Multi-omic molecular profiling of lung cancer in COPD

Brian J. Sandri, Adam Kaplan, Shane W. Hodgson, Mark Peterson, Svetlana Avdulov, Lee Ann Higgins, Todd Markowski, Ping Yang, Andrew H. Limper, Timothy J. Griffin, Peter Bitterman, Eric F. Lock, Chris H. Wendt

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Chronic obstructive pulmonary disease (COPD) is a known risk factor for developing lung cancer but the underlying mechanisms remain unknown. We hypothesise that the COPD stroma contains molecular mechanisms supporting tumourigenesis. 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 profiling of proteomic and mRNA (both cytoplasmic and polyribosomal). We used the Joint and Individual Variation Explained (JIVE) method to integrate and analyse across the three datasets. JIVE identified eight latent patterns that robustly distinguished and separated the three groups of tissue samples (tumour, adjacent and 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, were represented at the translatomic level. Pathway analysis revealed extracellular matrix and phosphatidylinositol-4,5-bisphosphate 3-kinase-protein kinase B signalling pathways as important signals in the tumour adjacent stroma. The multi-omic approach distinguishes tumour adjacent stroma in lung cancer and reveals two stromal expression patterns associated with cancer.

Original languageEnglish (US)
Article number1702665
JournalEuropean Respiratory Journal
Issue number1
StatePublished - Jul 1 2018

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine


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