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

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• 作者：Allan R. Brasier ; Sundar Victor ; Gary Boetticher ; Hyunsu Ju ; Chang Lee ; Eugene R. Bleecker ; Mario Castro ; William W. Busse ; William J. Calhoun
• 关键词：Asthma ; inflammation ; cytokines ; phenotypes ; ELISA ; hierarchical clustering ; bioinformatics ; class discovery
• 刊名：Journal of Allergy and Clinical Immunology
• 出版年：2008
• 出版时间：January 2008
• 年：2008
• 卷：121
• 期：1
• 页码：30-37.e6
• 全文大小：1592 K

文摘

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.