MUFFINN: cancer gene discovery via network analysis of somatic mutation data.
MUFFINN: cancer gene discovery via network analysis of somatic mutation data.
复制标题
DOI：
10.1186/s13059-016-0989-x
复制
发表时间：
2016-06-23
期刊：
影响因子：
12.3
通讯作者：
Lee I
中科院分区：
文献类型：
--
作者：
Cho A;Shim JE;Kim E;Supek F;Lehner B;Lee I
区分致癌驱动突变和无关紧要的乘客突变的一个主要挑战是癌症基因组中不经常突变的基因的长尾。在这里，我们提出并评估了一种癌症基因排序方法，该方法不仅考虑了单个基因的突变，还考虑了功能网络中相邻基因的突变，即MUFFINN（突变对网络邻居的功能影响）。与以基因为中心的突变数据分析相比，这种以途径为中心的方法具有较高的灵敏度。值得注意的是，当使用10%的TCGA患者样本时，仅观察到性能的轻微下降，这表明该方法可能会在小患者群体中增强癌症基因组计划。本文的在线版本（doi:10.1186/s13059-016-0989-x）包含补充材料，可供授权用户使用。
A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation data. Notably, only a marginal decrease in performance is observed when using 10 % of TCGA patient samples, suggesting the method may potentiate cancer genome projects with small patient populations. The online version of this article (doi:10.1186/s13059-016-0989-x) contains supplementary material, which is available to authorized users.