Abstract 1574: Leveraging bronchial airway gene expression to develop a nasal biomarker for lung cancer detection
Authors: Joseph Perez-Rogers, Joseph Gerrein, Christina Anderlind, Xiaohui Xiao, Hanqiao Liu, Rebecca Kusko, Joshua Campbell, Teresa Wang, Yuriy Alekseyev, Gang Liu, Kate Porta, Duncan Whitney, Avrum Spira, Marc Lenburg
Journal: Cancer Research (2015)

Abstract Rationale: Using nasal gene expression to predict the presence of lung cancer would offer a less invasive alternative to diagnostic approaches we have pioneered using bronchial airway epithelial (BE) gene expression. We have previously demonstrated that cytologically normal BE and nasal epithelial (NE) cells harbor gene expression differences that reflect tobacco-related lung disease and that these changes in the BE form the basis of a clinically informative lung cancer biomarker. Given the concordance of BE and NE gene-expression, we hypothesized that gene signatures associated with the presence of lung cancer extend from the airway to the nose and that lung cancer associated BE gene-expression could be leveraged to develop more accurate nasal lung cancer biomarkers. Methods: BE (n = 676) and NE (n = 280) brushings were collected from current and former smokers undergoing bronchoscopy for clinical suspicion of lung cancer. We leveraged two methods to determine the concordance between BE and NE gene-expression signal for cancer. First we applied the bronchial gene expression-based diagnostic test directly to our nasal cohort. Second, we used Gene Set Enrichment Analysis (GSEA) to quantify the relationship between the BE and NE. To develop the nasal gene expression biomarker, we examined the correlation of each gene between the BE and NE. Genes passing our selection criteria were passed to a biomarker discovery pipeline in which we examined the performance of different biomarker algorithm configurations using cross-validation. Results: Direct application of the bronchial airway gene-expression classifier to an independent set of nasal samples (n = 109) resulted in an AUC of 0.67. GSEA revealed high concordance (p&amp;lt;0.001) between cancer-associated nasal and bronchial gene expression profiles from the same patients. 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 BE and NE as well as significance for cancer in both tissues (p&amp;lt;0.05) perform better, on average, than biomarkers built from genes significant for cancer (p&amp;lt;0.05) in the NE alone. Conclusions. We have demonstrated concordance between BE and NE gene expression differences associated with lung cancer. We have further shown that gene expression in the NE reflects the presence of lung cancer and can serve as a diagnostic biomarker. These results demonstrate the feasibility of leveraging cancer-associated gene expression changes throughout the airway to develop a minimally invasive and robust nasal gene expression biomarker for lung cancer diagnosis. Citation Format: Joseph Perez-Rogers, Joseph Gerrein, Christina Anderlind, Xiaohui Xiao, Hanqiao Liu, Rebecca Kusko, Joshua Campbell, Teresa Wang, Yuriy Alekseyev, Gang Liu, Kate Porta, Duncan Whitney, Avrum Spira, Marc Lenburg. Leveraging bronchial airway gene expression to develop a nasal biomarker for lung cancer detection. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1574. doi:10.1158/1538-7445.AM2015-1574