ArticleBMC medical genomics2015
Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy.
Article in BMC medical genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed, 78 citations in OpenAlex.
- Alterations to the Bronchial Gene-Expression Effects of Tobacco Smoking in People Living With HIV.Journal of acquired immune deficiency syndromes (1999) · 2026Trial
- Effect of Intermittent Versus Continuous Low-Dose Aspirin on Nasal Epithelium Gene Expression in Current Smokers: A Randomized, Double-Blinded Trial.Cancer prevention research (Philadelphia, Pa.) · 2019Trial
- Gene network approach reveals co-expression patterns in nasal and bronchial epithelium.Scientific reports · 2019Trial
- Impact of a bronchial genomic classifier on clinical decision making in patients undergoing diagnostic evaluation for lung cancer.BMC pulmonary medicine · 2016Trial
- Characterization of the cancer-associated field of injury in the nasal epithelium in never-smokers.Lung cancer (Amsterdam, Netherlands) · 2026Article
- Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions.Genome medicine · 2026Article
- Upregulation of an Epithelial miRNA Is Associated with Immune Evasion in Progressive Bronchial Premalignant Lesions.Cancer immunology research · 2026Article
- A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024Review
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Review
- Comparison of Ultra-Magnifying Endocytoscopic and Hematoxylin-Eosin-Stained Images of Lung Specimens.Diagnostics (Basel, Switzerland) · 2023Article
- Smoking modulates different secretory subpopulations expressing SARS-CoV-2 entry genes in the nasal and bronchial airways.Scientific reports · 2022Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Assessment of a Large-Scale Unbiased Malignant Pleural Effusion Proteomics Study of a Real-Life Cohort.Cancers · 2022Article
- Emerging Approaches to Complement Low-Dose Computerized Tomography for Lung Cancer Screening: A Narrative Review.Cureus · 2022Review
- Clinical Study of Aspirin and Zileuton on Biomarkers of Tobacco-Related Carcinogenesis in Current Smokers.Cancers · 2022Article
- The Diagnostic Utility of Cell-Free DNA from Ex Vivo Bronchoalveolar Lavage Fluid in Lung Cancer.Cancers · 2022Article
- Percepta Genomic Sequencing Classifier and decision-making in patients with high-risk lung nodules: a decision impact study.BMC pulmonary medicine · 2022Article
- Article
- Development and validation of a flexible DNA extraction (PAN) method for liquid biopsy of multiple sample types.Journal of clinical laboratory analysis · 2021Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe gene expression profile of cytologically-normal bronchial airway epithelial cells has previously been shown to be altered in patients with lung cancer. Although bronchoscopy is often used for the diagnosis of lung cancer, its sensitivity is imperfect, especially for small and peripheral suspicious lesions. In this study, we derived a gene expression classifier from airway epithelial cells that detects the presence of cancer in current and former smokers undergoing bronchoscopy for suspect lung cancer and evaluated its sensitivity to detect lung cancer among patients from an independent cohort.
methodsWe collected bronchial epithelial cells (BECs) from the mainstem bronchus of 299 current or former smokers (223 cancer-positive and 76 cancer-free subjects) undergoing bronchoscopy for suspected lung cancer in a prospective, multi-center study. RNA from these samples was run on gene expression microarrays for training a gene-expression classifier. A logistic regression model was built to predict cancer status, and the finalized classifier was validated in an independent cohort from a previous study.
resultsWe found 232 genes whose expression levels in the bronchial airway are associated with lung cancer. We then built a classifier based on the combination of 17 cancer genes, gene expression predictors of smoking status, smoking history, and gender, plus patient age. This classifier had a ROC curve AUC of 0.78 (95% CI, 0.70-0.86) in patients whose bronchoscopy did not lead to a diagnosis of lung cancer (n = 134). In the validation cohort, the classifier had a similar AUC of 0.81 (95% CI, 0.73-0.88) in this same subgroup (n = 118). The classifier performed similarly across a range of mass sizes, cancer histologies and stages. The negative predictive value was 94% (95% CI, 83-99%) in subjects with a non-diagnostic bronchoscopy.
conclusionWe developed a gene expression classifier measured in bronchial airway epithelial cells that is able to detect lung cancer in current and former smokers who have undergone bronchoscopy for suspicion of lung cancer. Due to the high NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing in patients whose bronchoscopy is non diagnostic.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.