Department of Respiratory Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands.
Department of Pediatric Pneumology and Allergy, University Children's Hospital Regensburg (KUNO), Regensburg, Germany.
Curr Opin Allergy Clin Immunol. 2020 Apr;20(2):155-161. doi: 10.1097/ACI.0000000000000626.
Childhood asthma is a heterogeneous inflammatory disease comprising different phenotypes and endotypes and, particularly in its severe forms, has a large impact on the quality-of-life of patients and caregivers. The application of advanced omics technologies provides useful insights into underlying asthma endotypes and may provide potential clinical biomarkers to guide treatment and move towards a precision medicine approach.
The current article addresses how novel omics approaches have shaped our current understanding of childhood asthma and highlights recent findings from (pharmaco)genomics, epigenomics, transcriptomics, and metabolomics studies on childhood asthma and their potential clinical implications to guide treatment in severe asthmatics.
Until now, omics studies have largely expanded our view on asthma heterogeneity, helped understand cellular processes underlying asthma, and brought us closer towards identifying (bio)markers that will allow the prediction of treatment responsiveness and disease progression. There is a clinical need for biomarkers that will guide treatment at the individual level, particularly in the field of biologicals. The integration of multiomics data together with clinical data could be the next promising step towards development individual risk prediction models to guide treatment. However, this requires large-scale collaboration in a multidisciplinary setting.
儿童哮喘是一种异质性炎症性疾病,包括不同的表型和内型,特别是在严重形式下,对患者和护理人员的生活质量有很大影响。先进的组学技术的应用为哮喘内型提供了有用的见解,并可能为潜在的临床生物标志物提供指导治疗的方法,并朝着精准医学的方法发展。
本文介绍了新的组学方法如何改变我们对儿童哮喘的现有认识,并强调了(药物)基因组学、表观基因组学、转录组学和代谢组学研究在儿童哮喘方面的最新发现,及其对指导严重哮喘患者治疗的潜在临床意义。
到目前为止,组学研究在很大程度上扩大了我们对哮喘异质性的认识,帮助我们了解哮喘背后的细胞过程,并使我们更接近识别(生物)标志物,这些标志物将能够预测治疗反应和疾病进展。需要有能够在个体层面指导治疗的生物标志物,特别是在生物制剂领域。将多组学数据与临床数据相结合,可能是开发个体风险预测模型以指导治疗的下一个有前途的步骤。然而,这需要在多学科环境中进行大规模合作。