Correlations of Plasma Cytokine Levels and Anti-FVIII Antibodies during Immune Tolerance Induction
Notice bibliographique
Résumé
Abstract Introduction The development of neutralizing anti-FVIII antibodies (inhibitors) with reduced or absent activity of substituted factor VIII (FVIII) remains the most serious complication of hemophilia A therapy (Kempton, 2014). Frequent and high doses of FVIII with or without bypassing products can reestablish immune tolerance in 60-70% of patients. Polymorphism in immune response genes including IL-10 and TNFa were identified as risk factors for inhibitor development (Astermark, 2006). Cross-sectional studies showed different cytokine profiles in patients with hemophilia, especially in those with history of an inhibitor (Oliveira, 2013). In this study cytokine profiles were monitored longitudinally during immune tolerance induction (ITI). Methods 107 plasma samples from 18 patients were collected during the RES.I.S.T Experienced and Naive trial, which included patients with a poor prognosis for ITI success (Gringeri, 2007). We quantified 14 cytokines in each sample by using a Human High Sensitivity T-Cell Discovery Array 14-Plex (Eve Technologies Corp, Calgary, AB, Canada). ELISA based FVIII antibody assays were used for anti-FVIII IgG detection. FVIII inhibitor titers (Bethesda assay, BU) were measured and available for the analysis. The cut-off for a positive inhibitor was >0,6 BU mL-1. Bethesda titers (BUpos) between 0,6 - <5,0 were considered as low titer (BUlow), whereas BU ≥5 as high titer (BUhigh). Statistical analyses were performed using GraphPad Prism 7. Mann-Whitney U tests and Spearman correlation tests considered significant when P value <0.05. Results Plasma levels of TNFa (P=0,014) and IL-8 (P=0,048) were positively correlated with FVIII inhibitor titers. Negative correlation was found in levels of IL-10 (P=0,041), IL-12 (P=0,038) and IL-1B (P=0,026). When cytokine levels of plasma samples with detectable and undetectable FVIII inhibitor titers were compared, significant higher plasma levels of TNFa (median: 11,56pg/ml, 8,11pg/ml; P=0,016) and lower levels of IL-12 (median: 4,29 pg/ml, 6,25 pg/ml; P=0,047) and IL-23 (median: 1016 pg/ml, 1353 pg/ml; P=0,049) were measured in samples with detectable FVIII inhibitor (BUpos). Furthermore, TNFa levels were higher in BUlow (median: 10,83 pg/ml; P=0,047) as well as in BUhigh samples (median: 11,75 pg/ml; P=0,019), compared to BUneg (median: 8,11 pg/ml). Cytokine concentrations of IL-1B (median: 2,64 pg/ml, 3,77 pg/ml; P=0,023), IL-2 (median: 2,44 pg/ml, 2,97pg/ml; P=0,043) and IL-17 (median: 15,79 pg/ml, 19,42 pg/ml; P=0,036) were significantly lower in BUhigh plasma samples compared to BUneg. Additionally, plasma level of IL-10 correlated negatively with levels of anti-FVIII IgG (P=0.045). Conclusion This is the first study of cytokine measurement in a longitudinal setting as well as during ITI in patients with hemophilia. FVIII inhibitors and anti-FVIII IgG antibodies were correlated to IL-10 and TNFa levels - of note, polymorphisms in the genes of these cytokines are a known risk factor for inhibitor development. Furthermore, IL-12, IL-17 and IL-23 levels were higher in samples with loss of FVIII Inhibitors. In addition to prediction of inhibitor development, cytokine profiles might serve as prognostic factors for ITI success and considering the emerging evidence of the IL-17-IL-23 immune axis in autoimmunity might also be promising therapeutic approaches for higher ITI success rates. Disclosures Ewing: Genentech: Honoraria; Shire: Honoraria; Bayer: Honoraria; Grifols: Honoraria; CSL Behring: Honoraria; Novo Nordisk: Honoraria; Hema Biologics: Honoraria; Biogen: Research Funding. Koenigs:Jansen: Research Funding; Gilead: Research Funding; Biotest: Research Funding, Speakers Bureau; Bayer: Consultancy, Research Funding, Speakers Bureau; Pfizer: Research Funding, Speakers Bureau; Intersero: Research Funding; CSL Behring: Consultancy, Research Funding; EU (IMI, FP7): Research Funding; Sobi: Consultancy, Research Funding, Speakers Bureau; Shire: Consultancy, Research Funding; Novo Nordisk: Consultancy, Speakers Bureau; Bioverativ: Consultancy; Roche/Chugai: Consultancy.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».