Suárez-Fariñas Mayte, Grishin Alexander, Arif-Lusson Rihane, Bourgoin Pénélope, Matthews Katie, Campbell Dianne E, Busnel Jean-Marc, Sampson Hugh A
Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY; Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY.
Division of Pediatric Allergy and Immunology, Icahn School of Medicine at Mount Sinai, New York, NY.
J Allergy Clin Immunol Pract. 2024 Dec;12(12):3383-3392.e8. doi: 10.1016/j.jaip.2024.09.007. Epub 2024 Sep 14.
The basophil activation test (BAT) has been limited to research settings owing to technical issues. Novel approaches using dry, ready-to-use reagents and streamlined protocols offer greater flexibility and may open opportunities for easier implementation in clinical research.
Using a streamlined basophil activation test (sBAT) strategy and the settings of the baseline study of the Epicutaneous Immunotherapy in Toddlers with Peanut Allergy (EPITOPE) trial of EPicutaneous ImmunoTherapy, we aimed to assess the feasibility of implementing BAT in a multicenter trial and to evaluate its utility in predicting the outcomes of peanut double-blind placebo-controlled food challenge (DBPCFC).
Whole blood samples were collected from subjects aged 1 to 3 years (n = 241) undergoing baseline eligibility DBPCFC in the EPITOPE study across 15 clinical sites in North America. After preparation with sBAT reagents, processed samples were analyzed in a single central laboratory within 5 days of collection and preparation. The eliciting dose (ED) at DBPCFC was determined using, Practical Allergy (PRACTALL) criteria. Using a machine learning approach that incorporated BAT-derived features, clinical characteristics, and peanut-specific immunoglobulin E, the ability to predict outcomes of interest (ED ≤ 300 mg or > 300 mg] and use of epinephrine) was assessed using data randomly split into training (n = 182) and validation (n = 59) subsets.
The expression of basophil activation markers CD203c and CD63 correlated with ED and severity outcomes of DBPCFC. Most informative concentrations of peanut extract in the sBAT assay for these associations were 1 ng/mL and 10 ng/mL. Using machine learning to assess the ability to predict the outcomes of DBPCFC, the best models using only the BAT-derived features provided relatively high sensitivities of 0.86 and 0.85 for predicting ED and epinephrine use, respectively, whereas specificities were lower, ranging from 0.60 to 0.80. Although including specific immunoglobulin E and skin prick test data in addition to those from sBAT did not improve the ability to identify individuals most at risk for severe reactions, it did improve the ability to identify patients with an ED greater than 300 mg.
In addition to facilitating implementation in multicenter trials, sBAT retains the potential of BAT to characterize allergic patients and confirms its potential to contribute to predicting the outcome of oral food challenges.
由于技术问题，嗜碱性粒细胞活化试验（BAT）一直局限于研究环境。使用干燥、即用型试剂和简化方案的新方法提供了更大的灵活性，并可能为在临床研究中更轻松地实施创造机会。
采用简化的嗜碱性粒细胞活化试验（sBAT）策略以及幼儿花生过敏经皮免疫疗法（EPITOPE）试验的基线研究设置，我们旨在评估在多中心试验中实施BAT的可行性，并评估其在预测花生双盲安慰剂对照食物激发试验（DBPCFC）结果方面的效用。
从北美15个临床地点参与EPITOPE研究的1至3岁受试者（n = 241）中采集全血样本，这些受试者正在接受基线合格DBPCFC。用sBAT试剂制备后，处理过的样本在采集和制备后的5天内在单个中央实验室进行分析。使用实用过敏（PRACTALL）标准确定DBPCFC时的激发剂量（ED）。使用一种结合了BAT衍生特征、临床特征和花生特异性免疫球蛋白E的机器学习方法，通过随机分为训练（n = 182）和验证（n = 59）子集的数据，评估预测感兴趣结果（ED≤300mg或>300mg]和肾上腺素使用情况）的能力。
嗜碱性粒细胞活化标志物CD203c和CD63的表达与DBPCFC的ED和严重程度结果相关。sBAT试验中这些关联的花生提取物最具信息性的浓度为1ng/mL和10ng/mL。使用机器学习评估预测DBPCFC结果的能力，仅使用BAT衍生特征的最佳模型预测ED和肾上腺素使用的敏感性分别相对较高，为0.86和0.85，而特异性较低，范围为0.60至0.80。虽然除了sBAT的数据外，纳入特异性免疫球蛋白E和皮肤点刺试验数据并没有提高识别最有严重反应风险个体的能力，但确实提高了识别ED大于300mg患者的能力。
除了便于在多中心试验中实施外，sBAT保留了BAT对过敏患者进行特征描述的潜力，并证实了其有助于预测口服食物激发试验结果的潜力。
