ReviewPathologie (Heidelberg, Germany)2025
[Pathology of liver tumours in animal models].
Review in Pathologie (Heidelberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
9 authors.
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Abstract
backgroundLiver tumours are common and deadly diseases in humans and are often associated with a poor prognosis. To better understand the mechanisms and potential therapies for hepatocellular carcinoma and cholangiocarcinoma, experimental animal models are used in preclinical research and spontaneously occurring tumours in companion animals can also be investigated as models.
objectiveWe provide an overview of commonly used animal models in hepatic cancer research, including their applications, advantages, and disadvantages. Additionally, we present pathological findings of the most frequent spontaneous liver neoplasms in dogs and cats. MATERIALS AND
methodsWe performed a review of the scientific literature and of findings in animal models. Additionally, we performed an analysis of case statistics from a veterinary pathology submission laboratory.
resultsMouse models are most frequently used in the study of hepatic tumours. These include xenografts, in which human tumour cells are injected, chemically and diet-induced models for studying environmental and metabolic factors, and genetically modified mice. In addition to mice, other species such as rats, rabbits, and woodchucks are also utilized. Woodchucks carry their own hepatitis virus, making them particularly suitable for investigating viral pathogenesis. Furthermore, domestic animals such as dogs and cats develop spontaneous liver neoplasms, which makes them valuable models. Dogs predominantly develop hepatocellular adenomas and carcinomas, whereas benign, cystic bile duct lesions are more common in cats.
conclusionsA wide range of animal models is available for various research questions. Choosing the appropriate model is crucial for the success of any study and depends on multiple factors.
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