期刊
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
卷 195, 期 4, 页码 443-455出版社
AMER THORACIC SOC
DOI: 10.1164/rccm.201512-2452OC
关键词
severe asthma; corticosteroid insensitivity; T-helper type 2; exhaled nitric oxide; gene set variation analysis
资金
- Innovative Medicines Initiative joint undertaking from the European Union's Seventh Framework Programme (FP7) [115010]
- European Federation of Pharmaceutical Industries and Associations companies'
- Department of Thoracic Medicine, Linkou Chang-Gung Memorial Hospital, through Chang-Gung Medical Foundation, Taoyuan, Taiwan
- MRC [MR/L01632X/1, G1001367] Funding Source: UKRI
- Asthma UK [MRC-Asthma UK Centre] Funding Source: researchfish
- Medical Research Council [G1000758, G1001367, MR/L01632X/1] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0515-10016, NF-SI-0514-10085, NF-SI-0509-10080] Funding Source: researchfish
Rationale: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. Objectives: Using transcriptomic profiling of airway tissues, we sought to define the molecular phenotypes of severe asthma. Methods: The transcriptome derived from bronchial biopsies and epithelial brushings of 107 subjects with moderate to severe asthma were annotated by gene set variation analysis using 42 gene signatures relevant to asthma, inflammation, and immune function. Topological data analysis of clinical and histologic data was performed to derive clusters, and the nearest shrunken centroid algorithm was used for signature refinement. Measurements and Main Results: Nine gene set variation analysis signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper cell type 2 cytokines and lack of corticosteroid response (group 1 and group 3). Group 1 had the highest submucosal eosinophils, as well as high fractional exhaled nitric oxide levels, exacerbation rates, and oral corticosteroid use, whereas group 3 patients showed the highest levels of sputum eosinophils and had a high body mass index. In contrast, group 2 and group 4 patients had an 86% and 64% probability, respectively, of having noneosinophilic inflammation. Using machine learning tools, we describe an inference scheme using the currently available inflammatory biomarkers sputum eosinophilia and fractional exhaled nitric oxide levels, along with oral corticosteroid use, that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity. Conclusions: This analysis demonstrates the usefulness of a transcriptomics-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target T-helper cell type 2-mediated inflammation and/or corticosteroid insensitivity.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据