title: Population analysis of microsatellite genotypes reveals a signature associated with ovarian cancer.
abstract: Ovarian cancer (OV) ranks fifth in cancer deaths among women, yet there remain few informative biomarkers for this disease. Microsatellites are repetitive genomic regions which we hypothesize could be a source of novel biomarkers for OV and have traditionally been under-appreciated relative to Single Nucleotide Polymorphisms (SNPs). In this study, we explore microsatellite variation as a potential novel source of genomic variation associated with OV. Exomes from 305 OV patient germline samples and 54 tumors, sequenced as part of The Cancer Genome Atlas, were analyzed for microsatellite variation and compared to healthy females sequenced as part of the 1,000 Genomes Project. We identified a subset of 60 microsatellite loci with genotypes that varied significantly between the OV and healthy female populations. Using these loci as a signature set, we classified germline genomes as 'at risk' for OV with a sensitivity of 90.1% and a specificity of 87.6%. Cross-analysis with a similar set of breast cancer associated loci identified individuals 'at risk' for both diseases. This study revealed a genotype-based microsatellite signature present in the germlines of individuals diagnosed with OV, and provides the basis for a potential novel risk assessment diagnostic for OV and new personal genomics targets in tumors.
authors.affiliation: Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, USA.
authors.forename: Natalie C
authors.initials: NC
authors.lastname: Fonville
authors.affiliation: Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, USA.
authors.forename: Zalman
authors.initials: Z
authors.lastname: Vaksman
authors.affiliation: Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, USA.
authors.forename: Lauren J
authors.initials: LJ
authors.lastname: McIver
authors.affiliation: Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, USA.
authors.forename: Harold R
authors.initials: HR
authors.lastname: Garner
mesh_terms: D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000235:genetics*; D016022:Case-Control Studies; D019295:Computational Biology; D030541:Databases, Genetic; D059472:Exome; D005260:Female; D020869:Gene Expression Profiling; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D005828:Genetics, Population / Q000379:methods; D006801:Humans; D018895:Microsatellite Repeats; D010051:Ovarian Neoplasms / Q000235:genetics* / Q000473:pathology; D010641:Phenotype; D057285:Precision Medicine; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors
chemical_list: D014408:Biomarkers, Tumor
keywords: 1000 genomes project; The Cancer Genome Atlas; biomarkers; breast cancer; ovarian cancer