Exploring personalized prediction of clinical chemotherapy efficacy and revealing tumor heterogeneity using patient-derived 3D bioprinting gastric cancer models

作者：Liwei Du, Zicheng Zheng, Kai Zhang, Hang Sun, Yan Lü, Minghao Sun, Mingchang Pang, Shangze Jiang, Yixuan He, Shunda Du, Haitao Zhao, Yilei Mao, Weiming Kang, Penglei Ge, Huayu Yang · 发表于：Molecular Cancer · 年份：2025 · DOI：10.1186/s12943-025-02466-9 · 被引用次数：11 · 研究领域：Gastric Cancer Management and Outcomes、Cancer Cells and Metastasis、3D Printing in Biomedical Research

BACKGROUND: The pronounced chemotherapeutic heterogeneity observed in gastric cancer (GC) poses significant challenges to personalized treatment strategies, with current approaches lacking reliable predictive modalities for chemotherapy efficacy and postoperative prognosis. While patient-derived organoid (PDO) and xenograft (PDX) models serve as established three-dimensional platforms, their prohibitive costs and inherent batch effect limit faithful replication of native tumor extracellular matrix (ECM) complexity. METHODS: We utilized patient-derived GC tissues to construct individualized 3D bioprinting (3DP)-GC models. After screening bioinks for optimal mechanical properties and biocompatibility, we successfully and efficiently constructed 3DP-GC models of 33 patients, and performed histopathological and genomic analyses to determine that the 3DP-GC model effectively preserved the histological architecture, biomarker expression abundance and genetic mutation profiles of the parental tumors. Drug screening on the 3DP-GC models was conducted using clinical gastric cancer therapies. Retrospective analysis of patients’ post-neoadjuvant therapy and follow-up of those post-adjuvant therapies were performed to evaluate the model’s potential in predicting and selecting chemotherapeutic agents for gastric cancer patients. RESULTS: In this study, we successfully and efficiently constructed 3D in vitro models of 33 GC patients using 3D bioprinting technology, and performed histopatho...