A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles

Lin Wang, Fuhai Li, Jianting Sheng, Stephen TC Wong

Personalized genomics instability, e.g., somatic mutations, is believed to contribute to the heterogeneous drug responses in patient cohorts. However, it is difficult to discover personalized driver mutations that are predictive of drug sensitivity owing to diverse and complex mutations of individual patients. To circumvent this problem, a novel computational method is presented to discover potential drug sensitivity relevant cancer subtypes and identify driver mutation modules of individual subtypes by coupling differentially expressed genes (DEGs) based subtyping analysis with the driver mutation network analysis. The proposed method was applied to breast cancer and lung cancer samples available from The Cancer Genome Atlas (TCGA). Cancer subtypes were uncovered with significantly different survival rates, and more interestingly, distinct driver mutation modules were also discovered among different subtypes, indicating the potential mechanism of heterogeneous drug sensitivity. The research findings can be used to help guide the repurposing of known drugs and their combinations in order to target these dysfunctional modules and their downstream signaling effectively for achieving personalized or precision medicine treatment.