U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics.
Publisher DOI
PubMed ID
27773852
Abstract
BACKGROUND
Asthma is a heterogeneous disease in which there is a differential response to asthma treatments. This heterogeneity needs to be evaluated so that a personalized management approach can be provided.
OBJECTIVES
We stratified patients with moderate-to-severe asthma based on clinicophysiologic parameters and performed an omics analysis of sputum.
METHODS
Partition-around-medoids clustering was applied to a training set of 266 asthmatic participants from the European Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes (U-BIOPRED) adult cohort using 8 prespecified clinic-physiologic variables. This was repeated in a separate validation set of 152 asthmatic patients. The clusters were compared based on sputum proteomics and transcriptomics data.
RESULTS
Four reproducible and stable clusters of asthmatic patients were identified. The training set cluster T1 consists of patients with well-controlled moderate-to-severe asthma, whereas cluster T2 is a group of patients with late-onset severe asthma with a history of smoking and chronic airflow obstruction. Cluster T3 is similar to cluster T2 in terms of chronic airflow obstruction but is composed of nonsmokers. Cluster T4 is predominantly composed of obese female patients with uncontrolled severe asthma with increased exacerbations but with normal lung function. The validation set exhibited similar clusters, demonstrating reproducibility of the classification. There were significant differences in sputum proteomics and transcriptomics between the clusters. The severe asthma clusters (T2, T3, and T4) had higher sputum eosinophilia than cluster T1, with no differences in sputum neutrophil counts and exhaled nitric oxide and serum IgE levels.
CONCLUSION
Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.
Asthma is a heterogeneous disease in which there is a differential response to asthma treatments. This heterogeneity needs to be evaluated so that a personalized management approach can be provided.
OBJECTIVES
We stratified patients with moderate-to-severe asthma based on clinicophysiologic parameters and performed an omics analysis of sputum.
METHODS
Partition-around-medoids clustering was applied to a training set of 266 asthmatic participants from the European Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes (U-BIOPRED) adult cohort using 8 prespecified clinic-physiologic variables. This was repeated in a separate validation set of 152 asthmatic patients. The clusters were compared based on sputum proteomics and transcriptomics data.
RESULTS
Four reproducible and stable clusters of asthmatic patients were identified. The training set cluster T1 consists of patients with well-controlled moderate-to-severe asthma, whereas cluster T2 is a group of patients with late-onset severe asthma with a history of smoking and chronic airflow obstruction. Cluster T3 is similar to cluster T2 in terms of chronic airflow obstruction but is composed of nonsmokers. Cluster T4 is predominantly composed of obese female patients with uncontrolled severe asthma with increased exacerbations but with normal lung function. The validation set exhibited similar clusters, demonstrating reproducibility of the classification. There were significant differences in sputum proteomics and transcriptomics between the clusters. The severe asthma clusters (T2, T3, and T4) had higher sputum eosinophilia than cluster T1, with no differences in sputum neutrophil counts and exhaled nitric oxide and serum IgE levels.
CONCLUSION
Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.
Date Issued
2017-06
Publication Type
Article
Subject(s)
Subjects
Severe asthma
•
clustering
•
partition-around-medoids algorithm
•
sputum eosinophilia
Language(s)
en
Author(s)
Lefaudeux, Diane | |
De Meulder, Bertrand | |
Loza, Matthew J | |
Peffer, Nancy | |
Rowe, Anthony | |
Baribaud, Frédéric | |
Bansal, Aruna T | |
Lutter, Rene | |
Sousa, Ana R | |
Corfield, Julie | |
Pandis, Ioannis | |
Bakke, Per S | |
Caruso, Massimo | |
Chanez, Pascal | |
Dahlén, Sven-Erik | |
Fleming, Louise J | |
Fowler, Stephen J | |
Horvath, Ildiko | |
Krug, Norbert | |
Montuschi, Paolo | |
Sanak, Marek | |
Sandstrom, Thomas | |
Shaw, Dominic E | |
Sterk, Peter J | |
Roberts, Graham | |
Adcock, Ian M | |
Djukanovic, Ratko | |
Auffray, Charles | |
Chung, Kian Fan | |
Unbiased Biomarkers for the Prediction of Respiratory Disease Ou, U-BIOPRED |
Journal
The Journal of allergy and clinical immunology
Publisher
Elsevier
ISSN
1097-6825
Access(Rights)
restricted