'Latent drivers' expand the cancer mutational landscape. (June 2015) - Nussinov, Ruth; Tsai, Chung-Jung
Highlights: Currently mutations are classified into drivers and passengers. We suggest an additional 'latent driver' classification. Latent driver mutations behave as passengers and do not confer a cancer hallmark. Latent driver mutations emerge prior to and during cancer evolution. Coupled with other emerging mutations, they drive cancer. Abstract : A major challenge facing the community involves identification of mutations that drive cancer. Analyses of cancer genomes to detect, and distinguish, 'driver' from 'passenger' mutations are daunting tasks. Here we suggest that there is a third 'latent driver' category. 'Latent driver' mutations behave as passengers, and do not confer a cancer hallmark. However, coupled with other emerging mutations, they drive cancer development and drug resistance. 'Latent drivers' emerge prior to and during cancer evolution. These allosteric mutations can work through 'AND' all-or-none or incremental 'Graded' logic gate mechanisms. Current diagnostic platforms generally assume that actionable 'driver' mutations are those appearing most frequently in cancer. We propose that 'latent driver' detection may help forecast cancer progression and modify personalized drug regimes.