title: Using large-scale genomics data to identify driver mutations in lung cancer: methods and challenges.
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.
authors.affiliation: Signalling Networks in Cancer Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, M20 4BX, UK.
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Hudson
authors.affiliation: RNA Biology Group & Computational Biology Support Team, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK.
authors.forename: Christopher
authors.initials: C
authors.lastname: Wirth
authors.affiliation: Signalling Networks in Cancer Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, M20 4BX, UK.
authors.forename: Natalie L
authors.initials: NL
authors.lastname: Stephenson
authors.affiliation: ANDI Centre of Excellence for Biomedical & Biomaterial Research, University of Mauritius, Reduit, Mauritius.
authors.forename: Shameem
authors.initials: S
authors.lastname: Fawdar
authors.affiliation: Signalling Networks in Cancer Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, M20 4BX, UK.
authors.forename: John
authors.initials: J
authors.lastname: Brognard
authors.affiliation: RNA Biology Group & Computational Biology Support Team, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK.
authors.forename: Crispin J
authors.initials: CJ
authors.lastname: Miller
mesh_terms: D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D023281:Genomics / Q000379:methods; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D009154:Mutation / Q000235:genetics*; D012907:Smoking / Q000235:genetics
keywords: cancer genomics; challenges; driver mutation; genetic dependency screen; in silico analysis; lung cancer