IEEE/ACM Trans Comput Biol Bioinform. 2020 Mar-Apr;17(2):390-401. doi: 10.1109/TCBB.2018.2846262. Epub 2018 Jun 12.
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 percent 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.
鉴定驱动模块或途径是解释癌症潜在分子机制和发病机制的关键挑战。越来越多的研究表明，罕见突变的基因对于癌症的发展很重要。然而，由低频驱动突变的突变基因组成的驱动模块尚未得到很好的描述。为了识别含有罕见突变基因的驱动模块，我们提出了一种功能相似性指数，以量化罕见突变基因与同一模块中其他基因之间的功能关系。然后，我们通过整合功能相似性、覆盖度和互斥性，开发了一种检测罕见突变基因驱动模块（DMRG）的方法。通过在 HINT+HI2012、iRefIndex 和 MultiNet 三个网络上应用 DMRG 对 TCGA 癌症数据集进行分析，我们检测到了与已知信号通路和蛋白质复合物（如细胞周期通路和中介复合物）相交的驱动模块。DMRG 还可以通过随机选择 20%、40%、60%和 80%的样本有效地检测到驱动模块。与 HotNet2 相比，DMRG 检测到了更多罕见突变的癌症基因，并且通路富集度更高。总的来说，DMRG 为识别含有罕见突变基因的驱动模块提供了一种有效的方法。
