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Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine

OBJECTIVES: Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response tha...

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Autores principales: Harris, Michael, Bhuvaneshwar, Krithika, Natarajan, Thanemozhi, Sheahan, Laura, Wang, Difei, Tadesse, Mahlet G., Shoulson, Ira, Filice, Ross, Steadman, Kenneth, Pishvaian, Michael J., Madhavan, Subha, Deeken, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3888473/
https://www.ncbi.nlm.nih.gov/pubmed/24401833
http://dx.doi.org/10.1097/FPC.0000000000000015
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author Harris, Michael
Bhuvaneshwar, Krithika
Natarajan, Thanemozhi
Sheahan, Laura
Wang, Difei
Tadesse, Mahlet G.
Shoulson, Ira
Filice, Ross
Steadman, Kenneth
Pishvaian, Michael J.
Madhavan, Subha
Deeken, John
author_facet Harris, Michael
Bhuvaneshwar, Krithika
Natarajan, Thanemozhi
Sheahan, Laura
Wang, Difei
Tadesse, Mahlet G.
Shoulson, Ira
Filice, Ross
Steadman, Kenneth
Pishvaian, Michael J.
Madhavan, Subha
Deeken, John
author_sort Harris, Michael
collection PubMed
description OBJECTIVES: Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response that may help to personalize treatment in the clinic. METHODS: We analyzed two independent data sets: (a) genotype data from NCI-60 cell lines using the Affymetrix DMET 1.0 platform combined with gemcitabine cytotoxicity data in those cell lines, and (b) genome-wide association studies (GWAS) data from 351 pancreatic cancer patients treated on an NCI-sponsored phase III clinical trial. We also performed a subset analysis on the GWAS data set for 135 patients who were given gemcitabine+placebo. Statistical and systems biology analyses were performed on each individual data set to identify biomarkers significantly associated with gemcitabine response. RESULTS: Genetic variants in the ABC transporters (ABCC1, ABCC4) and the CYP4 family members CYP4F8 and CYP4F12, CHST3, and PPARD were found to be significant in both the NCI-60 and GWAS data sets. We report significant association between drug response and variants within members of the chondroitin sulfotransferase family (CHST) whose role in gemcitabine response is yet to be delineated. CONCLUSION: Biomarkers identified in this integrative analysis may contribute insights into gemcitabine response variability. As genotype data become more readily available, similar studies can be conducted to gain insights into drug response mechanisms and to facilitate clinical trial design and regulatory reviews.
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spelling pubmed-38884732014-01-13 Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine Harris, Michael Bhuvaneshwar, Krithika Natarajan, Thanemozhi Sheahan, Laura Wang, Difei Tadesse, Mahlet G. Shoulson, Ira Filice, Ross Steadman, Kenneth Pishvaian, Michael J. Madhavan, Subha Deeken, John Pharmacogenet Genomics Original Articles OBJECTIVES: Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response that may help to personalize treatment in the clinic. METHODS: We analyzed two independent data sets: (a) genotype data from NCI-60 cell lines using the Affymetrix DMET 1.0 platform combined with gemcitabine cytotoxicity data in those cell lines, and (b) genome-wide association studies (GWAS) data from 351 pancreatic cancer patients treated on an NCI-sponsored phase III clinical trial. We also performed a subset analysis on the GWAS data set for 135 patients who were given gemcitabine+placebo. Statistical and systems biology analyses were performed on each individual data set to identify biomarkers significantly associated with gemcitabine response. RESULTS: Genetic variants in the ABC transporters (ABCC1, ABCC4) and the CYP4 family members CYP4F8 and CYP4F12, CHST3, and PPARD were found to be significant in both the NCI-60 and GWAS data sets. We report significant association between drug response and variants within members of the chondroitin sulfotransferase family (CHST) whose role in gemcitabine response is yet to be delineated. CONCLUSION: Biomarkers identified in this integrative analysis may contribute insights into gemcitabine response variability. As genotype data become more readily available, similar studies can be conducted to gain insights into drug response mechanisms and to facilitate clinical trial design and regulatory reviews. Lippincott Williams & Wilkins 2014-02 2013-12-19 /pmc/articles/PMC3888473/ /pubmed/24401833 http://dx.doi.org/10.1097/FPC.0000000000000015 Text en © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins http://creativecommons.org/licenses/by-nc-nd/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivitives 3.0 License, where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially.
spellingShingle Original Articles
Harris, Michael
Bhuvaneshwar, Krithika
Natarajan, Thanemozhi
Sheahan, Laura
Wang, Difei
Tadesse, Mahlet G.
Shoulson, Ira
Filice, Ross
Steadman, Kenneth
Pishvaian, Michael J.
Madhavan, Subha
Deeken, John
Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine
title Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine
title_full Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine
title_fullStr Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine
title_full_unstemmed Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine
title_short Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine
title_sort pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3888473/
https://www.ncbi.nlm.nih.gov/pubmed/24401833
http://dx.doi.org/10.1097/FPC.0000000000000015
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