• Chih-Hsi Scott Kuo, Stelios Pavlidis, Matthew Loza, Fred Baribaud, Anthony Rowe, Ioannis Pandis, Uruj Hoda, Christos Rossios, Ana Sousa, Susan J Wilson, Peter Howarth, Barbro Dahlen, Sven-Erik Dahlen, Pascal Chanez, Dominick Shaw, Norbert Krug, Thomas Sandstrӧm, Bertrand De Meulder, Diane Lefaudeux, Stephen Fowler, Louise Fleming, Julie Corfield, Charles Auffray, Peter J Sterk, Ratko Djukanovic, Yike Guo, Ian M Adcock, Kian Fan Chung, and U-BIOPRED Project Team ‡ .
• 1 Department of Computing.
• Am. J. Respir. Crit. Care Med. 2017 Feb 15; 195 (4): 443-455.

RationaleAsthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms.ObjectivesUsing transcriptomic profiling of airway tissues, we sought to define the molecular phenotypes of severe asthma.MethodsThe 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 ResultsNine 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.ConclusionsThis 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.

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