Prediction of cancer driver mutations in protein kinases
Prediction of cancer driver mutations in protein kinases
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DOI：
10.1158/0008-5472.can-07-5283
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发表时间：
2008-03-15
期刊：
影响因子：
11.2
通讯作者：
Schork, Nicholas J.
中科院分区：
文献类型：
--
作者：
Torkamani, Ali;Schork, Nicholas J.
在肿瘤发生的过程中，大量的体细胞突变积累。这些突变的一个子集有助于肿瘤进展（称为“驱动”突变），而这些突变中的大多数是有效的中性（称为“乘客”突变）。区分驾驶员和乘客的能力对于即将到来的大规模癌症DNA重测序项目的成功至关重要。在这里，我们展示了一种能够区分最常见的癌症相关蛋白质家族蛋白激酶中的司机和乘客的方法。我们将这种方法应用于多个癌症数据集，通过显示它能够识别已知的驱动程序来验证其准确性，与以前对驱动程序频率的统计估计具有很好的一致性，并提供了强有力的证据，表明预测的驱动程序受到各种序列和结构分析的正选择。此外，我们确定了蛋白激酶中似乎在肿瘤发生中起作用的特定位置。最后，我们提供了候选驱动突变的排名列表。
A large number of somatic mutations accumulate during the process of tumorigenesis. A subset of these mutations contribute to tumor progression (known as "driver" mutations) whereas the majority of these mutations are effectively neutral (known as "passenger" mutations). The ability to differentiate between drivers and passengers will be critical to the success of upcoming large-scale cancer DNA resequencing projects. Here we show a method capable of discriminating between drivers and passengers in the most frequently cancer-associated protein family, protein kinases. We apply this method to multiple cancer data sets, validating its accuracy by showing that it is capable of identifying known drivers, has excellent agreement with previous statistical estimates of the frequency of drivers, and provides strong evidence that predicted drivers are under positive selection by various sequence and structural analyses. Furthermore, we identify particular positions in protein kinases that seem to play a role in oncogenesis. Finally, we provide a ranked list of candidate driver mutations.