Unsupervised machine learning identifies distinct systemic lupus erythematosus patient endotypes with differential response to belimumab

Roberto Depascale; Raffaele Da Mutten; Julius Lindblom; Nursen Cetrez; Leonardo Palazzo; Luca Iaccarino; Andrea Doria; Dionysis Nikolopoulos; Mariele Gatto; Ioannis Parodis

doi:10.1093/rheumatology/keaf215

Unsupervised machine learning identifies distinct systemic lupus erythematosus patient endotypes with differential response to belimumab

Rheumatology (Oxford). 2025 Apr 17:keaf215. doi: 10.1093/rheumatology/keaf215. Online ahead of print.

Affiliations

¹ Division of Rheumatology, Department of Medicine DIMED, University of Padua, Padua, Italy.
² Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, and Center for Molecular Medicine (CMM), Stockholm, Sweden.
³ Academic Rheumatology Centre, Department of Clinical and Biological Sciences, University of Turin, AO Mauriziano di Torino, Turin, Italy.
⁴ Department of Rheumatology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.

PMID: 40244828
DOI: 10.1093/rheumatology/keaf215

Abstract

Objective: To determine systemic lupus erythematosus (SLE) endotypes according to B cell immunophenotyping and serological profile and assess endotypes' response to belimumab.

Methods: We analysed data from 796 patients with SLE from the phase III trial BLISS-SC. Unsupervised machine learning employing factor analysis of mixed data (FAMD) and subsequent clustering determined endotypes based on B cell immunophenotyping and serological profiles. Cox regression was used to assess belimumab efficacy on inducing lupus low disease activity state (LLDAS) and Definitions of Remission in SLE (DORIS) remission within clusters.

Results: Three endotypes were determined. Compared to each other, cluster 1 (n = 191) displayed higher proportions of CD19+CD24b+CD27+ regulatory B cells [mean ± standard deviation (SD): 35.9%±12.6%], CD19+CD20+CD27+ bulk memory B cells (32.2%±9.9%), CD19+CD20+CD69+ activated B cells (0.2%±0.3%), CD19+CD20-CD138+ long-lived plasma cells (0.7%±1.0%), and CD19+CD38b+CD27b+ SLE-associated plasma cells (6.6%±7.0%). Cluster 2 (n = 366) displayed higher proportions of CD19+CD24bbrightCD38bbrightCD27- transitional B cells (6.3%±9.0%) and CD19+CD20+CD27- naïve B cells (85.4%±7.2%), and lower proportions of CD19+CD20-CD138+ peripheral long-lived plasma cells (0.2%±0.3%) and CD19+CD38b+CD27b+ SLE-associated plasma cells (1.6%±2.0%). Cluster 3 (n = 239) displayed a higher proportion of CD19+CD20+CD138+ short-lived plasma cells (0.1%±0.1%) and higher serological activity, being enriched in anti-double stranded(ds)DNA, anti-extractable nuclear antigens (ENA), antiphospholipid antibodies, and hypocomplementemia. Use of belimumab was superior to placebo in inducing sustained LLDAS (HR: 2.22; 95% CI: 1.18-4.17; p = 0.014) and DORIS remission (HR: 3.45; 95% CI: 1.2-9.94; p = 0.022) in cluster 2.

Conclusion: Three distinct SLE endotypes were identified based on B cell immunophenotyping and serological profiles, showing differential benefit from belimumab therapy.

Keywords: B cells; belimumab; machine learning; systemic lupus erythematosus.