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

• Published in: Arthritis research & therapy Vol. 17; no. 1; p. 78
• Main Authors: de Jong, Tamarah D, Vosslamber, Saskia, Blits, Marjolein, Wolbink, Gertjan, Nurmohamed, Mike T, van der Laken, Conny J, Jansen, Gerrit, Voskuyl, Alexandre E, Verweij, Cornelis L
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.03.2015; BioMed Central
• Subjects: Adult; Aged; Analysis; Antirheumatic Agents - therapeutic use; Arthritis; Arthritis, Rheumatoid - diagnosis; Arthritis, Rheumatoid - drug therapy; Arthritis, Rheumatoid - metabolism; Biological response modifiers; Care and treatment; Cohort Studies; Corticosteroids; Drug therapy; Female; Genes; Health aspects; Humans; Interferon; Interferon Type I - biosynthesis; Male; Middle Aged; Predictive Value of Tests; Prednisone; Prednisone - therapeutic use; Rheumatoid factor; Rituximab - therapeutic use; Steroids; Treatment Outcome; Taiwan
• ISSN: 1478-6354; 1478-6362; 1478-6354
• DOI: 10.1186/s13075-015-0564-y

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. 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. 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. 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.