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Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer
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 learn...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848405/ https://www.ncbi.nlm.nih.gov/pubmed/26755624 http://dx.doi.org/10.1093/bioinformatics/btw010 |
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author | Young, Jonathan H. Peyton, Michael Seok Kim, Hyun McMillan, Elizabeth Minna, John D. White, Michael A. Marcotte, Edward M. |
author_facet | Young, Jonathan H. Peyton, Michael Seok Kim, Hyun McMillan, Elizabeth Minna, John D. White, Michael A. Marcotte, Edward M. |
author_sort | Young, Jonathan H. |
collection | PubMed |
description | 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_paper. Contact: marcotte@icmb.utexas.edu or jon.young@utexas.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-4848405 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-48484052016-04-29 Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer Young, Jonathan H. Peyton, Michael Seok Kim, Hyun McMillan, Elizabeth Minna, John D. White, Michael A. Marcotte, Edward M. Bioinformatics Original Papers 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_paper. Contact: marcotte@icmb.utexas.edu or jon.young@utexas.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-05-01 2016-01-10 /pmc/articles/PMC4848405/ /pubmed/26755624 http://dx.doi.org/10.1093/bioinformatics/btw010 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Young, Jonathan H. Peyton, Michael Seok Kim, Hyun McMillan, Elizabeth Minna, John D. White, Michael A. Marcotte, Edward M. Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer |
title | Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer |
title_full | Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer |
title_fullStr | Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer |
title_full_unstemmed | Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer |
title_short | Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer |
title_sort | computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848405/ https://www.ncbi.nlm.nih.gov/pubmed/26755624 http://dx.doi.org/10.1093/bioinformatics/btw010 |
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