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

Journal: Scientific reports
PMID: 29891868

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.

Authors

  • Gaurav Pandey
    Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: gaurav.pandey@mssm.edu.
  • Om P Pandey
    Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA.
  • Angela J Rogers
    Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Mehmet E Ahsen
    Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Gabriel E Hoffman
    Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Benjamin A Raby
    Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham & Women's Hospital, and Harvard Medical School, Boston, MA, USA.
  • Scott T Weiss
    From Research Information Systems and Computing (V.M.C., V.G., S.M.), Partners Healthcare; Boston Children's Hospital Informatics Program (D.D., S.F., G.S.); Harvard Medical School (D.D., S.Y., A.C., M.A.-E.-B., N.A.S., S.M., S.T.W., R.D.); Department of Medicine (S.Y., S.T.W.), Department of Neurosurgery (A.C., M.A.-E.-B., R.D.), Division of Rheumatology, Immunology and Allergy (N.A.S.), and Channing Division of Network Medicine (S.T.W., R.D.), Brigham and Women's Hospital, Boston, MA; Center for Statistical Science (S.Y.), Tsinghua University, Beijing, China; Department of Neurology (S.M.), Massachusetts General Hospital; and Biostatistics (T.C.), Harvard School of Public Health, Boston, MA.
  • Eric E Schadt
    Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Supinda Bunyavanich
    Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. supinda@post.harvard.edu.

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