Scientists have developed a new gastric cancer model using advanced 3D bioprinting techniques and cancer tissue samples taken from patients. This groundbreaking model maintains the essential features of genuine patient tissues, allowing for quick assessment and prediction of how individual patients might respond to specific medications.
A collaborative research effort spearheaded by Professor Jinah Jang from POSTECH (Pohang University of Science and Technology) and Professor Charles Lee from The Jackson Laboratory for Genomic Medicine has successfully created a gastric cancer model employing 3D bioprinting and patient-derived cancer tissue fragments. This state-of-the-art model retains the properties of actual patient tissues and aims to facilitate rapid evaluation and prediction of drug responses for individual patients. The findings of this research are detailed in the international journal Advanced Science.
Tumor diversity presents a major obstacle in the creation and treatment of cancer therapies. Individual patients may respond differently to the same medication, and the timing of treatments is crucial for better prognosis. Thus, technologies that can forecast the effectiveness of cancer therapies are essential for reducing side effects and improving treatment outcomes. Existing methods, such as gene panel tests and patient-derived xenograft (PDX) models, have limitations regarding their applicability to specific patients, challenges in forecasting drug effects, and often require significant time and financial resources to implement.
In this research, the team created an in vitro gastric cancer model by utilizing 3D bioprinting technology alongside bioink tailored to include patient-derived tissue fragments.
The researchers encapsulated these cancer tissues in a decellularized extracellular matrix (dECM) hydrogel derived from stomach tissue, which allows for artificial cell-matrix interactions. By co-culturing these tissues with human gastric fibroblasts, they effectively replicated the interactions between cancer cells and the surrounding stroma, thus mimicking the in vivo tumor microenvironment in vitro.
This innovative model successfully retains the unique attributes of gastric tissues from individual patients, recreating both cell-stroma and cell-matrix interactions. It showed a high level of specificity in predicting how patients would respond to anticancer drugs and their prognosis. Additionally, the model’s gene profiles associated with cancer growth, advancement, and drug response closely mirrored those of actual patient tissues, outperforming traditional PDX models.
Moreover, the rapid creation of this model through bioprinting allows for drug evaluations to be conducted within just two weeks following the extraction of tumor tissues from patients. This efficient process is expected to significantly aid in the development of personalized cancer therapies.
Professor Charles Lee from The Jackson Laboratory for Genomic Medicine, who led the study, shared his optimistic outlook on the model: “By replicating the interactions between cancer cells and their microenvironment, this model improves the accuracy of drug response predictions while minimizing unnecessary drug treatments for patients who are unlikely to respond.”
Professor Jinah Jang from POSTECH highlighted the importance of this research: “This serves as a vital preclinical platform not only for crafting patient-specific treatments but also for testing new anticancer drugs and combination therapies.”
This study received support from the Basic Science Research Program via the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2020R1A6A1A03047902), and through NRF grants supported by the Korean government (MSIT) (No. 2022M3C1A3081359, No. 2021R1A2C2004981).
