The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

中文翻译：

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2,500 多个癌症基因组中的乘客突变：总体分子功能影响和后果。

癌症“司机”和“乘客”的二分模型认为，肿瘤中只有少数突变会强烈影响其进展，其余突变则无关紧要。在这里，我们利用 ICGC/TCGA 全基因组泛癌症分析 (PCAWG) 项目的综合变异数据集来证明，除了高影响变异和低影响变异的二分法之外，还存在第三组中等影响的变异。影响假定的乘客。此外，我们还发现分子影响与亚克隆结构（即早期突变与晚期突变）相关，并且不同的特征编码具有不同影响的突变。此外，我们采用了复杂特征研究中的加性效应模型，以表明假定乘客（包括未检测到的弱驾驶员）的聚合效应为预测癌症表型提供了显着的额外功效（约 12% 加性方差），超出了 PCAWG 识别的驾驶员范围突变。最后，该框架使我们能够估计缺乏任何明确表征的驱动程序改变的 PCAWG 样本中潜在弱驱动程序突变的频率。