Data-Driven Asthma Endotypes Defined from Blood Biomarker and Gene Expression Data

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Abstract

The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes) driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels.

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Most cited references 41

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What is principal component analysis?

Markus Ringnér (2008)

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T-helper type 2-driven inflammation defines major subphenotypes of asthma.

Prescott G Woodruff, Barmak Modrek, David F Choy … (2009)

T-helper type 2 (Th2) inflammation, mediated by IL-4, IL-5, and IL-13, is considered the central molecular mechanism underlying asthma, and Th2 cytokines are emerging therapeutic targets. However, clinical studies increasingly suggest that asthma is heterogeneous. To determine whether this clinical heterogeneity reflects heterogeneity in underlying molecular mechanisms related to Th2 inflammation. Using microarray and polymerase chain reaction analyses of airway epithelial brushings from 42 patients with mild-to-moderate asthma and 28 healthy control subjects, we classified subjects with asthma based on high or low expression of IL-13-inducible genes. We then validated this classification and investigated its clinical implications through analyses of cytokine expression in bronchial biopsies, markers of inflammation and remodeling, responsiveness to inhaled corticosteroids, and reproducibility on repeat examination. Gene expression analyses identified two evenly sized and distinct subgroups, "Th2-high" and "Th2-low" asthma (the latter indistinguishable from control subjects). These subgroups differed significantly in expression of IL-5 and IL-13 in bronchial biopsies and in airway hyperresponsiveness, serum IgE, blood and airway eosinophilia, subepithelial fibrosis, and airway mucin gene expression (all P < 0.03). The lung function improvements expected with inhaled corticosteroids were restricted to Th2-high asthma, and Th2 markers were reproducible on repeat evaluation. Asthma can be divided into at least two distinct molecular phenotypes defined by degree of Th2 inflammation. Th2 cytokines are likely to be a relevant therapeutic target in only a subset of patients with asthma. Furthermore, current models do not adequately explain non-Th2-driven asthma, which represents a significant proportion of patients and responds poorly to current therapies.

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Asthma exacerbations and sputum eosinophil counts: a randomised controlled trial.

Ruth H Green, Christopher E. Brightling, Susan Mckenna … (2002)

Treatment decisions in asthma are based on assessments of symptoms and simple measures of lung function, which do not relate closely to underlying eosinophilic airway inflammation. We aimed to assess whether a management strategy that minimises eosinophilic inflammation reduces asthma exacerbations compared with a standard management strategy. We recruited 74 patients with moderate to severe asthma from hospital clinics and randomly allocated them to management either by standard British Thoracic Society asthma guidelines (BTS management group) or by normalisation of the induced sputum eosinophil count and reduction of symptoms (sputum management group). We assessed patients nine times over 12 months. The results were used to manage those in the sputum management group, but were not disclosed in the BTS group. The primary outcomes were the number of severe exacerbations and control of eosinophilic inflammation, measured by induced sputum eosinophil count. Analyses were by intention to treat. The sputum eosinophil count was 63% (95% CI 24-100) lower over 12 months in the sputum management group than in the BTS management group (p=0.002). Patients in the sputum management group had significantly fewer severe asthma exacerbations than did patients in the BTS management group (35 vs 109; p=0.01) and significantly fewer patients were admitted to hospital with asthma (one vs six, p=0.047). The average daily dose of inhaled or oral corticosteroids did not differ between the two groups. A treatment strategy directed at normalisation of the induced sputum eosinophil count reduces asthma exacerbations and admissions without the need for additional anti-inflammatory treatment.

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Author and article information

Contributors

Vladimir V. Kalinichenko: Role: Academic Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 2 February 2015

Publication date Collection: 2015

Volume: 10

Issue: 2

Electronic Location Identifier: e0117445

Affiliations

[1 ]National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America

[2 ]National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America

[3 ]National Health and Environmental Effects Research Laboratory—Environmental Public Health Division, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America

[4 ]National Health and Environmental Effects Research Laboratory—Integrated Systems Toxicology Division, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America

[5 ]Department of Bioinformatics, Expression Analysis, a Quintiles company, Durham, North Carolina, United States of America

[6 ]Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America

Cincinnati Children’s Hospital Medical Center, UNITED STATES

Author notes

Competing Interests: The authors have declared that no competing interests exist.

Conceived and designed the experiments: JEG EEH ECH LN. Performed the experiments: BLH. Analyzed the data: BJG DMR SWE WJ ECH CRW. Wrote the paper: SWE BJG JEG.

[¤a]

Current address: Biological Sciences Department, North Carolina State University, Raleigh, North Carolina, United States of America

[¤b]

Current address: Biology Department, North Carolina Central University, Durham, North Carolina, United States of America

[¤c]

Current address: Duke Translational Medicine Institute, Duke University, Durham, North Carolina, United States of America

‡ These authors contributed equally to this work.

* E-mail: edwards.stephen@ 123456epa.gov

Article

Publisher ID: PONE-D-14-11673

DOI: 10.1371/journal.pone.0117445

PMC ID: 4314082

PubMed ID: 25643280

SO-VID: 73a1bf0c-17ef-42d1-b032-f1c5c98c3766

License:

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication

History

Date received : 14 March 2014

Date accepted : 25 December 2014

Page count

Figures: 3, Tables: 0, Pages: 19

Funding

This study was funded by the National Health and Environmental Effects Research Laboratory and the National Center for Computational Toxicology within the U.S. Environmental Protection Agency (EPA) Office of Research and Development. This manuscript has been subjected to review by the US Environmental Protection Agency, National Health and Environmental Effects Research Laboratory and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does the mention of trade names or commercial products constitute endorsement or recommendation for use. Except as noted above, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Custom metadata

Data Availability The gene expression data underlying the findings are available in GEO with accession number GSE35571. The clinical marker data contain potential PII and as a result we cannot provide it without IRB oversight and review. Requests regarding clinical marker data availability can be initiated by contacting Dr. Tim Wade, Branch Chief, Epidemiology Branch, EPHD, NHEERL, ORD, USEPA, at wade.tim@epa.gov.

Data-Driven Asthma Endotypes Defined from Blood Biomarker and Gene Expression Data

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Most cited references 41

What is principal component analysis?

T-helper type 2-driven inflammation defines major subphenotypes of asthma.

Asthma exacerbations and sputum eosinophil counts: a randomised controlled trial.

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