Machine learning-based development of a cytotoxicity prediction model for NK cell therapy in cancers.

PURPOSE: Natural killer (NK) cells mediate anti-tumor immunity through integrated signaling of inhibitory and activating receptors. The efficacy of NK cell adoptive transfer therapy varies among patients due to heterogeneous receptor-ligand expression. This study aimed to develop a predictive model based on receptor-ligand interactions to determine NK cells' therapeutic effects. METHODS: Through analyses of receptor-ligand expression profiles of NK and tumor cells and assessment of NK cell cytotoxicity, we developed a machine learning-based random forest model using 11 key receptor-ligand pairs selected through database mining and experimental screening. Flow cytometry was used to obtain receptor-ligand profiles, and combined predictors were calculated for each pair. The model was validated using independent datasets and evaluated for generalizability across different tumor types. RESULTS: The model showed significant predictive performance, achieving an accuracy of 84.2% and an area under the curve (AUC) of 0.908 in ovarian cancer cohorts. This predictive capability was validated in both in vitro experiments and clinical samples, revealing complex non-linear interactions between receptor-ligand expression and NK cell killing efficacy. Cancer-specific ligand expression patterns were identified. While showing optimal performance in studied cancer types, it exhibited moderate applicability to other cancers and demonstrated potential compatibility with transcriptomic data for prediction. CONCLUSIONS: This model provides tools and foundations for the precise treatment of tumors using NK immune cells and may be applied in clinical practice.