Currently, pancreatic cancer is the fourth cause of cancer death. In 2013, it is estimated that approximately 38,460 people will die of pancreatic cancer. Early detection of malignant cyst (pancreatic cancer precursor) is necessary to help prevent late diagnosis of the tumor. In this study, we characterized glycoproteins and non-glycoproteins on pooled mucinous (n=10) and non-mucinous (n=10) pancreatic cyst fluid to identify ‘proteins of interest’ to differentiate between mucinous cyst from non-mucinous cyst and investigate these proteins as potential biomarker targets. An automated multi-lectin affinity chromatography (M-LAC) platform was utilized for glycoprotein enrichment followed by nano-LC-MS/MS analysis. Spectral count quantitation allowed for the identification of proteins with significant differential levels in mucinous cysts from non-mucinous cysts of which one protein (periostin) was confirmed via immunoblotting. To exhaustively evaluate differentially expressed proteins, we used a number of proteomic tools including; gene ontology classification, pathway and network analysis, Novoseek data mining and chromosome gene mapping. Utilization of complementary proteomic tools, revealed that several of the proteins such as mucin 6 (MUC6), bile salt-activated lipase (CEL) and pyruvate kinase lysozyme M1/M2 with significant differential expression have strong association with pancreatic cancer. Further, chromosome gene mapping demonstrated co-expressions and co-localization of some proteins of interest including 14-3-3 protein epsilon (YWHAE), pigment epithelium derived factor (SERPINF1) and oncogene p53.

Cancer-related alterations in protein glycosylation may serve as diagnostic or prognostic biomarkers or may be used for monitoring disease progression. Clusterin is a medium abundance, yet heavily glycosylated, glycoprotein that is upregulated in clear cell renal cell carcinoma (ccRCC) tumors. We recently reported that the N-glycan profile of clusterin is altered in the plasma of ccRCC patients. Here, we characterized the occupancy and the degree of heterogeneity of individual N-glycosylation sites of clusterin in the plasma of patients diagnosed with localized ccRCC, before and after curative nephrectomy (n = 40). To this end, we used tandem mass spectrometry of immunoaffinity-enriched plasma samples to analyze the individual glycosylation sites in clusterin. We determined the levels of targeted clusterin glycoforms containing either a biantennary digalactosylated disialylated (A2G2S2) glycan or a core fucosylated biantennary digalactosylated disialylated (FA2G2S2) glycan at N-glycosite N374. We showed that the presence of these two clusterin glycoforms differed significantly in the plasma of patients prior to and after curative nephrectomy for localized ccRCC. Removal of ccRCC led to a significant increase in the levels of both FA2G2S2 and A2G2S2 glycans in plasma clusterin. These changes were further confirmed by lectin blotting of plasma clusterin. It is envisioned that these identified glycan alterations may provide an additional level of therapeutic or biomarker sensitivity than levels currently achievable by monitoring expression differences alone.

Clear cell renal cell carcinoma is the most prevalent of all reported kidney cancer cases, and currently there are no markers for early diagnosis. This has stimulated great research interest recently because early detection of the disease can significantly improve the low survival rate. Combining the proteome, glycoproteome, and N-glycome data from clear cell renal cell carcinoma plasma has the potential of identifying candidate markers for early diagnosis and prognosis and/or to monitor disease recurrence. Here, we report on the utilization of a multi-dimensional fractionation approach (12P-M-LAC) and LC–MS/MS to comprehensively investigate clear cell renal cell carcinoma plasma collected before (disease) and after (non-disease) curative nephrectomy (n = 40). Proteins detected in the subproteomes were investigated via label-free quantification. Protein abundance analysis revealed a number of low-level proteins with significant differential expression levels in disease samples, including HSPG2, CD146, ECM1, SELL, SYNE1, and VCAM1. Importantly, we observed a strong correlation between differentially expressed proteins and clinical status of the patient. Investigation of the glycoproteome returned 13 candidate glycoproteins with significant differential M-LAC column binding. Qualitative analysis indicated that 62% of selected candidate glycoproteins showed higher levels (upregulation) in M-LAC bound fraction of disease samples. This observation was further confirmed by released N-glycans data in which 53% of identified N-glycans were present at different levels in plasma in the disease vs non-disease samples. This striking result demonstrates the potential for significant protein glycosylation alterations in clear cell renal cell carcinoma cancer plasma. With future validation in a larger cohort, information derived from this study may lead to the development of clear cell renal cell carcinoma candidate biomarkers.

We validated the highly specific, stable and robust 12P-M-LAC platform using human plasma. An improved enrichment of low abundance proteins and glycoproteins with minimum sample loss was achieved demonstrating the suitability of this platform in future biomarker discovery studies.

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