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Background: SARS-CoV-2 infection and disease severity are influenced by viral entry (VE) gene expression patterns in airway epithelium. The similarities and differences of VE gene expression (ACE2, TMPRSS2, and CTSL) across nasal and bronchial compartments has not been fully characterized using matched samples from large cohorts. Results: Gene expression data from 793 nasal and 1,673 bronchial brushes obtained from individuals participating in lung cancer screening or diagnostic workup revealed that smoking was the only clinical factor significantly and reproducibly associated with VE gene expression. ACE2 and TMPRSS2 expression were higher in smokers in the bronchus but not in the nose. scRNA-seq of nasal brushings indicated that ACE2 co-expressed genes were highly expressed in club and C15orf48+ secretory cells while TMPRSS2 co-expressed genes were highly expressed in keratinizing epithelial cells. In contrast, these ACE2 and TMPRSS2 modules were highly expressed in goblet cells in scRNA-seq from bronchial brushings. Cell-type deconvolution of the RNA-seq confirmed that smoking increased the abundance of several secretory cell populations in the bronchus, but only goblet cells in the nose. Conclusions: The association of ACE2 and TMPRSS2 with smoking in the bronchus is due to their high expression in goblet cells which increase in abundance in current smoker airways. In contrast, in the nose these genes are not predominantly expressed in cell populations modulated by smoking. Smoking-induced VE gene expression changes in the nose likely has minimal impact on SARS-CoV-2 infection, but in the bronchus, smoking may lead to higher viral loads and more severe disease.

BACKGROUND: Although e-cigarette (ECIG) use has increased in the United States, their potential health effects remain uncertain. Understanding the effects of tobacco cigarette (TCIG) smoke on bronchial airway epithelial gene expression have previously provided insights into tobacco-related disease pathogenesis. Identifying the impact of ECIGs on airway gene expression could provide insights into their potential long-term health effects. We sought to compare the bronchial airway gene-expression profiles of former TCIG smokers now using ECIGs with the profiles of former and current TCIG smokers. METHODS: We performed gene-expression profiling of bronchial epithelial cells collected from current TCIG smokers (n = 9), current ECIG users who are former TCIG smokers (n = 15), and former TCIG smokers (n = 21). We then compared our findings with previous studies of the effects of TCIG use on bronchial epithelium, as well an in vitro model of ECIG exposure. RESULTS: Among 3,165 genes whose expression varied between the three study groups (q < 0.05), we identified 468 genes altered in ECIG users relative to former smokers (P < .05). Seventy-nine of these genes were up- or down-regulated concordantly among ECIG and TCIG users. We did not detect ECIG-associated gene-expression changes in known pathways associated with TCIG usage. Genes downregulated in ECIG users are enriched among the genes most downregulated by exposure of airway epithelium to ECIG vapor in vitro. CONCLUSIONS: ECIGs induce both distinct and shared patterns of gene expression relative to TCIGs in the bronchial airway epithelium. The concordance of the genes altered in ECIG users and in the in vitro study suggests that genes altered in ECIG users are likely to be changed as the direct effect of ECIG exposure.

Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 SARS-CoV-2), which infects host cells with help from the Viral Entry (VE) proteins ACE2, TMPRSS2, and CTSL1–4. Proposed risk factors for viral infection, as well as the rate of disease progression, include age5,6, sex7, chronic obstructive pulmonary disease7,8, cancer9, and cigarette smoking10–13. To investigate whether the proposed risk factors increase viral infection by modulation of the VE genes, we examined gene expression profiles of 796 nasal and 1,673 bronchial samples across four lung cancer screening cohorts containing individuals without COVID-19. Smoking was the only clinical factor reproducibly associated with the expression of any VE gene across cohorts. ACE2 expression was significantly up-regulated with smoking in the bronchus but significantly down-regulated with smoking in the nose. Furthermore, expression of individual VE genes were not correlated between paired nasal and bronchial samples from the same patients. Single-cell RNA-seq of nasal brushings revealed that an ACE2 gene module was detected in a variety of nasal secretory cells with the highest expression in the C15orf48+ secretory cells, while a TMPRSS2 gene module was most highly expressed in nasal keratinizing epithelial cells. In contrast, single-cell RNA-seq of bronchial brushings revealed that ACE2 andTMPRSS2 gene modules were most enriched in MUC5AC+ bronchial goblet cells. The CTSL gene module was highly expressed in immune populations of both nasal and bronchial brushings. Deconvolution of bulk RNA-seq showed that the proportion of MUC5AC+ goblet cells was increased in current smokers in both the nose and bronchus but proportions of nasal keratinizing epithelial cells, C15orf48+ secretory cells, and immune cells were not associated with smoking status. The complex association between VE gene expression and smoking in the nasal and bronchial epithelium revealed by our results may partially explain conflicting reports on the association between smoking and SARS-CoV-2 infection.

Introduction: Although three randomized control trials have proven mortality benefit of CT lung cancer screening (CTLS)

Background: Diesel engine exhaust (DEE) exposure causes lung cancer, but the molecular mechanisms by which this occurs are not well understood. Objectives: To assess transcriptomic alterations in nasal epithelium of DEE-exposed factory workers to better understand the cellular and molecular effects of DEE. Methods: Nasal epithelial brushings were obtained from 41 diesel engine factory workers exposed to relatively high levels of DEE (17.2–105.4 μg/m3), and 38 unexposed workers from factories without DEE exposure. mRNA was profiled for gene expression using Affymetrix microarrays. Linear modeling was used to identify differentially expressed genes associated with DEE exposure and interaction effects with current smoking status. Pathway enrichment among differentially expressed genes was assessed using EnrichR. Gene Set Enrichment Analysis (GSEA) was used to compare gene expression patterns between datasets. Results: 225 genes had expression associated with DEE exposure after adjusting for smoking status (FDR q

Introduction We have previously shown that gene expression alterations in the cytologically-normal mainstem bronchus can be leveraged as a biomarker for lung cancer detection (Silvestri et al. NEJM 2015), a test that is now used clinically. Extending this approach, we hypothesized that bronchial microRNA (miRNA) expression is altered in patients with lung cancer and that incorporating miRNA expression into the mRNA classifier may improve its performance. Methods Using bronchial brushes collected prospectively from current and former smokers undergoing bronchoscopy for suspect lung cancer across 28 medical centers as part of the AEGIS 1 and 2 clinical trials, we profiled miRNA expression via small RNA sequencing of 341 patients for which gene expression data was also available on the same bronchial brush sample. Patients were followed for up to one year post-bronchoscopy until a final diagnosis was established. 138 patients from AEGIS 1 (88 cancer-positive and 50 cancer-free) served as a discovery set, while other 203 patients from AEGIS 1 and 2 (103 cancer-positive and 100 cancer-free) were used as an independent test set. First, we identified miRNAs whose expression is associated with cancer by linear modeling in the discovery set. We next explored the relationships between the expression of these miRNAs and their predicted mRNA targets. Lastly, using logistic regression, we incorporated a cancer miRNA feature into our bronchial gene-expression classifier (Silvestri et al., NEJM 2015) and validated its performance in the test set. Results We found that expression profiles of 42 miRNAs were associated with cancer status in the discovery set (p Conclusions We have established that there are alterations in miRNA expression in the cytologically normal mainstem bronchus of smokers with lung cancer. Importantly, we demonstrated the potential of these miRNA alterations to improve the performance of an existing bronchial gene expression biomarker for lung cancer detection. Citation Format: Ana Brandusa Pavel, Joshua Campbell, Gang Liu, Sherry Zhang, Hanqiao Liu, Ji Xiao, Kate Porta, Duncan Whitney, Steven Dubinett, David Elashoff, Marc Lenburg, Avrum Spira. microRNA expression in bronchial epithelium for lung cancer detection. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1954.

We have previously shown that gene expression alterations in cytologically normal epithelial cells from the bronchial airway can be used as an early detection biomarker for lung cancer in smokers. We hypothesize that bronchial epithelial expression of microRNAs, as regulators of gene expression, may also be affected by the presence of cancer and may regulate some of these gene expression differences. We propose a novel method to identify microRNAs functionally associated with disease that leverages the relationship between microRNA and mRNA expression by determining the differential connectivity (DC) of microRNA-mRNA association networks between disease and normal states. Bronchial epithelial brushes were collected from 220 former and current smokers who underwent bronchoscopy for suspicion of lung cancer (120 lung cancer patients and 100 healthy controls). For these subjects, we profiled microRNA expression via small RNA sequencing and gene expression via microarray. Each microRNA node is assigned a DC score, which captures the overall difference in the pairwise microRNA-gene correlation strengths between lung cancer and control subjects. We quantify the change in both the directionality and strength of the correlations between a microRNA and the gene nodes. Then, the observed DC scores are compared to the DC scores obtained with permuted class labels to identify microRNAs with signinficant disease-specific differences in microRNA-mRNA connectivity. The proposed DC method identifies 54 microRNAs which are significantly differentially connected in lung cancer cases compared to controls (FDR We propose a novel approach for integrating microRNA and gene expression data to identify disease-associated changes in gene regulation by microRNAs and show that the microRNA-mRNA networks are significantly different between disease and normal states. These data suggest that changes in microRNA expression may drive some of the gene expression alterations observed in the cytologically normal epithelium from the proximal airway of patients with lung cancer and that airway microRNA-mRNA expression changes may ultimately serve as a biomarker for lung cancer detection. Citation Format: Ana Brandusa Pavel, Joshua D. Campbell, Gang Liu, Sherry Zhang, Hanqiao Liu, Lingqi Luo, Ji Xiao, Kate Porta, Duncan Whitney, Steven Dubinett, David Elashoff, Marc E. Lenburg, Avrum Spira. Dysregulation of microRNA-mRNA regulatory networks in the bronchial airway epithelium of smokers with lung cancer. [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 3077. doi:10.1158/1538-7445.AM2015-3077

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 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

Introduction: We have previously shown that smoking creates a “molecular field of injury” throughout the epithelial cells that line the respiratory tract and that gene-expression alterations in the cytologically-normal mainstem bronchus epithelium can serve as an early detection biomarker for lung cancer. We hypothesize that microRNAs (miRNAs) regulate these airway gene expression changes and that miRNA expression differences in this tissue can be used as biomarkers for lung cancer diagnosis. Methods: We profiled miRNA expression via small RNA sequencing in bronchial epithelial brushes collected from the mainstem bronchus of 230 subjects undergoing bronchoscopy for suspect lung cancer and gene expression (mRNA) via microarray for 201 matched samples. Bilal et al. (2013) have shown that incorporating biological knowledge into model building improves prediction. Therefore, we sought to test the hypothesis that including information about the expression levels of the predicted mRNA targets of miRNA may improve miRNA feature selection and aid in interpretation of signatures. We used mirConnX which combines miRNA with mRNA data to create disease-specific, genome-wide regulatory networks. Results: First, we show that there is a miRNA expression signal for cancer: across many combinations of feature selection methods, predictive models and different parameters within 100 bootstraps to predict cancer phenotype, we find the AUC values obtained are consistently higher compared to the random control procedure where we randomly shuffle the class labels (p < 0.001). Second, using a training set of 106 samples, we built networks separately for cancer and non-cancer samples, using 10-fold cross validation in order to determine robust cancer and non-cancer specific features. The disease-state specific networks are then aggregated by taking the overlapping features across the ten folds. Next, we select the non-overlapping features (miRNAs and genes) between cancer and non-cancer as those that capture the difference between the two phenotypes. The selected genes are enriched for relevant cancer related pathways, including KEGG pathways in cancer (p = 0.0003), cell cycle (p = 0.008), WNT signaling (p = 0.0001), basal cell carcinoma (p = 0.023), MAPK signaling pathway (p = 0.024), TGF-beta signaling pathway (p = 0.00014), p53 signaling pathway (p = 0.087) etc. Most importantly, the miRNA features from these disease specific networks are found to have higher predictive power (highest AUC 0.72) compared to all miRNA features (highest AUC 0.57), on a second set of 68 samples in cross-validation. Conclusion: Using novel integrative analysis, we improved miRNA biomarker prediction. This is the first report of cancer-associated miRNA expression differences in cytologically normal bronchial epithelium, for lung cancer diagnosis, and it extends our previous work focused on mRNA biomarkers from this tissue. Citation Format: Ana Pavel, Joshua Campbell, Gang Liu, Sherry Zhang, Hanqiao Liu, Steven Dubinett, David Elashoff, Kate Porta, Duncan Whitney, Marc Lenburg, Avrum Spira. Biomarker development for lung cancer diagnosis using integrative microRNA and gene expression networks. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1485. doi:10.1158/1538-7445.AM2014-1485