Bronchial Genomic Classifier May Improve Lung Cancer Detection

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A gene-expression classifier may improve the diagnostic performance of bronchoscopy in detecting pulmonary lesions that are suggestive of lung cancer, according to a recent study published in The New England Journal of Medicine.

Researchers led by Gerard Silvestri, MD, of the Medical University of South Carolina looked at 639 patients who were enrolled in 28 centers as part of the prospective AEGIS-1 and AEGIS-2 trials. Among them, 43 percent had undergone bronchoscopic examinations that were nondiagnostic for lung cancer, leading to invasive procedures after bronchoscopy in 35 percent with benign lesions.

They studied a gene-expression classifier in epithelial cells that was collected from normal-appearing mainstem bronchus in order to assess the probability of lung cnacer.

The researchers found that, in AEGIS-1, the classifier had a sensitivity of 88 percent and a specificity of 47 percent. In AEGIS-2, it had a sensitivity of 89 percent and a specificity of 47 percent.

Combination bronchoscopy and classifier had a sensitivity of 96 percent in AEGIS-1 and 98 percent in AEGIS-2. Among 101 patients who had intermediate pretest probability of cancer, negative predictive value of the classifier was 91 percent in those with nondiagnostic bronchoscopic examination.

“In intermediate-risk patients with a nondiagnostic bronchoscopic examination, a negative classifier score provides support for a more conservative diagnostic approach,” the authors concluded.

Variants of gene SLCO2B1 associated with progression and survival among patients undergoing ADT for
Gene-expression classifier may improve the diagnostic performance of bronchoscopy in detecting lung cancer.
We sought to validate a bronchial-airway gene-expression classifier that could improve the diagnostic performance of bronchoscopy.

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