Integrative proteomic and machine learning strategies identify key biomarkers associated with response to JAK inhibitor in rheumatoid arthritis

Janus kinase inhibitors (JAKi) have revolutionized the treatment of rheumatoid arthritis (RA), yet therapeutic responses vary widely across patients. Identifying proteomic biomarkers of response may enhance personalized treatment strategies and improve clinical outcomes. We conducted a prospective study involving 42 RA patients treated with tofacitinib, baricitinib, or upadacitinib. Longitudinal serum proteomic profiling was performed at baseline and 6 months using the Olink® Explore 384 platform. Treatment response was defined by ACR/EULAR criteria. Biomarkers associated with good responders were identified through differential expression analysis and ensemble feature selection. Prediction models were developed using selected features, and feature importance was quantified using SHapley Additive exPlanations (SHAP). Pretreatment levels of IL-12B, IL-17A, and IFN-γ were significantly elevated in JAKi non-responders. IL-17 signaling was associated with response to baricitinib and tofacitinib, while upadacitinib responders exhibited broader suppression of immune-related proteins including IL-4/IL-13 pathways. Prediction models using proteomic features outperformed those based on clinical variables alone (AUC ≥ 0.90 vs. 0.52 - 0.57), with further improvement upon integrating clinical and proteomic data (AUC = 0.95). Among all features, IL-12B emerged as the most robust and consistent predictor of treatment response, supported by both differential expression and multiple machine learning approaches. Serum proteomic profiling identifies elevated pretreatment IL-12B levels as a key predictor of JAKi non-response and uncovers distinct molecular responses to different JAK inhibitors. These findings support the utility of integrating proteomics with machine learning to advance personalized treatment strategies in RA.