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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.

SUBMITTER: Suarez-Farinas M

PROVIDER: S-EPMC7658687 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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

Suárez-Fariñas Mayte M Suprun Maria M Chang Helena L HL Gimenez Gustavo G Grishina Galina G Getts Robert R Nadeau Kari K Wood Robert A RA Sampson Hugh A HA

The Journal of allergy and clinical immunology 20181207 3

<h4>Background</h4>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.<h4>Objective</h4>We sought to determine whether IgE and IgG<sub>4</sub> antibody binding to allergenic milk protein epitopes changes with MOIT and whether this coul ...[more]

PMID: 30528770

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Project description:BackgroundComprehensive characterization of exposures and immune responses to viral infections is critical to a basic understanding of human health and disease. We previously developed the VirScan system, a programmable phage-display technology for profiling antibody binding to a library of peptides designed to span the human virome. Previous VirScan analytical approaches did not carefully account for antibody cross-reactivity among sequences shared by related viruses or for the disproportionate representation of individual viruses in the library.MethodsHere we present the AntiViral Antibody Response Deconvolution Algorithm (AVARDA), a multi-module software package for analyzing VirScan datasets. AVARDA provides a probabilistic assessment of infection with species-level resolution by considering sequence alignment of all library peptides to each other and to all human viruses. We employed AVARDA to analyze VirScan data from a cohort of encephalitis patients with either known viral infections or undiagnosed etiologies. We further assessed AVARDA's utility in associating viral infection with type 1 diabetes and lupus.FindingsBy comparing acute and convalescent sera, AVARDA successfully confirmed or detected encephalitis-associated responses to human herpesviruses 1, 3, 4, 5, and 6, improving the rate of diagnosing viral encephalitis in this cohort by 44%. AVARDA analyses of VirScan data from the type 1 diabetes and lupus cohorts implicated enterovirus and herpesvirus infections, respectively.InterpretationAVARDA, in combination with VirScan and other pan-pathogen serological techniques, is likely to find broad utility in the epidemiology and diagnosis of infectious diseases.FundingThis work was made possible by support from the National Institutes of Health (NIH), the US Army Research Office, the Singapore Infectious Diseases Initiative (SIDI), the Singapore Ministry of Health's National Medical Research Council (NMRC) and the Singapore National Research Foundation (NRF).

Dataset Information

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

Publications

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

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