Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer

Jonathan H. Young, Michael Peyton, Hyun Seok Kim, Elizabeth Mcmillan, John D. Minna, Michael A. White, Edward M. Marcotte

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Motivation: Novel approaches are needed for discovery of targeted therapies for non-small-cell lung cancer (NSCLC) that are specific to certain patients. Whole genome RNAi screening of lung cancer cell lines provides an ideal source for determining candidate drug targets. Results: Unsupervised learning algorithms uncovered patterns of differential vulnerability across lung cancer cell lines to loss of functionally related genes. Such genetic vulnerabilities represent candidate targets for therapy and are found to be involved in splicing, translation and protein folding. In particular, many NSCLC cell lines were especially sensitive to the loss of components of the LSm2-8 protein complex or the CCT/TRiC chaperonin. Different vulnerabilities were also found for different cell line subgroups. Furthermore, the predicted vulnerability of a single adenocarcinoma cell line to loss of the Wnt pathway was experimentally validated with screening of small-molecule Wnt inhibitors against an extensive cell line panel.

Original languageEnglish (US)
Pages (from-to)1373-1379
Number of pages7
JournalBioinformatics
Volume32
Issue number9
DOIs
StatePublished - May 1 2016

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability

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