Risk of ovarian cancer and inherited variants in relapse-associated genes.
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BACKGROUND: We previously identified a panel of genes associated with outcome of ovarian cancer. The purpose of the current study was to assess whether variants in these genes correlated with ovarian cancer risk. METHODS AND FINDINGS: Women with and without invasive ovarian cancer (749 cases, 1,041 controls) were genotyped at 136 single nucleotide polymorphisms (SNPs) within 13 candidate genes. Risk was estimated for each SNP and for overall variation within each gene. At the gene-level, variation within MSL1 (male-specific lethal-1 homolog) was associated with risk of serous cancer (p = 0.03); haplotypes within PRPF31 (PRP31 pre-mRNA processing factor 31 homolog) were associated with risk of invasive disease (p = 0.03). MSL1 rs7211770 was associated with decreased risk of serous disease (OR 0.81, 95% CI 0.66-0.98; p = 0.03). SNPs in MFSD7, BTN3A3, ZNF200, PTPRS, and CCND1A were inversely associated with risk (p<0.05), and there was increased risk at HEXIM1 rs1053578 (p = 0.04, OR 1.40, 95% CI 1.02-1.91). CONCLUSIONS: Tumor studies can reveal novel genes worthy of follow-up for cancer susceptibility. Here, we found that inherited markers in the gene encoding MSL1, part of a complex that modifies the histone H4, may decrease risk of invasive serous ovarian cancer.
Genetic Predisposition to Disease
Polymorphism, Single Nucleotide
Published Version (Please cite this version)10.1371/journal.pone.0008884
Publication InfoBerchuck, Andrew; Couch, FJ; Cunningham, JM; Elliott, EA; Fredericksen, ZS; Fridley, BL; ... White, KL (2010). Risk of ovarian cancer and inherited variants in relapse-associated genes. PLoS One, 5(1). pp. e8884. 10.1371/journal.pone.0008884. Retrieved from https://hdl.handle.net/10161/4525.
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James M. Ingram Professor of Gynecologic Oncology
Dr. Andrew Berchuck is Director of the Duke Division of Gynecologic Oncology and holds the James M. Ingram Distinguished Professorship. He is a practicing oncologist who is actively involved in the surgical and chemotherapy management of women with ovarian, endometrial and lower genital tract cancers. This includes minimally invasive laparoscopic surgical approaches. He also has developed a research program that focuses on the molecular-genetic alterations involved in malignant transformation of
Research Professor of Statistical Science
Bayesian statistical modeling with application to problems in genetic epidemiology and cancer research; models for epidemiological risk assessment, including hierarchical methods for combining related epidemiological studies; ascertainment corrections for high risk family data; analysis of high-throughput genomic data sets.
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