American journal of respiratory and critical care medicine · 2017
A Transcriptome-driven Analysis of Epithelial Brushings and Bronchial Biopsies to Define Asthma Phenotypes in U-BIOPRED.
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Kuo Chih-Hsi Scott, Pavlidis Stelios, Loza Matthew, Baribaud Fred, Rowe Anthony, Pandis Ioannis, Hoda Uruj, Rossios Christos, Sousa Ana, Wilson Susan J, Howarth Peter, Dahlen Barbro, Dahlen Sven-Erik, Chanez Pascal, Shaw Dominick, Krug Norbert, Sandstrӧm Thomas, De Meulder Bertrand, Lefaudeux Diane, Fowler Stephen, Fleming Louise, Corfield Julie, Auffray Charles, Sterk Peter J, Djukanovic Ratko, Guo Yike, Adcock Ian M, Chung Kian Fan, U-BIOPRED Project Team ‡
Аннотация
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.
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.