Genomic Rearrangement Signatures and Clinical Outcomes in High-Grade Serous Ovarian Cancer.
Ontology highlight
ABSTRACT: Background:To identify clinically relevant genomic rearrangement signatures in high-grade serous ovarian cancer (HGSOC), we conducted a retrospective analysis of sequenced HGSOC whole-tumor genomes. Methods:Clinical data and whole-genome sequencing (WGS) reads were obtained for primary HGSOC tumors sequenced by the Australian Ovarian Cancer Study (AOCS; n?=?80). Genomic rearrangements were identified, and non-negative matrix factorization (NMF) was used to extract rearrangement signatures. The cohort was then dichotomized around the median signature contribution, and overall survival (OS) was analyzed. An independent cohort from The Cancer Genome Atlas (TCGA) ovarian cancer study (n?=?490) was also examined. The TCGA cohort was dichotomized around the median similarity between tumor copy number profile and a prognostic rearrangement signature, and OS was analyzed. Outcomes were assessed using Kaplan-Meier and multivariable Cox regression methods. All statistical tests were two-sided. Results:We identified five genomic rearrangement signatures (Ov.RS1-5) in HGSOC. Ov.RS3 exhibited 10 kilobase to 10 megabase deletions and tandem duplications, and patients whose tumors exhibited a high contribution from Ov.RS3 had poor OS. The median OS was 22.7?months (95% confidence interval [CI] = 20.2 to 39.0?months) in the Ov.RS3-high group vs 38.2?months (95% CI?=?22.7 to 69.1?months) in the Ov.RS3-low group (hazard ratio [HR] = 1.86, 95% CI?=?1.12 to 3.09, P = .02). For the independent TCGA cohort, median OS rates were 38.0?months (95% CI?=?35.3 to 41.4?months) in the Ov.RS3 high-similarity group vs 48.9?months (95% CI?=?44.1 to 57.1?months) in the Ov.RS3 low-similarity group (HR?=?1.54, 95% CI?=?1.21 to 1.97, P < .001). Conclusion:A novel genomic rearrangement signature is associated with poor prognosis in HGSOC.
SUBMITTER: Hillman RT
PROVIDER: S-EPMC6054271 | biostudies-literature | 2018 Mar
REPOSITORIES: biostudies-literature
ACCESS DATA