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Novel cancer drivers: mining the kinome


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.

Type Journal
ISBN 1756-994X (Print)
Authors Biankin, A. V. ; Grimmond, S. M.;
Responsible Garvan Author (missing name)
Publisher Name Genome Medicine
Published Date 2013-03-01
Published Volume 5
Published Issue 2
Published Pages 19
Status Published in-print
URL link to publisher's version
OpenAccess link to author's accepted manuscript version