Extracellular vesicle-based assay for detecting metastases and dynamic monitoring of prostate cancer.

Abstract

182 Background: There is an unmet need to develop a noninvasive assay to detect the metastatic progression of prostate cancer (PCa) patients for timely treatment. We aim to optimize an extracellular vesicle (EV)-based Digital Scoring Assay to reflect the disease status of PCa. Methods: PCa EV Digital Scoring Assay integrated a PCa-derived EV purification device (i.e., EV Click Chip) and downstream reverse-transcription droplet digital PCR for transcriptomic profiling of PCa-derived EVs. The performance of the Assay for purifying PCa-derived EVs was assessed using artificial plasma samples with spiked EVs. In parallel, a panel of 11 PCa-relevant mRNA markers were selected and validated. PCa EV Digital Scoring Assay in conjugation with the 11-gene panel was tested using plasma from 20 localized, 20 metastatic PCa patients, and serial plasma from three PCa patients. mRNA expressions were computed using weighted Z score method. ROC analysis was utilized to distinguish localized from metastatic PCa. Results: After optimization, the capability of purifying PCa-derived EVs from artificial plasma samples by EV Click Chips (92.5 ± 5.4%) outperformed ultracentrifugation or precipitation methods (41.0 ± 3.2%; 34.9± 2.2%; P < 0.001). Eleven genes, i.e., ACP3, FOLH1, HOXB13, KLK2, KLK3, KLK4, MSMB, RLN1, SLC45A3, STEAP2, and TMPRSS, were selected using The Tissue Atlas and a gene set from a blood-based study as our panel. Strong signals of the panel were detected from three PCa cells (LNCaP, C4-2B, and 22Rv1) and their derived EVs, while rare signals were detected in either WBCs or EVs purified from male healthy donor. In the clinical study, the Z scores calculated from the selected 11-gene panel, were significantly higher in 20 metastatic PCa than 20 localized PCa with an area under the ROC curve of 0.88 (95% CI = 0.78-0.98). Lastly, longitudinal analyses of three PCa patients showed that the Z scores can reflect the clinical progression, response, and stable status, even when disease is undetectable by imaging. Conclusions: We develop a sensitive PCa EV Digital Scoring Assay to distinguish metastatic PCa and reveal the dynamic disease states in a noninvasive manner. This assay holds the potential to augment current imaging tools and tests for timely detection of PCa progression and improve care for patients.