Veracyte Announces Publication of Data Demonstrating Potential for Non-Invasive Nasal Test to Improve Lung Cancer Diagnosis
"The data published today provide compelling evidence that molecular biomarkers used to determine lung cancer risk in cells from the bronchial airway could provide similar information as cells obtained from a simple nasal swab. This discovery could offer a method to further reduce the uncertainty, risk and cost associated with the early detection of lung cancer," said
The new findings build on research previously conducted by
In the current study,
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
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.
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