e-Driver: a novel method to identify protein regions driving cancer
e-Driver: a novel method to identify protein regions driving cancer
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DOI：
10.1093/bioinformatics/btu499
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发表时间：
2014-11-01
期刊：
影响因子：
5.8
通讯作者：
Godzik, Adam
中科院分区：
文献类型：
--
作者：
Porta-Pardo, Eduard;Godzik, Adam
动机：大多数用于识别癌症驱动基因的方法都集中在整个基因上，并假设一个基因作为一个实体在癌症中具有特定的作用。这种方法在描述基因丢失或基因表达变化的影响方面可能是正确的;然而，突变可能具有不同的影响，包括它们与癌症的相关性，这取决于它们影响的基因区域。除了罕见的和众所周知的例外，没有足够的数据来可靠地统计单个位置，但是在单个位置和整个基因之间的中间水平的分析可能会给我们比前者更好的统计数据和比后者更好的分辨率方法。结果：我们开发了e-Driver，一种利用蛋白质功能区域之间体细胞错义突变的内部分布的方法这些蛋白质的突变率与相同蛋白质的其他区域相比显示出偏差，从而提供了正选择的证据，并表明这些蛋白质可能是实际的癌症驱动因素。我们将e-Driver应用于癌症基因组图谱中的大型癌症基因组数据集，并将其性能与其他四种方法进行了比较，结果表明e-Driver可以识别新的候选癌症驱动因子，并且由于其分辨率提高，可以更深入地了解其他方法识别的癌症驱动基因的潜在机制。
Motivation: Most approaches used to identify cancer driver genes focus, true to their name, on entire genes and assume that a gene, treated as one entity, has a specific role in cancer. This approach may be correct to describe effects of gene loss or changes in gene expression; however, mutations may have different effects, including their relevance to cancer, depending on which region of the gene they affect. Except for rare and well-known exceptions, there are not enough data for reliable statistics for individual positions, but an intermediate level of analysis, between an individual position and the entire gene, may give us better statistics than the former and better resolution than the latter approach.Results: We have developed e-Driver, a method that exploits the internal distribution of somatic missense mutations between the protein's functional regions (domains or intrinsically disordered regions) to find those that show a bias in their mutation rate as compared with other regions of the same protein, providing evidence of positive selection and suggesting that these proteins may be actual cancer drivers. We have applied e-Driver to a large cancer genome dataset from The Cancer Genome Atlas and compared its performance with that of four other methods, showing that e-Driver identifies novel candidate cancer drivers and, because of its increased resolution, provides deeper insights into the potential mechanism of cancer driver genes identified by other methods.