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

Mayte Suárez-Fariñas, Maria Suprun, Helena L. Chang, Gustavo Gimenez, Galina Grishina, Robert Getts, Kari Nadeau, Robert A. Wood, Hugh A. Sampson

Research output: Contribution to journalArticlepeer-review

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 IgG 4 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 IgG 4 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 IgG 4 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.

Original languageEnglish (US)
Pages (from-to)1038-1046
Number of pages9
JournalJournal of Allergy and Clinical Immunology
Volume143
Issue number3
DOIs
StatePublished - Mar 2019

Keywords

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

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology

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