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WO2012135635A2 - Marqueurs biologiques de cancer de l'ovaire - Google Patents

Marqueurs biologiques de cancer de l'ovaire Download PDF

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Publication number
WO2012135635A2
WO2012135635A2 PCT/US2012/031484 US2012031484W WO2012135635A2 WO 2012135635 A2 WO2012135635 A2 WO 2012135635A2 US 2012031484 W US2012031484 W US 2012031484W WO 2012135635 A2 WO2012135635 A2 WO 2012135635A2
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sample
ovarian cancer
snv
nucleic acid
mutations
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WO2012135635A3 (fr
Inventor
Jian-Bing Fan
Russell GROCOCK
Keira CHEETHAM
Richard Shaw
Jeremy R. CHIEN
Vijayalakshmi Shridhar
Lynn Hartmann
Dirk Evers
John Peden
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Mayo Foundation for Medical Education and Research
Illumina Inc
Mayo Clinic in Florida
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Mayo Foundation for Medical Education and Research
Illumina Inc
Mayo Clinic in Florida
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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
    • C12Q2600/156—Polymorphic or mutational 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.
  • Survival rate once a diagnosis is made is typically dismal, for example if ovarian cancer is detected and effectively treated prior to metastasis the 5 year survival rate can be as high as 73%, however if the cancer is not detected until it has metastasized then the long term survival rate drops to ⁇ 30%.
  • ovarian cancer often goes undetected until it has metastasized within the pelvis and abdomen, therefore the outcome for most women is grim as the cancer is difficult to treat and is often fatal.
  • early detection ovarian cancer is crucial to benefit those patients, for example, that present with no or vague symptoms or with tumors that are below the level of detection during a physical examination.
  • a considerable amount of research effort has been focused in discovering and developing early detection systems, however to date no effective screening method has been developed.
  • what are needed are ways to detect ovarian cancer, preferably at an early stage, for example before metastasis, thereby improving the long term survival of those women afflicted with this disease.
  • the present disclosure identifies biological markers, or biomarkers, indicative of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer.
  • the disclosed biomarkers allow for identification of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a subject.
  • the methods described herein utilize the biomarkers and provide alternatives to currently available ovarian cancer determinative, diagnostic and prognostic methodologies.
  • the biomarkers and methods of their use as disclosed herein can be applied to the characterization, classification, differentiation, grading, staging, diagnosis, or prognosis of ovarian cancer, ovarian cancer type and/or ovarian cancer stage.
  • Embodiments as described herein are based in part on the identification of reliable biomarkers for the improved determination, screening, diagnosis and/or prognosis of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a subject.
  • the disclosure provides a population of gene targets or gene related targets (i.e., biomarkers) and methods of use as described herein.
  • Biomarkers and methods of their use for determining ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a subject comprise TP53, MUC2, MY016, ARID1A, CT B 1, CSMD3, TRRAP, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1 S, CACNA2D1, PIKFYVE, PIK3CA, NIPBL, PKHD1, MLL3, ATBF 1, TPO, DNAH7, LRRIQ1, SDHA, TIAM1, TTN, SLC16A7, COL3A1 and HNRNPUL1.
  • methods for determining the presence of ovarian cancer in a subject comprises providing a nucleic acid sample from a subject, detecting the presence of one or more variant mutations in two or more genes selected from the group consisting essentially of ARID 1 A, CACNA2D1, CTTNB1, PKHD1, DNAH7, PIKC3A, TRRAP and CSMD3 in the sample, evaluating the probability that that the two or more genes are correlated with ovarian cancer, and determining the presence of ovarian cancer in the sample based on the probability of correlation.
  • evaluating the probability comprises comparing the nucleic acid sample suspected of having ovarian cancer to a matched normal nucleic acid sample, wherein both the nucleic acid samples (i.e., test and normal) are genomic DNA samples.
  • a gene is correlated with ovarian cancer at p ⁇ 0.05.
  • the gene DNAH7 is one of the two or more genes identified as being correlated with ovarian cancer at p ⁇ 0.05.
  • genomic DNA for testing for presence of ovarian cancer from a subject is isolated from a sample selected from a group consisting of a tissue sample, a biopsy sample, a cell sample, a circulating tumor cell sample, a fixed tissue sample, a frozen tissue sample or a lavage sample
  • methods for determining the presence of ovarian cancer in a subject comprises providing a nucleic acid sample from a subject, detecting the presence of one or more variant mutations in two or more genes selected from the group consisting of ARID1A, CACNA2D1, CTTNB1, PKHD 1 , DNAH7, PIKC3 A, TRRAP and CSMD3 , evaluating the probability that that the two or more genes are correlated with ovarian cancer, and determining the presence of ovarian cancer in the sample based on the probability of correlation.
  • detecting mutations in genes correlated with ovarian cancer comprises sequencing, such as sequence by synthesis methodologies, microarray analysis and/or polymerase chain reaction
  • methods are described herein for determining the presence of ovarian cancer in a sample comprising creating a DNA library from nucleic acids derived from a sample suspected of having ovarian cancer, sequencing the DNA library to identify one or more mutations in two or more genes from the list consisting essentially of ARID 1 A, CACNA2D1, CTTNB1, PKHD l, DNAH7, PIKC3A, TRRAP and CSMD3, computationally determining the probability that the two or more genes are correlated with ovarian cancer, and determining the presence of ovarian cancer in said sample based on the probability of correlation.
  • computational determination of ovarian cancer correlated comprising comparing the sequence of the nucleic acid sample suspected of having ovarian cancer to a matched normal sample.
  • a gene is correlated with ovarian cancer at p ⁇ 0.05.
  • DNAH7 is one of the two or more genes identified as being correlated with ovarian cancer.
  • a test sample suspected of having ovarian cancer is isolated from a sample selected from the group consisting of a tissue sample, a biopsy sample, a cell sample, a circulating tumor cell sample, a fixed tissue sample, a frozen tissue sample or a lavage sample, wherein a matched normal sample may be isolated from either a blood sample or a tissue sample that does not have ovarian cancer.
  • a DNA library is sequenced using sequence by synthesis
  • a method for confirming the presence of ovarian cancer in a sample comprises comparing the sequence of a nucleic acid sample derived from a tissue suspected of having ovarian cancer with the sequence of a nucleic acid from a tissue not suspected of having ovarian cancer, wherein the presence of one or more variant sequences identified in two or more of ARIDIA, CACNA2D1, CTTNB l, PKHDl, DNAH7, PIKC3A, TRRAP and CSMD3 in the test sample as compared to the normal sample indicates the presence of ovarian cancer in a sample.
  • methods are described herein for determining the presence of ovarian cancer in a sample comprising evaluating a sample for the presence of one or more variant mutations in two or more genes selected from the group comprising ARIDIA, CTNNBl, CSMD3, TRRAP, MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S,
  • a sample under evaluation is compared to a matched normal nucleic acid sample.
  • the nucleic acid sample is genomic DNA from a human subject as is the matched normal nucleic acid sample.
  • a genomic DNA sample is isolated from a tissue sample, a biopsy sample a cell sample, a circulating tumor cell sample, a fixed tissue sample, a frozen tissue sample or a lavage sample.
  • the presence of one or more variant mutations is found in two or more genes selected from the group consisting essentially of MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHDl, MLL3, ATBFl, TPO, DNAH7, LRRIQl, SDHA, TIAMl, TTN, SLC16A7, COL3A1 and HNRNPULl.
  • the presence of one or more variant mutations is found in two or more genes selected from the group consisting of MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S,
  • determining ovarian cancer comprises determining the presence of high grade serous ovarian cancer, whereas in other embodiments determining ovarian cancer comprises determining the presence of non-high grade serous ovarian cancer. In some embodiments, determining ovarian cancer comprises determining the presence of serous ovarian cancer.
  • the presence of one or more variant mutations is found in two or more genes selected from the group consisting of MUC2, MY016,
  • OR4A47 CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHDl, MLL3, ATBFl, TPO, DNAH7, LRRIQl, SDHA, TIAMl, and TTN, further comprising determining the presence of high grade serous ovarian cancer based on the presence of the variant mutations.
  • the presence of one or more variant mutations is found in a group comprising SLC16A7, wherein the presence of one or more variant mutation determines the presence of non-high grade serous ovarian cancer in a sample.
  • the presence of one or more variant mutations is found in two or more genes selected from the group consisting essentially of ARIDIA, CTNNB1, CSMD3, TRRAP, PIKC3A, MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHDl, MLL3, ATBFl, TPO, DNAH7, LRRIQl, SDHA, TIAMl, TTN, SLC16A7, COL3A1 and HNRNPULl, the presence of which determines the presence of serous ovarian cancer.
  • a sample is evaluated for the presence of one or more variant mutations in the group comprising MUC2, MYO 16, HNRNPULl and COL3A1, the presence of which determines the presence of serous ovarian cancer. In some embodiments, a sample is evaluated for the presence of one or more variant mutations in the group consisting of MUC2, MYO 16, HNRNPULl and COL3A1, the presence of which determines the presence of serous ovarian cancer.
  • methods are described herein for determining the presence of high grade serous ovarian cancer in a sample comprising evaluating the sample for the presence of one or more variant mutations in two or more genes selected from the group comprising MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHDl, MLL3, ATBFl, TPO, DNAH7, LRRIQl, SDHA, TIAMl and TTN.
  • evaluating the sample for the presence of one or more variant mutations comprises evaluating the nucleic acid sample for one or more variant mutations in one or more of MUC2 and MY016 and one or more of OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S,
  • the evaluation further comprises evaluating one or more of DNAH7, LRRIQl, SDHA, TIAMl and TTN.
  • methods are described herein for determining the presence of serous ovarian cancer in a sample comprising evaluating the sample for the presence of one or more variant mutations in two or more genes selected from the group comprising MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, COL3A1, CACNA1S, CACNA2D1, PIKFYVE, NIPBL,
  • evaluating the sample for the presence of one or more variant mutations comprises evaluating the nucleic acid sample for one or more variant mutations in one or more of HNRNPULl and COL3A1 and one or more of MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHDl, MLL3, ATBFl, TPO, DNAH7, LRRIQl, SDHA, TIAMl and TTN.
  • evaluating the sample for the presence of one or more variant mutations comprises evaluating the nucleic acid sample for one or more variant mutations in one or more of HNRNPULl and COL3 A 1 , one or more of MUC2 and MYO 16 and one or more of OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S,
  • evaluating the sample for the presence of one or more variant mutations comprises evaluating the nucleic acid sample for one or more variant mutations in one or more of HNRNPUL1 and COL3A1, one or more of MUC2 and MY016, one or more of OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1 S, CACNA2D1, PIKFYVE, NIPBL, PKHD1, MLL3, ATBF 1 , TPO and one or more of DNAH7, LRRIQ 1 , SDHA, TIAM 1 and TTN.
  • evaluating a nucleic acid sample for the presence of ovarian cancer, high grade serous ovarian cancer or serous ovarian cancer comprises sequencing the nucleic acid sample. In some embodiments, sequencing a sample comprises sequence by synthesis methodologies. In some embodiments, evaluation of a nucleic acid sample for the presence of ovarian cancer, high grade serous ovarian cancer or serous ovarian cancer is carried out on a microarray. In some embodiments, evaluating a nucleic acid sample for the presence of ovarian cancer, high grade serous ovarian cancer or serous ovarian cancer comprises performing polymerase chain reaction on the sample, for example quantitative or real-time PCR.
  • Figure 2 is exemplary of the number of serous ovarian cancer patient samples
  • test sample is intended to mean any biological fluid, cell, tissue, organ or portion thereof that contains genomic nucleic acids, for example genomic DNA or RNA, suitable for mutational detection 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 patient matched normal sample can be used to establish a mutational background for comparison to a patient 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 the presence or absence of ovarian cancer, differentiation between early and/or late stage ovarian cancer types, ovarian cancer epithelial type differentiation, or 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 diagnostic aim of a biomarker as disclosed herein.
  • 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 mutational status of biomarkers for that individual.
  • the normal sample, or 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.
  • a biomarker can be DNA or RNA, proteins, polypeptides, variants, fragments or functional equivalents thereof.
  • a biomarker is generally associated with a genomic nucleic acid such as a gene or gene associated region or location unless specified otherwise.
  • Biomarkers disclosed herein that are associated with ovarian cancer, a particular type of ovarian cancer and/or a particular stage of ovarian cancer comprise one or more single nucleotide variants and/or insertions/deletions (indels) located in a gene or gene associated region as compared to its equivalent in a normal sample.
  • a gene that contains one or more somatic mutations, such as variant mutations, identified in two or more patient samples is contemplated to be a biomarker that is useful in detecting, diagnosing or prognosing ovarian cancer, a particular type of ovarian cancer and/or a stage of ovarian cancer.
  • Somatic mutation is an alteration in the genome that occurs after conception resulting in a genetic difference of the genome at that particular location. Somatic mutations can occur in any cell in the body except the germ cells and are passed to the cell progeny during cell division. Somatic mutations include, but are not limited to, point mutations such as single nucleotide variants (SNVs), gene amplification or duplication, genetic insertions and/or deletions (indels), chromosomal translocations, chromosomal inversions and single nucleotide polymorphisms (SNPs). Somatic mutations can result in phenotypic changes, disease formation, cancer, etc. "Somatic mutations" as used herein, unless otherwise stated, include SNVs and/or indels present in genomic DNA, and are considered variant mutations, or mutations that may result in phenotypic changes, disease formation, cancer, etc.
  • Identification of somatic variants described herein was performed by looking for a SNV or indel in the patient cancer sample which was not present in the patient matched normal sample. If a somatic mutation was found in the cancer sample which was not present in the normal sample, then that mutation was identified as a SNV or indel, as the case may be. If a somatic mutation was present in both the cancer sample and the normal sample, then that mutation was considered part of that patient's genetic background and was not considered a variant.
  • a gene from two or more patient samples that has one or more variant mutations is considered a biomarker and useful in detecting, diagnosing and prognosing ovarian cancer, a particular type of ovarian cancer and/or a stage of ovarian cancer.
  • 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). Typically, 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. Sequences located 3' or downstream of the coding region and present on the mRNA are referred to as 3' non-translated sequences.
  • 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.
  • 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.
  • the majority of 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. The vast majority of
  • Type II, high-grade serous ovarian cancers are diagnosed at advanced stages and represent a major challenge in early detection (Chan et al, 2006, Obs and Gyn 108: 521-528).
  • 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.
  • LMP low malignant potential
  • 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.
  • 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.
  • 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.
  • a patient cohort was sequenced as described herein and a subset of the candidate list was created (Table 2).
  • Gene locations as identified in the tables herein, for example in Table 2 are as found in the Archive Ensembl Human database, release 59-Aug 2010 (http://aug2010.archive.ensembl.org/Homo_sapiens/Info/Index) which provides human genomic data as assembled from the Genome Reference Consortium (GRC).
  • GRC consists of the Wellcome Trust Sanger Institute, the Genome Center at Washington University, the European Bioinformatics Institute and the National Center for Biotechnology Information.
  • the subset comprises biomarkers identified during sequencing that were correlated with ovarian cancer, such that one or more of the patient samples were identified to have one or more variant mutations in a gene, thereby classifying that gene as a biomarker for detecting, diagnosing, and prognosing ovarian cancer, a particular type of ovarian cancer and/or a stage of ovarian cancer.
  • Table 2-List of an exemplary subset from candidate list
  • CACNA1S IS subunit chrl:201008642-201081694 calcium channel, voltage-dependent, alpha 2/delta
  • CNBD1 cyclic nucleotide binding domain containing 1 chr8:88218216-88394955
  • FRG2C FSHD region gene 2 family member C chr3:75713481-75716371
  • HNRNPUL1 heterogeneous nuclear ribonucleoprotein U-like 1 chrl9:41768391-41813811
  • IL13RA1 interleukin 13 receptor, alpha 1 chrX:117861535-117928502
  • Phosphoinositide-3-kinase catalytic, alpha
  • solute carrier family 16 member 7 (monocarboxylic
  • the Table 2 list of biomarkers was collated based on data from a retrospective study comprising genomic samples from high-grade serous, low-grade serous, endometrioid, mucinous and clear cell epithelial type ovarian cancer tumors from the first sample cohort of 25 patients (Table 3, and Example 1).
  • the tier grade was determined as described in Vang 2009.
  • samples were selected with high tumor content prior to sequencing by selecting the tumor samples with at least 70% tumor nuclei during pathological review of fresh frozen tumor samples. Average coverage of a haploid genome was 40 fold with approximately 93% of exomic sequence and approximately 86% of the whole genomic sequence covered by at least 10 reads (Table 4).
  • the tumor with 385,369 mutations was from a patient with Stage IC high-grade serous ovarian cancer and was determined to represent a hypermutator phenotype as the patient had neither prior chemotherapy nor radiation therapy treatment and no pre-existing cancer prior to ovarian cancer diagnosis. Excluding the hypermutated sample, approximately 2130 mutations within gene coding regions and 36,936 mutations outside gene coding regions (i.e., non-coding regions) were identified in approximately 256 genes in one or more tumor samples in the study.
  • the tumor protein 53 (TP53 or p53) was identified as the most frequently mutated gene in high-grade ovarian cancer, with 15 out of 16 tumors showing mutations in TP53. Mutations of TP53 were identified throughout the coding regions, consistent with the mutation profiles of tumor suppressor genes (Jones et al, 2010, Science 330:228-31; Vogelstein and Kinzler, 2004, Nat Med 10:789-99).
  • experimentation identified 338 gene associated locations having statistically significant mutations in non-coding regions when compared to random background mutations. Since previous studies have demonstrated that distal enhancer elements could be as far away as 500 kb in a genome, it is contemplated that mutations in non-coding regions several hundred kilobases away from a promoter may possess functional significance in regulating gene expression and therefore also serve as viable biomarkers indicative of cancer, such as ovarian cancer.
  • Validation studies were performed on a different second patient cohort (i.e., different from the fist patient cohort) of 40 tumor tissue samples and matched normal tissue samples (i.e., validation sample cohort).
  • a validation cohort of 40 tumor normal and normal matched samples was obtained.
  • the validation tumor samples were formalin fixed, paraffin embedded (FFPE) samples and normal matched FFPE samples were obtained from fallopian tubes, ovary, or normal lymph nodes.
  • FFPE formalin fixed, paraffin embedded
  • the validation data identified nine biomarkers in particular that can be considered hot spots for mutations in ovarian cancer.
  • the nine biomarkers as listed in Table 7 comprise ARID 1 A, CACNA2D1, CSMD3, CTN B1, DNAH7, PIKC3A, PKHDl, TP53 and TRRAP and were identified upon data analysis from the validation sample cohort, wherein mutations in these genes were found to be correlated (p ⁇ 0.05) with the presence of ovarian cancer. Gene ID and gene location are reported as found in the Ensembl database as previously described.
  • biomarkers and their methods of use are described below.
  • the biomarkers and their methods of use are not limited to these embodiments.
  • Biomarkers as described herein find utility, either alone or in combination, in methods for diagnosing ovarian cancer, a type of ovarian cancer and/or a stage of ovarian cancer. Biomarkers as described herein find utility, either alone or in combination, in methods for prognosing patient outcome diagnosed with ovarian cancer, a type of ovarian cancer and/or a stage of ovarian cancer.
  • Biomarkers as described herein find utility, either alone or in combination, in methods for screening patients for the presence or absence of ovarian cancer, a type of ovarian cancer and/or a stage of ovarian cancer, for example for patients that might be part of a high risk population predisposed to developing ovarian cancer (e.g., family history, genetic predisposition, etc.).
  • the biomarkers as described herein, either alone or in combination, find utility as diagnostic, prognostic, or screening tools in conjunction with additional tests and methods for identifying ovarian cancer.
  • Additional tests and methods for identifying ovarian cancer include, but are not limited to, transvaginal sonography, protein staining methods such as IHC or histopathological staining such as H&E, genetic probe assays such as in situ hybridization (ISH), TNM and/or FIGO staging, clinical staging, pathological staging, etc., for example as recognized by the American Joint Committee on Cancer (AJCC) and/or the World Health Organization (AJCC Cancer Staging Manual). Additional tests and method for identifying ovarian cancer may include experimental or discovery related tests and methods that are not yet recognized as mainstream, however find utility in providing support for a diagnosis of ovarian cancer nonetheless.
  • a biomarker is a gene or genetic location that was identified to comprise one or more variant mutations in patient test samples as compared to the gene or gene location in a patient matched normal sample.
  • a biomarker that was identified to have variants mutations in at least two patient samples is contemplated to represent a "hot spot", or gene that comprises variants mutations as compared to other genes in an ovarian cancer test sample (e.g., tissue, cell, circulating tumor cells, etc.).
  • Table 2 and Table 7 are exemplary of genes that were identified in two or more patient samples to have variant mutations compared to the same gene is a patient matched normal sample, thereby identifying them as potential biomarkers for the presence or absence of ovarian cancer, type of ovarian cancer, and/or stage of ovarian cancer.
  • variant mutations in biomarkers as described herein are located in a coding region of a gene.
  • biomarkers as described herein are located in non-coding regions of a gene.
  • biomarkers as described herein are located in intergenic regions.
  • biomarkers as described herein comprise single nucleotide variants (SNVs).
  • biomarkers as described herein comprise insertions and/or deletions (indels) of one or more genomic sequences.
  • a biomarker may comprise both SNVs and indels.
  • the biomarkers as disclosed herein are useful in detecting the presence or absence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a subject.
  • the biomarkers as disclosed herein are useful in diagnosing the presence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a subject.
  • the biomarkers as disclosed herein are useful in prognosing disease progression, treatment outcome and/or treatment regimen progress of a subject diagnosed with ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer. In some
  • the biomarkers as disclosed herein are useful in screening a subject for the possibility of developing ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a subject.
  • the biomarkers as described herein are useful in screening potential therapeutic options for treating a patient having ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a subject.
  • the present disclosure provides biomarkers comprising non-coding region variant mutations for detecting the presence or absence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer.
  • ROB02 one or more of ROB02, CNTNAP2, PTPRN2, GPC5, LRP 1B, CSMD3, DLG2, CNTNAP5, DPP6, PCDH15, and CDH12 are biomarkers indicative of ovarian cancer as demonstrated in Table 9.
  • ROB02 codes for a member of the roundabout family of proteins involved in axonal guidance and neuronal migration.
  • CNTNAP2 and CNTNAP5 contactin associated protein genes are members of the neurexin family which function as cell adhesion molecules.
  • PTPR 2 is a member of the receptor protein tyrosine
  • phosphatase gene family the proteins of which are involved in regulation of a variety of cellular processes including, but not limited to, cell growth and oncogenic
  • GPC5 a glypican gene, codes for a cell surface proteoglycan
  • biomarkers associated with the presence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer comprise one or more of ROB02, PTPRN2, GPC5, CSMD3, HNT and CNTNAP5.
  • biomarkers associated with the presence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer further comprise one or more of DPP6, KCNIP4 and PCDH15.
  • biomarkers associated with the presence of ovarian cancer in a sample further comprise CNTNAP2, LRP1B, and DLG2.
  • biomarkers associated with the presence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer TP53, MUC2, MY016, OR4A47, CNBDl, GRMl, BRCA2, SLC5A7, MFNl, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHD1, PIK3CA, ARID 1 A, CTNNB1, CSMD3, TRRAP, MLL3, ATBF1, TPO, DNAH7, LRRIQ1, SDHA, TIAMl, TTN, SLC16A7, COL3A1 and HNRNPULl.
  • biomarkers comprising variant mutations in ovarian cancer patient samples comprise one or more of, two or more of, TP53, MUC2, MY016, OR4A47, CNBDl, GRMl, BRCA2, SLC5A7, MFNl, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHD1, PIK3CA, ARID 1 A, CTNNB1, CSMD3, TRRAP, MLL3, ATBF1, TPO, DNAH7, LRRIQ1, SDHA, TIAMl, TTN, SLC16A7, COL3A1 and
  • biomarkers comprising variant mutations in ovarian cancer patient samples and associated with the presence of high grade serous ovarian cancer in a sample comprise one or more of TP53, MUC2 and MY016 and one or more of OR4A47, CNBDl, GRMl, BRCA2, SLC5A7, MFNl, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHD1, MLL3, ATBF1, TPO, DNAH7, LRRIQ1, SDHA, TIAMl and TTN ( Figure 1).
  • biomarkers comprising variant mutations in three or more patient samples and associated with the presence of high grade serous ovarian cancer in a sample comprise TP53, MUC2 and MY016.
  • biomarkers comprising variant mutations in two or more patient samples and associated with the presence of high grade serous ovarian cancer in a sample comprise one or more OR4A47, CNBDl, GRMl, BRCA2, SLC5A7, MFNl, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHD1, MLL3, ATBF1, TPO, DNAH7, LRRIQ1, SDHA, TIAMl and TTN.
  • a method for diagnosing the presence of high grade serous ovarian cancer in a subject comprises identifying variant mutations in one or more of TP53, MUC2, MY016,OR4A47, CNBDl, GRMl, BRCA2, SLC5A7, MFNl, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHD1, MLL3, ATBF1, TPO, DNAH7, LRRIQ1, SDHA, TIAMl and TTN in a sample from a subject.
  • a method for diagnosing the presence of high grade serous ovarian cancer in a subject comprises identifying variant mutations in one or more of TP53, MUC2 and MY016 and one or more of OR4A47, CNBDl, GRMl, BRCA2, SLC5A7, MFNl, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHD1, MLL3, ATBF1, TPO, DNAH7, LRRIQ1, SDHA, TIAMl and TTN in a sample from a subject.
  • a method for diagnosing the presence of high grade serous ovarian cancer in a subject comprises identifying variant mutations in one or more of TP53, MUC2 and MY016 and one or more of OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1 S, CACNA2D 1 , PIKFYVE, NIPBL, PKHD 1 , MLL3 , ATBF 1 , TPO and one or more of DNAH7, LRRIQ1, SDHA, TIAMl and TTN in a sample from a subject.
  • a sample from a subject used in methods for diagnosing ovarian cancer as described herein is a tissue sample, for example a biopsy tissue sample, for example an ovarian tissue biopsy sample.
  • a biopsy tissue sample used in diagnostic methods described herein is a fresh sample, or a sample that has been frozen or modified.
  • a modified sample is, for example, a sample that has been preserved or modified for storage and/or for use in
  • a sample from a subject is a liquid sample, such as a blood sample containing white blood cells.
  • a liquid sample contains circulating tumor cells.
  • the liquid sample is a lavage, for example a vaginal lavage, a cervical lavage, wherein cells are harvested from the lavage sample.
  • a sample is a cell sample, such as a cervical cell sample, for example as derived from a Papanicolaou test (PAP smear) slide.
  • nucleic acids are extracted and isolated from a sample, or portion thereof for subsequent use in methods as described herein for determining the presence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a sample.
  • white blood cells from a blood sample were utilized as a patient matched normal sample for comparison to a tissue sample.
  • blood samples were obtained from a patient and matched to that patient's tissue sample for evaluation of biomarker mutations as described herein (Example 1).
  • the genomic DNA was isolated from the white blood cells and served as a patient baseline (normal) for comparing mutations present in the test sample.
  • tissue sample that is free from the cancerous phenotype can also be utilized as a source of comparative normal genomic DNA for a patient where appropriate and available.
  • nucleic acids isolated from a sample, or a portion thereof are used in diagnostic and/or prognostic methods.
  • the one or more biomarkers as described herein can be used in methods for determining ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a subject. Further, the biomarkers find utility in combination with other biomarkers and/or other diagnostic tests in providing a diagnostician additional tools to determine ovarian cancer status of a subject. The methods as described herein find particular utility as diagnostic and prognostic tools. In embodiments of the present disclosure, methods described herein can be used to diagnose ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a subject.
  • methods comprising biomarkers as described herein are useful in differentiating early stage, high grade serous ovarian cancer from late stage, high grade serous ovarian cancer. In some embodiments, methods comprising biomarkers as described herein are prognostic for patient survival due to said differentiating early stage, high grade serous ovarian cancer from late stage, high grade serous ovarian cancer.
  • the biomarkers comprise variant genetic sequences in a genomic DNA sample compared to a genomic DNA normal sample.
  • Variant genetic sequences comprise single nucleotide variants, sequence insertions, sequence deletions, within genes that differ from a normal sample and indicate mutations that may indicate phenotypic changes, disease formation, cancer, etc.
  • the methods detect one or more altered genetic sequences as compared to a normal sample.
  • a comparison between gene sequences in a test sample (i.e., collected from a patient, subject, individual, etc.) to a normal or control sample (i.e., from the sample patient, subject, individual from which the test sample is collected) identifies the number of mutations associated with a particular gene, wherein the presence of a variant gene over a normal may associate that gene with ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer as described herein.
  • methods disclosed herein detect the insertion of one or more genetic sequences into a gene, deletion of one or more genetic sequences from a gene, or both as compared to a normal, or control, sample.
  • the methods detect one or more of single nucleotide variant(s) and/or insertion(s) and/or deletion(s) altered genetic sequences in a sample compared to a normal, or control sample.
  • Biomarkers and methods of their use as described herein can be used to differentiate between high grade serous and non-high grade serous (i.e., low grade serous, mucinous, endometrioid, clear cell type ovarian cancer) ovarian cancer
  • Biomarkers useful for differentiating between high grade serous and non- high grade serous ovarian cancer comprise TP53, MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S,
  • a sample of known ovarian cancer is further identified as high grade serous ovarian cancer by detecting variant mutations in one or more of TP53, MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHDl, MLL3, ATBFl, TPO, DNAH7, LRRIQI, SDHA, TIAMl and TTN as compared to a normal, or control sample.
  • a sample of known ovarian cancer is further identified as non-high grade serous ovarian cancer by detection variant mutations in SLC16A7.
  • biomarkers and methods of their use as described herein differentiate between serous and non-serous (i.e., mucinous, endometrioid, clear cell type ovarian cancer) ovarian cancer in a sample ( Figure 2).
  • Biomarkers useful for differentiating between serous and non-serous ovarian cancer comprise one or more of TP53, MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, COL3A1, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHDl, MLL3, ATBFl, HNRNPULl, TPO, DNAH7, LRRIQI, SDHA, TIAMl and TTN.
  • a method for diagnosing the presence of serous ovarian cancer in a subject comprises identifying variant mutations in one or more of TP53, MUC2 and MY016 and one or more of OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, COL3A1, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHDl, MLL3, ATBFl, HNRNPULl, TPO, DNAH7, LRRIQI, SDHA, TIAMl and TTN in a sample from a subject.
  • a method for diagnosing the presence of serous ovarian cancer in a subject comprises identifying variant mutations in one or more of TP53, MUC2 and MY016, one or more of OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, COL3A1, CACNA1S, CACNA2D1, PIKFYVE, NIPBL, PKHD1, MLL3, ATBF 1,
  • a test sample i.e., a sample to be assayed for presence of ovarian cancer
  • a second sample e.g., a normal or control sample (e.g., blood sample, tissue sample known not to have a cancerous phenotype) is collected from the same individual.
  • Genomic DNA is isolated from the sample(s) by techniques known in the art (for example, as found in Molecular Cloning, a Laboratory Manual, Eds. Sambrook, et al, Cold Spring Harbor Press.).
  • the isolated DNA from a sample is used in methods as described herein for detecting biomarkers indicative of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a sample.
  • the isolated DNA from the test and control samples are subjected to sequencing, for example next generation sequencing methodologies.
  • Sequence data from the test and the control DNA samples are compared, for example by aligning the two sequences, variant sequences are identified in the test sequence over the control sequence and the presence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a sample is identified based on said comparison.
  • isolated genomic DNA from a sample is used to identify variant mutations in a genetic sequence, wherein genes comprising variant mutations relative to a normal sample are associated with the presence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a sample.
  • a subset of biomarkers as found in Table 1 is used to determine the presence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a sample in a sample.
  • the subset can represent one or more, two or more, three or more, four or more, five or more, or six or more biomarkers with variant mutations from the subset of which is indicative of the presence of ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer in a sample in a sample.
  • the subset of biomarkers comprising AC008021.1, ADAMTS20, ANK3, ANKRD30A, ARID 1 A, ASPN, ATBFl, BRCA2, CACNA1S, CACNA2D1, CCDC141, CDH11, CHRM3, CNBD1, CAL3A1, CSMD3, CTNNB1, DNAH11, DNAH7, FAT3, GPR112, GRP115, GPR179, GRM3, HNRNPUL1, HSDL2, IL13RA1, KIAA1109, KIF24, KNDC1, LRRC53, LRRIQl, MFN1, MLL3, MUC16, MUC2, MUC4, MYH6, MY016, NEB, NHS, NIPBL, OR4A47, PIK3CA, PIKFYVE, PKHDl, RPGR, SACS, SDHA, SLC16A7, SLC5A7, THOC2, TIAM1, TP53, TPO, TRRAP, TTN, USP17
  • An additional subset comprising biomarkers TP53, PKHDl, PIK3CA, CACNA2D1, DNAH7, ARID 1 A, CTNNB1, CSMD3 and TRRAP may be useful for indicating the presence of ovarian cancer.
  • An additional subset encompassing biomarkers comprising TP53, MUC2, MY016, OR4A47, CNBD1, GRM1, BRCA2, SLC5A7, MFN1, CHRM3, NEB, CACNA1S,
  • CACNA2D1, PIKFYVE, NIPBL, PKHDl, MLL3, ATBFl, TPO, DNAH7, LRRIQl, SDHA, TIAMl and TTN may be useful for indicating the presence of high grade serous type ovarian cancer.
  • An additional subset encompassing biomarkers comprising SLC16A7 is useful for indicating the presence of non-high grade serous type ovarian cancer.
  • biomarkers with variant mutations as identified in two or more patient samples from the sample cohort or a subset thereof as described herein may be correlated with the presence of ovarian cancer and/or a type of ovarian cancer (e.g., high grade serous, serous, non- high grade serous, non-serous, endometrioid) and/or stage of ovarian cancer (e.g., early stage serous versus late stage serous, etc.).
  • a type of ovarian cancer e.g., high grade serous, serous, non- high grade serous, non-serous, endometrioid
  • stage of ovarian cancer e.g., early stage serous versus late stage serous, etc.
  • the gene Mucin 2 (MUC2) is characterized herein as a biomarker that was mutated in at least four patient samples and is herein associated with ovarian cancer, a type of ovarian cancer, and/or stage of ovarian cancer. This gene has not been previously associated with the presence of ovarian cancer.
  • the gene Cyclic Nucleotide Binding Domain Containing Protein 1 (CNBD1) has been characterized herein as a biomarker that was mutated in at least three patient samples and is herein associated with ovarian cancer, a type of ovarian cancer, and/or stage of ovarian. This gene has not been previously associated with the presence of ovarian cancer.
  • DNAH7 The gene dynein, axonemal, heavy chain 7 (DNAH7) is characterized herein as a biomarker that was mutated in at least five validation tumor samples and had not been previously statistically associated with ovarian cancer.
  • DNAH7 belongs to the dynein heavy chain family and is a component of the inner dynein arm of ciliary axonemes. These axomenes are expressed in the cytoplasm of bronchial epithelial cells and serve as the force generating protein of respiratory cilia by the motion of their microtubules.
  • Dynein has ATPase activity and the force producing power strike is contemplated to occur on release of ADP.
  • the fallopian tube consists
  • ovarian fimbriae predominately of two types of cells of which ciliated cells predominate throughout the tube, particularly in the ampulla and infundibulum which is surrounded by fimbriae and this includes the ovarian fimbriae that is attached to the ovary.
  • the majority of ovarian cancers can be classified as "epithelial” in type, however it has been suggested that the fallopian tube could also be the source of some types of ovarian cancer (2008, Piek et al, Adv Exp Med Biol 622:79-87; incorporated herein by reference in its entirety).
  • tubal fimbria is viewed as being a preferred site for early adenocarcinoma in women with familial ovarian cancer (2006, Medeiros et al, Am J Surg Pathol 30:230-236); incorporated herein by reference in its entirety). It is herein contemplated that, as DNAH7 is expressed in the ciliated cells of the fallopian tube and fimbriae, somatic variations within the gene may give rise to ovarian cancer and/or increase the aggressiveness of ovarian cancer. Diagnostic methods utilizing biomarkers as described herein are contemplated to be useful for identifying the presence or absence of ovarian cancer in a patient, the type of ovarian cancer present and/or the stage of ovarian cancer present in a patient.
  • epithelial ovarian carcinomas e.g., serous, mucinous, endometrioid, clear cell, transitional cell, squamous cell, undifferentiated and mixed epithelial tumors
  • the difficulty in diagnosing early stage epithelial ovarian carcinomas is high and the disease is typically left undiagnosed thereby leading to poor overall prognosis once the late stage carcinoma is diagnosed.
  • Diagnostic methods utilizing biomarkers as described herein are contemplated to provide for early stage disease diagnosis thereby providing a patient with a more favorable prognostic outcome.
  • Prognostic methods utilizing biomarkers as described herein are contemplated to be useful for determining a proper course of treatment for a patient having ovarian cancer.
  • a course of treatment refers to the therapeutic measures taken for a patient after diagnosis or after treatment for ovarian cancer. For example, a determination of the likelihood for cancer recurrence, spread, or patient survival, can assist in determining whether a more conservative or more radical approach to therapy should be taken, or whether treatment modalities should be combined. For example, when ovarian cancer recurrence is likely, it can be advantageous to precede or follow surgical treatment with chemotherapy, radiation, immunotherapy, biological modifier therapy, gene therapy, vaccines, and the like, or adjust the span of time during which the patient is treated.
  • a diagnosis or prognosis of an ovarian cancer state is contemplated to be correlated with one or more, for example a particular combination, of biomarkers described herein comprising two or more gene mutations.
  • Methods utilizing biomarkers as described herein are contemplated to be useful for monitoring recurrence of ovarian cancer following or during a course of treatment in a patient diagnosed with ovarian cancer.
  • Treatment of a patient diagnosed with ovarian cancer includes, but is not limited to, surgery, chemotherapy, radiation, immunotherapy, biological modifier therapy and gene therapy.
  • the methods utilizing biomarkers as described here are contemplated to be useful in determining the success, failure and/or progress of the treatment regimen.
  • Such information can be used by a clinician to determine a change in treatment course and/or future action or treatment for a patient.
  • a sample is obtained, nucleic acids for example DNA are isolated from the sample by established means known in the art, and the isolated nucleic acids are assayed by methods described herein.
  • a normal or control sample is typically obtained for comparison with the test sample.
  • Methods described herein are contemplated for use in, for example, characterizing the variant mutational status of one or more biomarkers as found in Table 1, wherein the variant mutational status of one or more biomarkers is useful in determining ovarian cancer status.
  • methods for characterization comprise sequencing technologies, for example next generation sequencing technologies.
  • microarray based technologies are utilized to characterize the mutational status of a biomarker as described herein for determining the status of ovarian cancer in a sample.
  • a sample is assayed for methylation status, the data of which is used to characterize a sample for ovarian cancer status.
  • Isolated genomic DNA from samples is typically modified prior to characterization.
  • genomic DNA libraries are created which are applied to downstream detection applications.
  • a library is produced, for example, by performing the methods as described in the NexteraTM DNA Sample Prep Kit (Epicentre® Biotechnologies, Madison WI), GL FLX Titanium Library Preparation Kit (454 Life Sciences, Branford CT), SOLiDTM Library Preparation Kits (Applied BiosystemsTM Life Technologies, Carlsbad CA), and the like.
  • the sample as described herein may be further amplified for sequencing by, for example, multiple stand displacement amplification (MDA) techniques.
  • MDA multiple stand displacement amplification
  • an amplified sample library is, for example, prepared by creating a DNA library as described in Mate Pair Library Prep kit, Genomic DNA Sample Prep kits or TruSeqTM Sample Preparation and Exome Enrichment kits (Illumina®, Inc., San Diego CA).
  • Useful cluster amplification methods are described, for example, in U.S. Patent No. 5,641,658; U.S. Patent Publ. No. 2002/0055100; U.S. Patent No. 7, 115,400; U.S. Patent Publ. No. 2004/0096853; U.S. Patent Publ. No. 2004/0002090; U.S. Patent Publ. No. 2007/0128624; and U.S. Patent Publ. No.
  • Genomic DNA libraries derived from a sample as described herein can be characterized for ovarian cancer status by sequencing for the presence of gene mutations. In one embodiment, sequencing can be performed following
  • BiosystemsTM Life Technologies (ABI PRISM® Sequence detection systems, SOLiDTM System), Ion Torrent® Life Technologies (Personal Genome Machine sequencer) further as those described in, for example, in United States patents and patent applications 5,888,737, 6, 175,002, 5,695,934, 6,140,489, 5,863,722,
  • current technology typically utilizes a light generating readable output, such as fluorescence or luminescence, however the present methods for detecting mutations in a biomarker for determining ovarian cancer status in a sample is not necessarily limited to the type of readable output as long as differences in output signal for a particular sequence of interest can be determined.
  • analysis software examples include, but are not limited to, Pipeline, CASAVA and GenomeStudio data analysis software (Illumina®, Inc.), SignalMap and NimbleScan data analysis software (Roche NimbleGen), GS Analyzer analysis software (454 Life Sciences), SOLiDTM, DNASTAR® SeqMan® NGen® and Partek® Genomics SuiteTM data analysis software (Life Technologies), Feature Extraction and Agilent Genomics Workbench data analysis software (Agilent Technologies), Genotyping ConsoleTM, Chromosome Analysis Suite data analysis software (Affymetrix®).
  • a skilled artisan will know of additional numerous commercially and academically available software alternatives for data analysis for sequencing generated output.
  • Embodiments described herein are not limited to any data analysis method.
  • the number of mutations in a biomarker can be detected using microarray methodologies.
  • 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 to detect the number of mutations in a biomarker include, but are not limited to, slide arrays, silicon wafer arrays, liquid arrays, bead-based arrays and others known in the art or set forth in further detail below.
  • the methods 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.
  • Exemplary methods and systems for microarray analysis includes, but is not limited to, those methods and systems commercialized by Roche NimbleGen,
  • An array of beads can also be in a fluid format such as a fluid stream of a flow cytometer or similar device.
  • fluid formats for distinguishing beads include, for example, those used in XMAPTM technologies from Luminex or MPSSTM methods from Lynx Therapeutics.
  • microarray methods and systems can be found in, for example, US patents 5,856,101, 5,981,733; 6,001,309; 6,023,540, 6, 110,426, 6,200,737, 6,221,653; 6,232,072, 6,266,459, 6,327,410, 6,355,431, 6,379,895, 6,429,027, 6,458,583, 6,667,394 6,770,441, 6,489,606 and 6,859,570, 7, 106,513, 7,126,755, and 7, 164,533, US patent applications 2005/0227252, 2006/0023310, 2006/006327, 2006/0071075, 2006/0119913 and PCT publications WO98/40726, W099/18434, WO98/50782, WO00/63437, WO04/024328 and WO05/033681 (each of which is incorporated herein by reference in their entireties). Microarray based technologies for characterizing ovarian cancer are contemplated to be useful either
  • PCR polymerase chain reaction
  • a plurality of probes may be utilized to amplify genomic regions contemplated to comprise one or more somatic variants wherein amplification data are used to determine the presence or absence of variants thereby associating a sample with ovarian cancer, or not, as the case may be.
  • Exemplary methods and systems for PCR analysis include, but are not limited to those methods and systems commercialized by Illumina®, Inc., Roche Applied Science and Applied BiosystemsTM. Polymerase chain reaction based technologies for characterizing ovarian cancer are contemplated to be useful either alone or in combination with other diagnostic and/or prognostic assays.
  • the genes described herein were identified from a group of 25 early-stage ovarian cancers, of predominantly serous histology. Twenty-five patients with known ovarian cancer were enrolled in a study to identify ovarian cancer related biomarkers under approved protocol from the Internal Review Board at the Mayo Foundation for Medical Education and Research, Rochester, MN. One of the 25 patient tumor samples was determined to represent a hypermutated tumor phenotype. Blood samples were collected from the enrolled patients and processed under approved IRB protocols. The blood sample from a patient was matched with its corresponding tissue sample and served as the patient matched normal sample for that patient in determining gene associated variant mutations.
  • Genomic DNA was extracted and isolated from a portion of a fresh frozen biopsy tissue sample using the Gentra® Puregene® Tissue Kit (QIAGEN, Inc., Valencia CA) following manufacturer's protocol.
  • a blood sample was drawn from each patient by venous puncture into a Vacutainer® tube containing EDTA. The tube was centrifuged to separate the white blood cells, or buffy coat, from the red blood cells.
  • DNA was extracted from 100- 200 ⁇ 1 of buffy coat cells using the AutoGen prep 965 (AutoGen, Holliston MA) or Gentra® Puregene® Tissue Kit following manufacturer's protocol.
  • the genomic blood DNA was matched with its respective tissue isolated genomic DNA for each patient.
  • Genomic DNA libraries were generated by adding 4 ⁇ g of sample DNA to the Paired End Sample prep kit PE-102-1001 (Illumina®, Inc.) following manufacturer's protocol. Briefly, DNA fragments are generated by random shearing and conjugated to a pair of oligonucleotides in a forked adaptor configuration. The ligated products are amplified using two oligonucleotide primers, resulting in double-stranded blunt- ended products having a different adaptor sequence on either end.
  • Clusters were formed prior to sequencing using the V3 cluster kit (Illumina®, Inc.). Briefly, products from a DNA library preparation are denatured and single strands annealed to complementary oligonucleotides on the flow-cell surface. A new strand is copied from the original strand in an extension reaction and the original strand is removed by denaturation. The adaptor sequence of the copied strand is annealed to a surface-bound complementary oligonucleotide, forming a bridge and generating a new site for synthesis of a second strand. Multiple cycles of annealing, extension and denaturation in isothermal conditions resulted in growth of clusters, each approximately 1 ⁇ in physical diameter.
  • the DNA in each cluster is linearized by cleavage within one adaptor sequence and denatured, generating single- stranded template for sequencing by synthesis (SBS) to obtain a sequence read.
  • SBS sequencing by synthesis
  • the products of read 1 are removed by denaturation, the template is used to generate a bridge, the second strand is re-synthesized and the opposite strand is then cleaved to provide the template for the second read.
  • the Genome Analyzer IIx is designed to perform multiple cycles of sequencing chemistry and imaging to collect sequence data automatically from each cluster on the surface of each lane of an eight-lane flow cell.
  • the aligned reads were aggregated and sorted into chromosomes based on alignment positions.
  • the sorted reads were used to call variants using Hyrax, a Bayesian S V caller and GROUPER.
  • the callers are part of the standard CASAVA 1.8 distribution and were run with default parameters. This process was carried out for the tumor and normal genomes.
  • Somatic single nucleotide variant subtraction for calling somatic SNVs was performed by taking the list of positions in the tumor genome with snp quality values greater than 15 (Q(snp)tumor >15) and high confidence of the assigned genotype given the polymorphic prior (Q(max_gt)tumor >20). For each putative SNV the normal sample was investigated. If a call was present in the normal sample at the same position as a putative SNV, and if the call had a quality value greater than 0 (Q(snp) normal >0), the position was filtered out as background.
  • the putative SNVs were recalled (using Hyrax) in the tumor sample, however for recalling additional information from candidate indel contigs constructed from the normal sample was used. This process was utilized to avoid any indels that were initially missed in the tumor due to low supporting evidence. A candidate SNV was called when there was complete agreement between the initial SNV call and the recall.
  • variants were also recalled in the normal sample, using Hyrax with all read filtering turned off. If the posterior probability of the tumor genotype was higher than a non-reference genotype, then that SNV was considered to have low confidence evidence in the normal sample and was discarded.
  • insertion/deletions only those indels that were confidently called in the tumor sample and not present in the normal sample were considered. Indels in the tumor with a Q score less than 30 (Q(indel) ⁇ 30) and those positions that had less than 10 reads coverage in the normal sample (for a 3 OX build) were filtered out. To be considered as evidence, a read had a single read alignment score >10 and a paired read alignment score >90. Positions were excluded if they mapped within 1000 bases of a known centromere or telomere (as obtained from the reference genome
  • the indel position was matched with the region and calls present in the normal sample. If a putative somatic indel position overlapped with an indel call originating from the normal sample, the indel was considered to be present in normal germline and hence the position was filtered out.
  • the putative somatic indel region was characterized by finding the shortest sequence around the indel that extended outside any repeats and that region was matched with each intersecting read in the normal sample. If there was evidence in the normal sample having the same pattern in the intersecting normal reads the candidate somatic indel was discarded. To account for homopolymer slippage (i.e., the sequencing polymerase misincorporates, or misses, one or more bases in a sequence of identical bases) during a run, one read was allowed in the normal sample to have the same indel as found in the tumor. After sequence calling, each class of variant was annotated against the Ensembl database release e59. Each somatic variant was queried for overlapping annotated features.
  • Regulatory regions e.g., 3' and 5' untranslated regions
  • Table 10 is exemplary of the variant mutations identified in gene coding regions from the different patient samples.
  • biomarkers identified in Table 9 wherein non-coding regions variants identify one or more biomarkers, to visualize the entire spectrum of genomic somatic mutations (e.g., coding regions, conserved regions, non-coding regions, repeat
  • EXAMPLE 4- Validation cohort sample sequencing and data analysis A validation cohort of 40 tumor and matched normal sample pairs (FFPE) were obtained and processed as described in Example 1 to provide genomic DNA for sequencing.
  • the study population comprised female adults from 18-80 years of age.
  • Some cytosine bases in DNA extracted from FFPE samples can be subject to random deamination resulting in a cytosine base being converted to uracil and in turn misread as a thymidine base. This conversion typically occurs randomly in approximately 0.5- 1.5% of all cytosine bases found in FFPE derived DNA.
  • the random deamination can lead to false positive predictions of somatic OT or G>A mutations during sequencing.
  • the validation design was a 1000X deep sequencing of a targeted pull down (i.e., enrichment).
  • a targeted pull down is an experimental design which targets a subset of the genome by designing complimentary DNA probes which hybridize to, or pull down, randomly fractionated genomic DNA (i.e., a DNA library as described in Example 1).
  • the hybridized DNA can be enriched for fragments that overlap the probe region and the enriched DNA eluted and sequenced to high depth.
  • the targeted pull down was achieved by using an Illumina, Inc. TruSeq Custom Enrichment Array Kit for in solution capture for targeting and enriching user selected human genomic regions.
  • AVP3, AVP4 and AVP5 were designed, each containing the content of previous generations plus additional probes to additional targeted genes.
  • the probes were designed to target previously defined DNA gene regions (i.e., such as those genes identified in Tables 1 and 2), or regions in genes subsequently identified as potential ovarian cancer gene biomarkers.
  • Table 1 1 describes the enrichment design for targeted genes in the validation cohort.
  • the enrichment design AVP3 utilized 4071 probes which targeted 1200213 bases over 215 genes (Table 12).
  • the enrichment design AVP4 utilized 5097 probes which targeted 1473936 bases over 245 genes (Table 12) and enrichment design AVP5 utilized 5490 probes which targeted a total of 1578057 bases over 271 genes (Table 12).
  • the probe designs for the TruSeqTM Custom Enrichment Array Kit consisted of a subset of probes taken from the TruSeqTM Exome Enrichment Kit, the probe design data of which is available within the kit. Further, a skilled artisan following known and established protocols will understand how to define probes for targeted enrichment assays. Table 12 describes the genes targeted, the enrichment assay and whether they were enriched in assay design AVP3, 4 and/or 5. Table 1 1- Enrichment assay design for validation cohort samples
  • sequence data was aligned to human reference genome HG19/GRCh37 using CASAVA-1.8 data analysis software and all optical and PCR duplicates were tagged as duplicates by the software using default parameters.
  • Tumour specific mutations were detected in each sample by selecting those reads with properly matched Rl and R2 read pairs as determined by CASAVA (i.e., those reads wherein the orientation of Rl and R2 read pairs and the size of the DNA fragment were within normal limits) and which were not tagged as duplicate reads, and by subtracting matching normal tissue variants from the tumor variants using the Strelka method for somatic SNV and small indel detection from sequencing data of matched
  • VEP Variant Effect Predictor
  • splice region variant were considered as having a functional impact on the downstream protein of any gene overlapping the mutations (see VEP documentation http://www.ensembl.org/info/docs/variation/predicted_data.html. All other exonic variants were classified as non- functional P-values from Fisher's exact test using number of functional versus all (functional and non- functional) mutations annotated to the gene (excluding intronic mutations).
  • a Condel score was also utilized to integrate the output of computational tools aimed at assessing the impact of non-synonymous SNVs on protein function by computing a weighted average of the scores (WAS) of computational tools, such as SIFT, Polyphen2, MAPP, LogR Pfam e-value (2004, Clifford et al, Bioinformatics 20: 1006-1014; incorporated herein by reference in its entirety) and MutationAssessor.
  • WAS weighted average of the scores
  • the scores of different methods are weighted using the complementary cumulative distributions produced by the five methods on a dataset of approximately 20000 missense mutations, both deleterious and neutral.
  • the probability that a predicted deleterious mutation is not a false positive of the method and the probability that a predicted neutral mutation is not a false negative are employed as weights.
  • a high Condel score, for example above 0.5, represents mutations more likely than not to be deleterious whereas a low Condel score represents the opposite (2011,

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Abstract

La présente invention concerne des marqueurs biologiques et des procédés pour les utiliser dans la détermination de la présence ou de l'absence d'un cancer. Dans des modes de réalisation préférés, des marqueurs biologiques et des procédés pour les utiliser permettent de déterminer la présence ou l'absence d'un cancer de l'ovaire, d'un type de cancer de l'ovaire et/ou d'un stade de cancer de l'ovaire dans un échantillon provenant d'un individu.
PCT/US2012/031484 2011-03-30 2012-03-30 Marqueurs biologiques de cancer de l'ovaire Ceased WO2012135635A2 (fr)

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WO2015156740A1 (fr) * 2014-04-08 2015-10-15 Agency For Science, Technology And Research Marqueurs du cancer de l'ovaire et leurs utilisations
CN111635942A (zh) * 2020-06-22 2020-09-08 中国人民解放军空军军医大学 评估女性恶性肿瘤风险的突变基因群、文库及试剂盒
CN114966027A (zh) * 2022-04-30 2022-08-30 重庆大学附属肿瘤医院 一种三个基因表达水平的检测试剂在制备卵巢癌样本干性鉴定试剂中的应用

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101432173B1 (ko) * 2013-01-22 2014-08-22 한국원자력의학원 Ｈｎｒｎｐｕｌ1을 측정하는 제제를 포함하는 방사선 저항성 또는 민감성 진단용 조성물 및 이의 용도
WO2015156740A1 (fr) * 2014-04-08 2015-10-15 Agency For Science, Technology And Research Marqueurs du cancer de l'ovaire et leurs utilisations
CN106460064A (zh) * 2014-04-08 2017-02-22 新加坡科技研究局 卵巢癌标志物及其用途
CN111635942A (zh) * 2020-06-22 2020-09-08 中国人民解放军空军军医大学 评估女性恶性肿瘤风险的突变基因群、文库及试剂盒
CN114966027A (zh) * 2022-04-30 2022-08-30 重庆大学附属肿瘤医院 一种三个基因表达水平的检测试剂在制备卵巢癌样本干性鉴定试剂中的应用

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