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Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics
Traditional means for scoring the effects of anti-cancer drugs on the growth and survival of cell lines is based on relative cell number in drug-treated and control samples and is seriously confounded by unequal division rates arising from natural biological variation and differences in culture cond...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5674849/ https://www.ncbi.nlm.nih.gov/pubmed/29112189 http://dx.doi.org/10.1038/sdata.2017.166 |
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author | Hafner, Marc Heiser, Laura M. Williams, Elizabeth H. Niepel, Mario Wang, Nicholas J. Korkola, James E. Gray, Joe W. Sorger, Peter K. |
author_facet | Hafner, Marc Heiser, Laura M. Williams, Elizabeth H. Niepel, Mario Wang, Nicholas J. Korkola, James E. Gray, Joe W. Sorger, Peter K. |
author_sort | Hafner, Marc |
collection | PubMed |
description | Traditional means for scoring the effects of anti-cancer drugs on the growth and survival of cell lines is based on relative cell number in drug-treated and control samples and is seriously confounded by unequal division rates arising from natural biological variation and differences in culture conditions. This problem can be overcome by computing drug sensitivity on a per-division basis. The normalized growth rate inhibition (GR) approach yields per-division metrics for drug potency (GR(50)) and efficacy (GR(max)) that are analogous to the more familiar IC(50) and E(max) values. In this work, we report GR-based, proliferation-corrected, drug sensitivity metrics for ~4,700 pairs of breast cancer cell lines and perturbagens. Such data are broadly useful in understanding the molecular basis of therapeutic response and resistance. Here, we use them to investigate the relationship between different measures of drug sensitivity and conclude that drug potency and efficacy exhibit high variation that is only weakly correlated. To facilitate further use of these data, computed GR curves and metrics can be browsed interactively at http://www.GRbrowser.org/. |
format | Online Article Text |
id | pubmed-5674849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-56748492017-11-09 Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics Hafner, Marc Heiser, Laura M. Williams, Elizabeth H. Niepel, Mario Wang, Nicholas J. Korkola, James E. Gray, Joe W. Sorger, Peter K. Sci Data Data Descriptor Traditional means for scoring the effects of anti-cancer drugs on the growth and survival of cell lines is based on relative cell number in drug-treated and control samples and is seriously confounded by unequal division rates arising from natural biological variation and differences in culture conditions. This problem can be overcome by computing drug sensitivity on a per-division basis. The normalized growth rate inhibition (GR) approach yields per-division metrics for drug potency (GR(50)) and efficacy (GR(max)) that are analogous to the more familiar IC(50) and E(max) values. In this work, we report GR-based, proliferation-corrected, drug sensitivity metrics for ~4,700 pairs of breast cancer cell lines and perturbagens. Such data are broadly useful in understanding the molecular basis of therapeutic response and resistance. Here, we use them to investigate the relationship between different measures of drug sensitivity and conclude that drug potency and efficacy exhibit high variation that is only weakly correlated. To facilitate further use of these data, computed GR curves and metrics can be browsed interactively at http://www.GRbrowser.org/. Nature Publishing Group 2017-11-07 /pmc/articles/PMC5674849/ /pubmed/29112189 http://dx.doi.org/10.1038/sdata.2017.166 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Hafner, Marc Heiser, Laura M. Williams, Elizabeth H. Niepel, Mario Wang, Nicholas J. Korkola, James E. Gray, Joe W. Sorger, Peter K. Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics |
title | Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics |
title_full | Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics |
title_fullStr | Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics |
title_full_unstemmed | Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics |
title_short | Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics |
title_sort | quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using gr metrics |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5674849/ https://www.ncbi.nlm.nih.gov/pubmed/29112189 http://dx.doi.org/10.1038/sdata.2017.166 |
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