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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...

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Autores principales: Safikhani, Zhaleh, Smirnov, Petr, Thu, Kelsie L., Silvester, Jennifer, El-Hachem, Nehme, Quevedo, Rene, Lupien, Mathieu, Mak, Tak W., Cescon, David, Haibe-Kains, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
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.
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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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