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Zebrafish xenograft models of cancer and metastasis for drug discovery

Abstract : Introduction: Patients with metastatic cancer suffer the highest rate of cancer-related death, but existing animal models of metastasis have disadvantages that limit our ability to understand this process. The zebrafish is increasingly used for cancer modelling, particularly xenografting of human cancer cell lines, and drug discovery, and may provide novel scientific and therapeutic insights. However, this model system remains underexploited. Areas covered: The authors discuss the advantages and disadvantages of the zebrafish xenograft model for the study of cancer, metastasis and drug discovery. They summarise previous work investigating the metastatic cascade, such as tumour-induced angiogenesis, intravasation, extravasation, dissemination and homing, invasion at secondary sites, assessing metastatic potential and evaluation of cancer stem cells in zebrafish. Expert opinion: The practical advantages of zebrafish for basic biological study and drug discovery are indisputable. However, their ability to sufficiently reproduce and predict the behaviour of human cancer and metastasis remains unproven. For this to be resolved, novel mechanisms must to be discovered in zebrafish that are subsequently validated in humans, and for therapeutic interventions that modulate cancer favourably in zebrafish to successfully translate to human clinical studies. In the meantime, more work is required to establish the most informative methods in zebrafish.
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Contributor : Dominique Heymann Connect in order to contact the contributor
Submitted on : Wednesday, March 15, 2017 - 6:40:08 PM
Last modification on : Wednesday, April 27, 2022 - 4:02:12 AM
Long-term archiving on: : Friday, June 16, 2017 - 2:58:23 PM


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Hannah K Brown, Kristina K Schiavone, Simon Tazzyman, Dominique Heymann, Thimothy J Chico. Zebrafish xenograft models of cancer and metastasis for drug discovery. Expert Opinion on Drug Discovery, Informa Healthcare, 2017, Epub ahead of print. ⟨10.1080/17460441.2017.1297416⟩. ⟨inserm-01490365v2⟩



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