Cargando…
Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis
OBJECTIVE: Approximately 30–40% of rheumatoid arthritis (RA) patients who are initially started on low‐dose methotrexate (MTX) will not benefit from the treatment. To date, no reliable biomarkers of MTX inefficacy in RA have been identified. The aim of this study was to analyze whole blood samples f...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328381/ https://www.ncbi.nlm.nih.gov/pubmed/30615300 http://dx.doi.org/10.1002/art.40810 |
_version_ | 1784757708801441792 |
---|---|
author | Plant, Darren Maciejewski, Mateusz Smith, Samantha Nair, Nisha Hyrich, Kimme Ziemek, Daniel Barton, Anne Verstappen, Suzanne |
author_facet | Plant, Darren Maciejewski, Mateusz Smith, Samantha Nair, Nisha Hyrich, Kimme Ziemek, Daniel Barton, Anne Verstappen, Suzanne |
author_sort | Plant, Darren |
collection | PubMed |
description | OBJECTIVE: Approximately 30–40% of rheumatoid arthritis (RA) patients who are initially started on low‐dose methotrexate (MTX) will not benefit from the treatment. To date, no reliable biomarkers of MTX inefficacy in RA have been identified. The aim of this study was to analyze whole blood samples from RA patients at 2 time points (pretreatment and 4 weeks following initiation of MTX), to identify gene expression biomarkers of the MTX response. METHODS: RA patients who were about to commence treatment with MTX were selected from the Rheumatoid Arthritis Medication Study. Using European League Against Rheumatism (EULAR) response criteria, 42 patients were categorized as good responders and 43 as nonresponders at 6 months following the initation of MTX treatment. Data on whole blood transcript expression were generated, and supervised machine learning methods were used to predict a EULAR nonresponse. Models in which transcript levels were included were compared to models in which clinical covariates alone (e.g., baseline disease activity, sex) were included. Gene network and ontology analysis was also performed. RESULTS: Based on the ratio of transcript values (i.e., the difference in log(2)‐transformed expression values between 4 weeks of treatment and pretreatment), a highly predictive classifier of MTX nonresponse was developed using L2‐regularized logistic regression (mean ± SEM area under the receiver operating characteristic [ROC] curve [AUC] 0.78 ± 0.11). This classifier was superior to models that included clinical covariates (ROC AUC 0.63 ± 0.06). Pathway analysis of gene networks revealed significant overrepresentation of type I interferon signaling pathway genes in nonresponders at pretreatment (P = 2.8 × 10(−25)) and at 4 weeks after treatment initiation (P = 4.9 × 10(−28)). CONCLUSION: Testing for changes in gene expression between pretreatment and 4 weeks post–treatment initiation may provide an early classifier of the MTX treatment response in RA patients who are unlikely to benefit from MTX over 6 months. Such patients should, therefore, have their treatment escalated more rapidly, which would thus potentially impact treatment pathways. These findings emphasize the importance of a role for early treatment biomarker monitoring in RA patients started on MTX. |
format | Online Article Text |
id | pubmed-9328381 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93283812022-07-30 Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis Plant, Darren Maciejewski, Mateusz Smith, Samantha Nair, Nisha Hyrich, Kimme Ziemek, Daniel Barton, Anne Verstappen, Suzanne Arthritis Rheumatol Rheumatoid Arthritis OBJECTIVE: Approximately 30–40% of rheumatoid arthritis (RA) patients who are initially started on low‐dose methotrexate (MTX) will not benefit from the treatment. To date, no reliable biomarkers of MTX inefficacy in RA have been identified. The aim of this study was to analyze whole blood samples from RA patients at 2 time points (pretreatment and 4 weeks following initiation of MTX), to identify gene expression biomarkers of the MTX response. METHODS: RA patients who were about to commence treatment with MTX were selected from the Rheumatoid Arthritis Medication Study. Using European League Against Rheumatism (EULAR) response criteria, 42 patients were categorized as good responders and 43 as nonresponders at 6 months following the initation of MTX treatment. Data on whole blood transcript expression were generated, and supervised machine learning methods were used to predict a EULAR nonresponse. Models in which transcript levels were included were compared to models in which clinical covariates alone (e.g., baseline disease activity, sex) were included. Gene network and ontology analysis was also performed. RESULTS: Based on the ratio of transcript values (i.e., the difference in log(2)‐transformed expression values between 4 weeks of treatment and pretreatment), a highly predictive classifier of MTX nonresponse was developed using L2‐regularized logistic regression (mean ± SEM area under the receiver operating characteristic [ROC] curve [AUC] 0.78 ± 0.11). This classifier was superior to models that included clinical covariates (ROC AUC 0.63 ± 0.06). Pathway analysis of gene networks revealed significant overrepresentation of type I interferon signaling pathway genes in nonresponders at pretreatment (P = 2.8 × 10(−25)) and at 4 weeks after treatment initiation (P = 4.9 × 10(−28)). CONCLUSION: Testing for changes in gene expression between pretreatment and 4 weeks post–treatment initiation may provide an early classifier of the MTX treatment response in RA patients who are unlikely to benefit from MTX over 6 months. Such patients should, therefore, have their treatment escalated more rapidly, which would thus potentially impact treatment pathways. These findings emphasize the importance of a role for early treatment biomarker monitoring in RA patients started on MTX. John Wiley and Sons Inc. 2019-03-19 2019-05 /pmc/articles/PMC9328381/ /pubmed/30615300 http://dx.doi.org/10.1002/art.40810 Text en © 2019 The Authors. Arthritis & Rheumatology published by Wiley Periodicals, Inc. on behalf of American College of Rheumatology. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Rheumatoid Arthritis Plant, Darren Maciejewski, Mateusz Smith, Samantha Nair, Nisha Hyrich, Kimme Ziemek, Daniel Barton, Anne Verstappen, Suzanne Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis |
title | Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis |
title_full | Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis |
title_fullStr | Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis |
title_full_unstemmed | Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis |
title_short | Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis |
title_sort | profiling of gene expression biomarkers as a classifier of methotrexate nonresponse in patients with rheumatoid arthritis |
topic | Rheumatoid Arthritis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328381/ https://www.ncbi.nlm.nih.gov/pubmed/30615300 http://dx.doi.org/10.1002/art.40810 |
work_keys_str_mv | AT plantdarren profilingofgeneexpressionbiomarkersasaclassifierofmethotrexatenonresponseinpatientswithrheumatoidarthritis AT maciejewskimateusz profilingofgeneexpressionbiomarkersasaclassifierofmethotrexatenonresponseinpatientswithrheumatoidarthritis AT smithsamantha profilingofgeneexpressionbiomarkersasaclassifierofmethotrexatenonresponseinpatientswithrheumatoidarthritis AT nairnisha profilingofgeneexpressionbiomarkersasaclassifierofmethotrexatenonresponseinpatientswithrheumatoidarthritis AT profilingofgeneexpressionbiomarkersasaclassifierofmethotrexatenonresponseinpatientswithrheumatoidarthritis AT hyrichkimme profilingofgeneexpressionbiomarkersasaclassifierofmethotrexatenonresponseinpatientswithrheumatoidarthritis AT ziemekdaniel profilingofgeneexpressionbiomarkersasaclassifierofmethotrexatenonresponseinpatientswithrheumatoidarthritis AT bartonanne profilingofgeneexpressionbiomarkersasaclassifierofmethotrexatenonresponseinpatientswithrheumatoidarthritis AT verstappensuzanne profilingofgeneexpressionbiomarkersasaclassifierofmethotrexatenonresponseinpatientswithrheumatoidarthritis |