Identifying driver modules or pathways is a key challenge to interpret the molecular mechanisms and pathogenesis underlying cancer. An increasing number of studies suggest that rarely mutated genes are important for the development of cancer. However, the driver modules consisting of mutated genes with low-frequency driver mutations are not well characterized. To identify driver modules with rarely mutated genes, we propose a functional similarity index to quantify the functional relationship between rarely mutated genes and other ones in the same module. Then, we develop a method to detect Driver Modules with Rarely mutated Genes (DMRG) by incorporating the functional similarity, coverage and mutual exclusivity. By applying DMRG on TCGA cancer dataset on three networks: HINT+HI2012, iRefIndex and MultiNet, we detect driver modules intersecting with the well-known signalling pathways and protein complexes, such as the cell cycle pathway and the mediator complex. DMRG can also detect driver modules effectively with 20%, 40%, 60% and 80% of samples by random selection. When compared with HotNet2, DMRG detects more rarely mutated cancer genes and has higher pathway enrichment. Overall, DMRG provides an effective method for the identification of driver modules with rarely mutated genes.

中文翻译：

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检测具有罕见突变基因的癌症驱动模块。

识别驱动模块或途径是解释癌症的分子机制和发病机理的关键挑战。越来越多的研究表明，很少突变的基因对于癌症的发展很重要。然而，由具有低频驱动子突变的突变基因组成的驱动子模块没有得到很好的表征。为了识别具有很少突变基因的驱动程序模块，我们提出了一个功能相似性指数，以量化很少突变基因与同一模块中其他基因之间的功能关系。然后，我们开发了一种通过合并功能相似性，覆盖范围和互斥性来检测具有罕见突变基因（DMRG）的驱动程序模块的方法。通过在以下三个网络的TCGA癌症数据集上应用DMRG：HINT + HI2012，iRefIndex和MultiNet，我们检测到与众所周知的信号通路和蛋白质复合物（例如细胞周期通路和介体复合物）相交的驱动程序模块。DMRG还可以通过随机选择有效地检测20％，40％，60％和80％样本的驱动器模块。与HotNet2相比，DMRG可检测到更罕见的突变癌症基因，并且具有更高的途径富集。总体而言，DMRG提供了一种有效的方法来鉴定具有很少突变基因的驱动程序模块。