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The nose knows
July 2018
by Lori Lesko  |  Email the author
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NEW YORK—Armed with a simple nasal brush, Mount Sinai researchers have identified a genetic biomarker of asthma that can differentiate asthma from other respiratory conditions such as allergic rhinitis, smoking, upper respiratory infection and cystic fibrosis.
 
Led by clinical and computational scientists in the Department of Genetics and Genomic Sciences, the Icahn Institute for Genomics and Multi-scale Biology and the Department of Pediatrics at the Icahn School of Medicine at Mount Sinai, the research team published its results in the June 2018 issue of Scientific Reports.
 
In the journal article, Mount Sinai Health System researchers described using a “nasal brushing” technique to collect RNA from the noses of 190 volunteers, 66 of whom had asthma and 124 of whom did not. After those samples were sequenced, machine learning algorithms were used to analyze the data to determine the differences between the RNA of the two groups.
 
As a result, a 90-gene biomarker was identified that is specific to people with asthma. It is hoped that soon doctors will be able to simply swab the inside of patients’ noses, then analyze the sample to see if the biomarker is present. The next step involves plans for a follow-up study involving a larger sample size.
 
“Mild to moderate asthma can be difficult to diagnose because symptoms change over time and can be complicated by other respiratory conditions,” Dr. Supinda Bunyavanich, a researcher at the Icahn School of Medicine, states. “Our nasal brush test takes seconds to collect. For time-strapped clinicians, particularly primary care providers at the front lines of asthma diagnosis, this could greatly improve patient outcomes through early and accurate diagnosis.”
 
Bunyavanich told DDNews that asthma “can be challenging to diagnose given its waxing and waning symptoms. Individuals may not recognize that they have asthma, and individuals may not be symptomatic when they see their doctor.”
 
“Studies have shown substantial proportions of the population have asthma-like symptoms without being diagnosed with asthma,” she adds. “Some of these people may actually have asthma and would benefit from diagnosis and appropriate treatment.” At the same time, “some of these people may not have asthma and would benefit from knowing asthma is unlikely, thus avoiding unnecessary treatment with asthma medications. Current guidelines recommend incorporating pulmonary function testing with clinical assessment to diagnose asthma, but such testing is frequently not available in primary care settings. Limited time and expertise also often preclude pulmonary function testing.”
 
“Our nasal brush-based classifier would be quick and easy to implement, yielding an automatable output of yes or no asthma for the clinician to consider,” Bunyavanich continues. “The potential outcomes of this include appropriate treatment of asthma if asthma is likely—and avoidance of unnecessary asthma treatment if asthma is unlikely.”
 
Currently, pulmonary function testing (PFT) is the most reliable diagnostic tool for asthma. However, the equipment and expertise needed to perform these tests are not always available in primary care settings where asthma is frequently diagnosed and treated. It is also difficult to differentiate between asthma and other respiratory diseases using PFT alone, while the nasal brush and subsequent analysis for this asthma biomarker provides a binary result of asthma or not asthma.
 
“One of the most exciting components of this study is demonstrating the power of machine learning when applied to biomedical data,” said Dr. Gaurav Pandey, who led data science efforts to develop the biomarker. “Collaborations between computational scientists and biomedical researchers and clinicians are advancing medicine at an inspiring pace. We have the power of many insights we didn’t have in the past, and that opens a window to an entirely new world of diagnostic tools and treatments.”
 
Pandey was lead researcher in the study, which was titled “Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.”
 
This classifier reportedly performed with strong predictive value and sensitivity across eight test sets, including a test set of independent asthmatic and control subjects profiled by RNA sequencing, two independent case-control cohorts of asthma profiled by microarray and five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate.
 
Code: E071821

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