Glycan Motif Profiling Reveals Plasma Sialyl-Lewis X Elevations in Pancreatic Cancers That Are Negative for Sialyl-Lewis A

A patient with an uncertain lesion of the pancreas typically is referred to a specialist for dedicated scans of the pancreas and, if available, additional procedures such as endoscopic imaging with fine-needle aspiration to obtain material for cytology. The diagnostic challenges include differentiating benign from neoplastic conditions and determining the type and potential aggressiveness of a neoplasm (1–4). Based on imaging and biopsy, each condition and type occasionally can mimic others, and obtaining definitive information from biopsy is not always possible (5). Molecular tests hold promise to improve this situation (6), as they could provide objective and detailed information about each patient's condition. But molecular markers to diagnose incipient pancreatic cancer are not available despite decades of research; the current best marker for pancreatic cancer, the CA19–9 test, was discovered in 1979 (7, 8). CA19–9 is elevated in about 75% of pancreatic cancers (9), which is useful for certain purposes, such as monitoring response to treatment, but not for diagnosis. The antigen detected by the CA19–9 test is a glycan, a tetrasaccharide known as the sialyl-Lewis A (sLeA)1 antigen. The discovery that CA19–9 antibodies recognize a glycan (10, 11) further revealed the prevalent nature of glycosylation alterations in cancer. Researchers have uncovered other glycans that show up with high abundance in cancer (12, 13), some of which contribute to cancer cell function and carry information about cell differentiation. Glycans, therefore, have good potential to serve as biomarkers of cancer. But for the diagnosis of pancreatic cancer, glycan-based markers are not yet effective because we do not have markers to detect the cancers that are low in sLeA. A strategy for improving upon the CA19–9 test is to identify biomarkers that are elevated in the patients who are low in sLeA. Previous research suggested that other glycans besides sLeA are overproduced in some cancers that are low in sLeA. All antibodies used in the CA19–9 assays primarily detect the sLeA glycan, which has the sequence Siaα2,3Galβ1,3(Fucα1,4)GlcNAc (where Sia is sialic acid, Gal is galactose, Fuc is fucose, and GlcNAc is N-acetylglucosamine), but some also detect other glycans (14, 15). The several available CA19–9 assays give divergent results for individual patients (15–17), indicating the occasional elevation of the off-target glycans. Additional evidence comes from the DUPAN2 antibody (18), which binds a non-fucosylated relative of sLeA called sialyl-Lewis C (19) (Siaα2,3Galβ1,3GlcNAc). DUPAN2 detection shows elevations in some pancreatic cancers that do not make sLeA (15, 20). The results cited above raise the possibility that knowledge of the differences in specificities between antibodies could guide discovery of glycans that are produced in pancreatic cancers. In theory, one could compare the levels of binding to a patient sample between antibodies and make inferences about the glycans that are present, based on the specificities of the antibodies. For example, if two antibodies recognize overlapping but distinct sets of glycans, and if only one antibody binds glycans in certain samples, then the glycans uniquely recognized by the antibody showing binding could be elevated in the samples. Several previous developments make such an approach possible. For one, we had detailed information about the specificities of glycan-specific antibodies available through glycan array technology. Glycan arrays enable measurements of the binding of antibodies or lectins to hundreds of different glycans in a single experiment (reviewed in references (21, 22)), from which one can derive the specificities of the glycan-binding proteins. We previously developed an algorithm and software to analyze glycan array data (23–25), along with a database of analyzed, publicly available glycan array data (26). With that information, we can select antibodies that target desired glycan motifs and precisely interpret measurements made using the antibodies. Secondly, antibody array technology gave the ability to efficiently test many antibody sandwich assays over multiple patient samples (27). Accordingly, we could acquire measurements from many glycan-binding antibodies over multiple samples and then use the information about the specificities of the antibodies to make predictions about the glycan motifs that are present in each sample. We previously developed an algorithm for that purpose, called motif prediction (28). We applied this approach, which we call motif profiling, to the problem of pancreatic cancer diagnostics, asking whether we could identify glycan motifs that are elevated in the cancer patients who are low in sLeA.