Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Yale School of Medicine, Yale University, New Haven, CT 06510, USA.
Cell. 2020 Mar 5;180(5):915-927.e16. doi: 10.1016/j.cell.2020.01.032. Epub 2020 Feb 20.
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.
癌症中“驱动者”和“乘客”的二分模型假设，肿瘤中只有少数突变会强烈影响其进展，而其余的突变则无足轻重。在这里，我们利用了 ICGC/TCGA 全基因组泛癌症分析（PCAWG）项目中的全面变异数据集，证明除了高影响和低影响变异的二分法之外，还有第三组中等影响的假定乘客。此外，我们还发现，分子影响与亚克隆结构相关（即早期和晚期突变），不同的特征编码具有不同影响的突变。此外，我们从复杂性状研究中采用了一种加性效应模型，表明假定乘客（包括未检测到的弱驱动突变）的累积效应，除了 PCAWG 确定的驱动突变之外，为预测癌症表型提供了显著的额外能力（约 12%的加性方差）。最后，该框架允许我们估计 PCAWG 样本中缺乏任何特征明确的驱动改变的潜在弱驱动突变的频率。
