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Am J Respir Crit Care Med. 2017 Feb 15;195(4):443-455. doi: 10.1164/rccm.201512-2452OC.

A Transcriptome-driven Analysis of Epithelial Brushings and Bronchial Biopsies to Define Asthma Phenotypes in U-BIOPRED.

Author information

1
1 Department of Computing.
2
2 Data Science Institute, and.
3
3 Airways Disease Section, National Heart and Lung Institute, Imperial College London, London, United Kingdom.
4
4 Janssen Research and Development, High Wycombe, United Kingdom.
5
5 Biomedical Research Unit, Royal Brompton & Harefield National Health Service Trust, London, United Kingdom.
6
6 Respiratory Therapeutic Unit, GlaxoSmithKline, Stockley Park, United Kingdom.
7
7 Faculty of Medicine, Southampton University, Southampton, United Kingdom.
8
8 Centre for Allergy Research, Karolinska Institute, Stockholm, Sweden.
9
9 Université de la Méditerranee, Marseille, France.
10
10 Centre for Respiratory Research, University of Nottingham, Nottingham, United Kingdom.
11
11 Fraunhofer Institute of Toxicology and Experimental Medicine, Hannover, Germany.
12
12 Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
13
13 European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, University of Lyon, Lyon, France.
14
14 Centre for Respiratory Medicine and Allergy, University of Manchester, Manchester, United Kingdom.
15
15 AstraZeneca R&D, Molndal, Sweden.
16
16 Areteva R&D, Nottingham, United Kingdom; and.
17
17 Faculty of Medicine, University of Amsterdam, Amsterdam, the Netherlands.

Abstract

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.

KEYWORDS:

T-helper type 2; corticosteroid insensitivity; exhaled nitric oxide; gene set variation analysis; severe asthma

PMID:
27580351
DOI:
10.1164/rccm.201512-2452OC
[Indexed for MEDLINE]
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