A workflow using diaPASEF global quantitative proteomic analysis reveals extracellular vesicle biomarker candidates for non-invasive diagnostics in non-small cell lung cancer

Abstract

Lung cancer is the second most diagnosed cancer in the world. Non-small cell lung cancer is the most common type of lung cancer in the United States. Tissue biopsy is the gold standard for detecting lung cancer but is highly invasive as it necessitates the extraction of a sample of tissue for histologic analysis. It also carries risks of bleeding and/or infection and is inconvenient from a patient perspective. The development of a minimally invasive test, such as one utilizing a blood or urine sample, and capable of providing accurate results for lung cancer detection and/or subtyping, would significantly enhance the clinical landscape and streamline patient care. In this study we utilize A549 and H1299 human lung cancer cell lines, differing in cell type, location within the lung, and genetic composition (Kras & p53 status), and employ diaPASEF for global quantitative proteomic analysis. We demonstrate that extracellular vesicle protein content provides enhanced resolution to differentiate between these two cell lines relative to protein lysate content and reveals alterations in signaling. Protein clusters are identified showing enrichment for distinct biological processes representing multiple gene ontology categories unique to each lung cancer subtype-oxidative phosphorylation, apical junction, and epithelial-mesenchymal transition. We subsequently delineate a short list of urine-detectable protein candidates that is prognostic in a second cohort of lung cancer patients. This list of protein candidates may be useful for the development of a non-invasive test to distinguish between these two subtypes of human lung cancer.