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<p>Tumor organoids preserve the cellular heterogeneity and structural complexity of native tumors, providing robust platforms for mechanistic studies, preclinical drug testing, and translational oncology research. However, their predictive performance is not an intrinsic property of organoids. It is shaped by platform design, including culture format, matrix composition, medium formulation, and the extent of multicellular reconstruction. This review examines how these design variables influence model fidelity, reproducibility, tumor microenvironment reconstruction, and drug-response interpretation. We compare major culture formats and matrix systems, discuss strategies for incorporating stromal and immune components, and evaluate current and emerging uses of AI-assisted analysis for organoid-derived phenotypic data. Overall, this review highlights how integrated tumor organoid design can strengthen pharmacological modeling and oncology translation.</p>

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Tumor organoid platform design for drug response modeling: culture architecture, microenvironmental complexity, and AI-assisted readouts

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Eun Ah Shin,
  • Won-Cheol Jeong,
  • Ye-Won Kim,
  • Ji Won Kim,
  • Miso Park

摘要

Tumor organoids preserve the cellular heterogeneity and structural complexity of native tumors, providing robust platforms for mechanistic studies, preclinical drug testing, and translational oncology research. However, their predictive performance is not an intrinsic property of organoids. It is shaped by platform design, including culture format, matrix composition, medium formulation, and the extent of multicellular reconstruction. This review examines how these design variables influence model fidelity, reproducibility, tumor microenvironment reconstruction, and drug-response interpretation. We compare major culture formats and matrix systems, discuss strategies for incorporating stromal and immune components, and evaluate current and emerging uses of AI-assisted analysis for organoid-derived phenotypic data. Overall, this review highlights how integrated tumor organoid design can strengthen pharmacological modeling and oncology translation.