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

Majd A Al-Hamaly1,2, Logan T Turner2,3, Angelica Rivera-Martinez3

  • 1Pharmacology and Nutritional Sciences, University of Kentucky, Lexington, KY 40356, USA.

Insights

Zebrafish cancer models offer a rapid and cost-effective way to study tumor heterogeneity and improve precision cancer medicine. These models help predict drug response by analyzing diverse cancer cell profiles within a tumor.

Area of Science:

  • Oncology
  • Genomics
  • Translational Medicine

Background:

  • Precision cancer medicine aims to match drugs to tumor molecular profiles for optimal patient benefit.
  • Intra-tumoral heterogeneity, with diverse mutation profiles and cell behaviors, significantly impacts therapy response.
  • Current precision medicine approaches do not fully address tumor heterogeneity.

Purpose of the Study:

  • To review recent advancements in zebrafish cancer avatar models.
  • To discuss the advantages of zebrafish models for studying cancer heterogeneity.
  • To explore the integration of zebrafish avatars into precision cancer medicine pipelines.

Main Methods:

  • Review of current literature on zebrafish cancer avatar models.
  • Discussion of zebrafish biological features relevant to cancer modeling.
  • Analysis of zebrafish avatars' potential for assessing drug response and heterogeneity.

Main Results:

  • Zebrafish avatars are emerging as a time-efficient and cost-effective alternative to traditional models.
  • Zebrafish models can recapitulate patient tumor characteristics and assess drug efficacy.
  • These models offer unique advantages for interrogating intra-tumoral heterogeneity.

Conclusions:

  • Zebrafish cancer avatar models show significant promise for advancing precision oncology.
  • Their speed and cost-effectiveness make them suitable for clinical decision-making pipelines.
  • Zebrafish models are valuable tools for understanding and overcoming challenges posed by tumor heterogeneity.

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