Preclinical mouse models for studying cholangiocarcinoma

Abstract

Cholangiocarcinoma (CCA) is a malignancy characterized by tumor cells originating in the liver or bile ducts, exhibiting features of cholangiocyte differentiation. It poses a significant clinical challenge due to the limited diagnostic and therapeutic options available. Robust animal models are essential for advancing our understanding of CCA pathogenesis and developing effective treatments. This review provides a comprehensive overview of CCA mouse models, highlighting various approaches, including chemical induction, genetically engineered models, and tumor xenografts. Each model is discussed in terms of its establishment techniques, pathological characteristics, and research significance, with a focus on intrahepatic CCA. Chemical induction models, such as diethylnitrosamine- and azoxymethane-induced models, offer insights into tumorigenesis processes, whereas genetically modified models involving alterations in key genes such as Kirsten rat sarcoma viral oncogene homolog, tumor protein 53, and isocitrate dehydrogenase serve as important tools for studying the molecular mechanisms underlying CCA. Xenograft models, including patient-derived xenografts, bridge the gap between experimental research and clinical applications, allowing for precise therapeutic evaluations. By comparing these models, this review underscores their respective advantages and limitations, paving the way for future studies aiming to optimize and innovate CCA modeling strategies.

Keywords

Cholangiocarcinoma (CCA) / Mouse models / Bile duct / Genetic engineering / Tumor initiation / Chemical carcinogens

Authors' contributions

Shanru Yang: Writing - review & editing, Writing - original draft. Chaodi Bao: Writing - review & editing, Writing - original draft. Yuan Li: Visualization, Conceptualization. Yangxiang Ou: Visualization, Conceptualization. Yifan Jiang: Visualization, Conceptualization. Wenjie Xu: Visualization, Conceptualization. Dongfang Zheng: Visualization, Conceptualization. Na Li: Visual-ization, Conceptualization. Mengjie Yang: Visualization, Concep-tualization. Fuyan Wang: Software. Xin Hou: Validation, Supervision, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that there is no conflicts of interest.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82470624 and No. U25A2019). Thanks for the technical support by the Core Facilities, Ningbo University School of Medicine. Thanks for the support by the Laboratory Animal Center of Ningbo University. Thanks to BioRender.com.

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