Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. We sought to identify the downstream expression effects of these perturbations and to find whether or not this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level. Applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on sample expression profiles we systematically interrogated the signatures associated with combinations of perturbations recurrent in cancer. This allowed us to catalogue the mutations with discernible downstream expression effects across a number of tumor cohorts as well as to uncover and characterize a multitude of cases where subsets of a genes mutations are clearly divergent in their function from the remaining mutations of the gene.

中文翻译：

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对突变分组的系统性研究揭示了关键癌症基因内不同的下游表达程序

涉及肿瘤发生的基因通常在其经常突变的肿瘤队列中表现出不同的基因组变异集。我们试图确定这些扰动的下游表达效应，并寻找基因组水平的异质性是否反映在转录组水平的相应异质性中。应用新颖的层次结构组织队列中存在的突变以及在样本表达谱上训练的机器学习管道，我们系统地询问了与癌症反复发作的组合相关的特征。