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. 2025 May 26.
doi: 10.1002/art.43255. Online ahead of print.

Identifying Predictive Biomarkers of Response in Patients With Rheumatoid Arthritis Treated With Adalimumab Using Machine Learning Analysis of Whole-Blood Transcriptomics Data

Chuan Fu Yap  1 , Nisha Nair  2 , Ann W Morgan  3 , John D Isaacs  4 , Anthony G Wilson  5 , Kimme Hyrich  6 , Guillermo Barturen  7 , María Riva-Torrubia  8 , Marta Gut  9 , Ivo Gut  9 , Marta E Alarcón Riquelme  10 , Anne Barton  2 , Darren Plant  2
Affiliations

Identifying Predictive Biomarkers of Response in Patients With Rheumatoid Arthritis Treated With Adalimumab Using Machine Learning Analysis of Whole-Blood Transcriptomics Data

Chuan Fu Yap et al. Arthritis Rheumatol. .

Abstract

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.

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References

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