Researchers reported validation of a liquid biopsy-based model that predicts overall survival in recurrent ovarian cancer. The model uses a combination of blood protein markers and clinical factors to improve patient risk stratification and guide future research.
The analysis was based on data from the phase 3 ANITA/ENGOT-OV41/GEICO 69-O trial, testing an 8-variable model in 54 patients. It included four baseline proteins (VISTA, CA125, CD96, VEGF-A) and four clinical factors (BRCA status, PD-L1 status, treatment-free interval after platinum therapy, and FIGO stage). The model successfully separated patients into risk groups, with high-risk patients showing significantly shorter overall survival than low-risk patients.
Additional analyses in 207 patients before maintenance therapy identified proteins linked to better or worse survival outcomes, including immune-related and tumor markers. Another analysis of 310 patients showed that changes in protein levels during treatment were also associated with survival, suggesting that dynamic biomarker monitoring may help predict prognosis and guide therapy decisions in ovarian cancer.