Predicting development of sustained unresponsiveness to milk oral immunotherapy using epitope-specific antibody binding profiles

Abstract

Background: In a recent trial of milk oral immunotherapy (MOIT) with or without omalizumab in 55 patients with milk allergy treated for 28 months, 44 of 55 subjects passed a 10-g desensitization milk protein challenge; 23 of 55 subjects passed the 10-g sustained unresponsiveness (SU) challenge 8 weeks after discontinuing MOIT.

Objective: We sought to determine whether IgE and IgG4 antibody binding to allergenic milk protein epitopes changes with MOIT and whether this could predict the development of SU.

Methods: By using a novel high-throughput Luminex-based assay to quantitate IgE and IgG4 antibody binding to 66 sequential epitopes on 5 milk proteins, serum samples from 47 subjects were evaluated before and after MOIT. Machine learning strategies were used to predict whether a subject would have SU after 8 weeks of MOIT discontinuation.

Results: MOIT profoundly altered IgE and IgG4 binding to epitopes, regardless of treatment outcome. At the initiation of MOIT, subjects achieving SU exhibited significantly less antibody binding to 40 allergenic epitopes than subjects who were desensitized only (false discovery rate ≤ 0.05 and fold change > 1.5). Based on baseline epitope-specific antibody binding, we developed predictive models of SU. Using simulations, we show that, on average, IgE-binding epitopes alone perform significantly better than models using standard serum component proteins (average area under the curve, >97% vs 80%). The optimum model using 6 IgE-binding epitopes achieved a 95% area under the curve and 87% accuracy.

Conclusion: Despite the relatively small sample size, we have shown that by measuring the epitope repertoire, we can build reliable models to predict the probability of SU after MOIT. Baseline epitope profiles appear more predictive of MOIT response than those based on serum component proteins.

Keywords: Cow's milk allergy; allergenic epitopes; bootstrap aggregating strategy; desensitization; elastic net algorithm; epitope-specific antibodies; machine learning; omalizumab; oral immunotherapy; sustained unresponsiveness.

Copyright © 2018 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Figures

FIG 1.

Changes induced by MOIT in epitope-specific IgE or IgG4 binding by adjuvant treatment arm. A, Heat map representing (standardized) binding of 108 epitopes (48 IgE and 60 IgG4), with significant changes (fold change [FCH] > 1.5 and FDR ≤ 0.05) at month 32 from baseline. B, Box plot of the MOIT-induced log2 FCHs in epitope binding by adjuvant treatment. C, Bar plots of MOIT-induced changes and 95% CIs in log10 levels of SCPs. Asterisks represent a significant change at month 32 from baseline: *P < .05, **P < .01, and ***P < .001. DS, Desensitization; β-LG, β-lactoglobulin.

FIG 2.

Changes in ESAB by adjuvant treatment and MOIT outcome. A and B, Bar plots representing changes induced by MOIT in patients achieving only desensitization (DS) or SU by adjuvant treatment arm for each IgE and IgG4 epitope. The height of the bars represents the log2 fold change (FCH) in binding from baseline to month 32. Colors represent the direction of change (red, increase; blue, decrease) for the differential epitope-specific antibody binding (DESAB; FCH > 1.5 and FDR < 0.05). Colored asterisks indicate significance in changes between SU and DS outcomes for each adjuvant treatment (light blue, placebo; light pink, omalizumab); black asterisks represent DESAB, with significant differences between omalizumab and placebo for each outcome. C, Box plot of MOIT-induced changes in ESAB by MOIT outcome. NR, Nonresponder.

FIG 3.

Differences in baseline epitope and SCP levels between groups achieving desensitization (DS) or SU 8 weeks after MOIT discontinuation. A, Box plots of the baseline overall (z score) epitope-specific IgE binding by MOIT outcome, with significantly different z scores between the SU and DS groups. NR, Nonresponders. B, Heat map representing baseline epitope-specific IgE binding for each patient for the set of epitopes with significant differences (fold change [FCH] > 1.5 and FDR < 0.05) between DS and SU outcomes at baseline. β-LG, β-Lactoglobulin. C, Bar plots of the least square mean (Ismean) with the 95% CI of log10 SCP levels at baseline by MOIT outcome.

FIG 4.

Prediction of desensitization (DS) versus SU outcome at month 32 (8 weeks after MOIT discontinuation) by using baseline levels with machine learning algorithms. A, Performance metrics of models fitted with different features: epitope-specific IgE or IgG4 binding only, SCPs (total IgE; milk-specific IgE; casein-specific IgE, IgG, and IgG4; and β-lactoglobulin–specific IgE and IgG), or the combination of SCPs and IgE and IgG4 epitopes in 300 bootstrapping simulations. Means and 95% CIs are presented. IgE epitopes are sufficient to achieve maximal predictive performance. Accuracy and receiver operating characteristic (ROC) curve are presented here; other metrics can be found in Fig E8. B, ROC curve and coefficients for the best model with the 6 most robust IgE epitopes identified as important predictors in at least 75% of the bootstrapping simulations (BF = 75%). C, Predicted probabilities (x-axis) versus actual MOIT outcomes of all subjects (colored bars) as predicted by using an IgE-based epitope model with a BF of 75%. If the predicted probability is greater than 0.5, the subject is classified as SU and DS otherwise. Pink and red bars represent actual MOIT outcomes: DS (pink) and SU (red). D, Performance metrics of 4 models, including epitopes with BFs of 60% to 80%. Estimates with 95% CIs are presented for cross-validation (CV) statistics.