Kidwai Sarah, Barbiero Pietro, Meijerman Irma, Tonda Alberto, Perez-Pardo Paula, Lio Pietro, van der Maitland-Zee Anke H, Oberski Daniel L, Kraneveld Aletta D, Lopez-Rincon Alejandro
Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
Department of Computer Science and Technology, University of Cambridge, Cambridge, UK.
Clin Transl Allergy. 2023 Nov;13(11):e12306. doi: 10.1002/clt2.12306.
Not being well controlled by therapy with inhaled corticosteroids and long-acting β2 agonist bronchodilators is a major concern for severe-asthma patients. The current treatment option for these patients is the use of biologicals such as anti-IgE treatment, omalizumab, as an add-on therapy. Despite the accepted use of omalizumab, patients do not always benefit from it. Therefore, there is a need to identify reliable biomarkers as predictors of omalizumab response.
Two novel computational algorithms, machine-learning based Recursive Ensemble Feature Selection (REFS) and rule-based algorithm Logic Explainable Networks (LEN), were used on open accessible mRNA expression data from moderate-to-severe asthma patients to identify genes as predictors of omalizumab response.
With REFS, the number of features was reduced from 28,402 genes to 5 genes while obtaining a cross-validated accuracy of 0.975. The 5 responsiveness predictive genes encode the following proteins: Coiled-coil domain- containing protein 113 (CCDC113), Solute Carrier Family 26 Member 8 (SLC26A), Protein Phosphatase 1 Regulatory Subunit 3D (PPP1R3D), C-Type lectin Domain Family 4 member C (CLEC4C) and LOC100131780 (not annotated). The LEN algorithm found 4 identical genes with REFS: CCDC113, SLC26A8 PPP1R3D and LOC100131780. Literature research showed that the 4 identified responsiveness predicting genes are associated with mucosal immunity, cell metabolism, and airway remodeling.
Both computational methods show 4 identical genes as predictors of omalizumab response in moderate-to-severe asthma patients. The obtained high accuracy indicates that our approach has potential in clinical settings. Future studies in relevant cohort data should validate our computational approach.
吸入性糖皮质激素和长效β2受体激动剂支气管扩张剂治疗效果不佳是重度哮喘患者的主要担忧。目前针对这些患者的治疗选择是使用生物制剂，如抗IgE治疗药物奥马珠单抗作为附加疗法。尽管奥马珠单抗已被广泛应用，但患者并非总能从中获益。因此，需要确定可靠的生物标志物作为奥马珠单抗反应的预测指标。
两种新的计算算法，基于机器学习的递归集成特征选择（REFS）和基于规则的算法逻辑可解释网络（LEN），被应用于来自中重度哮喘患者的可公开获取的mRNA表达数据，以确定作为奥马珠单抗反应预测指标的基因。
使用REFS时，特征数量从28,402个基因减少到5个基因，同时交叉验证准确率达到0.975。这5个反应性预测基因编码以下蛋白质：卷曲螺旋结构域包含蛋白113（CCDC113）、溶质载体家族26成员8（SLC26A）、蛋白磷酸酶1调节亚基3D（PPP1R3D）、C型凝集素结构域家族4成员C（CLEC4C）和LOC100131780（未注释）。LEN算法与REFS发现了4个相同的基因：CCDC113、SLC26A8、PPP1R3D和LOC100131780。文献研究表明，这4个已确定的反应性预测基因与黏膜免疫、细胞代谢和气道重塑相关。
两种计算方法均显示4个相同的基因可作为中重度哮喘患者奥马珠单抗反应的预测指标。所获得的高精度表明我们的方法在临床环境中具有潜力。未来在相关队列数据中的研究应验证我们的计算方法。
