ABSTRACT: Background: The clinical and pathologic diversity of systemic lupus erythematosus (SLE) has hindered diagnosis, management, and treatment development. This study clustered adult SLE patients through comprehensive molecular phenotyping to improve distinctions with prognostic and therapeutic relevance. Methods: Plasma, serum, and RNA were collected from 198 adult SLE patients. Disease activity was scored by modified SELENA-SLEDAI. Twenty-nine co-expression module scores were calculated from microarray gene-expression data. Plasma soluble mediators (n=23) and autoantibodies (n=13) were assessed by multiplex bead-based assays and ELISAs. Phenotypic patient clusters were identified by machine learning combining K-means clustering and random forest analysis of co-expression module scores and soluble mediators. Findings: SLEDAI scores correlated strongly with interferon module scores, more modestly with plasma cell and select cell cycle modules, and with circulating IFNα, IL21, IL1α, IL17A, IP10, and MIG levels. Co-expression modules and soluble mediators differentiated seven clusters of SLE patients with unique molecular phenotypes. Inflammation and interferon modules were elevated in Clusters 1 (moderately) and 4 (strongly), with decreased T cell modules in Cluster 4. The other clusters differed in monocyte, neutrophil, plasmablast, B cell, and T cell modules. Clusters 1 and 4 had higher SLEDAI scores, and more frequent anti-dsDNA, low complement, and renal activity. These features were also prominent in Cluster 3, which lacked the interferon and inflammation signatures. Arthritis and rashes were common in all clusters. Interpretation: Molecular profiles can distinguish SLE subsets. Prospective longitudinal studies of these profiles may help to improve prognostic evaluation, clinical trial design, and precision medicine approaches.
