The new findings build on research previously conducted by Dr. Spira and a team from Boston University, which demonstrated that cells in the central bronchial airways of the lung exhibit measurable cancer-associated gene-expression changes due to smoking. These collective genomic alterations comprise a "field of injury," which serves as a biomarker distinguishing ever-smokers with lung cancer from those with benign lung disease, independent of clinical risk factors. The field-of-injury innovation is the foundation for Veracyte's Percepta Bronchial Genomic Classifier, which evaluates patient samples obtained by bronchoscopy to improve lung cancer screening and diagnosis.
In the current study, Boston University researchers sought to expand upon the earlier findings, evaluating whether smoking produces similar field-of-injury genomic alterations in the cells lining human nasal passages. The investigators prospectively collected nasal epithelial cells from 505 current and former smokers undergoing diagnostic evaluation for pulmonary lesions in two, prospective multi-center clinical studies (AEGIS-1 and AEGIS-2).
After one year of follow up, researchers identified differentially expressed genes in the nasal epithelium of AEGIS-1 patients who were diagnosed with lung cancer vs. those with benign disease (p<0.001). They also confirmed significant consistency between the field-of-injury alterations found in paired samples of cells from the lower and upper airways (i.e., bronchial and nasal epithelial cells).
Finally, the researchers determined that a non-invasive nasal classifier comprising 30 of the most differentially expressed genes significantly enhanced the ability of a risk model based only on clinical factors (e.g., age, smoking status) to predict lung-cancer status.
"The landmark findings published today reinforce the validity and clinical importance of the field-of-injury innovation underlying our Percepta Bronchial Genomic Classifier, which has already demonstrated the ability to reduce ambiguity in lung cancer diagnosis and thereby reduce the unnecessary, invasive and costly procedures that can result," said Bonnie Anderson, chairman of the board, president and chief executive officer of Veracyte. "We look forward to exploring the potential for a non-invasive genomic nasal classifier that can expand upon the meaningful benefits provided by the Percepta test."
The Veracyte Percepta classifier detects field of injury-associated genomic changes in cells collected by bronchoscopy, a minimally invasive procedure, to determine the likelihood that a lung nodule or lesion detected by CT scan is cancerous. In the AEGIS-1 and -2 studies, Percepta combined with bronchoscopy demonstrated a cancer-detection sensitivity of 97 percent as compared to 75 percent sensitivity for bronchoscopy alone. The test has demonstrated a high degree of accuracy (negative predictive value of 91 percent) in identifying patients at low (<10 percent) risk of cancer, allowing these patients to be monitored noninvasively with imaging and avoid further unnecessary, risky and costly invasive procedures.
Veracyte acquired rights to the Percepta classifier and its underlying technology and intellectual property through its 2014 acquisition of Allegro Diagnostics.
Veracyte (NASDAQ: VCYT) is a leading genomic diagnostics company that is fundamentally improving patient care by resolving diagnostic uncertainty with evidence that is trustworthy and actionable. The company's products uniquely combine genomic technology, clinical science and machine learning to provide answers that give physicians and patients a clear path forward without risky, costly surgery that is often unnecessary. Since its founding in 2008, Veracyte has commercialized three genomic tests, which are transforming the diagnosis of thyroid cancer, lung cancer and idiopathic pulmonary fibrosis and collectively target a $2 billion market opportunity. Veracyte is based in South San Francisco, California. For more information, please visit www.veracyte.com and follow the company on Twitter (@veracyte).
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