Abstract
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 histopathological and genomic validation to find that the 3DP-GC model well preserved the expression abundance and mutation profiles of markers in the parental tumors. A significant correlation was observed in drug sensitivity between the 3DP-GC platform and the actual clinical efficacy observed in patients.
Conclusion:
Our study establishes a robust and stable 3DP-GC model. Crucially, drug testing of 3DP-GC model can accurately predict the clinical chemotherapy of patients in a shorter time and at a lower cost, offering a promising tool for high-throughput drug screening and personalized treatment decision-making.
Supplementary Information:
The online version contains supplementary material available at 10.1186/s12943-025-02466-9.