Toward precision oncology: Leveraging bone metastatic organoid models for mechanistic, translational, and therapeutic discovery

Abstract

Bone metastasis represents a frequent late-stage complication in cancers such as lung, breast, and prostate, severely affecting patients’ quality of life. Conventional two-dimensional cultures and animal models fail to recapitulate the complex bone microenvironment. Patient-derived organoids (PDOs) offer a physiologically relevant three-dimensional platform to recapitulate bone metastasis by preserving native tumor features and modeling tumor-bone interactions. This review systematically outlines the current methodologies for constructing bone metastatic organoid models, evaluates their applications, and identifies future directions. We describe the essential components of culture systems and critically discuss their strengths and limitations in modeling bone-specific signaling. Furthermore, we highlight the capacity of PDOs to elucidate key aspects of bone metastasis, including tumor cell adaptation to the osseous niche, bidirectional remodeling of the microenvironment, and the dynamic monitoring of disease progression. Bone organoids are also discussed as a means of establishing a standardized bone microenvironment, offering a controllable in vitro platform for investigating interactions between tumor cells and the bone matrix. Furthermore, we present the translational potential of organoids for informing individualized therapy selection, evaluating clinical drug sensitivity, and facilitating the development of organoid biobanks. Looking forward, the development of patient-specific “bone metastasis-on-a-chip” systems with artificial intelligence-driven digital twins may transform the research paradigm from experimental simulation to precision prediction, ultimately advancing personalized therapeutic strategies for bone metastatic disease.

Keywords

Bone metastasis / Patient-derived organoids / Bone organoids / Tumor microenvironment / Precision medicine

Acknowledgments

None.

Funding

This work was supported in part by the National Natural Science Foundation of China (grant numbers: 82522063, 82472786, 82173168).

Conflict of interest

Jiacan Su serves as the Editorial Board Member of the journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Other authors declare they have no competing interests.

Author contributions

Conceptualization: Jiacan Su, Tong Meng

Visualization: Chencong Lv, Tong Meng

Writing-original draft: Chencong Lv, Xiao Chen

Writing-review & editing: Hongjing Dou, Zhenping Cao

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

Not applicable.

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