BELIMUMAB REDUCES THE RISK OF FLARES ASSOCIATED WITH THE BAFF OVEREXPRESSING TNFSF13B GENE VARIANT: A HINT FOR PERSONALIZED TREATMENT IN SLE.
Notice bibliographique
Résumé
PV183 / #357 Poster Topic: AS20 - Precision Medicine Background/Purpose The TNFSF13B gene functional variant (BAFFvar) is an insertion-deletion (GCTGT→A) introducing an alternative polyadenylation motif generating a truncated/shorter gene transcript that escapes miRNA inhibition, yielding increased production of soluble BAFF.[1] The consequent overexpression of BAFF, in turn, up-regulates humoral immunity, increases the production of autoantibodies, and increases the risk of developing SLE. The present study investigates if Baffvar status influences the risk of overall and renal SLE flares and whether patients stratified according to Baffvar status might show a differential benefit from anti-BAFF treatment. Methods This study used data from patients included in a monocentric SLE inception cohort between 1 January 2006 and 31 December 2022. Inclusion criteria were: (a) SLE classified according to the ACR/EULAR 2019 and/or SLICC 2012 and/or ACR 1997 criteria; (b) evaluation in at least 3 consecutive visits (not less than 2 visits every 12 months); (c) genotyping for BAFFvar. Demographic, clinical, serologic, and treatment variables were recorded. Flare was defined as the onset of a new SLE manifestation or worsening of a preexisting clinical manifestation resulting in a therapy change. Renal flares were nephritic (≥10 RBCs/hpf with or without a decrease in eGFR by ≥10%, irrespective of changes in proteinuria) or nephrotic (doubling of proteinuria to >1g/24h or to >2g/24h depending on the previous complete or partial response). Kaplan-Meier curves were used to analyze the association between BAFFvar and overall or renal SLE flares. Multivariate Cox regression models were built, including demographic, clinical, and serologic data and past or ongoing treatment as covariates. Results 194 (89.2% female) out of 256 screened patients were analyzed (Table 1). The mean age was 41.1 (± 14.8) years, the mean number of follow-up visits was 17 (± 8) and 119 (61.3%) were BAFFvar carriers. During follow-up, 119 patients (56.2%) experienced at least 1 flare, with 60 patients (30.9%) having more than 1 flare. The median number of flares was higher (p=0.038) in Baffvar carriers (1; IQR 0-2) than in BAFF-wt carriers (0; IQR 0-1). Cox regression model showed BAFFvar (HR 1.5 per copy variant; 95% CI 1.2 - 2.0; p = 0.002), disease duration <1 year (HR 0.46; 95% CI 0.30 – 0.71; p<0.001), DORIS remission (HR 0.41; 95% CI 0.24 – 0.71; p = 0.001), renal (HR 1.7; 95% CI 1.1 – 2.6; p = 0.017), and musculoskeletal (HR 5.3; 95% CI 1.3 – 21.5; 0.019) involvement as baseline factors independently associated with the risk of flare development. Out of 38 patients with biopsy-confirmed LN, 33 (86.8%) were female, 21 (55.3%) were BAFFvar carriers, and 24 (63.2%) were diagnosed with proliferative lupus nephritis class III or IV. Flares occurred in 12 (33.3%) patients, with 22 flares (5 nephritic and 17 nephrotic). The BAFFvar was independently associated with the risk of renal flare (HR 9.3; 95% CI 1.8 to 49.5; p=0.008). Out of 35 patients treated with belimumab after a flare, 9 had at least 1 flare during a median 48-month follow-up (total of 11 flares). Patients with BAFFvar had a flare rate of 13.6% (3/22), while BAFFwt carriers had a flare rate of 46.1% (6/13) (HR 0.22; 95% CI 0.05-0.90; p=0.035). Table 1. Baseline characteristics of the lupus cohort at entry into the study. Conclusions Belimumab reduces the risk of overall and renal SLE flares conferred by the BAFFvar. BAFFvar identifies patients at higher risk for flare and those best responders to belimumab and may represent a potential predicting and prognostic biomarker for personalized treatment in SLE patients. References: [1.] Steri M. NEJM 2017;376:1615-26.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».