Gene Expression and Autoantibody Analysis Revealing Distinct Ancestry-Specific Profiles Associated With Response to Rituximab in Refractory Systemic Lupus Erythematosus.

OBJECTIVE:Gene expression profiles are associated with the clinical heterogeneity of systemic lupus erythematosus (SLE) but are not well studied as biomarkers for therapy. We studied gene expression and response to rituximab in a multiethnic UK cohort who were refractory to standard therapy. METHODS:We evaluated baseline expression levels of transcripts known to associate with clinical features of SLE using a 96-probe TaqMan array and whole blood samples from 213 patients with active SLE who had been prospectively enrolled in the British Isles Lupus Assessment Group (BILAG) Biologics Register. We measured autoantibodies using immunoprecipitation and enzyme-linked immunosorbent assays. We determined responses to first-cycle rituximab at 6 months from treatment start in 110 SLE patients by assessing BILAG 2004 disease activity. RESULTS:Interferon gene expression scores were lower in patients of European ancestry than in all other ancestry groups. The relationship between blood interferon gene expression scores and scores annotated to plasmablasts, neutrophils, myeloid lineage, inflammation, and erythropoiesis differed between patients of European and non-European ancestries. Hierarchical clustering revealed 3 distinct non-European ancestry patient subsets with stratified responses to rituximab that were not explained by sociodemographic and clinical variables, with responses lowest in an interferon-low, neutrophil-high cluster and highest in a cluster with high expression levels across all signatures (P < 0.001). Clusters in European ancestry patients did not predict response to rituximab but segregated patients by global disease activity and renal involvement. In both ancestral groups, interferon-high clusters were associated with U1 RNP/Sm antibodies. CONCLUSION:Ancestry appears central to the immunologic and clinical heterogeneity in SLE. These results suggest that ancestry, disease activity, and transcriptional signatures could each assist in predicting the effectiveness of B cell depletion therapies.