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Identifying modules of cooperating cancer drivers |
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| Authors: | Michael I Klein Vincent L Cannataro Jeffrey P Townsend Scott Newman David F Stern Hongyu Zhao |
| Institution: | 1. Program in Computational Biology and Bioinformatics, Yale University, New Haven CT, USA ; 2. Bioinformatics R&D, Sema4, Stamford CT, USA ; 3. Department of Biology, Emmanuel College, Boston MA, USA ; 4. Department of Biostatistics, Yale School of Public Health, New Haven CT, USA ; 5. Yale Cancer Center, Yale University, New Haven CT, USA ; 6. Department of Pathology, Yale School of Medicine, New Haven CT, USA |
| 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/. |
| Keywords: | cancer etiology driver‐ gene combinations multi‐ gene biomarkers patient stratification precision oncology |