In cancer genomics, it is of great importance to distinguish driver mutations, which contribute to cancer progression, from causally neutral passenger mutations. We propose a random-effect regression approach to estimate the effects of mutations on the expressions of genes in tumor samples, where the estimation is assisted by a prespecified gene network. The model allows the mutation effects to vary across subjects. We develop a subject-specific mutation score to quantify the effect of a mutation on the expressions of its downstream genes, so mutations with large scores can be prioritized as drivers. We demonstrate the usefulness of the proposed methods by simulation studies and provide an application to a breast cancer genomics study.

翻译：在癌症基因组学中，区分导致癌症进展的驱动突变与因果中性的过客突变具有重要意义。我们提出了一种随机效应回归方法，用于估计突变对肿瘤样本基因表达的影响，其中估计过程借助了预设的基因网络。该模型允许突变效应在不同个体间存在差异。我们开发了一个个体特异性突变评分，用于量化突变对其下游基因表达的影响，从而将评分较高的突变优先考虑为驱动突变。通过模拟研究，我们证明了所提方法的有效性，并将其应用于一项乳腺癌基因组学研究。