Liquid biopsy of novel extracellular vesicle and particle biomarkers for prostate cancer diagnosis, risk stratification, and metastasis monitoring

Prostate cancer (PCa) remains the most frequently diagnosed malignancy among men worldwide. Despite advances in diagnostics and treatment, current strategies for early detection, risk stratification, and prognosis remain clinically unreliable. Extracellular vesicles and particles (EVPs), which encapsulate biomolecules from their cells of origin and are detectable in biofluids, represent a promising non-invasive source to enhance PCa diagnosis, monitor disease progression, predict metastasis, and support personalised therapies. The aims of this PhD research are threefold: (1) to optimise isolation protocols for four EVP subpopulations, including large extracellular vesicles (lEVs), small extracellular vesicles (sEVs), exomeres, and supermeres; to identify candidate protein biomarkers using label-free proteomics in four PCa cell line models; and to validate these candidates in clinical plasma samples; (2) to explore EVP protein biomarkers in a pilot plasma cohort and to validate selected candidates in an independent clinical cohort to improve early diagnosis and risk stratification; and (3) to establish single-vesicle detection strategies targeting selected EVP-associated proteins for precision diagnostics in PCa. This study demonstrates that each EVP subtype exhibits distinct characteristics that contribute to the molecular landscape of PCa. Using label-free proteomics, four novel biomarker candidates were identified, namely ATP6V1A (lEVs), USP13 (sEVs), FGL1 (exomeres), and ZBTB18 (supermeres). These proteins are linked to PCa progression and metastasis. Notably, they displayed stage-specific expression patterns in plasma EVPs from PCa patients, highlighting their utility as non-invasive biomarkers for early diagnosis, risk stratification, and longitudinal monitoring. Importantly, a single-vesicle analysis platform using nanoflow cytometry demonstrated the potential for EVP-based molecular subtyping in PCa. This approach provides a comprehensive understanding of the molecular landscape of PCa and has significant potential for future clinical translation. In summary, this thesis establishes optimised methods for isolating and profiling distinct EVP subpopulations and identifies a panel of clinically relevant protein biomarkers derived from PCa cell lines and plasma samples. These findings provide a strong foundation for developing non-invasive and personalised diagnostic and monitoring tools, advancing precision medicine in managing PCa.