High levels of anti-factor VIII immunoglobulin G4 and immunoglobulin G total are associated with immune tolerance induction failure in people with congenital hemophilia A and high-responding inhibitors.

Background: Immune tolerance induction (ITI) is the treatment of choice to eradicate neutralizing anti-factor (F)VIII alloantibodies (inhibitors) in people with inherited hemophilia A. However, it is not successful in 10% to 40% of the cases. The biological mechanisms and biomarkers associated with ITI outcome are largely unknown. Objectives: The aim of this study was to investigate the association of plasma cytokines (interferon-γ, tumor necrosis factor, interleukin [IL]-2, IL-4, IL-5, IL-6, IL-10, and IL-17A), chemokines (IL-8/CXCL8, RANTES/CCL5, MIG/CXCL9, MCP-1/CCL2, and IP-10/CXCL10), and anti-FVIII immunoglobulin (Ig) G total, IgG1, and IgG4 with ITI outcome. Methods: In this cross-sectional analysis of the Brazilian Immune Tolerance Study, we assessed plasma levels of anti-FVIII IgGs using an enzyme-linked immunosorbent assay with plasma-derived FVIII and recombinant FVIII as target antigens, immobilized in microplates. Results: We assayed 98 plasma samples of moderately severe and severe (FVIII activity, <2%) people with hemophilia A after completion of a first ITI course. Levels of anti-recombinant FVIII IgG total and IgG4 were higher in people with hemophilia A who failed ITI (IgG total optical density [OD], 0.37; IQR, 0.15-0.73; IgG4 OD, 2.19; IQR, 0.80-2.52) than in those who had partial (IgG total OD, 0.03; IQR, 0.00-0.14; IgG4 OD, 0.39; IQR, 0.09-1.11; P < .0001 for both) or complete success (IgG total OD, 0.04; IQR, 0.00-0.07; IgG4 OD, 0.07; IQR, 0.06-0.40; P < .0001 for both). Plasma cytokines, chemokines, and anti-FVIII IgG1 were not associated with ITI outcome. Conclusion: Our results show that high levels of plasma anti-FVIII IgG4 and IgG total are associated with ITI failure.

Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately severe hemophilia A using a machine-learning network.

BACKGROUND: Prediction of inhibitor development in patients with hemophilia A (HA) remains a challenge. OBJECTIVES: To construct a predictive model for inhibitor development in HA using a network of clinical variables and biomarkers based on the individual similarity network. METHODS: Previously untreated and minimally treated children with severe/moderately severe HA, participants of the HEMFIL Cohort Study, were followed up until reaching 75 exposure days (EDs) without inhibitor (INH-) or upon inhibitor development (INH+). Clinical data and biological samples were collected before the start of factor (F)VIII replacement (T0). A predictive model (HemfilNET) was built to compare the networks and potential global topological differences between INH- and INH+ at T0, considering the network robustness. For validation, the "leave-one-out" cross-validation technique was employed. Accuracy, precision, recall, and F1-score were used as evaluation metrics for the machine-learning model. RESULTS: We included 95 children with HA (CHA), of whom 31 (33%) developed inhibitors. The algorithm, featuring 37 variables, identified distinct patterns of networks at T0 for INH+ and INH-. The accuracy of the model was 74.2% for CHA INH+ and 98.4% for INH-. By focusing the analysis on CHA with high-risk F8 mutations for inhibitor development, the accuracy in identifying CHA INH+ increased to 82.1%. CONCLUSION: Our machine-learning algorithm demonstrated an overall accuracy of 90.5% for predicting inhibitor development in CHA, which further improved when restricting the analysis to CHA with a high-risk F8 genotype. However, our model requires validation in other cohorts. Yet, missing data for some variables hindered more precise predictions.