Baseline Epitope-Specific IgE Profiles Are Predictive of Sustained Unresponsiveness or High Threshold 1-Year Post Oral Immunotherapy in the POISED Trial

This study aimed to determine whether baseline immunoglobulin E (IgE) binding patterns to specific peanut protein epitopes can predict which patients with peanut allergy will achieve sustained unresponsiveness or sustained high tolerance after completing peanut oral immunotherapy (OIT). Building upon findings from the POISED (Peanut Oral Immunotherapy Study: Safety, Efficacy, and Discovery) trial, the goal was to identify immunologic biomarkers that could help guide clinical decisions regarding OIT.The study included participants aged 7 to 55 years with confirmed peanut allergy, as diagnosed by a double-blind, placebo-controlled food challenge (DBPCFC) showing a reaction to 500 mg or less of peanut protein. All participants were previously enrolled in the POISED trial and were randomized to receive either placebo or 24 months of peanut OIT, during which they built up to a daily maintenance dose of 4000 mg of peanut protein. At the end of 24 months, individuals who passed a 4000-mg DBPCFC were randomized again to either continue with 300 mg of daily peanut or completely avoid peanut for an additional 12 months. At the end of this maintenance or avoidance phase, participants underwent a final DBPCFC. Based on these outcomes, participants were classified as having achieved sustained unresponsiveness if they tolerated 4000 mg after 12 months of avoidance (8/55 patients), sustained high tolerance if they tolerated 4000 mg after continued low-dose peanut exposure (13/28 patients), or transient desensitization if they passed the 24-month challenge but reacted during the final DBPCFC.This was a prospective analysis using serum samples collected at baseline and other time points, with the primary analysis focused on pretreatment data. Researchers employed high-resolution epitope mapping to measure IgE binding to linear epitopes from major peanut allergens, including Ara h 1, Ara h 2, and Ara h 3. Baseline IgE binding profiles were analyzed and compared across outcome groups. Additional immunologic markers included total peanut-specific IgE and IgG4, as well as component-specific IgE levels. Statistical methods included multivariate modeling and machine learning approaches to identify which patterns of epitope-specific IgE binding were most predictive of achieving either sustained unresponsiveness or sustained high tolerance after OIT.Participants with lower baseline IgE binding to specific peanut protein epitopes, particularly within Ara h 2, were more likely to achieve sustained unresponsiveness or tolerate higher doses of peanut after 2 years of OIT. In contrast, broader or more intense IgE responses to multiple epitopes at baseline were associated with a reduced likelihood. These findings suggest that specific IgE binding profiles at the epitope level may serve as useful biomarkers for predicting the success of peanut oral immunotherapy.The findings of this study suggest that detailed assessment of epitope-specific IgE at baseline may serve as a useful biomarker for identifying patients most likely to benefit from peanut oral immunotherapy and for guiding personalized treatment strategies. This kind of personalized immunologic assessment could improve patient selection and treatment planning, ultimately leading to better outcomes for individuals undergoing food allergy immunotherapy.Although there is a growing body of evidence on OIT, there are limited data to guide the duration of therapy. Most patients lose protection once stopping OIT, but as shown by the previous POISED study, there is a small subset of patients that do not. This study addresses a clinical need by demonstrating that detailed IgE epitope binding patterns and machine learning algorithms may help predict which patients may fall into this latter category. Future studies should include a larger patient cohort, as well as younger patients, as there may be different predictors in this age group. It is also important to note that epitope-specific IgE are not yet commercially available tests. In the future, should these become available, this machine learning model could become a useful tool for clinicians and families in shared decision-making between different therapies for food allergy.