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Ovarian cancer (OC) is the gynecological malignancy with the highest mortality rate, characterized by insidious onset, significant heterogeneity, and a high risk of recurrence. In recent years, immune checkpoint inhibitors (ICIs) have brought new hope for OC treatment; however, their overall clinical efficacy remains limited, partly due to the inherently immunosuppressive tumor microenvironment (TME) and the lack of effective predictive biomarkers. This review systematically searched relevant literature from 2015 to 2024 to comprehensively elaborate on the current clinical applications, challenges, and research progress of various predictive biomarkers for ICIs in OC. It indicates that although biomarkers such as PD-L1 expression, tumor mutational burden (TMB), homologous recombination deficiency (HRD) status, and tumor-infiltrating lymphocytes (TILs) show certain potential, their predictive value is constrained by factors like tumor spatiotemporal heterogeneity and lack of standardized detection methods. Emerging microbiome (gut/vaginal) biomarkers and integrated models based on multi-omics data represent important research directions. Furthermore, this article discusses practical principles for integrating existing biomarkers into clinical decision-making pathways and analyzes the importance of biomarker-guided stratified therapy in enhancing the cost-effectiveness of ICI treatment. Finally, the review outlines key future directions, including deepening mechanistic research, promoting the validation of integrated models, overcoming tumor heterogeneity, optimizing combination strategies, and conducting prospective biomarker-driven clinical trials, to advance OC immunotherapy toward an era of precision and individualization.
Li et al. (Tue,) studied this question.