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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. Graphical abstract [ABSTRACT FROM AUTHOR]

Background: Patients with peanut allergy have highly stable pathologic antibody repertoires to the immunodominant B-cell epitopes of the major peanut allergens Ara h 1 to 3. Objective: We used a peptide microarray technique to analyze the effect of treatment with peanut oral immunotherapy (OIT) on such repertoires. Methods: Measurements of total peanut-specific IgE (psIgE) and peanut-specific IgG4 (psIgG4) were made with CAP-FEIA. We analyzed sera from 22 patients with OIT and 6 control subjects and measured serum specific IgE and IgG4 binding to epitopes of Ara h 1 to 3 using a high-throughput peptide microarray technique. Antibody affinity was measured by using a competitive peptide microarray, as previously described. Results: At baseline, psIgE and psIgG4 diversity was similar between patients and control subjects, and there was broad variation in epitope recognition. After a median of 41 months of OIT, polyclonal psIgG4 levels increased from a median of 0.3 μg/mL (interquartile range [25% to 75%], 0.1-0.43 μg/mL) at baseline to 10.5 μg/mL (interquartile range [25% to 75%], 3.95-45.48 μg/mL; P < .0001) and included de novo specificities. psIgE levels were reduced from a median baseline of 85.45 kUA/L (23.05-101.0 kUA/L) to 7.75 kUA/L (2.58-30.55 kUA/L, P < .0001). Affinity was unaffected. Although the psIgE repertoire contracted in most OIT-treated patients, several subjects generated new IgE specificities, even as the total psIgE level decreased. Global epitope-specific shifts from IgE to IgG4 binding occurred, including at an informative epitope of Ara h 2. Conclusion: OIT differentially alters Ara h 1 to 3 binding patterns. These changes are variable between patients, are not observed in control subjects, and include a progressive polyclonal increase in IgG4 levels, with concurrent reduction in IgE amount and diversity. [Copyright &y& Elsevier]

Background: Peanut allergy is relatively common, typically permanent, and often severe. Double-blind, placebo-controlled food challenge is considered the gold standard for the diagnosis of food allergy–related disorders. However, the complexity and potential of double-blind, placebo-controlled food challenge to cause life-threatening allergic reactions affects its clinical application. A laboratory test that could accurately diagnose symptomatic peanut allergy would greatly facilitate clinical practice. Objective: We sought to develop an allergy diagnostic method that could correctly predict symptomatic peanut allergy by using peptide microarray immunoassays and bioinformatic methods. Methods: Microarray immunoassays were performed by using the sera from 62 patients (31 with symptomatic peanut allergy and 31 who had outgrown their peanut allergy or were sensitized but were clinically tolerant to peanut). Specific IgE and IgG4 binding to 419 overlapping peptides (15 mers, 3 offset) covering the amino acid sequences of Ara h 1, Ara h 2, and Ara h 3 were measured by using a peptide microarray immunoassay. Bioinformatic methods were applied for data analysis. Results: Individuals with peanut allergy showed significantly greater IgE binding and broader epitope diversity than did peanut-tolerant individuals. No significant difference in IgG4 binding was found between groups. By using machine learning methods, 4 peptide biomarkers were identified and prediction models that can predict the outcome of double-blind, placebo-controlled food challenges with high accuracy were developed by using a combination of the biomarkers. Conclusions: In this study, we developed a novel diagnostic approach that can predict peanut allergy with high accuracy by combining the results of a peptide microarray immunoassay and bioinformatic methods. Further studies are needed to validate the efficacy of this assay in clinical practice. [ABSTRACT FROM AUTHOR]