Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA.
BMC Bioinformatics. 2021 May 6;22(1):233. doi: 10.1186/s12859-021-04147-y.
Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. For many genes, neither the transcriptomic effects of these variants nor their relationship to one another in cancer processes have been well-characterized. We sought to identify the downstream expression effects of these mutations and to determine whether this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level.
By applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on samples' expression profiles we systematically interrogated the signatures associated with combinations of mutations 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 over a hundred cases where subsets of a gene's mutations are clearly divergent in their function from the remaining mutations of the gene. These findings successfully replicated across a number of disease contexts and were found to have clear implications for the delineation of cancer processes and for clinical decisions.
The results of cataloguing the downstream effects of mutation subgroupings across cancer cohorts underline the importance of incorporating the diversity present within oncogenes in models designed to capture the downstream effects of their mutations.
在肿瘤队列中经常发生突变的肿瘤发生相关基因通常表现出多种基因组变异。对于许多基因，这些变异的转录组效应及其在癌症过程中的相互关系尚未得到很好的描述。我们试图确定这些突变的下游表达效应，并确定在基因组水平上的这种异质性是否反映在转录组水平上的相应异质性。
通过应用一种新颖的分层框架，沿着具有基于样本表达谱训练的机器学习管道组织队列中存在的突变，我们系统地研究了与癌症中反复出现的突变组合相关的特征。这使我们能够对许多肿瘤队列中的具有明显下游表达效应的突变进行编目，并且能够发现和描述一百多个基因的突变子集在功能上明显不同于基因的其余突变的情况。这些发现成功地在多个疾病环境中复制，并对癌症过程的描述和临床决策具有明确的意义。
对跨癌症队列的突变亚组的下游效应进行编目的结果强调了在设计旨在捕获其突变的下游效应的模型时纳入癌基因中存在的多样性的重要性。
