Molecular phenotyping of severe asthma using pattern recognition of bronchoalveolar lavage-derived cytokines

Allan R. Brasier, Sundar Victor, Gary Boetticher, Hyunsu Ju, Chang Lee, Eugene R. Bleecker, Mario Castro, William W. Busse, William J. Calhoun

Research output: Contribution to journalArticlepeer-review

100 Scopus citations

Abstract

Background: Asthma is a heterogeneous clinical disorder. Methods for objective identification of disease subtypes will focus on clinical interventions and help identify causative pathways. Few studies have explored phenotypes at a molecular level. Objective: We sought to discriminate asthma phenotypes on the basis of cytokine profiles in bronchoalveolar lavage (BAL) samples from patients with mild-moderate and severe asthma. Methods: Twenty-five cytokines were measured in BAL samples of 84 patients (41 severe, 43 mild-moderate) using bead-based multiplex immunoassays. The normalized data were subjected to statistical and informatics analysis. Results: Four groups of asthmatic profiles could be identified on the basis of unsupervised analysis (hierarchical clustering) that were independent of treatment. One group, enriched in patients with severe asthma, showed differences in BAL cellular content, reductions in baseline pulmonary function, and enhanced response to methacholine provocation. Ten cytokines were identified that accurately predicted this group. Classification methods for predicting methacholine sensitivity were developed. The best model analysis predicted hyperresponders with 88% accuracy in 10 trials by using a 10-fold cross-validation. The cytokines that contributed to this model were IL-2, IL-4, and IL-5. On the basis of this classifier, 3 distinct hyperresponder classes were identified that varied in BAL eosinophil count and PC20 methacholine. Conclusion: Cytokine expression patterns in BAL can be used to identify distinct types of asthma and identify distinct subsets of methacholine hyperresponders. Further biomarker discovery in BAL may be informative.

Original languageEnglish (US)
Pages (from-to)30-37.e6
JournalJournal of Allergy and Clinical Immunology
Volume121
Issue number1
DOIs
StatePublished - Jan 2008

Keywords

  • Asthma
  • ELISA
  • bioinformatics
  • class discovery
  • cytokines
  • hierarchical clustering
  • inflammation
  • phenotypes

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology

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