Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
Bioinformatics R&D, Sema4, Stamford, CT, USA.
Mol Syst Biol. 2021 Mar;17(3):e9810. doi: 10.15252/msb.20209810.
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/.
识别驱动改变的协作模块可以深入了解癌症的病因，并推进有效个性化治疗的发展。我们提出了癌症规则集优化（CRSO），用于推断在个体患者中协同驱动肿瘤形成的改变组合。对 19 种 TCGA 癌症类型的应用揭示了每种癌症平均有 11 个核心驱动组合，每个组合包含 2-6 个改变，平均占每种癌症类型样本的 70%。CRSO 与基于统计共现的方法不同，我们证明这是研究驱动合作的次优标准。CRSO 确定了一些经过充分研究的驱动组合，而这些组合没有被其他方法检测到，并提名了在多种癌症类型中与临床结果相关的新组合。在NRAS 突变黑色素瘤中发现了新的协同作用，这可能具有治疗相关性。在四种癌症类型中发现了涉及 NFE2L2 突变的核心驱动组合，这支持了 NRF2 通路抑制的治疗潜力。CRSO 可在 https://github.com/mikekleinsgit/CRSO/ 获得。
