Abstract B1-50: Leveraging gene expression in the bronchial airway to develop a nasal biomarker for early detection of lung cancer (2015)

Abstract Rationale: Lung cancer results in five times more deaths per year than car accidents in the United States. Approximately 57% of lung cancers diagnosed this year will be diagnosed at a late stage and these patients will exhibit a 5-year survival rate of only 4%. Annual screening of high-risk current and former smokers by chest-CT can reduce cancer mortality, however this procedure has a 95% false positive rate. It is therefore critical to develop methods to rapidly and accurately determine which patients with nodules on chest CT have lung cancer and potentially spare those with benign disease an unnecessary invasive procedure. We have previously demonstrated that specific gene expression alterations in cytologically normal bronchial epithelial cells from patients with lung cancer can be leveraged to form a clinically informative lung cancer biomarker in the population of patients undergoing bronchoscopy for suspect lung cancer. We hypothesized that there might be similar expression differences in nasal epithelium and that these could form the basis of a less invasive test that could be applied more broadly to individuals with screen detected nodules on chest CT. Methods: Bronchial (n=676) and nasal (n=280) epithelial brushings were collected from current and former smokers undergoing bronchoscopy for clinical suspicion of lung cancer within the AEGIS clinical trial. 271 subjects had matched bronchial and nasal samples. RNA was extracted and hybridized to Affymetrix Human Gene ST 1.0 Arrays. To establish a connection between bronchial and nasal epithelial gene expression signal for cancer, we first applied the bronchial gene expression based diagnostic test, BronchoGen, directly to our nasal cohort. Gene Set Enrichment Analysis was then used to determine the concordance of cancer signal between the bronchial and nasal epithelium. To develop the nasal gene-expression biomarker for lung cancer detection, we examined the correlation of each gene between the bronchial and nasal epithelium as well as the significance of each gene's association with cancer in each tissue. Genes passing our selection criteria were passed to a biomarker discovery pipeline in which we examined the performance of different biomarker algorithm configurations (e.g. feature-selection algorithms, classification algorithms, and other biomarker parameters) using cross-validation. Results: Direct application of BronchoGen to our nasal cohort resulted in an AUC of 0.64 on a set of NE samples (n=110) with a matched bronchial sample in the training set used to develop the test. On an independent set of nasal samples (n=109), BronchoGen achieved an AUC of 0.67. Gene Set Enrichment Analysis revealed high levels of concordance between cancer-associated nasal and bronchial gene expression. Using a cross-validation approach, we found that nasal biomarkers built from sets of genes showing significant correlation (p&amp;lt;0.05) between the bronchial and nasal epithelium as well as significance for cancer in both tissues (p&amp;lt;0.05) perform better, on average, than biomarkers build from genes significant for cancer (p&amp;lt;0.05) in the nasal epithelium alone. Conclusions: Given the larger sample size, more isolated location in the airway, and higher RIN scores that characterize the bronchial cohort, we sought to leverage bronchial airway epithelial gene expression to inform which genes in the nasal epithelium should be indicative of the presence of cancer. We have shown that gene expression in the nasal epithelium reflects the presence of lung cancer and can serve as a diagnostic biomarker. We have further demonstrated concordance between bronchial and nasal airway gene expression differences associated with lung cancer. These results suggest the potential to develop a robust nasal gene expression biomarker for lung cancer diagnosis that leverages cancer-associated gene expression differences occurring at other airway sites. Citation Format: Joseph F. Perez-Rogers, Joseph Gerrein, Christina Anderlind, Rebecca L. Kusko, Joshua D. Campbell, Teresa Wang, Kate Porta, Duncan Whitney, Avrum Spira, Marc Lenburg. Leveraging gene expression in the bronchial airway to develop a nasal biomarker for early detection of lung cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-50.