Veracyte presents data on genomic classifier for Idiopathic Pulmonary Fibrosis
May 30, 2017 – SOUTH SAN FRANCISCO. Veracyte, Inc., a genomic diagnostics company focused on reducing unnecessary surgeries and healthcare costs by resolving diagnostic uncertainty, announced today that pivotal clinical validation data demonstrating the performance of its Envisia Genomic Classifier were presented at the American Thoracic Society 2017 International Conference (ATS 2017) held in Washington, DC last week. The genomic test is used to improve diagnosis of idiopathic pulmonary fibrosis (IPF), a common and severe form of interstitial lung disease (ILD), which is often challenging to diagnose without surgery.
At the ATS meeting today, investigators presented pivotal clinical validation study results confirming the Envisia classifier’s ability to detect usual interstitial pneumonia (UIP), a pattern whose presence is essential to IPF diagnosis, without the need for surgery. The genomic test identified UIP vs. non-UIP with high specificity of nearly 90% and demonstrated sensitivity of 67 percent, meaning it would be expected to identify two thirds of UIP cases with a high degree of accuracy. This performance was compared to a reference standard of histopathology review by a central panel of pathologists with expertise in ILD. The findings are from the 30-site, prospective BRAVE trial and involved 236 transbronchial biopsy (TBB) samples from 49 patients.
“By providing information that today can often only be obtained through surgery, the Envisia classifier has the potential of enabling the diagnosis of IPF,” said Ganesh Raghu, M.D., professor of medicine in the Division of Pulmonary and Critical Care Medicine and director of the Center for Interstitial Lung Disease at the University of Washington and senior author of the study. “This new molecular approach to diagnosis of IPF will hopefully serve the patients better in ascertaining the diagnosis of IPF.”
Data were also presented from an analytical verification study demonstrating the Envisia classifier’s strong accuracy, reproducibility, and robustness in distinguishing UIP from non-UIP under conditions that emulate the operational and biological variation that may be encountered in routine testing. The study included assessing the classifier’s ability to determine gene expression in a pooled RNA sample composed of multiple (3-5) TBB samples per patient, with varying mixtures of each sample, illustrating that the test is robust across sampling variations.
“The data presented today demonstrate the significant role that the Envisia Genomic Classifier can play in resolving uncertainty in IPF diagnosis so that patients can get the answers they need without undergoing surgery,” said Bonnie Anderson, Veracyte’s chief executive officer and chairman. “These data also represent remarkable progress as we build the library of clinical evidence to support physician adoption and payer reimbursement for the test.”
The Envisia Genomic Classifier is designed to improve physicians’ ability to differentiate IPF from other ILDs without the need for surgery. The 190-gene classifier uses machine learning coupled with powerful, deep RNA sequencing to detect the presence or absence of usual interstitial pneumonia, or UIP, a classic diagnostic pattern whose presence is essential for the diagnosis of IPF, using samples obtained through less-invasive bronchoscopy.