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斑马鱼癌症模型:用于分析肿瘤异质性和预测患者预后的转化平台。

Zebrafish Cancer Avatars: A Translational Platform for Analyzing Tumor Heterogeneity and Predicting Patient Outcomes.

机构信息

Pharmacology and Nutritional Sciences, University of Kentucky, Lexington, KY 40356, USA.

Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.

出版信息

Int J Mol Sci. 2023 Jan 24;24(3):2288. doi: 10.3390/ijms24032288.

Abstract

The increasing number of available anti-cancer drugs presents a challenge for oncologists, who must choose the most effective treatment for the patient. Precision cancer medicine relies on matching a drug with a tumor's molecular profile to optimize the therapeutic benefit. However, current precision medicine approaches do not fully account for intra-tumoral heterogeneity. Different mutation profiles and cell behaviors within a single heterogeneous tumor can significantly impact therapy response and patient outcomes. Patient-derived avatar models recapitulate a patient's tumor in an animal or dish and provide the means to functionally assess heterogeneity's impact on drug response. Mouse xenograft and organoid avatars are well-established, but the time required to generate these models is not practical for clinical decision-making. Zebrafish are emerging as a time-efficient and cost-effective cancer avatar model. In this review, we highlight recent developments in zebrafish cancer avatar models and discuss the unique features of zebrafish that make them ideal for the interrogation of cancer heterogeneity and as part of precision cancer medicine pipelines.

摘要

可供选择的抗癌药物数量不断增加,这给肿瘤学家带来了挑战,他们必须为患者选择最有效的治疗方法。精准癌症医学依赖于将药物与肿瘤的分子特征相匹配,以优化治疗效果。然而,目前的精准医学方法并没有充分考虑到肿瘤内异质性。单个异质性肿瘤内的不同突变谱和细胞行为会显著影响治疗反应和患者预后。患者来源的模拟物在动物或培养皿中再现了患者的肿瘤,并提供了功能评估异质性对药物反应影响的手段。小鼠异种移植和类器官模拟物已经建立,但生成这些模型所需的时间对于临床决策来说并不实用。斑马鱼正成为一种高效且经济的癌症模拟物模型。在这篇综述中,我们强调了斑马鱼癌症模拟物模型的最新进展,并讨论了斑马鱼的独特特征,使其成为研究癌症异质性的理想模型,并作为精准癌症医学管道的一部分。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee51/9916713/b3a73f0bf048/ijms-24-02288-g001.jpg

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