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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月14日

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  • Liwei Du,
  • Zicheng Zheng,
  • Kai Zhang,
  • Hang Sun,
  • Yuce Lu,
  • Minghao Sun,
  • Mingchang Pang,
  • Shangze Jiang,
  • Yixuan He,
  • Shunda Du,
  • Haitao Zhao,
  • Yilei Mao,
  • Weiming Kang,
  • Penglei Ge,
  • Huayu Yang

摘要

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.