PT  - JOURNAL ARTICLE
AU  - Gaurav Pandey
AU  - Om P. Pandey
AU  - Angela J. Rogers
AU  - Gabriel E. Hoffman
AU  - Benjamin A. Raby
AU  - Scott T. Weiss
AU  - Eric E. Schadt
AU  - Supinda Bunyavanich
TI  - A Novel Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data
AID  - 10.1101/145771
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 145771
4099  - http://biorxiv.org/content/early/2017/07/10/145771.short
4100  - http://biorxiv.org/content/early/2017/07/10/145771.full
AB  - 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.