A small extracellular vesicle protein-based model for ovarian cancer relapse detection and monitoring.

Abstract

5564 Background: Recurrence surveillance of epithelial ovarian cancer (EOC) is a major clinical challenge, given the suboptimal performance of serum CA125 levels and limitations of radiological assessments. Serum small extracellular vesicle (sEV)-based liquid biopsy provides tumor-enriched and stable biomarkers, with potential to improve the precision of recurrence monitoring and support post-treatment surveillance. This study aimed to develop and preliminarily validate a serum sEV protein-based model to enhance recurrence monitoring in ovarian cancer. Methods: The study was designed with two stages (model development and validation) and plans to prospectively enroll 200 patients with EOC (NCT06925126). This analysis reports preliminary stage I data, and patients were categorized into relapse and non-relapse groups based on imaging-confirmed recurrence status. All relapsed patients were platinum-sensitive with low tumor burden. Quantitative assessment was performed on biomarkers derived from sEVs (specifically, E-CA125, E-HE4, and E-C5a) as well as corresponding serum markers measured in routine clinical practice (namely, H-CA125 and H-HE4), utilizing peripheral blood samples. Biomarker distributions were compared between groups, and predictive performance for recurrence detection was evaluated using receiver operating characteristic (ROC) analysis, including area under the curve (AUC), accuracy, sensitivity, and specificity. Results: A total of 113 patients were analyzed (42 non-relapsed, 71 relapsed), with a median age of 57 years. The majority (95/113) of patients had high-grade serous ovarian cancer, while other histological types were also included. Levels of E-CA125, E-HE4, H-CA125, and H-HE4 were significantly higher in relapsed patients compared with non-relapsed patients (all p < 0.01), while E-C5a showed no discriminative value between the two groups. Among individual sEV biomarkers evaluated, E-CA125 demonstrated the highest performance for recurrence detection (AUC = 0.877), followed by E-HE4 (AUC = 0.689) and E-C5a (AUC = 0.528). Moreover, the sEV protein-based model achieved an accuracy of 77.9% with a sensitivity of 71.8% and a specificity of 90.9%. This performance surpassed that of the conventional serum CA125, which exhibited an accuracy of 50.0%, a sensitivity of 26.8%, and a specificity of 100.0%. These results indicate that the sEV protein-based model may provide added clinical value when serum CA125 monitoring is unreliable after prior therapy. Conclusions: The sEV-derived CA125 and the sEV model demonstrate promising performance for recurrence detection in EOC. These findings support the feasibility of a serum sEV-based surveillance strategy that may improve the reliability of post-treatment monitoring. Prospective validation is ongoing to determine its potential role in guiding clinical surveillance and intervention. Clinical trial information: NCT06925126 .