Towards Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: a Pharmacogenomics-driven Machine Learning Approach

Objective To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA). Methods Demographic, clinical and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n=336) and validation cohort (n=307) were used. The genomic data comprised 160 single nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European League Against Rheumatism (EULAR) response criteria at 3-month follow-up. Supervised ML methods were trained with 5-repeats and 10-fold cross-validation using the training cohort. Prediction performance was validated in the independent validation cohort. Results Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score with 28-joint count (DAS28) and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 (p=0.05) in the training cohort and achieved a prediction accuracy of 76% (p=0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 were among the top predictors of methotrexate response. Conclusion Pharmacogenomic biomarkers combined with baseline DAS28 can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies