RT Journal Article
SR Electronic
T1 A Novel Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 145771
DO 10.1101/145771
A1 Gaurav Pandey
A1 Om P. Pandey
A1 Angela J. Rogers
A1 Gabriel E. Hoffman
A1 Benjamin A. Raby
A1 Scott T. Weiss
A1 Eric E. Schadt
A1 Supinda Bunyavanich
YR 2017
UL http://biorxiv.org/content/early/2017/06/07/145771.abstract
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.