Unsupervised machine learning identifies distinct SLE patient endotypes with differential response to belimumab.

ABSTRACT:

Objective

To determine 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 with each other, cluster 1 (n = 191) displayed higher proportions of CD19+CD24b+CD27+ regulatory B cells (mean ± SD: 35.9%±12.6%), CD19+

SUBMITTER: Depascale R 

PROVIDER: S-EPMC12316369 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature