Identifying Modules of Cooperating Cancer Drivers

A bstract Identifying cooperating modules of driver alterations can provide biological insights to cancer causation and would advance the development of effective personalized treatments. We present Cancer Rule-Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types found a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination, and accounting for a mean of 70% of samples per cancer. CRSO departs from methods based on statistical cooccurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS -mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/ .