The early detection of pancreatic ductal adenocarcinoma is a complex clinical obstacle yet is key to improving the overall likelihood of patient survival. Current and prospective carbohydrate biomarkers CA19-9 and sTRA are sufficient for surveilling disease progression yet are not approved for delineating PDAC from other abdominal cancers and non-cancerous pancreatic pathologies. To further understand these glycan epitopes, an imaging mass spectrometry approach was utilized to assess the N-glycome of the human pancreas and pancreatic cancer in a cohort of PDAC patients represented by tissue microarrays and whole tissue sections. Orthogonally, these same tissues were characterized by multi-round immunofluorescence which defined expression of CA19-9 and sTRA as well as other lectins towards 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 bi-antennary N-glycans were detected specifically in normal pancreatic islets. N-glycans from CA19-9 expressing tissues tended to be bi-, tri- and tetra-antennary structures with both core and terminal fucose residues and bisecting N-acetylglucosamines. These N-glycans were detected in less abundance in sTRA-expressing tumor tissues, which favored tri- 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-IHC and 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.

Purpose: A subset of pancreatic ductal adenocarcinomas (PDACs) is highly resistant to systemic chemotherapy, but no markers are available in clinical settings to identify this subset. We hypothesized that a glycan biomarker for PDACs called sialylated tumor-related antigen (sTRA) could be used for this purpose. Experimental Design: We tested for differences between PDACs classified by glycan expression in multiple systems: sets of cell lines, organoids, and isogenic cell lines; primary tumors; and blood plasma from human subjects. Results: The sTRA-expressing models tended to have stem-like gene expression and the capacity for mesenchymal differentiation, in contrast to the nonexpressing models. The sTRA cell lines also had significantly increased resistance to seven different chemotherapeutics commonly used against pancreatic cancer. Patients with primary tumors that were positive for a gene expression classifier for sTRA received no statistically significant benefit from adjuvant chemotherapy, in contrast to those negative for the signature. In another cohort, based on direct measurements of sTRA in tissue microarrays, the patients who were high in sTRA again had no statistically significant benefit from adjuvant chemotherapy. Furthermore, a blood plasma test for the sTRA glycan identified the PDACs that showed rapid relapse following neoadjuvant chemotherapy. Conclusions: This research demonstrates that a glycan biomarker could have value to detect chemotherapy-resistant PDAC in clinical settings. This capability could aid in the development of stratified treatment plans and facilitate biomarker-guided trials targeting resistant PDAC.

Multimarker fluorescence analysis of tissue specimens offers the opportunity to probe the expression levels and locations of multiple markers in a single sample. Software is needed to fully capitalize on the advantages of this technology for sensitive, quantitative, and multiplexed data collection. A major challenge has been the automated identification and quantification of signals. We report on the software SignalFinder-IF, which meets that need. SignalFinder-IF uses a newly developed algorithm called Segment-Fit Thresholding, which showed robust performance for automated signal identification in side-by-side comparisons with several current methods. Two utilities provided with SignalFinder-IF enable downstream analyses. The first allows the quantification and mapping of relationships between an unlimited number of markers through user-defined sequences of AND, OR, and NOT operators. The second produces composite pictures of the signals or colocalization analysis on brightfield hematoxylin and eosin images, which is useful for understanding the morphologies and locations of cells meeting specific marker criteria. SignalFinder-IF enables high-throughput, rigorous analyses of whole-slide, multimarker data, and it promises to open new possibilities in many research and clinical applications.

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<p>Gene-expression analyses</p>