Center for Bioinformatics and Computational Biology, Institute of Advanced Computer Studies, Department of Computer Science, University of Maryland, College Park, MD 20742;
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894.
Proc Natl Acad Sci U S A. 2018 Nov 20;115(47):E11101-E11110. doi: 10.1073/pnas.1807256115. Epub 2018 Nov 7.
How mutation and selection determine the fitness landscape of tumors and hence clinical outcome is an open fundamental question in cancer biology, crucial for the assessment of therapeutic strategies and resistance to treatment. Here we explore the mutation-selection phase diagram of 6,721 tumors representing 23 cancer types by quantifying the overall somatic point mutation load (ML) and selection (dN/dS) in the entire proteome of each tumor. We show that ML strongly correlates with patient survival, revealing two opposing regimes around a critical point. In low-ML cancers, a high number of mutations indicates poor prognosis, whereas high-ML cancers show the opposite trend, presumably due to mutational meltdown. Although the majority of cancers evolve near neutrality, deviations are observed at extreme MLs. Melanoma, with the highest ML, evolves under purifying selection, whereas in low-ML cancers, signatures of positive selection are observed, demonstrating how selection affects tumor fitness. Moreover, different cancers occupy specific positions on the ML-dN/dS plane, revealing a diversity of evolutionary trajectories. These results support and expand the theory of tumor evolution and its nonlinear effects on survival.
突变和选择如何决定肿瘤的适合度景观,从而影响临床结果,这是癌症生物学中的一个开放性基本问题,对于评估治疗策略和治疗耐药性至关重要。在这里,我们通过量化每个肿瘤整个蛋白质组的整体体细胞点突变负荷(ML)和选择(dN/dS),探索了代表 23 种癌症类型的 6721 个肿瘤的突变-选择相图。我们表明,ML 与患者生存强烈相关,在临界点周围显示出两种相反的模式。在低 ML 癌症中,大量突变表明预后不良,而高 ML 癌症则显示出相反的趋势,可能是由于突变崩溃。尽管大多数癌症在接近中性的情况下进化,但在极端 ML 下会观察到偏差。具有最高 ML 的黑色素瘤在纯化选择下进化,而在低 ML 癌症中,则观察到阳性选择的特征,这表明选择如何影响肿瘤的适应性。此外,不同的癌症在 ML-dN/dS 平面上占据特定的位置,揭示了不同的进化轨迹。这些结果支持并扩展了肿瘤进化理论及其对生存的非线性影响。