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A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data

By Gaurav Pandey, Om P. Pandey, Angela J. Rogers, Mehmet E. Ahsen, Gabriel E. Hoffman, Benjamin A. Raby, Scott T. Weiss, Eric E. Schadt, Supinda Bunyavanich

Posted 07 Jun 2017
bioRxiv DOI: 10.1101/145771 (published DOI: 10.1038/s41598-018-27189-4)

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 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 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 classifier had a low to zero rate of misclassification. Following validation in large, prospective cohorts, this classifier could be developed into a nasal brush-based biomarker of asthma.

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