Type I interferon response gene expression in established rheumatoid arthritis is not associated with clinical parameters

BACKGROUND: A peripheral blood interferon (IFN) signature (i.e., elevated type I interferon response gene [IRG] expression) has been described in a subset of patients with rheumatoid arthritis (RA). In the present study, we systematically assessed the association between this IRG expression and clinical parameters. METHODS: Expression of 19 IRGs was determined in peripheral blood from 182 consecutive patients with RA and averaged into an IFN score per individual. Correlation and unpaired analyses were performed on the complete patient group. The analyses were internally validated by using an algorithm to randomize the patient group 1000 times into two equally sized sets, and then analyses were performed on both sets. RESULTS: Associations were assessed between IFN score and disease duration, 28-joint Disease Activity Score and its components, the occurrence of erosions and nodules, autoantibody positivity, and immunosuppressive treatment. This analysis revealed lower IFN scores in patients using hydroxychloroquine, prednisone, and/or sulfasalazine, but it did not show significant associations between the other parameters and the IFN score. Selecting patients who were not treated with hydroxychloroquine, prednisone, and/or sulfasalazine (n = 95) did not reveal any significant associations either. CONCLUSIONS: IRG expression in RA is affected by immunosuppressive treatment with prednisone, hydroxychloroquine, and/or sulfasalazine, but it is not evidently associated with other clinical parameters. Hence, the IFN signature appears to describe a subgroup of patients with RA but does not seem to reflect disease activity

Additional file 1: Table S1. of Type I interferon response gene expression in established rheumatoid arthritis is not associated with clinical parameters

List of the 19 IFN response genes measured. Table S2. Results of analysis for associations between IFN scores and clinical parameters after 1000 times of random sampling using only patients who were not treated with prednisone, HCQ, or SSZ. (PDF 82 kb

Effect of prednisone on type I interferon signature in rheumatoid arthritis: consequences for response prediction to rituximab

INTRODUCTION: Elevated type I interferon (IFN) response gene (IRG) expression has proven clinical relevance in predicting rituximab non-response in rheumatoid arthritis (RA). Interference between glucocorticoids (GCs) and type I IFN signaling has been demonstrated in vitro. Since GC use and dose are highly variable among patients before rituximab treatment, the aim of this study was to determine the effect of GC use on IRG expression in relation to rituximab response prediction in RA. METHODS: In two independent cohorts of 32 and 182 biologic-free RA patients and a third cohort of 40 rituximab-starting RA patients, peripheral blood expression of selected IRGs was determined by microarray or quantitative real-time polymerase chain reaction (qPCR), and an IFN-score was calculated. The baseline IFN-score was tested for its predictive value towards rituximab response in relation to GC use using receiver operating characteristics (ROC) analysis in the rituximab cohort. Patients with a decrease in disease activity score (∆DAS28) >1.2 after 6 months of rituximab were considered responders. RESULTS: We consistently observed suppression of IFN-score in prednisone users (PREDN(+)) compared to non-users (PREDN(−)). In the rituximab cohort, analysis on PREDN(−) patients (n = 13) alone revealed improved prediction of rituximab non-response based on baseline IFN-score, with an area under the curve (AUC) of 0.975 compared to 0.848 in all patients (n = 40). Using a group-specific IFN-score cut-off for all patients and PREDN(−) patients alone, sensitivity increased from 41% to 88%, respectively, combined with 100% specificity. CONCLUSIONS: Because of prednisone-related suppression of IFN-score, higher accuracy of rituximab response prediction was achieved in PREDN(−) patients. These results suggest that the IFN-score-based rituximab response prediction model could be improved upon implementation of prednisone use