Tumor heterogeneity presents a significant challenge in cancer therapy development and treatment, as patient responses to the same medication vary, and treatment timing affects prognosis. Technologies that predict anticancer treatment effectiveness are essential for minimizing side effects and improving therapeutic outcomes.
Existing methods, such as patient-derived xenograft (PDX) models and gene panel-based tests, have limitations in predicting drug responses, are only applicable to specific patient groups, and require significant time and resources to establish.
In this study, the research team developed an in vitro gastric cancer model using 3D bioprinting technology and tissue-specific bioink containing patient-derived tissue fragments.
The approach involved encapsulating cancer tissues within a hydrogel composed of a decellularized extracellular matrix (dECM) derived from the stomach, facilitating artificial cell-matrix interactions. By co-culturing these tissues with human gastric fibroblasts, the researchers replicated the in vivo tumor microenvironment, successfully modeling cancer cell-stroma interactions.
By simulating both cell-stroma and cell-matrix interactions, the model preserved the distinct characteristics of gastric tissues from different patients. It demonstrated specificity in predicting patient prognosis and anticancer drug responses. Additionally, the model outperformed traditional PDX models, as its gene expression profiles for treatment response, tumor development, and progression closely aligned with those of patient tissues.
The rapid bioprinting process enables drug screening within two weeks of tumor tissue extraction, offering an efficient platform for developing 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.
Charles Lee, Professor and Study Lead, The Jackson Laboratory for Genomic Medicine
“This is a critical preclinical platform not only for developing patient-specific treatments but also for validating new anticancer drugs and combination therapies,” Jinah Jang, Professor, Pohang University of Science and Technology (POSTECH) added.
National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022M3C1A3081359, No. 2021R1A2C2004981) and the Basic Science Research Program through the NRF funded by the Ministry of Education (No. 2020R1A6A1A03047902) provided support for this study.
Journal Reference:
Choi, Y., et al. (2025) Prediction of Patient Drug Response via 3D Bioprinted Gastric Cancer Model Utilized Patient-Derived Tissue Laden Tissue-Specific Bioink. doi.org/10.1002/advs.202411769