Decoding ascitic immunological niches with multi-modal machine learning reveals prognostic and chemoresistant determinants in ovarian cancer

作者：Lin Yang, Tianhui He, Jing Wang, Xiaolan Zhang, Lin Zeng, Qinkun Sun, Yuelin Song, Yufei Nie, Xinran Gao, Chunliang Shang, Hongyan Guo · 发表于：Frontiers in Immunology · 年份：2025 · DOI：10.3389/fimmu.2025.1698793 · 被引用次数：1 · 研究领域：Ferroptosis and cancer prognosis、Single-cell and spatial transcriptomics、Ovarian cancer diagnosis and treatment

Background Malignant ascites in high-grade serous ovarian cancer (HGSOC) represent a fluid extension of the tumor microenvironment, embedding immune programs that may inform prognosis and treatment response. We investigated whether ascitic T-cell phenotypes, integrated with clinical variables, improve prediction of overall survival (OS), progression-free survival (PFS), progression-free interval (PFI), and platinum-based drug chemotherapy resistance (P-DCR). Methods We retrospectively analyzed 87 patients with FIGO III/IV HGSOC with treatment-naïve ascites treated at Peking University Third Hospital (May 2019–Mar 2024; median follow-up, 33 months). Ascites (>1,000 mL) underwent standardized processing and multiparametric flow cytometry to quantify T-cell subsets. To prevent information leakage, we used repeated nested cross-validation with event-stratified folds: inner folds performed endpoint-specific screening with Benjamini–Hochberg FDR control, redundancy reduction, and multicollinearity checks; clinical covariates were added by incremental contribution testing. Cox proportional hazards, Random Survival Forests (RSFs), and DeepSurv modeled survival endpoints; a random-forest classifier modeled P-DCR. Performance was summarized on outer folds [C-index for survival; receiver operating characteristic–area under the curve (ROC-AUC) for P-DCR]. Model interpretability used Shapley Additive Explanations (SHAP). Results Across endpoints, combined clinical + ascites feature...