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

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

Research output: Contribution to journalArticle

Abstract

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control 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 cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

Original languageEnglish (US)
Article number8826
JournalScientific Reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018
Externally publishedYes

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    Pandey, G., Pandey, O. P., Rogers, A. J., Ahsen, M. E., Hoffman, G. E., Raby, B. A., Weiss, S. T., Schadt, E. E., & Bunyavanich, S. (2018). A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data. Scientific Reports, 8(1), [8826]. https://doi.org/10.1038/s41598-018-27189-4