., CAPN2_A, CAPN2_B), and BCAT1 (see Table 9; Example III) contain DNA methylation markers.

[0126] In some embodiments, ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., Chromosomal regions having annotations selected from the group consisting of CDO1_A, CDO1_B), GP5, DSCR6, SKI, SIM2_A, AGRN_8794, BCAT1_6015, KCNA3_7518, KCNA3_7320, LOC10013136, GYPC_C, SRC (e.g., SRC_A, SRC_B), NR2F6, TSHZ3, CELF2 (e.g., CELF2_A, CELF2_B), TACC2 (e.g., TACC2_A, TACC2_B), VIPR2 (e.g., VIPR2_A, VIPR2_B), and SPOCK2_74333 (see Table 10, Example III) contain DNA methylation markers.

[0127] In some embodiments, such a method includes determining the methylation status of two DNA methylation markers. In some embodiments, such a method includes determining the methylation status of a pair of DNA methylation markers shown in Table 1A and / or Table 6A.

[0128] In certain embodiments, the technology provides a method for characterizing samples obtained from human patients (e.g., ovarian tissue samples, plasma samples, whole blood samples, serum samples, fecal samples). In some embodiments, such a method includes determining the methylation status of DNA methylation markers in a sample containing bases in a DMR selected from the group consisting of DMR1 to 560 in Tables 1A and 6A; comparing the methylation status of DNA methylation markers in a patient sample with the methylation status of DNA methylation markers in a normal control sample from a human subject that does not have ovarian cancer and / or a specific form of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC); and determining the confidence interval and / or p-value of the difference in methylation status between the human patient and the normal control sample. In some embodiments, the confidence intervals are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9%, or 99.99%, and the p-values ​​are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, or 0.0001.

[0129] In certain embodiments, the technology provides a method for characterizing samples obtained from human subjects (e.g., ovarian tissue samples, plasma samples, whole blood samples, serum samples, fecal samples), the method comprising: reacting a nucleic acid containing DMR with a reagent capable of modifying DNA in a methylation-specific manner (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents) to produce a methylation-specific modified nucleic acid; sequencing the methylation-specific modified nucleic acid to obtain the nucleotide sequence of the methylation-specific modified nucleic acid; and comparing the nucleotide sequence of the methylation-specific modified nucleic acid with the nucleotide sequence of a DMR-containing nucleic acid from a subject without ovarian cancer to identify differences between the two sequences.

[0130] In certain embodiments, the technology provides a system for characterizing samples obtained from human subjects (e.g., ovarian tissue samples, plasma samples, fecal samples), the system including an analytical component configured to determine the methylation status of the sample, a software component configured to compare the methylation status of the sample with the methylation status of a control or reference sample recorded in a database, and a warning component configured to determine a single value based on a combination of methylation statuses and to warn the user of ovarian cancer-related methylation statuses. In some embodiments, the sample includes nucleic acids containing DMRs.

[0131] In some embodiments, such a system further includes components for isolating nucleic acids. In some embodiments, such a system further includes components for collecting samples.

[0132] In some embodiments, the sample is a fecal sample, a tissue sample, an ovarian tissue sample, a blood sample (e.g., a plasma sample, a whole blood sample, a serum sample), or a urine sample.

[0133] In some embodiments, the database includes nucleic acid sequences containing DMRs. In some embodiments, the database includes nucleic acid sequences from subjects who do not have ovarian cancer.

[0134] Further embodiments will become apparent to those skilled in the art based on the teachings contained herein. [Brief description of the drawing] [Figure 1] Information on marker chromosome regions and associated primers and probes used for various methylated DNA markers listed in Tables 1A and 6A. Naturally occurring sequences (WT) and bisulfite-modified sequences (BST) of the PCR target region are shown.

[0135] definition To facilitate understanding of this technology, several terms and phrases are defined below. Further definitions will be provided throughout the detailed explanation.

[0136] Throughout this specification and the claims, the following terms have the meaning expressly as relating to this specification unless otherwise clearly indicated by the context. The phrase "in one embodiment" may, but not necessarily, refer to the same embodiment. Furthermore, the phrase "in another embodiment" may, but not necessarily, refer to a different embodiment. Thus, various embodiments of the present invention can be readily combined without departing from the scope or spirit of the invention, as described below.

[0137] Furthermore, as used herein, the term "or" is an inclusive "or" operator and is equivalent to the term "and / or" unless otherwise explicitly indicated by the context. The term "based on" is not exclusive and allows for basing on additional factors not listed unless otherwise explicitly indicated by the context. Furthermore, throughout this specification, the meanings of "a," "an," and "the" include multiple references. The meaning of "in" includes "inside" and "on top of."

[0138] The transitional phrase "essentially becomes from" is used in the claims of this application, as in In re Herz, 537 F.2d 549, 551-52, 190 USPQ As stated in 461,463 (CCPA 1976), the scope of the claims is limited to a specific substance or step of the claimed invention that "does not substantially affect the basic and novel features(s)". For example, a composition "essentially consisting of" the enumerated elements may contain unenumerated impurities in levels that are present but do not alter the function of the enumerated composition compared to a pure composition, i.e., a composition "consisting of" the enumerated components.

[0139] As used herein, “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 acid” includes, but is not limited to, single-stranded 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. Therefore, DNA having a modified skeleton for stability or other reasons is a “nucleic acid.” As used herein, the term “nucleic acid” encompasses, in addition to such chemical, enzymatic, or metabolically modified forms of nucleic acids, chemical forms of DNA characteristic of viruses and cells (including, for example, simple and complex cells).

[0140] The terms “oligonucleotide,” “polynucleotide,” “nucleotide,” or “nucleic acid” refer to molecules having two or more, preferably more than three, and usually more than ten deoxyribonucleotides or ribonucleotides. The exact size will depend on many factors, and then on the final function or use of the oligonucleotide. Oligonucleotides can be produced by any method, including chemosynthesis, DNA replication, reverse transcription, or a combination thereof. Typical deoxyribonucleotides in DNA are thymine, adenine, cytosine, and guanine. Typical ribonucleotides in RNA are uracil, adenine, cytosine, and guanine.

[0141] As used herein, the terms “locus” or “region” of a nucleic acid refer to a small region of nucleic acid, such as a gene on a chromosome, a single nucleotide, or a CpG island.

[0142] The terms "complementary" and "complementarity" refer to nucleotides (e.g., a single nucleotide) or polynucleotides (e.g., a sequence of nucleotides) that are linked by base pairing rules. For example, the sequence 5'-AGT-3' is complementary to the sequence 3'-TCA-5'. Complementarity may be "partial," in which case only some of the nucleic acid bases match according to base pairing rules. Alternatively, "complete" or "whole" complementarity may exist between nucleic acids. The degree of complementarity between nucleic acid strands results in the efficiency and strength of hybridization between nucleic acid strands. This is particularly important in amplification reactions and in detection methods that rely on binding between nucleic acids.

[0143] The term “gene” refers to a nucleic acid (e.g., DNA or RNA) sequence containing the coding sequence necessary for the production of RNA, polypeptides, or their precursors. Functional polypeptides can be coded by their full-length coding sequence or by any portion of the coding sequence, as long as the desired activity or functional property of the polypeptide (e.g., enzymatic activity, ligand binding, signal transduction, etc.) is preserved. The term “part,” when used in relation to a gene, refers to a fragment of that gene. A fragment can range in size from a few nucleotides to the entire gene sequence minus one nucleotide. Thus, “nucleotides containing at least a portion of a gene” can include a gene fragment or the entire gene.

[0144] The term “gene” also encompasses the coding region of a structural gene and includes sequences located adjacent to the coding region at both the 5' and 3' ends, for example, at a distance of approximately 1 kb on either end, thereby corresponding to the length of the gene's full-length mRNA (e.g., including the coding sequence, regulatory sequence, structural sequence, and other sequences). Sequences located at the 5' end of the coding region and present on the mRNA are referred to as 5' non-translated or 5' untranslated sequences. Sequences located at the 3' end or downstream of the coding region and present on the mRNA are referred to as 3' non-translated or 3' untranslated sequences. The term “gene” encompasses both the cDNA and genomic morphology of a gene. In some organisms (e.g., eukaryotes), the genomic morphology or clone of a gene contains coding regions that are interrupted by non-coding sequences, referred to as “introns” or “intervening regions” or “intervening sequences.” Introns are segments of genes that are transcribed into nuclear RNA (hnRNA), and they may contain regulatory elements such as enhancers. Introns are removed from the nucleus or primary transcript, or "removed by splicing," and therefore are not present in messenger RNA (mRNA) transcripts. mRNA functions during translation to determine the sequence or order of amino acids in nascent polypeptides.

[0145] In addition to the inclusion of introns, the genomic morphology of a gene may also include sequences located at both the 5' and 3' ends of the sequence present on the RNA transcript. These sequences are referred to as “adjacent” sequences or regions (these adjacent sequences are located at the 5' or 3' end of the untranslated sequence present on the mRNA transcript). The 5' adjacent region may contain regulatory sequences such as promoters and enhancers that control or influence gene transcription. The 3' adjacent region may contain sequences that direct transcription termination, post-transcriptional cleavage, and polyadenylation.

[0146] The term "wild-type," when referring to a gene, refers to a gene that possesses the characteristics of a gene isolated from a naturally occurring source. The term "wild-type," when referring to a gene product, refers to a gene product that possesses the characteristics of a gene product isolated from a naturally occurring source. The term "naturally occurring," when used for an object, refers to the fact that the object can be found naturally. For example, a polypeptide or polynucleotide sequence present in an organism (including a virus) that can be isolated from a natural source and has not been intentionally modified by human hands in a laboratory is naturally occurring. A wild-type gene is often the gene or allele that is most frequently observed in a population and is therefore arbitrarily referred to as the "normal" or "wild-type" form of that gene. In contrast, the terms "modified" or "mutant," when referring to a gene or gene product, refer to a gene or gene product that exhibits modifications (e.g., altered characteristics) to its sequence and / or functional properties compared to a wild-type gene or gene product. Note that naturally occurring mutants can be isolated, and these are identified by the fact that they possess altered characteristics compared to a wild-type gene or gene product.

[0147] The term "allele" refers to genetic diversity, which includes, but is not limited to, variants and mutants, polymorphism loci, single nucleotide polymorphism loci, frameshifts, and splice mutations. Alleles may exist naturally in a population or arise during the lifespan of any particular individual in a population.

[0148] Therefore, when the terms “variant” and “mutant” are used in relation to nucleotide sequences, they refer to nucleic acid sequences that differ by one or more nucleotides from another, usually related, nucleotide sequence. “Variability” is the difference between two different nucleotide sequences, usually one of which is the reference sequence.

[0149] Amplification is a special case of nucleic acid replication involving template specificity. This is in contrast to non-specific template replication (e.g., template-dependent replication, but not dependent on a specific template). Template specificity is distinguished herein from replication fidelity (e.g., synthesis of a suitable polynucleotide sequence) and nucleotide (ribonucleotide or deoxyribonucleotide) specificity. Template specificity is often described in terms of "target" specificity. Target sequences are "targets" in the sense that they are required to be selected from other nucleic acids. Amplification techniques are primarily designed for this selection.

[0150] In relation to nucleic acids, the term "amplification" or "amplification" typically refers to the generation of multiple copies of a polynucleotide, or a portion of a polynucleotide, starting from a small amount of polynucleotide (e.g., a single polynucleotide molecule), and the amplified product or amplicon is generally detectable. Polynucleotide amplification 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 polymerase chain reaction (PCR) or ligase chain reaction (LCR, e.g., see U.S. Patent No. 5,494,810, which is incorporated herein by reference in its entirety) is a form of amplification. Further types of amplification include allele-specific PCR (see, e.g., U.S. Patent No. 5,639,611, which is incorporated herein by reference in its entirety), assembly PCR (see, e.g., U.S. Patent No. 5,965,408, which is incorporated herein by reference in its entirety), helicase-dependent amplification (see, e.g., U.S. Patent No. 7,662,594, which is incorporated herein by reference in its entirety), hot-start PCR (see, e.g., U.S. Patents No. 5,773,258 and No. 5,338,671, each of which is incorporated herein by reference in its entirety), sequence-specific PCR, reverse PCR (see, e.g., Trigli, et al. (1988) Nucleic Acids Res., 16:8186, which is incorporated herein by reference in its entirety), and ligation-mediated PCR (see, e.g., Guilfoyle, R. et al., Nucleic Acids See Research, 25:1854-1858 (1997), U.S. Patent No. 5,508,169 (each of which is incorporated herein by reference in whole), methylation-specific PCR (see, e.g., Herman, et al., (1996) PNAS 93(13) 9821-9826 (which is incorporated herein by reference in whole), miniprimer PCR, multiplex ligation-dependent probe amplification (see, e.g., Schouten, et al.See (2002) Nucleic Acids Research 30(12):e57, which is incorporated herein by reference in its entirety), multiplex PCR (e.g., see 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 is incorporated herein by reference in its entirety), nested PCR, overlap extension PCR (e.g., see Higuchi, et al., (1988) Nucleic Acids Research 16(15)7351-7367, which is incorporated herein by reference in its entirety), real-time PCR (e.g., Higuchi, et al. See al., (1992) Biotechnology 10:413-417, Higuchi, et al., (1993) Biotechnology 11:1026-1030, each of which is incorporated herein by reference in its entirety), reverse transcription PCR (see, for example, Bustin, SA (2000) J. Molecular Endocrinology 25:169-193, which is incorporated herein by reference in its entirety), solid-phase PCR, thermally asymmetric interlaced PCR, and touchdown PCR (see, for example, Don, et al., Nucleic Acids Research (1991) 19(14) 4008, Roux, K. (1994) Biotechniques 16(5) 812-814, Hecker, et al., (1996) Biotechniques See 20(3)478-485, each of which is incorporated herein by reference in whole. Examples include, but are not limited to, these. Polynucleotide amplification can also be performed using digital PCR (e.g., Kalinina, et al., Nucleic Acids Research).See 25;1999–2004,(1997), Vogelstein and Kinzler, Proc Natl Acad Sci USA.96;9236–41,(1999), International Patent Publication WO05023091A2, and U.S. Patent Application Publication 20070202525 (each of which is incorporated herein by reference in whole).

[0151] The term "polymerase chain reaction" ("PCR") refers to the methods described in KBMullis's U.S. Patents 4,683,195, 4,683,202, and 4,965,188, which describe methods for increasing the concentration of a target sequence segment in a mixture of genomes or other DNA or RNA without cloning or purification. This process for amplifying a target sequence consists of introducing a large excess of two oligonucleotide primers into a DNA mixture containing the desired target sequence, followed by a thermal cycle in the presence of DNA polymerase in a precise sequence. The two primers are complementary to their individual strands in the double-stranded target sequence. To bring about amplification, the mixture is denatured, and then the primers are annealed to their complementary sequences in the target molecule. After annealing, the primers are extended with polymerase to form a new pair of complementary strands. By repeatedly performing the steps of denaturation, primer annealing, and polymerase extension (i.e., denaturation, annealing, and extension constitute one “cycle,” and numerous “cycles” may exist), a highly concentrated amplified segment of the desired target sequence can be obtained. The length of the amplified segment of the desired target sequence is determined by the relative positions of the primers to each other, and therefore this length is a controllable parameter. Due to the iterative aspect of the process, this method is called a “polymerase chain reaction” (“PCR”). The desired amplified segments of the target sequence are said to be “PCR amplified” because they become the dominant sequence (in terms of concentration) in the mixture, and they are “PCR products” or “amplicons.” Those skilled in the art will understand that the term “PCR” encompasses many variations of the initially described method, such as real-time PCR, nested PCR, reverse transcription PCR (RT-PCR), single-primer and arbitrary-prime PCR, etc.

[0152] Template specificity is achieved in most amplification techniques through enzyme selection. Amplifying enzymes are those that, under the conditions in which they are used, process only specific sequences of nucleic acids in a heterogeneous mixture of nucleic acids. 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 acids are not replicated by this amplifying enzyme. Similarly, in the case of T7 RNA polymerase, this amplifying enzyme has stringent specificity to its own promoter (Chamberlin). (et al, Nature, 228:227

[1970] ). In the case of T4 DNA ligases, the enzyme will not ligate two oligonucleotides or polynucleotides if there is a mismatch between the oligonucleotide or polynucleotide substrate and template at the ligation site (Wu and Wallace (1989) Genomics 4:560). Finally, thermostable template-dependent DNA polymerases (e.g., Taq and Pfu DNA polymerases) have been found to exhibit high specificity for the sequence to which primers bind and are thereby defined by the primers, due to their ability to function at high temperatures. High temperatures provide thermodynamic conditions that are favorable for primer hybridization with target sequences but not hybridization with non-target sequences (HAErlich (ed.), PCR Technology, Stockton Press

[1989] ).

[0153] As used herein, the term “nucleic acid detection assay” refers to any method for determining the nucleotide composition of a nucleic acid of interest. Examples of nucleic acid detection assays include DNA sequencing, probe hybridization, structure-specific cleavage assays (e.g., INVADER assay (Hologic, Inc.)), and, for example, U.S. Patents No. 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 U.S. Patent No. 9,096,893, each of which is incorporated herein by reference in whole for any purpose); Enzyme mismatch cleavage methods (e.g., Variagenics, U.S. Patents No. 6,110,684, No. 5,958,692, and No. 5,851,770, which are incorporated herein by reference in whole); the aforementioned polymerase chain reaction (PCR); branched hybridization methods (e.g., Chiron, U.S. Patents No. 5,849,481, No. 5,710,264, No. 5,124,246, and No. 5,624,802, which are incorporated herein by reference in whole); rolling circle replication (e.g., which are incorporated herein by reference in whole U.S. Patents 6,210,884, 6,183,960, and 6,235,502, incorporated herein by reference); NASBA (e.g., U.S. Patent 5,409,818, which is incorporated entirely by reference); Molecular beacon technology (e.g., U.S. Patent 6,150,097, which is incorporated entirely by reference); E-sensor technology (Motorola, U.S. Patents 6,248,229, 6,221,583, 6,013,170, and 6,063,573, which are incorporated entirely by reference); Cycling probe technology (e.g., U.S. Patents 5,403,711, 5,011,769, and 5,660,988, which are incorporated entirely by reference); Dade Examples include, but are not limited to, Behring signal amplification methods (e.g., U.S. Patents 6,121,001, 6,110,677, 5,914,230, 5,882,867, and 5,792,614, all of which are incorporated herein by reference); ligase chain reactions (e.g., Baranay Proc. Natl. Acad. Sci USA 88,189-93 (1991)); and sandwich hybridization methods (e.g., U.S. Patent 5,288,609, all of which are incorporated herein by reference).

[0154] The term "amplifiable nucleic acid" refers to nucleic acids that can be amplified by any amplification method. "Amplifiable nucleic acid" is usually intended to include a "sample template."

[0155] The term "sample template" refers to nucleic acids derived from the sample being analyzed for the presence of the "target" (defined below). In contrast, "background template" is used for nucleic acids other than the sample template, which may or may not be present in the sample. Background templates are almost always accidental. Background templates may be the result of carryover, or they may be due to the presence of nucleic acid contaminants that need to be purified and removed from the sample. For example, nucleic acids from organisms other than the target organism may be present as background in the test sample.

[0156] The term "primer" refers to an oligonucleotide, which may occur naturally as a nucleic acid fragment from a restriction digest or be produced by synthesis. This oligonucleotide can function as a starting point for synthesis when placed under conditions that induce the synthesis of a primer extension product complementary to the nucleic acid template (e.g., in the presence of an inducer such as nucleotides and DNA polymerase, and at a suitable temperature and pH). Primers are preferably single-stranded to maximize amplification efficiency, but may be double-stranded instead. In the case of double-stranded primers, they are first treated to separate their strands and then used for the preparation of the extension product. Preferably, the primer is an oligodeoxyribonucleotide. The primer must be long enough to prime the synthesis of the extension product in the presence of an inducer. The exact length of the primer will depend on numerous factors, including temperature, primer source, and method used.

[0157] The term “probe” refers to an oligonucleotide (e.g., a sequence of nucleotides) that occurs naturally, as well as purified restriction digests, or is produced by synthesis, recombination, or PCR amplification, and which can hybridize to another oligonucleotide of interest. Probes may be single-stranded or double-stranded. Probes are useful for the detection, identification, and isolation of specific gene sequences (e.g., “capture probes”). Any probe used in the present invention may, in some embodiments, be labeled with any “reporter molecule” and as a result, be detectable by any detection system, including but not limited to enzymes (e.g., ELISA and enzyme-based histochemical assays), fluorescence, radioactivity, and luminescence systems. This is not intended to limit the present invention to any particular detection system or labeling.

[0158] The term "target," as used herein, refers to a nucleic acid that is to be sorted from other nucleic acids, for example, by probe binding, amplification, isolation, or capture. For example, when used in relation to polymerase chain reaction, "target" refers to a region of nucleic acid bound by a primer used in the polymerase chain reaction, whereas when used in assays where the target DNA is not amplified, for example in some embodiments of an entry cleavage assay, the target includes a site to which a probe and an entry oligonucleotide (e.g., INVADER oligonucleotide) bind to form an entry cleavage structure, thereby enabling the detection of the presence of the target nucleic acid. "Segment" is defined as a region of nucleic acid within a target sequence.

[0159] As used herein, “methylation” refers to cytosine methylation at the C5 or N4 position of cytosine, adenine at the N6 position, or other types of nucleic acid methylation. In vitro amplified DNA is usually not methylated because typical in vitro DNA amplification methods do not preserve the methylation pattern of the amplified template. However, “unmethylated DNA” or “methylated DNA” may also refer to amplified DNA whose original template was not methylated, or amplified DNA whose original template was methylated, respectively.

[0160] Therefore, as used herein, “methylated nucleotide” or “methylated nucleotide base” refers to the presence of a methyl moiety on a nucleotide base, and the methyl moiety is not present in typical nucleotide bases that are recognized. 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, while 5-methylcytosine is a methylated nucleotide. In another example, thymine contains a methyl moiety at position 5 of its pyrimidine ring, but since thymine is a typical nucleotide base of DNA, for the purposes of this specification, thymine is not considered a methylated nucleotide when present in DNA.

[0161] As used herein, "methylated nucleic acid molecule" refers to a nucleic acid molecule containing one or more methylated nucleotides.

[0162] As used herein, the “methylation state,” “methylation profile,” and “methylation status” of a nucleic acid molecule refer to the presence or absence of one or more methylated nucleotide bases in the nucleic acid molecule. For example, a nucleic acid molecule containing methylated cytosine is considered methylated (for example, the methylation state of the nucleic acid molecule is methylated). A nucleic acid molecule that does not contain any methylated nucleotides is considered unmethylated.

[0163] The methylation status of a specific nucleic acid sequence (e.g., a gene marker or DNA region described herein) may indicate the methylation status of all bases in the sequence, or the methylation status of a subset of bases within the sequence (e.g., one or more cytosines), or may indicate information about the local methylation density within the sequence, with or without providing precise location information within the sequence where methylation occurs.

[0164] The methylation status of a nucleotide locus in a nucleic acid molecule refers to the presence or absence of methylated nucleotides at a specific locus within the nucleic acid molecule. For example, the methylation status of cytosine at the seventh nucleotide in a nucleic acid molecule is methylated if the nucleotide present at the seventh nucleotide is 5-methylcytosine. Similarly, the methylation status of cytosine at the seventh nucleotide in a nucleic acid molecule is unmethylated if the nucleotide present at the seventh nucleotide is cytosine (but not 5-methylcytosine).

[0165] Methylation status can optionally be expressed or indicated by a "methylation value" (e.g., representing methylation frequency, proportion, ratio, percentage, etc.). Methylation values ​​can be generated, for example, by quantifying the amount of intact nucleic acid present after restriction digestion using methylation-dependent restriction enzymes, by comparing amplification profiles after bisulfite reactions, or by comparing the sequences of bisulfite-treated and untreated nucleic acids. Therefore, a value, such as a methylation value, represents the methylation status and can thus be used as a quantitative indicator of methylation status across multiple copies of a gene locus. This is a specific application when it is desirable to compare the methylation status of a sequence in a sample with a threshold or reference value.

[0166] As used herein, “methylation frequency” or “methylation percentage (%)” refers to the number of instances in which a molecule or locus is methylated compared to the number of instances in which the molecule or locus is not methylated.

[0167] Therefore, methylation status describes the state of methylation in nucleic acids (e.g., genome sequences). Furthermore, methylation status refers to the characteristics of a nucleic acid segment at a specific genomic locus associated with methylation. Such characteristics include, but are not limited to, whether any of the cytosine (C) residues in this DNA sequence are methylated, the location of the methylated C residue(s), the frequency or percentage of methylated C across any particular region of the nucleic acid, and alleleal differences in methylation due to, for example, differences in allele origin. The terms “methylation status,” “methylation profile,” and “methylation condition” also refer to the relative, absolute, or pattern of methylated or unmethylated C across any particular region of nucleic acid in a biological sample. For example, if cytosine (C) residue(s) in a nucleic acid sequence are methylated, it may be referred to as having “high methylation” or “increased methylation,” while if cytosine (C) residue(s) in a DNA sequence are not methylated, it may be referred to as having “low methylation” or “decreased methylation.” Similarly, if cytosine (C) residues(s) in a nucleic acid sequence are methylated compared to another nucleic acid sequence (e.g., from a different region or from a different individual), that sequence is considered to have high methylation or increased methylation compared to the other nucleic acid sequence. Alternatively, if cytosine (C) residues(s) in a DNA sequence are not methylated compared to another nucleic acid sequence (e.g., from a different region or from a different individual), that sequence is considered to have low methylation or decreased methylation compared to the other nucleic acid sequence. Furthermore, as used herein, the term “methylation pattern” refers to a collection of methylated and unmethylated nucleotides across a region of a nucleic acid. Two nucleic acids may have the same or similar methylation frequency or methylation percentage, but may have different methylation patterns, if the number of methylated and unmethylated nucleotides is the same or similar across the region, but the locations of the methylated and unmethylated nucleotides are different.Sequences are said to be "variable methylated," have "methylation differences," or have "different methylation states" if they differ in the degree of methylation (e.g., one has increased or decreased methylation compared to the other), frequency, or pattern. The term "variable methylation" refers to the difference in the level or pattern of nucleic acid methylation in cancer-positive samples compared to the level or pattern of nucleic acid methylation in cancer-negative samples. It may also refer to the difference in level or pattern between patients whose cancer recurred after surgery and those whose cancer did not. Specific levels or patterns of variable methylation and DNA methylation are prognostic and predictive biomarkers, for example, after precise cutoffs or predictive characteristics have been defined.

[0168] Methylation status frequencies can be used to describe samples derived from a population or a single individual. For example, a nucleotide locus with a 50% methylation status frequency is methylated in 50% of cases and unmethylated in 50% of cases. Such frequencies can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a population or nucleic acid aggregate. Therefore, if the methylation in a first population or pool of nucleic acid molecules differs from that in a second population or pool of nucleic acid molecules, the methylation status frequency of the first population or pool will differ from that of the second population or pool. Such frequencies can also 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 frequencies can be used to describe the degree to which cells from a tissue sample are methylated or unmethylated at a nucleotide locus or nucleic acid region.

[0169] As used herein, "nucleotide locus" refers to the position of a nucleotide within a nucleic acid molecule. A nucleotide locus of a methylated nucleotide refers to the position of a methylated nucleotide within a nucleic acid molecule.

[0170] Typically, methylation of human DNA occurs on adjacent guanine and cytosine-containing dinucleotide sequences (also known as CpG dinucleotide sequences) where cytosine is located at the 5' position of guanine. While many cytosines within CpG dinucleotides are methylated in the human genome, some remain unmethylated in specific CpG dinucleotide-rich genomic regions known as CpG islands (e.g., Antequera et al.). See al. (1990) Cell 62:503-514).

[0171] As used herein, “CpG island” refers to a G:C-rich region of genomic DNA containing an increased number of CpG dinucleotides compared to the whole genomic DNA. A CpG island may be at least 100, 200, or more base pairs long, in which case the G:C content of the region is at least 50%, and the ratio of observed CpG frequency to predicted frequency is 0.6. In some cases, a CpG island may be at least 500 base pairs long, in which case the G:C content of the region is at least 55%, and the ratio of observed CpG frequency to predicted frequency is 0.65. The ratio of observed CpG frequency to predicted 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 relative to the predicted frequency can be calculated according to the formula R = (A × B) / (C × D), where R is the ratio of the observed CpG frequency to the predicted frequency, A is the number of CpG dinucleotides in the 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 status is usually determined within CpG islands, for example, in promoter regions. However, it will be understood that other sequences in the human genome also have a tendency toward DNA methylation such as CpA and CpT (Ramsahoye (2000) Proc. Natl. Acad. Sci. USA 97:5237-5242, Salmon). See also 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, and Woodcock (1987) Biochem. Biophys. Res. Commun. 145:888-894.

[0172] As used herein, “methylation-specific reagent” refers to a reagent that modifies the nucleotides of a nucleic acid molecule in accordance with the methylation state of the nucleic acid molecule, or a methylation-specific reagent refers to a compound, composition, or other agent that can alter the nucleotide sequence of a nucleic acid molecule in a manner that reflects the methylation state of the nucleic acid molecule. A method of treating a nucleic acid molecule with such a reagent may include contacting the nucleic acid molecule with the reagent and, if desired, linking it to additional steps to achieve a desired alteration of the nucleotide sequence. Such a method may be applied in such a way that unmethylated nucleotides (e.g., each unmethylated cytosine) are modified into different nucleotides. For example, in some embodiments, such a reagent can deaminate unmethylated cytosine nucleotides to produce deoxyuracil residues. Examples of such reagents include, but are not limited to, methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents.

[0173] Alterations to nucleic acid nucleotide sequences by methylation-specific reagents can also result in nucleic acid molecules in which each methylated nucleotide is modified into a different nucleotide.

[0174] The term "methylation assay" refers to any assay for determining the methylation status of one or more CpG dinucleotide sequences within a nucleic acid sequence.

[0175] The term "MS AP-PCR" (methylation-sensitive optional prime polymerase chain reaction) refers to a recognized technique in the art that uses CG-rich primers to perform a whole-genome scan and focus on the regions most likely to contain CpG dinucleotides, as described by Gonzalgo et al. (1997) Cancer Research 57:594-599.

[0176] The term "MethyLight (trademark)" refers to a fluorescence-based real-time PCR technology recognized in the art, as described by Eads et al. (1999) Cancer Res. 59:2302-2306.

[0177] The term "HeavyMethyl®" refers to an assay in which a methylation-specific blocking probe (also referred to herein as a blocker) that covers or is covered by the amplification primers at CpG positions between amplification primers enables methylation-specific selective amplification of a nucleic acid sample.

[0178] The term "HeavyMethyl®MethyLight® assay" refers to the HeavyMethyl®MethyLight® assay, which is a variation of the MethyLight® assay, in which the MethyLight® assay is combined with a methylation-specific blocking probe that covers CpG positions between amplification primers.

[0179] The term "Ms-SNuPE" (methylation-sensitive single nucleotide primer elongation) refers to an assay recognized in the art, as described by Gonzalgo & Jones (1997) Nucleic Acids Res. 25:2529-2531.

[0180] The term "MSP" (methylation-specific PCR) refers to methylation assays recognized in the art, as described by Herman et al. (1996) Proc. Natl. Acad. Sci. USA 93:9821-9826 and by U.S. Patent No. 5,786,146.

[0181] The term "COBRA" (compound bisulfite restriction analysis) refers to a methylation assay recognized in the art, as described by Xiong & Laird (1997) Nucleic Acids Res. 25:2532-2534.

[0182] The term "MCA" (methylated CpG island amplification) refers to the methylation assay described by Toyota et al. (1999) Cancer Res. 59:2307-12 and WO00 / 26401A1.

[0183] As used herein, “selected nucleotide” means one of the four nucleotides normally present in a nucleic acid molecule (C, G, T, and A for DNA, and C, G, U, and A for RNA), and may include methylated derivatives of normally present nucleotides (for example, if C is the selected nucleotide, both methylated and unmethylated C are included in the meaning of selected nucleotide), while “methylated selected nucleotide” specifically refers to a methylated normally present nucleotide, and “unmethylated selected nucleotide” specifically refers to an unmethylated normally present nucleotide.

[0184] The term "methylation-specific restriction enzyme" refers to a restriction enzyme that selectively digests nucleic acids depending on the methylation status of its recognition site. In the case of a restriction enzyme that specifically cleaves when the recognition site is unmethylated or hemimethylated (methylation-sensitive enzyme), if the recognition site is methylated on one or both strands, cleavage does not occur (or occurs with significantly reduced efficiency). In the case of a restriction enzyme that specifically cleaves only when the recognition site is methylated (methylation-dependent enzyme), if the recognition site is not methylated, cleavage does not occur (or occurs with significantly reduced efficiency). Methylation-specific restriction enzymes are preferred, and their recognition sequences contain a CG dinucleotide (e.g., a recognition sequence such as CGCG or CCCGGG). More preferably in some embodiments is a restriction enzyme that does not cleave if the cytosine in this dinucleotide is methylated at carbon atom C5.

[0185] As used herein, “different nucleotide” means a nucleotide that is chemically different from the selected nucleotide, and usually, as a result, the different nucleotide has different Watson-Crick base pairing properties than the selected nucleotide, so that a commonly occurring nucleotide complementary to the selected nucleotide is not the same as a commonly occurring nucleotide complementary to the different nucleotide. For example, if C is the selected nucleotide, U or T may be a different nucleotide, as exemplified by the complementarity of C to G and the complementarity of U or T to A. As used herein, a nucleotide complementary to the selected nucleotide, or complementary to a different nucleotide, means a nucleotide that, under high stringency conditions, base pairs with the selected nucleotide or the different nucleotide with a higher affinity than the base pairing of complementary nucleotides with three of the four commonly occurring nucleotides. An example of complementarity is the Watson-Crick base pairing of DNA (e.g., AT and CG) and RNA (e.g., AU and CG). Therefore, for example, under high stringency conditions, G base pairs with C with a higher affinity than G base pairs with G, A, or T. For this reason, if C is a selected nucleotide, then G is a nucleotide complementary to the selected nucleotide.

[0186] As used herein, the “sensitivity” of a given marker (or a set of markers used together) refers to the percentage of samples that report DNA methylation values ​​above a threshold that distinguishes neoplastic samples from non-neoplastic samples. In some embodiments, a positive result is defined as a histologically confirmed neoplasm reporting a DNA methylation value above the threshold (e.g., within a disease-associated range), and a false negative result is defined as a histologically confirmed neoplasm reporting a DNA methylation value below the threshold (e.g., within a non-disease-associated range). Thus, the sensitivity value reflects the probability that a DNA methylation measurement of a given marker obtained from a known affected sample will fall within the range of disease-associated measurements. As defined herein, the clinical relevance of the calculated sensitivity value represents an estimate of the probability of detecting the presence of a clinical condition when a given marker is applied to a subject with that clinical condition.

[0187] As used herein, “specificity” of a given marker (or a set of markers used together) refers to the percentage of non-tumor samples that report DNA methylation values ​​below a threshold that distinguishes a tumorigenic sample from a non-tumorgenic sample. In some embodiments, a negative result is defined as a histologically confirmed non-tumor sample that reports DNA methylation values ​​below a threshold (e.g., within a range not associated with disease), and a false positive result is defined as a histologically confirmed non-tumor sample that reports DNA methylation values ​​above a threshold (e.g., within a range associated with disease). Thus, the value of specificity reflects the probability that a DNA methylation measurement of a given marker obtained from a known non-tumor sample will fall within the range of a non-disease-related measurement. As defined herein, the clinical relevance of the calculated specificity value represents an estimate of the probability of detecting the absence of a clinical condition when a given marker is applied to a patient who does not have that clinical condition.

[0188] The term "AUC," as used herein, is an abbreviation for "Area under the curve." In particular, AUC refers to the area under the receiver operating characteristic (ROC) curve. The ROC curve is a plot of the true positive rate against the false positive rate at various possible cut points of a diagnostic test. The ROC curve 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 the ROC curve (AUC) is a measure of the accuracy of a diagnostic test (a larger area indicates better accuracy, with 1 being optimal; a randomized test has a diagonally located ROC curve and an area of ​​0.5. See JPEgan. (1975) Signal Detection Theory and ROC Analysis, Academic Press, New York).

[0189] As used herein, the term "neoplasm" refers to any new abnormal growth of tissue. Therefore, a neoplasm may be a pre-malignant neoplasm or a malignant neoplasm.

[0190] The term “neoplasm-specific marker,” as used herein, refers to any biomaterial or element that may be used to indicate the presence of a neoplasm. Examples of biomaterials include, but are not limited to, nucleic acids, polypeptides, carbohydrates, fatty acids, cellular components (e.g., cell membranes and mitochondria), and whole cells. In some cases, the marker is a specific nucleic acid region (e.g., a gene, an intragenic region, a specific gene locus, etc.). A nucleic acid region that is a marker may be referred to, for example, a “marker gene,” a “marker region,” a “marker sequence,” or a “marker gene locus.”

[0191] As used herein, the term “adenoma” refers to a benign tumor of glandular origin. While these growths are benign, they may progress over time and become malignant.

[0192] The terms "precancerous" or "preneoplastic" and their equivalents refer to any cell proliferation disorder that has undergone malignant transformation.

[0193] The "location" of neoplasms, adenomas, cancers, etc., refers to the tissue, organ, cell type, anatomical region, or body part in the body where the neoplasm, adenoma, cancer, etc., is located.

[0194] As used herein, the application of a “diagnostic” test includes detecting or identifying a disease state or condition in a subject, determining the likelihood of a subject developing a given disease or condition, determining the likelihood of a subject with a disease or condition responding to therapy, determining the prognosis (or likelihood of progression or regression) of a subject with a disease or condition, and determining the effectiveness of a treatment for a subject with a disease or condition. For example, a diagnosis may be used to detect the presence or likelihood of a subject having a neoplasm, or the likelihood that such a subject would respond favorably to a compound (e.g., a pharmaceutical, e.g., a drug) or other treatment.

[0195] The term "isolated," when used in reference to nucleic acids, as in "isolated oligonucleotide," refers to a nucleic acid sequence identified and isolated from at least one contaminating nucleic acid that is normally associated with its natural source. Isolated nucleic acids exist in a form or context different from that which is found in nature. In contrast, unisolated nucleic acids, such as DNA and RNA, are found in the state in which they are found in nature. Examples of unisolated nucleic acids include a given DNA sequence (e.g., a gene) found on a host cell chromosome adjacent to an adjacent gene, an RNA sequence, or a specific mRNA sequence encoding a particular protein, such as one found in a cell as a mixture with many other mRNAs encoding numerous proteins. However, isolated nucleic acids encoding a particular protein include, for example, such nucleic acids in cells that normally express that protein, in which case the nucleic acid is located at a chromosomal location different from that of the natural cell, or otherwise adjacent to a nucleic acid sequence different from that which is found in nature. Isolated nucleic acids or oligonucleotides can exist in single-stranded or double-stranded form. When isolated nucleic acids or oligonucleotides are used to express proteins, these oligonucleotides may contain at least a sense strand or coding strand (i.e., the oligonucleotide may be single-stranded), but may also contain both a sense strand and an antisense strand (i.e., the oligonucleotide may be double-stranded). Isolated nucleic acids, after isolation from their natural or normal environment, can be combined with other nucleic acids or molecules. For example, isolated nucleic acids may be present in host cells, where they are placed for heterologous expression, for instance.

[0196] The term “purified” refers to a molecule, either a nucleic acid or an amino acid sequence, that has been removed, isolated, or separated from its natural environment. Thus, “isolated nucleic acid sequence” may be a purified nucleic acid sequence. A “substantially purified” molecule contains at least 60%, preferably at least 75%, and more preferably at least 90% of other components naturally associated with them. As used herein, the terms “purified” or “for purification” also refer to the removal of contaminants from a sample. Removal of contaminating proteins increases the percentage of the target polypeptide or nucleic acid in the sample. In another example, a recombinant polypeptide is expressed in a plant, bacterium, yeast, or mammalian host cell, and this polypeptide is purified by the removal of host cell proteins, thereby increasing the percentage of recombinant polypeptide in the sample.

[0197] The term "composition comprising" a given polynucleotide sequence or polypeptide broadly refers to any composition containing a given polynucleotide sequence or polypeptide. The composition may include aqueous solutions containing a salt (e.g., NaCl), a surfactant (e.g., SDS), and other components (e.g., Denhardt's solution, milk powder, salmon sperm DNA, etc.).

[0198] The term "sample" is used in its broadest sense. In one sense, a sample may refer to animal cells or tissues. In another sense, a sample may refer to specimens or cultures obtained from any source, as well as biological and environmental samples. Biological samples may be obtained from plants or animals (including humans) and include fluids, solids, tissues, and gases. Environmental samples include environmental materials such as surface materials, soil, water, and industrial samples. These examples should not be construed as limiting the types of samples to which the present invention is applicable.

[0199] As used herein, “remote sample” refers, in some contexts, to a sample that is indirectly recovered from a site that is not the source of the sample’s cells, tissues, or organs. For example, if sample material derived from the pancreas is evaluated in a fecal sample (e.g., not directly taken from the ovary), the sample is a remote sample.

[0200] As used herein, the terms “patient” or “subject” refer to the organism subjected to the various tests provided by this technology. The term “subject” includes animals, preferably mammals, including humans. In a preferred embodiment, the subject is a primate. In a more preferred embodiment, the subject is a human. With respect to diagnostic methods, the preferred subject is a vertebrate subject. Preferred vertebrates are warm-blooded, and preferred warm-blooded vertebrates are mammals. The 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. Thus, this technology enables the diagnosis of mammals such as humans, as well as mammals that are important because they are endangered, such as Amur tigers, economically important mammals such as animals raised on farms for human consumption, and / or animals that are socially important 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; pigs, boars, and swine, including wild boars; ruminants and / or ungulates such as cattle, bulls, sheep, giraffes, deer, goats, bison, and camels; pinnipeds; and horses. Thus, the diagnosis and treatment of livestock, including but not limited to domesticated pigs, ruminants, ungulates, and horses (including racehorses), are further provided. The subject matter disclosed herein further includes a system for diagnosing lung cancer in a subject. This system may be provided as a commercially available kit that can be used, for example, to screen for lung cancer risk in a subject from which a biological sample has been collected, or to diagnose lung cancer. An exemplary system provided in accordance with this technology includes evaluating the methylation status of markers described herein.

[0201] As used herein, the term “kit” refers to any delivery system for delivering a substance. In relation to a reaction assay, such a delivery system includes a system that enables the storage, transport, or delivery of reaction reagents (e.g., oligonucleotides, enzymes, etc., in appropriate containers) and / or supporting materials (e.g., buffers, written instructions for performing the assay) from one location to another. For example, a kit includes one or more enclosed containers (e.g., boxes) containing the relevant reaction reagents and / or supporting materials. As used herein, the term “subdivision kit” refers to a delivery system comprising two or more separate containers, each containing a fraction of the entire components of the kit. These containers may be delivered together or separately to the intended recipient. For example, the first container may contain an enzyme for use in an assay, while the second container contains an oligonucleotide. The term “subdivision kit” is intended to encompass, but is not limited to, kits containing analyte-specific reagents (ASRs) regulated under Section 520(e) of the Federal Food, Drug, and Cosmetic Act. In fact, any delivery system comprising two or more separate containers, each containing a small portion of the entire kit's components, falls under the term "fragmentation kit." In contrast, a "composite kit" refers to a delivery system containing all components of a reaction assay in a single container (for example, in a single box containing each of the desired components). The term "kit" encompasses both fragmentation kits and composite kits.

[0202] As used herein, the term “ovarian cancer” refers to any cancerous growth originating from the ovary, including, but not limited to, conventionally diagnosed ovarian cancer, fallopian tube cancer, and primary peritoneal cancer. In some embodiments, ovarian cancer is a type of cancer that develops in the tissue of the ovary. In other embodiments, ovarian cancer is either ovarian epithelial carcinoma (a cancer that begins in cells on the surface of the ovary) or malignant germ cell tumor (a cancer that begins in egg cells).

[0203] As used herein, the term “information” refers to any set of facts or data. With respect to information stored or processed using computer systems, including but not limited to the Internet, the term refers to any data stored in any form (e.g., analog, digital, optical, etc.). As used herein, the term “information relating to a subject” refers to facts or data relating to a subject (e.g., human, plant, or animal). The term “genomic information” refers to information relating to a genome, including but not limited to nucleic acid sequences, genes, methylation percentages, allele frequencies, RNA expression levels, protein expression, phenotypes related to genotype, etc. “Allele frequency information” refers to facts or data relating to allele frequencies, including but not limited to allele identification information, statistical correlations between the presence of alleles and characteristics of a subject (e.g., a human subject), the presence or absence of alleles in an individual or population, and the percentage likelihood of alleles present in individuals having one or more specific characteristics. [Modes for carrying out the invention] In this detailed description of various embodiments, numerous specific details are provided for illustrative purposes to provide a complete understanding of the disclosed embodiments. However, those skilled in the art will understand that these various embodiments can be carried out with or without these specific details. In other cases, the structure and mechanism are shown in block diagram form. Furthermore, those skilled in the art will readily understand that the particular order in which the methods are presented and carried out is exemplary, and that the order can be changed, but still intended to remain within the spirit and scope of the various embodiments disclosed herein.

[0204] Provided herein are, but are not limited to, techniques for ovarian cancer screening, methods, compositions, and related applications for detecting the presence of ovarian cancer and / or specific forms of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC). Where the techniques are described herein, the section headings used are for illustrative purposes only and should not be construed as limiting the subject matter in any way.

[0205] Indeed, as described in Examples I, II, and III, experiments conducted during the process of elucidating embodiments of the present invention identified a novel set of 560 variable methylation regions (DMRs) for distinguishing ovarian cancer-derived DNA from non-tumor control DNA. From these 560 novel DNA methylation markers, further experiments identified markers that can distinguish various types of ovarian cancer from normal tissue and plasma samples. For example, individual sets of DMRs were identified that can distinguish 1) clear cell ovarian cancer tissue from normal tissue, 2) endometrioid ovarian cancer tissue from normal tissue, 3) mucinous ovarian cancer tissue from normal tissue, 4) serous ovarian cancer tissue from normal tissue, and 5) ovarian cancer in blood samples.

[0206] The disclosures herein refer to certain exemplary embodiments, which should be understood to be presented as examples only and not as limitations.

[0207] In certain embodiments, the technology provides compositions and methods for identifying, determining, and / or classifying cancers such as ovarian cancer and / or subtypes of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC). The method involves determining the methylation status of at least one methylation marker in a biological sample isolated from a subject (e.g., fecal sample, ovarian tissue sample, plasma sample), where changes in the methylation status of the marker indicate the presence, class, or site of ovarian cancer and / or its subtype. Certain embodiments relate to markers containing variable methylation regions (DMRs, e.g., DMR1-560; see Tables 1A and 6A) used for the diagnosis (e.g., screening) of ovarian cancer and various types of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC).

[0208] In addition to embodiments provided herein in which methylation analysis of at least one marker, region of a marker, or base of a marker is analyzed, including DMRs (e.g., DMRs, e.g., DMR1-560) shown in Tables 1A and 6A, the Technology also provides a panel of markers including at least one marker, region of a marker, or base of a marker, including DMRs useful for the detection of cancer, particularly ovarian cancer.

[0209] Some embodiments of this technology are based on the analysis of the CpG methylation status of at least one marker including a DMR, a region of the marker, or a base of the marker.

[0210] In some embodiments, the technique allows for the determination of the methylation status of CpG dinucleotide sequences in at least one marker, including DMRs (e.g., DMR1-560, see Tables 1A and 6A), by using reagents that modify DNA in a methylation-specific manner (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents) in combination with one or more methylation assays. Genomic CpG dinucleotides can be methylated or demethylated (or known as up-methylated and down-methylated, respectively). However, the method of the present invention is suitable for the analysis of heterogeneous biological samples, such as low concentrations of tumor cells or biomaterials derived therefrom, in the background of a distant sample (e.g., blood, organ excrement, or feces). Therefore, when analyzing the methylation status of CpG locations in such samples, quantitative assays can be used to determine the level of methylation (e.g., percentage, proportion, ratio, or degree) at a particular CpG location.

[0211] According to this technology, determining the methylation status of CpG dinucleotide sequences in markers containing DMRs is useful for both the diagnosis and characterization of cancers such as ovarian cancer.

[0212] combination of markers In some embodiments, the technique relates to evaluating the methylation status of marker combinations including DMRs (e.g., DMR numbers 1-560) from Tables 1A and 6A. In some embodiments, evaluating the methylation status of multiple markers improves the specificity and / or sensitivity of screening or diagnosis for identifying neoplasms (e.g., ovarian cancer) in a subject.

[0213] Various cancers can be predicted by different combinations of markers, for example, by statistical techniques related to the specificity and sensitivity of predictions. This technique provides a method for identifying predictive and validated predictive combinations for several cancers.

[0214] Method for assaying methylation status In certain embodiments, a method for analyzing nucleic acids for the presence of 5-methylcytosine involves treating DNA with a reagent that modifies DNA in a methylation-specific manner. Examples of such reagents include, but are not limited to, methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents.

[0215] A frequently used method for analyzing nucleic acids for the presence of 5-methylcytosine is the bisulfite method described by Frommer et al. for the detection of 5-methylcytosine in DNA (Frommer et al. (1992) Proc. Natl. Acad. Sci. USA 89:1827-31, the whole of which is expressly incorporated herein by reference for all purposes) or for the detection of their diversity. The bisulfite method for mapping 5-methylcytosine is based on the knowledge that cytosine reacts with bisulfite ions (also known as bisulfites) (however 5-methylcytosine does not react). The reaction is usually carried out in the following steps: First, cytosine reacts with bisulfite to form sulfonated cytosine. Next, spontaneous deamination of the sulfonation reaction intermediate yields sulfonated uracil. Finally, sulfonated uracil is desulfonated under alkaline conditions to form uracil. Uracil pairs with adenine (and therefore behaves like thymine), while 5-methylcytosine pairs with guanine (and therefore behaves like cytosine), making detection possible. This allows detection by, for example, bisulfite genome sequencing (Grigg G, & Clark S, Bioessays (1994) 16:431-36, Grigg G, DNA Seq. (1996) 6:189-98), methylation-specific PCR (MSP) as disclosed in, for example, U.S. Patent No. 5,786,146, or assays including sequence-specific probe cleavage, such as the QuARTS flap endonuclease assay (e.g., Zou et al. (2010) “Sensitive quantification of methylated markers”). By using a novel methylation-specific technology (see Clin Chem 56:A199, and U.S. Patents No. 8,361,720, 8,715,937, 8,916,344, and 9,212,392), it becomes possible to distinguish between methylated and unmethylated cytosine.

[0216] Several conventional techniques involve methods that include encapsulating the DNA to be analyzed in an agarose matrix to prevent DNA diffusion and unraveling (bisulfite reacts only with single-stranded DNA), and replacing the precipitation and purification steps with rapid dialysis (Olek A, et al. (1996) “A modified and improved method for bisulfite based cytosine methylation analysis” Nucleic Acids Res. 24:5064-6). Therefore, it is possible to analyze the methylation status of individual cells and demonstrate the usefulness 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.

[0217] Bisulfite techniques typically involve amplifying specific short fragments of known nucleic acids after bisulfite treatment, and then assaying the product by either sequencing (Olek & Walter (1997) Nat. Genet. 17:275-6) or primer extension reaction (Gonzalgo & Jones (1997) Nucleic Acids Res. 25:2529-31, WO95 / 00669, U.S. Patent No. 6,251,594) to analyze individual cytosine positions. Some methods utilize enzymatic digestion (Xiong & Laird (1997) Nucleic Acids Res. 25:2532-4). Detection by hybridization has also been described in the art (Olek et al., WO99 / 28498). Furthermore, the use of bisulfite techniques for detecting methylation in individual genes is described (Grigg & 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, WO9746705, WO9515373).

[0218] Various methylation assay procedures can be used in conjunction with bisulfite treatment using this technology. These assays allow for the determination of the methylation status of one or more CpG dinucleotides (e.g., CpG islands) within a nucleic acid sequence. Such assays include, among other techniques, sequencing of bisulfite-treated nucleic acids, PCR (for sequence-specific amplification), Southern blot analysis, and the use of methylation-specific restriction enzymes, such as methylation-sensitive or methylation-dependent enzymes.

[0219] For example, genome sequencing has been simplified for the analysis of methylation patterns and 5-methylcytosine distribution by using bisulfite treatment (Frommer et al. (1992) Proc. Natl. Acad. Sci. USA 89:1827-1831). Furthermore, restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA is used to assess methylation status, for example, as described by Sadri & Hornsby (1997) Nucleic Acids Res. 24:5058-5059, or as embodied by a method known as COBRA (compound bisulfite restriction analysis) (Xiong & Laird (1997) Nucleic Acids Res. 25:2532-2534).

[0220] COBRA® analysis is a quantitative methylation assay useful for determining DNA methylation levels at specific loci using small amounts of genomic DNA (Xiong & Laird, Nucleic Acids Res. 25:2532-2534, 1997). Briefly, restriction enzyme digestion is used to reveal methylation-dependent sequence differences in the PCR product of sodium bisulfite-treated DNA. Methylation-dependent sequence differences are first introduced into genomic DNA by standard bisulfite treatment according to the procedure described by Frommer et al. (Proc. Natl. Acad. Sci. USA 89:1827-1831, 1992). Next, PCR amplification of the bisulfite-converted DNA is performed using primers specific to the target CpG island, followed by restriction endonuclease digestion, gel electrophoresis, and detection using a specifically labeled hybridization probe. The methylation level in the original DNA sample is expressed by the relative amounts of digested and undigested PCR products using a linear quantitative method that covers a wide range of DNA methylation levels. Furthermore, this technique can be reliably applied to DNA obtained from micro-dissected paraffin-embedded tissue samples.

[0221] Typical reagents for COBRA® analysis (e.g., those found in a typical COBRA®-based kit) include, but are not limited to, PCR primers for specific loci (e.g., specific genes, markers, DMRs, gene regions, marker regions, bisulfite-treated DNA sequences, CpG islands, etc.), restriction enzymes and appropriate buffers, gene hybridization oligonucleotides, control hybridization oligonucleotides, kinase labeling kits for oligonucleotide probes, and labeled nucleotides. Furthermore, bisulfite conversion reagents include DNA denaturation buffers; sulfonation buffers; DNA recovery reagents or kits (e.g., precipitation, ultrafiltration, affinity columns); desulfonation buffers; and DNA recovery components.

[0222] Assays such as "MethyLight®" (fluorescence-based real-time PCR technology) (Eads et al., Cancer Res. 59:2302-2306, 1999), Ms-SNuPE® (methylation-sensitive single nucleotide primer extension) reaction (Gonzalgo & 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. Patent 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.

[0223] The "HeavyMethyl®" assay technology is a quantitative method for evaluating methylation differences based on methylation-specific amplification of bisulfite-treated DNA. Methylation-specific blocking probes ("blockers") that cover CpG positions between amplification primers, or are covered by the amplification primers, enable methylation-specific selective amplification of nucleic acid samples.

[0224] The term "HeavyMethyl® MethyLight® assay" refers to the HeavyMethyl® MethyLight® assay, which is a variation of the MethyLight® assay, in which the MethyLight® assay is combined with a methylation-specific blocking probe that covers CpG positions between amplification primers. The HeavyMethyl® assay can also be used in combination with methylation-specific amplification primers.

[0225] Typical reagents for HeavyMethyl® analysis (e.g., those found in a typical MethyLight®-based kit) include, but are not limited to, PCR primers for specific loci (e.g., specific genes, markers, gene regions, marker regions, bisulfite-treated DNA sequences, CpG islands, or bisulfite-treated DNA sequences or CpG islands); blocking oligonucleotides; optimized PCR buffers and deoxynucleotides; and Taq polymerase.

[0226] Methylation-Specific PCR (MSP) allows for the evaluation of the methylation status of virtually any group of CpG sites within a CpG island, independently of the use of methylation-sensitive restriction enzymes (Herman et al. Proc. Natl. Acad. Sci. USA 93:9821-9826, 1996; U.S. Patents 5,786,146). Briefly, DNA is modified with sodium bisulfite, which converts unmethylated cytosine to uracil (but not methylated cytosine), and the product is then amplified with primers specific to methylated DNA compared to unmethylated DNA. MSP requires only a small amount of DNA, is sensitive to 0.1% of methylated alleles in a given CpG island locus, and can be performed with DNA extracted from paraffin-embedded samples. Typical reagents for MSP analysis (e.g., those found in a typical MSP-based kit) include, but are not limited to, methylation and demethylation PCR primers for specific loci (e.g., specific genes, markers, gene regions, marker regions, bisulfite-treated DNA sequences, CpG islands, etc.); optimized PCR buffers and deoxyribonucleotides; and specific probes.

[0227] The MethyLight® assay is a high-throughput quantitative methylation assay utilizing fluorescence-based real-time PCR (e.g., TaqMan®) that requires no further manipulation 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, by standard procedure, is converted into a mixed pool of methylation-dependent sequence differences during a sodium bisulfite reaction (the bisulfite process converts unmethylated cytosine residues to uracil). Fluorescence-based PCR is then performed in a “biased” reaction using PCR primers that overlap, for example, known CpG dinucleotides. Sequence identification is performed both at the amplification level and the fluorescence detection level.

[0228] The MethyLight® assay is used for quantitative analysis of methylation patterns in nucleic acid samples, such as genomic DNA, with sequence identification performed at the probe hybridization level. In the quantitative version, a PCR reaction yields methylation-specific amplification in the presence of a fluorescent probe that overlaps a specific putative methylation site. An unbiased control is provided by a reaction in which neither the primers nor the probe overlap any CpG dinucleotides. Alternatively, qualitative analysis of genomic methylation is performed by probing a biased PCR pool using either a control oligonucleotide that does not cover known methylation sites (e.g., a fluorescent-based version of HeavyMethyl® and MSP technology) or an oligonucleotide that covers potential methylation sites.

[0229] The MethyLight® process is used with any suitable probe (e.g., TaqMan® probes, Lightcycler® probes, 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, for example, MSP primers and / or HeavyMethylblocker oligonucleotides and TaqMan® probes. TaqMan® probes are double-labeled with fluorescent "reporter" and "quencher" molecules and are designed to be specific to a relatively high GC content region so that the probe melts at a temperature approximately 10°C higher than the forward or reverse primer during the PCR cycle. This allows the TaqMan® probe to continue hybridizing sufficiently during the PCR annealing / extension step. Taq polymerase enzymatically synthesizes new strands during PCR, ultimately reaching the annealed TaqMan® probe. Next, the 5' to 3' endonuclease activity of Taq polymerase digests the TaqMan® probe and releases the fluorescent reporter molecule, thereby removing the probe, in order to quantitatively detect the currently unquenched signal of the fluorescent reporter molecule using a real-time fluorescence detection system.

[0230] Typical reagents for MethyLight® analysis (e.g., those found in a typical MethyLight®-based kit) include, but are not limited to, PCR primers for specific loci (e.g., specific genes, markers, gene regions, marker regions, bisulfite-treated DNA sequences, CpG islands, etc.); TaqMan® or Lightcycler® probes; optimized PCR buffers and deoxyribonucleotides; and Taq polymerase.

[0231] The QM(trademark) (Quantitative Methylation) assay is an alternative quantitative test for methylation patterns in genomic DNA samples, where sequence identification is performed at the probe hybridization level. In this quantitative version, PCR reactions provide unbiased amplification in the presence of a fluorescent probe that overlaps specific putative methylation sites. An unbiased control in terms of input DNA quantity is provided by a reaction in which neither primers nor probes overlap any CpG dinucleotides. Alternatively, qualitative testing of genomic methylation is performed by probing a biased PCR pool using either a control oligonucleotide that does not cover known methylation sites (e.g., a fluorescence-based version of HeavyMethyl(trademark) and MSP technology) or an oligonucleotide that covers potential methylation sites.

[0232] The QM® process can be used in the amplification process with any suitable probe, such as the "TaqMan®" probe or the Lightcycler® probe. For example, double-stranded genomic DNA is treated with sodium bisulfite and supplied to unbiased primers and the TaqMan® probe. The TaqMan® probe is double-labeled with fluorescent "reporter" and "quencher" molecules and is designed to be specific to a relatively high GC content region so that it melts at a temperature approximately 10°C higher than the forward or reverse primer during the PCR cycle. This allows the TaqMan® probe to continue hybridizing sufficiently during the PCR annealing / extension step. The Taq polymerase enzymatically synthesizes new strands during PCR, ultimately reaching the annealed TaqMan® probe. Next, the 5' to 3' endonuclease activity of Taq polymerase removes the TaqMan® probe by digesting it and releasing the fluorescent reporter molecule, allowing for quantitative detection of the currently unquenched signal of the fluorescent reporter molecule using a real-time fluorescence detection system. Typical reagents for QM® analysis (e.g., those that may be found in a typical QM®-based kit) include, but are not limited to, PCR primers for specific loci (e.g., specific genes, markers, gene regions, marker regions, bisulfite-treated DNA sequences, CpG islands, etc.); TaqMan® or Lightcycler® probes; optimized PCR buffer and deoxyribonucleotides; and Taq polymerase.

[0233] The Ms-SNuPE™ technology is a quantitative method for evaluating differences in methylation at specific CpG sites based on bisulfite treatment of DNA followed by single-nucleotide primer extension (Gonzalgo & Jones, Nucleic Acids Res. 25:2529-2531, 1997). Briefly, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine to uracil without altering 5-methylcytosine. The desired target sequence is then amplified using PCR primers specific to the bisulfite-converted DNA, and the resulting product is isolated and used as a template for methylation analysis at the target CpG site. Small amounts of DNA can be analyzed (e.g., microscopic pathological sections), thus avoiding the use of restriction enzymes to determine methylation status at CpG sites.

[0234] Typical reagents for Ms-SNuPE® analysis (e.g., those found in a typical Ms-SNuPE®-based kit) include, but are not limited to, PCR primers for specific loci (e.g., specific genes, markers, gene regions, marker regions, bisulfite-treated DNA sequences, CpG islands, etc.); optimized PCR buffers and deoxyribonucleotides; gel extraction kits; positive control primers; Ms-SNuPE® primers for specific loci; reaction buffers (related to the Ms-SNuPE reaction); and labeled nucleotides. Furthermore, bisulfite conversion reagents include DNA denaturation buffers; sulfonation buffers; DNA recovery reagents or kits (e.g., precipitation, ultrafiltration, affinity columns); desulfonation buffers; and DNA recovery components.

[0235] Reduced Representation (RRBS) bisulfite sequencing begins with bisulfite treatment of nucleic acids to convert all unmethylated cytosines to uracil, followed by restriction enzyme digestion (e.g., by an enzyme that recognizes sites containing CG sequences, such as Mspl), and then full sequencing of the fragments after binding to an adapter ligand. Restriction enzyme selection enriches fragments from CpG-high-density regions and reduces the number of redundant sequences that may map to multiple gene locations during analysis. Therefore, RRBS reduces the complexity of nucleic acid samples by selecting a subset of restriction fragments for sequencing (e.g., by size selection using preparation gel electrophoresis). In contrast to whole-genome bisulfite sequencing, all fragments produced by restriction enzyme digestion contain DNA methylation information for at least one CpG dinucleotide. Therefore, RRBS enriches samples of promoters, CpG islands, and other genomic traits with high-frequency restriction enzyme cleavage sites in these regions, thereby providing an assay for evaluating the methylation status of one or more genomic loci.

[0236] A typical RRBS protocol includes steps such as digesting the nucleic acid sample with restriction enzymes like MspI, filling the overhangs, A-tailing, ligating the adapter, bisulfite conversion, and PCR. See, for example, et al. (2005) "Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution" Nat Methods 7:133-6 and Meissner et al. (2005) "Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis" Nucleic Acids Res. 33:5868-77.

[0237] In some embodiments, quantitative allele-specific real-time target and signal amplification (QuARTS) assays are used to assess methylation status. Each QuARTS assay involves three consecutive reactions: the primary reaction includes amplification (reaction 1) and target probe cleavage (reaction 2), and the secondary reaction includes FRET cleavage and fluorescence signal generation (reaction 3). When a target nucleic acid is amplified with a specific primer, a specific detection probe with a flap sequence loosely binds to the amplicon. The presence of a specific invading oligonucleotide at the target binding site allows a 5' nuclease, such as FEN-1 endonuclease, to cleave between the detection probe and the flap sequence, releasing the flap sequence. The flap sequence is complementary to the non-hairpin portion of the corresponding FRET cassette. Thus, the flap sequence functions as an invading oligonucleotide on the FRET cassette, resulting in cleavage between the FRET cassette fluorophore and the quencher, generating a fluorescence signal. The cleavage reaction can cleave multiple probes per target, thereby releasing multiple fluorophores per flap and resulting in exponential signal amplification. QuARTS can detect multiple targets in a single reaction well by using FRET cassettes with different dyes. See, for example, Zou et al. (2010) “Sensitive quantification of methylated markers with a novel methylation specific technology” Clin Chem 56:A199, and U.S. Patents 8,361,720, 8,715,937, 8,916,344, and 9,212,392, each of which is incorporated herein by reference for all purposes.

[0238] The term “bisulfite reagent” refers to reagents comprising bisulfite, disulfite, bisulfite, or combinations thereof, which are useful for distinguishing methylated CpG dinucleotide sequences from unmethylated CpG dinucleotide sequences, as disclosed herein. Methods for such treatment are known in the art (e.g., PCT / EP2004 / 011715 and WO2013 / 116375, each of which is incorporated herein by reference in whole). In some embodiments, the bisulfite treatment is carried out in the presence of a denaturing solvent, for example, but not limited to, n-alkylene glycol or diethylene glycol dimethyl ether (DME), or in the presence of dioxane or a dioxane derivative. In some embodiments, the denaturing solvent is used at a concentration of 1% to 35% (v / v). In some embodiments, the bisulfite reaction is carried out in the presence of a scavenger, for example, but not limited to, chroman derivatives, such as 6-hydroxy-2,5,7,8,-tetramethylchroman 2-carboxylic acid or trihydroxybenzoic acid and their derivatives, such as gallic acid (see PCT / EP2004 / 011715, which is incorporated by reference in its entirety). In certain preferred embodiments, the bisulfite reaction includes treatment with ammonium bisulfite, for example, as described in WO2013 / 116375.

[0239] In some embodiments, the processed DNA fragments are amplified using a set of primer oligonucleotides according to the present invention (see, for example, Tables 1C and 6B) and an amplification enzyme. Amplification of several DNA segments may be carried out simultaneously in the same reaction vessel. Amplification is typically carried out using polymerase chain reaction (PCR). The amplicons are typically 100 to 2000 base pairs long.

[0240] In another embodiment of this method, the methylation status of CpG positions in or near markers containing DMRs (e.g., DMR1-560, Tables 1A and 6A) can be detected using methylation-specific primer oligonucleotides. This technique (MSP) is described in Herman's U.S. Patent No. 6,265,171. The use of methylation-specific primers for amplification of bisulfite-treated DNA makes it possible to distinguish between methylated and unmethylated nucleic acids. An MSP primer pair contains at least one primer that hybridizes to a bisulfite-treated CpG dinucleotide. Therefore, the sequence of the primer contains at least one CpG dinucleotide. An MSP primer specific to unmethylated DNA contains a "T" at the C position in the CpG.

[0241] The fragments obtained by amplification may retain a directly or indirectly detectable label. In some embodiments, the label is a fluorescent label, radionuclide, or desorbable molecular fragment having a typical mass detectable by a mass spectrometer. When the label is a mass label, in some embodiments, good detectability in a mass spectrometer is enabled by the labeled amplicon having a single positive or negative net charge. Detection can be performed and visualized, for example, by matrix-assisted laser desorption / ionization mass spectrometry (MALDI) or using electron spray mass spectrometry (ESI).

[0242] Methods for isolating DNA suitable for these assay techniques are known in the art. In particular, some embodiments include the isolation of nucleic acids described in U.S. Patent Application No. 13 / 470,251, “Isolation of Nucleic Acids,” which is incorporated herein by reference in whole.

[0243] In some embodiments, the markers described herein are used in QUARTS assays performed on fecal samples. In some embodiments, methods are provided for generating DNA samples, particularly those comprising small amounts (e.g., less than 100 microliters, less than 60 microliters) of highly purified low-abundance nucleic acids, and substantially and / or effectively free from substances that inhibit assays (e.g., PCR, INVADER, QUARTS assays, etc.) used to test the DNA sample. Such DNA samples are used in diagnostic assays to qualitatively detect the presence of genes, gene variants (e.g., alleles), or gene modifications (e.g., methylation) present in a sample taken from a patient, or to quantitatively measure their activity, expression, or quantity. For example, some cancers correlate with the presence of specific mutant alleles or specific methylation states, and therefore, the detection and / or quantification of such mutant alleles or methylation states has predictive value in the diagnosis and treatment of cancer.

[0244] Many beneficial genetic markers are present in extremely small amounts in samples, and many of the events that generate such markers are rare. Consequently, even highly sensitive detection methods such as PCR require large amounts of DNA to adequately provide low-abundance targets to meet or invalidate the assay's detection threshold. Furthermore, the presence of inhibitors, even in small amounts, impairs the accuracy and precision of these assays aimed at detecting such low-abundance targets. Therefore, provided herein are methods that provide the necessary volume and concentration controls for generating such DNA samples.

[0245] In some embodiments, the sample includes blood, serum, leukocytes, plasma, or saliva. In some embodiments, the subject is human. Such samples can be obtained by any number of means known in the art, such as those that will be apparent to those skilled in the art. Cell-free or substantially cell-free samples can be obtained by subjecting the sample to a variety of techniques known to those skilled in the art, including but not limited to centrifugation and filtration. While it is generally preferred to obtain the sample using non-invasive techniques, it may still be preferred to obtain samples such as tissue homogenates, tissue sections, and biopsy specimens. The techniques described herein are not limited to the methods used to prepare the sample and provide nucleic acids for testing. For example, in some embodiments, DNA is isolated from a fecal sample, or from a blood or plasma sample, using direct gene capture, e.g., as detailed in U.S. Patents 8,808,990 and 9,169,511, and WO2012 / 155072, or by related methods.

[0246] The analysis of markers may be performed separately or simultaneously with additional markers within a single test sample. For example, several markers may be combined in one test to efficiently process multiple samples and potentially provide higher accuracy in diagnosis and / or prognosis. Furthermore, those skilled in the art will recognize the value of testing multiple samples from the same subject (e.g., at consecutive points in time). Such testing of a series of samples makes it possible to identify changes in the methylation status of markers over time. In addition to changes in methylation status, the absence of changes in methylation status can provide useful information about the disease state, including, but not limited to, the identification of the approximate time since the onset of the event, the presence and amount of recoverable tissue, the appropriateness of drug therapy, the effectiveness of various therapies, and the identification of the subject's outcome, including the risk of future events.

[0247] Biomarker analysis can be performed in various physical forms. For example, the use of microtiter plates or automation can be used to facilitate the processing of a large number of test samples. Alternatively, single-sample formats can be developed to facilitate timely treatment and diagnosis, for example, in outpatient transport or emergency room situations.

[0248] Embodiments of this technology are intended to be provided in the form of a kit. The kit includes embodiments of the compositions, devices, apparatus, etc., described herein, and instructions for the use of the kit. Such instructions describe a suitable method for preparing an analyte from a sample, e.g., a method for recovering the sample and preparing nucleic acids from the sample. The individual components of the kit are packaged in suitable containers and packaging (e.g., vials, boxes, blister packs, ampoules, bottles, tubes, etc.), and the components are packaged together in a suitable container (e.g., box(s)) for convenient storage, transport, and / or use by the user of the kit. It is understood that liquid components (e.g., buffers) may be provided in a lyophilized form that is reconstituted by the user. The kit may include controls or references for evaluating, verifying, and / or guaranteeing the performance of the kit. For example, a kit for assaying the amount of nucleic acid present in a sample may include controls containing the same or a different nucleic acid at known concentrations for comparison, and in some embodiments, may include detection reagents (e.g., primers) specific to the control nucleic acid. The kit is suitable for use in a clinical setting and, in some embodiments, for use at the user's home. The components of the kit, in some embodiments, provide the functionality of a system for preparing nucleic acid solutions from samples. In some embodiments, specific components of the system are provided by the user.

[0249] method In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma or ovarian tissue) with at least one reagent or series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker containing DMR (e.g., DMR1-560 as shown in Tables 1A and 6A), and 2) A step to detect ovarian cancer, clear cell OC, endometrioid OC, mucinous OC, or serous OC (e.g., with a sensitivity of 80% or higher and a specificity of 80% or higher).

[0250] In some embodiments of this technology, a method comprising the following steps is provided: 1) Nucleic acids obtained from the subject (e.g., body fluids such as blood or plasma, or isolated from ovarian tissue, e.g., genomic DNA) are designated as AGRN_A, ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, BCAT1, CCND2_D, CMTM3_A, ELMO1_A, ELMO1_B, ELMO1_C, EMX1, EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D, FAIM2_A, FLJ34208_A, GPRIN1, GYPC_A, INA_A, ITGA4_B, KCNA3_A, KCNA3_ C, LBH, LIME1_A, LIME1_B, LOC646278, LRRC4, LRRC41_A, MAX.chr1.110626771-110626832, MAX.chr1.147790358-147790381, MAX.chr1.161591532-1 61591608, MAX.chr15.28351937-28352173, MAX.chr15.28352203-28352671, MAX.chr15.29131258-29131734, MAX.chr4.8859995-8860062, MAX.chr 5.42952182-42952292, MDFI, NCOR2, NKX2-6, OPLAH_A, PARP15, PDE10A, PPP1R16B, RASSF1_B, SEPTIN9, SKI, SLC12A8, SRC_A, SSBP4_B, ST8SIA1, TACC 2_A, TSHZ3, UBTF, VIM, VIPR2_A, ZBED4, ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZNF382_A, ZNF469_B, ATP6V1B1_A, BZRAP1, GDF6, IFFO1_A, IFFO1_B, KCNAB2, LIMD 2. The step of contacting at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of MAML3_B, MAX.chr14.102172350-102172770, MAX.chr16.85482307-85482494, MAX.chr17.76254728-76254841, MAX.chr5.42993898-42994179, and RASAL3, and 2) A step to detect ovarian cancer (for example, with a sensitivity of 80% or more and a specificity of 80% or more).

[0251] In some embodiments of this technology, a method comprising the following steps is provided: 1) Nucleic acids obtained from the subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) are classified as GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), SRC (e.g., SRC_A, SRC_B), SIM2 (e.g., SIM2_A, SIM2_B), AGRN (e.g., AGRN_A, AGRN_B, AGRN_C, AGRN_8794), FAIM2 (e.g., FAIM2_A, FAIM2 The steps include contacting at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of _B), CELF2 (e.g., CELF2_A, CELF2_B), DSCR6, GYPC (e.g., GYPC_A, GYPC_B, GYPC_C), CAPN2 (e.g., CAPN2_A, CAPN2_B), and BCAT1, and 2) A step to detect ovarian cancer (for example, with a sensitivity of 80% or more and a specificity of 80% or more).

[0252] In some embodiments of this technology, a method comprising the following steps is provided: 1) Nucleic acids obtained from the subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) are classified as ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g.) For example, C1QL3_A, C1QL3_B), FAIM2 (for example, FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (for example, CMTM3_A, CMTM3_B), ZMIZ1 (for example, ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (for example, GPRIN1_A, GPRIN1_B), CDO1 (for example, CDO The steps include contacting at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of 1_A, CDO1_B), GP5, DSCR6, SKI, SIM2_A, AGRN_8794, BCAT1_6015, KCNA3_7518, KCNA3_7320, LOC10013136, GYPC_C, SRC (e.g., SRC_A, SRC_B), NR2F6, TSHZ3, CELF2 (e.g., CELF2_A, CELF2_B), TACC2 (e.g., TACC2_A, TACC2_B), VIPR2 (e.g., VIPR2_A, VIPR2_B), and SPOCK2_74333, and 2) A step to detect ovarian cancer (for example, with a sensitivity of 80% or more and a specificity of 80% or more).

[0253] In some embodiments of this technology, a method comprising the following steps is provided: 1) A step of measuring the level of CA-125 in blood samples obtained from the subject (e.g., plasma samples, whole blood samples, leukocyte samples, serum samples), 2) Nucleic acids obtained from the subject (e.g., genomic DNA isolated from blood samples (e.g., plasma samples, whole blood samples, leukocyte samples, serum samples) are converted into ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMT) The steps include contacting at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of M3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, and SIM2_A, and 3) A step to detect ovarian cancer (for example, with a sensitivity of 80% or more and a specificity of 80% or more).

[0254] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) with at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of MAX.chr16.85482307-85482494, GDF6, IFFO_A, MAX.chr5.42993898-42994179, MAX.chr17.76254728-76254841, MAX.chr14.102172350-102172770, RASAL3, BZRAP1, and LIMD2, and 2) A step to detect ovarian cancer (for example, with a sensitivity of 80% or more and a specificity of 80% or more).

[0255] In some embodiments of this technology, a method comprising the following steps is provided: 1) A step of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or ovarian tissue) with at least one reagent or a series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAML3_A, SKI, DNMT3A_A, and C2CD4D, and 2) A step to detect ovarian cancer (for example, with a sensitivity of 80% or more and a specificity of 80% or more).

[0256] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) with at least one reagent or a series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of TACC2_A, LRRC41_A, EPS8L2, LBH, LIME1_B, MDFI, FAIM2_A, GYPC_A, AGRN_B, and ZBED4, and 2) A step to detect clear cell ovarian cancer (e.g., with a sensitivity of 80% or more and a specificity of 80% or more).

[0257] In some embodiments of this technology, a method comprising the following steps is provided: 1) Nucleic acids obtained from the subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) were identified as MT1A_A, CELF2_A, KCNA3_A, MDFI, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, MAX.chr14.10551217 The steps include contacting at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of 8-105512224, EPS8L2_E, SKI, GPRIN1_A, MAX.chr8.142215938-142216298, CDO1_A, DNMT3A_A, SIM2_A, SKI, MT1A_B, GYPC_A, BCL2L11, PISD, and C2CD4D, and 2) A step to detect clear cell ovarian cancer (e.g., with a sensitivity of 80% or more and a specificity of 80% or more).

[0258] In some embodiments of this technology, a method comprising the following steps is provided: 1) Nucleic acids obtained from the subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) are classified as NCOR2, MT1A_B, CELF2_A, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225 The steps include contacting at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of DSCR6, MAML3_A, SKI, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, MT1A_B, GYPC_A, BCL2L11, GDF6, and C2CD4D, and 2) A step to detect clear cell ovarian cancer (e.g., with a sensitivity of 80% or more and a specificity of 80% or more).

[0259] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or ovarian tissue) with at least one reagent or series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of AGRN_8794, BHLHE23_8339, EPS8L2_F, RASSF1_8293, MDFI_6321, SKI, GYPC_C, NKX2-6_4159, LOC100131366, FAIM2_B, GPRIN1_B, LRRC41_B, TACC2_B, LBH, SIM2_B, CDO1_A, and DSCR6, and 2) A step to detect clear cell ovarian cancer (e.g., with a sensitivity of 80% or more and a specificity of 80% or more).

[0260] In some embodiments of this technology, a method comprising the following steps is provided: 1) A step of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or ovarian tissue) with at least one reagent or a series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides in at least one marker selected from a chromosomal region having annotations selected from the group consisting of PARP15, GPRIN1_A, GYPC1_A, FLJ34208, MAX.chr1.147790358-147790381, FAIM2_A, SH2B3, KCNQ5, IRF4, and BCAT1, and 2) A step to detect endometrioid ovarian cancer (e.g., with a sensitivity of 80% or higher and a specificity of 80% or higher).

[0261] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) with at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of NCOR2, CELF2_A, PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, MAML3_A, SKI, GPRIN1_A, SKI, BCL2L11, and C2CD4D, and 2) A step to detect endometrioid ovarian cancer (e.g., with a sensitivity of 80% or higher and a specificity of 80% or higher).

[0262] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) with at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of NCOR2, PALLD, PRDM14, MAX.chr1.147790358-147790381, MAX.chr11.14926602-14926671, DSCR6, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, and C2CD4D, and 2) A step to detect endometrioid ovarian cancer (e.g., with a sensitivity of 80% or higher and a specificity of 80% or higher).

[0263] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) with at least one reagent or a series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of BCAT1_6015, EPS8L2_F, SKI, NKX2-6_4159, C1QL3_B, GPRIN1_B, PARP15, OXT_C, SIM2_B, DNMT3A_A, and CELF2_A, and 2) A step to detect endometrioid ovarian cancer (e.g., with a sensitivity of 80% or higher and a specificity of 80% or higher).

[0264] In some embodiments of this technology, a method comprising the following steps is provided: 1) A step of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or ovarian tissue) with at least one reagent or a series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of CMTM3_A, ATP10A_C, TSHZ3, ZMIZ1_B, ATP10A_B, ELMO1_B, TACC2_A, LRRC4, VIM, and ZNF382_A, and 2) A step to detect mucinous ovarian cancer (e.g., with a sensitivity of 80% or more and a specificity of 80% or more).

[0265] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) with at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of NCOR2, MT1A_A, KCNA3_A, ZMIZ1_C, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, BCL2L11, and GATA2, and 2) A step to detect mucinous ovarian cancer (e.g., with a sensitivity of 80% or more and a specificity of 80% or more).

[0266] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) with at least one reagent or a series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of NCOR2, PALLD, TACC2_A, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, and BCL2L11, and 2) A step to detect mucinous ovarian cancer (e.g., with a sensitivity of 80% or more and a specificity of 80% or more).

[0267] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) with at least one reagent or a series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of BCAT1_6015, ELMO1_9100, KCNA3_7518, KCNA3_7320, MDFI_6321, SKI, VIPR_B, ZNF382_B, ATP10A_E, CMTM3_B, ZMIZ1_D, SRC_B, HDGFRP3, TACC2_B, TSHZ3, LBH, and DNMT3A_A, and 2) A step to detect mucinous ovarian cancer (e.g., with a sensitivity of 80% or more and a specificity of 80% or more).

[0268] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) with at least one reagent or set of reagents that distinguishes methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of MAX.chr1.147790358-147790381, MAML3, NR2F6, DNMT3A_A, SKI, SOBP, UBTF, AGRN_C, MAX.chr12.30975740-30975780, and CAPN2_A, and 2) A step to detect serous ovarian cancer (e.g., with a sensitivity of 80% or more and a specificity of 80% or more).

[0269] In some embodiments of this technology, a method comprising the following steps is provided: 1) The steps of contacting nucleic acids obtained from a subject (e.g., genomic DNA isolated from bodily fluids such as blood or plasma, or from ovarian tissue) with at least one reagent or set of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from a chromosomal region having annotations selected from the group consisting of PALLD, PRDM14, MAX.chr1.147790358-147790381, CAPN2_A, MAX.chr6.10382190-10382225, SKI, NR2F6, IFFO1_A, MT1A_B, IFFO1_B, GDF6, and C2CD4D, and 2) A step to detect serous ovarian cancer (e.g., with a sensitivity of 80% or more and a specificity of 80% or more).

[0270] In some embodiments of this technology, a method comprising the following steps is provided: 1) Contacting nucleic acid obtained from a subject (e.g., genomic DNA isolated from a body fluid such as blood or plasma or ovarian tissue) with at least one reagent or series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from chromosomal regions having an annotation selected from the group consisting of NCOR2, MAX.chr1.147790358 - 147790381, MAX.chr6.10382190 - 10382225, IFFO1_A, GDF6, and C2CD4D, and 2) Detecting serous ovarian cancer (e.g., obtained with a sensitivity of 80% or more and a specificity of 80% or more).

[0271] In some embodiments of the present technology, a method is provided that includes the following steps: 1) Contacting nucleic acid obtained from a subject (e.g., genomic DNA isolated from a body fluid such as blood or plasma or ovarian tissue) with at least one reagent or series of reagents that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker selected from chromosomal regions having an annotation selected from the group consisting of SKI, PEAR1_B, CAPN2_B, SIM2_B, DNMT3A_A, CDO1_A, and NR2F6, and 2) Detecting serous ovarian cancer (e.g., obtained with a sensitivity of 80% or more and a specificity of 80% or more).

[0272] In some embodiments of the present technology, a method is provided that includes the following steps: 1) Measuring the methylation level of one or more genes in a biological sample of a human individual by treating genomic DNA in the biological sample with a reagent that modifies DNA in a methylation - specific manner (e.g., where the reagent is a bisulfite reagent, a methylation - sensitive restriction enzyme, or a methylation - dependent restriction enzyme), wherein the one or more genes are selected from one of the following groups: ·AGRN_A、ATP10A_A、ATP10A_B、ATP10A_C、ATP10A_D、BCAT1、CCND2_D、CMTM3_A、ELMO1_A、ELMO1_B、ELMO1_C、EMX1、EPS8L2_A、EP S8L2_B, EPS8L2_C, EPS8L2_D, FAIM2_A, FLJ34208_A, GPRIN1, GYPC_A, INA_A, ITGA4_B, KCNA3_A, KCNA3_C, LBH, LIME1_A, LIME1_B LOC646278, LRRC4, LRRC41_A, MAX.chr1.110626771-110626832, MAX.chr1.147790358-147790381, MAX.chr1.161591532-161 591608. MAX.chr15.28351937-28352173. MAX.chr15.28352203-28352671. MAX.chr15.29131258-29131734 -8860062, MAX.chr5.42952182-42952292, MDFI, NCOR2, NKX2-6, OPLAH_A, PARP15, PDE10A, PPP1R16B, RASSF1_B, SEPTIN9, SKI. SLC12A8, SRC_A, SSBP4_B, ST8SIA1, TACC2_A, TSHZ3, UBTF, VIM, VIPR2_A, ZBED4, ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZNF382_A, ZNF469_ B, ATP6V1B1_A, BZRAP1, GDF6, IFFO1_A, IFFO1_B, KCNAB2, LIMD2, MAML3_B, MAX.chr14.102172350-102172770, MAX.chr16.8548 2307-85482494 MAX.chr17.76254728-76254841 MAX.chr5.42 993898-42994179 and RASAL3(Page 1A, Plot 1B, Plot 6A, Plot 6B; ·MAX.chr16.85482307-85482494, GDF6, IFFO_A, MAX.chr5.42993898-42994179, MAX.chr17.76254728-76254841, MAX.chr14.102172350-102172770, RASAL3, BZRAP1, and LIMD2 (see Table 3; Example I); ·PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAML3_A, SKI, DNMT3A_A, and C2CD4D (see Table 4A; Example I); and BCAT1_6015, SKI, SIM2_B, DNMT3A_A, CDO1_A, and DSCR6 (see Table 8A; Example II); 2) A step of amplifying the treated genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture.

[0273] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the methylation level of one or more genes in a biological sample of a human individual (e.g., blood sample, plasma sample) by treating the genomic DNA in the biological sample with a reagent that modifies the DNA in a methylation-specific manner (e.g., the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein one or more genes are GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), S A step selected from RC (e.g., SRC_A, SRC_B), SIM2 (e.g., SIM2_A, SIM2_B), AGRN (e.g., AGRN_A, AGRN_B, AGRN_C, AGRN_8794), FAIM2 (e.g., FAIM2_A, FAIM2_B), CELF2 (e.g., CELF2_A, CELF2_B), DSCR6, GYPC (e.g., GYPC_A, GYPC_B, GYPC_C), CAPN2 (e.g., CAPN2_A, CAPN2_B), and BCAT1; 2) A step of amplifying the treated genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture.

[0274] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the methylation level of one or more genes in a biological sample of a human individual (e.g., blood sample, plasma sample) by treating the genomic DNA in the biological sample with a reagent that modifies the DNA in a methylation-specific manner (e.g., the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein the one or more genes are ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM 3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, SIM2_A, AGRN_8794, BCAT1_6015, KCNA3_751 8. A step selected from KCNA3_7320, LOC10013136, GYPC_C, SRC (e.g., SRC_A, SRC_B), NR2F6, TSHZ3, CELF2 (e.g., CELF2_A, CELF2_B), TACC2 (e.g., TACC2_A, TACC2_B), VIPR2 (e.g., VIPR2_A, VIPR2_B), and SPOCK2_74333; 2) A step of amplifying the treated genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture.

[0275] In some embodiments of this technology, a method comprising the following steps is provided: 1) A step of measuring the level of CA-125 in blood samples obtained from a human individual (e.g., plasma samples, whole blood samples, leukocyte samples, serum samples); 2) A step of measuring the methylation level of one or more genes in a blood sample of a human individual (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample) by treating the genomic DNA in the biological sample with a reagent that modifies the DNA in a methylation-specific manner (e.g., the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein one or more genes are ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8 A step selected from L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, and SIM2_A; 3) A step of amplifying the treated genomic DNA using a set of primers for one or more selected genes; and 4) A step of determining the methylation level of one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture.

[0276] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the amount of at least one methylation marker gene in the DNA from the sample, wherein one or more genes are selected from one of the following groups: ·AGRN_A、ATP10A_A、ATP10A_B、ATP10A_C、ATP10A_D、BCAT1、CCND2_D、CMTM3_A、ELMO1_A、ELMO1_B、ELMO1_C、EMX1、EPS8L2_A、EP S8L2_B, EPS8L2_C, EPS8L2_D, FAIM2_A, FLJ34208_A, GPRIN1, GYPC_A, INA_A, ITGA4_B, KCNA3_A, KCNA3_C, LBH, LIME1_A, LIME1_B LOC646278, LRRC4, LRRC41_A, MAX.chr1.110626771-110626832, MAX.chr1.147790358-147790381, MAX.chr1.161591532-161 591608. MAX.chr15.28351937-28352173. MAX.chr15.28352203-28352671. MAX.chr15.29131258-29131734 -8860062, MAX.chr5.42952182-42952292, MDFI, NCOR2, NKX2-6, OPLAH_A, PARP15, PDE10A, PPP1R16B, RASSF1_B, SEPTIN9, SKI. SLC12A8, SRC_A, SSBP4_B, ST8SIA1, TACC2_A, TSHZ3, UBTF, VIM, VIPR2_A, ZBED4, ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZNF382_A, ZNF469_ B, ATP6V1B1_A, BZRAP1, GDF6, IFFO1_A, IFFO1_B, KCNAB2, LIMD2, MAML3_B, MAX.chr14.102172350-102172770, MAX.chr16.8548 2307-85482494 MAX.chr17.76254728-76254841 MAX.chr5.42 993898-42994179 and RASAL3(Page 1A, Plot 1B, Plot 6A, Plot 6B; ·MAX.chr16.85482307-85482494, GDF6, IFFO_A, MAX.chr5.42993898-42994179, MAX.chr17.76254728-76254841, MAX.chr14.102172350-102172770, RASAL3, BZRAP1, and LIMD2 (see Table 3; Example I); · GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), SRC (e.g., SRC_A, SRC_B), SIM2 (e.g., SIM2_A, SIM2_B), AGRN (e.g., AGRN_A, AGRN_B, AGRN_C, AGRN_8794), FAIM2 (e.g., FAIM2_A, FAIM2_B), CELF2 (e.g., CELF2_A, CELF2_B), DSCR6, GYPC (e.g., GYPC_A, GYPC_B, GYPC_C), CAPN2 (e.g., CAPN2_A, CAPN2_B), and BCAT1 (see Table 9; Example III); ·ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, G PRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, SIM2_A, AGRN_8794, BCAT1_6015, KCNA3_7518, KCNA3_7320, LOC10013136, GYPC_C, SRC (e.g., SRC_A, SRC_B), NR2F6, TSHZ3, CELF2 (e.g., CELF2_A, CELF2_B), TACC2 (e.g., TACC2_A, TACC2_B), VIPR2 (e.g., VIPR2_A, VIPR2_B), and SPOCK2_74333 (see Table 10, Example III); ·PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAML3_A, SKI, DNMT3A_A, and C2CD4D (see Table 4A; Example I); and BCAT1_6015, SKI, SIM2_B, DNMT3A_A, CDO1_A, and DSCR6 (see Table 8A; Example II); 2) The step of measuring the amount of at least one reference marker in the DNA; and 3) A step of calculating the amount of at least one methylation marker gene measured in DNA as a percentage of the amount of a reference marker gene measured in DNA, wherein this value represents the amount of at least one methylation marker DNA measured in the sample.

[0277] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the methylation level of CpG sites of one or more genes in a biological sample of a human individual by treating the genomic DNA in the biological sample with bisulfite reagents (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents) which are reagents capable of modifying DNA in a methylation-specific manner; 2) The step of amplifying modified genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of a CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genome sequencing PCR, The step of selecting one or more genes from one of the following groups: ·AGRN_A、ATP10A_A、ATP10A_B、ATP10A_C、ATP10A_D、BCAT1、CCND2_D、CMTM3_A、ELMO1_A、ELMO1_B、ELMO1_C、EMX1、EPS8L2_A、EP S8L2_B, EPS8L2_C, EPS8L2_D, FAIM2_A, FLJ34208_A, GPRIN1, GYPC_A, INA_A, ITGA4_B, KCNA3_A, KCNA3_C, LBH, LIME1_A, LIME1_B LOC646278, LRRC4, LRRC41_A, MAX.chr1.110626771-110626832, MAX.chr1.147790358-147790381, MAX.chr1.161591532-161 591608. MAX.chr15.28351937-28352173. MAX.chr15.28352203-28352671. MAX.chr15.29131258-29131734 -8860062, MAX.chr5.42952182-42952292, MDFI, NCOR2, NKX2-6, OPLAH_A, PARP15, PDE10A, PPP1R16B, RASSF1_B, SEPTIN9, SKI. SLC12A8, SRC_A, SSBP4_B, ST8SIA1, TACC2_A, TSHZ3, UBTF, VIM, VIPR2_A, ZBED4, ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZNF382_A, ZNF469_ B, ATP6V1B1_A, BZRAP1, GDF6, IFFO1_A, IFFO1_B, KCNAB2, LIMD2, MAML3_B, MAX.chr14.102172350-102172770, MAX.chr16.8548 2307-85482494 MAX.chr17.76254728-76254841 MAX.chr5.42 993898-42994179 and RASAL3(Page 1A, Plot 1B, Plot 6A, Plot 6B; · GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), SRC (e.g., SRC_A, SRC_B), SIM2 (e.g., SIM2_A, SIM2_B), AGRN (e.g., AGRN_A, AGRN_B, AGRN_C, AGRN_8794), FAIM2 (e.g., FAIM2_A, FAIM2_B), CELF2 (e.g., CELF2_A, CELF2_B), DSCR6, GYPC (e.g., GYPC_A, GYPC_B, GYPC_C), CAPN2 (e.g., CAPN2_A, CAPN2_B), and BCAT1 (see Table 9; Example III); ·ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, G PRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, SIM2_A, AGRN_8794, BCAT1_6015, KCNA3_7518, KCNA3_7320, LOC10013136, GYPC_C, SRC (e.g., SRC_A, SRC_B), NR2F6, TSHZ3, CELF2 (e.g., CELF2_A, CELF2_B), TACC2 (e.g., TACC2_A, TACC2_B), VIPR2 (e.g., VIPR2_A, VIPR2_B), and SPOCK2_74333 (see Table 10, Example III); ·MAX.chr16.85482307-85482494, GDF6, IFFO_A, MAX.chr5.42993898-42994179, MAX.chr17.76254728-76254841, MAX.chr14.102172350-102172770, RASAL3, BZRAP1, and LIMD2 (see Table 3; Example I); ·PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAML3_A, SKI, DNMT3A_A, and C2CD4D (see Table 4A; Example I); and BCAT1_6015, SKI, SIM2_B, DNMT3A_A, CDO1_A, and DSCR6 (see Table 8A; Example II).

[0278] In some embodiments of this technology, a method comprising the following steps is provided: 1) A step of measuring the methylation level of one or more genes in a biological sample of a human individual by treating the genomic DNA in the biological sample with a reagent that modifies the DNA in a methylation-specific manner (for example, the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein one or more genes are selected from one of the following groups: TACC2_A, LRRC41_A, EPS8L2, LBH, LIME1_B, MDFI, FAIM2_A, GYPC_A, AGRN_B, and ZBED4 (see Table 2A; Example I); · MT1A_A, CELF2_A, KCNA3_A, MDFI, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, MAX.chr14.105512178-105512224, EPS8L2_E, SKI, GPRIN1_A, MAX.chr8.142215938-142216298, CDO1_A, DNMT3A_A, SIM2_A, SKI, MT1A_B, GYPC_A, BCL2L11, PISD, and C2CD4D (see Table 4B; see Example I); · NCOR2, MT1A_B, CELF2_A, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, SKI, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, MT1A_B, GYPC_A, BCL2L11, GDF6, and C2CD4D (see Table 5B; see Example I); and · AGRN_8794, BHLHE23_8339, EPS8L2_F, RASSF1_8293, MDFI_6321, SKI, GYPC_C, NKX2-6_4159, LOC100131366, FAIM2_B, GPRIN1_B, LRRC41_B, TACC2_B, LBH, SIM2_B, CDO1_A, and DSCR6 (see Table 8B; see Example II); 2) Amplifying the processed genomic DNA using a set of primers for one or more selected genes; and 3) Determining the methylation level of one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture.

[0279] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the amount of at least one methylation marker gene in the DNA from the sample, wherein one or more genes are selected from one of the following groups: TACC2_A, LRRC41_A, EPS8L2, LBH, LIME1_B, MDFI, FAIM2_A, GYPC_A, AGRN_B, and ZBED4 (see Table 2A; Example I); ·MT1A_A, CELF2_A, KCNA3_A, MDFI, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790 381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML 3_A, MAX.chr14.105512178-105512224, EPS8L2_E, SKI, GPRIN1_A, MAX.chr8.142215938-142216298, CDO1_A, DNMT3A_A, SIM2_A, SKI, MT1A_B, GYPC_A, BCL2L11, PISD, and C2CD4D (see Table 4B; Example I); ·NCOR2, MT1A_B, CELF2_A, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, SKI, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, MT1A_B, GYPC_A, BCL2L11, GDF6, and C2CD4D (see Table 5B; Example I); and ·AGRN_8794, BHLHE23_8339, EPS8L2_F, RASSF1_8293, MDFI_6321, SKI, GYPC_C, NKX2-6_4159, LOC100131366, FAIM2_B, GPRIN1_B, LRRC41_B, TACC2_B, LBH, SIM2_B, CDO1_A, and DSCR6 (see Table 8B; Example II); 2) The step of measuring the amount of at least one reference marker in the DNA; and 3) A step of calculating the amount of at least one methylation marker gene measured in DNA as a percentage of the amount of a reference marker gene measured in DNA, wherein this value represents the amount of at least one methylation marker DNA measured in the sample.

[0280] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the methylation level of CpG sites of one or more genes in a biological sample of a human individual by treating the genomic DNA in the biological sample with bisulfite reagents (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents) which are reagents capable of modifying DNA in a methylation-specific manner; 2) The step of amplifying modified genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of a CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genome sequencing PCR, The step of selecting one or more genes from one of the following groups: TACC2_A, LRRC41_A, EPS8L2, LBH, LIME1_B, MDFI, FAIM2_A, GYPC_A, AGRN_B, and ZBED4 (see Table 2A; Example I); ·MT1A_A, CELF2_A, KCNA3_A, MDFI, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790 381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML 3_A, MAX.chr14.105512178-105512224, EPS8L2_E, SKI, GPRIN1_A, MAX.chr8.142215938-142216298, CDO1_A, DNMT3A_A, SIM2_A, SKI, MT1A_B, GYPC_A, BCL2L11, PISD, and C2CD4D (see Table 4B; Example I); ·NCOR2, MT1A_B, CELF2_A, PALLD, PRDM14, PARP15, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, AGRN_B, MAX.chr6.10382190-10382225, DSCR6, MAML3_A, SKI, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, MT1A_B, GYPC_A, BCL2L11, GDF6, and C2CD4D (see Table 5B; Example I); and ·AGRN_8794, BHLHE23_8339, EPS8L2_F, RASSF1_8293, MDFI_6321, SKI, GYPC_C, NKX2-6_4159, LOC100131366, FAIM2_B, GPRIN1_B, LRRC41_B, TACC2_B, LBH, SIM2_B, CDO1_A, and DSCR6 (see Table 8B; Example II).

[0281] In some embodiments of this technology, a method comprising the following steps is provided: 1) A step of measuring the methylation level of one or more genes in a biological sample of a human individual by treating the genomic DNA in the biological sample with a reagent that modifies the DNA in a methylation-specific manner (for example, the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein one or more genes are selected from one of the following groups: PARP15, GPRIN1_A, GYPC1_A, FLJ34208, MAX.chr1.147790358-147790381, FAIM2_A, SH2B3, KCNQ5, IRF4, and BCAT1 (see Table 2B; Example I); NCOR2, CELF2_A, PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, MAML3_A, SKI, GPRIN1_A, SKI, BCL2L11, and C2CD4D (see Table 4C; Example I); ·NCOR2, PALLD, PRDM14, MAX.chr1.147790358-147790381, MAX.chr11.14926602-14926671, DSCR6, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, and C2CD4D (see Table 5C; Example I); and ·BCAT1_6015, EPS8L2_F, SKI, NKX2-6_4159, C1QL3_B, GPRIN1_B, PARP15, OXT_C, SIM2_B, DNMT3A_A, and CELF2_A (see Table 8C; Example II); 2) A step of amplifying the treated genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture.

[0282] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the amount of at least one methylation marker gene in the DNA from the sample, wherein one or more genes are selected from one of the following groups: PARP15, GPRIN1_A, GYPC1_A, FLJ34208, MAX.chr1.147790358-147790381, FAIM2_A, SH2B3, KCNQ5, IRF4, and BCAT1 (see Table 2B; Example I); NCOR2, CELF2_A, PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, MAML3_A, SKI, GPRIN1_A, SKI, BCL2L11, and C2CD4D (see Table 4C; Example I); ·NCOR2, PALLD, PRDM14, MAX.chr1.147790358-147790381, MAX.chr11.14926602-14926671, DSCR6, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, and C2CD4D (see Table 5C; Example I); and ·BCAT1_6015, EPS8L2_F, SKI, NKX2-6_4159, C1QL3_B, GPRIN1_B, PARP15, OXT_C, SIM2_B, DNMT3A_A, and CELF2_A (see Table 8C; Example II); 2) The step of measuring the amount of at least one reference marker in the DNA; and 3) A step of calculating the amount of at least one methylation marker gene measured in DNA as a percentage of the amount of a reference marker gene measured in DNA, wherein this value represents the amount of at least one methylation marker DNA measured in the sample.

[0283] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the methylation level of CpG sites of one or more genes in a biological sample of a human individual by treating the genomic DNA in the biological sample with bisulfite reagents (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents) which are reagents capable of modifying DNA in a methylation-specific manner; 2) The step of amplifying modified genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of a CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genome sequencing PCR, The step of selecting one or more genes from one of the following groups: PARP15, GPRIN1_A, GYPC1_A, FLJ34208, MAX.chr1.147790358-147790381, FAIM2_A, SH2B3, KCNQ5, IRF4, and BCAT1 (see Table 2B; Example I); NCOR2, CELF2_A, PALLD, PRDM14, MAX.chr1.147790358-147790381, BCAT1, MAX.chr11.14926602-14926671, MAML3_A, SKI, GPRIN1_A, SKI, BCL2L11, and C2CD4D (see Table 4C; Example I); ·NCOR2, PALLD, PRDM14, MAX.chr1.147790358-147790381, MAX.chr11.14926602-14926671, DSCR6, GPRIN1_A, CDO1_A, SIM2_A, IFFO1_A, and C2CD4D (see Table 5C; Example I); and BCAT1_6015, EPS8L2_F, SKI, NKX2-6_4159, C1QL3_B, GPRIN1_B, PARP15, OXT_C, SIM2_B, DNMT3A_A, and CELF2_A (see Table 8C; Example II).

[0284] In some embodiments of this technology, a method comprising the following steps is provided: 1) A step of measuring the methylation level of one or more genes in a biological sample of a human individual by treating the genomic DNA in the biological sample with a reagent that modifies the DNA in a methylation-specific manner (for example, the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein one or more genes are selected from one of the following groups: CMTM3_A, ATP10A_C, TSHZ3, ZMIZ1_B, ATP10A_B, ELMO1_B, TACC2_A, LRRC4, VIM, and ZNF382_A (see Table 2C; Example I); NCOR2, MT1A_A, KCNA3_A, ZMIZ1_C, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, BCL2L11, and GATA2 (see Table 4D; Example I); ·NCOR2, PALLD, TACC2_A, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, and BCL2L11 (see Table 5D; see Example I); and • BCAT1_6015, ELMO1_9100, KCNA3_7518, KCNA3_7320, MDFI_6321, SKI, VIPR_B, ZNF382_B, ATP10A_E, CMTM3_B, ZMIZ1_D, SRC_B, HDGFRP3, TACC2_B, TSHZ3, LBH, DNMT3A_A (Table 8D; see Example II); 2) A step of amplifying the treated genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture.

[0285] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the amount of at least one methylation marker gene in the DNA from the sample, wherein one or more genes are selected from one of the following groups: CMTM3_A, ATP10A_C, TSHZ3, ZMIZ1_B, ATP10A_B, ELMO1_B, TACC2_A, LRRC4, VIM, and ZNF382_A (see Table 2C; Example I); NCOR2, MT1A_A, KCNA3_A, ZMIZ1_C, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, BCL2L11, and GATA2 (see Table 4D; Example I); ·NCOR2, PALLD, TACC2_A, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, and BCL2L11 (see Table 5D; see Example I); and ·BCAT1_6015, ELMO1_9100, KCNA3_7518, KCNA3_7320, MDFI_6321, SKI, VIPR_B, ZNF382_B, ATP10A_E, CMTM3_B, ZMIZ1_D, SRC_B, HDGFRP3, TACC2_B, TSHZ3, LBH, DNMT3A_A (Table 8D; see Example II); 2) Step of measuring the amount of at least one reference marker in the DNA; and 3) A step of calculating the amount of at least one methylation marker gene measured in DNA as a percentage of the amount of a reference marker gene measured in DNA, wherein this value represents the amount of at least one methylation marker DNA measured in the sample.

[0286] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the methylation level of CpG sites of one or more genes in a biological sample of a human individual by treating the genomic DNA in the biological sample with bisulfite reagents (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents) which are reagents capable of modifying DNA in a methylation-specific manner; 2) The step of amplifying modified genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of a CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genome sequencing PCR, The step of selecting one or more genes from one of the following groups: CMTM3_A, ATP10A_C, TSHZ3, ZMIZ1_B, ATP10A_B, ELMO1_B, TACC2_A, LRRC4, VIM, and ZNF382_A (see Table 2C; Example I); NCOR2, MT1A_A, KCNA3_A, ZMIZ1_C, TACC2_A, MAX.chr1.147790358-147790381, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, BCL2L11, and GATA2 (see Table 4D; Example I); ·NCOR2, PALLD, TACC2_A, BCAT1, AGRN_B, SKI, SLC12A8, ZMIZ1_B, and BCL2L11 (see Table 5D; see Example I); and ·BCAT1_6015, ELMO1_9100, KCNA3_7518, KCNA3_7320, MDFI_6321, SKI, VIPR_B, ZNF382_B, ATP10A_E, CMTM3_B, ZMIZ1_D, SRC_B, HDGFRP3, TACC2_B, TSHZ3, LBH, DNMT3A_A (See Table 8D; Example II).

[0287] In some embodiments of this technology, a method comprising the following steps is provided: 1) A step of measuring the methylation level of one or more genes in a biological sample of a human individual by treating the genomic DNA in the biological sample with a reagent that modifies the DNA in a methylation-specific manner (for example, the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein one or more genes are selected from one of the following groups: ·MAX.chr1.147790358-147790381, MAML3, NR2F6, DNMT3A_A, SKI, SOBP, UBTF, AGRN_C, MAX.chr12.30975740-30975780, and CAPN2_A (see Table 2D; Example I); PALLD, PRDM14, MAX.chr1.147790358-147790381, CAPN2_A, MAX.chr6.10382190-10382225, SKI, NR2F6, IFFO1_A, MT1A_B, IFFO1_B, GDF6, and C2CD4D (see Table 4E; Example I); ·NCOR2, MAX.chr1.147790358-147790381, MAX.chr6.10382190-10382225, IFFO1_A, GDF6, and C2CD4D (see Table 5A; Example I); and ·SKI, PEAR1_B, CAPN2_B, SIM2_B, DNMT3A_A, CDO1_A, and NR2F6 (see Table 8E; Example II); 2) A step of amplifying the treated genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture.

[0288] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the amount of at least one methylation marker gene in the DNA from the sample, wherein one or more genes are selected from one of the following groups: ·MAX.chr1.147790358-147790381, MAML3, NR2F6, DNMT3A_A, SKI, SOBP, UBTF, AGRN_C, MAX.chr12.30975740-30975780, and CAPN2_A (see Table 2D; Example I); PALLD, PRDM14, MAX.chr1.147790358-147790381, CAPN2_A, MAX.chr6.10382190-10382225, SKI, NR2F6, IFFO1_A, MT1A_B, IFFO1_B, GDF6, and C2CD4D (see Table 4E; Example I); ·NCOR2, MAX.chr1.147790358-147790381, MAX.chr6.10382190-10382225, IFFO1_A, GDF6, and C2CD4D (see Table 5A; Example I); and ·SKI, PEAR1_B, CAPN2_B, SIM2_B, DNMT3A_A, CDO1_A, and NR2F6 (see Table 8E; Example II); 2) The step of measuring the amount of at least one reference marker in the DNA; and 3) A step of calculating the amount of at least one methylation marker gene measured in DNA as a percentage of the amount of a reference marker gene measured in DNA, wherein this value represents the amount of at least one methylation marker DNA measured in the sample.

[0289] In some embodiments of this technology, a method comprising the following steps is provided: 1) Measuring the methylation level of CpG sites of one or more genes in a biological sample of a human individual by treating the genomic DNA in the biological sample with bisulfite reagents (e.g., methylation-sensitive restriction enzymes, methylation-dependent restriction enzymes, and bisulfite reagents) which are reagents capable of modifying DNA in a methylation-specific manner; 2) The step of amplifying modified genomic DNA using a set of primers for one or more selected genes; and 3) A step of determining the methylation level of a CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genome sequencing PCR, The step of selecting one or more genes from one of the following groups: ·MAX.chr1.147790358-147790381, MAML3, NR2F6, DNMT3A_A, SKI, SOBP, UBTF, AGRN_C, MAX.chr12.30975740-30975780, and CAPN2_A (see Table 2D; Example I); PALLD, PRDM14, MAX.chr1.147790358-147790381, CAPN2_A, MAX.chr6.10382190-10382225, SKI, NR2F6, IFFO1_A, MT1A_B, IFFO1_B, GDF6, and C2CD4D (see Table 4E; Example I); ·NCOR2, MAX.chr1.147790358-147790381, MAX.chr6.10382190-10382225, IFFO1_A, GDF6, and C2CD4D (see Table 5A; Example I); and SKI, PEAR1_B, CAPN2_B, SIM2_B, DNMT3A_A, CDO1_A, and NR2F6 (see Table 8E; Example II).

[0290] Determining the methylation level of any of these markers using any of the methods listed in Table 1C or Table 6B is performed using the primers listed in Table 1C or Table 6B.

[0291] Preferably, the sensitivity of such a method is about 70% to about 100%, or about 80% to about 90%, or about 80% to about 85%. Preferably, the specificity is about 70% to about 100%, or about 80% to about 90%, or about 80% to about 85%.

[0292] Genomic DNA can be isolated by any means, including the use of commercially available kits. Briefly, if the DNA of interest is encapsulated by a cell membrane, the biological sample must be disrupted and lysed by enzymatic, chemical, or mechanical means. Proteins and other contaminants can then be removed from the DNA solution, for example, by digestion with proteinase K. The genomic DNA is then recovered from the solution. This can be carried out by various methods, including salting out, organic extraction, or binding of the DNA to a solid support. The choice of method is influenced by several factors, including time, cost, and the amount of DNA required. All types of clinical samples containing neoplastic or pre-neoplastic material, such as cell lines, histological slides, biopsies, paraffin-embedded tissues, body fluids, feces, ovarian tissue, colonic excrement, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, and combinations thereof, are suitable for use in this method.

[0293] This technology is not limited to methods used for preparing samples and providing nucleic acids for testing. For example, in some embodiments, DNA is isolated from fecal, blood, or plasma samples using direct gene capture, e.g., as detailed in U.S. Patent Application No. 61 / 485386, or by related methods.

[0294] Next, the genomic DNA sample is treated with at least one reagent, or a series of reagents, that distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides within at least one marker containing a DMR (e.g., DMR1-560, as shown in Tables 1A and 6A).

[0295] In some embodiments, the reagent converts an unmethylated cytosine base at the 5' position to uracil, thymine, or another base that differs from cytosine in terms of hybridization behavior. However, in some embodiments, the reagent may be a methylation-sensitive restriction enzyme.

[0296] In some embodiments, genomic DNA samples are treated in such a way that unmethylated cytosine bases at the 5' position are converted to uracil, thymine, or other bases that are different from cytosine in terms of hybridization behavior. In some embodiments, this treatment is carried out by bisulfite (bisulfite, disulfite) followed by alkaline hydrolysis.

[0297] Next, the processed nucleic acids are analyzed to determine the methylation status of the target gene sequence (DMR, for example, at least one gene, genome sequence, or nucleotide from a marker containing at least one DMR selected from, for example, DMR1 to 560, as shown in Tables 1A and 6A). The analytical method can be selected from those known in the art, including those shown herein, for example, QuARTS and MSP as described herein.

[0298] Abnormal methylation, more specifically hypermethylation of markers including DMRs (e.g., DMR1-560, as shown in Tables 1A and 6A), is associated with ovarian cancer.

[0299] This technology relates to the analysis of any sample associated with ovarian cancer. For example, in some embodiments, the sample includes tissue and / or bodily fluids obtained from a patient. In some embodiments, the sample includes secretions. In some embodiments, the sample includes blood, serum, plasma, gastric secretions, pancreatic juice, gastrointestinal biopsy samples, microanatomically dissected cells from ovarian tissue biopsies, and / or cells recovered from feces. In some embodiments, the sample includes ovarian tissue. In some embodiments, the subject is human. The sample may include cells, secretions, or tissues from the ovaries, breasts, liver, bile ducts, pancreas, stomach, colon, rectum, esophagus, small intestine, appendix, duodenum, polyps, gallbladder, anus, and / or peritoneum. In some embodiments, the sample includes cell fluid, ascites, urine, feces, pancreatic juice, fluids obtained during endoscopy, blood, mucus, or saliva. In some embodiments, the sample is a fecal sample.

[0300] Such samples can be obtained by any number of means known in the art, such as those that will be obvious to those skilled in the art. For example, urine and fecal samples are readily available, while blood, ascites, serum, or pancreatic fluid samples can be obtained parenterally, for example, by using needles and syringes. Cell-free or substantially cell-free samples can be obtained by subjecting the sample to a variety of techniques known to those skilled in the art, including but not limited to centrifugation and filtration. While it is generally preferred to obtain samples using non-invasive techniques, it may still be preferred to obtain samples such as tissue homogenates, tissue sections, and biopsy specimens.

[0301] In some embodiments, the technology relates to a method for treating a patient (e.g., a patient with ovarian cancer) (e.g., a patient with one or more clear cell OCs, endometrioid OCs, mucinous OCs, and serous OCs), the method comprising determining the methylation status of one or more DMRs provided herein, and treating the patient based on the results of determining the methylation status. The treatment may be the administration of a pharmaceutical compound, the administration of a vaccine, the performance of surgery, the imaging of the patient, or the performance of another test. Preferably, the use is in methods for clinical screening, methods for prognosis assessment, methods for monitoring the outcomes of therapy, methods for identifying patients most likely to respond to a particular therapeutic procedure, methods for imaging patients or subjects, and methods for screening and developing drugs.

[0302] In some embodiments of this technology, a method for diagnosing a given ovarian cancer is provided. The terms “diagnose” and “diagnose,” as used herein, refer to a method by which a person skilled in the art can estimate and further determine whether a subject has a given disease or condition, or is likely to develop such a condition in the future. A person skilled in the art often makes a diagnosis based on one or more diagnostic indicators, such as biomarkers (e.g., DMRs disclosed herein), the methylation status of which indicates the presence, severity, or absence of the condition.

[0303] In addition to diagnosis, clinical cancer prognosis involves determining the aggressiveness of the cancer and the likelihood of tumor recurrence, and planning the most effective therapy. If a more accurate prognosis can be made, or even if the potential risk of developing cancer can be assessed, appropriate therapy, and in some cases, less harsh therapy for the patient, can be selected. Evaluation of cancer biomarkers (e.g., determining methylation status) is useful in differentiating between patients with a good prognosis and / or a low risk of developing cancer who require no therapy or only limited therapy, and those who are more likely to develop cancer or experience recurrence and may benefit from more aggressive treatment.

[0304] Therefore, “to make a diagnosis” or “to diagnose,” as used herein, further includes determining the risk of developing cancer or determining the prognosis, which may enable predicting clinical outcomes (with or without medical treatment), selecting appropriate treatment (or whether treatment is effective), or monitoring current treatment and, if applicable, modifying treatment, based on measures of diagnostic biomarkers (e.g., DMR) disclosed herein. Furthermore, in some embodiments of the subject matter disclosed herein, multiple determinations of biomarkers over time can be made to facilitate diagnosis and / or prognosis. Changes in biomarkers over time can be used to predict clinical outcomes, monitor the progression of ovarian cancer, and / or monitor the effectiveness of appropriate therapies targeting cancer. In such embodiments, for example, it may be expected that changes in the methylation status of one or more biomarkers (e.g., DMR) (and, if monitored, one or more additional biomarkers) disclosed herein) in a biological sample over time during the course of effective therapy.

[0305] The subject matter disclosed herein further provides, in some embodiments, methods for determining whether to initiate or continue prevention or treatment of a target cancer. In some embodiments, the method includes obtaining a series of biological samples from a subject over a period of time, analyzing the series of biological samples to determine the methylation status of at least one biomarker disclosed herein in each biological sample, and comparing any measurable changes in the methylation status of one or more biomarkers in each biological sample. Any changes in the methylation status of a biomarker over a period of time can be used to predict the risk of developing cancer, to predict clinical outcomes, to determine whether to initiate or continue prevention or treatment of cancer, and to determine whether the current treatment is effectively treating the cancer. For example, a first time point may be selected before the initiation of treatment, and a second time point may be selected at some point after the initiation of treatment. The methylation status can be measured in each of the samples taken at different time points, and qualitative and / or quantitative differences are recorded. Changes in the methylation status of biomarker levels from different samples may correlate with ovarian cancer risk, prognosis, determination of treatment effectiveness, and / or cancer progression in the subject.

[0306] In preferred embodiments, the methods and compositions of the present invention are for the treatment or diagnosis of a disease at an early stage, for example, before the symptoms of the disease appear. In some embodiments, the methods and compositions of the present invention are for the treatment or diagnosis of a disease during the clinical stage.

[0307] As already mentioned, in some embodiments, multiple determinations of one or more diagnostic or prognostic biomarkers can be made, and the change in the markers over time can be used to determine the diagnosis or prognosis. For example, a diagnostic marker can be determined initially and again in subsequent measurements. In such embodiments, an increase in the marker from the first to the second measurement may diagnose a particular type or severity of cancer, or a given prognosis. Similarly, a decrease in the marker from the first to the second measurement may indicate a particular type or severity of cancer, or a given prognosis. Furthermore, the degree of change in one or more markers may be related to the severity of cancer and future adverse events. Those skilled in the art will understand that in certain embodiments, comparative measurements of the same biomarker may be performed at multiple time points, but a given biomarker may also be measured at one time point and a second biomarker at a second time point, and the comparison of these markers may yield diagnostic information.

[0308] As used herein, the phrase “determine prognosis” means a method by which a person skilled in the art can predict the course or outcome of a condition in question. The term “prognosis” does not mean the ability to predict the course or outcome of a condition with 100% accuracy, or furthermore, the ability to predict whether a given course or outcome is as likely to occur as expected based on the methylation status of a biomarker (e.g., DMR). Instead, a person skilled in the art will understand the term “prognosis” to mean the probability that a particular course or outcome will occur, i.e., the increased probability that the course or outcome is more likely to occur in an object exhibiting a given condition compared to those individuals that do not exhibit that condition. For example, an individual that does not exhibit the condition (e.g., has normal methylation status of one or more DMRs) may have a very low probability of a given outcome (e.g., developing ovarian cancer).

[0309] In some embodiments, statistical analysis associates prognostic indicators with predispositions to adverse outcomes. For example, in some embodiments, a methylation status different from that in normal control samples obtained from patients without cancer may, when determined by the level of statistical significance, indicate that the subject is more likely to develop cancer than subjects with more similar levels of methylation status in the control sample. Furthermore, changes in methylation status from baseline (e.g., "normal") levels may reflect the prognosis of the subject, and the degree of change in methylation status may be related to the severity of adverse events. Statistical significance is often determined by comparing two or more populations and determining confidence intervals and / or p-values. For example, Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New See York, 1983. Exemplary confidence intervals for this subject 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.

[0310] In other embodiments, thresholds for changes in the methylation status of prognostic or diagnostic biomarkers (e.g., DMRs) disclosed herein can be established, and the degree of change in the methylation status of a biomarker in a biological sample is simply compared to the threshold degree for changes in the methylation status. Preferred thresholds for changes in the methylation status of biomarkers provided herein are about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 50%, about 75%, about 100%, and about 150%. In yet another embodiment, a “nomogram” can be established, thereby directly relating the methylation status of a prognostic or diagnostic indicator (biomarker or combination of biomarkers) to a predisposition associated with a given outcome. Since individual sample measurements rather than population mean values ​​are referenced, those skilled in the art are familiar with using such nomograms to correlate two numerical values, understanding that the uncertainty of this measurement is the same as the uncertainty of the marker concentration.

[0311] In some embodiments, control samples are analyzed simultaneously with biological samples, so that the results obtained from biological samples can be compared with the results obtained from control samples. Furthermore, it is intended that standard curves can be provided, and that the standard curves can be compared with the assay results of biological samples. Such standard curves present the methylation status of biomarkers depending on the assay unit, for example, the fluorescence signal intensity if fluorescent labeling is used. Using samples taken from multiple donors, standard curves can be obtained for the control methylation status of one or more biomarkers in normal tissue, and for the "risky" level of one or more biomarkers in tissue taken from donors with dysplasia or donors with ovarian cancer. In certain embodiments of the method, a subject is identified as having dysplasia if abnormal methylation status of one or more DMRs provided herein is identified in a biological sample obtained from the subject. In other embodiments of the method, the subject is identified as having cancer by the detection of abnormal methylation status of one or more such biomarkers in a biological sample obtained from the subject.

[0312] The analysis of markers may be performed separately or simultaneously with additional markers within a single test sample. For example, several markers may be combined in one test to efficiently process multiple samples and potentially provide higher accuracy in diagnosis and / or prognosis. Furthermore, those skilled in the art will recognize the value of testing multiple samples from the same subject (e.g., at consecutive points in time). Such testing of a series of samples makes it possible to identify changes in the methylation status of markers over time. In addition to changes in methylation status, the absence of changes in methylation status can provide useful information about the disease state, including, but not limited to, the identification of the approximate time since the onset of the event, the presence and amount of recoverable tissue, the appropriateness of drug therapy, the effectiveness of various therapies, and the identification of the subject's outcome, including the risk of future events.

[0313] Biomarker analysis can be performed in various physical forms. For example, the use of microtiter plates or automation can be used to facilitate the processing of a large number of test samples. Alternatively, single-sample formats can be developed to facilitate timely treatment and diagnosis, for example, in outpatient transport or emergency room situations.

[0314] In some embodiments, a subject is diagnosed with ovarian cancer if there is a measurable difference in the methylation status of at least one biomarker in the sample compared to a control methylation status. Conversely, if no change in the methylation status of the biological sample is identified, the subject may be identified as not having ovarian cancer, not being at risk of cancer, or having a low risk of cancer. In this regard, subjects with cancer or at risk can be distinguished from subjects with low risk of cancer or those substantially without them. Those subjects at risk of developing ovarian cancer may be placed on a more intensive and / or periodic screening schedule, including endoscopic surveillance. On the other hand, subjects at low risk to substantially without risk can be avoided from further tests for ovarian cancer (e.g., invasive procedures) until future screening, e.g., screening performed according to this technology, indicates that the risk of ovarian cancer has manifested in those subjects.

[0315] As described above, depending on the embodiment of the method of this technology, detecting a change in the methylation state of one or more biomarkers may be a qualitative or quantitative determination. Therefore, the step of diagnosing a subject as having ovarian cancer or being at risk of developing ovarian cancer involves performing a certain threshold measurement, for example, indicating that the methylation state of one or more biomarkers in a biological sample differs from a predetermined control methylation state. In some embodiments of this method, the control methylation state is any detectable methylation state of the biomarker. In other embodiments of the method in which a control sample is tested simultaneously with the biological sample, the predetermined methylation state is the methylation state in the control sample. In other embodiments of this method, the predetermined methylation state is based on and / or identified by a standard curve. In other embodiments of this method, the predetermined methylation state is a specific state or range of states. Therefore, the predetermined methylation state may be selected, in part, based on the embodiment of the method being performed and the desired specificity, within acceptable limits that will be apparent to those skilled in the art.

[0316] Furthermore, regarding the diagnostic method, the preferred subject is a vertebrate subject. The preferred vertebrate is warm-blooded, and the preferred warm-blooded vertebrate is a mammal. The 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. Thus, this technology enables the diagnosis of mammals such as humans, as well as mammals that are important because they are endangered, such as Amur tigers, economically important mammals such as animals raised on farms for human consumption, and / or animals that are socially important 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, sows, and wild boars; ruminants and / or ungulates such as cattle, bulls, sheep, giraffes, deer, goats, bison, and camels; and horses. Therefore, the diagnosis and treatment of livestock, including but not limited to domesticated pigs, ruminants, ungulates, and horses (including racehorses), are further provided.

[0317] The subject matter disclosed herein further includes systems for diagnosing ovarian cancer and / or specific forms of ovarian cancer (e.g., clear cell OC, endometrioid OC, mucinous OC, serous OC) in subjects. These systems may be provided as commercially available kits, which can be used, for example, to screen for ovarian cancer risk in subjects from which biological samples have been collected, or to diagnose ovarian cancer. Exemplary systems provided in accordance with this technology include evaluating the methylation status of DMRs as shown in Tables 1A and 6A. [Examples]

[0318] Example I. Tissues and blood samples were obtained from the Mayo Clinic's biological specimen repository under the supervision of the institution's IRB. Samples were selected in strict accordance with the study approval and inclusion / exclusion criteria. Cancer subtypes included 1) serous occlusion, 2) clear cell occlusion, 3) mucinous occlusion, and 4) endometrioid occlusion. Controls included non-neoplastic fallopian tube tissue and whole blood-derived leukocytes. Tissues were macro-dissected and examined for histological structure by a specialist gynecological pathologist. Samples were age-matched, randomized, and blinded. Sample DNA from 77 frozen tissues (18 serous occlusions, 15 clear cell occlusions, 6 mucinous occlusions, 18 endometrioid occlusions, 6 benign fallopian tubes, and 14 benign fallopian tube brush samples) and 19 buffy coat samples from non-cancer women was purified using the QIAamp DNA Tissue Mini Kit and the QIAamp DNA Blood Mini Kit (Qiagen, Valencia CA), respectively. DNA was re-purified using AMPure XP beads (Beckman-Coulter, Brea CA) and quantified using PicoGreen (Thermo-Fisher, Waltham MA). DNA integrity was evaluated using qPCR. Four ovarian cancer cell lines were also sequenced (TOV21G, SKOV3, OVCAR3, CAOV3).

[0319] RRBS sequencing libraries were prepared according to a modified Meissner protocol (Gu et al. Nature Protocols 2011 Apr;6(4):468-81). Samples were converted to 4plex format and sequenced using an Illumina HiSeq 2500 instrument (Illumina, San Diego, CA) at the Mayo Genomics Facility. Reads were processed using the Illumina pipeline module for image analysis and base calling. Secondary analysis was performed using SAAP-RRBS, a bioinformatics suite developed by Mayo. Briefly, reads were cleaned up using Trim-Galore and aligned to a GRCh37 / hg19 reference genome constructed with BSMAP. Methylation rates were determined by calculating C / (C+T) or, conversely, G / (G+A) for read mapping to the reverse strand, for CpGs with coverage of 10x or more and a base quality score of 20 or more.

[0320] Individual CpGs were ranked by their methylation rate, i.e., the number of methylated cytosines relative to the total number of cytosines at a given locus. For cases, the ratio had to be ≥0.20 (20%); for tissue controls, ≤0.05 (5%); and for buffy coat controls, ≤0.01 (1%). CpGs that did not meet these criteria were discarded. Candidate CpGs were then binned into DMRs (variable methylation regions) ranging from approximately 60 to 200 bp, with a minimum cutoff of 5 CpGs per region, based on genomic location. DMRs with excessively high CpG density (>30%) were excluded to avoid GC-related amplification problems during the validation phase. For each candidate region, a 2D matrix was created comparing individual CpGs sample-by-sample against both cases and controls. Comparisons of the entire OC, as well as subtypes, against all benign ovarian tissue and / or non-cancerous buffy coat were analyzed. Next, these CpG matrices were compared to reference sequences to assess whether genomically consecutive methylation sites had been discarded during the initial filtering. From a subset of this region, final selection required coordinated and consecutive (case-specific) hypermethylation of individual CpGs across the entire DMR sequence at a sample-by-sample level. Conversely, control samples needed at least one-tenth the methylation of cases, and the CpG pattern had to be more random and less coordinated. At least 10% of cancer samples within the subtype cohort needed to have at least 50% hypermethylation at all CpG sites within the DMR.

[0321] In another analysis, DMRs were derived based on mean methylation values ​​of CpGs using a proprietary DMR identification pipeline and regression package. Differences in mean methylation percentages were compared among OC cases, tissue controls, and buffy coat controls, and DMRs with control methylation less than 5% were identified using tiled reading frames within each 100 base pairs of mapped CpGs. DMRs were analyzed only if the total coverage depth averaged 10 reads per subject and the variance between subgroups was greater than 0. Assuming an increase in the biologically relevant odds ratio of more than 3 and a coverage depth of 10 reads, more than 18 samples per group were required to achieve 80% power with a two-tailed test at a 5% significance level and to assume a binomial variance inflation factor of 1.

[0322] After regression, DMRs were ranked by the difference in p-values, area under the receiver operating characteristic curve (AUC), and magnification changes between cases and all controls. Since independent validation had been planned in advance, no adjustment for false positives was performed at this stage.

[0323] Using a proprietary methodology for sample preparation, sequencing, analytical pipelines, and filtering, we identified variable methylation regions (DMRs), accurately identified these gynecological cancers, and narrowed down the selection to DMRs that were superior in a clinical trial setting. Tissue-specific analysis identified 471 hypermethylated ovarian cancer (OC) DMRs (Tables 1A and 1B). These included OC-specific regions, OC subtype-specific regions, and regions covering a more general range of cancers. DMRs ranked by top subtypes are shown in Tables 2A, 2B, 2C, and 2D. Tissue-versus-leukocyte (buffy coat) analysis yielded 55 hypermethylated ovarian tissue DMRs with less than 1% noise in WBCs (DMRs 472-525, shown in Tables 1A and 1B). The overall top buffy DMRs are shown in Table 3. From the tissue and buffy marker groups, 68 candidates were selected as initial pilots. Methylation-specific PCR assays were developed and tested on two sets of tissue samples: sequenced (frozen) and from a relatively large, independent cohort (FFPE). Short amplicon primers (<150 bp) were designed to target the most discriminant CpG within the DMR and tested with controls to ensure that well-methylated fragments were strongly and linearly amplified, while unmethylated and / or unconverted fragments were not. 136 primer sequences are shown in Table 1C. Ultimately, 54 assays proceeded (14 assays were discontinued before reaching QC).

[0324] The results of the first validation were analyzed logistically to determine AUC and magnification changes. Previous studies have shown that the epigenetics of cancer subtypes within organs differ, and that the best panel is derived from a combination of subtype markers. Tissue and buffy coat control analyses were performed separately. The results are presented in Tables 4A, 4B, 4C, 4D, and 4E. Many assays differentiated buffy coat samples from OCs with 100% accuracy, and nearly 100% accuracy when comparing OCs with benign fallopian tubes.

[0325] These results provided a rich source of information regarding high-performance candidates for adoption in independent sample testing. Of the original 54 assays, 33 were selected. Most were within the AUC range of 0.90–1.00, but others were included that exhibited very high FC numbers (extremely low background) and / or complementarity with other methylated DNA markers (MDMS). All MDM assays demonstrated high analytical performance, namely linearity, efficacy, sequence specificity (assessed using melting curve analysis), and strong amplification.

[0326] In the second validation, the experiment was conducted using the entire sample and marker set in a single batch, similar to the previous step. Approximately 10 ng of FFPE-derived sample DNA was used for each marker (350 in total). The results for individual MDM OC subtypes versus normal tissue and buffy coat are shown in Tables 5A, 5B, 5C, and 5D. Several MDMs showed significant changes in methylation ratios (10 to over 1000) compared to the control across all OC tissue structures.

[0327] Data were plotted in a heat matrix format to enable visualization of complementarity. A cross-validated 2-MDM panel was obtained from rPART modeling (C2CD4D, NCOR2) that distinguished the entire occlusion (OC) from benign fallopian tube tissue with 99% sensitivity and 97% specificity. Complete identification of all tissue structures was achieved using subtype rPART and random forest modeling (AUC=1).

[0328] Through sequencing of the entire methylome, rigorous filtering criteria, and biological validation, excellent candidate MDMs for ovarian cancer were obtained. Some MDMs distinguish all OC tissue structures from controls with relatively high sensitivity, while others accurately differentiate between tissue structures. Due to their high discriminative ability and ease of assay, such MDMs deserve further investigation for clinical application as early detection markers.

[0329] Table 1A shows DMR information, including chromosome number, gene annotation, and DMR start / end location for these markers. Table 1B shows the difference in p-values, area under the receiver operating characteristic curve (AUC), and magnification changes between OC cases and all controls. Table 1C shows primer sequence information for the various markers shown in Tables 1A and 1B.

[0330] [Table 1]

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[0343]

Table 2

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[0356]

Table 3

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[0362] A subset of DMRs was selected for further development. The criterion was primarily the area under the ROC curve, a logistic-derived measure that provides performance evaluation of region discriminability. An AUC of 0.85 was selected as the cutoff. In addition, the methylation ratio change rate (mean cancer hypermethylation rate / mean control hypermethylation rate) was calculated, using a lower limit of 10 for tissue-to-tissue comparisons and a lower limit of 20 for tissue-to-buffy coat comparisons. The P-value had to be less than 0.01. DMRs had to be documented in both the mean and individual CpG selection processes. Quantitative methylation-specific PCR (qMSP) primers were designed for candidate regions using MethPrimer (Li LC and Dahiya R. Bioinformatics 2002 Nov;18(11):1427-31), and 20 ng (6250 equivalents) of positive and negative genomic methylation controls were QC tested. Multiple annealing temperatures were tested for optimal discrimination. Validation was performed using a two-stage qMSP. The first stage consisted of retesting of sequenced DNA samples. This was done to demonstrate that DMR is indeed highly discriminative and not a result of overfitting from an extremely large next-generation dataset. The second stage utilized a relatively large set of independent samples (serous OC-36 samples, clear cell OC-21 samples, mucinous OC-14 samples, endometrioid OC-23 samples, control benign fallopian tube-29 samples, control buffy coat-28 samples).

[0363] Tissue was identified as before by expert clinical and pathological examination. DNA purification was performed as described above. The EZ-96 DNA Methylation Kit (Zymo Research, Irvine CA) was used for the bisulfite conversion step. 10 ng of converted DNA (per marker) was amplified using SYBR Green detection on a Roche 480 LightCycler (Roche, Basel, Switzerland). Serially diluted universal methylated genomic DNA (Zymo Research) was used as a quantitative standard. A CpG-independent ACTB (β-actin) assay was used as input reference and normalization control. Results were expressed as methylated copy (specific marker) / ACTB copy.

[0364] The results were analyzed logistically for the performance of individual MDMs (methylated DNA markers). Two techniques were used for marker combinations. First, the rPart technique was applied to the entire MDM set, then limited to combinations of three MDMs, and the probability of cancer predicted by rPart was calculated. In the second approach, 500 individual rPart models were generated to fit a bootstrap sample of the original data (approximately two-thirds of the training data), and random forest regression (rForest) was used to estimate the cross-validation error across the entire MDM panel (one-third of the test data). This was repeated 500 times to avoid spurious splits that would underestimate or overestimate the true cross-validation measure. The results were then averaged over the 500 iterations.

[0365] Table 2A shows 10 methylation regions that distinguish clear cell occlusive dressing tissue from buffy coat control and control fallopian tube tissue (methylation percentages for control buffy coat, control fallopian tube tissue, and clear cell occlusive dressing tissue) (AUC and p-values ​​between methylation percentages of clear cell tissue and control fallopian tube).

[0366] [Table 4]

[0367] Table 2B shows 10 methylation regions that distinguish endometrioid OC tissue from buffy coat control and control fallopian tube tissue (methylation percentages for control buffy coat, control fallopian tube tissue, and endometrioid OC tissue) (AUC and p-values ​​between methylation percentages of endometrioid tissue and control fallopian tubes).

[0368] [Table 5]

[0369] Table 2C shows 10 methylation regions that distinguish mucinous occlusive dressing tissue from buffy coat control and control fallopian tube tissue (methylation percentages for control buffy coat, control fallopian tube tissue, and mucinous occlusive dressing tissue) (AUC and p-values ​​between methylation percentages of mucinous tissue and control fallopian tube).

[0370] [Table 6]

[0371] Table 2D shows 10 methylation regions that distinguish serous occlusive dressing tissue from buffy coat control and control fallopian tube tissue (methylation percentages for control buffy coat, control fallopian tube tissue, and serous occlusive dressing tissue) (AUC and p-values ​​between methylation percentages of serous tissue and control fallopian tubes).

[0372] [Table 7]

[0373] Table 3 shows the 10 methylation regions that distinguish OC tissue from buffy coat control (showing the difference in methylation percentage between OC and control buffy coat; showing the difference in methylation percentage between OC and control fallopian tube; showing AUC; showing the difference in magnification change; and showing the p-value).

[0374] [Table 8]

[0375] Tables 4A–E show the results of the initial tissue validation in which more than 60 top DMRs were selected from sequencing data and qMSP assays were designed. These DMRs were performed on occlusive carcinoma (OC) tissue, clear cell OC tissue, endometrioid OC tissue, mucinous OC tissue, serous OC tissue, and control fallopian tube tissue. Next, a larger, independent tissue validation was performed to test new, untested cases and controls (see Table 5).

[0376] [Table 9]

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[0378] [Table 10]

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[0380] [Table 11]

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[0382] [Table 12]

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[0384] [Table 13]

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[0386] Table 5A shows the area under the curve for various markers in Table 1 that distinguish serous OC tissue from benign ovarian tissue and buffy coat.

[0387] [Table 14]

[0388] Table 5B shows the area under the curve for various markers in Table 1 that distinguish clear cell OC tissue from benign ovarian tissue and buffy coat.

[0389] [Table 15]

[0390] Table 5C shows the area under the curve for various markers in Table 1 that distinguish endometrioid OC tissue from benign ovarian tissue and buffy coat.

[0391] [Table 16]

[0392] Table 5D shows the area under the curve for various markers in Table 1 that distinguish mucinous occlusive dressing tissue from benign ovarian tissue and buffy coat.

[0393] [Table 17]

[0394] Example II. This example describes the identification of ovarian cancer tissue markers, clear cell ovarian cancer tissue markers, endometrioid ovarian cancer tissue markers, mucinous ovarian cancer tissue markers, and serous ovarian cancer tissue markers.

[0395] Candidate methylation markers for detecting ovarian cancer, clear cell OC, endometrioid OC, mucinous OC, and serous OC were identified by RRBS of ovarian tissue samples, clear cell OC tissue samples, endometrioid OC tissue samples, mucinous OC tissue samples, serous OC tissue samples, and normal ovarian tissue samples. To identify methylated DNA markers, 149 samples per patient group (see Table 7) were subjected to the RRBS process and subsequently aligned to a bisulfite-converted human genome. Using normal ovarian tissue and buffy coat as reference, CpG regions with high methylation rates in ovarian cancer, clear cell OC, endometrioid OC, mucinous OC, and serous OC were selected and mapped to their gene names.

[0396] [Table 18]

[0397] After selecting markers using RRBS, a total of 49 methylation markers were identified, and targeted enrichment long probe quantitative amplification signal assays were designed and ordered (for general techniques, see, for example, WO2017 / 075061 and U.S. Patent Application No. 15 / 841,006). Table 6A shows the marker chromosome regions used for the 49 methylation markers. Table 6B shows the primer and probe information for the markers. Figure 1 further shows the marker chromosome regions used for the 49 methylation markers, as well as the associated primers and probes.

[0398] [Table 19]

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[0400] [Table 20]

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[0409] The sensitivity of each methylation marker was calculated for each subtype with a 95% cutoff and is shown in Tables 8A (ovarian cancer), 8B (clear cell OC), 8C (endometrioid OC), 8D (mucinous OC), and 8E (serous OC). Tables 8A-8 show the 95% specificity sensitivity of the markers shown in Table 6A for ovarian cancer and subtype tissues for OC, clear cell OC, endometrioid OC, mucinous, and serous OC.

[0410] [Table 21]

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[0412] [Table 22]

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[0414] [Table 23]

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[0416] [Table 24]

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[0418] [Table 25]

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[0420] Example III. This example describes the identification of plasma markers for detecting ovarian cancer (OC).

[0421] DNA methylation is an early-stage carcinogenic event and can be detected in plasma samples from cancer patients. Experiments (described in Examples I and II) using DNA extracted from tissue first identified and then validated candidate methylated DNA markers (MDMs) that were highly discriminant for OC in tissue samples. Subsequent experiments involved individually testing plasma from women with and without OC, identifying and validating methylated DNA markers for plasma detection of OC, demonstrating their clinical feasibility.

[0422] For discovery, DNA from 67 frozen tissues (18 high-grade serous (HGS), 18 endometrioid, 15 clear cell (CC), 6 mucinous ocytoplasmic tubal epithelium (OC), 10 benign tubal epithelium (FTE), and 19 buffy coat from non-cancer women) was subjected to reduced-resolution bisulfite sequencing (RRBS) to identify MDMs associated with OC. Candidate MDM selection was based on receiver operating characteristic (ROC) discrimination, methylation ratio changes, and low background methylation between controls. Blinded biological validation was performed using MSP on DNA extracted from independent FFPE tissues of OC (36 HGS, 22 endometrioid, 21 CC, and 14 mucinous) and 29 FTE. Top-ranking MDMs in the tissues were tested using long-probe quantitative signal assays in independent pre-treatment plasma samples from newly diagnosed OC women and healthy women sampled from the population. Random forest modeling analysis was performed to generate disease predictive probabilities. Results in We performed 500-fold cross-validation using silico.

[0423] Following the discovery and biological validation of RRBS, 33 MDMs showed significant methylation ratio changes (10–1000) relative to FTE across all OC tissue structures. The top 11 MDMs (GPRIN1, CDO1, SRC, SIM2, AGRN, FAIM2, CELF2, DSCR6, GYPC, CAPN2, BCAT1) were tested in plasma from 91 women with OC (76 HGS (84%)) and 91 women without OC. The cross-validated 11-MDM panel highly differentiated between OC and control (95% specificity, 79% sensitivity, and AUC 0.91 (0.86–0.96)). Of the HGS, the panel accurately identified 83%, including 5 / 6 stage I / II and the majority of other subtypes (Table 9).

[0424] Through sequencing of the entire methylome, rigorous filtering criteria, and biological validation, we obtained an excellent candidate MDM for OC that can be performed in plasma and is expected to have high sensitivity and specificity.

[0425] [Table 26]

[0426] Using 66 plasma samples from patients with OC (e.g., 6 stage I OCs, 3 stage II OCs, 27 stage III OCs, 12 stage IV OCs, and 18 non-diagnosed), the following marker MDM was additionally tested and compared with 237 control plasma samples from patients without OC: ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CM TM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, SIM2_A, AGRN_8794, BCAT1_6015, KCNA3_7518, K CNA3_7320, LOC10013136, GYPC_C, SRC (e.g., SRC_A, SRC_B), NR2F6, TSHZ3, CELF2 (e.g., CELF2_A, CELF2_B), TACC2 (e.g., TACC2_A, TACC2_B), VIPR2 (e.g., VIPR2_A, VIPR2_B), and SPOCK2_74333. Table 10 shows the percentage of sensitivity and specificity for each marker for detecting OC.

[0427] [Table 27]

[0428] Subsequent experiments demonstrated that the identification of cancer cells (OCs) is clinically feasible by detecting a combination of 1) elevated cancer antigen 125 (CA-125) levels compared to non-cancerous normal levels, and 2) changes in measured methylation levels of the following markers compared to non-cancerous normal methylation levels: ATP10A (e.g., ATP10A_A, ATP10A_B, ATP10A_C, ATP10A_D, ATP10A_E), EPS8L2 (e.g., EPS8L2_A, EPS8L2_B, EPS 8L2_C, EPS8L2_D), C1QL3 (e.g., C1QL3_A, C1QL3_B), FAIM2 (e.g., FAIM2_A, FAIM2_B), CAPN2_B, LBH, CMTM3 (e.g., CMTM3_A, CMTM3_B), ZMIZ1 (e.g., ZMIZ1_A, ZMIZ1_B, ZMIZ1_C, ZMIZ1_D), GPRIN1 (e.g., GPRIN1_A, GPRIN1_B), CDO1 (e.g., CDO1_A, CDO1_B), GP5, DSCR6, SKI, and SIM2_A.

[0429] The marker MDM was tested using 66 plasma samples from patients with OC (e.g., 6 stage I OCs, 3 stage II OCs, 27 stage III OCs, 12 stage IV OCs, and 18 non-diagnosed cases), and compared to 237 control plasma samples from patients without OC. CA-125 levels were also measured in the 66 plasma samples and the 237 control plasma samples. Table 11 shows the 90% specificity for detecting OC with MDM. Table 12 shows the 90% specificity for detecting OC with CA-125. Table 13 shows the 90% specificity for detecting OC with both MDM and CA-125.

[0430] [Table 28]

[0431] [Table 29]

[0432] [Table 30]

[0433] Built-in by reference The entirety of each disclosure of patent documents and scientific papers referenced herein is incorporated by reference for all purposes.

[0434] Equivalents The present invention may be implemented in other specific forms without departing from its spirit or essential features. Therefore, the embodiments described herein should be considered illustrative in all respects rather than limiting the invention as described herein. Accordingly, the scope of the invention is indicated by the appended claims rather than the foregoing description, and all modifications that fall within the equivalent meaning and scope of the claims are intended to be encompassed therein. [Brief explanation of the drawing]

[0435] [Figure 1-1] Tables 1A and 6A list various methylated DNA markers and provide information on the marker chromosome regions used for them, as well as the associated primers and probes. The naturally occurring (WT) and bisulfite-modified (BST) sequences of the PCR target regions are shown. [Figure 1-2] Continuation of Figure 1-1. [Figure 1-3] Continuation of Figure 1-2. [Figure 1-4] Continuation of Figure 1-3. [Figure 1-5] Continuation of Figure 1-4. [Figure 1-6] Continuation of Figure 1-5. [Figure 1-7] Continuation of Figure 1-6. [Figure 1-8] Continuation of Figure 1-7. [Figure 1-9] Continuation of Figure 1-8. [Figure 1-10] Continuation of Figure 1-9. [Figure 1-11] Continuation of Figure 1-10. [Figure 1-12] Continuation of Figure 1-11. [Figure 1-13]Continuation of Figure 1-12. [Figure 1-14] Continuation of Figure 1-13. [Figure 1-15] Continuation of Figure 1-14. [Figure 1-16] Continuation of Figure 1-15. [Figure 1-17] Continuation of Figure 1-16. [Figure 1-18] Continuation of Figure 1-17. [Figure 1-19] Continuation of Figure 1-18. [Figure 1-20] Continuation of Figure 1-19. [Figure 1-21] Continuation of Figure 1-20. [Figure 1-22] Continuation of Figure 1-21. [Figure 1-23] Continuation of Figure 1-22. [Figure 1-24] Continuation of Figure 1-23.

Claims

[Claim 1] a) The methylation level of one or more genes in a biological sample of a human individual, Treating the genomic DNA in the aforementioned biological sample with a reagent that modifies the DNA in a methylation-specific manner; Amplifying the treated genomic DNA using a set of primers for one or more selected genes; and Determining the methylation level of one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nucleases, mass-based separation, and target capture; This includes measuring through A method in which one or more of the aforementioned genes are selected from FAIM2, CAPN2, and SIM2.

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