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Gene isoforms as expression-based biomarkers predictive of drug response in vitro
Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high freque...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5655668/ https://www.ncbi.nlm.nih.gov/pubmed/29066719 http://dx.doi.org/10.1038/s41467-017-01153-8 |
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author | Safikhani, Zhaleh Smirnov, Petr Thu, Kelsie L. Silvester, Jennifer El-Hachem, Nehme Quevedo, Rene Lupien, Mathieu Mak, Tak W. Cescon, David Haibe-Kains, Benjamin |
author_facet | Safikhani, Zhaleh Smirnov, Petr Thu, Kelsie L. Silvester, Jennifer El-Hachem, Nehme Quevedo, Rene Lupien, Mathieu Mak, Tak W. Cescon, David Haibe-Kains, Benjamin |
author_sort | Safikhani, Zhaleh |
collection | PubMed |
description | Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high frequency of mRNA splicing in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery. To identify robust transcriptomic biomarkers for drug response across studies, we develop a meta-analytical framework combining the pharmacological data from two large-scale drug screening datasets. We use an independent pan-cancer pharmacogenomic dataset to test the robustness of our candidate biomarkers across multiple cancer types. We further analyze two independent breast cancer datasets and find that specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 are significantly associated with AZD6244, lapatinib, erlotinib, and paclitaxel, respectively. Our results support isoform expressions as a rich resource for biomarkers predictive of drug response. |
format | Online Article Text |
id | pubmed-5655668 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-56556682017-10-26 Gene isoforms as expression-based biomarkers predictive of drug response in vitro Safikhani, Zhaleh Smirnov, Petr Thu, Kelsie L. Silvester, Jennifer El-Hachem, Nehme Quevedo, Rene Lupien, Mathieu Mak, Tak W. Cescon, David Haibe-Kains, Benjamin Nat Commun Article Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high frequency of mRNA splicing in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery. To identify robust transcriptomic biomarkers for drug response across studies, we develop a meta-analytical framework combining the pharmacological data from two large-scale drug screening datasets. We use an independent pan-cancer pharmacogenomic dataset to test the robustness of our candidate biomarkers across multiple cancer types. We further analyze two independent breast cancer datasets and find that specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 are significantly associated with AZD6244, lapatinib, erlotinib, and paclitaxel, respectively. Our results support isoform expressions as a rich resource for biomarkers predictive of drug response. Nature Publishing Group UK 2017-10-24 /pmc/articles/PMC5655668/ /pubmed/29066719 http://dx.doi.org/10.1038/s41467-017-01153-8 Text en © The Author(s) 2017 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/. |
spellingShingle | Article Safikhani, Zhaleh Smirnov, Petr Thu, Kelsie L. Silvester, Jennifer El-Hachem, Nehme Quevedo, Rene Lupien, Mathieu Mak, Tak W. Cescon, David Haibe-Kains, Benjamin Gene isoforms as expression-based biomarkers predictive of drug response in vitro |
title | Gene isoforms as expression-based biomarkers predictive of drug response in vitro |
title_full | Gene isoforms as expression-based biomarkers predictive of drug response in vitro |
title_fullStr | Gene isoforms as expression-based biomarkers predictive of drug response in vitro |
title_full_unstemmed | Gene isoforms as expression-based biomarkers predictive of drug response in vitro |
title_short | Gene isoforms as expression-based biomarkers predictive of drug response in vitro |
title_sort | gene isoforms as expression-based biomarkers predictive of drug response in vitro |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5655668/ https://www.ncbi.nlm.nih.gov/pubmed/29066719 http://dx.doi.org/10.1038/s41467-017-01153-8 |
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