Proteins linked to type II interferon response in Sjögren’s disease: novel indicators for disease monitoring and predicting treatment response to leflunomide and hydroxychloroquine combination therapy - Wong, Wing-Yi; Leavis, Helen L.; Blokland, Sofie L. M.; Baloche, Valentin M. D.; van Roon, Joel A. G. (Frontiers in Immunology, 2025)
ObjectiveTo identify biomarkers and endotypes predictive of treatment response, monitor disease activity, and explore pathways associated with the clinical efficacy of leflunomide and hydroxychloroquine combination therapy (LEF/HCQ) in patients with primary Sjögren’s disease (SjD).MethodsSerum proteome (Olink Immuno-oncology panel, analyzing 92 proteins) of 29 patients with SjD of the RepurpSS-I study and 8 healthy controls was analyzed at baseline and after 24 weeks of LEF/HCQ. Proteomic changes were correlated to standard and novel clinical endpoints. Transcriptome data of blood mononuclear cells and monocytes were used to assess type I and II IFN scores.ResultsAt baseline, 29 proteins were differentially expressed between SjD and HC. LEF/HCQ significantly downregulated 22 out of 27 over-expressed proteins, which was not observed in the placebo-arm. Fourteen baseline proteins and the changes of four of these proteins, CXCL10, CXCL11, TNF, and soluble CD70 concentrations, were correlated with clinical response (|r| 0.40–0.62, p<0.05). Principal Component Analysis revealed an IFN-γ-associated set of coherent proteins. At baseline, using only two proteins, CXCL10 and CXCL11 effectively distinguished patients from healthy controls and responders from non-responders (all p<0.05). Finally, in addition to changes in type I IFN signatures, type II IFN signatures were observed in monocytes that were associated with changes in disease activity.ConclusionThese data support a significant role for a type II IFN-associated immune response in SjD pathogenesis, which is targeted by LEF/HCQ. Proteins associated with type II IFN-driven immune responses hold potential to monitor disease activity and predict treatment response.