Systemic lupus erythematosus (SLE) is a heterogeneous disease with respect to disease manifestations, disease progression and treatment response. Therefore, strategies to identify biomarkers that help distinguishing SLE subgroups are a major focus of biomarker research. We reasoned that a multiparametric autoantibody profiling approach combined with data mining tools could be applied to identify SLE patient clusters. We used a bead-based array containing 86 antigens including diverse nuclear and immune defense pathway proteins. Sixty-four autoantibodies were significantly (p < 0.05) increased in SLE (n = 69) compared to healthy controls (HC, n = 59). Using binary cut-off thresholds (95% quantile of HC), hierarchical clustering of SLE patients yields five clusters, which differ qualitatively and in their total number of autoantibodies. In two patient clusters the overall accumulated autoantibody reactivity of all antigens tested was 31% and 48%, respectively. We observed a positive association between the autoantibody signature present in these two patient clusters and the clinical manifestation of glomerulonephritis (GLMN). In addition, groups of autoantibodies directed against distinct intracellular compartments and/or biological motifs characterize the different SLE subgroups. Our findings highlight the relevant potential of multiparametric autoantibody detection and may contribute to a deeper understanding of the clinical and serological diversity of SLE.
Keywords: Systemic lupus erythematosus; antigen; autoantibody signature; biomarker; cluster.
© The Author(s) 2016.