Novel cancer drivers: mining the kinome

被引：2

作者：

Biankin, Andrew V. ^{[1
,2
,3
,4
]}

Grimmond, Sean M. ^{[5
]}

机构：

[1] Kinghorn Canc Ctr, Darlinghurst, NSW, Australia

[2] Garvan Inst Med Res, Canc Res Program, Sydney, NSW 2010, Australia

[3] Bankstown Hosp, Dept Surg, Sydney, NSW 2200, Australia

[4] Univ New S Wales, Fac Med, South Western Sydney Clin Sch, Liverpool, NSW 2170, Australia

[5] Univ Queensland, Queensland Ctr Med Genom, Inst Mol Biosci, Brisbane, Qld 4072, Australia

来源：

GENOME MEDICINE | 2013年 / 5卷

关键词：

GENOMES;

D O I：

10.1186/gm423

中图分类号：

Q3 [遗传学];

学科分类号：

071007 [遗传学];

摘要：

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.

引用

页数：3

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