A network-based approach for identifying suitable biomarkers for oral immunotherapy of food allergy

• Published in: BMC bioinformatics Vol. 20; no. 1; p. 206
• Main Authors: van Bilsen, Jolanda H M, Verschuren, Lars, Wagenaar, Laura, Vonk, Marlotte M, van Esch, Betty C A M, Knippels, Léon M J, Garssen, Johan, Smit, Joost J, Pieters, Raymond H H, van den Broek, Tim J
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.04.2019; BioMed Central; BMC
• Subjects: Allergens; Allergies; Allergy; Animal experimentation; Bayesian analysis; Bayesian network analyses; Bioinformatics; Biological markers; Biomarkers; Cancer; Care and treatment; Computation; Computational biology; Computer applications; Cow's milk; Data analysis; Data mining; Data structures; Diagnosis; Diet; Disease; Diseases; Effectiveness; Epidemiology; Experimental food allergy; Food; Food allergies; Food hypersensitivity; Fructooligosaccharides; Future predictions; Genomics; Health aspects; Identification and classification; Immunotherapy; Information management; Mathematical models; Medical prognosis; Milk; Milk allergy; Oral immunotherapy; Parameters; Peanut allergy; Peanuts; Safety; Signs and symptoms; Topological data analyses; Netherlands; Bayesian network analyses; Topological data analyses; Experimental food allergy; Bioinformatics; Oral immunotherapy
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-019-2802-9

Oral immunotherapy (OIT) is a promising therapeutic approach to treat food allergic patients. However, concerns with regards to safety and long-term efficacy of OIT remain. There is a need to identify biomarkers that predict, monitor and/or evaluate the effects of OIT. Here we present a method to select candidate biomarkers for efficacy and safety assessment of OIT using the computational approaches Bayesian networks (BN) and Topological Data Analysis (TDA). Data were used from fructo-oligosaccharide diet-supported OIT experiments performed in 3 independent cow's milk allergy (CMA) and 2 independent peanut allergy (PNA) experiments in mice. Bioinformatical approaches were used to understand the data structure. The BN predicted the efficacy of OIT in the CMA with 86% and indicated a clear effect of scFOS/lcFOS on allergy parameters. For the PNA model, this BN (trained on CMA data) predicted an efficacy of OIT with 76% accuracy and shows similar effects of the allergen, treatment and diet as compared to the CMA model. The TDA identified clusters of biomarkers closely linked to biologically relevant clinical symptoms and also unrelated and redundant parameters within the network. Here we provide a promising application of computational approaches to a) compare mechanistic features of two different food allergies during OIT b) determine the biological relevance of candidate biomarkers c) generate new hypotheses to explain why CMA has a different disease pattern than PNA and d) select relevant biomarkers for future studies.