Lu, R.; Wagner, C. A.; Fava, A.; Jones, B.; Izmirly, P.; Belmont, H. M.; Clancy, R. M.; Anolik, J.; Barnas, J. L.; Putterman, C.; Wofsy, D.; Weisman, M. H.; Davidson, A.; Fine, D. M.; Holers, V. M.; Utz, P. J.; Accelerating Medicines Partnership in RA/SLE Network, ; diamond, b.; Buyon, J.; Petri, M.; Guthridge, J. M.; James, J. A.

Lupus nephritis (LN), a severe manifestation of systemic lupus erythematosus (SLE), features heterogeneous renal pathology and reliance on invasive biopsies for diagnosis, prognosis, and treatment selection. Current peripheral clinical markers inadequately capture disease activity and progression. Here, we performed comprehensive serum proteomic profiling of over 5,000 proteins in the large, longitudinal Accelerating Medicines Partnership Rheumatoid Arthritis/SLE cohort of 270 LN patients and 63 healthy controls. Machine learning identified distinct molecular signatures that classified LN versus controls, differentiated histological classes, and delineated activity- and chronicity-associated pathways, including inflammatory cytokine, PI3K/AKT, TGFb;, and complement/coagulation pathways. An increase in VSIG4, CD27, HAVCR1, and LAIR1 consistently emerged as top biomarkers across multiple clinical contexts, and early decreases in these markers at 3 months were associated with complete treatment response at 1 year. By resolving coordinated serum protein modules linked to key inflammatory, PI3K/AKT, TGFb;, and complement pathways, these signatures mechanistically connect circulating proteomic perturbations to intrarenal immune activation, tissue injury, and repair in LN. These findings demonstrate that serum proteomics reflect complex intrarenal immunopathology and offer a promising noninvasive liquid biopsy approach to refine LN classification and guide personalized management, potentially reducing the need for repeated invasive biopsies and improving therapeutic decision-making.