Biometric Research Branch, National Cancer Institute, Bethesda, MD 20892-7434, USA.
Bioinformatics. 2011 Jan 15;27(2):175-81. doi: 10.1093/bioinformatics/btq630. Epub 2010 Dec 17.
Major tumor sequencing projects have been conducted in the past few years to identify genes that contain 'driver' somatic mutations in tumor samples. These genes have been defined as those for which the non-silent mutation rate is significantly greater than a background mutation rate estimated from silent mutations. Several methods have been used for estimating the background mutation rate.
We propose a new method for identifying cancer driver genes, which we believe provides improved accuracy. The new method accounts for the functional impact of mutations on proteins, variation in background mutation rate among tumors and the redundancy of the genetic code. We reanalyzed sequence data for 623 candidate genes in 188 non-small cell lung tumors using the new method. We found several important genes like PTEN, which were not deemed significant by the previous method. At the same time, we determined that some genes previously reported as drivers were not significant by the new analysis because mutations in these genes occurred mainly in tumors with large background mutation rates.
The software is available at: http://linus.nci.nih.gov/Data/YounA/software.zip.
在过去的几年中，已经进行了主要的肿瘤测序项目，以鉴定肿瘤样本中含有“驱动”体细胞突变的基因。这些基因被定义为那些非同义突变率明显高于从沉默突变估计的背景突变率的基因。已经使用了几种方法来估计背景突变率。
我们提出了一种新的方法来鉴定癌症驱动基因，我们相信这提供了更高的准确性。新方法考虑了突变对蛋白质的功能影响、肿瘤之间背景突变率的差异以及遗传密码的冗余性。我们使用新方法重新分析了 188 个非小细胞肺癌肿瘤中 623 个候选基因的序列数据。我们发现了一些重要的基因，如 PTEN，以前的方法认为它们不显著。同时，我们确定了一些先前被报道为驱动基因的基因在新的分析中并不显著，因为这些基因的突变主要发生在背景突变率较高的肿瘤中。
该软件可在以下网址获得：http://linus.nci.nih.gov/Data/YounA/software.zip。
