Review of patient-derived cancer organoids on drug-sensitivity prediction from the aspect of clinical oncotherapy

Patient-derived organoids (PDOs) have emerged as a pivotal platform for precision oncology, offering novel technical support for formulating personalized treatment strategies. However, the spatial heterogeneity of tumor tissues and variations in culture systems across laboratories continue to pose challenges to the reproducibility and clinical reliability of PDOs-based drug sensitivity testing. Through a systematic literature review, this study analyzed the impact of various factors—including cancer types (e.g., colorectal, breast, and lung cancers), clinical stages(with a focus on advanced-stage patients), and therapeutic agents(covering first-/second-line chemotherapy drugs and common targeted drugs)—on the sensitivity of PDOs drug testing. The results indicate that PDOs drug sensitivity testing achieves an overall clinical predictive accuracy of approximately 80%, while demonstrating particularly high negative predictive values(approaching 100%) in advanced-stage patients. Furthermore, significant heterogeneity in drug responses was observed across different cancer types and drug categories (such as 5-FU, platinum-based agents, and EGFR inhibitors), which closely correlated with the mutational status of key driver genes like TP53 and EGFR. Notably, the establishment of PDOs-immune cell co-culture systems provides a promising approach for evaluating immunotherapy efficacy, though their broader application across cancer types, especially in solid tumors, remains limited. Concurrently, biopharmaceutical companies worldwide are accelerating the commercial adoption of organoid technology in clinical drug sensitivity testing and drug discovery. This review systematically summarizes the clinical and technical factors influencing the performance of PDOs drug testing and outlines the latest advances in the translational application of organoid technology. Based on current evidence, we propose key future directions: establishing standardized protocols for PDOs culture and drug testing; integrating microfluidic technology for precise modulation of the organoid microenvironment; leveraging artificial intelligence to optimize high-throughput drug sensitivity data analysis; and promoting multicenter clinical studies to validate the predictive power of PDOs. Implementing these strategies will facilitate the translation of PDOs from laboratory research to routine clinical practice, ultimately advancing optimized precision therapy in oncology.