Background: Invasive ovarian cancer is a significant cause of gynecologic cancer mortality. Methods: We examined whether this mortality was associated with inherited variation in approximately 170 candidate genes/regions [993 single-nucleotide polymorphisms (SNPs)] in a multistage analysis based initially on 312 Mayo Clinic cases (172 deaths). Additional analyses used The Cancer Genome Atlas (TCGA; 127 cases, 62 deaths). For the most compelling gene, we immunostained Mayo Clinic tissue microarrays (TMA, 326 cases) and conducted consortium-based SNP replication analysis (2,560 cases, 1,046 deaths). Results: The strongest initial mortality association was in HGF (hepatocyte growth factor) at rs1800793 (HR = 1.7, 95% CI = 1.3-2.2, P = 2.0 × 10 -5) and with overall variation in HGF (gene-level test, P = 3.7 × 10 -4). Analysis of TCGA data revealed consistent associations [e.g., rs5745709 (r 2 = 0.96 \with rs1800793): TCGA HR = 2.4, CI = 1.4-4.1, P = 2.2 × 10 -3; Mayo Clinic + TCGA HR = 1.6, CI = 1.3-1.9, P = 7.0 × 10 -5] and suggested genotype correlation with reduced HGF mRNAlevels (P = 0.01). In Mayo Clinic TMAs, protein levels of HGF, its receptor MET (C-MET), and phospho-MET were not associated with genotype and did not serve as an intermediate phenotype; however, phospho-MET was associated with reduced mortality (P = 0.01) likely due to higher expression in early-stage disease. In eight additional ovarian cancer case series, HGF rs5745709 was not associated with mortality (HR = 1.0, CI = 0.9-1.1, P = 0.87). Conclusions: Weconclude that although HGF signaling is critical to migration, invasion, and apoptosis, it is unlikely that HGF genetic variation plays a major role in ovarian cancer mortality. Furthermore, any minor role is not related to genetically-determined expression. Impact: Our study shows the utility of multiple data types and multiple data sets in observational studies.
ASJC Scopus subject areas