Pervasive conditional selection of driver mutations and modular epistasis networks in cancer
Pervasive conditional selection of driver mutations and modular epistasis networks in cancer
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DOI：
10.1016/j.celrep.2022.111272
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发表时间：
2022-08-23
期刊：
影响因子：
8.8
通讯作者：
V. Koonin, Eugene
中科院分区：
文献类型：
--
作者：
Iranzo, Jaime;Gruenhagen, George;V. Koonin, Eugene
癌症驱动突变通常表现出相互排斥或共同出现，强调了epis-tasis在致癌作用中的关键作用。然而，估计上位性的大小和量化其对肿瘤演变的影响仍然是一个挑战。我们开发了一种方法（Coselens）来量化癌症基因中非同义取代过量的条件选择。Coselens使用基于协方差的突变模型推断癌症基因组学数据集的不同分区中每个基因的驱动因子数量，并确定基因中的编码突变是否影响任何其他基因中驱动因子的选择。使用Coselens，我们在TCGA数据集中的16种癌症类型中确定了296个条件选择的基因对。条件选择在具有>2个驱动因子的肿瘤中影响25%-50%的驱动因子置换。协同选择的基因形成模块化网络，其结构挑战了对通路内互斥性和跨通路协同作用的传统解释，表明基因特异性跨通路上位性形成分化的癌症亚型的更复杂的情况。
Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epis-tasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection af-fects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclu-sivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across -pathway epistasis shapes differentiated cancer subtypes.