Marc E. Lenburg, Ph.D.
Associate Professor of Medicine, Division of Computational Biomedicine
Associate Professor, Bioinformatics Program, College of Engineering
Associate Professor of Pathology
Deputy Director, CTSI Translational Bioinformatics Core
Co-Director, Boston University Microarray Resource
B.A., Biology (summa cum laude),Wesleyan University, Connecticut, 1990
Ph.D.,Biochemistry, University of California, San Francisco, 2000
Genomic approaches to understanding lung disease. Our lab approaches lung disease from a variety of angles, but one unifying theme is our use of comprehensive genome-wide gene-expression profiling (whether using microarray-based technology or now RNAseq) together with rigorous computational data analysis methods to discover unexpected distinctions between disease states that provide us not only with clues as to how disease develops, but also sensitive tools for detecting disease.
The physiologic response to tobacco smoke. As many of our research goals are aimed at improving the treatment of patients with smoking-related lung diseases, we are interested in understanding how the body responds to tobacco smoke, and using this to better understand how tobacco smoke contributes to disease. Using genomic approaches, we have identified smoking-related gene expression changes that occur throughout the respiratory tract and identified a subset that remain altered in people who have quit smoking. These irreversibly changed genes are especially interesting since disease risk remains elevated after smoking cessation. That many of the gene expression changes deep in the airway are also altered in cells that line the nose has led us to explore whether we can combine a simple nose test together with genome-wide approaches to answer basic questions such as: how physiologic responses to tobacco smoke vary amongst people who are exposed to different levels of tobacco smoke (or people who are only exposed to second-hand smoke), if differences in responses between individuals might contribute to differing levels of disease susceptibility, and if other inhaled pollutants cause similar differences in gene expression. This work is supported by grants from the NIEHS.
Detecting lung cancer. Using genomic approaches that allow us to comprehensively identify gene expression differences, we have identified a number of differences between smokers who have lung cancer and others who were thought to potentially have lung cancer but turn out instead to have a benign disease. We detect these expression differences in normal-looking cells from the large airway that are collected during bronchoscopy: a routine clinical procedure that is often employed as an early step in figuring out whether someone has lung cancer. We have shown that we can use a combination of several such genes to make a biomarker that is both sensitive and specific for distinguishing smokers with lung cancer from those with benign disease and that this biomarker is more sensitive than the standard workup done as part of the routine bronchoscopy procedure. This biomarker has been licensed to a company that is seeking to validate its performance and make it available for clinicians to use as an adjunct to bronchoscopy. We are now determining whether these cancer-specific signals can also be detected in samples from the nose and whether there are gene expression differences that occur prior to the development of clinically detectable cancer in the hope that such differences could be used as a biomarker for assessing lung cancer risk. Lung cancer risk assessment could be used to determine which current and former smokers might benefit from increased lung cancer screening, or those who are good candidates for drugs that might prevent lung cancer. This work is supported by grants from the NCI and the Department of Defense.
Assessing and understanding COPD and emphysema. Chronic Obstructive Pulmonary Disease (COPD) and emphysema are debilitating smoking-related lung diseases that often develop over an extended period and can be remarkably different between different patients. Using a cohort of over 200 patients and very similar approaches as our work in smoking and lung cancer, we have begun to identify gene expression differences that occur in the airway in patients with COPD and emphysema. Interestingly, the COPD-related gene-expression differences in the larger airways that we’ve studied are similar to the differences that occur in the small airways and alveolae: the tissues that are thought to be the main sites of disease. Moreover, these gene expression differences are more severe in patients with more severe disease and are diminished following treatment with anti-COPD therapy. These studies open the possibility of being able to molecularly dissect the clinical differences between patients with COPD using airway tissue readily obtained during bronchoscopy, and to develop biomarkers for monitoring a patient’s response to therapy. This work is supported by grants from the NHLBI.
Mechanisms of disease pathogenesis. In addition to developing biomarkers for assessing lung disease in clinical samples, we are also interested in using genome-wide approaches to improve our molecular understanding of lung disease pathogenesis. One strategy that we have used to model disease progression in both emphysema and lung cancer is to perform gene-expression profiling on multiple tissues from the same patient collected from regions of differing disease severity. Using this approach we have identified specific molecular processes involved in tissue remodeling that are specifically altered in regions of more severe emphysema. By combining these data with computational approaches to search databases of drugs, we have identified an existing drug as a potential emphysema therapeutic and validated that this drug reverses aspects of the emphysema gene expression signature and molecular defects in tissue remodeling pathways.
A second approach has involved identifying microRNA expression differences associated with disease. While we are exploring using microRNA (miRNA) expression differences as the basis for biomarkers similar to our gene (mRNA) expression biomarkers, the regulatory function of miRNA makes them especially attractive for understanding the regulation of disease processes. The majority of our miRNA profiling work has been performed using high throughput RNA sequencing technology that has allowed us to develop a comprehensive portrait of all the small RNA that are expressed both in airway and lung tissue and discover a number of new miRNA. We have identified specific miRNA that are important regulators of the response to smoking as well as other miRNA that contribute to airway epithelial cell differentiation and repress aspects of lung carcinogenesis.
A third approach to understanding disease pathogenesis has involved the use of high throughput RNA sequencing to provide a comprehensive genome-wide view of the lung transcriptome at single nucleotide resolution. We are mining these data to identify disease-associated differences in transcript structure, expression of non-coding RNAs, etc. in the hope that they could serve as biomarkers, but more importantly that they might also provide specific clues as to the regulation of processes that contribute to disease pathogenesis.
Our work on the molecular regulation of the response to smoking and lung disease pathogenesis is supported by grants from the NIEHS, NHLBI and the Department of Defense.
Computational tools for clinical genomics. A critical challenge with the genome-wide expression technologies that we use in our research is sifting through the large volumes of data they generate to find disease-associated differences that are informative either for use as biomarkers or for understanding the mechanisms of disease. A large portion of our activity is therefore focused on identifying the computational, statistical, and bioinformatic strategies that are most powerful for doing this. One area of research involves developing approaches to use the large volume of publicly available gene-expression data as a source of knowledge about how genes are coexpressed across diverse conditions; and to develop methods that allow this information to be incorporated into the biomarker discovery process or used to identify biologically related conditions based on their resulting in similar differences in gene expression. While these sorts of methods may have broad applicability beyond the study of lung disease, our hope is that they will enhance our ability to gain useful insights and clinically useful tools for the treatment of lung disease. This work is supported by grants from the NCRR.
Spira, A., J. Beane, V. Shah, K. Steiling, G. Liu, F. Schembri, S. Gilman, Y.-M. Dumas, P. Calner, P. Sebastiani, S. Sridhar, J. Beamis, C. Lamb, T. Anderson, N. Gerry, J. Keane, M. Lenburg, J.S. Brody. 2007. Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer. Nature Med. 13:361-366. PMID: 17334370.
Beane, J.E., P. Sebastiani, G. Liu, J.S. Brody, M.E. Lenburg, A. Spira. 2007. Reversible and Permanent effects of Tobacco Smoke Exposure on Airway Epithelial Gene Expression. Genome Biology. 8: R201. PMID: 17894889.
Beane, J.E., P. Sebastiani, T.H. Whitfield, K. Steiling, Y-M. Dumas, M.E. Lenburg, A. Spira. 2008. A prediction model for diagnosing lung cancer that integrates genomic and clinical features. Cancer Prevention Research. 1:56-64. PMID: 19138936.
Merritt, W., Y. G. Lin, L. Y. Han, A. A. Kamat, W. A. Spannuth, R. Schmandt, D. Urbauer, L. A. Pennacchio, J-F Cheng, A. Zeidan, H. Wang, P. Mueller, M. E. Lenburg, J. W. Gray, S. Mok, M. J. Birrer, G. Lopez-Berestein, R. L. Coleman, M. Bar-Eli, A. K. Sood. 2008. Decreased expression of RNA interference machinery, Dicer and Drosha, is associated with poor outcome in ovarian cancer patients. New England Journal of Medicine. 359:2641-2650. PMID: 19092150.
Schembri, F., S. Sridhar, C. Perdomo, A. Gustafson, X. Zhang, A. Ergun, J. Lu, G. Liu, X. Zhang, J. Bowers, K. Sensinger, J.J. Collins, J. Brody, R. Getts, M.E. Lenburg, A. Spira. 2009. MicroRNAs as modulators of smoking-induced gene-expression changes in human airway epithelium. Proceedings of the National Academy of Sciences. 106:2319-24. PMID: 19168627.
Zhang, X., G. Liu, F. Schembri, X. Zhang, Y.-M. Dumas, E.M. Langer, Y. Alekseyev, G.T. O’Connor, D.R. Brooks, P. Sebastiani, M.E. Lenburg*, A. Spira*. 2010. Similarity and differences in effect of cigarette smoking on gene expression in nasal and bronchial epithelium. Physiological Genomics. 41:1-8. (* contributed equally). PMID: 19952278.
Gustafson, A., R. Soldi, C. Anderlind, M.B Scholand, X. Zhang, D. Walker, A. McWilliams, G. Liu, E. Szabo, M.E. Lenburg, S. Lam, A.H. Bild, A. Spira. 2010. Deregulation of the phosphatidylinositol 3-kinase pathway in the bronchial airway epithelium is an early and reversible event in the development of lung cancer. Science Translational Medicine. 2:26ra25. PMID: 20375364.
Beane, J.E., J. Vick, F. Schembri, C. Anderlind, A.C. Gower, J. Campbell, L. Luo, X. Zhang, J. Xiao, Y.O. Alekseyev, S. Wang, S. Levy, P.P. Massion, M.E. Lenburg, A. Spira. 2011. Characterizing the impact of smoking and lung cancer on the airway transcriptome using RNA-seq. Cancer Prevention Research. 4:803-817. PMID: 21636547.
Campbell, J.D, A. Spira, M.E. Lenburg. 2011. Applying gene-expression microarrays to pulmonary disease. Respirology. 16:407-418. PMID: 21299687.
Gower, A.C., A. Spira, M.E. Lenburg. 2011. Discovering biological connections between experimental conditions based on common patterns of differential gene expression. BMC Bioinformatics. 12:381. PMID: 21951600.
Campbell. J.D., J.E. McDonough, J.E. Zeskind, D.V. Pechkovsky, C.-A. Brandsma, M. Suzuki, J.V. Gosselink, G. Liu, Y.O. Alekseyev, J. Xiao, X. Zhang, S. Hayashi, J.D. Cooper, W. Timens, D.S. Postma, D.A. Knight, M.E. Lenburg*, J.C. Hogg*, A. Spira*. A gene expression signature of emphysema-related lung destruction and its reversal by the tripeptide GHK. Genome Medicine. 4(8):67, Aug 2012. * contributed equally