Understand SLE heterogeneity in the era of omics, big data, and artificial intelligence

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease characterized by extraordinary heterogeneity, due to the complex pathogenesis and diverse manifestations. Stratification of patients for therapy and prognosis represents a major challenge to manage SLE. Conventional biomarkers for disease diagnosis and activity assessment provide very limited insight into immunological pathogenesis and therapeutic response rates. The advancement of "omics" technologies including genomics, transcriptomics, proteomics, and metabolomics has constituted an unprecedented opportunity to characterize the immunopathological landscape in individual patients with SLE. Indeed, genomic studies reveal a subset of SLE patients carrying one or more functional single nucleotide polymorphisms (SNPs) underlying immune dysregulation while transcriptomic studies have revealed subgroups in SLE patients showing distinct signatures for Type I interferon (TI-IFN) pathway activation or aberrant differentiation of B cells into plasma cells. This review will summarize results from the latest studies using omics technology to understand SLE heterogeneity. In addition, we propose that the application of artificial intelligence, such as by machine learning-based nonlinear dimensionality reduction method uniform manifold approximation and projection (UMAP) can further strengthen the analysis of omics big data. The combination of new technology and novel analysis pipeline can lead to breakthroughs in stratifying SLE patients for a better monitoring of disease activity and more precise design of treatment regime, not only for conventional immunosuppression but also novel immunotherapies targeting B-cell activating factor (BAFF), TI-IFN, and interleukin 2 (IL-2).