A collaborative research team led by Professor Jinah Jang from the Department of Mechanical Engineering and the Department of Creative IT Engineering at POSTECH (Pohang University of Science and Technology) and Professor Charles Lee from The Jackson Laboratory for Genomic Medicine in the United States has successfully 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.

Tumour heterogeneity poses a significant challenge in the development and treatment of cancer therapies, as patient responses to the same drug varies, and the timing of treatment is a critical factor influencing prognosis.

Therefore, technologies that predict the efficacy of anticancer treatments play a vital role in minimising side effects and enhancing treatment efficiency.

Existing approaches, such as gene panel-based tests and patient-derived xenograft (PDX) models, are limited in their applicability to certain patients, have constraints in predicting drug effects, and require substantial time and costs to establish.

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 decellularised 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 tumour 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 tumour tissue extraction from the patient.

This efficient platform is anticipated to significantly contribute to the development of personalised 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.”

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

Image: Recreating the Tumor Microenvironment with Bioprinting Technology for Personalized Drug Response Identification and Prediction Credit: POSTECH

Source: Pohang University of Science & Technology (POSTECH)