Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis. Issue 5 (19th March 2019)

• Record Type: Journal Article
• Authors: Plant, Darren; Maciejewski, Mateusz; Smith, Samantha; Nair, Nisha; Hyrich, Kimme; Ziemek, Daniel; Barton, Anne; Verstappen, Suzanne
• Is Part Of: Arthritis & rheumatology. Volume 71:Issue 5(2019)
• Page Start: 678
• Page End: 684
• Publication Date: 2019-03-19
• Subjects: Arthritis -- Periodicals; Rheumatism -- Periodicals; 616.72
• DOI: 10.1002/art.40810
• Languages: English
• ISSNs: 2326-5191

Abstract

Abstract : 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 log2 ‐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.