Extracellular vesicles (EVs) are lipid nano-to-micro-sized vesicles increasingly identified as valuable liquid biopsy tools for medical applications. However, the heterogeneity of cargo and the lack of convenient quantification methods to characterize EVs pose challenges in identifying vesicles with specific markers. In this study, we show the isolation, characterization, detection, and quantification of a cancer-specific marker, Trop2, on circulating extracellular vesicles in serum (EV-Trop2). This work combines the unique advantages of our user-friendly isolation method with serum diagnostics to identify high-risk prostate cancer cases and predict recurrence after prostate surgery. To our knowledge, this is the first demonstration to isolate and quantify EV-Trop2 from prostate cancer patient serum to study its analytical validity and potential clinical utility as an EV-based liquid biopsy. Initial study with patient serum samples from three clinical groups: high-risk prostate cancer (n = 22), low-risk prostate cancer (n = 23), and cancer-free groups (n = 21), demonstrates the potential of this approach in distinguishing prostate cancer aggressiveness. We observed significantly different levels of EV-Trop2 expression between the high-risk and low-risk patient groups (p = 0.0015), and between high-risk patient and cancer-free groups (p < 0.0001). Furthermore, employing machine learning algorithms, EV-Trop2 was shown to enhance classifier metrics across the three sample groups, aiding both in risk stratification and predicting recurrence post-prostatectomy. The availability of such tool could have a broad impact across multiple cancers by enabling minimally invasive liquid biopsy sampling.

U.D. is a co-founder of and has an equity interest in: (i) Vetmotl Inc., (ii) LevitasBio, (iii) Hermes Biosciences. His interests were reviewed and managed in accordance with his institutional conflict-of-interest policies.

This work was supported by the Stanford Clinical and Translational Science Award (CTSA) to Spectrum (UL1TR003142) and the SPARK at Stanford Program (through Weston-Havens Foundation). The CTSA program is led by the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work was also partly supported by NIH grant R01EB029805. P.D.S. acknowledges support from the James D. Plummer Graduate Fellowship, EDGE Doctoral Fellowship Program, Summer First Fellowship Program, Dean's Office of the Stanford School of Engineering, Cancer Imaging & Early Detection Award, Canary-ACED Graduate Fellowship, and Stanford Bio-X Interdisciplinary Initiatives Program (IIP) Seed Grant. M.O.O. and E.C.H. acknowledge partial support from the SPARK at Stanford Program (through Weston-Havens Foundation) and NIH grant R37CA240822. P.D.S. and E.D. would like to thank the Canary CREST Program (NIH R25CA217729). T.S. is supported by the National Institutes of Health/National Cancer Institute (NCI) R37CA240822, R01CA274978, and R01CA244281, as well as the US Army Medical Research Acquisition Activity, No. GRANT13686517. S.L. is supported by the UCLA Jonsson Comprehensive Cancer Center.

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The resource has been continuously reviewed and approved by the Institutional Review Board of Stanford University. The samples have been used in compliance with the IRB approved protocol #59490. All experiments were carried out in accordance with the relevant ethical regulations of Stanford University.

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