Using large-scale genomics data to identify driver mutations in lung cancer: methods and challenges

Hudson, A. M., Wirth, C., Stephenson, N. L., Fawdar, S., Brognard, J. and Miller, C. J. (2015) Using large-scale genomics data to identify driver mutations in lung cancer: methods and challenges. Pharmacogenomics, 16(10), pp. 1149-1160. (doi: 10.2217/pgs.15.60) (PMID:26230733)

Abstract

Lung cancer is the commonest cause of cancer death in the world and carries a poor prognosis for most patients. While precision targeting of mutated proteins has given some successes for never- and light-smoking patients, there are no proven targeted therapies for the majority of smokers with the disease. Despite sequencing hundreds of lung cancers, known driver mutations are lacking for a majority of tumors. Distinguishing driver mutations from inconsequential passenger mutations in a given lung tumor is extremely challenging due to the high mutational burden of smoking-related cancers. Here we discuss the methods employed to identify driver mutations from these large datasets. We examine different approaches based on bioinformatics, in silico structural modeling and biological dependency screens and discuss the limitations of these approaches.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Miller, Professor Crispin
• Authors: Hudson, A. M., Wirth, C., Stephenson, N. L., Fawdar, S., Brognard, J., and Miller, C. J.
• College/School: College of Medical Veterinary and Life Sciences > School of Cancer Sciences
• Journal Name: Pharmacogenomics
• Publisher: Future Medicine
• ISSN: 1462-2416
• ISSN (Online): 1744-8042
• Published Online: 31 July 2015
• Copyright Holders: Copyright © 2015 Andrew M Hudson
• First Published: First published in Pharmacogenomics 16(10):1149-1160
• Publisher Policy: Reproduced under a Creative Commons Licence