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Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies

BACKGROUND: An individual patient’s response to a particular drug is influenced by multiple factors, which may include genetic predisposition. Pharmacogenetic studies attempt to discover and estimate the contributions of genetic variants to the variability in response to a drug treatment. The task o...

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Autores principales: Standish, Kristopher A., Huang, C. Chris, Curran, Mark E., Schork, Nicholas J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427602/
https://www.ncbi.nlm.nih.gov/pubmed/28494788
http://dx.doi.org/10.1186/s13075-017-1299-8
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author Standish, Kristopher A.
Huang, C. Chris
Curran, Mark E.
Schork, Nicholas J.
author_facet Standish, Kristopher A.
Huang, C. Chris
Curran, Mark E.
Schork, Nicholas J.
author_sort Standish, Kristopher A.
collection PubMed
description BACKGROUND: An individual patient’s response to a particular drug is influenced by multiple factors, which may include genetic predisposition. Pharmacogenetic studies attempt to discover and estimate the contributions of genetic variants to the variability in response to a drug treatment. The task of identifying the genetic contribution is often complicated by response phenotypes that are based on imprecise or subjective clinical observations. Because the success of a pharmacogenetic study depends on the analysis of a heritable phenotype, it is important to identify phenotypes with a significant heritable component to ensure reliable and reproducible results in subsequent genetic association studies. METHODS: We retrospectively analyzed data collected from 436 rheumatoid arthritis patients treated with golimumab during the phase III GO-FURTHER study. We investigated the reliability of several potential response outcomes after golimumab treatment. Using whole-genome sequencing of the clinical trial cohort, we estimated the heritability of each potential outcome measure. We further performed a longitudinal analysis of the clinical data to estimate variability of outcome measures over time and the degree to which each response metric could be confounded by placebo response. RESULTS: We determined that the high degree of within-patient variation over time makes a single follow-up visit insufficient to assess an individual patient’s response to golimumab treatment. We found that different potential response outcomes had varying degrees of heritability and that averaging across multiple follow-up visits yielded higher heritability estimates than single follow-up estimates. Importantly, we found that the change in swollen and tender joint counts were the most heritable outcome metrics we tested; however, we showed that they are also more likely to be confounded by a placebo response than objective phenotypes like the change in C-reactive protein levels. CONCLUSIONS: Our rigorous approach to finding robust and heritable response phenotypes could be beneficial to all pharmacogenetic studies and may lead to more reliable and reproducible results. TRIAL REGISTRATION: Clinicaltrials.gov NCT00973479. Registered 4 September 2009. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13075-017-1299-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-54276022017-05-15 Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies Standish, Kristopher A. Huang, C. Chris Curran, Mark E. Schork, Nicholas J. Arthritis Res Ther Research Article BACKGROUND: An individual patient’s response to a particular drug is influenced by multiple factors, which may include genetic predisposition. Pharmacogenetic studies attempt to discover and estimate the contributions of genetic variants to the variability in response to a drug treatment. The task of identifying the genetic contribution is often complicated by response phenotypes that are based on imprecise or subjective clinical observations. Because the success of a pharmacogenetic study depends on the analysis of a heritable phenotype, it is important to identify phenotypes with a significant heritable component to ensure reliable and reproducible results in subsequent genetic association studies. METHODS: We retrospectively analyzed data collected from 436 rheumatoid arthritis patients treated with golimumab during the phase III GO-FURTHER study. We investigated the reliability of several potential response outcomes after golimumab treatment. Using whole-genome sequencing of the clinical trial cohort, we estimated the heritability of each potential outcome measure. We further performed a longitudinal analysis of the clinical data to estimate variability of outcome measures over time and the degree to which each response metric could be confounded by placebo response. RESULTS: We determined that the high degree of within-patient variation over time makes a single follow-up visit insufficient to assess an individual patient’s response to golimumab treatment. We found that different potential response outcomes had varying degrees of heritability and that averaging across multiple follow-up visits yielded higher heritability estimates than single follow-up estimates. Importantly, we found that the change in swollen and tender joint counts were the most heritable outcome metrics we tested; however, we showed that they are also more likely to be confounded by a placebo response than objective phenotypes like the change in C-reactive protein levels. CONCLUSIONS: Our rigorous approach to finding robust and heritable response phenotypes could be beneficial to all pharmacogenetic studies and may lead to more reliable and reproducible results. TRIAL REGISTRATION: Clinicaltrials.gov NCT00973479. Registered 4 September 2009. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13075-017-1299-8) contains supplementary material, which is available to authorized users. BioMed Central 2017-05-12 2017 /pmc/articles/PMC5427602/ /pubmed/28494788 http://dx.doi.org/10.1186/s13075-017-1299-8 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Standish, Kristopher A.
Huang, C. Chris
Curran, Mark E.
Schork, Nicholas J.
Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies
title Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies
title_full Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies
title_fullStr Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies
title_full_unstemmed Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies
title_short Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies
title_sort comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427602/
https://www.ncbi.nlm.nih.gov/pubmed/28494788
http://dx.doi.org/10.1186/s13075-017-1299-8
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