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

被引:22
作者
Al-Hamaly, Majd A. A. [1 ,2 ]
Turner, Logan T. T. [2 ,3 ]
Rivera-Martinez, Angelica [3 ]
Rodriguez, Analiz [4 ]
Blackburn, Jessica S. S. [2 ,3 ]
机构
[1] Univ Kentucky, Pharmacol & Nutr Sci, Lexington, KY 40356 USA
[2] Univ Kentucky, Markey Canc Ctr, Lexington, KY 40536 USA
[3] Univ Kentucky, Mol & Cellular Biochem, Lexington, KY 40356 USA
[4] Univ Arkansas Med Sci, Dept Neurosurg, Little Rock, AR 72205 USA
基金
美国国家卫生研究院;
关键词
co-clinical trials; intra-tumoral heterogeneity; precision medicine; xenograft; zPDX; CHRONIC MYELOID-LEUKEMIA; BREAST-CANCER; PRECISION MEDICINE; MOUSE MODELS; IMMUNE CELLS; XENOGRAFTS; GENERATION; GROWTH; PROLIFERATE; PROGRESSION;
D O I
10.3390/ijms24032288
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
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页数:24
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