A multi-biomarker panel for predicting Tocilizumab response in Rheumatoid arthritis patients

Abstract

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammation in the synovial lining of the joints. Key inflammatory cytokines such as interleukin-6 (IL-6), TNF-α, and others play a critical role in the activation of local synovial leukocytes and the induction of chronic inflammation. Tocilizumab (TCZ), a humanized anti-IL-6 receptor monoclonal antibody, has demonstrated significant clinical efficacy in treating RA patients. However, similar to other inflammatory cytokine blockers, such as TNF-alpha inhibitors, Interleukin-1 inhibitors, or CD20 inhibitors, some patients do not respond to treatment. To address this challenge, our study employed a high-precision proteomics approach to identify protein biomarkers capable of predicting clinical responses to Tocilizumab in RA patients. Through the use of data-independent acquisition (DIA) mass spectrometry, we analyzed serum samples from both TCZ responders and non-responders to discover potential biomarker candidates. These candidates were subsequently validated using individual serum samples from two independent cohorts: a training set (N = 70) and a test set (N = 18), allowing for the development of a robust multi-biomarker panel. The constructed multi-biomarker panel demonstrated an average discriminative power of 86 % between response and non-response groups, with a high area under the curve (AUC) value of 0.84. Additionally, the panel exhibited 100 % sensitivity and 60 % specificity. Collectively, our multi-biomarker panel holds promise as a diagnostic tool to predict non-responders to TCZ treatment in RA patients.