Multi-Omic Molecular Profiling of Lung Cancer in Chronic Obstructive Pulmonary Disease
- Brian J. Sandri1,*,
- Adam Kaplan2,*,
- Shane W Hodgson3,
- Mark Peterson1,
- Svetlana Avdulov1,
- LeeAnn Higgins4,
- Todd Markowski4,
- Ping Yang5,
- Andrew H. Limper6,
- Timothy J. Griffin4,
- Peter Bitterman1,
- Eric F. Lock2 and
- Chris H. Wendt1,5
- 1Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, MN, 55455, U.S.
- 2Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, 55455, U.S.
- 3Pulmonary, Allergy, Critical Care, and Sleep Medicine, Veterans Affairs Medical Center, Minneapolis, MN, 55455, U.S.
- 4Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, U.S.
- 5Division of Epidemiology, Mayo Clinic, Rochester, MN, 55902, U.S.
- 6Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, 55902, U.S.
- Chris Wendt, MD, 1 Veterans Drive, Minneapolis, MN 55417. E-mail: wendt005{at}umn.edu
* Equally contributing first authors
Abstract
Introduction: Chronic obstructive pulmonary disease (COPD) is a known risk factor for developing lung cancer but the underlying mechanisms remain unknown. We hypothesize that the COPD stroma contains molecular mechanisms supporting tumorigenesis.
Materials/Methods: We conducted an unbiased multi-omic analysis to identify gene expression patterns that distinguish COPD stroma in patients with or without lung cancer. We obtained lung tissue from patients with COPD and lung cancer (tumour and adjacent non-malignant tissue) and those with COPD without lung cancer for proteomic and mRNA (cytoplasmic and polyribosomal) profiling. We used the joint and individual variation explained (JIVE) method to integrate and analyze across the three datasets.
Results: JIVE identified eight latent patterns that robustly distinguished and separated the three groups of tissue samples (tumour, COPD adjacent and COPD control). Predictive variables that associated with the tumour, compared to adjacent stroma, were mainly represented in the transcriptomic data, whereas, predictive variables associated with adjacent tissue compared to controls was represented at the translatomic level. Pathway analysis revealed extracellular matrix and PI3K-Akt signaling pathways as important signals in the tumour adjacent stroma.
Conclusion: Multi-omic approach distinguishes tumour adjacent stroma in lung cancer and reveals two stromal expression patterns associated with cancer.
Abstract
A multi-omic approach identified gene expression programmes distinguishing COPD lung stroma associated with lung cancer
Footnotes
This manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.
Conflict of interest: Dr. Sandri has nothing to disclose.
Conflict of interest: Mr. Kaplan has nothing to disclose.
Conflict of interest: Mr. Hodgson has nothing to disclose.
Conflict of interest: Mr. Peterson has nothing to disclose.
Conflict of interest: Dr. Avdulov has nothing to disclose.
Conflict of interest: Dr. Higgins has nothing to disclose.
Conflict of interest: Mr. Markowski has nothing to disclose.
Conflict of interest: Dr. Yang has nothing to disclose.
Conflict of interest: Dr. Limper has nothing to disclose.
Conflict of interest: Dr. Griffin has nothing to disclose.
Conflict of interest: Dr. Bitterman has nothing to disclose.
Conflict of interest: Dr. Lock has nothing to disclose.
Conflict of interest: Dr. Wendt has nothing to disclose.
This is a PDF-only article. Please click on the PDF link above to read it.
- Copyright ©ERS 2018