Prediction of Patient Drug Response via 3D Bioprinted Gastric Cancer Model Utilized Patient-Derived Tissue Laden Tissue-Specific Bioink

Yoo-Mi Choi ¹, Deukchae Na ², Goeun Yoon ³

¹Center for 3D Organ Printing and Stem cells (COPS), Pohang University of Science and Technology (POSTECH), Pohang, 37666, Republic of Korea.
²Ewha Institute of Convergence Medicine, Ewha Womans University Mokdong Hospital, Seoul, 07985, Republic of Korea.
³Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37666, Republic of Korea.
⁴School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology (POSTECH), Pohang, 37666, Republic of Korea.
⁵Department of Rural and Biosystems Engineering, Chonnam National University, Gwangju, 61186, Republic of Korea.
⁶Department of Internal Medicine, Liver Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
⁷Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, 06273, Republic of Korea.
⁸The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
⁹Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37666, Republic of Korea.
¹⁰Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, 03722, Republic of Korea.

⁰Center for 3D Organ Printing and Stem cells (COPS), Pohang University of Science and Technology (POSTECH), Pohang, 37666, Republic of Korea.

Advanced Science (weinheim, Baden-Wurttemberg, Germany) +

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January 3, 2025

View abstract on PubMed

Summary

A novel 3D bioprinted gastric cancer model accurately predicts patient chemotherapy response. This patient-specific preclinical tool enables rapid drug evaluation, improving personalized cancer treatment strategies.

Area Of Science

Biomedical Engineering
Oncology
Drug Discovery

Background

Tumor heterogeneity complicates cancer treatment and anticancer drug discovery.
Patient response to chemotherapy varies, necessitating personalized treatment approaches.
Existing preclinical models often lack patient-specific stromal components, limiting predictive accuracy.

Purpose Of The Study

To develop a patient-specific 3D bioprinted gastric cancer (pGC) model for rapid preclinical chemotherapy evaluation.
To assess the pGC model's ability to replicate patient tumor characteristics and predict drug response.
To establish a more accurate preclinical tool for personalized cancer therapy.

Main Methods

Utilized extrusion-based 3D bioprinting technology with patient-derived tumor chunks and tissue-specific bioinks.
Developed a printed gastric cancer (pGC) model incorporating patient-derived tumor tissues and human gastric fibroblasts.
Analyzed drug response-related gene profiles in pGC tissues and compared them with patient tissues.

Main Results

The pGC model successfully retained original tumor characteristics.
Rapid drug evaluation was achieved within two weeks of patient tumor isolation.
The gene expression profile of pGC tissues co-cultured with fibroblasts mirrored that of patient tissues.
The pGC model demonstrated high similarity to patients in chemotherapy response and prognostic predictability.

Conclusions

The developed pGC model serves as a promising preclinical tool for personalized and precise cancer treatments.
This 3D bioprinting approach overcomes limitations of traditional preclinical models by including patient-derived stromal cells.
The pGC model offers a rapid and accurate method for evaluating patient-specific drug responses, advancing anticancer drug discovery.

Keywords:

drug efficacy testing gastric cancer patient‐derived xenograft gastric tissue‐derived decellularized extracellular matrix tumor tissue printing