: 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).

Detection of Tumor-Associated Autoantibodies in the Sera of Pancreatic Cancer Patients Using Engineered MUC1 Glycopeptide Nanoparticle Probes / Corzana F.; Asin A.; Eguskiza A.; De Tomi E.; Martin-Carnicero A.; Martinez-Moral M.P.; Mangini V.; Papi F.; Breton C.; Oroz P.; Lagartera L.; Jimenez-Moreno E.; Avenoza A.; Busto J.H.; Nativi C.; Asensio J.L.; Hurtado-Guerrero R.; Peregrina J.M.; Malerba G.; Martinez A.; Fiammengo R.. - In: ANGEWANDTE CHEMIE. - ISSN 1521-3773. - ELETTRONICO. - (2024), pp. 0-0. [10.1002/anie.202407131]

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

Nativi C.
Conceptualization
;
2024

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).
2024
0
0
Corzana F.; Asin A.; Eguskiza A.; De Tomi E.; Martin-Carnicero A.; Martinez-Moral M.P.; Mangini V.; Papi F.; Breton C.; Oroz P.; Lagartera L.; Jimenez...espandi
File in questo prodotto:
File Dimensione Formato  
Angewandte Chemie - 2024 - Corzana - Detection of Tumor‐Associated Autoantibodies in the Sera of Pancreatic Cancer Patients.pdf

Accesso chiuso

Tipologia: Pdf editoriale (Version of record)
Licenza: Tutti i diritti riservati
Dimensione 1.55 MB
Formato Adobe PDF
1.55 MB Adobe PDF   Richiedi una copia

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1378072
Citazioni
  • ???jsp.display-item.citation.pmc??? 6
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 12
social impact