Prediction of cancer driver mutations in protein kinases

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.