Integrated Multi-Omic Analysis Reveals Immunosuppressive Phenotype Associated with Poor Outcomes in High-Grade Serous Ovarian Cancer

SIMPLE SUMMARY: Tumor classification based on genomic features may be able to identify new clinically relevant subtypes and disease characteristics. By integrating multiple levels of genetic and epigenetic data, distinct clusters can be defined among tumors with similar histology and previously unknown distinguishing features. Here, we aimed to find prognostic subtypes among high-grade serous ovarian tumors by integrating their transcriptomic and methylomic features. Feature selection was applied to retain only those features most significantly correlated with disease recurrence. By using consensus clustering and machine learning algorithms, we describe four groups of tumors characterized by unique genetic and epigenetic properties which were associated with significant differences in clinical outcomes. By using both techniques in succession, we uncovered both differential features between groups and defining ontologies therein. One such group was associated with stromal and immune diverse cell populations and was associated with poor clinical outcomes. Our findings identify unique contributors to disease recurrence in high-grade serous ovarian cancer. ABSTRACT: High-grade serous ovarian cancer (HGSOC) is characterized by a complex genomic landscape, with both genetic and epigenetic diversity contributing to its pathogenesis, disease course, and response to treatment. To better understand the association between genomic features and response to treatment among 370 patients with newly diagnosed HGSOC, we utilized multi-omic data and semi-biased clustering of HGSOC specimens profiled by TCGA. A Cox regression model was deployed to select model input features based on the influence on disease recurrence. Among the features most significantly correlated with recurrence were the promotor-associated probes for the NFRKB and DPT genes and the TREML1 gene. Using 1467 transcriptomic and methylomic features as input to consensus clustering, we identified four distinct tumor clusters—three of which had noteworthy differences in treatment response and time to disease recurrence. Each cluster had unique divergence in differential analyses and distinctly enriched pathways therein. Differences in predicted stromal and immune cell-type composition were also observed, with an immune-suppressive phenotype specific to one cluster, which associated with short time to disease recurrence. Our model features were additionally used as a neural network input layer to validate the previously defined clusters with high prediction accuracy (91.3%). Overall, our approach highlights an integrated data utilization workflow from tumor-derived samples, which can be used to uncover novel drivers of clinical outcomes.