A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPREDTools Khuo, 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 E., Krug, Norbert, Sandström, Thomas, De Meulder, Betrand, Lefaudeux, Diane, Fowler, Stephen, Fleming, Louise, Corfield, Julie, Auffray, Charles, Sterk, Peter J., Djukanovic, Ratko, Guo, Yike, Adcock, Ian M. and Chung, Kian Fan (2016) A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED. American Journal of Respiratory and Critical Care Medicine . ISSN 1535-4970 Full text not available from this repository.AbstractRationale and objectives: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. We used transcriptomic profiling of airway tissues to help define asthma phenotypes. Methods: The transcriptome from bronchial biopsies and epithelial brushings of 107 moderate-to-severe asthmatics were annotated by gene-set variation analysis (GSVA) using 42 gene-signatures relevant to asthma, inflammation and immune function. Topological data analysis (TDA) of clinical and histological data was used to derive clusters and the nearest shrunken centroid algorithm used for signature refinement. Results: 9 GSVA signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper type 2 (Th-2) cytokines and lack of corticosteroid response (Group 1 and Group 3). Group 1 had the highest submucosal eosinophils, high exhaled nitric oxide (FeNO) levels, exacerbation rates and oral corticosteroid (OCS) use whilst Group 3 patients showed the highest levels of sputum eosinophils and had a high BMI. In contrast, Group 2 and Group 4 patients had an 86% and 64% probability of having non-eosinophilic inflammation. Using machine-learning tools, we describe an inference scheme using the currently-available inflammatory biomarkers sputum eosinophilia and exhaled nitric oxide levels along with OCS use that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity. Conclusion: This analysis demonstrates the usefulness of a transcriptomic-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target Th2-mediated inflammation and/or corticosteroid insensitivity.
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