Integrating genetic and gene expression data in network-based stratification analysis of cancers

Abstract

Cancers are complex diseases that have heterogeneous genetic drivers and varying clinical outcomes. A critical area of cancer research is organizing patient cohorts into subtypes and associating subtypes with clinical and biological outcomes for more effective prognosis and treatment. Large-scale studies have collected a plethora of omics data across multiple tumor types. These studies provide an extensive dataset for stratifying patient cohorts. Network-based stratification (NBS) approaches have been presented to classify cancer tumors using somatic mutation data. A challenge in cancer stratification is integrating omics data to yield clinically meaningful subtypes. In this study, we integrate somatic mutation data with RNA sequencing data within the NBS framework and investigate the effectiveness of integrated NBS on three cancers: ovarian, bladder, and uterine cancer. We show that integrated NBS subtypes are more significantly associated with overall survival or histology. Integrated NBS networks also reveal highly influential genes that drive cancer initiation and progression. This comprehensive approach underscores the significance of integrating genomic data types in cancer subtyping, offering profound implications for personalized prognosis and treatment strategies.