TNO, PO Box 360, 3700, AJ, Zeist, The Netherlands.
Institute of Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
BMC Bioinformatics. 2019 Apr 23;20(1):206. 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.
口服免疫疗法（OIT）是治疗食物过敏患者的一种很有前途的治疗方法。然而，OIT 的安全性和长期疗效仍存在担忧。需要确定预测、监测和/或评估 OIT 效果的生物标志物。在这里，我们提出了一种使用贝叶斯网络（BN）和拓扑数据分析（TDA）的计算方法来选择 OIT 疗效和安全性评估的候选生物标志物的方法。
使用了来自果糖低聚糖饮食支持的 OIT 实验的数据，这些实验在 3 个独立的牛奶过敏（CMA）和 2 个独立的花生过敏（PNA）实验中在小鼠中进行。生物信息学方法用于了解数据结构。BN 预测了 CMA 中 OIT 的疗效，准确率为 86%，并表明 scFOS/lcFOS 对过敏参数有明显影响。对于 PNA 模型，该 BN（在 CMA 数据上训练）预测 OIT 的疗效准确率为 76%，并显示出与 CMA 模型类似的过敏原、治疗和饮食的作用。TDA 确定了与生物相关的临床症状密切相关的生物标志物群，并且在网络内还确定了与生物学无关和冗余的参数。
在这里，我们提供了一种计算方法的有希望的应用，用于 a）比较两种不同食物过敏在 OIT 期间的机制特征，b）确定候选生物标志物的生物学相关性，c）生成新的假设来解释为什么 CMA 比 PNA 具有不同的疾病模式，以及 d）选择用于未来研究的相关生物标志物。
