Identifying biomarkers for methotrexate efficacy in rheumatoid arthritis: a machine learning approach to whole-blood transcriptomic analysis.

OBJECTIVES

Methotrexate (MTX) is the first-line treatment for rheumatoid arthritis (RA), yet inadequate response is reported in 30-40% of patients. Predicting MTX response early could enable more personalised and effective treatment. This study aimed to identify biomarkers predictive of MTX response at 6 months through whole-blood transcriptomic signatures using machine learning.

METHODS

RNA-sequencing data were generated in whole-blood samples taken from 100 MTX-naïve RA patients at baseline (pre-treatment) and following 4-weeks post treatment with MTX. Machine learning models were trained to classify MTX response following 6-months of treatment using gradient boosted trees and interpreted using SHAP values to identify predictive genes.

RESULTS

Machine learning models trained on baseline and 4-weeks data achieved AUCs of 0.89 and 0.90 respectively. Stability of SHAP values showed that the baseline model was generally more stable, and therefore potentially more generalisable. Key predictive genes at baseline, which were downregulated in responders, included CAV1, LCN12, and GLB1L.

CONCLUSION

Blood-based gene expression profiling at baseline and after 4-weeks of MTX treatment can predict treatment response with high confidence revealing relevant gene pathways and candidate gene targets but requires independent validation. These findings highlight the potential for transcriptomic biomarkers to inform early treatment decision in RA, supporting precision medicine approaches.