TY - JOUR
T1 - Predicting development of sustained unresponsiveness to milk oral immunotherapy using epitope-specific antibody binding profiles
AU - Suárez-Fariñas, Mayte
AU - Suprun, Maria
AU - Chang, Helena L.
AU - Gimenez, Gustavo
AU - Grishina, Galina
AU - Getts, Robert
AU - Nadeau, Kari
AU - Wood, Robert A.
AU - Sampson, Hugh A.
N1 - Publisher Copyright:
© 2018 American Academy of Allergy, Asthma & Immunology
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - Cow's milk allergy
KW - allergenic epitopes
KW - bootstrap aggregating strategy
KW - desensitization
KW - elastic net algorithm
KW - epitope-specific antibodies
KW - machine learning
KW - omalizumab
KW - oral immunotherapy
KW - sustained unresponsiveness
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U2 - 10.1016/j.jaci.2018.10.028
DO - 10.1016/j.jaci.2018.10.028
M3 - Article
C2 - 30528770
AN - SCOPUS:85057739853
SN - 0091-6749
VL - 143
SP - 1038
EP - 1046
JO - Journal of Allergy and Clinical Immunology
JF - Journal of Allergy and Clinical Immunology
IS - 3
ER -