3D Bioprinted Gastric Cancer Model Uses Patient-Derived Tissue Fragments to Predict Drug Response

Tumor heterogeneity presents a major obstacle in the development and treatment of cancer therapies, as patients' responses to the same drug can differ, and the timing of treatment significantly influences prognosis. Consequently, technologies that predict the effectiveness of anticancer treatments are essential in minimizing side effects and improving treatment efficiency. Current methods, such as gene panel-based tests and patient-derived xenograft (PDX) models, have limitations in their applicability to certain patients, have challenges in predicting drug effects, and require significant time and costs to develop. Now, researchers have successfully created a gastric cancer model using 3D bioprinting technology and patient-derived cancer tissue fragments. This groundbreaking model preserves the characteristics of actual patient tissues and is expected to rapidly assess and predict individual patient drug responses.

In collaborative research by Pohang University of Science & Technology (POSTECH, Gyeongbuk, Korea) and The Jackson Laboratory for Genomic Medicine (Farmington, CT, USA), scientists have developed an in vitro gastric cancer model by utilizing 3D bioprinting technology and tissue-specific bioink that incorporated patient-derived tissue fragments. Notably, they encapsulated cancer tissues within a stomach-derived decellularized extracellular matrix (dECM) hydrogel, which artificially facilitated cell-matrix interactions. By co-culturing these tissues with human gastric fibroblasts, they successfully replicated cancer cell-stroma interactions, thereby recreating the in vivo tumor microenvironment in vitro.

Published in the international journal Advanced Science, the research shows that this model preserved the distinct characteristics of gastric tissues from individual patients by replicating both cell-stroma and cell-matrix interactions. It demonstrated high specificity in predicting the patient's anticancer drug responses and prognosis. Additionally, the model's gene profiles related to cancer development, progression, and drug response closely mirrored those of patient tissues, outperforming conventional PDX models. The rapid fabrication process of this model through bioprinting also allows drug evaluation within two weeks of tumor tissue extraction from the patient. This efficient platform is expected to make a significant contribution to the development of personalized cancer treatments.

“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,” said Professor Charles Lee from The Jackson Laboratory for Genomic Medicine, who led the study.

“This is a critical preclinical platform not only for developing patient-specific treatments but also for validating new anticancer drugs and combination therapies,” added Professor Jinah Jang of POSTECH.

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