Published in:
Open Access 01-12-2013 | Research
A qPCR-based metric of Th2 airway inflammation in asthma
Published in: Clinical and Translational Allergy | Issue 1/2013
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Background
Using microarray profiling of airway epithelial cells, we previously identified a Th2-high molecular phenotype of asthma based on expression of periostin, CLCA1 and serpinB2 and characterized by specific inflammatory, remodeling, and treatment response features. The goal of the current study was to develop a qPCR-based assay of Th2 inflammation to overcome the limitations of microarray-based methods.
Methods
Airway epithelial brushings were obtained by bronchoscopy from two clinical studies comprising 44 healthy controls and 62 subjects with asthma, 39 of whom were studied before and after a standardized 8 week course of inhaled corticosteroids (ICS). The qPCR-based expression of periostin, CLCA1 and serpinB2 were combined into a single metric.
Results
In asthma, the three-gene-mean of periostin, CLCA1 and serpinB2 correlated with FeNO (r = 0.75, p = 0.0002), blood eosinophils (r = 0.58, p = 0.003) and PC20 methacholine (r = -0.65, p = 0.0006), but not total serum IgE (r = 0.33, p = 0.1). Higher baseline three-gene-mean correlated with greater improvement in FEV1 with ICS at 2, 4 and 8 weeks (all p < 0.05). By ROC analysis, the area under the curve (AUC) of the three-gene-mean for FEV1 improvement with ICS at 4 and 8 weeks was 0.94 and 0.87, respectively, which are higher than the AUCs of FeNO, blood eosinophils, IgE or PC20. Th2 airway inflammation as measured by this three-gene-mean also had predictive capacity for an improvement in symptoms.
Conclusions
The three-gene-mean of periostin, CLCA1 and serpinB2 in airway epithelial brushings identifies Th2-high and low populations, is correlated with other Th2 biomarkers, and performs well for prediction of FEV1 improvement with ICS. The three-gene-mean provides a measurement of Th2 airway inflammation that is clinically relevant and that can serve as a valuable tool to evaluate non-invasive biomarkers to predict treatment responses to existing and emerging asthma therapies.