Extracellular vesicles (EVs) hold great potential as novel biomarkers for minimally invasive detection of early stage tumors, since tumors abundantly secrete EVs that accumulate in the circulation and these EVs can transport factors that regulate tumor initiation, progression, and metastasis. However, most current EV analysis methods require pre-isolation of EVs prior to analysis and are low-throughput and impractical for clinical use. We recently developed a rapid, robust, isolation-free and inexpensive assay that directly quantifies tumor-derived EVs in small volumes (1~5 L) of serum or plasma. In this assay, EVs that bind probes specific for two different EV target proteins produce a distance-regulated nanoplasmon-enhanced scattering (nPES) effect that allows sensitive detection of specific EVs. In a pilot study we used a nPES assay for a pancreatic cancer (PC)- associated EV marker to distinguish PC cases from non-malignant controls (patients with pancreatitis and healthy individuals) with high reproducibility, specificity, and sensitivity. This assay also differentiated PC tumor stages and tumor responses to neoadjuvant, outperforming CA19-9, a biomarker widely used for PC therapy assessment. Our nPES assay platform has multiple features required for research and clinical translation: 1) It is rapid, high-throughput, and inexpensive; 2) it does not employ EV isolation, avoiding a major source of EV assay variation; 3) it robustly and reproducibly quantifies EV biomarkers from small volumes of serum, plasma or urine, allowing its use in longitudinal analysis of mouse models of human disease; and 4) it can be readily adapted to diagnose and monitor cancers that express other EV biomarkers. Based on the success of our pilot study, we propose to develop and validate an automated and highly reproducible nPES EV assay to allow rapid and accurate PC diagnosis in clinical settings. We hypothesize that a nPES-based digital EV reader will equal or outperform the analytical performance of our current manual assay. We will build a diagnostic EV assay model for early PC detection by examining the ability of proteins reported to be enriched on the surface of EVs derived from PC stem cells or PC-initiating cells (e.g., CD44, CD133 and EpCAM) to diagnose patients with early stage PC and to differentiate them from patients with pre-malignant pancreatic lesions, hereditary syndromes or family history of PC, and individuals with normal pancreases. Specifically, we propose to: 1) Development and fabrication of Chip-nPES platform to achieve single EV resolution; 2) automate and refine our nPES-based digital EV reader to enhance assay reliability and reproducibility. We will also select and validate candidate EV capture and detection antibodies for PC diagnosis; 3) establish and evaluate a diagnostic model that integrates EV biomarkers with known cancer-associations; and 4) perform a pre-clinical validation of this assay in a third-party laboratory. The successful results of this work would have a significant translational impact in cancer management, through reliable and accessible screens for early detection of pancreatic cancer.
Extracellular vesicles (EVs) hold great promise as biomarkers for minimally-invasive disease detection, but most current EV analysis methods are low-throughput and require EV pre-isolation steps, which can greatly decrease their reproducibility, and are impractical for clinical use. To address this problem, we have developed a rapid, robust, and isolation-free nanoparticle-based EV assay that directly quantitates tumor-derived EVs in serum or plasma samples to diagnose pancreatic cancer (PC), including early disease, with high reproducibility, sensitivity and specificity. This proposal is designed to automate and further improve the accuracy and reproducibility of our novel assay with a single vesicle resultion for PC diagnosis, and perform analytical validation of this assay according to Clinical and Laboratory Standards Institute guidelines in CLIA- certified laboratory.
