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UNSUPERVISED MACHINE LEARNING IDENTIFIES DISTINCT SYSTEMIC LUPUS ERYTHEMATOSUS PATIENT ENDOTYPES BASED ON B CELL PHENOTYPING AND AUTOANTIBODY PROFILES WITH DIFFERENTIAL RESPONSE TO BELIMUMAB
University of Padua, Division of Rheumatology, Department of Medicine DIMED, Padua, Italy.
Karolinska Institutet, Division of Rheumatology, Department of Medicine Solna, Stockholm, Sweden.
Karolinska Institutet, Division of Rheumatology, Department of Medicine Solna, Stockholm, Sweden.
University of Padua, Division of Rheumatology, Department of Medicine DIMED, Padua, Italy.
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2024 (English)In: Annals of the Rheumatic Diseases, ISSN 0003-4967, E-ISSN 1468-2060, Vol. 83, no Suppl. 1, article id POS0734Article in journal, Meeting abstract (Other academic) Published
Abstract [en]

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterised by involvement of multiple organ systems and a wide range of clinical phenotypes. Prior research has endeavoured to stratify SLE patients into distinct endotypes based on their unique serological profiles, but how B cell subtypes could contribute to such patient characterisation remains unclear.

A more refined characterisation of SLE patient subgroups might enhance our understanding of underlying pathogenesis and serve as a guide in therapeutic decisions.

Objectives: To determine SLE patient endotypes according to B cell immunophenotype and serological profile and assess their response to belimumab.

Methods: We analysed data from 796 patients with SLE from the phase III trial BLISS-SC clinical trial of belimumab. An unsupervised machine learning algorithm employing principal components analysis and a subsequent unsupervised clustering methodology was used to explore patient subgroups based on B cell immunophenotyping and serology. Cox regression analysis was used to assess the effect of belimumab vs placebo on inducing sustained lupus low disease activity state (LLDAS) or sustained DORIS remission across clusters.

Results: Cluster 1 (n=193) was characterised by higher proportions [mean (SD)] of CD19+CD24b+CD27+ regulatory B cells [35.8%, (12.6%)], CD19+CD20+CD27+ bulk memory B cells [32.0% (9.9%)], CD19+CD20+CD69+ activated B cells [0.2%, (0.3%)], CD19+CD20-CD138+ long-lived plasma cells [0.6%, (1.0%)], and CD19+CD38b+CD27b+ SLE-associated plasma cells [6.6%, (7.0%)]. Cluster 2 (n=358) comprised higher proportions of CD19+CD24b+CD38b+CD27- transitional B cells [6.5% (9.2%)], and CD19+CD20+CD27- naïve B cells [85.5% (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% (6.1%)]. Cluster 3 was characterised by a higher proportion of CD19+CD20+CD138+ short-lived plasma cells [0.1%, (0.1%)]. Cluster 2 was dominated by musculoskeletal and mucocutaneous manifestations. Patients within cluster 3 had the greatest baseline Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) scores and average prednisone dose, and a greater proportion of patients in this cluster had renal involvement. Use of belimumab in cluster 2 yielded an increased probability of attaining sustained LLDAS [HR 2.12; 95% CI: 1.1-4.0; p<0.005] and DORIS remission [HR 3.45; 95% CI: 1.2-9.9; p<0.005] compared with placebo, while no such benefit from belimumab was seen in clusters 1 and 3.

Conclusion: Three distinct SLE endotypes were identified based on B cell immunophenotyping and autoantibody profiles. Cluster 2, dominated by an abundance of immature B cells and musculoskeletal/mucocutaneous manifestations, appears to be more benefited by belimumab therapy.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 83, no Suppl. 1, article id POS0734
Keywords [en]
Adaptive immunity, Artificial Intelligence, Autoantibodies
National Category
Rheumatology
Identifiers
URN: urn:nbn:se:oru:diva-120917DOI: 10.1136/annrheumdis-2024-eular.4886ISI: 001470410400052OAI: oai:DiVA.org:oru-120917DiVA, id: diva2:1956848
Conference
European Congress of Rheumatology (EULAR 2024), Vienna, Austria, June 12-15, 2024
Available from: 2025-05-07 Created: 2025-05-07 Last updated: 2025-05-07Bibliographically approved

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Parodis, Ioannis

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