Profiling the Pancreatic Cancer Secretome with Metabolic Glycoengineering

Profiling the secretome for biomarkers offers an attractive, minimally invasive strategy to detect and monitor cancer. Several challenges, however, must be overcome including the broad dynamic range of biomolecules in the secretome and the requirement for selective detection of tumor-associated markers. Here, we employed a metabolic glycoengineering (MGE) strategy, using 1,3,4-O-Bu3ManNAz, an azido-tagged, bioorthogonal metabolic precursor of sialic acid, to label the glycome of pancreatic near-normal and cancer cells to improve conventional LC-MS/MS proteomics-based biomarker discovery. By using this "MGE-LC-MS/MS" approach that incorporates MGE-enrichment into conventional LC-MS/MS proteomics, we identified several unique proteins from the secretomes of cancer cells evaluated in vitro. In addition to proteins known to be secreted, we identified several putatively intracellular, non-N-glycosylated proteins such β-glucocerebrosidase and paladin linked to pancreatic cancer (PC) as well as proteins associated with extracellular vesicles (EV) in PC such as DCTPP1. The identification of EV-associated proteins was consistent with our discovery that ManNAc analogs used in the MGE-LC-MS/MS workflow enhance EV production, creating a more complete secretome profile of PC cells. Pointing towards clinical relevance, we used MGE-LC-MS/MS to enrich PC-derived glycoproteins from plasma harvested from mice bearing xenografted human pancreatic tumors, unambiguously demonstrating that this approach can interrogate the secretomes of cancer cells for biomarker discovery. Finally, we discovered that MGE dramatically improved the production of EVs, which both aids in biomarker discovery (this study) and holds potential to facilitate biomanufacturing of these nascent drugs.