Detection of Tumor-Associated Autoantibodies in the Sera of Pancreatic Cancer Patients Using Engineered MUC1 Glycopeptide Nanoparticle Probes

Abstract

Pancreatic cancer is one of the deadliest cancers worldwide, mainly due to late diagnosis. Therefore, there is an urgent need for novel diagnostic approaches to identify the disease as early as possible. We have developed a diagnostic assay for pancreatic cancer based on the detection of naturally occurring tumor associated autoantibodies against Mucin-1 (MUC1) using engineered glycopeptides on nanoparticle probes. We used a structure-guided approach to develop unnatural glycopeptides as model antigens for tumor-associated MUC1. We designed a collection of 13 glycopeptides to bind either SM3 or 5E5, two monoclonal antibodies with distinct epitopes known to recognize tumor associated MUC1. Glycopeptide binding to SM3 or 5E5 was confirmed by surface plasmon resonance and rationalized by molecular dynamics simulations. These model antigens were conjugated to gold nanoparticles and used in a dot-blot assay to detect autoantibodies in serum samples from pancreatic cancer patients and healthy volunteers. Nanoparticle probes with glycopeptides displaying the SM3 epitope did not have diagnostic potential. Instead, nanoparticle probes displaying glycopeptides with high affinity for 5E5 could discriminate between cancer patients and healthy controls. Remarkably, the best-discriminating probes show significantly better true and false positive rates than the current clinical biomarkers CA19-9 and carcinoembryonic antigen (CEA).

Keywords: autoantibodies; cancer; glycopeptides; gold nanoparticles; molecular recognition.