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A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED

A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED
A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED
Rationale 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.
1073-449X
443-455
Kuo, Chih-Hsi
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Pavlidis, Stelios
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Loza, Matthew
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Baribaud, Fred
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Rowe, Anthony
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Pandis, Ioannis
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Hoda, Uruj
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Rossios, Christos
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Sousa, Ana
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Wilson, Susan J.
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Howarth, Peter
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Chanez, Pascal
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Shaw, Dominick
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Krug, Norbert
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Sandstorm, Thomas
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Meulder, Bertrand
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Lefaudeux, Diane
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Fowler, Stephen
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Flemming, Louise
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Corfield, Julie
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Auffray, Charles
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Sterk, Peter
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Djukanovic, Ratko
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Guo, Yike
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Adcock, Ian
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Chung, Kian
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Kuo, Chih-Hsi
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Pavlidis, Stelios
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Loza, Matthew
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Baribaud, Fred
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Rowe, Anthony
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Pandis, Ioannis
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Hoda, Uruj
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Rossios, Christos
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Sousa, Ana
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Wilson, Susan J.
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Howarth, Peter
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Dahlen, Barbro
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Dahlen, Sven-Erik
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Chanez, Pascal
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Shaw, Dominick
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Krug, Norbert
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Sandstorm, Thomas
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Meulder, Bertrand
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Lefaudeux, Diane
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Fowler, Stephen
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Flemming, Louise
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Corfield, Julie
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Auffray, Charles
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Sterk, Peter
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Djukanovic, Ratko
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Guo, Yike
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Adcock, Ian
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Chung, Kian
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Kuo, Chih-Hsi, 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, Sandstorm, Thomas, Meulder, Bertrand, Lefaudeux, Diane, Fowler, Stephen, Flemming, Louise, Corfield, Julie, Auffray, Charles, Sterk, Peter, Djukanovic, Ratko, Guo, Yike, Adcock, Ian and Chung, Kian (2017) 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, 195 (4), 443-455. (doi:10.1164/rccm.201512-2452OC). (PMID:27580351)

Record type: Article

Abstract

Rationale 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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Accepted/In Press date: 29 August 2016
e-pub ahead of print date: 31 August 2016
Published date: 2017
Organisations: Clinical & Experimental Sciences

Identifiers

Local EPrints ID: 404791
URI: https://eprints.soton.ac.uk/id/eprint/404791
ISSN: 1073-449X
PURE UUID: 69c1f47d-c54e-4589-9390-5daeb4b20a5c
ORCID for Ratko Djukanovic: ORCID iD orcid.org/0000-0001-6039-5612

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Date deposited: 23 Jan 2017 14:52
Last modified: 03 Dec 2019 06:22

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Contributors

Author: Chih-Hsi Kuo
Author: Stelios Pavlidis
Author: Matthew Loza
Author: Fred Baribaud
Author: Anthony Rowe
Author: Ioannis Pandis
Author: Uruj Hoda
Author: Christos Rossios
Author: Ana Sousa
Author: Susan J. Wilson
Author: Peter Howarth
Author: Barbro Dahlen
Author: Sven-Erik Dahlen
Author: Pascal Chanez
Author: Dominick Shaw
Author: Norbert Krug
Author: Thomas Sandstorm
Author: Bertrand Meulder
Author: Diane Lefaudeux
Author: Stephen Fowler
Author: Louise Flemming
Author: Julie Corfield
Author: Charles Auffray
Author: Peter Sterk
Author: Yike Guo
Author: Ian Adcock
Author: Kian Chung

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