Translational implications of newly characterized pathogenic pathways in systemic lupus erythematosus

: Improved characterization of relevant pathogenic pathways in systemic lupus erythematosus (SLE) has been further delineated over the last decades. This led to the development of targeted treatments including belimumab and anifrolumab, which recently became available in clinics. Therapeutic targets in SLE encompass interferon (IFN) signaling, B-T costimulation including immune checkpoints, and increasing modalities of B lineage targeting, such as chimeric antigen receptor (CAR) T cells directed against CD19 or sequential anti-B cell targeting. Patient profiling based on characterization of underlying molecular abnormalities, often performed through comprehensive omics analyses, has recently been shown to better predict patients' treatment responses and also holds promise to unravel key molecular mechanisms driving SLE. SLE carries two key signatures, namely the IFN and B lineage/plasma cell signatures. Recent advances in SLE treatments clearly indicate that targeting innate and adaptive immunity is successful in such a complex autoimmune disease. Although those signatures may interact at the molecular level and provide the basis for the first selective treatments in SLE, it remains to be clarified whether these distinct treatments show different treatment responses among certain patient subsets. In fact, notwithstanding the remarkable amount of novel clues for innovative SLE treatment, harmonization of big data within tailored treatment strategies will be instrumental to better understand and treat this challenging autoimmune disorder. This review will provide an overview of recent improvements in SLE pathogenesis, related insights by analyses of big data and machine learning as well as technical improvements in conducting clinical trials with the ultimate goal that translational research results in improved patient outcomes.