A large number of somatic mutations accumulate during the process of tumorigenesis. A subset of these mutations contributes to tumor progression (known as “driver” mutations) while 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. In this issue, Torkamani and Schork describe a method capable of discriminating between drivers and passengers in the most frequently cancer-associated protein family, protein kinases. The structural distribution of predicted cancer drivers is depicted as a heat map overlayed on the crystal structure of PKA, with red being the highest density of drivers, followed by yellow, green, and blue. Identification of cancer drivers is a crucial first step towards customizing or individualizing the treatment of a cancer patient based on his or her specific tumorigenic profile. With this knowledge, kinase inhibitors can be redesigned to target tumor-promoting mutant protein kinases more effectively. For details, see the article by Torkamani and Schork on page 1675 of this issue.

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