Abstract
Identifying cooperating modules of driver alterations can provide insights into cancer etiology and 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 revealed 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 type. CRSO is distinct from methods based on statistical co‐occurrence, 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/.
Highlights
Cells must deregulate multiple genetic pathways in in order to become cancerous
A major driving force behind the co-occurrence is that loss of the G1/S checkpoint (CDKN2A/B) is necessary to avoid oncogene induced senescence caused by KRAS oncogenic signaling [7, 8, 9]
We show that Cancer Rule-Set Optimization (CRSO) can identify known and novel combinations of driver alterations in 19 tissue types from The Cancer Genome Atlas (TCGA) [26, 27], and that some of these combinations correlate with clinical outcomes
Summary
Cells must deregulate multiple genetic pathways in in order to become cancerous. Most recent estimates are that 2-8 ‘hits’ are necessary for a precursor cell to become neoplastic [1, 2, 3]. A major driving force behind the co-occurrence is that loss of the G1/S checkpoint (CDKN2A/B) is necessary to avoid oncogene induced senescence caused by KRAS oncogenic signaling [7, 8, 9]. Mutations in these two genes cooperate to produce a pro-growth phenotype. Many methods can identify mutually exclusive candidate driver genes [10, 11, 12, 13, 14, 15, 16], but there are comparatively few that identify functionally relevant modules of cooccurring gene alterations in individual patients. This extra layer of biological information and its possible relevance to therapy is not captured by any of the above methods and it would be beneficial to derive new approaches to identify groups of cooperating mutations
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