An extracellular vesicle-based assay for noninvasive detection of metastases and monitoring prostate cancer.

Abstract

e17004 Background: Optimizing outcomes in prostate cancer (PCa) requires timely detect of metastatic progression of prostate cancer patients. We aim to optimize an extracellular vesicle (EV)-based Digital Scoring Assay for detecting PCa metastasis and noninvasive monitoring disease progression. Methods: PCa EV Digital Scoring Assay was developed by combining EV Click Chip, a nanotechnology-enabled microfluidic device for purifying PCa-derived EV and reverse-transcription droplet digital PCR for profiling disease-relevant mRNAs. A panel of 11 PCa-relevant mRNA markers were selected from The Tissue Atlas and a blood-based biomarker study. Plasma samples from 20 localized, 20 metastatic PCa patients, and serial plasma from three PCa patients were used to assess the performance of PCa EV Digital Scoring Assay with the 11-gene panel. mRNA expression of the panel was computed using weighted Z score method. ROC analysis was applied to discriminate localized from metastatic PCa. Results: After optimization, the capability of EV Click Chips (92.5 ± 5.4%) for capturing PCa-derived EVs from artificial plasma samples spiked with EVs outperformed ultracentrifugation (41.0 ± 3.2%) or precipitation methods (34.9± 2.2%; P< 0.001). Strong signals of the 11-gene panel, i.e., ACP3, FOLH1, HOXB13, KLK2, KLK3, KLK4, MSMB, RLN1, SLC45A3, STEAP2, and TMPRSS2, 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 11-gene panel were significantly higher in metastatic than localized PCa with an area under the ROC curve of 0.88 (95% CI: 0.78-0.98). Finally, longitudinal analyses of three PCa patients with clinical progression, response, and stable diseases, respectively, showed the Z scores can precisely reflect the disease status even when it is undetectable by imaging. Conclusions: We demonstrate a sensitive PCa EV Digital Scoring Assay to identify metastatic PCa and dynamically monitor disease states in a noninvasive manner. This assay may augment current imaging tools for timely detection of PCa progression and provide a means to better personalized care.