A Fluorescence Resonance Energy Transfer-Based Assay Targeting Tumor-Derived Extracellular Vesicles for Highly Specific Pancreatic Cancer Detection

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Pancreatic ductal adenocarcinoma (PDAC) remains one of the most challenging cancers to detect, with significant limitations compared to screening methods for lung, breast, colon, and cervical cancers. Current methods are often plagued by high false-positive rates, necessitating costly, complex, and invasive confirmatory procedures. These challenges arise from the low incidence of PDAC and the need for highly specific and sensitive screening methods. To address these limitations, we developed EV-FRET, a fluorescence resonance energy transfer (FRET)-based assay designed as a rapid, specific, noninvasive, single-step, and low-cost detection method for PDAC. EV-FRET targets extracellular vesicles (EVs), small vesicles secreted by cells, including tumor cells, leveraging their unique size (∼160 nm) and composition for cancer detection. The EV-FRET assay targets two key markers: β-sheet-rich tumorous proteins (pan-cancer) and N -acetyl- d -galactosamine on epithelial cells (pancreas-specific), using thioflavin T and Dolichos biflorus agglutinin conjugated with fluorescein isothiocyanate fluorophores, which generate a FRET signal exclusively in PDAC-derived EVs within the circulating system. EV-FRET demonstrated superior diagnostic accuracy, achieving an area under the curve of 0.95 compared to 0.72 for CA19–9, the current clinical standard for PDAC detection. The assay offers additional advantages, including high reproducibility (coefficient of variation <4%), fast processing time (<15 min), low cost (estimated reagent cost < $15/test), and a simple, single-step operation that eliminates the need for EV enrichment or isolation. By integrating organ-specific markers and a tumor-specific biomarker, EV-FRET provides a scalable and highly specific diagnostic solution for pancreatic cancer. These features position EV-FRET as a transformative tool in cancer diagnostics, with the potential to significantly improve patient outcomes through more precise detection.

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