Ehab Billatos1, Samuel Y Ash2, Fenghai Duan3, Ke Xu4, Justin Romanoff3, Helga Marques3, Elizabeth Moses5, MeiLan K Han6, Elizabeth A Regan7, Russell P Bowler8, Stefanie E Mason2, Tracy J Doyle9, Rubén San José Estépar10, Ivan O Rosas9, James C Ross11, Xiaohui Xiao5, Hanqiao Liu5, Gang Liu5, Gauthaman Sukumar12, Matthew Wilkerson12, Clifton Dalgard13, Christopher Stevenson14, Duncan Whitney14, Denise Aberle15, Avrum Spira16, Raúl San José Estépar10, Marc E Lenburg4, George R Washko2. 1. Department of Medicine, Section of Pulmonary and Critical Care Medicine, Boston University, Boston, MA; Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA. Electronic address: ebillato@bu.edu. 2. Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA; Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA. 3. Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI. 4. Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA; Bioinformatics Program, Boston University College of Engineering, Boston, MA. 5. Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA. 6. Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI. 7. Department of Medicine, Division of Rheumatology, National Jewish Health, Denver, CO. 8. Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, National Jewish Health, Denver, CO. 9. Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA. 10. Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA; Department of Radiology, Brigham and Women's Hospital, Boston, MA. 11. Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA. 12. Department of Anatomy, Physiology & Genetics, The American Genome Center, Collaborative Health Initiative Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD. 13. Department of Anatomy, Physiology & Genetics, The American Genome Center, Collaborative Health Initiative Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD. 14. Lung Cancer Initiative at Johnson & Johnson, New Brunswick, NJ. 15. Department of Radiology, University of California at Los Angeles, Los Angeles, CA. 16. Department of Medicine, Section of Pulmonary and Critical Care Medicine, Boston University, Boston, MA; Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA; Lung Cancer Initiative at Johnson & Johnson, New Brunswick, NJ.
Abstract
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. These clusters were replicated in the Detection of Early Lung Cancer Among Military Personnel (DECAMP) cohort (n = 360), and were further characterized using bronchial epithelial gene expression. RESULTS: Three clusters (preserved, interstitial predominant and emphysema predominant) were identified. Compared to the preserved cluster, the interstitial and emphysema clusters had worse lung function, exercise capacity and quality of life. In longitudinal follow-up, individuals from the emphysema group had greater declines in exercise capacity and lung function, more emphysema, more exacerbations, and higher mortality. Similarly, genes involved in inflammatory pathways (tumor necrosis factor-α, interferon-β) are more highly expressed in bronchial epithelial cells from individuals in the emphysema cluster, while genes associated with T-cell related biology are decreased in these samples. Samples from individuals in the interstitial cluster generally had intermediate levels of expression of these genes. INTERPRETATION: Using quantitative CT imaging, we identified three groups of individuals in older ever-smokers that replicate in two cohorts. Airway gene expression differences between the three groups suggests increased levels of inflammation in the most severe clinical phenotype, possibly mediated by the tumor necrosis factor-α and interferon-β pathways. CLINICAL TRIAL REGISTRATION: COPDGene (NCT00608764), DECAMP-1 (NCT01785342), DECAMP-2 (NCT02504697).
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. These clusters were replicated in the Detection of Early Lung Cancer Among Military Personnel (DECAMP) cohort (n = 360), and were further characterized using bronchial epithelial gene expression. RESULTS: Three clusters (preserved, interstitial predominant and emphysema predominant) were identified. Compared to the preserved cluster, the interstitial and emphysema clusters had worse lung function, exercise capacity and quality of life. In longitudinal follow-up, individuals from the emphysema group had greater declines in exercise capacity and lung function, more emphysema, more exacerbations, and higher mortality. Similarly, genes involved in inflammatory pathways (tumor necrosis factor-α, interferon-β) are more highly expressed in bronchial epithelial cells from individuals in the emphysema cluster, while genes associated with T-cell related biology are decreased in these samples. Samples from individuals in the interstitial cluster generally had intermediate levels of expression of these genes. INTERPRETATION: Using quantitative CT imaging, we identified three groups of individuals in older ever-smokers that replicate in two cohorts. Airway gene expression differences between the three groups suggests increased levels of inflammation in the most severe clinical phenotype, possibly mediated by the tumor necrosis factor-α and interferon-β pathways. CLINICAL TRIAL REGISTRATION: COPDGene (NCT00608764), DECAMP-1 (NCT01785342), DECAMP-2 (NCT02504697).
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