Researchers Develop 3D-Printed Gastric Cancer Model for Personalized Drug Testing - Video

A research team has developed a gastric cancer model using 3D bioprinting technology and patient-derived cancer tissue fragments. This innovative model preserves the characteristics of actual patient tissues and is expected to rapidly evaluate and predict individual patient drug responses. The research has been published in the international journal Advanced Science.

In this study, the research team developed an in vitro gastric cancer model by leveraging 3D bioprinting technology and tissue-specific bioink incorporating patient-derived tissue fragments.

Notably, they encapsulated cancer tissues within a stomach-derived decellularized extracellular matrix (dECM) hydrogel, artificially enabling cell-matrix interactions. By co-culturing these tissues with human gastric fibroblasts, they successfully mimicked cancer cell-stroma interactions, thereby recreating the in vivo tumor microenvironment in vitro.

This model demonstrated the ability to preserve the unique characteristics of gastric tissues from individual patients by replicating both cell-stroma and cell-matrix interactions. It exhibited high specificity in predicting the patient's anticancer drug responses and prognosis. Furthermore, the model's gene profiles related to cancer development, progression, and drug response closely resembled those of patient tissues, surpassing the performance of conventional PDX models.

Additionally, the rapid fabrication method of this model via bioprinting enables drug evaluation within two weeks of tumor tissue extraction from the patient. This efficient platform is anticipated to significantly contribute to the development of personalized cancer treatments.

Professor Charles Lee from The Jackson Laboratory for Genomic Medicine, who led the study, expressed his expectations for the model: "By reproducing cancer cell-stroma and cell-matrix interactions, this model enhances the accuracy of drug response predictions and reduces unnecessary drug administration to non-responsive patients."

Reference: Y. Choi, D. Na, G. Yoon, J. Kim, S. Min, H.-G. Yi, S.-J. Cho, J. H. Cho, C. Lee, J. Jang, Prediction of Patient Drug Response via 3D Bioprinted Gastric Cancer Model Utilized Patient-Derived Tissue Laden Tissue-Specific Bioink. Adv. Sci. 2024, 2411769. https://doi.org/10.1002/advs.202411769