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

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.

FIG 1

Hierarchical clustering of 18 cytokines. Shown is a heat map of clustering cytokine values. Each row is an individual patient. At left, dendogram showing similarity of groups. Right, four major groups (G) are indicated by vertical bars (G1-G4).

FIG 2

Treatment patterns as a result of glucocorticoid therapy. Patients were separately clustered based on glucocorticoid therapy at the time of BAL. Left, subjects taking glucocorticoids (inhaled or oral) vs right, subjects not on glucocorticoids. Note similar cytokine patterns are seen in both groups.

FIG 3

Cytokine classifiers for G1. Shown is a rank ordered list of the 10 cytokines that minimize cross validation error for G1 asthmatics. Left, centroid of G1; right centroid of combined G2-G4 (threshold of 1.2). X axis, is deviation from the overall class centroid.

FIG 4

Identification of HR subjects. Shown is a frequency histogram of the 67 patients where PC20 Methacholine sensitivity was measured. Patients with PC₂₀ methacholine response of <0.5 mg/ml were classified as HR.

FIG 5

CART classification. C.4.5 decision tree was performed on the Z-Score normalized cytokine data. Shown is the most accurate model. For each node (rectangle) the classification and number of correctly grouped subjects is indicated. The identity of HR Classes A, B and C are indicated in upper right hand corner of each terminal leaf.

FIG 6

Pairwise comparison of methacholine HR Classes. Demographic variables of methacholine HR Class A, B and C were compared by ANOVA. Top panel, BAL eosinophils; bottom panel, PC₂₀ methacholine.