Effect of XXXX on type I interferon signature in @entity309 : consequences for response prediction to rituximab.

@entity1 @entity229 @entity309
INTRODUCTION: Elevated type I interferon (IFN) response gene (IRG) expression has proven clinical relevance in predicting rituximab non-response in @entity309 ( @entity309 ). Interference between glucocorticoids (GCs) and type I IFN signaling has been demonstrated in vitro. Since GC use and dose are highly variable among @entity1 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 @entity309 . METHODS: In two independent cohorts of 32 and 182 biologic-free @entity309 @entity1 and a third cohort of 40 rituximab-starting @entity309 @entity1 , 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. @entity1 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 @entity229 users (PREDN(+)) compared to non-users (PREDN(-)). In the rituximab cohort, analysis on PREDN(-) @entity1 (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 @entity1 (n   =   40). Using a group-specific IFN-score cut-off for all @entity1 and PREDN(-) @entity1 alone, sensitivity increased from 41% to 88%, respectively, combined with 100% specificity. CONCLUSIONS: Because of @entity229 -related suppression of IFN-score, higher accuracy of rituximab response prediction was achieved in PREDN(-) @entity1 . These results suggest that the IFN-score-based rituximab response prediction model could be improved upon implementation of @entity229 use.
