A Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer

Abstract

Background: Bronchoscopy is frequently nondiagnostic in patients with pulmonary lesions suspected to be lung cancer. This often results in additional invasive testing, although many lesions are benign. We sought to validate a bronchial-airway gene-expression classifier that could improve the diagnostic performance of bronchoscopy.

Methods: Current or former smokers undergoing bronchoscopy for suspected lung cancer were enrolled at 28 centers in two multicenter prospective studies (AEGIS-1 and AEGIS-2). A gene-expression classifier was measured in epithelial cells collected from the normal-appearing mainstem bronchus to assess the probability of lung cancer.

Results: A total of 639 patients in AEGIS-1 (298 patients) and AEGIS-2 (341 patients) met the criteria for inclusion. A total of 43% of bronchoscopic examinations were nondiagnostic for lung cancer, and invasive procedures were performed after bronchoscopy in 35% of patients with benign lesions. In AEGIS-1, the classifier had an area under the receiver-operating-characteristic curve (AUC) of 0.78 (95% confidence interval [CI], 0.73 to 0.83), a sensitivity of 88% (95% CI, 83 to 92), and a specificity of 47% (95% CI, 37 to 58). In AEGIS-2, the classifier had an AUC of 0.74 (95% CI, 0.68 to 0.80), a sensitivity of 89% (95% CI, 84 to 92), and a specificity of 47% (95% CI, 36 to 59). The combination of the classifier plus bronchoscopy had a sensitivity of 96% (95% CI, 93 to 98) in AEGIS-1 and 98% (95% CI, 96 to 99) in AEGIS-2, independent of lesion size and location. In 101 patients with an intermediate pretest probability of cancer, the negative predictive value of the classifier was 91% (95% CI, 75 to 98) among patients with a nondiagnostic bronchoscopic examination.

Conclusions: The gene-expression classifier improved the diagnostic performance of bronchoscopy for the detection of lung cancer. In intermediate-risk patients with a nondiagnostic bronchoscopic examination, a negative classifier score provides support for a more conservative diagnostic approach. (Funded by Allegro Diagnostics and others; AEGIS-1 and AEGIS-2 ClinicalTrials.gov numbers, NCT01309087 and NCT00746759.).

Figures

Figure 1. Classifier Performance in the AEGIS-1 and AEGIS-2 Studies

Shown are receiver-operating-characteristic curves for all patients (gray) and the subset of patients with a nondiagnostic bronchoscopic examination (black) in the AEGIS-1 and AEGIS-2 cohorts. In AEGIS-1, the area under the curve (AUC) was 0.78 (95% CI, 0.73 to 0.83) for all patients and 0.76 (95% CI, 0.68 to 0.83) for patients with a nondiagnostic examination (P = 0.31). In AEGIS-2, the AUC was 0.74 (95% CI, 0.68 to 0.80) and 0.75 (95% CI, 0.68 to 0.82), respectively (P = 0.85). The AUC was also not significantly different for patients with a nondiagnostic examination in the comparison between AEGIS-1 and AEGIS-2 (P = 0.61).

Figure 2. Posttest Probability of Cancer based on the Pretest Probability and the Negative Likelihood Ratio of the Classifier and Bronchoscopy

The posttest probability of lung cancer is shown in relation to the pretest probability based on a non-diagnostic bronchoscopic examination and a negative classifier score (adjusted with the use of the negative likelihood ratio). The curve shows that for patients with a pretest probability of cancer of less than 66% (short vertical line), the posttest probability is less than 10% (broken line) when bronchoscopic findings are negative and the classifier score is negative.