Chung Kian Fan, Adcock Ian M
aExperimental Studies, National Heart and Lung Institute, Imperial College London bRoyal Brompton NIHR Biomedical Research Unit cAirways Disease Section, National Heart and Lung Institute, Imperial College London, London, UK.
Curr Opin Allergy Clin Immunol. 2015 Feb;15(1):56-62. doi: 10.1097/ACI.0000000000000134.
Asthma is a common disease which presents in various clinical forms and levels of severity. The current 'one size fits all' approach to treatment is suboptimal. Using unbiased cluster analysis has identified several asthma phenotypes. Understanding the underlying mechanisms driving these clusters may lead to better patient-orientated medicines.
Clustering was initially performed on clinical features only, but the addition of biomarkers that characterize sputum and blood cellular profiles has enabled the prediction of responses to targeted therapies. Clusters of severe asthma include those on high-dose corticosteroid treatment associated with severe airflow obstruction and those with discordance between symptoms and sputum eosinophilia. Sputum eosinophilia can predict therapeutic responses to T-helper type 2 cytokine blockade. Further molecular phenotyping or endotyping of asthma will be necessary to determine new treatment strategies. Low T-helper type 2 expression may be predictive of poor therapeutic response to inhaled corticosteroids, but much less is known about this type of asthma.
Phenotype-driven treatment of asthma will be further boosted by the integration of genetic, transcriptomic and proteomic technologies to defining distinct severe asthma phenotypes and biomarkers of therapeutic responses. This will lead towards stratified medicine for asthma.
哮喘是一种常见疾病，有多种临床形式和严重程度级别。当前“一刀切”的治疗方法并不理想。使用无偏聚类分析已识别出几种哮喘表型。了解驱动这些聚类的潜在机制可能会带来更好的以患者为导向的药物。
聚类最初仅基于临床特征进行，但添加表征痰液和血细胞特征的生物标志物能够预测对靶向治疗的反应。重度哮喘的聚类包括那些接受高剂量皮质类固醇治疗且伴有严重气流阻塞的患者，以及症状与痰液嗜酸性粒细胞增多不一致的患者。痰液嗜酸性粒细胞增多可预测对2型辅助性T细胞细胞因子阻断的治疗反应。哮喘的进一步分子表型分析或内型分析对于确定新的治疗策略将是必要的。低2型辅助性T细胞表达可能预示对吸入性皮质类固醇的治疗反应不佳，但对于这类哮喘了解较少。
将遗传、转录组学和蛋白质组学技术整合以定义不同的重度哮喘表型和治疗反应生物标志物，将进一步推动基于表型的哮喘治疗。这将朝着哮喘的分层医学发展。
