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Objective: The type I interferon (IFN) response in rheumatoid arthritis (RA) has been extensively studied in relation to therapy with biological DMARDs (bDMARDs). However, the effect of conventional synthetic (cs)DMARDs and glucocorticoids (GCs) on IFN response gene (IRG) expression remains largely unknown, even though csDMARDS are used throughout all disease phases, including simultaneously with biologic therapy. This study was aimed to determine the dynamics of IFN response upon immunosuppressive treatment. Methods: Whole blood was collected in PAXgene tubes from 35 RA patients who received either COBRA therapy (combination of prednisone, initially 60 mg, methotrexate and sulfasalazine) ( n = 14) or COBRA-light therapy (prednisone, initially 30 mg, and methotrexate) ( n = 21). Expression of 10 IRGs was determined by real-time PCR at baseline (T0), after 4 weeks (T4), and 13 weeks (T13) of treatment. IRG selection was based on the differential presence of transcription factor binding sites (TFBS), in order to study the therapy effect on different pathway components involved in IFN signaling. Results: Seven of the 10 IRGs displayed significant changes during treatment ( p ≤ 0.016). These 7 IRGs all displayed a particularly pronounced decrease between T0 and T4 (≥1.6-fold, p ≤ 0.0059). The differences between IRG sensitivity to the treatment appeared related to the presence of TFBS for STAT1 and IRF proteins within the genes. The extent of the decreases between T0 and T4 was similar for the COBRA- and COBRA-light-treated group, despite the differences in drug combination and doses in those groups. Between T4 and T13, however, IRG expression in the COBRA-light-treated group displayed a significant increase, whereas it remained stable or decreased even further in most COBRA-treated patients (comparison of mean fold changes, p = 0.011). A significant association between IRG dynamics and clinical response to therapy was not detected. Conclusions: Immunosuppressive treatment with csDMARDs, in this case a combination of prednisolone, methotrexate and sulfasalazine, substantially downregulates the IFN response in RA patients. The dynamics of this downregulation were partly dependent on the presence of TFBS within the IRGs and the combination and dosages of agents, but they were irrespective of the clinical response to therapy.

Objectives: To validate the IFN response gene (IRG) set for the prediction of non-response to rituximab in rheumatoid arthritis (RA) and assess the predictive performance upon combination of this gene set with clinical parameters., Methods: In two independent cohorts of 93 (cohort I) and 133 (cohort II) rituximab-starting RA patients, baseline peripheral blood expression of eight IRGs was determined, and averaged into an IFN score. Predictive performance of IFN score and clinical parameters was assessed by logistic regression. A multivariate prediction model was developed using a forward stepwise selection procedure. Patients with a decrease in disease activity score (ΔDAS28)≥1.8 after 6 months of therapy were considered responders., Results: The mean IFN score was higher in non-responders compared to responders in both cohorts, but this difference was most pronounced in patients who did not use prednisone, as described before. Univariate analysis in cohort I showed that baseline DAS28, IFN score, DMARD use and negativity for IgM-RF and/or ACPA were associated with rituximab non-response. The multivariate model consisted of DAS28, IFN score and DMARD use, which showed an area under the curve (AUC) of 0.82. In cohort II, this model revealed a comparable AUC in PREDN-negative patients (0.78), but AUC in PREDN-positive patients was significantly lower (0.63), which seemed due to effect modification of the IFN score by prednisone., Conclusions: Combination of predictive parameters provided a promising model for the prediction of non-response to rituximab, with possibilities for optimization via definition of the exact interfering effect of prednisone on IFN score., Trial Registration (cohort Ii, Smart Trial): NCT01126541, registered 18 May 2010., (Copyright © 2017. Published by Elsevier SAS.)

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.