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

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 di...

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Bibliographic Details
Published in:Pharmacogenomics 2015-07, Vol.16 (10), p.1149-1160
Main Authors: Hudson, Andrew M, Wirth, Christopher, Stephenson, Natalie L, Fawdar, Shameem, Brognard, John, Miller, Crispin J
Format: Article
Language:English
Subjects:
analysis
cancer genomics
Cancer research
challenges
Computational Biology - methods
Databases, Genetic
Development and progression
driver mutation
Gene mutation
Genetic aspects
genetic dependency screen
Genetic research
Genomics - methods
Health aspects
Humans
Identification and classification
Lung cancer
Lung Neoplasms - genetics
Mutation - genetics
Precision medicine
Smoking - genetics
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Summary: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, structural modeling and biological dependency screens and discuss the limitations of these approaches.
ISSN:1462-2416
1744-8042
DOI:10.2217/pgs.15.60