Identifying Predictive Biomarkers of Response in Patients With Rheumatoid Arthritis Treated With Adalimumab Using Machine Learning Analysis of Whole‐Blood Transcriptomics Data
Authors' conclusion
This study advances the understanding of transcriptomic alterations in RA treatment and enhances our understanding of treatment response mechanisms.
Study context
- Population
- Patients with rheumatoid arthritis (RA) starting tumornecrosis factor inhibitors (TNFi) therapy (adalimumab), assessed for treatment response at 6 months97 samples passing quality control
- Participants
- 100 patients
- Outcome
- Biomarkers predictive of adalimumab treatment response
- Follow-up
- 6 months
Methodological detailswhole-blood transcriptomics (RNA sequencing), machine learning classifiers, network analysis, survival analysis
- Whole-blood transcriptomics (RNA sequencing)
- Machine learning classifiers
- Network analysis
- Survival analysis
Main finding
As reported in the abstract
Transcriptomic predictors of treatment response
Genes associated with treatment response (differential expression)
84
Random forest classifiers, AUC up to
0.86
Observational data: associations, not effects.
Original publication
Identifying Predictive Biomarkers of Response in Patients With Rheumatoid Arthritis Treated With Adalimumab Using Machine Learning Analysis of Whole‐Blood Transcriptomics Data. Arthritis Rheumatol. Published 4 August 2025. doi:10.
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