Abstract
The clinical and pathologic diversity of systemic lupus erythematosus (SLE) hinders diagnosis, management, and treatment development. This study addresses heterogeneity in SLE through comprehensive molecular phenotyping and machine learning clustering. Adult SLE patients (n = 198) provided plasma, serum, and RNA. 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. Patient clusters were identified by machine learning combining K-means clustering and random forest analysis of co-expression module scores and soluble mediators. SLEDAI scores correlated with interferon, plasma cell, and select cell cycle modules, and with circulating IFN-α, IP10, and IL-1α 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. Monocyte, neutrophil, plasmablast, B cell, and T cell modules distinguished the remaining clusters. ...Continue Reading
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