Envisia is expected to be the first commercially available test to help patients with suspected idiopathic pulmonary fibrosis (IPF) secure accurate diagnoses more quickly than the currently available process and without the need for invasive surgery.

Each year in the United States and Europe, up to 200,000 patients are suspected of having an interstitial lung disease (ILD), including IPF, which is among the most common and most deadly of these lung-scarring diseases.

IPF is notoriously difficult to diagnose, often leading to treatment delays, repeated misdiagnoses, patient distress and added healthcare expense. Veracyte estimates that by helping to improve care for these patients, the company is targeting a $500 million market in the United States and Europe.

The Envisia classifier uses machine learning coupled with powerful, deep RNA sequencing to detect the presence or absence of usual interstitial pneumonia (UIP), a classic diagnostic pattern that is essential for the diagnosis of IPF.

Physicians routinely use high-resolution CT imaging (HRCT) to identify UIP, but this approach frequently provides inconclusive results, leading many patients to undergo surgery to secure a more definitive diagnosis using surgical histopathology.

 Veracyte scientists trained the Envisia classifier to differentiate UIP from non-UIP on patient samples obtained through less-invasive outpatient bronchoscopy.

During a private event with leading pulmonologists and other IPF-treating physicians at CHEST 2016, Giulia C. Kennedy, Ph.D., chief scientific officer of Veracyte, presented data demonstrating the clinical validity of the Envisia classifier.

 The test was trained on over 350 samples obtained through bronchoscopy from 90 patients participating in the ongoing, prospective 30-site BRAVE study. The researchers then evaluated the clinical validity of the locked 190-gene classifier using an independent set of samples from 49 BRAVE study participants, confirming that Envisia detects UIP vs. non-UIP with high specificity (88 percent).

The classifier reported sensitivity of 67 percent, meaning it would be expected to identify nearly two-thirds of UIP cases. These results show high concordance with the presence or absence of a UIP pattern reported on surgical histopathology review by a centralized panel of pathologists with expertise in ILD.

"The availability of a clinically validated genomic classifier for patients with suspected IPF will be a significant step forward in ensuring an accurate and timely diagnosis," 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.

"In the current diagnostic pathway, an accurate diagnosis may require a surgical lung biopsy to confirm the presence of UIP. This is an invasive, costly and potentially risky procedure."

"Patients with suspected ILD, including IPF, endure a significant delay in diagnosis, frequent misdiagnosis and often require invasive procedures to get definitive answers," said Gregory Cosgrove, M.D., associate professor of medicine, National Jewish Health and CMO of the Pulmonary Fibrosis Foundation (PFF).

Veracyte will begin making the Envisia Genomic Classifier available to a limited number of institutions in December, as the company builds the clinical evidence it believes will be necessary to support Medicare reimbursement.

This strategic roll out follows the successful commercialization and reimbursement approach that the company used with its tests in thyroid cancer (Afirma® Gene Expression Classifier) and lung cancer (Percepta® Bronchial Genomic Classifier).

"Veracyte now has three commercial products in major disease markets that we estimate to be more than $2 billion," said Bonnie Anderson, president and chief executive officer of Veracyte.