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

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    Authors
    Hudson, Andrew M
    Wirth, C
    Stephenson, Natalie L
    Fawdar, S
    Brognard, John
    Miller, Crispin J
    Affiliation
    Signalling Networks in Cancer Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, M20 4BX
    Issue Date
    2015-07
    
    Metadata
    Show full item record
    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.
    Citation
    Using large-scale genomics data to identify driver mutations in lung cancer: methods and challenges. 2015, 16 (10):1149-60 Pharmacogenomics
    Journal
    Pharmacogenomics
    URI
    http://hdl.handle.net/10541/579070
    DOI
    10.2217/pgs.15.60
    PubMed ID
    26230733
    Type
    Article
    Language
    en
    ISSN
    1744-8042
    ae974a485f413a2113503eed53cd6c53
    10.2217/pgs.15.60
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research

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