Multi-omics-based analysis of high grade serous ovarian cancer subtypes reveals distinct molecular processes linked to patient prognosis

Abstract

Despite advancements in treatment, high-grade serous ovarian cancer (HGSOC) is still characterized by poor patient outcomes. To understand the molecular heterogeneity of this disease, which underlies the challenge in selecting optimal treatments for HGSOC patients, we have integrated genomic, transcriptomic, and epigenetic information to identify seven new HGSOC subtypes using a multiscale clustering method. These subtypes not only have significantly distinct overall survival, but also exhibit unique patterns of gene expression, microRNA expression, DNA methylation, and copy number alterations. As determined by our analysis, patients with similar clinical outcomes have distinct profiles of activated or repressed cellular processes, including cell cycle, epithelial-to-mesenchymal transition, immune activation, interferon response, and cilium organization. Furthermore, we performed a multiscale gene co-expression network analysis to identify subtype-specific key regulators and predicted optimal targeted therapies based on subtype-specific gene expression. In summary, this study provides new insights into the cellular heterogeneity of the HGSOC genomic, epigenetic, and transcriptomic landscapes and provides a basis for future studies into precision medicine for HGSOC patients.