Browse by author
Lookup NU author(s): Professor John IsaacsORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 The Author(s). Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology. Objective: Tumornecrosis factor inhibitors (TNFi) have significantly improved rheumatoid arthritis (RA) management, yet variability in patient response remains a substantial challenge, with approximately 40% of patients discontinuing TNFi due to nonresponse or adverse effects. This study aimed to identify biomarkers predictive of adalimumab treatment response using whole-blood transcriptomics, leveraging machine learning models for data mining observed by targeted statistical analysis. Methods: A cohort of patients with RA starting TNFi therapy (n = 100) was assessed for treatment response at 6 months, with RNA sequencing performed on baseline (pretreatment) and 3-month follow-up samples. Machine learning classifiers were built to identify predictive biomarkers for treatment outcomes. This was observed by a network analysis on the biomarkers to elucidate the most influential biomarker, which was subsequently confirmed through survival analysis. Results: Differential gene expression analysis in 97 samples passing quality control identified 84 genes associated with treatment response. Random forest classifiers achieved high predictive accuracy with area under the receiver operating characteristic curves up to 0.86, identifying genes contributing to treatment outcomes. Network analysis further elucidated gene interactions, highlighting marginal zone B And B1 cell–specific protein 1 (MZB1) as a novel biomarker not captured by machine learning alone. MZB1's role in B cell development and antibody production was associated with antidrug antibody formation, impacting treatment efficacy. Conclusion: This study advances the understanding of transcriptomic alterations in RA treatment and enhances our understanding of treatment response mechanisms. Although the gene signatures identified require independent replication, the study serves as a starting point to pave the way for personalized therapeutic strategies in patients commencing TNFi therapy in RA.
Author(s): Yap CF, Nair N, Morgan AW, Isaacs JD, Wilson AG, Hyrich K, Barturen G, Riva-Torrubia M, Gut M, Gut I, Alarcon Riquelme ME, Barton A, Plant D
Publication type: Article
Publication status: Published
Journal: Arthritis and Rheumatology
Year: 2025
Pages: Epub ahead of print
Online publication date: 26/05/2025
Acceptance date: 07/05/2025
Date deposited: 20/08/2025
ISSN (print): 2326-5191
ISSN (electronic): 2326-5205
Publisher: John Wiley and Sons Inc.
URL: https://doi.org/10.1002/art.43255
DOI: 10.1002/art.43255
PubMed id: 40415615
Altmetrics provided by Altmetric
