Mass spectrometry-based proteomic analyses of the cell surface of lung cancer reveals candidate biomarkers of disease and therapeutic response

A114 Introduction:Lung cancer is the most commonly diagnosed cancer worldwide and a leading cause of cancer-related deaths. With a 5-year survival rate of 15%, there is a clear need for improved lung cancer therapeutics and diagnostic biomarkers to guide patient treatment options. Towards this end, we have performed mass spectrometry (MS) analysis of lung cancer cell lines and fresh lung tumors together with normal tissue samples for comparison. Analysis focused on the cell surface proteome to identify both therapeutic antibody targets and diagnostic biomarkers.Methodology: To reduce complexity for MS and to enrich for the plasma membrane proteome, a sequential process was incorporated including isolation of viable cells from fresh tissues, enrichment of epithelial cells, capture of plasma membrane proteins, and capture of cysteine tryptic peptides. Peptide ions were identified by liquid chromatography-MS analysis followed by MS/MS analysis and the MS/MS spectra searched against protein databases.Results: Greater than 450 cell surface and secreted proteins were identified as at least 4 fold over-expressed in lung cancer which included known cancer-related proteins such as EGFR, CD44, and CEACAM1. Functional family classification indicated approximately 20% were cell adhesion proteins, 15% were kinase/receptor proteins, and 10% transporter proteins. Correlating MS data with clinical information allowed for evaluation of protein expression across disease staging and histological type. IHC and flow cytometry (FACS) on a subset of targets validated over-expression in lung cancer and expanded the expression profile in lung cancer and other cancers. Additionally, we have utilized the lung cancer cell surface proteome data to identify candidate predictive markers of response in cancer cell lines with varying sensitivity to lung cancer chemotherapeutics and targeted kinase inhibitors. Where feasible, we confirmed differential expression by FACS and examined expression in lung tumors by MS.Conclusions: MS analysis of lung tumors and cancer cell lines revealed more than 450 over-expressed cell surface/secreted proteins which, in association with clinical data, could assist with disease classification. We have utilized MS to demonstrate differential expression of cell surface proteins in lung cancer cell lines displaying varying sensitivities to chemotherapeutics. The results from this study demonstrate that MS Proteomics can be an effective platform for identifying both candidate biomarkers of disease and predictive biomarkers of therapeutic response.