Integrated multiomic predictors for ovarian cancer survival

• Published in: Carcinogenesis (New York) Vol. 39; no. 7; pp. 860 - 868
• Main Authors: Fu, Alan, Chang, Helena R, Zhang, Zuo-Feng
• Format: Journal Article
• Language: English
• Published: UK Oxford University Press 03.07.2018
• Subjects: Biomarkers, Tumor - genetics; Cancer Biomarkers and Molecular Epidemiology; DNA Methylation - genetics; Epigenesis, Genetic - genetics; Exome - genetics; Female; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic - genetics; Genetic Variation - genetics; Genome, Human - genetics; Humans; Middle Aged; Ovarian Neoplasms - genetics; Prognosis; Progression-Free Survival; Transcriptome - genetics
• ISSN: 0143-3334; 1460-2180
• DOI: 10.1093/carcin/bgy055

Integrative analysis was performed on tumor exome-, transcriptome- and methylome-wide molecular profiles from TCGA to construct a robust multiomic predictor of ovarian cancer survival. Abstract Increasingly affordable high-throughput molecular profiling technologies have made feasible the measurement of omics-wide interindividual variations for the purposes of predicting cancer prognosis. While multiple types of genetic, epigenetic and expression changes have been implicated in ovarian cancer, existing prognostic biomarker strategies are constrained to analyzing a single class of molecular variations. The extra predictive power afforded by the integration of multiple omics types remains largely unexplored. In this study, we performed integrative analysis on tumor-based exome-, transcriptome- and methylome-wide molecular profiles from The Cancer Genome Atlas (TCGA) for variations in cancer-relevant genes to construct robust, cross-validated multiomic predictors for ovarian cancer survival. These integrated polygenic survival scores (PSSs) were able to predict 5-year overall (OS) and progression-free survival in the Caucasian subsample with high accuracy (AUROC = 0.87 and 0.81, respectively). These findings suggest that the PSSs are able to predict long-term OS in TCGA patients with accuracy beyond that of previously proposed protein-based biomarker strategies. Our findings reveal the promise of an integrated omics-based approach in enhancing existing prognostic strategies. Future investigations should be aimed toward prospective external validation, strategies for standardizing application and the integration of germline variants.