Innovative Bioprinted Organoids Enable Tumor Feature Capture and Prognosis Prediction
A novel 3D bioprinted tumor model combines patient-derived organoids with AI to accurately capture tumor features and predict patient prognosis, advancing personalized cancer treatment strategies.
Researchers from Ulsan National Institute of Science and Technology have developed a groundbreaking 3D bioprinted tumor model that closely mimics the in vivo conditions of patient-derived cancers. This advanced model, called Embedded Bioprinting-enabled Arrayed Patient-Derived Organoids (Eba-PDOs), replicates key tumor microenvironment features such as high matrix stiffness and hypoxia, crucial for studying cancer behavior, especially in colorectal cancer cases.
The team utilized bioinks composed of gelatin and extracellular matrix components to print bead-like tumor structures derived from patient cancer cells, allowing precise control over tumor shape and morphology. These artificial tumors maintained consistent structures within individual patient samples but showed variability across different patient sources, reflecting genuine biological diversity.
A significant innovation was the integration of artificial intelligence (AI) to analyze tumor morphology from microscopic images. The AI model could accurately predict the expression of the prognostic gene CEACAM5—often overexpressed in colorectal and other solid tumors—and achieved an impressive 90% correlation with actual patient tumor gene expression, surpassing previous models. This enabled personalized predictions of tumor behavior and responsiveness to treatments like chemotherapy.
The study also demonstrated that these bioprinted organoids could simulate individual patient responses, offering a promising tool for tailored cancer therapies. Future directions include adding immune cells and vascular structures to further enhance the model's biological fidelity, aiming to improve its utility in personalized medicine.
Published in Advanced Science, this research highlights a significant step toward more accurate in vitro tumor models that can predict disease progression and treatment outcomes, providing a powerful platform for future cancer research and therapeutic development.
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