Abstract
Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers. Differentiating 'driver' mutations that are functionally relevant from 'passenger' mutations is a major challenge in cancer genomics. While recurrent mutations in a gene provides supporting evidence of 'driver' status, novel computational methods and model systems are greatly improving our ability to identify genes important in carcinogenesis. Reimand and Bader have recently shown that driver gene discovery in discrete gene classes (in this case the kinome) is possible across multiple cancer types and has the potential to yield new druggable targets and clinically relevant leads.
Highlights
Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers
Available by the International Cancer Genome Consor tium (ICGC) [2], the Cancer Genome Atlas (TCGA) [3,4] and independent groups [5] were analyzed using methods designed to enrich for cancer drivers
While cohortbased cata loguing of genomic aberrations initially reveals candidate driver events in different cancer types, this group and many others are interrogating these data using innovative approaches to distinguish between driver and passenger mutations
Summary
Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers. New approaches that increase confidence in candidate driver prediction are required to generate hypotheses for further study. A recently published study by Reimand and Bader provides a timely example of the importance of largescale efforts in cancer genomics, and the valuable insights that mining these datasets can yield [1].
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