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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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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. |
format | Online Article Text |
id | pubmed-5427602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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