Uncovering the hidden glycan structures in metastatic melanoma

In the search for glycan disease biomarkers current glycoanalytical methods may not be revealing a complete picture of precious biological samples, and we may be missing potentially valuable information on structures that fall outside of current analysis approaches. The separation capabilities of porous graphitized carbon liquid chromatography with electrospray ionization mass spectrometry (PGC-LC-ESI-MS) is a powerful tool for the detailed characterisation and relative quantitation of glycans released from glycoproteins in human cells, tissues and body fluids. However the limitations of this approach have not been fully explored and much of the data analysis can only be achieved by expert manual annotation. This thesis expands on the capabilities of the PGC-LC-ESI-MS glycoanalysis and develops new approaches that facilitate the interpretation of the resultant data to enable the discovery of glycan structures that may be hidden from view. In this thesis, N-linked glycan structures released from proteins were assigned based on PGC elution order, and MS fragmentation patterns in the negative ESI mode. To further validate assignments the released glycans were treated with a full array of exoglycosidase enzymes to confirm monosaccharide linkage and terminal epitopes. Subsequently, the improved PGC-LC-ESI-MS analytical approach was used to fully characterise the N-glycosylation profile of melanoma lymph node tumour tissue and cultured cell samples. Metastasis accounts for the majority of mortality associated with melanoma, as limited treatment options exist for advanced stages of the disease. Alterations in cell surface protein glycosylation, in particular, increase the highly branched N-glycan structures that contribute to the invasive and metastatic potential of melanoma cells. Despite a growing understanding of the role of N-linked oligosaccharides in melanoma biology, there has been little progress in using glycosylation changes as a predictor of prognosis for patients with metastatic melanoma. The global membrane N-glycosylation profile of lymph node melanoma tumour tissue of good and poor prognosis patient samples from stage III and IV patient cohorts was compared. Glycan structures were quantitated by LC-MS before and after selected exoglycosidase combinations to confirm differences in structural features including the degree of branching, sialylation and fucosylation. Over 90 glycan structures were identified, including high mannose, pauci mannose, hybrid and complex type glycans. In addition, the glycosylation profile of a human melanoma cell line was compared to the glycosylation of the known melanoma prognostic marker, cell adhesion glycoprotein MCAM. Here we identified a group of bi-antennary structures with the LacdiNAc epitope and a high abundance of tetra-antennary glycans in the profile of MCAM that were not seen in the total cell membrane profile. By reducing the complexity of glycan pools using specific exoglycosidase enzymes it was shown that the abundance of tri- and tetra-antennary glycans were severely underrepresented in an undigested profile. Therefore a targeted strategy was developed combining PGC-LC-ESI-MS with exoglycosidases to improve the relative quantitation of tri- and tetra-antennary glycan classes. This method was applied to determine if the abundance of tetra antennary N-glycans could be correlated with clinical outcome.The use of this strategy provided additional valuable structural information on the length of poly LacNAc chains present, degree of sialylation, linkage of sialic acid residues and previously unreported epitopes including LacdiNAc carried by cell surface proteins, which may play a role in melanoma metastasis. This is the first in-depth characterisation of melanoma samples enabled by the extension and optimisation of PGC-LC-ESI-MS/MS. Our findings contribute to the understanding of glycosylation alterations in melanoma metastasis and suggest the complementary use of specific glycosylation changes as prognostic markers.