Dataset Information
Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer.
Ontology highlight
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.Availability and implementation
The clustering algorithm is implemented in Python and is freely available at https://bitbucket.org/youngjh/nsclc_paperContact
marcotte@icmb.utexas.edu or jon.young@utexas.eduSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Young JH
PROVIDER: S-EPMC4848405 | biostudies-literature | 2016 May
REPOSITORIES: biostudies-literature
Publications
Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer.
Bioinformatics (Oxford, England) 20160110 9
<h4>Motivation</h4>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.<h4>Results</h4>Unsupervised learning algorithms uncovered patterns of differential vulnerability across lung cancer cell lines to loss of functionally related genes. Such genetic vulnerabilities represent candidate targe ...[more]