Identification of driver modules in pan-cancer via coordinating coverage and exclusivity
Identification of driver modules in pan-cancer via coordinating coverage and exclusivity
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通过协调覆盖范围和排他性识别泛癌中的驱动模块
DOI：
10.18632/oncotarget.16433
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发表时间：
2017-05-30
期刊：
影响因子：
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通讯作者：
Huang, Xiuzhen
中科院分区：
文献类型：
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作者：
Gao, Bo;Li, Guojun;Huang, Xiuzhen
人们普遍认为，癌症是由个体一生中积累的体细胞突变驱动的。癌症突变可以靶向相对少量的细胞功能模块。不同癌症患者的异质性使得难以识别与癌症相关的驱动突变或功能模块。生物学上期望能够通过覆盖和排他性之间的协调来识别癌症途径模块。已经有一些方法为此目的而开发，但由于其计算复杂性和预测精度，它们在实践中都有局限性。我们提出了一种基于网络的方法，CovEx，预测特定的面向患者的模块1）发现候选模块为每个考虑的基因，2）通过协调覆盖和排他性提取重要的候选人，3）进一步选择面向患者的模块的基础上设置覆盖模型。将CovEx应用于从公共数据库TCGA收集的跨越12种癌症类型的泛癌症数据集，它在性能上表现出比当前领先竞争对手显著的优势。它是在GNU通用公共许可证下发布的，源代码可在以下网址获得：https://sourceforge.net/projects/cancer-pathway/files/
It is widely accepted that cancer is driven by accumulated somatic mutations during the lifetime of an individual. Cancer mutations may target relatively small number of cell functional modules. The heterogeneity in different cancer patients makes it difficult to identify driver mutations or functional modules related to cancer. It is biologically desired to be capable of identifying cancer pathway modules through coordination between coverage and exclusivity. There have been a few approaches developed for this purpose, but they all have limitations in practice due to their computational complexity and prediction accuracy. We present a network based approach, CovEx, to predict the specific patient oriented modules by 1) discovering candidate modules for each considered gene, 2) extracting significant candidates by harmonizing coverage and exclusivity and, 3) further selecting the patient oriented modules based on a set cover model. Applying CovEx to pan-cancer datasets spanning 12 cancer types collecting from public database TCGA, it demonstrates significant superiority over the current leading competitors in performance. It is published under GNU GENERAL PUBLIC LICENSE and the source code is available at:https://sourceforge.net/projects/cancer-pathway/files/