Correlations of Plasma Cytokine Levels and Anti-FVIII Antibodies during Immune Tolerance Induction
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".