Novel cancer drivers: mining the kinome
1 The Kinghorn Cancer Centre, 370 Victoria Street, Darlinghurst, and the Cancer Research Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney, New South Wales 2010, Australia
2 Department of Surgery, Bankstown Hospital, Eldridge Road, Bankstown, Sydney, New South Wales 2200, Australia
3 South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Liverpool, New South Wales 2170, Australia
4 Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, University of Queensland, St Lucia, Brisbane, Queensland 4072, Australia
Genome Medicine 2013, 5:19 doi:10.1186/gm423Published: 28 February 2013
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.