Distinguishing Smoking-Related Lung Disease Phenotypes Via Imaging and Molecular Features.

Background: Chronic tobacco smoke exposure results in a broad range of lung pathologies including emphysema, airway disease and parenchymal fibrosis as well as a multitude of extra-pulmonary comorbidities. Prior work using CT imaging has identified several clinically relevant subgroups of smoking related lung disease, but these investigations have generally lacked organ specific molecular correlates.

Research Question: Can CT imaging be used to identify clinical phenotypes of smoking related lung disease that have specific bronchial epithelial gene expression patterns to better understand disease pathogenesis?

Study Design And Methods: Using K-means clustering, we clustered participants from the COPDGene study (n = 5,273) based on CT imaging characteristics and then evaluated their clinical phenotypes.

Gene Expression Alterations in the Bronchial Epithelium of e-Cigarette Users.

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.

Assessment of a Highly Multiplexed RNA Sequencing Platform and Comparison to Existing High-Throughput Gene Expression Profiling Techniques.

The need to reduce per sample cost of RNA-seq profiling for scalable data generation has led to the emergence of highly multiplexed RNA-seq. These technologies utilize barcoding of cDNA sequences in order to combine multiple samples into a single sequencing lane to be separated during data processing. In this study, we report the performance of one such technique denoted as sparse full length sequencing (SFL), a ribosomal RNA depletion-based RNA sequencing approach that allows for the simultaneous sequencing of 96 samples and higher.

Detection of early lung cancer among military personnel (DECAMP) consortium: study protocols.

Background: Lung cancer is the leading cause of cancer-related death due in large part to our inability to diagnose it at an early and potentially curable stage. Screening for lung cancer via low dose computed tomographic (LDCT) imaging has been demonstrated to improve mortality but also results in a high rate of false positive tests. The identification and application of non-invasive molecular biomarkers that improve the performance of CT imaging for the detection of lung cancer in high risk individuals would aid in clinical decision-making, eliminate the need for unnecessary LDCT follow-up, and further refine the screening criteria for an already large high-risk population.

Molecular Impact of Electronic Cigarette Aerosol Exposure in Human Bronchial Epithelium.

Little evidence is available regarding the physiological effects of exposure to electronic cigarette (ECIG) aerosol. We sought to determine the molecular impact of ECIG aerosol exposure in human bronchial epithelial cells (HBECs). Gene-expression profiling was conducted in primary grown at air liquid interface and exposed to 1 of 4 different ECIG aerosols, traditional tobacco cigarette (TCIG) smoke, or clean air.