U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics

Research output: Contribution to journalArticle

  • Authors:
  • Diane Lefaudeux
  • Bertrand De Meulder
  • Matthew J Loza
  • Nancy Peffer
  • Anthony Rowe
  • Frédéric Baribaud
  • Aruna T Bansal
  • Rene Lutter
  • Ana R Sousa
  • Julie Corfield
  • Ioannis Pandis
  • Per S Bakke
  • Massimo Caruso
  • Pascal Chanez
  • Sven-Erik Dahlén
  • Louise J Fleming
  • Stephen Fowler
  • Ildiko Horvath
  • Norbert Krug
  • Paolo Montuschi
  • Marek Sanak
  • Thomas Sandstrom
  • Dominic E Shaw
  • Florian Singer
  • Peter J Sterk
  • Graham Roberts
  • Ian M Adcock
  • Ratko Djukanovic
  • Charles Auffray
  • Kian F Chung
  • U-BIOPRED Study Group

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 personalised management approach can be provided.

OBJECTIVES: We stratified patients with moderate-to-severe asthma based on clinico-physiological parameters and performed an -omics analysis of sputum.

METHODS: Partition-around-medoid clustering was applied to a training set of 266 asthma participants from the European U-BIOPRED adult cohort using 8 pre-specified clinic-physiological variables. This was repeated in a separate validation set of 152 asthmatics. The clusters were compared based on sputum proteomic and transcriptomic data.

RESULTS: Four reproducible and stable clusters of asthmatics were identified. The training set cluster T1 consists of well-controlled moderate-to-severe asthmatics, while cluster T2 is a group of late-onset severe asthmatics with history of smoking and chronic airflow obstruction. Cluster T3 is similar to cluster T2 in terms of chronic airflow obstruction but is composed of non-smokers. Cluster T4 is predominantly composed of obese female uncontrolled severe asthmatics 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 T1 with no differences in sputum neutrophil counts, exhaled nitric oxide and serum IgE levels.

CONCLUSION: Clustering based on clinico-physiological parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.

CLINICAL IMPLICATIONS: The definition of four distinct clusters of asthma linked to different pathobiological pathways provides a better template for the phenotyping and personalised treatment of severe asthma, where high unmet needs remain.

CAPSULE SUMMARY: Unsupervised clustering of asthma on clinical features alone has led to the definition of four phenotypes. Sputum 'omics' analysis has revealed different biological pathways pointing towards potential new treatments.

Bibliographical metadata

Original languageEnglish
Pages (from-to)1797
Number of pages1807
JournalJournal of Allergy and Clinical Immunology
Volume139
Issue number6
Early online date20 Oct 2016
DOIs
Publication statusPublished - Jun 2017

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