Cancer genomics efforts have identified genes and regulatory elements driving cancer development and neoplastic progression. From a microevolution standpoint, these are subject to positive selection. Although elusive in current studies, genes whose wild-type coding sequences are needed for tumor growth are also of key interest. They are expected to experience negative selection and stay intact under pressure of incessant mutation. The detection of significantly mutated (or undermutated) genes is completely confounded by the genomic heterogeneity of cancer mutation. Here we present a hierarchical framework that allows modeling of coding point mutations. Application of the model to sequencing data from 17 cancer types demonstrates an increased power to detect known cancer driver genes and identifies new significantly mutated genes with highly plausible biological functions. The signal of negative selection is very subtle, but is detectable in several cancer types and in a pan-cancer data set. It is enriched in cell-essential genes identified in a CRISPR screen, as well as in genes with reported roles in cancer.

中文翻译：

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贝叶斯推断人类癌症中的阴性和阳性选择。

癌症基因组学研究已经确定了驱动癌症发展和肿瘤进展的基因和调控元件。从微观进化的观点来看，这些都受到积极的选择。尽管在当前的研究中难以捉摸，但其野生型编码序列对于肿瘤生长所必需的基因也引起了人们的极大兴趣。预期他们会经历负面选择，并在不断发生突变的压力下保持完整。癌症突变的基因组异质性完全混淆了显着突变（或突变不足）基因的检测。在这里，我们介绍了一个分层框架，该框架允许对编码点突变进行建模。该模型在来自17种癌症类型的测序数据中的应用证明了检测已知癌症驱动基因的能力增强，并鉴定出具有高度可信的生物学功能的新的明显突变的基因。阴性选择的信号非常微妙，但是在几种癌症类型和全癌数据集中可以检测到。它富含在CRISPR筛选中鉴定的细胞必需基因，以及据报道在癌症中起作用的基因。