Human tumor organoids have advanced cancer modeling by preserving patient-specific heterogeneity and functional drug responses. However, translating organoid findings into routine decision-making remains challenging due to variability in culture conditions and incomplete reconstruction of the tumor microenvironment. In this review, we present a clear and actionable framework that positions tumor organoids as dynamic living biosensors, linking mechanistic studies, tumor microenvironment reconstruction, functional drug-response phenotyping, and precision-therapy decision-making.
The review summarizes major sources and establishment strategies, and highlights how culture engineering and biomaterials shape phenotype stability and drug-response interpretability. We further integrate tumor organoid microenvironment co-culture designs and emerging enabling technologies that improve scalability and standardization for translational use.
Key findings from this review include:

1. A living-biosensor framework for translation:
   We propose a unified framework in which tumor organoids connect mechanism, microenvironment context, functional readouts, and therapy selection, providing a practical roadmap from model building to clinically relevant outputs.
2. Culture engineering determines interpretability:
   Biophysical and biochemical cues, together with biomaterials design (synthetic matrices, decellularized extracellular matrix, scaffold-free formats, and dynamic perfusion systems), can shift tumor states and drug responses, directly affecting reproducibility and translatability.
3. Reconstructing tumor microenvironment interactions is essential:
   We synthesize reproducible co-culture strategies that incorporate stromal elements such as cancer-associated fibroblasts, immune components, vascularized modules, and extracellular matrix remodeling, and we summarize practical validation readouts to support standardized assessment.
4. Key technologies accelerate scale-up and standardization:
   Organoids-on-chip platforms, three-dimensional bioprinting, high-throughput miniaturized screening, and data-driven analysis using machine learning and multi-omics profiling are key drivers that move organoids from static cultures toward scalable translational platforms.

Overall, this review provides an integrated perspective on how tumor organoids can be engineered and applied for cancer modeling and precision therapy. These insights support improved clinical correlation and more reliable functional testing. Future work should prioritize quality control, cross-laboratory standardization, and ethical governance to enable broader real-world implementation.


DOI：10.1093/procel/pwag007