IMPACT OF BELIMUMAB ON EFFICACY, SAFETY AND IMMUNE PHENOTYPES IN REFRACTORY AND ACTIVE LUPUS NEPHRITIS IN REAL-WORLD LOOPS REGISTRY
Bibliographic record
Abstract
PT011 / #594 Topic: AS15 - Lupus Nephritis-Clinical POSTER TOUR 03: RECENT ADVANCEMENTS IN SLE CLINICAL OUTCOMES AND THERAPY 23-05-2025 10:00 AM - 10:40 AM Background/Purpose Belimumab (BEL) is a human monoclonal antibody against soluble B cell activating factor (BAFF). The BLISS-LN trial demonstrated the efficacy and safety of induction therapy combined with BEL in patients with active lupus nephritis (LN). In this study, we aimed to reveal how BEL alters the peripheral blood immune phenotype and to identify the immunophenotypic characteristics of patients with active LN suitable for BEL. Methods In this retrospective multicenter study, patients with biopsy-proven ISN/RPS class III or IV LN who received standard of care (SoC: glucocorticoid [GC] and either mycophenolate mofetil [MMF] or cyclophosphamide [CYC]) were included. The efficacy and safety of BEL combined with SoC (BEL+SoC group, n = 38) were compared with SoC alone (SoC group, n = 35). Based on a comprehensive eight-color flow cytometric analysis for human immune system termed “the Human Immunology Project” by NIH and FOCIS, we performed peripheral blood immunophenotyping to compare patients with active LN (n = 73) with age- and sex-matched healthy controls (HC, n = 120), and compared patients with LN pre- and post-treatment. Results The baseline patient characteristics were not significantly different between the SoC and BEL+SoC groups. The BEL+SoC group showed significantly higher complete renal response (CRR) (SoC vs BEL+SoC = 37.1% vs 73.0%, P = 0.004) at 52 weeks. GC dosage (mg/day) (SoC vs BEL+SoC = 6.8 ± 2.7 vs 4.7 ± 1.9, P < 0.001), SLICC Damage Index (SoC vs BEL+SoC = 0.5 ± 0.7 vs 0.2 ± 0.4, P = 0.009) and the rate of all adverse events (SoC vs BEL+SoC = 65.7% vs 37.8%, P = 0.021) at 52 weeks were significantly lower in the BEL+SoC group. Immunophenotyping revealed that, compared with HC, patients with active LN had significantly higher percentages of CD3+CD4+CD38+HLA-DR+ activated T helper cells (HC vs LN = 0.7 ± 0.5 vs 2.0 ± 1.8, P < 0.001), CD3+CD8+CD38+HLA-DR+ activated cytotoxic T cells (HC vs LN = 1.1 ± 3.1 vs 6.3 ± 5.6, P < 0.001), CD3−CD19+CD27−IgD− double-negative (DN) B cells (HC vs LN = 0.5 ± 0.4 vs 1.6 ± 2.0, P < 0.001) and CD3−CD19+CD27+CD20-CD38+ plasmocytes (HC vs LN = 0.3 ± 0.5 vs 1.7 ± 1.6, P < 0.001) at baseline. There were no significant differences in baseline immunophenotypes between the SoC and BEL+SoC groups. The BEL+SoC group had significantly higher reduction rates of DN B cells (SoC vs BEL+SoC = +2.9 ± 102.2 vs −44.6 ± 58.3, P = 0.033) at 52 weeks than the SoC group. In the BEL+SoC group, patients who achieved CRR had a significantly higher percentage of pretreatment plasmocytes (nonresponders vs responders = 1.0 ± 0.8 vs 2.5 ± 2.0, P = 0.041) than those who did not. No immunophenotypic characteristics were associated with CRR in the SoC group. Conclusions In induction therapy for patients with active LN, combination therapy with BEL (BEL+SoC) significantly reduced the proportion of DN B cells compared to SoC alone (GC+MMF/CYC). BAFF inhibition by BEL may prevent differentiation of transitional/naïve B cells into self-reactive DN B cells, thereby controlling disease activity, enabling early GC reduction, and potentially reducing organ damage and adverse events. BEL may be particularly effective in patients with increased peripheral blood plasmocytes before treatment. Given that BAFF promotes plasmocyte differentiation, increased plasmocytes indicate elevated BAFF levels, which may explain the enhanced effectiveness of anti-BAFF therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".