Multi-Omic Molecular Profiling of Lung Cancer Risk in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a known risk factor for developing lung cancer suggesting that the COPD stroma contains factors supporting tumorigenesis. Since cancer initiation is complex we used a multi-omic approach 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 (tumor 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 analysis across the three datasets. JIVE identified eight latent patterns that robustly distinguished and separated the three groups of tissue samples. Predictive variables that associated with the tumor, 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. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis revealed extracellular matrix (ECM) and PI3K-Akt signaling pathways as important signals in the pre-malignant stroma. COPD stroma adjacent to lung cancer is unique and differs from non-malignant COPD tissue and is distinguished by the extracellular matrix and PI3K-Akt signaling pathways. Overall design: Polysome-profiling of lung tumor, adjacent non-cancerous lung stroma tissue samples from the same patient compared to patients without lung cancer across a range of forced expiratory volume in one second (FEV1)