Pancreatic cancer is one of the deadliest forms of cancer, mainly because it is very hard to detect early. Classic screening methods are unsatisfactory, and current markers often are too non-specific or simply not sensitive enough. Now a technique reported in the journal Angewandte Chemie can significantly increase diagnostic accuracy by specifically identifying certain antibodies in blood samples.
This study is focused on autoantibodies against tumor-associated mucin-1, a protein usually overexpressed in many cancers, including pancreatic cancer. Mucin-1 is a glycosylated protein expressed in gland tissues of epithelial origin and has different glycosylation in normal tissue compared to tumors. This research tested the presence of autoantibodies directed against TA-MUC1 as an unequivocal indicator for pancreatic cancer.
To develop the method, a research team synthesized different glycopeptide fragments of TA-MUC1. The introduction of unnatural modifications increased the likelihood that autoantibody subgroups correlated with pancreatic cancer would be discovered. The synthetic antigens were then immobilized onto gold nanoparticles, generating probes for a serological assay (dot-blot assay). The new diagnostic test was validated using samples from patients diagnosed with pancreatic cancer and a healthy control group. The results showed some nanoparticle probes excellent differentiation between the cancerous and non-cancerous samples, therefore proving superior detection of tumor-associated autoantibodies compared with the existing clinical biomarkers.
The study found that smaller glycopeptide probes representing single epitopes worked better than larger probes that mimicked several epitopes. This not only makes it easier to synthesize but also increases the detection of specific autoantibodies. In particular, a short glycopeptide displaying only a non-natural sugar modification was found to be very effective in the detection of discriminating autoantibodies. Such a novel structure-based method may help significantly enhance the accuracy of early pancreatic cancer diagnosis by refining the selection of subgroups of autoantibodies with higher tumor specificity.
ANI
