CELL INTELLIGENCE · 肿瘤细胞治疗研究

Ovarian cancer remains a major cause of gynecologic cancer mortality; and, although immune checkpoint inhibitors have transformed treatment in other solid tumors, their activity in ovarian cancer has been limited by tumor heterogeneity, an immunosuppressive tumor microenvironment, and the lack of robust predictive biomarkers. Recent research therefore has shifted toward combination and precision-based strategies, including dual-checkpoint blockade, integration with chemotherapy, antiangiogenic therapy, and poly(adenosine diphosphate-ribose) polymerase inhibitors, as well as adoptive cell therapies, vaccines, and oncolytic viruses. To date, added benefit from checkpoint inhibition has been clearest in chemotherapy-based combinations for programmed death-ligand-positive, platinum-resistant disease, whereas its contribution to maintenance therapy with poly(adenosine diphosphate-ribose) polymerase inhibitors and/or bevacizumab in first-line or platinum-sensitive relapse appears limited. Emerging evidence also supports the role of antibody-drug conjugates as both direct cytotoxic agents and immune modulators. In parallel, biomarkers such as programmed death-ligand 1, tumor mutational burden, microsatellite instability high, and immune-cell infiltrates are being evaluated to improve patient selection, while artificial intelligence and machine learning are increasingly being explored to integrate clinical, pathologic, radiomic, and molecular data for response prediction. Research directions to make ovarian cancer more susceptible to immunotherapy are moving from empiric treatment to more biologically informed, multimodal approaches aimed at overcoming immune suppression, enhancing antitumor immunity, and personalizing treatment for patients with ovarian cancer.