WO2013096661A1 - Biomarqueurs de méthylation utilisés pour le cancer de l'ovaire - Google Patents

Biomarqueurs de méthylation utilisés pour le cancer de l'ovaire Download PDF

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WO2013096661A1
WO2013096661A1 PCT/US2012/071031 US2012071031W WO2013096661A1 WO 2013096661 A1 WO2013096661 A1 WO 2013096661A1 US 2012071031 W US2012071031 W US 2012071031W WO 2013096661 A1 WO2013096661 A1 WO 2013096661A1
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methylation
ovarian cancer
sample
dec
genomic
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PCT/US2012/071031
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Jian-Bing Fan
Marina Bibikova
Thomas Royce
Jeremy R. Chien
Lynn Hartmann
Vijayalakshmi Shridhar
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Illumina, Inc.
Mayo Foundation For Medical Education And Research
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Publication of WO2013096661A1 publication Critical patent/WO2013096661A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers

Definitions

  • Ovarian cancer is the among the top ten most common cancers among women, and the fifth leading cause of death for women with cancer in the United States.
  • ovarian cancer the most lethal gynecological disease among women in developed countries is ovarian cancer.
  • the American Cancer Society estimates that about 22,000 new cases will be diagnosed this year and approximately 15,000 women will die from ovarian cancer in the United States alone.
  • the incidence rate of ovarian cancer is roughly 13 per 100,000 women per year and even though the median age at diagnosis is around 63, no age group is immune to the disease. Further, even though incidence of ovarian cancer is slightly higher among white women, no race or ethnic background is immune.
  • Changes in cellular genetic information such as mutations in gene sequences which can affect gene expression and/or protein sequence, are associated with many diseases and cancers. However, changes can also occur to genes that affect gene expression; changes caused by mechanisms other than genetic mutations.
  • Epigenetics is the study of changes in gene expression caused by mechanisms other than changes in the underlying DNA sequence, the methylation of DNA being one of those mechanisms. Methylation of DNA, the addition of a methyl group to the 5 position of a cytosine pyrimidine ring or the positional sixth nitrogen of an adenine purine ring, is widespread and plays a critical role in the regulation of gene expression in development and differentiation of diseases such as multiple sclerosis, diabetes, schizophrenia, aging, and cancers.
  • DNA methylation typically occurs in regions where a cytosine nucleotide (C) is found next to a guanine nucleotide (G) where the C and G are linked by a phosphate group (p), the linear construct being referred to as a "CpG” island.
  • C cytosine nucleotide
  • G guanine nucleotide
  • p phosphate group
  • Methylation in particular gene regions for example in gene promoter regions, can augment or inhibit the expression of these genes. Recent work has shown that the gene silencing effect of methylated regions is accomplished through the interaction of methylcytosine binding proteins with structural components of chromatin which, in turn, makes the DNA inaccessible to transcription factors through histone deacetylation and chromatin structure changes.
  • hypomethylation of genomic DNA are observed in tumor cells. Further, a correlation between hypomethylation and increased gene expression has been reported for many oncogenes. Monitoring global changes in methylation pattern has been applied to molecular classification of cancers, for example, gene hypermethylation has been associated with clinical risk groups in neuroblastoma and hormone receptor status correlation with response to tamoxifen in breast cancer.
  • the present disclosure identifies biological markers, or biomarkers, indicative of ovarian cancer.
  • biomarkers and methods of their use described herein provide alternatives to currently available ovarian cancer determinative, diagnostic and prognostic methodologies.
  • Biomarkers and methods of their use can also be used to assess an individual prior to, during and/or subsequent to a treatment course for eradicating ovarian cancer (e.g., chemotherapy, radiation therapy, drug therapy, etc.) for determining the effectiveness of a treatment course.
  • the present disclosure provides methods for identifying differentially methylated genomic CpG dinucleotide sequences associated with ovarian cancer in an individual comprising measuring the level of methylation in a biological test sample from an individual suspected of having ovarian cancer at a CpG dinucleotide sequence in one or more genomic targets of SEQ ID NOs: 1-300, comparing the level of methylation at the one or more CpG dinucleotide sequences in said biological test sample to a reference level of methylation of said genomic CpG dinucleotide sequences in a normal biological sample wherein a difference in the methylation levels between the test sample and the normal sample identifies differentially methylated genomic CpG dinucleotide sequences associated with ovarian cancer in said individual.
  • the level of methylation of the differentially methylated genomic CpG dinucleotide sequence is used to diagnose ovarian cancer in said individual.
  • the methylation level in a biological sample is decreased as compared to a reference sample level, whereas in other embodiments the methylation level in a biological sample is increased relative to a control reference sample.
  • the level of methylation in the biological sample is decreased in SEQ ID NOs: 1-2, 10, 13-14, 17-18, 21, 28, 30-31, 34, 41-42, 46, 51, 54- 58, 63, 65-66, 73, 80-81, 83, 88, 91-96, 98-99, 104-105, 110, 115-1 16, 125-126, 130- 131, 133, 136, 138-139, 143-145, 147-150, 153, 157, 159-160, 163, 166, 168-169, 171, 175, 178-180, 183, 185, 187-189, 197, 200, 202, 204, 212-213, 215-216, 223- 225, 231-233, 239, 241, 245-247, 250-252, 254, 256-260, 264-266, 268, 271-272, 274, 276-277, 280-287, 289, 290, 292, 297 and 299-300 in comparison to
  • the differentially methylated genomic CpG is differentially methylated genomic CpG
  • dinucleotide sequences are observed in a subset of said genomic targets, wherein the subset comprises two or more of the genomic targets set forth as SEQ ID NOS: 200, 58, 41, 153, 57, 274, 168, 223, 197, 18, 289, 266, 26, 60, 190, 122, 1 12, 1 1 1, 22, 151, 146, 251, 104, 72, 6, 62, 33, 277, 204, 1 10, 42, 1 13, 165, 132, 212, 179, 128, 167, 238, 103, 295, 69, 74, 156, 77, 43, 102, 61, 234, 100, 27, 244, 89, 82, 275, 255, 230, 211, 79, 85, 263, 121, 15, 243, 172, 154, 71, 86, 228, 164, 134, 235, 174, 38, 222, 269, 70, 123, 45, 36, 87, 49, 226, 84,
  • genomic CpG dinucleotides indicative of ovarian cancer comprises a population of genomic targets comprising SEQ ID NOS: 1-300 or a subset of said population.
  • the population targets are capable of exhibiting differential methylation of genomic CpG dinucleotide sequences, wherein said differential methylation is diagnostic for the presence of ovarian cancer in an individual.
  • the population of genomic targets comprises a subset of SEQ ID NOS: SEQ ID NOS: 200, 58, 41, 153, 57, 274, 168, 223, 197, 18, 289, 266, 26, 60, 190, 122, 112, 11 1, 22, 151, 146, 251, 104, 72, 6, 62, 33, 277, 204, 1 10, 42, 113, 165, 132, 212, 179, 128, 167, 238, 103, 295, 69, 74, 156, 77, 43, 102, 61, 234, 100, 27, 244, 89, 82, 275, 255, 230, 21 1, 79, 85, 263, 121, 15, 243, 172, 154, 71, 86, 228, 164, 134, 235, 174, 38, 222, 269, 70, 123, 45, 36, 87, 49, 226, 84, 166, 34, 125, 265, 144, 63,
  • differential methylation in a population of genomic targets is decreased methylation level of said CpG dinucleotide sequences in a sample from an individual in comparison to a reference level, for example as found in SEQ ID NOs: 1-2, 10, 13-14, 17-18, 21, 28, 30-31, 34, 41-42, 46, 51, 54-58, 63, 65-66, 73, 80-81, 83, 88, 91-96, 98-99, 104-105, 1 10, 115-116, 125-126, 130-131, 133, 136, 138-139, 143-145, 147-150, 153, 157, 159-160, 163, 166, 168-169, 171, 175, 178- 180, 183, 185, 187-189, 197, 200, 202, 204, 212-213, 215-216, 223-225, 231-233, 239, 241, 245-247, 250-252, 254, 256-260, 264-266, 26
  • the differential methylation of a population of genomic targets is increased methylation level of said CpG dinucleotide sequences in a sample from an individual in comparison to a reference level, for example as found in SEQ ID NOs: 3-9, 1 1-12, 15-16, 19-20, 22-27, 29, 32-33, 35-40, 43-45, 47-50, 52-53, 59-62, 64, 67- 72, 74-79, 82, 84-87, 89-90, 97, 100-103, 106-109, 1 1 1-114, 117-124, 127-129, 432, 134-135, 137, 140-142, 146, 151-152, 154-156, 158, 161-162, 164-165, 167, 170, 172-174, 176-177, 181-182, 184,186, 190-196, 198-199, 201, 203, 205-21 1, 214, 217- 222, 226-230, 234-238
  • Figure 1 is an exemplary receiver-operator characteristic (ROC) curve for the subset of DNA methylation markers (1-100) of Figure 1. Data used for the graph are distinct from the data used to select the markers.
  • ROC receiver-operator characteristic
  • Figure 2 demonstrates the specificity of cumulative subsets of DNA methylation markers.
  • the large, bolded circle represents the area under the curve (AUC) maximized at 300 markers. Data used for the graph are distinct from the data used to select the markers.
  • Figure 3 shows a series of box plot graphs from subsets of markers (k) of Figure 1.
  • the graphs demonstrate a comparison between the percentage of markers that have ⁇ values greater/less than the threshold defined for that marker (y axis) between the ovarian tumor formaldehyde fixed, paraffin embedded (FFPE) tissue samples, normal ovarian tissue samples and normal non-ovarian tissue samples (Other Tissue).
  • 300 markers corresponds to the first 300 markers
  • test sample is intended to mean any biological fluid, cell, tissue, organ or portion thereof that contains genomic nucleic acids, for example genomic DNA, suitable for methylation status determination via the disclosed methods.
  • a test sample can include or be suspected to include a cell, such as a cell from an ovary, uterus, fallopian tube, vagina, or other organ or tissue that contains or is suspected to contain a cancerous cell.
  • the term includes samples present from an individual as well as samples obtained or derived from an individual.
  • a sample can be a histologic section of a specimen obtained by biopsy, cell scraping, etc. or cells that are placed in or adapted to tissue culture.
  • a sample further can be a sub-cellular fraction or extract, or a crude or isolated nucleic acid molecule.
  • a sample further can be a serum or other fluidic sample suspected of containing circulating cells.
  • a normal sample can be used to establish a methylation background pattern for comparison to a test sample.
  • a sample may be obtained in a variety of ways known in the art. Samples may be obtained according to standard techniques from all types of biological sources that are usual sources of genomic DNA including, but not limited to cells or cellular components which contain DNA, cell lines, circulating tumor cells, biopsies, bodily fluids such as blood, lavage specimens, tissue samples such as tissue that are formalin fixed and embedded in paraffin such as tissue from ovaries, endometrium, cervix, fallopian tubes, omentum, histological object slides, and all possible combinations thereof. Further, tissues can be fresh, fresh frozen, etc. Accordingly, a sample can be from an archived, stored or fresh source as suits a particular application of the methods set forth herein.
  • the methods described herein can be performed on one or more samples from ovarian cancer patients such as samples obtained by vaginal lavage, endometrial biopsy, ovarian biopsy, and/or blood draw.
  • Sample analysis can be applied, for example, to determine the methylation status of cells suspected of being ovarian cancer cells, to determine the methylation status of cells for differentiation between early and/or late stage ovarian cancer types, ovarian cancer epithelial type differentiation, or to determine the methylation status of cells in order to monitor cancer progression or response to treatment.
  • a suitable sample can be collected and acquired that is either known to comprise ovarian cancer cells or is subsequent to the formulation of the diagnosis of ovarian cancer.
  • a sample can be derived from a population of cells or from a tissue that is predicted to be afflicted with, or phenotypic of, ovarian cancer.
  • the genomic DNA can be derived from a high-quality source such that the sample contains only the tissue type of interest, minimum contamination and minimum DNA fragmentation.
  • samples are contemplated to be representative of the tissue or cell type of interest that is to be handled by an assay.
  • a population or set of samples from an individual source can be analyzed to maximize confidence in the results for an individual.
  • a sample from an individual is matched and compared to a normal sample from that same individual to identify the DNA methylation status of certain CpG dinucleotides for that individual.
  • a normal sample such as a patient matched normal sample, can be from the same or similar organ, tissue or fluid as the sample to which it is compared.
  • the normal sample will typically display a phenotype that is different from a phenotype of the sample to which it is compared.
  • isolated or purified when used in relation to a nucleic acid refers to a nucleic acid sequence that is extracted and separated from at least one component or contaminant with which it is ordinarily associated in its natural source. As such, an isolated or purified nucleic acid is present in a form or setting that is different from that in which it is found in nature.
  • gene refers to a nucleic acid sequence, such as DNA, that comprises coding sequences associated with the production of a polypeptide, precursor, or RNA (e.g., rRNA, tRNA).
  • a gene also includes non-coding and intergenic sequences.
  • the term can encompass the coding region of a gene and the sequences located adjacent to the coding region on both the 5' and 3' such that the gene corresponds to the length of the full-length mRNA. Sequences located 5' of the coding region and present on the mRNA are referred to as 5' non-translated sequences and comprise promoter, enhancer, transcription factor binding sites, and the like which affect gene expression.
  • sequences located 3' or downstream of the coding region and present on the mRNA are referred to as 3' non-translated sequences.
  • the term "gene” encompasses both cDNA and genomic forms of a gene.
  • a genomic form or clone of a gene contains the coding region interrupted with non-coding sequences such as introns, intervening regions, intervening sequences or intergenic regions.
  • a "non-gene associated region” are those genetic regions in a genome that are currently not identified as being related to a particular gene as identified above.
  • the term "reference level" refers to a control level of a marker used to evaluate a test level of a biomarker in a sample of a patient.
  • the level of methylation of one or more CpG dinucleotides, in a gene region or a non-gene associated region, in a patient sample can be higher than the reference level, wherein the cells are considered to have a higher level of methylation as compared to the methylation level found in a normal reference cell sample.
  • the level of methylation of one or more CpG dinucleotides in the test cells of a patient can be lower than the reference level, wherein the test cells are considered to have a lower level of methylation relative to the normal cell sample.
  • a reference level can be determined based on samples collected from normal classes of adjacent tissues and/or with normal peripheral blood lymphocytes.
  • the reference level can be determined by any of a variety of methods.
  • the reference level can be determined by, for example, measuring the level of methylation of a biomarker in non-tumorous cells from the same tissue as the tissue of the neoplastic cells to be tested.
  • the reference level can also be a level of a biomarker of in vitro cultured cells which can be manipulated to yield methylation levels which accurately reflect methylation levels of a normal cell or tissue sample.
  • the reference level can also be determined by comparison of the level of a biomarker, such as methylation of one or more genes, in populations of patients having the same cancer.
  • Two or more separate groups of patients can be determined by identification of subset populations of the cohort which have the same or similar levels of the biomarker. Determination of the reference level can then be made based on a level which best distinguishes these separate groups.
  • a reference level also can represent the levels of two or more markers. Two or more markers can be represented, for example, by a ratio of values for levels of each biomarker.
  • the reference level can be a single number, equally applicable to every patient, or the reference level can vary, according to specific subpopulations of patients. For example, individuals with a certain subtype of cancer might have a different reference level than individuals of a different subtype of cancer, say subtypes of ovarian cancer.
  • the reference level might be a certain ratio of a biomarker in the neoplastic cells of a patient relative to the biomarker levels in non-tumor cells within the same patient.
  • the reference level for each patient can be proscribed by a reference ratio of one or more genomic markers, such as methylation of one or more genes or non-gene related CpG dinucleotides, wherein the reference ratio can be determined by any of the methods for determining the reference levels described herein.
  • the reference level corresponds to the level of one or more methylated genomic CpG dinucleotide sequences present in a corresponding sample that allows comparison to the desired phenotype.
  • a reference level can be based on a sample that is derived from a cancer- free origin so as to allow comparison to the biological test sample for purposes of diagnosis.
  • a method of staging a cancer it can be useful to apply in parallel a series of reference levels, each based on a sample that is derived from a cancer that has been classified based on parameters established in the art, for example, phenotypic or cytological characteristics, as representing a particular cancer stage so as to allow comparison to the biological test sample for purposes of staging.
  • progression of the course of a condition can be determined by determining the rate of change in the level or pattern of methylation of genomic CpG dinucleotide sequences by comparison to reference levels derived from reference samples that represent time points within an established progression rate.
  • a user will be able to select the reference sample and establish the reference level based on the particular purpose of the comparison.
  • the term "neoplastic cell” refers to any cell that is transformed such that it proliferates without normal homeostatic growth control, for example a cancer cell, in particular an ovarian cancer cell. Such cells can result in a benign or malignant lesion of proliferating cells. Such a lesion can be located in a variety of tissues and organs of the body, in particular from female reproductive tissues.
  • cancer is intended to mean a class of diseases characterized by the uncontrolled growth of aberrant cells, including all known cancers, and neoplastic conditions, whether characterized as malignant, benign, soft tissue or solid tumor.
  • disease- free survival refers to the lack of tumor recurrence and/or spread and the fate of a patient after diagnosis, for example, a patient who is alive without tumor recurrence.
  • tumor recurrence refers to further growth of neoplastic or cancerous cells after diagnosis of cancer. Particularly, recurrence can occur when further cancerous cell growth occurs in the cancerous tissue.
  • Tumor spread refers to dissemination of cancer cells into local or distant tissues and organs, for example during tumor metastasis. Tumor recurrence, in particular, metastasis, is a significant cause of mortality among patients who have undergone surgical treatment for cancer. Therefore, tumor recurrence or spread is correlated with disease free and overall patient survival.
  • Ovarian cancer is often referred to as a silent killer because of its subtle symptoms that lead to delayed discovery, diagnosis and treatment.
  • the majority of ovarian cancers are diagnosed when the cancer has already reached an advanced stage, for example >80% of serous ovarian cancers are diagnosed at Stage III or Stage IV leading to a very low chance of long-term survival in these patients.
  • Screening and/or detecting ovarian cancer in women who might be at higher risk of developing ovarian cancer, such as those with a strong family history of such cancer is problematic.
  • the two most common screening tests for ovarian cancer include transvaginal sonography and identification of a protein marker, CA-125. However, both tests have limitations.
  • transvaginal sonography can identify a mass in the ovary however the sonogram is unable to distinguish whether the mass is cancerous or not.
  • the protein marker CA-125 is not specific to the presence of ovarian cancer as other cancers also exhibit high levels of CA-125.
  • ovarian tumor cancers are of the epithelial histologic type, which can be further divided into different tumor subtypes, for example serous, endometrioid, clear cell, mucinous, Brenner or transitional cell, squamous cell, undifferentiated and mixed epithelial cell types (AJCC Cancer Staging Manual 7 th Ed., p.422).
  • TAM tumor node metastasis
  • FIGO Federation Internationale de Gynecologie et d'Obstetrique
  • Ovarian cancer can also be classified into two groups based on molecular progression. For example, Type I ovarian tumors of mucinous, clear cell, endometrioid, and low-grade serous type develop in stepwise fashion from adenomas to carcinomas, whereas Type II tumors of high-grade serous develop de novo from undefined precursor lesions and progress rapidly with no apparent stepwise progression (Ie and Kurman, 2004, Am J Pathol 164: 151 1-1518). Further explanation of cancer staging and grading can be found at, for example, AJCC Cancer Staging Manual, Edge, SB et al, Eds., Springer-Verlag, New York.
  • ovarian cancers The vast majority of Type II, high-grade serous ovarian cancers (OvCa) are diagnosed at advanced stages and represent a major challenge in early detection (Chan et al, 2006, Obs and Gyn 108: 521-528).
  • the most common type of ovarian cancer arises from epithelial cells that line the surface of the ovary. Approximately 50% of epithelial ovarian tumors are classified as serous, or tumors with glandular features, and make up approximately 80% of all ovarian tumors.
  • Other types of ovarian cancers can arise from germ cells (e.g., cancer of the ovarian egg-making cells) and sarcomas.
  • High-grade serous tumors denote highly aggressive, invasive tumors as compared to low malignant potential (LMP) tumors. Whether an invasive serous tumor is classified as either high or low grade is based on the clinical course of the disease. For example, high grade serous tumors were found to over express genes that control various cellular functions associated with cancer cells, for example genes that control cell growth, DNA stability (or lack thereof) and genes that silence other genes. Conversely, LMP tumors were not found to overexpress these types of genes and LMP tumors were alternatively characterized by expression of growth control pathways, such as tumor protein 53 (TP53 or p53) pathways.
  • TP53 or p53 tumor protein 53
  • a high grade serous tumor has a high degree of chromosomal instability, has mutated TP53 gene, demonstrates very fast tumor development, typically has nuclei that are non-uniform, enlarged and irregularly shaped and has high mitotic index.
  • staging and grading cancers are subjective and rely on a diagnostician to interpret morphology, histology, anatomy and other related indices. Further, as ovarian cancer is typically left undiagnosed until late stage cancer due to, for example, its asymptomatic phenotype, the staging and grading do nothing to identify early stage cancer, or identify ovarian cancer earlier in the disease progression in the absence of disease related symptoms. As such, there is a critical need for tools, methods and strategies that can be used for detecting, diagnosing, and prognosing ovarian cancer in a patient.
  • DNA methylation is a cancer type-specific epigenetic event that plays an important role in tumor development and the identification of cancer-specific epigenetic changes has promise as a potential tool in molecular classification and disease stratification.
  • Experiments conducted with regards to determining epigenetic changes associated with ovarian cancer have resulted in the identification of aberrant DNA methylation that is present in ovarian cancer.
  • Assays to determine the aberrant DNA methylation patterns, or DNA methylation biomarkers, as defined herein can be performed not only on cells, but also on DNA from circulating tumor cells in the blood thereby providing an assay for ovarian cancer that is non-invasive.
  • inventions disclosed herein characterize ovarian cancer by differential methylation of genomic CpG dinucleotide sequences and provide populations of genomic targets useful for detecting the differential methylation of genomic CpG dinucleotide sequences in diagnosing an individual with ovarian cancer, determining the prognosis of an individual identified with ovarian cancer, and/or treatment assessment options for an individual afflicted with ovarian cancer.
  • methylation of the identified CpG dinucleotides was consistent between the FF or FFPE samples, as such tissue manipulation is not considered a variable when determining the methylation status of the identified CpG dinucleotides for ovarian cancer.
  • tissue manipulation is not considered a variable when determining the methylation status of the identified CpG dinucleotides for ovarian cancer.
  • the methylation levels of CpG dinucleotides from the cancer tissues were compared to levels from normal ovarian and female reproductive tissue reference samples.
  • Normal ovarian reference samples included 12 normal fallopian tube epithelial samples, 4 samples representing pooled normal ovarian surface epithelial cells and 2 normal ovarian tissues samples (Table 1).
  • non-ovarian non-tumor tissue samples were included as general, reference samples, those tissues included adipose, adrenal, bladder, blood, brain, breast, diaphragm, duodenum, heart, kidney, liver, lung, lymphnode, pancreas, skeletomuscle, skin, spleen, stomach, testis and ureter. It was determined during experimentation that the methylation levels of CpG dinucleotides from these disparate tissues did not align with those identified as being indicative of ovarian cancer, and indeed were akin to the levels found in the normal reference samples for the listed CpG dinucleotides (see Figure 3 Other Tissue).
  • the tissue samples were assayed for differential methylation utilizing the Infinium Assay for Methylation (I or II, one probe or two probes, respectively) followed by data analysis using GenomeStudio® Methylation Module vl .8 (Illumina, Inc.).
  • Accuracy of a diagnostic assay or test can be measured by the area under a receiver-operator characteristic curve (ROC).
  • ROC receiver-operator characteristic curve
  • the accuracy of a diagnostic test depends on how well the test can separate the group being tested, in this case ovarian cancer tissue samples, from those without the disease (normal ovarian tissue samples).
  • An area of 1.00 represents a perfect test, 0.90-1 and excellent test, 0.80-0.90 a good, 0.70-0.80 a fair test and 0.50 and below a worthless test.
  • Figure 1 shows an exemplary ROC curve for the first 100 methylation markers SEQ ID NO: 1-100.
  • the ROC is very near 1.0 as such the use of the 100 markers is an excellent diagnostic test for ovarian cancer.
  • the samples' classifications that are used in computing the ROC are from samples not used in the selection of these markers.
  • One way to summarize the ROC of Figure 1 is to calculate the area under the curve
  • Data analysis identified 300 differentially methylated CpG dinucleotides with an area under the curve (AUC) of >0.992 as demonstrated in Figure 2. Additional biomarkers from the originally identified set of approximately 12,930 markers provided no real advantage in increasing the AUC and thus increasing the diagnostic ability of the proposed biomarkers. Further evaluation of CpG dinucleotide subsets of those 300 biomarkers was undertaken.
  • Table 2 identifies 300 biomarkers useful in diagnostic and/or prognostic assays for ovarian cancer.
  • the 300 biomarkers comprise those of known and unknown gene regions and adjacent regulatory regions.
  • a methylation marker associated with a gene has been given the gene name as the Target ID to identify that particular marker, for example SEQ ID NO: 1 was given the Target ID of FOXM1 as this CpG dinucleotide is located in the gene region for forkhead box Ml (FOXM1).
  • Target ID is cg24871371 (as generated by Infinium (INF) I or II assay).
  • the beta-average ( ⁇ -AVE) for both FF and FFPE ovarian cancer samples is reported as is the ⁇ -AVE for normal tissue samples.
  • the ⁇ -average is the methylation level of the CpG dinucleotide in the group of samples; in this case the FF group, the FFPE group and the normal reference group (Table 1).
  • the methylation status (METH STATUS) of the CpG dinucleotide biomarker (SEQ ID NOs: 1-300) identifies whether the methylation pattern of that particular marker in ovarian cancer tissue is increased relative to the normal reference control.
  • methylation of the CpG dinucleotide of SEQ ID NO: 1 is typically decreased relative to that of the normal control based on the ⁇ -average between the sample groups.
  • Table 2 reports probability values (P -VALUE) for each marker indicating how likely the observed difference would arise by chance. P-values were computed using the Wilcoxon rank sum test in R (for example, see www.r-project.com).
  • IRX1 29 1 0.2858995 0.7551908 1.09E-11 0.6558658 9.07E-08 INC cgl6415411 30 1 0.7761870 0.3874091 7.86E-13 0.5036563 1.50E-12 DEC
  • CDKN2A 32 II 0.1514849 0.6069571 4.73E-11 0.4628200 4.73E-11 INC
  • JPH4 39 1 0.3162632 0.5865893 7.11E-10 0.5349135 4.80E-04 INC cgl5092219 40 II 0.3265441 0.7030458 2.32E-11 0.5774651 4.57E-05 INC
  • L1TD1;L1TD1 120 1 0.1969600 0.6201168 9.28E-11 0.3587767 5.59E-04 INC
  • NFKBIL2 136 1 0.6523424 0.2485812 5.61E-13 0.3331853 3.26E-10 DEC
  • NKX6-2 156 1 0.2357596 0.6787889 2.96E-11 0.5863062 3.84E-08 INC cg22550229 157 II 0.5226255 0.1871181 1.61E-14 0.2336168 1.50E-12 DEC
  • CDH4 223 0.8387435 0.4495015 1.03E-09 0.5551217 1.61E-14 DEC
  • WISP1 241 II 0.8938278 0.5776590 2.96E-11 0.7136048 5.87E-10 DEC
  • Figure 3 shows exemplary box plot graphs of subsets of methylation
  • Methylation biomarkers SEQ ID Nos: 1-10 (10 Markers), SEQ ID Nos: 1-50
  • detecting the biomarkers are easily developed by a skilled artisan for determining the methylation status of one or more of the CpG dinucleotides as found in Table 3 for use in methods, for example as described herein.
  • PCDHGA4 45 5 140801482 GAAAGGGCGCAGTTCCCATT[CG]TGTGGTGGTCCTCGATGTAA
  • PCDHGA4 49 5 140802432 GACCCG CCCCTCAG CAGCAA [CG ]TGTCG CTG AG CCTGTTCGTG
  • PCDHB11 61 5 140580769 CTGCACATCGGCAGTGTCAG[CG]CTACAGACAGAGACTCAGGC
  • NRN1 68 6 6000601 G ACTAAATATAATCCCAACC [CG ]TG ATCG ATTCCTG G GG CG CT
  • NKX2-8 69 14 37053169 GACCTGGCTCGGATGCGGCGG [CGJGCGGCCCTGTCAAGCCCCGC
  • PCDHGA4 71 5 140741730 GAGGTATTGCCAGACCTCAG[CG]ACCGCCGGGAGCCCTCTGAC
  • NKX6-2 74 10 134598496 GTTTGTGCTTCTTGAGCAGC[CG]CGTGATCTTCTCGTCGTCCG cg20677570 75 11 15962841 GCTCTCCAG CAAGTCG CTCC [CG ] G GTCCCAG GCCAAG GG CTG C cg02324227 76 5 180591594 GGAATTCGAACCCATGCCTC[CG]AAGACACCGGCGCCTTAATC
  • PCDHGA4 79 5 140803130 CCAGCCCAACTATGGGGACA[CG]CTCATCAGCCAGGAGAGCTG cgl5293759 80 10 86040024 CACCCGAATATTGCGCTTTT[CG]GACCAGCTTAAAAAACGGCG
  • PCDHB2 82 5 140475611 TGATAAGCACGGCCCTGGAC[CG]GGAGACCAGATCCGAATACA cg04675342 83 10 113822450 CACCTGAATACTGCGCTTTT[CG]GACGGGCTTAAAAAACGGCG
  • PCDHGB1 84 5 140731930 ATCTCAGTGCTCTTTCTCCT[CG]CGGTGATTCTAGCGATCGCC
  • PCDHGA4 85 5 140794962 GGCGGCTTGACAGGTGTGTC[CG]GCTCGCACTTTGTGGGCGTG
  • PCDHGA4 86 5 140800983 CCCCGGGAGCTGGCGAAGCG[CG]GAGTCCGCATCGTCTCCAGA
  • PCDHGA4 87 5 140794664 AGCCCTGCTGGACAGAGACG[CG]CTCAAGCAAAGCCTCGTAGT
  • PCDHB2 89 5 140476324 GTGCTGGTCAAGGACAATGG[CG]AGCCTCCGCGCTCGGCCACC cg00864171 90 11 67383662 GATGGGGCCCGGGCTCACAT[CG] ATGGCCATGTGCTCCTGCATG cgl0243855 91 17 1096705 AGCACGTAACCTTCACTCTT [CG ] CAAG CAG CCTCTCTATGTG G
  • PTPRS 95 19 5341595 CTG ACAACAG CTACCAG CCA [CG ] CCCCACTCCACTCTATCCCA cgl5208832 96 1 65467997 GCTCGGTACCGCCACGCACC[CG]TCCTATGTGTTACCTGGTCG ALX4 97 11 44332958 CGCGTGCAGCCCCTCGATTT[CG]CTTCATGACAACCGTGCTAG
  • PCDHB19P 100 5 140621344 AGGGCTGTTCGGCGTGTGGG[CG]CACAATGCACTGACAGGCTG
  • PCDHB10 102 5 140574095 TCCTCGCTCGGCCACCGCCA[CG]CTGCACTTGCTCCTGGTGGA
  • GSTP1 104 11 67350491 AACTTCAAATAAAAGTTG G A [CG ] G CCAG GCGTG GTG GCTCACG cg00177787 105 10 3330480 GCGTGAGGTGAGGGCTCCCC[CG]CGCCGCCCTGGCGTCCAGGT cg25324047 106 10 22765840 ACGCTCCCCAATTTCCCCAT[CG]AGCCTTCTCCTCCCGAGTCT
  • L1TD1;L1TD1 120 1 62660861 TGGTCCAGGCGCGAAGGGCG[CG]GGGTGCCCCGGGTAAGGCTG
  • TBPL2 127 14 55907299 CAGCGAGGCGGGGCGGCCCT[CG]GCCCAGGTTCCTGCAGAGGG
  • MMP23B 132 1 1566351 GCAGGGAGTTAGTTGGGGGG [CGJTCCCAGGCAGGGTCTGGGGG
  • HLX 140 1 221054273 TGTGGCGTTCTTGGAAGACA[CG]TGAAAGTGAGGCCGTAAGCC cgl7555825 141 5 76924190 CGCACGCAGCCCGCGAGGGG[CG]CCCTCCGCGCCACTGCCCCA cgll762968 142 13 95354190 GAGGAAGGCCTTAAGGCCCA[CG]GAAGCCTCATCCCGCCAAGC cgll305991 143 10 131844324 GTGGTAGGGCTCAGGGCAGA[CG]GAACGTGGAACAGGGAGTGG
  • PTPRN2 144 7 158045996 CGGGCGGGGGCCTGAGGACT[CG] AGGACTGACCGGGGCCAGCAC cg02576528 145 10 131845025 GGCCACCAGCTCGCCCACGC[CG]CCCGCTCACCTGCACGCGGA
  • GRXCR2 147 5 145252094 CCAACAGTTCTGGTTGGTAC[CG]GAGGGGGAGCAAGACACAAA cg06609496 148 5 1174426 GCTCCAGGGCACCCCCTCCC[CG]GGATGCCCCACACGAAGCACCCG VRK1 149 14 97262732 AAGTGAGGTCCTGAAGCTTA[CG]CTTCATTGGCTTCATTGTTA cg27228712 150 7 570041 TTTCCTCCACACCCAGCGTC[CG]GATGCTCTGTGATGTTAA
  • TBPL2 152 14 55907198 CCCACTGTTGGGGGTGGGGG [CG]GGTAAGAGGGTAAGCGCGGA
  • CDH4 153 20 60470166 GGCAGGAATGCACTGGCGTG[CG]GCACAGCCTCCTCTCTCTCT
  • NKX6-2 156 10 134598352 AATTTATTG ATG ATACAAAG [CG ] ACTCG CG CCCACCCG G GG CC cg22550229 157 18 13138034 CTGAGCAGGCTCAAGGGCTG[CG]TTATACAATCTTCTCGGTCC
  • C1QL4 158 12 49730055 CTGGTGGTCCAGGGGGCCCC[CG]CAGGCCTGCTTTCCCGCGCC cg21214521 159 7 569979 GTCCTCAGGGCGCGACACGA[CG]CTGCCCGCTGAGGCGTCAGA cg25595388 160 5 2684004 CCACCGTGAGTCTGGAAGCC[CG]TGGAGGGTGCCTGCAGCCCT
  • TBX15 161 1 119528638 GGCCCTAGACGGGCTCCGTG[CG]ATCTGGGGCCTCCCAAGAGA
  • CNST 180 1 246728663 ATACTTTGTG GTGTCATTAA [CG] ATACAG ACTCTACG CTTATG
  • C1QTNF4 181 11 47611780 GAGTGGTTGGTGCCGCGGCC[CG]GGGCCAGCGTCCGAGCCCAC cgll687036 182 3 147139563 GCTCCCCAGTGAGGCGGGAG[CG]AAAAGAGTGCAGGCCCACTC
  • SRCIN1 191 17 36719654 ATGCCCATG GTG AG CTTCTG [CG ] G G AACATGTG CG CG ATG AGT cg01891252 192 1 50881219 CCCAGAAGCGGAGACTCAAT[CG]CCCCCCTGCCCCTGCTACCA
  • IRX2 193 5 2751041 CAGCCGGCGAGCCGAGGGGA[CG]CGCGGGGGGCGCGCGGGTCC cgll729934 194 6 1601693 CCAGCTCTCAGTGAGGACTT[CG]CGGTCCTGGGGCCAGTAGGT
  • TAL1;TAL1 195 1 47694919 GAATAGGATCTCCACTCCGC[CG]GAAAGGGGCGGAAGCCGAGG cg02683197 196 6 28174875 GCAGTGCTCCAGTACTGGTT[CG]ATTTCCGCAAAGTCCTTCTT
  • CDH4 197 20 60472331 CCATTGCAAGCCCTACCCCT[CG]TCTCTGGTCCCCATCCCGAG cg07276621 198 11 10750870 CAAG G GTCTTCGTTGG CTTC [CG] CCCAAATCTCACACAGTG CG cgl6142855 199 4 379900 TGCCCAATCAGGGATGCAGC[CG]GAGAGGAGAGGGCGGCATCC
  • MDC1 204 6 30684478 CAGGACTGGATTAGGGGAAC[CG]TGTCTTTCCCCTAGGGTCCA
  • ZNF274 206 19 58716004 ACIAGIAAI 111 CI GCI CI G[CG]ACCG 1111 ICIGCGCIGIAA
  • NRXN2 207 11 64398015 G GTAATGTCGTCCGTG CCCA [CG [TTAAAG ATCACCCCCACGGT cgll616547 208 6 28782301 TCGCGGCGTTGAGAACGCCT[CG]CAGCTCCTTTACTGGCTGGG
  • TBPL2 209 14 55907427 AGGGGTGGGCCGGAGAGGGG [CGJTCTCGCCTGGGGGCAGTCAC cg07805777 210 11 15962932 GCCTGGGGGGCCGGGCAGGC[CG]AGGCCCCTGCCCGTCGCAGT
  • PCDHGA4 211 5 140798095 G GTG GGGACCCTCCCCGAAG[CG] GTACTG CTCAG ATAAG AATC
  • KCNS1 220 20 43726544 TACTGGGCGCCAGCAGGAGG[CG]CGACGACACCTCGAAGCTGA cgl3327545 221 10 22623548 CGCTCTCCAAAGTTGGACCC[CG]TGGCGAGCGGCGGCGACAGC
  • PCDHGA4 222 5 140810726 ACGACAATGCGCCTTACTTT[CG]TGAAAGTGAATTAGAAATAA
  • CDH4 223 20 60116795 CI 111 ICI ICICACI IAAI [CG]ICI I AGCCICICCCC I
  • FAM188B 224 7 30810183 AAACCATCCAG CTGACACCT[CG] ATCTTG GTCTGTAG CCCTCA cg00589791 225 5 30429329 AG 1 CAGAAAAGCGCA 1 IAI 1 [CG]GG 1 GGGAG 1 ACC 1 GA 1111
  • KPNA5 231 6 117001170 TATGGACTCATGATTGGCAA[CG]CTTTCTGGTAACACTGCCCA cg23057567 232 19 47017629 1 1111 CCA 111 AAAA 1 CC 1 [CG] 1111 AG 111 CAA 11 C 1 C 1 C
  • TBX5 236 12 114845868 AAGAGGCAACCAGGCGATAG[CG]ACTATCTCACCAGCCGCTGC cg05107535 237 16 3242850 GCI IGCAGI IGAGCI 111 GG[CG]G 1 CCG 1 GGG 1 1 CI GAC 1
  • TCL1B 239 14 96152706 CCCCGCCCCCACTGCCGGCC[CG]GGCCCCACCCACGCCGGAGC
  • PAX6 240 11 31819219 GCAGCCAGGCGGTGACCTAG[CG]GCTGCTCTTACATAAAATGG
  • PCDHGA4 243 5 140802135 C 1 AA 1 1 GCAGA 1 GACAA[CG] A 1 AACCC 1 CCCG 11111 CC 1
  • PCDHB19P 244 5 140619446 CAGAGCAGATTGCCTACCAA[CG]CAACATAGGCATCCGGACCC
  • RCC2 250 1 17766917 CAGTGGAACGCACAGCCTAA[CG]GAAAGACAGATCAGTAAACA
  • GSTP1 251 11 67350499 ATAAAAGTTGGACGGCCAGG[CG]TGGTGGCTCACGCCTGTAAT
  • PCDHGA4 263 5 140811253 ACAATAGGGGAGTTGGACCA[CG]AGGAGTCAGGATTCTACCAG
  • GPR158 264 10 25755241 CCGTGAGACCACGAACCCTT[CG]ATCGAGAAAAGACCTTCAAT
  • TBPL2 270 14 55907460 GGCAGTCACCCCACTCTGCC[CG]CAGCTGGGAAGGGCCTGGGC
  • RELB 272 19 45504295 TTGAGGAAACTGAGGTGTAG[CG]AGACCTTGGAGGTTTCCGAA cgl8143296 273 3 157812763 CAAG GCCTGCG CACTG AATA [CG ] G CCCAAATCCTGTTCAGTGG
  • CDH4 274 20 60397766 GGGC I I CAC I GAG I 1 1 GGGG[CG]GGAAAAGG 1 AA 1 1 I CA I C
  • PCDHGA2 275 5 140735027 CAGCGGCACCTTGGTCACCG[CG]GGTAGGATAGACAGGGAGGA cg07502874 276 16 1040578 AG CTG G GCTTGGTG CGG CTG [CG ] G CCAGTACG CTGGTCCTG CC
  • HELLS 280 10 96304193 ATGTCCG ACACTAAAATGTC [CG ] G G CGTG GTG G CACG CACCTG
  • NKX2-3 295 10 101294300 1 1 ACA 1 AGAACAGA 1 1 1 1 [CG]CGCAACCCA I GCCCACCGGC cg22340508 296 19 22891978 CCGGGGTCGCCTAGCCCAGGAA[CG]CCTTAGTTGCAACCCTGCGT
  • Pattern of methylation refers to whether a particular biomarker (from a test sample) demonstrates an increased or decreased level of methylation relative to the level of methylation of that biomarker in a reference sample.
  • Methylation of CpG dinucleotide sequences can be measured using any of a variety of techniques used in the art for the analysis of specific CpG dinucleotide methylation status.
  • methylation can be measured by employing a restriction enzyme based technology, which utilizes methylation sensitive restriction endonucleases for the differentiation between methylated and unmethylated cytosines.
  • Restriction enzyme based technologies include, for example, restriction digest with methylation-sensitive restriction enzymes followed by Southern blot analysis, use of methylation-specific enzymes and PCR, restriction landmark genomic scanning (RLGS) and differential methylation hybridization (DMH).
  • Restriction enzymes characteristically hydro lyze DNA at and/or upon recognition of specific sequences or recognition motifs that are typically between 4- to 8- bases in length.
  • methylation sensitive restriction enzymes are distinguished by the fact that they either cleave, or fail to cleave DNA according to the cytosine methylation state present in the recognition motif, in particular, of the CpG sequences.
  • the digested DNA fragments can be separated, for example, by gel electrophoresis, on the basis of size, and the methylation status of the sequence is thereby deduced, based on the presence or absence of particular fragments.
  • a post-digest PCR amplification step is added wherein a set of two oligonucleotide primers, one on each side of the methylation sensitive restriction site, is used to amplify the digested genomic DNA.
  • PCR products are not detectable where digestion of the subtended methylation sensitive restriction enzyme site occurs.
  • Techniques for restriction enzyme based analysis of genomic methylation are well known in the art and include the following: differential methylation hybridization (DMH) (Huang et al, 1999, Human Mol. Genet.
  • Not I-based differential methylation hybridization for example, WO02/086163A1; restriction landmark genomic scanning (RLGS) (Plass et al, 1999, Genomics 58:254-62); methylation sensitive arbitrarily primed PCR (AP-PCR) (Gonzalgo et al, 1997, Cancer Res. 57: 594-599); methylated CpG island amplification (MCA) (Toyota et. al, 1999, Cancer Res. 59: 2307-2312).
  • RGS restriction landmark genomic scanning
  • AP-PCR methylation sensitive arbitrarily primed PCR
  • MCA methylated CpG island amplification
  • Other useful methods for detecting genomic methylation are described, for example, in US Patent Application publication 2003/0170684 or WO 04/05122.
  • Methylation of CpG dinucleotide sequences can also be measured by employing cytosine conversion based technologies, which rely on methylation status-dependent chemical modification of CpG sequences within isolated genomic DNA, or fragments thereof, followed by DNA sequence analysis.
  • Chemical reagents that are able to distinguish between methylated and non-methylated CpG dinucleotide sequences include hydrazine, which cleaves the nucleic acid, and bisulfite treatment. Bisulfite treatment followed by alkaline hydrolysis specifically converts non- methylated cytosine to uracil, leaving 5-methylcytosine unmodified as described by Olek A., 1996, Nucleic Acids Res.
  • the bisulfite-treated DNA can subsequently be analyzed by conventional molecular techniques, such as PCR amplification, sequencing, and detection comprising oligonucleotide hybridization.
  • Techniques for the analysis of bisulfite treated DNA can employ methylation- sensitive primers for the analysis of CpG methylation status with isolated genomic DNA as described by Herman et al, 1996, Proc. Natl. Acad. Sci. USA 93:9821-9826, and in U.S. Patents. 5,786, 146 and 6,265,171.
  • Methylation sensitive PCR allows for the detection of a specific methylated CpG position within, for example, the regulatory region of a gene.
  • the DNA of interest is treated such that methylated and non-methylated cytosines are differentially modified, for example, by bisulfite treatment, in a manner discernable by their hybridization behavior.
  • PCR primers specific to each of the methylated and non-methylated states of the DNA are used in PCR amplification. Products of the amplification reaction are then detected, allowing for the deduction of the methylation status of the CpG position within the genomic DNA.
  • Ms-SNuPE methylation- sensitive single nucleotide primer extension
  • MethyLightTM the art-recognized fluorescence-based real-time PCR technique MethyLightTM
  • methylation assay methods can be used for the determination of the methylation status of particular genomic CpG positions.
  • Methods which require bisulfite conversion include, for example, bisulfite sequencing, methylation-specific PCR, methylation-sensitive single nucleotide primer extension (Ms-SnuPE), MALDI mass spectrometry and methylation-specific oligonucleotide arrays are described, for example, in U.S. Patent 7,611,869 and International Patent Application
  • methylation of genomic CpG positions in a sample can be detected using an array of probes.
  • a plurality of different probe molecules can be attached to a substrate or otherwise spatially distinguished in an array.
  • Exemplary arrays that can be used in the invention include, without limitation, slide arrays, silicon wafer arrays, liquid arrays, bead-based arrays and others known in the art or set forth in further detail herein.
  • the methods of the invention can be practiced with array technology that combines a miniaturized array platform, a high level of assay multiplexing, and scalable automation for sample handling and data processing.
  • An array of arrays also referred to as a composite array, having a plurality of individual arrays that is configured to allow processing of multiple samples can be used.
  • Exemplary composite arrays that can be used in the invention are described in U.S. Patent 6,429,027 and US Patent Application publication 2002/0102578 and include, for example, one component system in which each array is located in a well of a multi-well plate or two component systems in which a first component has several separate arrays configured to be dipped simultaneously into the wells of a second component.
  • a substrate of a composite array can include a plurality of individual array locations, each having a plurality of probes and each physically separated from other assay locations on the same substrate such that a fluid contacting one array location is prevented from contacting another array location.
  • Each array location can have a plurality of different probe molecules that are directly attached to the substrate or that are attached to the substrate via rigid particles in wells (also referred to herein as beads in wells).
  • an array substrate can be a fiber optical bundle or array of bundles, such as those generally described in U.S. Patents 6,023,540, 6,200,737 and 6,327,410; and PCT publications WO9840726, W09918434 and WO9850782.
  • An optical fiber bundle or array of bundles can have probes attached directly to the fibers or via beads.
  • Other substrates having probes attached to a substrate via beads are described, for example, in US Patent Application publication 2002/0102578 and US Patent 6,770,441.
  • a substrate, such as a fiber or silicon chip can be modified to form discrete sites or wells such that only a single bead is associated with the site or well.
  • wells can be made in a terminal or distal end of individual fibers by etching, with respect to the cladding, such that small wells or depressions are formed at one end of the fibers.
  • Beads can be non-covalently associated in wells of a substrate or, if desired, wells can be chemically functionalized for covalent binding of beads.
  • Other discrete sites can also be used for attachment of particles including, for example, patterns of adhesive or covalent linkers.
  • an array substrate can have an array of particles each attached to a patterned surface.
  • a surface of a substrate can include physical alterations to attach probes or produce array locations.
  • the surface of a substrate can be modified to contain chemically modified sites that are useful for attaching, either-covalently or non-covalently, probe molecules or particles having attached probe molecules.
  • Chemically modified sites can include, but are not limited to the linkers and reactive groups set forth above.
  • polymeric probes can be attached by sequential addition of monomeric units to synthesize the polymeric probes in situ.
  • Probes can be attached using any of a variety of methods known in the art including, but not limited to, an ink-jet printing method as described, for example, in US Patents 5,981,733; 6,001,309; 6,221,653; 6,232,072 or 6,458,583; a spotting technique such as one described in US Patent 6, 1 10,426; a photolithographic synthesis method such as one described in US Patents 6,379,895 or 5,856,101 ; or printing method utilizing a mask as described in US Patent 6,667,394. Accordingly, arrays described in the aforementioned references can be used in a method described herein.
  • an array used in the invention can vary depending on the probe composition and desired use of the array. Arrays containing from about 2 different probes to many millions can be made. Generally, an array can have from two to as many as a billion or more probes per square centimeter. Very high density arrays are useful in methods described herein, for example, those having from about 10,000,000 probes/cm 2 to about 2,000,000,000 probes/cm 2 or from about 100,000,000 probes/cm 2 to about 1,000,000,000 probes/cm 2 . High density arrays can also be used including, for example, those in the range from about 100,000 probes/cm 2 to about 10,000,000 probes/cm 2 or about 1,000,000 probes/cm 2 to about 5,000,000 probes/cm 2 .
  • Moderate density arrays useful in methods described herein can range from about 10,000 probes/cm 2 to about 100,000 probes/cm 2 , or from about 20,000 probes/cm 2 to about 50,000 probes/cm 2 .
  • Low density arrays are generally less than 10,000 probes/cm 2 with from about 1,000 probes/cm 2 to about 5,000 probes/cm 2 being useful in particular embodiments.
  • Very low density arrays having less than 1,000 probes/cm 2 from about 10 probes/cm 2 to about 1000 probes/cm 2 , or from about 100 probes/cm 2 to about 500 probes/cm 2 are also useful in some applications.
  • Methods for determining ovarian cancer described herein can provide a robust and ultra high-throughput technology for simultaneously measuring methylation at many specific sites in a genome.
  • the methods further provide cost-effective methylation profiling of thousands of samples in a reproducible, well-controlled system.
  • the disclosed methods allow implementation of a process, including sample preparation, bisulfite treatment, genotyping-based assay and PCR amplification that can be carried out on a robotic platform.
  • the methods can be carried out at a level of multiplexing that is 96-plex or even higher including, for example, as high as 1,500-plex.
  • An advantage of methods described herein is that the amount of genomic DNA used for detection of methylated sequences is low including, for example, less that 1 ng of genomic DNA per locus.
  • the throughput of the methods can be 96 samples per run, with
  • a further object of the disclosed methods is to provide assays to survey methylation status the 5' - regulatory regions of at least 1,000 human genes per sample. Particular genes of interest are tumor suppressor genes or other cancer-related genes, as well as genes identified through RNA profiling.
  • methods for assaying samples for the presence of ovarian cancer based on methylation status of one or more CpG dinucleotides comprises a bead based assay. Genomic DNA is initially quantitated by any known method.
  • DNA quantitation methods such as PicoGreen (Molecular Probes) are preferred as this method can quantitate small amounts of DNA and measures DNA directly, thereby minimizing detection of contaminating RNA and/or proteins.
  • the present method is not limited to any particular method for DNA quantitation.
  • an amount of the genomic DNA can be processed and analyzed for methylation status.
  • a genomic DNA sample is exposed to sodium bisulfite for conversion of unmethylated cytosines to uracils, while leaving methylated cytosine unchanged for methylation analysis.
  • Methylation detection in bisulfite converted DNA is based on the different sensitivity of cytosine and 5- methylcytosine to deamination by bisulfite. Under acidic conditions, cytosine undergoes conversion to uracil, while methylated cytosine remains unreactive.
  • Bisulfite conversion of DNA is well known in the art and there are numerous commercial kits and methods, for example EZ DNA Methylation Kit by Zymo, for practicing bisulfite conversion of DNA for methylation analysis.
  • the present method is not limited to any particular method for bisulfite conversion of DNA, however complete conversion of cytosine to uracil is highly recommended as incomplete conversion can lead to false positive methylation signals, and thusly reduce the quality and effectiveness of a diagnostic assay.
  • the sample can be denatured, for example by addition of NaOH, and the resultant single stranded DNA neutralized for in preparation for amplification.
  • Single stranded DNA can be isothermally amplified such that whole genome amplification occurs resulting in a uniform increase in the amount of DNA sample and minimizing amplification bias which can occurs during thermal (e.g., traditional polymerase chain reaction at high temperatures).
  • Isothermal amplification can be carried out, for example, by incubating the DNA sample overnight at 37°C in the presence of dNTPs, DNA polymerase with strand displacement activity and primers for example as described in US Patent publication 2008/0009420 (incorporated herein by reference in its entirety).
  • the present method is not limited by the type of amplification performed for increasing the amount of DNA for analysis; however an unbiased method for amplification is preferable.
  • the amplified, bisulfite converted DNA can be fragmented. Fragmentation can be by any means, however enzymatic or chemical fragmentation is preferred as it is more easily manipulated to provide a more homogenous mixture of fragments.
  • the sample can be incubated in the presence of a cleavage enzyme or chemical at 37°C for a period of time (e.g., 1 hour, etc.) to generate DNA fragments, for example of between 100-lOOObp long.
  • the fragments of DNA can then be precipitated out by any means, for example by using 2- propanol according to standard techniques.
  • the precipitated fragments can be washed and resuspended in a resuspension buffer, for example a Tris-EDTA based buffer, for subsequent hybridization to a substrate for methylation analysis.
  • the fragments can be hybridized to a substrate for analysis.
  • beads comprising immobilized (e.g., covalently bound) probes for capturing genomic DNA can be incubated with the DNA fragments such that hybridization of the fragments to homologous probes immobilized on the beads is carried out.
  • the fragmented DNA under hybridization conditions known to a skilled artisan, can by allowed to hybridize to the bead probes, for example overnight at 48°C, thereby immobilizing the fragmented DNA for methylation assay.
  • the beads can be in a tube or deposited on a substrate such as a slide, for example a slide that allows for multiple bead populations and multiple samples to be assayed at one time, or multiplexed applications.
  • Such applications are found in the Illumina's iNFiNiUM beadchip Methylation Assays using one probe (I) or two probes (II) affixed to beads which are located on slides such that one probe can be used to interrogate a CpG dinucleotide sequence or two probes can be used to interrogate a CpG dinucleotide sequence, depending on the CpG location and how resulting probe design.
  • the substrate containing the immobilized, fragmented DNA can be washed to remove unbound DNA fragments.
  • the probes bound to the fragmented DNA can be extended, for example by single base extension, by providing dNTPs, DNA polymerase, and other components necessary for DNA extension to occur.
  • the single base extension can incorporate one or more labeled nucleotides, for example hapten labeled nucleotides such as biotin and nitrophenol (DNP) labeled nucleotides.
  • hapten labeled nucleotides such as biotin and nitrophenol (DNP) labeled nucleotides.
  • DNP nitrophenol
  • adenines and thymines could be biotin labeled and cytosine and guanines could be DNP labelled.
  • fluorescently labeled binding partners to the labeled nucleotides are introduced and fluorescence of the extensions is detected, for example by a fluorescent scanner.
  • Software can then be used to decipher the fluorescence data captured by the fluorescent scanner and methylation status along with additional data parameters can be output to the user.
  • GENOMESTUDIO Methylation software can be used to analyze methylation data from scanned microarray images collected from fluorescent scanners and output a variety of reports, graphs, tables etc. characterizing the sample(s) methylation status.
  • embodiments described herein make available diagnostic and/or prognostic assays for the analysis of the methylation status of CpG dinucleotide sequence positions as markers for ovarian cancer.
  • the methods described herein provide a systematic method for the identification, assessment and validation of genomic targets as well as a systematic means for the identification and verification of multiple condition relevant CpG positions to be used alone, or in combination with other CpG positions, for example, as a panel or array of markers, that form the basis of a clinically relevant diagnostic or prognostic assay.
  • the disclosed method enables differentiation between two or more phenotypically distinct classes of biological matter and allows for the comparative analysis of the methylation patterns of CpG dinucleotides within each of the classes.
  • methylation detection interrogates genomic DNA, but not RNA or protein, it offers several technological advantages in a clinical diagnostic setting: (1) readily available source materials. This is particularly important for prognostic research, when only DNA can be reliably extracted from archived paraffin-embedded samples for study; (2) capability for multiplexing, allowing simultaneous
  • RNA-based gene expression profiling and/or protein-based immunoassays with DNA methylation profiling is contemplated to provide a multi-pronged combination tool that is a sensitive, accurate and robust tool for cancer diagnosis and prognosis (Wong, et al, Curr Oncol Rep. 4(6): 471-7 (2002)).
  • the present disclosure is directed to a biomarkers and methods using the biomarkers for the identification of differentially methylated CpG dinucleotides within genomic DNA that are particularly informative with respect to the
  • biomarkers disclosed herein can be used alone, in combinations of two or more, three or more, four or more, five or more, ten or more, 20 or more, 50 or more, 100 or more or 200 or more, such as components of a gene panel, further in combination with one or more additional diagnostic assays, for diagnostic and/or prognostic assays of ovarian cancer.
  • the biomarkers useful in determining, diagnosing or prognosing ovarian cancer in a subject comprise one or more of SEQ ID NO: 1-300. In some embodiments, at least one or more of SEQ ID NO: 1-10 can be included in methods for determining, diagnosing or prognosing ovarian cancer. In one embodiment, biomarkers useful in methods to determine, diagnose or prognose ovarian cancer in a subject comprise one or more of SEQ ID NO: 1-5. In some embodiments, one or more of SEQ ID NO: 6-10 can be further included in ovarian cancer diagnostic or prognostic methods. In further embodiments, one or more of SEQ ID NO: 11- 300 are included in methods in conjunction with one or more of SEQ ID NO: 1-10.
  • a plurality of any number of biomarkers of SEQ ID NO: 1-300 can be used in methods and assays for ovarian cancer diagnostics and prognostics.
  • a plurality can be two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, 15 or more, 20 or more, 30 or more, 50 or more, 75 or more, 85 or more, 100 or more 200 or more or all 300 biomarkers as disclosed herein, or any subset thereof.
  • methods for determining the presence of ovarian cancer comprise comparison of genomic DNA from a test sample, for example a sample suspected of having ovarian cancer such as a tissue or cell sample with genomic DNA from a normal tissue, or reference, sample, for example tissue that does not have ovarian cancer.
  • a genomic DNA sample from the test, or suspect tissue is assayed for methylation status at one or more CpG dinucleotides by any of a number of methods as described herein.
  • a genomic DNA reference normal sample is also assayed and methylation status at one or more CpG dinucleotides of the reference sample is obtained.
  • the methylation status i.e.
  • biomarker methylation can be decreased relative to a reference sample or increased relative to a reference sample, thereby indicating the presence of ovarian cancer in that patient sample.
  • those methylation biomarkers that demonstrate a decreased level of methylation at a particular CpG dinucleotide as compared to the level of methylation at the same location in a reference sample include SEQ ID NO: 1-2, 10, 13-14, 17-18, 21, 28, 30-31, 34, 41-42, 46, 51, 54-58, 63, 65-66, 73, 80-81, 83, 88, 91-96, 98-99, 104-105, 1 10, 1 15-1 16, 125-126, 130-131, 133, 136, 138-139, 143-145, 147-150, 153, 157, 159-160, 163, 166, 168-169, 171, 175, 178-180, 183, 185, 187-189, 197, 200, 202, 204, 212-213, 215-216, 223-225, 231-233, 239, 241, 245-247, 250-252, 254, 256-260, 264-266, 268, 27
  • a method for determining the presence of ovarian cancer in a sample from an individual comprises evaluating the methylation status of CpG dinucleotide sequences of a gene or gene region. In some embodiments, evaluating CpG dinucleotide methylation status in one or more of ALX3, ALX4, APRT, AQPl 1, BARX1, C10RF96, C1QL4, C1QTNF4, C1QTNF8, C2ORF60, CACNA1A,
  • SLC25A2, SLC25A33, SLC26A5, SLFN12L, SOX1, SRCIN1, STIM2, SULT1A1, TALI, TBPL2, TBX15, TBX5, TCL1B, TUBB, VENTX, VRK1, WISP1, ZC3H4, ZIC1, ZIC4, ZNF154, ZNF274, ZNF321, ZNF517 and ZNF572 is used in methods to determine the presence of ovarian cancer in a sample.
  • evaluating CpG dinucleotide methylation status in two or more or CACNA1H, CDH4, ELFNl, EMX20S, GSTP1, GUCA1A, IRX2, KCND3, MDCl, MELK, MMP23, MYT1L, NKAPL, NKX2, NKX6, PCDH, PRAME, PTPRN2, SFT2D2, SULT1A1, TBPL2, TUBB, ZIC4, ZNF154 and ZNF274 is used in methods to determine the presence of ovarian cancer in a sample.
  • a method for determining the presence of ovarian cancer in a sample from an individual comprises evaluating the methylation status of CpG dinucleotide sequences in one or more regions not associated with a particular gene. For example, evaluating CpG dinucleotide sequences in one or more regions not associated with a gene or gene region such as those identified in Table 2, is used in methods to determine the presence of ovarian cancer in a sample.
  • methods for determining the presence of ovarian cancer in a sample from an individual comprise evaluating the methylation status of one or more CpG dinucleotide sequences of a gene or gene region, and one or more CpG dinucleotide sequences of non-gene related region(s).
  • the CpG dinucleotide sequences useful in methods described herein are identified in Table 2.
  • the DNA methylation patterns described herein were identified from a group of 21 ovarian cancers compared to normal reference controls (i.e., no diagnosis of ovarian cancer).
  • the cancer tissue samples were categorized, with ovarian cancer subtypes represented for both FF and FFPE tissue samples including those classified as endometroid, serous, mucinous, and clear cell type cancers.
  • the fresh frozen ovarian cancer tissue samples were from a patient cohort previously diagnosed with ovarian cancer under approved protocol from the Internal Review Board at the Mayo Foundation for Medical Education and Research, Rochester, MN. In preparing the isolated genomic DNA samples for methylation analysis
  • ILLUMINA Infinium HumanMethylation450 BeadChip microarray assay isolated genomic DNA was bisulfite treated to convert unmethylated cytosines to uracils while maintaining methylated cytosines using the EZ DNA Methylation Kit (Cat. #D5001, Zymo Research) following manufacturer's recommendations.
  • One of two Infinium assays were performed for each sample, either the ILLUMINA Infinium assay I or II.
  • the samples were denatured every 60 minutes by heating to 95°C for 30 seconds.
  • the methylated DNA was prepared for microarray analysis following manufacturer's protocol (Infinium HD Assay for Methylation, ILLUMINA, Inc.).
  • the FFPE samples were processed using Infinium HD DNA restoration protocol (Part # 15014614 Rev C, ILLUMINA, Inc.).
  • the Infinium HD DNA Restoration protocol restores degraded FFPE DNA to a state that is amplifiable by the Infinium HD FFPE methylation whole genome amplification protocol.
  • Bisulfite converted FF genomic DNA samples or bisulfite converted and restored FFPE genomic DNA samples were processed using Infinium methylation assay. The assay was carried out as described in the Infinium HD Methylation Assay Guide (Part #15019519 Rev B, ILLUMINA, Inc.).
  • Bisulfite-converted DNA (+/- restoration) was used in the whole-genome amplification (WGA) reaction.
  • the DNA was enzymatically fragmented, precipitated and re-suspended in a hybridization buffer. All subsequent steps were performed following the standard Infinium protocol (User Guide part #15019519 B). Fragmented DNA was dispensed onto the HumanMethylation450 BeadChips, and hybridization performed in hybridization oven. After hybridization, the array was processed through a primer extension and an immunohistochemistry staining protocol to allow detection of a single-base extension reaction. Finally, BeadChips were coated and imaged on an Illumina iScan.
  • the bisulfite converted DNA was denatured and neutralized in preparation of amplification.
  • Multi-sample amplification mix MAI
  • MAI Multi-sample amplification mix
  • RPM random primer mix
  • MSM multi-sample amplification master mix
  • the denatured and degraded DNA was isothermally amplified by incubating the samples between 20-24 hrs at 37°C in a hybridization oven. Following incubation, the bisulfite converted and amplified DNA was fragmented.
  • FMS Fragmentation solution
  • the precipitated, dried sample DNA was resuspended in RAl (resuspension, hybridization and wash solution), the 96 well plate tightly sealed and the samples incubated in a hybridization oven for 1 hr at 48°C in preparation for hybridizing the fragmented DNA to the HumanMethylation450 BeadChip.
  • Humidifying buffer (PB2) was added to the buffer reservoirs in the assembled hybridization chambers and the BeadChips were correctly oriented into the chamber for barcode reading.
  • the 96 well plate containing the fragmented sample DNA was heated to 95°C for 20 min to denature the sample DNA fragments. Following denaturation, samples were loaded onto the appropriate BeadChip section and visualization confirmed sample dispersion over the entire associated BeadChip section.
  • the hybridization chamber was closed and incubated in a hybridization oven at 48°C for 20 hours.
  • the BeadChips were washed by repeated submersion in the wash buffer PB 1 in preparation for processing the BeadChip for
  • the washed BeadChips with the hybridized fragmented sample DNA were placed onto the Multi-sample BeadChip Alignment Fixture (prefilled with PB1), submersed in PB lsuch that the BeadChip barcodes were correctly oriented and a spacer placed on top of each BeadChip.
  • PB1 Multi-sample BeadChip Alignment Fixture
  • a glass back plate was placed on the spacer of each BeadChip and the BeadChip/back plate was clamped together with metal clamps to create a flow-through BeadChip chambers for subsequent DNA extension and labeling.
  • a Chamber Rack which can hold multiple flow-through BeadChip chambers was used for extension and labeling of the DNA immobilized on the BeadChip.
  • the BeadChip chambers were placed into a preheated 44°C heated Chamber Rack and RAl solution was added into the reservoir for each BeadChip chamber, followed by the stepwise addition of XStain BeadChip solution 1 (XC1), 10 min incubation, addition of XStain BeadChip solution 2 (XC2), 10 min incubation, addition of Two- color extension master mix (TEM), 15 min incubation, addition of 95%
  • DNA from ovarian cancer patient samples known healthy ovarian tissue samples and DNA from non-ovarian, non-cancerous tissue samples as described in Example 1.
  • the DNA methylation status at approximately 500,000 loci was determined.
  • the BeadChips were imaged using the iScan Reader (ILLUMINA, Inc.) and data analyzed using the GENOMESTUDIO Methylation Module (ILLUMINA, Inc.) software following manufacturer's protocol (GENOMESTUDIO Methylation Module vl.8).
  • the median beta value was calculated for each of the three tissue types.
  • the beta value reflects the methylation status of each CpG locus, ranging from 0 in the cases of completely unmethylated sites to 1 is completely methylated sites.
  • the absolute difference in median beta was computed between the cancerous and normal ovarian samples.
  • the absolute difference in median beta was also computed between the cancerous and non-ovarian tissue. The minimum of the two values represents the difference metric. All loci with a difference metric below 0.2 were excluded from further analysis. Following exclusion, 12,929 loci had a difference metric above 0.2.
  • the healthy ovarian and non- ovarian tissues were grouped together and labeled "non-cancerous.”
  • a beta value was determined, that when used as a beta value threshold, minimized misclassification of cancerous and non-cancerous samples.
  • the 12,929 loci were ranked with respect to their misclassification rate. If there were loci of equal rank, those loci were ranked using the reciprocal of the previously computed difference metric of the loci.
  • the top n loci (for example, 300 markers) were used to classify the loci.
  • the beta value calculated for each of the n markers was used to classify a new sample, using the optimum beta-value threshold for each marker thereby resulting in n classifications.
  • a summary metric was computed as the percentage of markers that classify the new sample as "cancerous” as opposed to "non-cancerous". For example, if 30 out of 300 markers classified the sample as “cancerous”, the Methylation sample score would be 0.1 and the sample would be classified as “non-cancerous", using 0.2 as the cutoff score. Conversely, if 150 markers classified the sample as “cancerous”, the Methylation sample score would be 0.5 and the sample would be classified as "cancerous”. It was determined that a threshold of 0.2 would accurately distinguish the sample classes.
  • the M-marker set of loci was evaluated on an independent set of seventy-six FFPE ovarian cancer samples, 9 healthy ovarian samples, and 14 non-ovarian samples.
  • ROC receiver-operator-characteristic
  • Figure 1 demonstrates an example when setting n equal to one hundred. The accuracy of an assay depends on how well the test samples (i.e., ovarian cancer samples) separate from the control samples (i.e., normal and non-cancerous tissues).
  • the area is for all intents and purposes 1, thereby representing almost perfect sensitivity and specificity in the classification system utilized herein for discriminating and correctly classifying ovarian cancer from non-ovarian cancer in the sample tissues based on methylation status.
  • the AUC was maximized at 300 markers (circle) for determining ovarian cancer methylation markers as disclosed herein as it is evident from Figure 2 that the area of 300 markers demonstrates excellent sensitivity
  • AUC ⁇ 1 for discrimination between ovarian cancer and non-ovarian cancer.
  • Figure 2 also demonstrates that as little as 10 of the 300 biomarkers has an AUC of >0.900, as such still an excellent test for diagnostic purposes.
  • the box plots of Figure 3 constructed from summary of the 300 marker data further illustrates the fact that the set and subsets of the 300 markers significantly separate cancerous (tumor) and non-cancerous (normal) tissues.
  • methylated CpG dinucleotide islands were associated with 115 genes or gene families and 93 CpG loci were not associated with any particular gene region.
  • the 93 non- gene associated CpG loci were determined to be associated with a number of different genetic features, for example empirically determined differentially methylated regions (10), enhancer regions (16), regulatory regions (13) or DNase I hypersensitive regions (11). Over half of the loci located outside gene regions are associated with CpG islands (42 annotated and 78 hidden Markov Model islands).
  • Table 3 lists the 300 CpG dinucleotide markers (SEQ ID NO: 1-300), the relative position (MAP INFO) on the chromosome (CHR) of the C in the CG dinucleotide based on the Genome build 37 and from which Infinium design (INF, I or II) they were identified.
  • Example 2 The following example demonstrations the application of the methods as found in Examples 1 and 2 in determining methylation status in a sample.
  • the DNA samples were prepped as described in Example 1 and microarray analysis was carried out as described in Example 2.
  • Protocadherin gamma gene cluster is one of three related clusters tandemly linked on chromosome five.
  • the gene cluster has an immunoglobulin-like
  • the gamma gene cluster includes 22 genes divided into 3 subfamilies. Subfamily A contains 12 genes, subfamily B contains 7 genes and 2 pseudogenes, and the more distantly related subfamily C contains 3 genes.
  • the tandem array of 22 large, variable region exons is followed by a constant region, containing 3 exons shared by all genes in the cluster. Each variable region exon encodes the extracellular region, which includes 6 cadherin ectodomains and a transmembrane region.
  • the constant region exons encode the common cytoplasmic region.
  • the methylation profile for the protocadherin gamma gene cluster from an ovarian cancer sample and a normal ovarian sample identified 30 CpG loci as differentially methylated between the ovarian tumor and normal ovarian samples as reported in Table 2 (PCDHGnn).

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Abstract

Cette invention concerne des séquences de dinucléotides CpG génomiques à méthylation spécifique associées avec le cancer. L'invention concerne en particulier des dinucléotides CpG génomiques à méthylation spécifique et leur utilisation dans des méthodes de diagnostic et de pronostic du cancer de l'ovaire.
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WO2016115354A1 (fr) * 2015-01-14 2016-07-21 Taipei Medical University Méthodes de diagnostic et de pronostic du cancer
CN107034295A (zh) * 2017-06-05 2017-08-11 天津医科大学肿瘤医院 用于癌症早期诊断和危险度评价的dna甲基化指标及其应用
CN111020034A (zh) * 2019-12-31 2020-04-17 上海奕谱生物科技有限公司 新型的诊断肿瘤的标志物及其应用
WO2020109818A1 (fr) * 2018-11-29 2020-06-04 Ucl Business Ltd Méthylation différentielle
WO2021087275A1 (fr) * 2019-10-31 2021-05-06 Mayo Foundation For Medical Education And Research Détection du cancer de l'ovaire
WO2021240001A1 (fr) * 2020-05-29 2021-12-02 The Queen's University Of Belfast Méthode de diagnostic, de pronostic et/ou de surveillance du cancer de l'ovaire
CN113913522A (zh) * 2021-11-18 2022-01-11 武汉艾米森生命科技有限公司 子宫内膜癌检测的试剂及试剂盒
CN117887848A (zh) * 2024-01-16 2024-04-16 湖南宏雅基因技术有限公司 一种卵巢良恶性病变的生物标志物及其应用
EP4265738A4 (fr) * 2022-03-03 2025-05-07 Beijing Originpoly Bio Tec Co Ltd Combinaison de marqueurs de méthylation de l'adn, utilisation, sonde d'amorce pour la détection précoce du cancer ovarien et kit

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WO2016115354A1 (fr) * 2015-01-14 2016-07-21 Taipei Medical University Méthodes de diagnostic et de pronostic du cancer
CN107034295A (zh) * 2017-06-05 2017-08-11 天津医科大学肿瘤医院 用于癌症早期诊断和危险度评价的dna甲基化指标及其应用
CN107034295B (zh) * 2017-06-05 2021-04-06 天津医科大学肿瘤医院 用于癌症早期诊断和危险度评价的dna甲基化指标及其应用
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CN111020034B (zh) * 2019-12-31 2023-09-19 上海奕谱生物科技有限公司 新型的诊断肿瘤的标志物及其应用
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