Bronchial washings for lung cancer diagnostic
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ABSTRACT:
PROVIDER: PRJEB38273 | ENA |
REPOSITORIES: ENA
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Project description:BACKGROUND: In patients with suspicious pulmonary lesions, bronchoscopy is frequently non-diagnostic. This often results in additional invasive testing, including surgical biopsy, although many patients have benign disease. We sought to validate an airway gene-expression classifier for lung cancer in patients undergoing diagnostic bronchoscopy. METHODS: Two multicenter prospective studies (AEGIS 1 and 2) enrolled 1357 current or former smokers undergoing bronchoscopy for suspected lung cancer. Bronchial epithelial cells were collected from normal appearing mucosa in the mainstem bronchus during bronchoscopy. Patients without a definitive diagnosis from bronchoscopy were followed for 12 months. A gene-expression classifier was used to assess the risk of lung cancer, and its performance was evaluated. RESULTS: A total of 298 patients from AEGIS 1 and 341 from AEGIS 2 met criteria for analysis. Bronchoscopy was non-diagnostic for cancer in 272 of 639 patients (43%; 95%CI, 39-46%). The gene expression classifier correctly identified 431 of 487 patients with cancer (89% sensitivity; 95%CI, 85-91%), and 72 of 152 patients without cancer (47% specificity; 95%CI, 40-55%). The combination of the classifier and bronchoscopy had a sensitivity of 97% (95%CI, 95-98%), which was independent of size, location, stage, and histological subtype of lung cancer. In patients with an intermediate pre-test risk (10-60%) of lung cancer, the NPV of the classifier was 91% (95%CI 75-98%). CONCLUSIONS: In patients with an intermediate risk of lung cancer and a non-diagnostic bronchoscopy, a gene-expression classification of “low-risk” warrants consideration of a more conservative diagnostic approach that could reduce unnecessary invasive testing in patients with benign disease. 680 CEL files from 639 BEC specimens, 152 benign and 487 malignant samples
2015-05-17 | E-GEOD-66499 | biostudies-arrayexpress
Project description:Introduction: Lung cancer screening by computed tomography (CT) reduces mortality but exhibited high false-positive rates. We established a diagnostic classifier combining chest CT features with bronchial genomics. Materials and Methods: Patients with CT-detected suspected lung cancer were enrolled. The sample collected by bronchial brushing was used for RNA sequencing. R software was applied to build the model. Results: A total of 283 patients, including 183 with lung cancer and 100 with benign lesions, were included. When incorporating genomic data with radiological characteristics, the advanced model yielded 0.903 AUC with 81.1% NPV. Moreover, the classifier performed well regardless of lesion size, location, stage, histologic type, or smoking status. Pathway analysis showed enhanced epithelial differentiation, tumor metastasis, and impaired immunity were predominant in smokers with cancer, whereas tumorigenesis played a central role in non-smokers with cancer. Apoptosis and oxidative stress contributed critically in metastatic lung cancer; by contrast, immune dysfunction was pivotal in locally advanced lung cancer. Conclusions: We devised a minimal-to-noninvasive, efficient diagnostic classifier for smokers and non-smokers with lung cancer, which provides evidence for different mechanisms of cancer development and metastasis associated with smoking. A negative classifier result will help the physician make conservative diagnostic decisions.
2021-02-18 | GSE166926 | GEO
Project description:BACKGROUND: In patients with suspicious pulmonary lesions, bronchoscopy is frequently non-diagnostic. This often results in additional invasive testing, including surgical biopsy, although many patients have benign disease. We sought to validate an airway gene-expression classifier for lung cancer in patients undergoing diagnostic bronchoscopy. METHODS: Two multicenter prospective studies (AEGIS 1 and 2) enrolled 1357 current or former smokers undergoing bronchoscopy for suspected lung cancer. Bronchial epithelial cells were collected from normal appearing mucosa in the mainstem bronchus during bronchoscopy. Patients without a definitive diagnosis from bronchoscopy were followed for 12 months. A gene-expression classifier was used to assess the risk of lung cancer, and its performance was evaluated. RESULTS: A total of 298 patients from AEGIS 1 and 341 from AEGIS 2 met criteria for analysis. Bronchoscopy was non-diagnostic for cancer in 272 of 639 patients (43%; 95%CI, 39-46%). The gene expression classifier correctly identified 431 of 487 patients with cancer (89% sensitivity; 95%CI, 85-91%), and 72 of 152 patients without cancer (47% specificity; 95%CI, 40-55%). The combination of the classifier and bronchoscopy had a sensitivity of 97% (95%CI, 95-98%), which was independent of size, location, stage, and histological subtype of lung cancer. In patients with an intermediate pre-test risk (10-60%) of lung cancer, the NPV of the classifier was 91% (95%CI 75-98%). CONCLUSIONS: In patients with an intermediate risk of lung cancer and a non-diagnostic bronchoscopy, a gene-expression classification of “low-risk” warrants consideration of a more conservative diagnostic approach that could reduce unnecessary invasive testing in patients with benign disease.
2015-05-17 | GSE66499 | GEO
Project description:Prior microarray studies of smokers at high risk for lung cancer have demonstrated that heterogeneity in bronchial airway epithelial cell gene expression response to smoking can serve as an early diagnostic biomarker for lung cancer. This study examines the relationship between gene expression variation and genetic variation in a central molecular pathway (NRF2-mediated antioxidant response) associated with smoking exposure and lung cancer. We assessed global gene expression in histologically normal airway epithelial cells obtained at bronchoscopy from smokers who developed lung cancer (SC, n=20), smokers without lung cancer (SNC, n=24), and never smokers (NS, n=8). Functional enrichment showed that the NRF2-mediated antioxidant response pathway differed significantly among these groups. Keywords: Global mRNA expression profiling 21 total arrays (20 unique patients) run on total RNA obtained from Bronchial Epithelium of Smokers with Lung Cancer 30 total arrays (24 unique patients) run on total RNA obtained from Bronchial Epithelium of Smokers without Lung Cancer 9 total arrays (8 unique patients) run on total RNA obtained from Bronchial Epithelium of Never Smokers
2010-09-30 | E-GEOD-19027 | biostudies-arrayexpress
Project description:Liquid Application Dosing Alters the Physiology of Air-Liquid Interface Primary Bronchial Epithelial Cell/Lung Fibroblast Co-Cultures and In Vitro Testing Relevant Endpoints
2023-08-08 | GSE198884 | GEO
Project description:RNA was obtained from histologically normal bronchial epithelium of smokers during time of clinical bronchoscopy from relatively accessible airway tissue. Gene expression data from smokers with lung cancer was compared with samples from smokers without lung cancer. This allowed us to generate a diagnostic gene expression profile that could distinguish the two classes. This profile could provide additional clinical benefit in diagnosing cancer amongst smokers with suspect lung cancer. Keywords: Disease state analysis
2006-02-27 | GSE4115 | GEO
Project description:Prior microarray studies of smokers at high risk for lung cancer have demonstrated that heterogeneity in bronchial airway epithelial cell gene expression response to smoking can serve as an early diagnostic biomarker for lung cancer. This study examines the relationship between gene expression variation and genetic variation in a central molecular pathway (NRF2-mediated antioxidant response) associated with smoking exposure and lung cancer. We assessed global gene expression in histologically normal airway epithelial cells obtained at bronchoscopy from smokers who developed lung cancer (SC, n=20), smokers without lung cancer (SNC, n=24), and never smokers (NS, n=8). Functional enrichment showed that the NRF2-mediated antioxidant response pathway differed significantly among these groups. Keywords: Global mRNA expression profiling
2010-10-01 | GSE19027 | GEO
Project description:We have previously shown that gene-expression alterations in cytologically normal appearing bronchial epithelial cells can be used as a biomarker for lung cancer detection in smokers (Whitney et al., BMC Medical Genomics 2015; Silvestri et al., NEJM 2015). In this study, we have established that there are also alterations in bronchial microRNA-expression of smokers with lung cancer. Importantly, the performance of an existing bronchial mRNA-biomarker has been improved by integrating microRNA with mRNA expression.
2018-03-08 | GSE93284 | GEO
Project description:A description of the transcriptome of human bronchial epithelium should provide a basis for studying lung diseases including cancer. We demonstrate here that minute epithelial specimens obtained by bronchial brushings afford reliable profiling by serial analysis of gene expression (SAGE) leading to lung gene discovery. We have deduced global gene expression profiles of bronchial epithelium and lung parenchyma, based upon a vast data set of nearly two million sequence tags from 21 SAGE libraries generated from individuals with a history of smoking. Cluster and linear regression analysis demonstrate the repeatability and reproducibility of bronchial SAGE libraries, and suggest that the transcriptome of the bronchial epithelium is distinct from that of lung parenchyma and other tissue types. This distinction is highlighted by the abundant expression of signature genes that reflect tissue-specific and region-specific functions. Through our analysis we have identified novel bronchial-enriched genes and a novel transcript variant for surfactant, pulmonary-associated protein B in lung parenchyma. Conspicuously, gene expression associated with ciliogenesis is evident in bronchial epithelium. Additionally, it is noted that a large number of unmapped tags awaits further investigation. This study represents a comprehensive delineation of the bronchial and parenchyma transcriptomes, identifying more than 20,000 known and hypothetical genes expressed in the human lung, constituting one of the largest human SAGE studies reported to date. Keywords: SuperSeries This reference Series links data in the following related Series: GSE3707 Expression profiling of bronchial epithelium GSE3708 Expression profiling of normal lung parenchyma
2006-11-20 | E-GEOD-3754 | biostudies-arrayexpress
Project description:A description of the transcriptome of human bronchial epithelium should provide a basis for studying lung diseases including cancer. We demonstrate here that minute epithelial specimens obtained by bronchial brushings afford reliable profiling by serial analysis of gene expression (SAGE) leading to lung gene discovery. We have deduced global gene expression profiles of bronchial epithelium and lung parenchyma, based upon a vast data set of nearly two million sequence tags from 21 SAGE libraries generated from individuals with a history of smoking. Cluster and linear regression analysis demonstrate the repeatability and reproducibility of bronchial SAGE libraries, and suggest that the transcriptome of the bronchial epithelium is distinct from that of lung parenchyma and other tissue types. This distinction is highlighted by the abundant expression of signature genes that reflect tissue-specific and region-specific functions. Through our analysis we have identified novel bronchial-enriched genes and a novel transcript variant for surfactant, pulmonary-associated protein B in lung parenchyma. Conspicuously, gene expression associated with ciliogenesis is evident in bronchial epithelium. Additionally, it is noted that a large number of unmapped tags awaits further investigation. This study represents a comprehensive delineation of the bronchial and parenchyma transcriptomes, identifying more than 20,000 known and hypothetical genes expressed in the human lung, constituting one of the largest human SAGE studies reported to date. This SuperSeries is composed of the SubSeries listed below.
2006-11-21 | GSE3754 | GEO