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    Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma

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    Authors
    Bennett, Laura
    Howell, Matthew
    Memon, Danish
    Smowton, Christopher
    Zhou, Cong
    Miller, Crispin J
    Affiliation
    RNA Biology Group, CRUK Manchester Institute, The University of Manchester, Alderley Park, Manchester, SK10 4TG, UK
    Issue Date
    2018
    
    Metadata
    Show full item record
    Abstract
    The genomic lesions found in malignant tumours exhibit a striking degree of heterogeneity. Many tumours lack a known driver mutation, and their genetic basis is unclear. By mapping the somatic mutations identified in primary lung adenocarcinomas onto an independent coexpression network derived from normal tissue, we identify a critical gene network enriched for metastasis-associated genes. While individual genes within this module were rarely mutated, a significant accumulation of mutations within this geneset was predictive of relapse in lung cancer patients that have undergone surgery. Since it is the density of mutations within this module that is informative, rather than the status of any individual gene, these data are in keeping with a 'mini-driver' model of tumorigenesis in which multiple mutations, each with a weak effect, combine to form a polygenic driver with sufficient power to significantly alter cell behaviour and ultimately patient outcome. These polygenic mini-drivers therefore provide a means by which heterogeneous mutation patterns can generate the consistent hallmark changes in phenotype observed across tumours.
    Citation
    Bennett L, Howell M, Memon D, Smowton C, Zhou C, Miller CJ. Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma. Scientific Reports [Internet]. 2018 Oct 4;8(1).
    Journal
    Sci Rep
    URI
    http://hdl.handle.net/10541/621368
    DOI
    10.1038/s41598-018-33276-3
    PubMed ID
    30287876
    Additional Links
    https://dx.doi.org/10.1038/s41598-018-33276-3
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41598-018-33276-3
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research

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