Immunoaffinity-enriched extracellular vesicle proteomics for high-grade serous ovarian cancer - A UKOPS nested case-control analysis.

OBJECTIVE: To determine whether plasma and serum extracellular vesicles (EV) are interchangeable for immunoaffinity-enriched proteomic profiling in high-grade serous ovarian cancer (HGSOC), and whether an EV protein signature improves diagnostic performance beyond CA-125 for detecting ovarian cancer. METHODS: We performed immunoaffinity-enriched proteomic profiling of EV subpopulations from 243 paired EDTA plasma and serum samples in a nested case-control cohort from the UK Ovarian Cancer Population Study (controls, benign, borderline, and HGSOC). Analyses assessed proteomic depth, plasma-serum concordance, and diagnostic performance using machine learning. RESULTS: Plasma-derived EV subpopulations showed greater proteomic depth than serum, with 6439 versus 5451 protein groups identified and 2838 versus 840 proteins detected at 50% prevalence. Protein intensities were highly concordant between matrices (r = 0.86, p < 0.001), maintained across disease groups and cancer stages. For distinguishing HGSOC from non-cancers, an EV-augmented CA-125 model achieved an AUROC of 0.97 in plasma and 0.96 in serum, improving plasma sensitivity to 99% (95% CI 89-100) and specificity to 85% (95% CI 68-91) versus 96% (95% CI 82-100) sensitivity and 65% (95% CI 15-73) specificity for CA-125 alone, with similarly high discrimination in serum. CONCLUSIONS: Surface epitope-targeted EV subpopulation enrichment yields specific proteomic profiling of HGSOC with highly concordant biomarker signatures across plasma and serum, supporting their interchangeable use in clinical and biobank settings. The EV-augmented CA-125 model outperformed CA-125 alone, with recurrent EV proteins reflecting mucin-mediated immune evasion, and metabolic reprogramming, underscoring the translational potential of this approach for blood-based ovarian cancer detection.