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A tool for discovering drug sensitivity and gene expression associations in cancer cells

The sensitivity of cancer cells to anticancer drugs is a crucial factor for developing effective treatments. However, it is still challenging to precisely predict the effectiveness of therapeutics in humans within a complex genomic and molecular context. We developed an interface which allows the us...

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Detalles Bibliográficos
Autores principales: Qin, Yong, Conley, Anthony P., Grimm, Elizabeth A., Roszik, Jason
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409143/
https://www.ncbi.nlm.nih.gov/pubmed/28453553
http://dx.doi.org/10.1371/journal.pone.0176763
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author Qin, Yong
Conley, Anthony P.
Grimm, Elizabeth A.
Roszik, Jason
author_facet Qin, Yong
Conley, Anthony P.
Grimm, Elizabeth A.
Roszik, Jason
author_sort Qin, Yong
collection PubMed
description The sensitivity of cancer cells to anticancer drugs is a crucial factor for developing effective treatments. However, it is still challenging to precisely predict the effectiveness of therapeutics in humans within a complex genomic and molecular context. We developed an interface which allows the user to rapidly explore drug sensitivity and gene expression associations. Predictions for how expression of various genes affect anticancer drug activity are available for all genes for a set of therapeutics based on data from various cell lines of different origin in the Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer projects. Our application makes discovery or validation of drug sensitivity and gene expression associations efficient. Effectiveness of this tool is demonstrated by multiple known and novel examples.
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spelling pubmed-54091432017-05-12 A tool for discovering drug sensitivity and gene expression associations in cancer cells Qin, Yong Conley, Anthony P. Grimm, Elizabeth A. Roszik, Jason PLoS One Research Article The sensitivity of cancer cells to anticancer drugs is a crucial factor for developing effective treatments. However, it is still challenging to precisely predict the effectiveness of therapeutics in humans within a complex genomic and molecular context. We developed an interface which allows the user to rapidly explore drug sensitivity and gene expression associations. Predictions for how expression of various genes affect anticancer drug activity are available for all genes for a set of therapeutics based on data from various cell lines of different origin in the Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer projects. Our application makes discovery or validation of drug sensitivity and gene expression associations efficient. Effectiveness of this tool is demonstrated by multiple known and novel examples. Public Library of Science 2017-04-28 /pmc/articles/PMC5409143/ /pubmed/28453553 http://dx.doi.org/10.1371/journal.pone.0176763 Text en © 2017 Qin et al 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 use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Qin, Yong
Conley, Anthony P.
Grimm, Elizabeth A.
Roszik, Jason
A tool for discovering drug sensitivity and gene expression associations in cancer cells
title A tool for discovering drug sensitivity and gene expression associations in cancer cells
title_full A tool for discovering drug sensitivity and gene expression associations in cancer cells
title_fullStr A tool for discovering drug sensitivity and gene expression associations in cancer cells
title_full_unstemmed A tool for discovering drug sensitivity and gene expression associations in cancer cells
title_short A tool for discovering drug sensitivity and gene expression associations in cancer cells
title_sort tool for discovering drug sensitivity and gene expression associations in cancer cells
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409143/
https://www.ncbi.nlm.nih.gov/pubmed/28453553
http://dx.doi.org/10.1371/journal.pone.0176763
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