Detection of Combinatorial Mutational Patterns in Human Cancer Genomes by Exclusivity Analysis.
Detection of Combinatorial Mutational Patterns in Human Cancer Genomes by Exclusivity Analysis.
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DOI：
10.1007/978-1-4939-7493-1_1
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发表时间：
2018
影响因子：
--
通讯作者：
Hua Tan;Xiaobo Zhou
中科院分区：
文献类型：
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作者：
Hua Tan;Xiaobo Zhou
在特定癌症的肿瘤样本中，癌症基因可能倾向于以共突变或互斥的方式发生突变，这构成了给定基因集的两种已知组合突变模式。以前的研究已经证实，在不同信号通路中发挥作用的基因可以在同一样本中发生突变，即在一个患者的肿瘤中，而在相同途径中工作的基因在同一癌症基因组中很少发生突变。因此，可靠地识别候选癌症基因的组合突变模式对于推断特定癌症类型的信号网络模块具有重要的意义。虽然已经提出了基于癌症基因突变的互斥性来发现突变驱动通路的算法，但仍然缺乏一种系统的流水线来识别共突变和互斥模式并进行合理的显著性估计。在这里，我们描述了一个可靠的框架和详细的程序，可以同时从公共横截面基因突变数据中探索这两种组合突变模式。
Cancer genes may tend to mutate in a co-mutational or mutually exclusive manner in a tumor sample of a specific cancer, which constitute two known combinatorial mutational patterns for a given gene set. Previous studies have established that genes functioning in different signaling pathways can mutate in the same sample, i.e., a tumor from one patient, while genes operating in the same pathway are rarely mutated in the same cancer genome. Therefore, reliable identification of combinatorial mutational patterns of candidate cancer genes has important ramifications in inferring signaling network modules in a particular cancer type. While algorithms for discovering mutated driver pathways based on mutual exclusivity of mutations in cancer genes have been proposed, a systematic pipeline for identifying both co-mutational and mutually exclusive patterns with rational significance estimation is still lacking. Here, we describe a reliable framework with detailed procedures to simultaneously explore both combinatorial mutational patterns from public cross-sectional gene mutation data.