Toward Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: A Pharmacogenomics‐Driven Machine Learning Approach

Abstract

on behalf of the Pharmacogenetics of Methotrexate in Rheumatoid Arthritis Consortium 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 Alliance of Associations for Rheumatology (EULAR) response criteria at the 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 in 28 joints (DAS28) scores 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 scores were among the top predictors of methotrexate response.Conclusion. Pharmacogenomic biomarkers combined with baseline DAS28 scores 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.

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

“…Overfitting and class imbalance were directly addressed; the sample included only White Europeans, so generalizability remains limited. The incorporation of genetic data in the prediction algorithm substantially improved prediction accuracy, supporting the feasibility of pharmacogenomic markers for precision medicine, although the overall response rate remained low 34 .…”

“…Several recent publications in RMDs reflect the goals of precision medicine, which can be understood as the provision of the right treatment 32 , at the right dose 33 , to the right person, at the right time 34 , while minimizing unnecessary testing ,side effects and overuse issues, including opioid use and abuse [35][36][37] , specifically opioid use around TJR [38][39][40] , and to explore issues of inequity in classification 41 .…”

“…Future work will benefit from larger samples with robust outcomes and truly independent external validation sets to avoid overfitting and to mitigate feature instability (often challenging in RDs). Another example is work utilizing consortium data to develop an algorithm to predict methotrexate response in patients with early RA (n=643) 34 . A RF model was trained on all UK patients (n=336) and externally validated on independent, non-UK patients (n=307, Sweden, Netherlands).…”

“…It will be important in the future to collect these types of multiomics data on larger and more representative samples and fully integrate the different data types into ML models. A few studies have incorporated such multi-modal data 34 , and others are collecting it 30,77 , but additional rigorously designed longitudinal studies will be needed to establish this knowledge base and allow discovery and validation using existing and newly developed methodologies capable of handling this type of multi-modal information.…”

Clinical predictors of response to methotrexate in patients with rheumatoid arthritis: a machine learning approach using clinical trial data

“…The modest discrimination value (AUC 0.79) of our LASSO models is non-inferior to the previous models including clinical predictors of response to MTX in RA [16,29] and dictates the need for additional biomarkers aiming at the improved performance of individualized predictive models. Studies are underway by our group to augment clinical predictors with genomic, metabolomic, and microbiome data [42,43].…”