Effect of Prednisone on Type I Interferon Signature in RA
Effect of Prednisone on Type I Interferon Signature in RA
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 (ΔDAS28) >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.
Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic joint inflammation which may lead to cartilage and bone destruction. It is a heterogeneous disease, as reflected by differences in severity, pathogenesis and treatment outcome. From diagnosis onwards, RA patients often receive immunosuppressive treatment with non-biologic disease-modifying anti-rheumatic drugs (DMARDs) and/or glucocorticoids (GCs). When patients no longer benefit from the non-biologic therapy, they usually start on treatment with biologics, such as TNFα-blockers and B-cell depletion therapy using rituximab (RTX). Approximately 30% to 50% of patients do not achieve a favorable response to biologics. To increase treatment efficacy and to develop personalized treatment, predictors of therapy response are needed.
Independent studies have shown that activation of the type I interferon (IFN) system is associated with the clinical outcome of RTX therapy. This so-called 'IFN signature' represents a response program consisting of genes that are activated by type I IFNs and is present in approximately 50% of RA patients. Induction of type I IFN response genes (IRG) is triggered via activation of the JAK-STAT signaling pathway, more specifically via JAK1, TYK2, STAT1 and STAT2, followed by recruitment of IRF9 and formation of the ISGF3 transcription factor complex. It was shown that patients with a good response to RTX have low IRG expression prior to the start of treatment, whereas non-responders display relatively high IRG expression. Potential clinical utility of IRG expression reflected as an IFN-score to predict the clinical outcome of RTX treatment was demonstrated by an area under the receiver operating characteristics (ROC) curve of 87%. Hence, knowledge of IRG expression in a RA patient before the start of RTX treatment is of crucial importance to predict the success of the clinical outcome.
It has been reported that GCs can interfere with the type I IFN system by modulation of IFN induction as well as downstream IFN signaling. GCs were initially prescribed to RA patients in high doses (≥10 mg/day) to suppress flares of inflammation, but nowadays long-term treatment with low-dose GCs is commonly used as well. Since use and dose of GCs are highly variable among patients prior to the start of treatment with RTX, we aimed to determine what the effect of GC use is on IRG expression in relation to the clinical response to RTX.
Abstract and Introduction
Abstract
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 (ΔDAS28) >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.
Introduction
Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic joint inflammation which may lead to cartilage and bone destruction. It is a heterogeneous disease, as reflected by differences in severity, pathogenesis and treatment outcome. From diagnosis onwards, RA patients often receive immunosuppressive treatment with non-biologic disease-modifying anti-rheumatic drugs (DMARDs) and/or glucocorticoids (GCs). When patients no longer benefit from the non-biologic therapy, they usually start on treatment with biologics, such as TNFα-blockers and B-cell depletion therapy using rituximab (RTX). Approximately 30% to 50% of patients do not achieve a favorable response to biologics. To increase treatment efficacy and to develop personalized treatment, predictors of therapy response are needed.
Independent studies have shown that activation of the type I interferon (IFN) system is associated with the clinical outcome of RTX therapy. This so-called 'IFN signature' represents a response program consisting of genes that are activated by type I IFNs and is present in approximately 50% of RA patients. Induction of type I IFN response genes (IRG) is triggered via activation of the JAK-STAT signaling pathway, more specifically via JAK1, TYK2, STAT1 and STAT2, followed by recruitment of IRF9 and formation of the ISGF3 transcription factor complex. It was shown that patients with a good response to RTX have low IRG expression prior to the start of treatment, whereas non-responders display relatively high IRG expression. Potential clinical utility of IRG expression reflected as an IFN-score to predict the clinical outcome of RTX treatment was demonstrated by an area under the receiver operating characteristics (ROC) curve of 87%. Hence, knowledge of IRG expression in a RA patient before the start of RTX treatment is of crucial importance to predict the success of the clinical outcome.
It has been reported that GCs can interfere with the type I IFN system by modulation of IFN induction as well as downstream IFN signaling. GCs were initially prescribed to RA patients in high doses (≥10 mg/day) to suppress flares of inflammation, but nowadays long-term treatment with low-dose GCs is commonly used as well. Since use and dose of GCs are highly variable among patients prior to the start of treatment with RTX, we aimed to determine what the effect of GC use is on IRG expression in relation to the clinical response to RTX.
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