Torkamani Ali, Schork Nicholas J
Graduate Program in Biomedical Sciences, Center for Human Genetics and Genomics, University of California, San Diego, CA, USA.
Cancer Res. 2008 Mar 15;68(6):1675-82. doi: 10.1158/0008-5472.CAN-07-5283.
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.
在肿瘤发生过程中会积累大量的体细胞突变。这些突变中的一部分促成肿瘤进展（称为“驱动”突变），而大多数突变实际上是中性的（称为“乘客”突变）。区分驱动突变和乘客突变的能力对于即将开展的大规模癌症DNA重测序项目的成功至关重要。在此，我们展示了一种能够在最常与癌症相关的蛋白质家族——蛋白激酶中区分驱动突变和乘客突变的方法。我们将此方法应用于多个癌症数据集，通过证明其能够识别已知的驱动突变、与先前对驱动突变频率的统计估计具有高度一致性，并通过各种序列和结构分析提供有力证据表明预测的驱动突变正处于正选择之下，从而验证了其准确性。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，我们提供了一份候选驱动突变的排名列表。
