ABSTRACT
Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. One hundred ninety subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline, comprised of feature selection, classification, and statistical analyses, identified a diagnostic classifier of asthma consisting of 90 nasally expressed genes interpreted via an L2-regularized logistic regression classification model. This nasal brush-based classifier performed with strong predictive value and sensitivity across eight validation test sets, including (1) a test set of independent asthmatic and non-asthmatic subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five independent cohorts of subjects with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the panel had a low to zero rate of misclassification. Translational development of this classifier into a diagnostic nasal brush-based biomarker for clinical use could aid in asthma detection and care.