The early detection of pancreatic ductal
adenocarcinoma (PDAC) is a complex clinical obstacle yet is key to improving the overall likelihood of patient survival. Current and prospective
carbohydrate biomarkers carbohydrate antigen 19-9 (CA19-9) and sialylated
tumor-related
antigen (sTRA) are sufficient for surveilling
disease progression yet are not approved for delineating PDAC from other abdominal
cancers and noncancerous pancreatic pathologies. To further understand these
glycan epitopes, an imaging mass spectrometry (IMS) approach was used to assess the N-glycome of the human pancreas and
pancreatic cancer in a cohort of patients with PDAC represented by tissue microarrays and whole-tissue sections. Orthogonally, these same tissues were characterized by multiround immunofluorescence that defined expression of CA19-9 and sTRA as well as other
lectins toward
carbohydrate epitopes with the potential to improve PDAC diagnosis. These analyses revealed distinct differences not only in N-
glycan spatial localization across both healthy and diseased tissues but importantly between different
biomarker-categorized tissue samples. Unique sulfated biantennary N-
glycans were detected specifically in normal pancreatic islets. N-
glycans from CA19-9-expressing tissues tended to be biantennary, triantennary, and tetra-antennary structures with both core and terminal
fucose residues and bisecting GlcNAc. These N-
glycans were detected in less abundance in sTRA-expressing
tumor tissues, which favored triantennary and tetra-antennary structures with
polylactosamine extensions. Increased sialylation of N-
glycans was detected in all
tumor tissues. A candidate new
biomarker derived from IMS was further explored by fluorescence staining with selected
lectins on the same tissues. The
lectins confirmed the expression of the
epitopes in
cancer cells and revealed different
tumor-associated staining patterns between
glycans with bisecting GlcNAc and those with terminal GlcNAc. Thus, the combination of
lectin-immunohistochemistry and
lectin-IMS techniques produces more complete information for
tumor classification than the individual analyses alone. These findings potentiate the development of early assessment technologies to rapidly and specifically identify PDAC in the clinic that may directly impact patient outcomes.