Next Article in Journal
Breast Tumor Tissue Image Classification Using Single-Task Meta Learning with Auxiliary Network
Previous Article in Journal
Induction of Immunological Antitumor Effects by the Combination of Adenovirus-Mediated Gene Transfer of B7-1 and Anti-Programmed Cell Death-1 Antibody in a Murine Squamous Cell Carcinoma Model
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 16
Issue 7
10.3390/cancers16071361
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Zebrafish—A Suitable Model for Rapid Translation of Effective Therapies for Pediatric Cancers

by
Debasish Roy
1,
Bavani Subramaniam
1,
Wai Chin Chong
1,
Miriam Bornhorst
1,
Roger J. Packer
1 and
Javad Nazarian
1,2,*
1
Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC 20012, USA
2
DIPG/DMG Research Center Zurich, Children’s Research Center, Department of Pediatrics, University Children’s Hospital Zürich, 8032 Zurich, Switzerland
*
Author to whom correspondence should be addressed.
Cancers 2024, 16(7), 1361; https://doi.org/10.3390/cancers16071361
Submission received: 15 March 2024 / Revised: 27 March 2024 / Accepted: 27 March 2024 / Published: 30 March 2024
(This article belongs to the Section Methods and Technologies Development)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

Cancer stands as the leading cause of death among children and adolescents in the United States. Some of these pediatric cancers are highly aggressive and complex in nature. Current in vitro models often fail to accurately replicate the tumor microenvironment, while in vivo models face time and cost constraints. This review article emphasizes the unique advantages of zebrafish models in pediatric cancer research due to their genetic similarity to humans, short experimental timeline, ease of genetic manipulations, rapid in vivo tumor development, and transparent bodies that facilitate precise tumor cell tracking at single-cell resolution. Through a comprehensive analysis of existing literature and experimental findings, the article highlights the potential of zebrafish as a valuable preclinical model for studying tumor biology, expediting drug discovery and screening processes, and implementing personalized medicine strategies for treating pediatric cancers.

Abstract

Pediatric cancers are the leading cause of disease-related deaths in children and adolescents. Most of these tumors are difficult to treat and have poor overall survival. Concerns have also been raised about drug toxicity and long-term detrimental side effects of therapies. In this review, we discuss the advantages and unique attributes of zebrafish as pediatric cancer models and their importance in targeted drug discovery and toxicity assays. We have also placed a special focus on zebrafish models of pediatric brain cancers—the most common and difficult solid tumor to treat.

1. Introduction

Cancer is the leading cause of death by disease in children and adolescents in the United States, with an incidence rate of approximately 175 per 1 million [1]. Leukemia constitutes 28% of all childhood cancers, while all solid tumors make up approximately 40% of childhood cancers [2]. Among solid tumors, tumors involving the central nervous system (CNS), including the brain and/or spine, are the most common solid tumors in this population [1]. Although many patients with solid tumors are successfully treated with multimodal therapy (chemotherapy, radiation therapy, and surgery), other tumors including relapsed/recurrent sarcomas, malignant melanomas, stage four neuroblastoma, and malignant brain tumors have poor overall survival [3,4]. In addition, treatment often results in long-term side effects and impacts quality of life. The recent understanding of tumor etiology has provided an opportunity to establish biology-informed clinical interventions. Through advanced sequencing techniques and large cooperative studies, new therapeutic approaches that target molecular alterations specific to the tumor and the tumor environment are being developed. These therapies will likely be critical for successful management and long-term survival for many solid tumors in the future.
Preclinical efficacy assays and safety studies are an essential component of novel therapy development, as this allows researchers to study both on-target and off-target effects of novel agents and provides information about potential toxicity and dose requirements for clinical trial development [5]. Pre-clinical models that are easy to use, relatively inexpensive, timely, and representative of the tumor and tumor micro-environment are most informative. In recent times, zebrafish have acquired huge popularity as a reliable model for cancer research due to their genetic similarities with humans, ease of genetic manipulation, in-vivo monitoring of tumor progression due to their transparent body structure in early developmental stages, high fecundity, low maintenance cost, and the ability to conduct drug and chemical screening in large numbers of animals. In this article, we review the potential and the unique attributes of zebrafish as pediatric cancer research models, as well as their comparative advantages over other preclinical models.

2. In Vitro Models for Pediatric Cancers

Cancer cell lines are the most applicable and cost-effective models for studying a tumor’s molecular characteristics as well as high throughput screening for the identification of potential drug targets. Cancer cells that are predominantly used for investigating tumor biology and in vitro drug testing fall into two categories: (i) cell lines developed from human samples and (ii) cell lines derived from animal models. These two categories are further subdivided into (i) primary tumor cells, which are derived directly from the tumor samples; (ii) genetically altered healthy cells to convert them into converted to tumor cells; and (iii) iPSC-derived tumor cells [6,7,8,9,10]. These cell lines normally retain their tumorigenic characteristics and are often immortalized to obtain an indefinite life span. Most of the cancer cell lines are simple to create, can be stored for decades, and are useful in studying the molecular mechanisms of tumor development and metastasis [11,12].
The major drawbacks of the in vitro cell culture models are their inability to provide a proper tumor microenvironment and failure to address the cause of cancer recurrence [13,14]. The tumor microenvironment is made up of tumor cells; stromal cells such as endothelial cells, immune cells, and fibroblasts; and extra-cellular matrix (ECM) components, like collagen, laminin, fibronectin, etc. [15,16,17]. These tumor–stromal–ECM interactions cannot be captured in traditional monolayer cell culture. Recent advancements in molecular cell biology and tissue engineering have resulted in the development of more robust ‘3D cell culture’ and ‘organoid’ models to address these shortcomings. In 3D cell culture, cells are allowed to grow and interact with the surrounding ECM framework in three dimensions. Organoids, which are also developed in a 3D cultural framework, are miniature versions of organs derived from stem cells that replicate the morphology, cell types, and functions of their in vivo counterparts [18,19,20]. Organoids can provide tumor microenvironment and cellular interactions to a certain extent, which makes them more efficient for drug screening than conventional cell culture models [21]. In 2020, Krieger et al. developed a cerebral organoid model that served as a scaffold for glioblastoma (GBM) cell invasion. Transcriptional analysis of GBM organoids revealed possible ligand–receptor interactions between tumor and organoid cells [22]. Huang et al. developed an organoid model for meningioma, and immunohistochemistry analysis revealed striking similarities in cellular heterogeneity between meningioma patient samples and meningioma organoids, which include tumor cells, T-lymphocytes, macrophages, and vascular endothelial cells [23]. However, high-cost, complex model development protocols, lack of proper vasculature, and poor controllability limit the applications and clinical implications of these models [24].

3. In Vivo Models for Pediatric Cancers

A number of animal models have been established for studying pediatric tumors over the past six decades. Invertebrates, including C. elegans and Drosophila, to vertebrates, such as rodents, felines, canines, and non-human primates [25,26,27,28,29], have been utilized to evaluate potential therapeutic strategies and cures for this deadly disease. Rodent models, particularly mice and rats, are the most used animal models in pediatric cancer research. Having more than 80% genetic similarity to humans as well as comparable anatomical, molecular, and biological characteristics make them the most popular models for human disease research [30,31]. Mouse tumor models, the most popular model among cancer biologists, can be broadly divided into two categories: (i) xenograft models, and (ii) transgenic or genetically engineered mouse models (GEMMs). Traditional patient-derived xenograft (PDX) models are created by implanting or injecting human malignant cells into immunodeficient mice, as wild-type mice with functional immune systems reject foreign tumor cells or tissues. By contrast, patient-derived orthotopic xenografts (PDOXs) are created by implanting tumor tissue or cells from a patient into a mouse in the same anatomic location in order to further emulate the tumor microenvironment. The major disadvantages of rodent models are the differences in physiological parameters compared with humans, the lower complexity of the tumor microenvironment, and the rapid clearance of drugs from the body due to fast liver metabolism [32,33,34,35,36]. In addition to that, they are also time-consuming and expensive to develop and cannot be shared among scientists, and perhaps most significantly, they require immunocompromised animals for developing patient-derived xenograft models [37,38]. Although scientists have created immunocompetent syngeneic mouse models (SMMs) and engrafted tumor cell lines from the same genetic background to avoid immune rejection, they lack inter- and intra-patient tumor microenvironment heterogeneity and differ in tumor growth kinetics [39,40]. GEMMs, on the other hand, are developed to study the role of specific genes by deleting, overexpressing, or mutating, which results in spontaneous tumor formation. Although they are the most accurate histological and genetic models of pediatric cancers, they have several drawbacks, including the variable and unpredictable nature of tumor development, a lack of complex genomic landscapes found in human tumors, and a longer tumor development time [41,42]. Zebrafish pediatric cancer models bridge this gap. In the following sections, we will detail the benefits of zebrafish models and highlight shortcomings that need to be addressed in future studies. A comparison between different in vitro and in vivo models is summarized in Table 1.

4. Zebrafish—A Tiny Human

Zebrafish (Danio rerio) were first discovered by F. Hamilton in the Ganges River in northeastern India in the 1820s and were described as “beautiful fish” with “several blue and silver stripes on each side” [43,44]. Zebrafish are tropical, freshwater fish belonging to the minnow family (Cyprinidae) of the order Cypriniformes. It is an ideal organism to maintain under laboratory conditions due to its small size, high fecundity rate, and ability to reach sexual maturity by 3 months of age [45]. Zebrafish breed throughout the year and produce a large number of embryos per cross. Furthermore, fertilization is external, and developing embryos are transparent, allowing the visualization of almost all organs using a simple dissection microscope [46]. The embryo develops into a free-swimming larva 3–4 days post fertilization (dpf). By observing these advantages, Dr. George Streisinger at the University of Oregon realized the potential of zebrafish as a suitable model organism and introduced them as a model system in biological research in 1972. Sanger’s Institute initiated the Zebrafish Reference Genome Sequencing project in 2001, which was completed in 2013 [47]. The sequencing data revealed 71.4% of the human genome to be conserved in zebrafish, with 82% of human disease-related genes having at least one ortholog in zebrafish [47]. In 2020, Yang et al. developed a detailed map of zebrafish transcriptomes, cis-regulatory elements, heterochromatin regions, methylomes, and 3D genome organization using a combination of advanced techniques, including RNA sequencing, assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-Seq), whole-genome bisulfite sequencing (WGBS), and chromosome conformation capture (Hi-C) experiments. When comparing zebrafish regulatory elements with those of humans and mice, they found both evolutionarily conserved and species-specific regulatory sequences and networks [48].
Zebrafish and humans share conserved organ systems, including the mouth, eyes, brain and spinal cord, intestine, pancreas, liver with bile ducts, kidney, esophagus, heart, ear, nose, muscle, blood, bone, cartilage, and teeth (Figure 1). Most of the important biological and metabolic pathways are also similar in zebrafish (KEGG pathway: zebrafish) providing the opportunity to study an array of human diseases using zebrafish models.

5. Zebrafish Models for Human Disorders

Since the 1980s, zebrafish have been employed in biomedical research to model a broad spectrum of human diseases. Zebrafish models have been successfully developed to study disorders ranging from neurodevelopmental disorders to those that are metabolic in nature [49,50].
Zebrafish models demonstrated comparable and quantitative changes in their social and cognitive behavior, altered locomotion, and even increased head size, all of which are typical of autism spectrum disorders (ASDs) [49,51,52,53]. Similarly, another neurodevelopmental disorder, Rett syndrome, was effectively modeled in zebrafish, which had observable behavioral alterations throughout their early infancy, including spontaneous and sensory-evoked motor abnormalities as well as thigmotaxis deficits [54,55]. Zebrafish have also been effectively utilized to model neurodegenerative diseases [56], behavioral disorders [57,58], neuromuscular disorders [59,60,61,62,63], aging-related disorders [64,65,66], hematopoietic disorders [67,68,69,70], renal diseases [71,72,73,74], liver diseases [74,75,76,77], and eye diseases [78,79,80,81], to mention a few. In recent years, zebrafish have also been used to study infectious viral diseases, such as Zika [82] and COVID-19 [83].

6. Zebrafish Models for Cancer Research

The utilization of zebrafish in cancer research has a long history. It was first reported in 1965 when Dr. Stantion induced irreversible liver damage and hepatic neoplasms in zebrafish by using the water-soluble carcinogen diethylnitrosamine [84]. Cancer modeling in zebrafish is mostly achieved in three ways: (i) the forward genetics method, (ii) the reverse genetics approach, and (iii) xenotransplantation. In 1996, Haffter et al. published their landmark study of a large-scale mutagenesis screen, generating numerous mutant zebrafish lines that are still useful in present-day cancer research [85]. Utilizing a similar forward genetics approach, Lee G. Beckwith and Dr. Jan M. Spitsbergen developed many neoplasms, such as papilloma, hemangiomas, hepatocellular adenoma, and rhabdomyosarcoma, by using common mutagens, like ethylnitrosourea (ENU) and N-methyl-nitrosoguanidine (MNNG) [86,87]. Several innovative strategies for gene knockdown, gene editing, and transgene insertion into the zebrafish genome have emerged in recent years. These reverse genetic techniques attempt to create a loss-of-function phenotype or transfer genes identified to be altered in human patients with cancer into fish.
Mutations in cancer predisposition genes (CPGs) are often associated with an increased susceptibility to cancer. Inherited mutations in CPGs cause approximately 10% of cancers in humans. More than 100 CPGs with diverse cellular and molecular functions have been identified, which might provide insight into the prevention, diagnosis, and optimization of cancer management [88,89]. Mutation CPGs linked to DNA repair, genome stability, signaling pathways, transcriptional regulation, epigenetic modifications, telomere maintenance, and metabolism often increase the susceptibility to tumor development. The utilization of zebrafish models to investigate the roles of CPGs in cancer was extensively discussed in a review by Kobar et al. [90].

7. Pediatric Cancer Models in Zebrafish

Compared with adult tumors, pediatric tumors often exhibit low mutational burdens and are frequently driven by singular driver genes, oncoproteins, or copy number variations [91]. The most common types of cancers diagnosed in children and adolescents (ages 0–19 years) are leukemias, CNS tumors, and sarcomas [92]. In the subsequent sections, we elaborate on non-central nervous system (CNS) and CNS pediatric cancer models in zebrafish.

8. Non-CNS Pediatric Cancer Models in Zebrafish

The most commonly diagnosed cancer in children is acute lymphoblastic leukemia (ALL). Although the survival rate for this type of cancer is high, many long-term detrimental side effects are seen in these patients due to the current treatment procedures [93]. Given these considerations, ongoing studies aim to minimize treatment-related toxicity and develop targeted therapies for recurrence and high-risk patients. ALL can be broadly classified into B-cell acute lymphoblastic leukemia (B-ALL), and T-cell acute lymphoblastic leukemia (T-ALL). Mariotto et al. developed a xenograft model for B-ALL in zebrafish. They identified BCL2-associated athanogene-1 (BAG1) as a potential target due to its increased expression during cancer relapse. They conducted transient knockdown of BAG1 protein expression in RS4;11 leukemia cells and xenografted them into zebrafish embryos two days post fertilization (dpf). Their study revealed that anti-cancer drugs such as dexamethasone, daunorubicin, and the BCL2 inhibitor ABT-737 exhibited greater sensitivity in BAG1-depleted cells without any toxicity, whereas pan-BCL inhibitors caused cytotoxic effects in zebrafish [94]. T-ALLs are biologically different from B-ALLs, and they can be subgrouped according to targetable pathways, such as Notch, Jak/Stat, PI3K/Akt/mTOR, and MAPK [95]. Hedgehog pathway mutations, particularly those affecting PTCH1 expressions, are quite common (16%) in patients with T-cell acute lymphoblastic leukemia (T-ALL). Burns et al. developed a ptch1 CRISPR knockout zebrafish model for T-ALL. Their findings indicated that ptch1 mutations expedited the onset of notch1-induced T-ALL. Additionally, the study suggested that the inhibition of the Hedgehog pathway could serve as a targeted therapy for high-risk T-ALL [96].
Rhabdomyosarcoma, which is the most common soft-tissue sarcoma diagnosed in children, constitutes approximately 3–4% of all pediatric cancers [97,98]. Alterations in the RAS/MAPK signaling pathway are reported in many patients with rhabdomyosarcoma [99] Kahsay et al. demonstrated a significant downregulation of the RAS/MAPK pathway in pax3 double mutant zebrafish (pax3a−/−; pax3b−/−), which resulted in a delayed progression of kRAS-induced rhabdomyosarcoma [100].
While dysregulation of HES3 was observed in children with fusion-positive rhabdomyosarcoma, the precise mechanism of HES3 involvement in this pediatric cancer remained unclear. To elucidate this, Kent et al. developed a her3 (ortholog of human HES3) knockout zebrafish model of rhabdomyosarcoma. Transcriptomic analysis of the her3 mutant zebrafish unveiled the impact on several cancer-related gene pathways, along with the downregulation of genes involved in organ development, such as pctp and grinab [101].
We have listed examples of non-CNS pediatric cancer models in zebrafish in Table 2.

9. Pediatric CNS Cancer Models in Zebrafish

CNS tumors are the second-most common tumors and the most common solid tumors in children and the leading cause of pediatric cancer-related death [1,134,135]. Alongside various in-vitro and in vivo models, many zebrafish models have also been developed to study these deadly pediatric cancers. Before discussing different zebrafish brain cancer models, we believe it is important to emphasize the remarkable similarities between zebrafish and human brain anatomy and functions.

10. Zebrafish Brain

Although the zebrafish is a teleost, the general macro-organization of the brain and cellular anatomy of zebrafish and humans are remarkably similar (Figure 2). The zebrafish brain has all the key brain anatomical components seen in humans [136]. Adult zebrafish have well-developed forebrain, midbrain, and hindbrain with prominent optic tectum, thalamic, and hypothalamic regions [137]. The embryonic forebrain of zebrafish develops into the telencephalon, diencephalon, hypothalamus, and retina [138,139]. The zebrafish telencephalon comprises the pallium, sub-pallium, and olfactory bulb, and it is important for their social behavior, memory, and emotions [140,141]. The thalamus, pineal body, and habenula constitute the zebrafish diencephalon, which regulates their attention, alertness, and circadian patterns [142]. The tectum and tegmentum are two key structures in the zebrafish midbrain that are important for vision, hearing, motivation, and reward [143,144]. The zebrafish hindbrain appears as a distinct structure posterior to the midbrain at embryonic developmental stages. It is separated from the midbrain by a temporary structure called the midbrain–hindbrain border (MHB), which is absent in adult zebrafish. The hindbrain regulates eye, jaw, and head movement and gives rise to the cerebellum, which is responsible for motor control, sensory input reception and response, cognition, emotion, and learning [145,146,147,148,149]. Human and zebrafish developmental gene expression shifting patterns are also remarkably comparable, highlighting the significance of this model in studying childhood brain disorders [150]. The neurochemical aspects of the zebrafish brain are also very similar to those of humans. Zebrafish possess all major neuromediators, including receptors, neurotransmitters, transporters, and enzymes [151,152,153,154,155,156,157,158,159]. Zebrafish have well-developed functional neuroendocrine systems like those present in mammals. The zebrafish stress response is driven by the hypothalamic-pituitary hormonal cascade and is regulated by cortisol, which acts through the glucocorticoid receptor, which is very similar in humans [160,161,162,163].

11. Zebrafish Models of Childhood CNS Cancer

Gliomas are the most frequent pediatric tumors, accounting for around half of all brain cancer cases recorded in children [134,135]. These CNS tumors can be classified as low- or high-grade gliomas depending on their malignant nature. Zebrafish glioma models, like other in vitro and in vivo models, have been effectively utilized for investigating these lethal CNS tumors, drug toxicity, and novel therapeutic inventions. Among Glioblastomas, the NF1 gene was found to be mutated in 20% of these cases. Shin et al. developed a glioblastoma model by developing a stable mutant nf1 zebrafish line using zinc finger nuclease (ZFN) and targeting induced local lesions in genomes (TILLING) gene editing techniques. They reported that the zebrafish carried at least one copy of the type nf1 allele (nf1a or nf1b), were viable and fertile, and showed no tumor formation. However, when both the alleles of nf1a and nf1b genes were absent, the nf1a–/–; nf1b–/– larvae displayed melanophore defects 6 days post fertilization (dpf), and they did not survive beyond 10 dpf. Although these nf1a–/–; nf1b–/– larvae did not develop tumors, they exhibited defects in glial cells and pigment cells, which are often observed in NF1 human patients. Moreover, in a p53 mutant background, nf1a+/–; nf1b–/–; p53e7/e7 zebrafish developed adult-onset high-grade gliomas and malignant peripheral nerve sheath tumors, which resemble human NF1 HGGs [165].
The tumor microenvironment is critical for tumor cell survival and metastasis, and microglia and infiltrating macrophages are two major cell types found in about 30% of HGG tumor tissues [166]. Chia et al. reported in 2018 that the neuron-specific overexpression of human AKT1 in zebrafish resulted in a large increase in macrophage and microglia populations. They developed the transgenic zebrafish lines Tg(NBT:∆LexPR-lexOP-pA; mpeg1:EGFP) and Tg(mpeg1:mCherry; p2ry12:p2ry12-GFP) and also utilized cxcr4b−/− mutant zebrafish to study the macrophage and microglial cell behavior inside the developing zebrafish brain. They demonstrated that the peripheral macrophage infiltration into the brain occurred through Sdf1b–Cxcr4b signaling [167].
CNS primitive neuro-ectodermal tumors (CNS-PNETs), presently classified as embryonal tumors, belong to the embryonal family of malignant childhood brain tumors. CNS primitive neuro-ectodermal tumors (CNS-PNETs) constitute a heterogeneous group of brain embryonal tumors that includes all CNS embryonal malignancies not diagnosed as medulloblastoma, atypical teratoid/rhabdoid tumors (AT/RT), or embryonal tumors with multilayered rosettes (ETMRs). Histologically, CNS-PNETs are identified by the presence of small, poorly differentiated cells and a mixed population of both glial and neuronal lineages. The 5-year progression-free survival rate for CNS-PNETs is currently as low as 30% [168,169,170,171]. Schultz et al. created an embryonal brain tumor model with homozygous rb1 loss of function via CRISPR mutagenesis that closely mirrored human CNS-PNETs [172]. Zebrafish rb1 tumors, like the human oligoneural OLIG2+/SOX10+ CNS-PNET subtype, displayed an over-expression of neural progenitor transcription factors, such as sox8b, sox10, and olig2, as well as the erbb3a oncogene [172]. Another zebrafish embryonal tumor model was developed by Modzelewska et al. through NRAS activation in Olig2+/Sox10+ oligoneural precursor cells. Genomic analysis revealed a striking similarity with human NB-FOXR2 CNS-PNET subgroup tumors. They also generated an orthotropic embryonic brain tumor transplantation assay for drug screening by allografting tumors from Tg (mitfaw2; p53M214K; Tg(sox10:mCherry-NRASWT) fish into 2dpf mitfw2 embryos and found that MEK inhibitors could remove these tumors in 79% of the fish [173].
Apart from transgenic tumor models, there are many excellent studies in which the investigators used zebrafish xenograft models (both tumor cell lines and patient-derived tumor cells) to study tumor biology, drug screening, and future immunotherapeutics. Zebrafish have a delayed adaptive immune system, as T and NK cells develop at 5 dpf and B cells by 21 dpf, which provides researchers with a short but valuable window to perform xenograft experiments [174]. These xenograft models are fast and can be developed only in 5 days, and most importantly, they match mouse xenograft models in terms of tumor growth and initiation [175]. Zebrafish xenograft models can be used for studying tumor biology or for drug discovery purposes. Hamilton et al. developed a xenograft model by injecting U87 and U251 glioblastoma cells to investigate the interaction between microglia and glioma in vivo. Their findings revealed variations in the growth rates and microglial interactions of different glioma cells. Notably, when U87 cells were xenografted into irf8−/− zebrafish mutant embryos, which lack microglia, the injected tumor cells exhibited a significant reduction in cell survival compared with wild-type embryos. This study underscores the crucial role of microglia in the dynamics of glioma growth [176]. Pudelko et al. orthotopically injected patient-derived tumor cells in zebrafish larvae to study the effect of MTH1 inhibitors in glioblastoma in real-time. They found that their in-house-developed MTH1 inhibitor, TH1579, was able to successfully irradicate glioblastoma stem stems in zebrafish [177].
Most xenograft experiments in zebrafish are performed between 5–7 days to avoid immune rejection. To address this issue, Prof. David Langenau’s group at Massachusetts General Hospital and the Harvard Stem Cell Institute developed an immunodeficient (prkdc−/−, il2rga−/−) casper-strain zebrafish that lacked T, B, and natural killer (NK) cells. These immunodeficient zebrafish can survive at 37 °C, which is ideal for tumor cell growth, and they can be engrafted with up to 1.5 × 106 tumor cells. The xenografted cells were able to survive in adult zebrafish for 28 days [178,179]. These fish models can be utilized in long-term xenograft studies and studies on accurate preclinical drug dosing in adult zebrafish [180]. We have listed the most common childhood brain cancer models developed in zebrafish in Table 3.

12. Humanized Zebrafish

Although xenograft and transgenic models are excellent tools for studying human illnesses because they provide a better microenvironment, none of them can mimic one hundred percent of human conditions. To bridge this gap, scientists are continuously developing humanized model organisms. Zhu et.al. published an excellent review on humanized mouse models and their benefits in studying human health and disease [185]. Similar to mice, humanized zebrafish models have been created to better understand human disorders and bridge the gap between species differences. The first such humanized zebrafish model was developed by Rajan et. al. in 2020, who created ‘GSS fish’ that expressed human hematopoietic-specific cytokines, GM-CSF, SCF, and SDF1α. By stimulating the self-renewal and multilineage differentiation of human hematopoietic stem and progenitor cells, these GSS zebrafish could establish a better microenvironment for human leukemia cells [186]. Similarly, another humanized zebrafish model was developed by Häberlein et. al. to study multiple sclerosis. They first created a zebrafish gpr17 loss-of-function transgenic animal and inserted the human GPR17 gene into their genome so that they could only express human GPR17 protein. This humanized zebrafish model could be essential in studying multiple sclerosis and identifying pro-remyelination compounds [187]. Many more humanized zebrafish will be generated in the near future to identify the optimal treatment for various human diseases at a personalized level.

13. Zebrafish as a Tool for Cancer Drug Discovery

The zebrafish model serves as a link between in vitro and in vivo investigations in mammals. This model is powerful in terms of its range of applicability and research tractability. In past decades, numerous studies have used zebrafish models to understand disease biology, drug efficacy, and toxicity. In cancer research, drug toxicity is a major concern. When compared with in vitro and other in vivo models, drug screening and toxicity studies in zebrafish can be performed in a comparatively short period of time and with more physiological relevance. Patient-derived avatar models in early larval stages in zebrafish are ideal for large-scale drug screening purposes. Xenografted embryos can be maintained in 96-well plates, and they easily absorb water-soluble drugs from the culture media, which is mostly E3 medium or fish water [188]. Drugs that are non-toxic and effective against tumor cells are selected for future studies, which include target validations, in vivo murine models, and, finally, translation to clinical trials (Figure 3). These drug toxicity tests are often performed in accordance with OECD recommendations, which are compilations of the most relevant internationally agreed-upon testing methodologies used by governments, industry, and independent laboratories to assess the safety of chemicals (https://www.oecd.org (accessed on 1 May 2023)).
To measure toxicity in zebrafish embryos, specific endpoints, such as egg coagulation, lack of somite development, non-detachment of the tail, and the absence of heartbeat, are used. In addition to these endpoints, researchers frequently quantify the heartbeat, coiling behavior, hatching percentage, yolk sack edema, pericardial edema, yolk sac necrosis, and tail curvature to measure the toxicity level of any compound (Figure 4). These additional observations are important for assessing the maximum tolerated dose/concentration.

14. Zebrafish: A Rapid and Cost-Effective Pediatric Cancer Model Organism

Time is essential in pediatric cancer treatment. The overall survival time of patients with pediatric cancer varies depending on the cancer type. CNS tumors, particularly aggressive ones like atypical teratoid tumors, exhibit a remarkably low overall survival time of just 6 months, whereas in patients with diffuse intrinsic pontine gliomas (DIPGs), the survival rate is less than one year [189,190]. Developing xenograft models in rodents to study these cancers takes at least 3–6 months [191]. Moreover, conducting large-scale drug screenings in rodent models is not only time-consuming but also presents ethical concerns and substantial costs. On the other hand, zebrafish models offer a cost-effective and rapid in vivo platform for studying brain cancers. The estimated cost for the in vivo screening of a single drug in zebrafish is approximately USD 300, making it 500 times more economical than comparable studies in rat models. In a parallel two-week in vivo study comparing zebrafish and mice, zebrafish models were found to be five times more cost-effective than their mouse counterparts [45]. Most of the xenograft studies in zebrafish can be completed within a week and yield results comparable to those of their mouse xenograft counterparts [175].

15. Concluding Remarks and Future Perspectives

Although high-grade childhood cancers are often termed as ‘rare’ diseases, they are a major concern across the medical field because of their severity and social impact. Aggressive pediatric cancers are also different from adult tumors in terms of age of onset, occurrence, progression, and treatment. Most of the time, patients’ mean survival time is far shorter, and it is difficult or impossible to develop rodent PDX models in that timeframe. In recent decades, the zebrafish has become an important model organism in strengthening our understanding of cancer etiologies, the involvement of the immune system in the cancer microenvironment, and drug screening. Whereas transgenic zebrafish models can be used to study tumor biology, disease progression, and cellular interactions in detail, xenograft models can be created rapidly and utilized to test hundreds of medicines, and most importantly, they can be used to find new therapies on a personalized level. Recent advances in zebrafish research include ‘humanized zebrafish’, which allows for a more intimate tumor microenvironment, and ‘immunodeficient zebrafish’, which allows for long-term xenograft trials, making this model more relevant and attractive.
There are some disadvantages of using zebrafish as a model for toxicity and drug testing. In the embryo toxicity assay, commonly, drugs or compounds are directly added to the water, and zebrafish embryos are exposed to these solutions. In the early stages of development, the protective chorion of zebrafish embryos may prevent the entry of compounds that have molecular weights of more than 4000 Da [192]. Additionally, the zebrafish skin also acts as a barrier to many drugs. In both low-water-soluble and water-insoluble drug testing, low sensitivity and inconsistent results are common outcomes. Various factors, including different routes of drug exposure, inaccuracies in determining the drug concentration within the embryos, temperature variations, lack of adaptive immunity in larvae, and external environmental conditions, can collectively reduce the clinical relevance of the obtained results. Consequently, alternative administration routes, such as gavaging and microinjection, become necessary, leading to time-consuming and technically challenging procedures. Addressing these challenges requires a deep understanding of the drug/compound’s structure, size, and solubility as well as careful experimental design and the implementation of various administration methods.
In the future, many unanswered questions need to be addressed. The refinement of existing models, drug delivery methods, and the development of more immunodeficient and humanized zebrafish models will help us solve many unresolved questions related to childhood cancers. Zebrafish have already demonstrated their relevance in several human disease studies, including studies on pediatric cancers, and we hope that they will soon be acknowledged as a model animal for clinical trials.

Author Contributions

Conceptualization, D.R. and J.N.; Writing—Original draft preparation, D.R., B.S. and M.B.; Review and editing, D.R. and J.N.; Visualization, D.R., B.S. and W.C.C.; Supervision, J.N.; Funding acquisition, J.N. and R.J.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by an anonymous family donor, grant number: PID-30007084.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Siegel, D.A.; King, J.B.; Lupo, P.J.; Durbin, E.B.; Tai, E.; Mills, K.; Van Dyne, E.; Buchanan Lunsford, N.; Henley, S.J.; Wilson, R.J. Counts, Incidence Rates, and Trends of Pediatric Cancer in the United States, 2003–2019. JNCI J. Natl. Cancer Inst. 2023, 115, 1337–1354. [Google Scholar] [CrossRef] [PubMed]
  2. Agarwal, S. Pediatric Cancers: Insights and Novel Therapeutic Approaches. Cancers 2023, 15, 3537. [Google Scholar] [CrossRef] [PubMed]
  3. Smeland, S.; Bielack, S.S.; Whelan, J.; Bernstein, M.; Hogendoorn, P.; Krailo, M.D.; Gorlick, R.; Janeway, K.A.; Ingleby, F.C.; Anninga, J.; et al. Survival and Prognosis with Osteosarcoma: Outcomes in More than 2000 Patients in the EURAMOS-1 (European and American Osteosarcoma Study) Cohort. Eur. J. Cancer 2019, 109, 36–50. [Google Scholar] [CrossRef] [PubMed]
  4. Ostrom, Q.T.; Cioffi, G.; Waite, K.; Kruchko, C.; Barnholtz-Sloan, J.S. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014–2018. Neuro Oncol. 2021, 23 (Suppl. 3), iii1–iii105. [Google Scholar] [CrossRef] [PubMed]
  5. Huang, W.; Percie du Sert, N.; Vollert, J.; Rice, A.S.C. General Principles of Preclinical Study Design. In Handbook of Experimental Pharmacology; Springer: Berlin/Heidelberg, Germany, 2020; Volume 257, pp. 55–69. [Google Scholar] [CrossRef]
  6. Hahn, W.C.; Counter, C.M.; Lundberg, A.S.; Beijersbergen, R.L.; Brooks, M.W.; Weinberg, R.A. Creation of Human Tumour Cells with Defined Genetic Elements. Nature 1999, 400, 464–468. [Google Scholar] [CrossRef] [PubMed]
  7. Chen, L.; Kasai, T.; Li, Y.; Sugii, Y.; Jin, G.; Okada, M.; Vaidyanath, A.; Mizutani, A.; Satoh, A.; Kudoh, T.; et al. A Model of Cancer Stem Cells Derived from Mouse Induced Pluripotent Stem Cells. PLoS ONE 2012, 7, e33544. [Google Scholar] [CrossRef] [PubMed]
  8. Stackhouse, C.T.; Gillespie, G.Y.; Willey, C.D. Cancer Explant Models. In Three Dimensional Human Organotypic Models for Biomedical Research; Current Topics in Microbiology and Immunology; Springer: Cham, Switzerland, 2021; Volume 430, p. 131. [Google Scholar] [CrossRef]
  9. Choorapoikayil, S.; Overvoorde, J.; Den Hertog, J. Deriving Cell Lines from Zebrafish Embryos and Tumors. Zebrafish 2013, 10, 316. [Google Scholar] [CrossRef] [PubMed]
  10. Papapetrou, E.P. Patient-Derived Induced Pluripotent Stem Cells in Cancer Research and Precision Oncology. Nat. Med. 2016, 22, 1392. [Google Scholar] [CrossRef]
  11. Simeonova, I.; Huillard, E. In Vivo Models of Brain Tumors: Roles of Genetically Engineered Mouse Models in Understanding Tumor Biology and Use in Preclinical Studies. Cell. Mol. Life Sci. 2014, 71, 4007. [Google Scholar] [CrossRef]
  12. Webber, M.M.; Bello, D.; Quader, S. Immortalized and Tumorigenic Adult Human Prostatic Epithelial Cell Lines: Characteristics and Applications Part 2. Tumorigenic Cell Lines. Prostate 1997, 30, 58–64. [Google Scholar] [CrossRef]
  13. Lindström, H.J.G.; Friedman, R. Inferring Time-Dependent Population Growth Rates in Cell Cultures Undergoing Adaptation. BMC Bioinform. 2020, 21, 583. [Google Scholar] [CrossRef]
  14. Harrison, N.J.; Baker, D.; Andrews, P.W. The Significance of Culture Adaptation of Embryonic Stem Cells for Regenerative Medicine. In Advances in Stem Cell Research; Springer: Cham, Switzerland, 2012; pp. 17–27. [Google Scholar] [CrossRef]
  15. Jahanban-Esfahlan, R.; Seidi, K.; Monhemi, H.; Adli, A.D.F.; Minofar, B.; Zare, P.; Farajzadeh, D.; Farajnia, S.; Behzadi, R.; Abbasi, M.M.; et al. RGD Delivery of Truncated Coagulase to Tumor Vasculature Affords Local Thrombotic Activity to Induce Infarction of Tumors in Mice. Sci. Rep. 2017, 7, 8126. [Google Scholar] [CrossRef]
  16. Hanahan, D.; Coussens, L.M. Accessories to the Crime: Functions of Cells Recruited to the Tumor Microenvironment. Cancer Cell 2012, 21, 309–322. [Google Scholar] [CrossRef] [PubMed]
  17. Huang, Y.L.; Liang, C.Y.; Ritz, D.; Coelho, R.; Septiadi, D.; Estermann, M.; Cumin, C.; Rimmer, N.; Schötzau, A.; López, M.N.; et al. Collagen-Rich Omentum Is a Premetastatic Niche for Integrin A2-Mediated Peritoneal Metastasis. eLife 2020, 9, e59442. [Google Scholar] [CrossRef] [PubMed]
  18. Langhans, S.A. Three-Dimensional in Vitro Cell Culture Models in Drug Discovery and Drug Repositioning. Front. Pharmacol. 2018, 9, 334617. [Google Scholar] [CrossRef]
  19. Edmondson, R.; Broglie, J.J.; Adcock, A.F.; Yang, L. Three-Dimensional Cell Culture Systems and Their Applications in Drug Discovery and Cell-Based Biosensors. Assay Drug Dev. Technol. 2014, 12, 207–218. [Google Scholar] [CrossRef] [PubMed]
  20. Kim, J.; Koo, B.K.; Knoblich, J.A. Human Organoids: Model Systems for Human Biology and Medicine. Nat. Rev. Mol. Cell Biol. 2020, 21, 571–584. [Google Scholar] [CrossRef]
  21. Huang, Y.; Huang, Z.; Tang, Z.; Chen, Y.; Huang, M.; Liu, H.; Huang, W.; Ye, Q.; Jia, B. Research Progress, Challenges, and Breakthroughs of Organoids as Disease Models. Front. Cell Dev. Biol. 2021, 9, 3259. [Google Scholar] [CrossRef]
  22. Krieger, T.G.; Tirier, S.M.; Park, J.; Jechow, K.; Eisemann, T.; Peterziel, H.; Angel, P.; Eils, R.; Conrad, C. Modeling Glioblastoma Invasion Using Human Brain Organoids and Single-Cell Transcriptomics. Neuro Oncol. 2020, 22, 1138. [Google Scholar] [CrossRef]
  23. Huang, M.; Xu, S.; Li, Y.; Shang, L.; Zhan, X.; Qin, C.; Su, J.; Zhao, Z.; He, Y.; Qin, L.; et al. Novel Human Meningioma Organoids Recapitulate the Aggressiveness of the Initiating Cell Subpopulations Identified by ScRNA-Seq. Adv. Sci. 2023, 10, 2205525. [Google Scholar] [CrossRef]
  24. Liu, J.; Huang, X.; Huang, L.; Huang, J.; Liang, D.; Liao, L.; Deng, Y.; Zhang, L.; Zhang, B.; Tang, W. Organoid: Next-Generation Modeling of Cancer Research and Drug Development. Front. Oncol. 2022, 11, 5897. [Google Scholar] [CrossRef] [PubMed]
  25. Kirienko, N.V.; Mani, K.; Fay, D.S. Cancer Models in Caenorhabditis Elegans. Dev. Dyn. 2010, 239, 1413–1448. [Google Scholar] [CrossRef]
  26. Mirzoyan, Z.; Sollazzo, M.; Allocca, M.; Valenza, A.M.; Grifoni, D.; Bellosta, P. Drosophila Melanogaster: A Model Organism to Study Cancer. Front. Genet. 2019, 10, 51. [Google Scholar] [CrossRef] [PubMed]
  27. Bayens-Simmonds, J.; Boisvert, D.P.J.; Castro, M.E.; Johnson, E.S. A Feline Model for Experimental Studies of Peritumor Brain Edema. J. Neurooncol. 1988, 6, 371–378. [Google Scholar] [CrossRef]
  28. Hicks, J.; Platt, S.; Kent, M.; Haley, A. Canine Brain Tumours: A Model for the Human Disease? Vet. Comp. Oncol. 2017, 15, 252–272. [Google Scholar] [CrossRef] [PubMed]
  29. Xia, H.J.; Chen, C.S. Progress of Non-Human Primate Animal Models of Cancers. Dongwuxue Yanjiu 2011, 32, 70–80. [Google Scholar] [CrossRef]
  30. Huszthy, P.C.; Daphu, I.; Niclou, S.P.; Stieber, D.; Nigro, J.M.; Sakariassen, P.O.; Miletic, H.; Thorsen, F.; Bjerkvig, R. In Vivo Models of Primary Brain Tumors: Pitfalls and Perspectives. Neuro Oncol. 2012, 14, 979–993. [Google Scholar] [CrossRef]
  31. Tratar, U.L.; Horvat, S.; Cemazar, M. Transgenic Mouse Models in Cancer Research. Front. Oncol. 2018, 8, 268. [Google Scholar] [CrossRef]
  32. Davies, B.; Morris, T. Physiological Parameters in Laboratory Animals and Humans. Pharm. Res. 1993, 10, 1093–1095. [Google Scholar] [CrossRef]
  33. Hetze, S.; Sure, U.; Schedlowski, M.; Hadamitzky, M.; Barthel, L. Rodent Models to Analyze the Glioma Microenvironment. ASN Neuro 2021, 13, 17590914211005074. [Google Scholar] [CrossRef]
  34. Terpstra, A.H.M. Differences between Humans and Mice in Efficacy of the Body Fat Lowering Effect of Conjugated Linoleic Acid: Role of Metabolic Rate. J. Nutr. 2001, 131, 2067–2068. [Google Scholar] [CrossRef] [PubMed]
  35. Chen, X.; Li, Y.; Yao, T.; Jia, R. Benefits of Zebrafish Xenograft Models in Cancer Research. Front. Cell Dev. Biol. 2021, 9, 616551. [Google Scholar] [CrossRef] [PubMed]
  36. Choi, T.Y.; Choi, T.I.; Lee, Y.R.; Choe, S.K.; Kim, C.H. Zebrafish as an Animal Model for Biomedical Research. Exp. Mol. Med. 2021, 53, 310–317. [Google Scholar] [CrossRef] [PubMed]
  37. Richter-Pechańska, P.; Kunz, J.B.; Bornhauser, B.; von Knebel Doeberitz, C.; Rausch, T.; Erarslan-Uysal, B.; Assenov, Y.; Frismantas, V.; Marovca, B.; Waszak, S.M.; et al. PDX Models Recapitulate the Genetic and Epigenetic Landscape of Pediatric T-Cell Leukemia. EMBO Mol. Med. 2018, 10, e9443. [Google Scholar] [CrossRef] [PubMed]
  38. Roussel, M.F.; Stripay, J.L. Modeling Pediatric Medulloblastoma. Brain Pathol. 2020, 30, 703–712. [Google Scholar] [CrossRef] [PubMed]
  39. Olson, B.; Li, Y.; Lin, Y.; Liu, E.T.; Patnaik, A. Mouse Models for Cancer Immunotherapy Research. Cancer Discov. 2018, 8, 1358. [Google Scholar] [CrossRef] [PubMed]
  40. Zeng, Z.; Wong, C.J.; Yang, L.; Ouardaoui, N.; Li, D.; Zhang, W.; Gu, S.; Zhang, Y.; Liu, Y.; Wang, X.; et al. TISMO: Syngeneic Mouse Tumor Database to Model Tumor Immunity and Immunotherapy Response. Nucleic Acids Res. 2022, 50, D1391–D1397. [Google Scholar] [CrossRef] [PubMed]
  41. Becher, O.J.; Holland, E.C. Genetically Engineered Models Have Advantages over Xenografts for Preclinical Studies. Cancer Res. 2006, 66, 3355–3359. [Google Scholar] [CrossRef] [PubMed]
  42. Sharpless, N.E.; DePinho, R.A. The Mighty Mouse: Genetically Engineered Mouse Models in Cancer Drug Development. Nat. Rev. Drug Discov. 2006, 5, 741–754. [Google Scholar] [CrossRef]
  43. Hamilton, F. An Account of the Fishes Found in the River Ganges and Its Branches; Archibald Constable and Company: Edinburgh, Scotland, 1822; Volume 1. [Google Scholar]
  44. Parichy, D.M. The Natural History of Model Organisms: Advancing Biology through a Deeper Understanding of Zebrafish Ecology and Evolution. eLife 2015. [Google Scholar] [CrossRef]
  45. Kalueff, A.V.; Stewart, A.M.; Gerlai, R. Zebrafish as an Emerging Model for Studying Complex Brain Disorders. Trends Pharmacol. Sci. 2014, 35, 63. [Google Scholar] [CrossRef] [PubMed]
  46. Hsu, C.-H.; Wen, Z.-H.; Lin, C.-S.; Chakraborty, C. The Zebrafish Model: Use in Studying Cellular Mechanisms for a Spectrum of Clinical Disease Entities. Curr. Neurovasc. Res. 2007, 4, 111–120. [Google Scholar] [CrossRef] [PubMed]
  47. Howe, K.; Clark, M.D.; Torroja, C.F.; Torrance, J.; Berthelot, C.; Muffato, M.; Collins, J.E.; Humphray, S.; McLaren, K.; Matthews, L.; et al. The Zebrafish Reference Genome Sequence and Its Relationship to the Human Genome. Nature 2013, 496, 498–503. [Google Scholar] [CrossRef] [PubMed]
  48. Yang, H.; Luan, Y.; Liu, T.; Lee, H.J.; Fang, L.; Wang, Y.; Wang, X.; Zhang, B.; Jin, Q.; Ang, K.C.; et al. A Map of Cis-Regulatory Elements and 3D Genome Structures in Zebrafish. Nature 2020, 588, 337–343. [Google Scholar] [CrossRef] [PubMed]
  49. Bernier, R.; Golzio, C.; Xiong, B.; Stessman, H.A.; Coe, B.P.; Penn, O.; Witherspoon, K.; Gerdts, J.; Baker, C.; Vulto-Van Silfhout, A.T.; et al. Disruptive CHD8 Mutations Define a Subtype of Autism Early in Development. Cell 2014, 158, 263–276. [Google Scholar] [CrossRef] [PubMed]
  50. Seth, A.; Stemple, D.L.; Barroso, I. The Emerging Use of Zebrafish to Model Metabolic Disease. Dis. Models Mech. 2013, 6, 1080. [Google Scholar] [CrossRef] [PubMed]
  51. de Abreu, M.S.; Genario, R.; Giacomini, A.C.V.V.; Demin, K.A.; Lakstygal, A.M.; Amstislavskaya, T.G.; Fontana, B.D.; Parker, M.O.; Kalueff, A.V. Zebrafish as a Model of Neurodevelopmental Disorders. Neuroscience 2020, 445, 3–11. [Google Scholar] [CrossRef]
  52. Meshalkina, D.A.; Kizlyk, M.N.; Kysil, E.V.; Collier, A.D.; Echevarria, D.J.; Abreu, M.S.; Barcellos, L.J.G.; Song, C.; Warnick, J.E.; Kyzar, E.J.; et al. Zebrafish Models of Autism Spectrum Disorder. Exp. Neurol. 2018, 299, 207–216. [Google Scholar] [CrossRef]
  53. Rea, V.; Van Raay, T.J. Using Zebrafish to Model Autism Spectrum Disorder: A Comparison of ASD Risk Genes Between Zebrafish and Their Mammalian Counterparts. Front. Mol. Neurosci. 2020, 13, 207. [Google Scholar] [CrossRef]
  54. Nozawa, K.; Lin, Y.; Kubodera, R.; Shimizu, Y.; Tanaka, H.; Ohshima, T. Zebrafish Mecp2 Is Required for Proper Axonal Elongation of Motor Neurons and Synapse Formation. Dev. Neurobiol. 2017, 77, 1101–1113. [Google Scholar] [CrossRef]
  55. Pietri, T.; Roman, A.C.; Guyon, N.; Romano, S.A.; Washbourne, P.; Moens, C.B.; de Polavieja, G.G.; Sumbre, G. The First Mecp2-Null Zebrafish Model Shows Altered Motor Behaviors. Front. Neural Circuits 2013, 7, 118. [Google Scholar] [CrossRef] [PubMed]
  56. Chia, K.; Klingseisen, A.; Sieger, D.; Priller, J. Zebrafish as a Model Organism for Neurodegenerative Disease. Front. Mol. Neurosci. 2022, 15, 376. [Google Scholar] [CrossRef] [PubMed]
  57. Blaser, R.E.; Chadwick, L.; McGinnis, G.C. Behavioral Measures of Anxiety in Zebrafish (Danio Rerio). Behav. Brain Res. 2010, 208, 56–62. [Google Scholar] [CrossRef] [PubMed]
  58. Norton, W.; Bally-Cuif, L. Adult Zebrafish as a Model Organism for Behavioural Genetics. BMC Neurosci. 2010, 11, 90. [Google Scholar] [CrossRef] [PubMed]
  59. Singh, J.; Patten, S.A. Modeling Neuromuscular Diseases in Zebrafish. Front. Mol. Neurosci. 2022, 15, 698. [Google Scholar] [CrossRef] [PubMed]
  60. Sztal, T.E.; Sonntag, C.; Hall, T.E.; Currie, P.D. Epistatic Dissection of Laminin-Receptor Interactions in Dystrophic Zebrafish Muscle. Hum. Mol. Genet. 2012, 21, 4718–4731. [Google Scholar] [CrossRef] [PubMed]
  61. Parsons, M.J.; Campos, I.; Hirst, E.M.A.; Stemple, D.L. Removal of Dystroglycan Causes Severe Muscular Dystrophy in Zebrafish Embryos. Development 2002, 129, 3505–3512. [Google Scholar] [CrossRef]
  62. Bolaos-Jiménez, F.; Bordais, A.; Behra, M.; Strähle, U.; Sahel, J.; Rendón, A. Dystrophin and Dp71, Two Products of the DMD Gene, Show a Different Pattern of Expression during Embryonic Development in Zebrafish. Mech. Dev. 2001, 102, 239–241. [Google Scholar] [CrossRef]
  63. Bassett, D.I.; Currie, P.D. The Zebrafish as a Model for Muscular Dystrophy and Congenital Myopathy. Hum. Mol. Genet. 2003, 12 (Suppl. 2), R265–R270. [Google Scholar] [CrossRef]
  64. Arbones, M.L.; Thomazeau, A.; Nakano-Kobayashi, A.; Hagiwara, M.; Delabar, J.M. DYRK1A and Cognition: A Lifelong Relationship. Pharmacol. Ther. 2019, 194, 199–221. [Google Scholar] [CrossRef]
  65. Liang, S.T.; Audira, G.; Juniardi, S.; Chen, J.R.; Lai, Y.H.; Du, Z.C.; Lin, D.S.; Hsiao, C. Der. Zebrafish Carrying Pycr1 Gene Deficiency Display Aging and Multiple Behavioral Abnormalities. Cells 2019, 8, 453. [Google Scholar] [CrossRef]
  66. Anchelin, M.; Murcia, L.; Alcaraz-Pérez, F.; García-Navarro, E.M.; Cayuela, M.L. Behaviour of Telomere and Telomerase during Aging and Regeneration in Zebrafish. PLoS ONE 2011, 6, e16955. [Google Scholar] [CrossRef]
  67. Gore, A.V.; Pillay, L.M.; Venero Galanternik, M.; Weinstein, B.M. The Zebrafish: A Fintastic Model for Hematopoietic Development and Disease. Wiley Interdiscip. Rev. Dev. Biol. 2018, 7, e312. [Google Scholar] [CrossRef] [PubMed]
  68. Rasighaemi, P.; Basheer, F.; Liongue, C.; Ward, A.C. Zebrafish as a Model for Leukemia and Other Hematopoietic Disorders. J. Hematol. Oncol. 2015, 8, 29. [Google Scholar] [CrossRef] [PubMed]
  69. Berman, J.; Hsu, K.; Look, A.T. Zebrafish as a Model Organism for Blood Diseases. Br. J. Haematol. 2003, 123, 568–576. [Google Scholar] [CrossRef] [PubMed]
  70. Huang, Y.; Yu, S.H.; Zhen, W.X.; Cheng, T.; Wang, D.; Lin, J.B.; Wu, Y.H.; Wang, Y.F.; Chen, Y.; Shu, L.P.; et al. Tanshinone I, a New EZH2 Inhibitor Restricts Normal and Malignant Hematopoiesis through Upregulation of MMP9 and ABCG2. Theranostics 2021, 11, 6891–6904. [Google Scholar] [CrossRef]
  71. Outtandy, P.; Russell, C.; Kleta, R.; Bockenhauer, D. Zebrafish as a Model for Kidney Function and Disease. Pediatr. Nephrol. 2019, 34, 751–762. [Google Scholar] [CrossRef]
  72. Poureetezadi, S.J.; Wingert, R.A. Little Fish, Big Catch: Zebrafish as a Model for Kidney Disease. Kidney Int. 2016, 89, 1204–1210. [Google Scholar] [CrossRef]
  73. Dang, L.; Cao, X.; Zhang, T.; Sun, Y.; Tian, S.; Gong, T.; Xiong, H.; Cao, P.; Li, Y.; Yu, S.; et al. Nuclear Condensation of CDYL Links Histone Crotonylation and Cystogenesis in Autosomal Dominant Polycystic Kidney Disease. J. Am. Soc. Nephrol. 2022, 33, 1708–1725. [Google Scholar] [CrossRef]
  74. Devane, J.; Ott, E.; Olinger, E.G.; Epting, D.; Decker, E.; Friedrich, A.; Bachmann, N.; Renschler, G.; Eisenberger, T.; Briem-Richter, A.; et al. Progressive Liver, Kidney, and Heart Degeneration in Children and Adults Affected by TULP3 Mutations. Am. J. Hum. Genet. 2022, 109, 928–943. [Google Scholar] [CrossRef]
  75. Ko, S.; Russell, J.O.; Tian, J.; Gao, C.; Kobayashi, M.; Feng, R.; Yuan, X.; Shao, C.; Ding, H.; Poddar, M.; et al. Hdac1 Regulates Differentiation of Bipotent Liver Progenitor Cells During Regeneration via Sox9b and Cdk8. Gastroenterology 2019, 156, 187–202.e14. [Google Scholar] [CrossRef] [PubMed]
  76. de Oliveira, S.; Houseright, R.A.; Graves, A.L.; Golenberg, N.; Korte, B.G.; Miskolci, V.; Huttenlocher, A. Metformin Modulates Innate Immune-Mediated Inflammation and Early Progression of NAFLD-Associated Hepatocellular Carcinoma in Zebrafish. J. Hepatol. 2019, 70, 710–721. [Google Scholar] [CrossRef] [PubMed]
  77. Goessling, W.; Sadler, K.C. Zebrafish: An Important Tool for Liver Disease Research. Gastroenterology 2015, 149, 1361. [Google Scholar] [CrossRef] [PubMed]
  78. Sehgal, P.; Mathew, S.; Sivadas, A.; Ray, A.; Tanwar, J.; Vishwakarma, S.; Ranjan, G.; Shamsudheen, K.V.; Bhoyar, R.C.; Pateria, A.; et al. LncRNA VEAL2 Regulates PRKCB2 to Modulate Endothelial Permeability in Diabetic Retinopathy. EMBO J. 2021, 40, e107134. [Google Scholar] [CrossRef] [PubMed]
  79. Wohlfart, D.P.; Lou, B.; Middel, C.S.; Morgenstern, J.; Fleming, T.; Sticht, C.; Hausser, I.; Hell, R.; Hammes, H.P.; Szendrödi, J.; et al. Accumulation of Acetaldehyde in Aldh2.1-/- Zebrafish Causes Increased Retinal Angiogenesis and Impaired Glucose Metabolism. Redox Biol. 2022, 50, 102249. [Google Scholar] [CrossRef] [PubMed]
  80. Ping, X.; Liang, J.; Shi, K.; Bao, J.; Wu, J.; Yu, X.; Tang, X.; Zou, J.; Shentu, X. Rapamycin Relieves the Cataract Caused by Ablation of Gja8b through Stimulating Autophagy in Zebrafish. Autophagy 2021, 17, 3323–3337. [Google Scholar] [CrossRef] [PubMed]
  81. Chhetri, J.; Jacobson, G.; Gueven, N. Zebrafish—On the Move towards Ophthalmological Research. Eye 2014, 28, 367–380. [Google Scholar] [CrossRef] [PubMed]
  82. Maleski, A.L.A.; Rosa, J.G.S.; Bernardo, J.T.G.; Astray, R.M.; Walker, C.I.B.; Lopes-Ferreira, M.; Lima, C. Recapitulation of Retinal Damage in Zebrafish Larvae Infected with Zika Virus. Cells 2022, 11, 1457. [Google Scholar] [CrossRef] [PubMed]
  83. Tyrkalska, S.D.; Candel, S.; Pedoto, A.; García-Moreno, D.; Alcaraz-Pérez, F.; Sánchez-Ferrer, Á.; Cayuela, M.L.; Mulero, V. Zebrafish Models of COVID-19. FEMS Microbiol. Rev. 2023, 47, fuac042. [Google Scholar] [CrossRef]
  84. Stanton, M.F. Diethylnitrosamine-Induced Hepatic Degeneration and Neoplasia in the Aquarium Fish, Brachydanio Rerio. JNCI J. Natl. Cancer Inst. 1965, 34, 117–130. [Google Scholar] [CrossRef]
  85. Haffter, P.; Granato, M.; Brand, M.; Mullins, M.C.; Hammerschmidt, M.; Kane, D.A.; Odenthal, J.; Van Eeden, F.J.M.; Jiang, Y.J.; Heisenberg, C.P.; et al. The Identification of Genes with Unique and Essential Functions in the Development of the Zebrafish, Danio rerio. Development 1996, 123, 1–36. [Google Scholar] [CrossRef] [PubMed]
  86. Beckwith, L.G.; Moore, J.L.; Tsao-Wu, G.S.; Harshbarger, J.C.; Cheng, K.C. Ethylnitrosourea Induces Neoplasia in Zebrafish (Danio rerio). Lab. Investig. 2000, 80, 379–385. [Google Scholar] [CrossRef] [PubMed]
  87. Spitsbergen, J.M.; Tsai, H.W.; Reddy, A.; Miller, T.; Arbogast, D.; Hendricks, J.D.; Bailey, G.S. Neoplasia in Zebrafish (Danio rerio) Treated with N-Methyl-N’nitro-N-Nitrosoguanidine by Three Exposure Routes at Different Developmental Stages. Toxicol. Pathol. 2000, 28, 716–725. [Google Scholar] [CrossRef] [PubMed]
  88. Wang, Q. Cancer Predisposition Genes: Molecular Mechanisms and Clinical Impact on Personalized Cancer Care: Examples of Lynch and HBOC Syndromes. Acta Pharmacol. Sin. 2016, 37, 143. [Google Scholar] [CrossRef] [PubMed]
  89. Rahman, N. Realizing the Promise of Cancer Predisposition Genes. Nature 2014, 505, 302–308. [Google Scholar] [CrossRef] [PubMed]
  90. Kobar, K.; Collett, K.; Prykhozhij, S.V.; Berman, J.N. Zebrafish Cancer Predisposition Models. Front. Cell Dev. Biol. 2021, 9, 660069. [Google Scholar] [CrossRef] [PubMed]
  91. Gröbner, S.N.; Worst, B.C.; Weischenfeldt, J.; Buchhalter, I.; Kleinheinz, K.; Rudneva, V.A.; Johann, P.D.; Balasubramanian, G.P.; Segura-Wang, M.; Brabetz, S.; et al. The Landscape of Genomic Alterations across Childhood Cancers. Nature 2018, 555, 321–327. [Google Scholar] [CrossRef]
  92. Cunningham, R.M.; Walton, M.A.; Carter, P.M. The Major Causes of Death in Children and Adolescents in the United States. N. Engl. J. Med. 2018, 379, 2468. [Google Scholar] [CrossRef]
  93. Ward, E.; Desantis, C.; Robbins, A.; Kohler, B.; Jemal, A. Childhood and Adolescent Cancer Statistics, 2014. CA Cancer J. Clin. 2014, 64, 83–103. [Google Scholar] [CrossRef]
  94. Mariotto, E.; Corallo, D.; Pantile, M.; Giarin, E.; Pigazzi, M.; Basso, G.; Viola, G.; Aveic, S. BAG1 Down-Regulation Increases Chemo-Sensitivity of Acute Lymphoblastic Leukaemia Cells. J. Cell. Mol. Med. 2021, 25, 9060–9065. [Google Scholar] [CrossRef]
  95. Raetz, E.A.; Teachey, D.T. T-Cell Acute Lymphoblastic Leukemia. Hematol. Am. Soc. Hematol. Educ. Program 2016, 2016, 580. [Google Scholar] [CrossRef] [PubMed]
  96. Burns, M.A.; Liao, Z.W.; Yamagata, N.; Pouliot, G.P.; Stevenson, K.E.; Neuberg, D.S.; Thorner, A.R.; Ducar, M.; Silverman, E.A.; Hunger, S.P.; et al. Hedgehog Pathway Mutations Drive Oncogenic Transformation in High-Risk T-Cell Acute Lymphoblastic Leukemia. Leukemia 2018, 32, 2126. [Google Scholar] [CrossRef] [PubMed]
  97. Martin-Giacalone, B.A.; Weinstein, P.A.; Plon, S.E.; Lupo, P.J. Pediatric Rhabdomyosarcoma: Epidemiology and Genetic Susceptibility. J. Clin. Med. 2021, 10, 2028. [Google Scholar] [CrossRef] [PubMed]
  98. McEvoy, M.T.; Siegel, D.A.; Dai, S.; Okcu, M.F.; Zobeck, M.; Venkatramani, R.; Lupo, P.J. Pediatric Rhabdomyosarcoma Incidence and Survival in the United States: An Assessment of 5656 Cases, 2001–2017. Cancer Med. 2023, 12, 3644. [Google Scholar] [CrossRef] [PubMed]
  99. Campbell, B.B.; Galati, M.A.; Stone, S.C.; Riemenschneider, A.N.; Edwards, M.; Sudhaman, S.; Siddaway, R.; Komosa, M.; Nunes, N.M.; Nobre, L.; et al. Mutations in the RAS/MAPK Pathway Drive Replication Repair–Deficient Hypermutated Tumors and Confer Sensitivity to MEK Inhibition. Cancer Discov. 2021, 11, 1454. [Google Scholar] [CrossRef] [PubMed]
  100. Kahsay, A.; Rodriguez-Marquez, E.; López-Pérez, A.; Hörnblad, A.; von Hofsten, J. Pax3 Loss of Function Delays Tumour Progression in KRAS-Induced Zebrafish Rhabdomyosarcoma Models. Sci. Rep. 2022, 12, 17149. [Google Scholar] [CrossRef] [PubMed]
  101. Kent, M.R.; Calderon, D.; Silvius, K.M.; Kucinski, J.P.; LaVigne, C.A.; Cannon, M.V.; Kendall, G.C. Zebrafish Her3 Knockout Impacts Developmental and Cancer-Related Gene Signatures. Dev. Biol. 2023, 496, 1–14. [Google Scholar] [CrossRef] [PubMed]
  102. Daakour, S.; Hajingabo, L.J.; Kerselidou, D.; Devresse, A.; Kettmann, R.; Simonis, N.; Dequiedt, F.; Twizere, J.C. Systematic Interactome Mapping of Acute Lymphoblastic Leukemia Cancer Gene Products Reveals EXT-1 Tumor Suppressor as a Notch1 and FBWX7 Common Interactor. BMC Cancer 2016, 16, 335. [Google Scholar] [CrossRef] [PubMed]
  103. Borga, C.; Park, G.; Foster, C.; Burroughs-Garcia, J.; Marchesin, M.; Shah, R.; Hasan, A.; Ahmed, S.T.; Bresolin, S.; Batchelor, L.; et al. Simultaneous B and T Cell Acute Lymphoblastic Leukemias in Zebrafish Driven by Transgenic MYC: Implications for Oncogenesis and Lymphopoiesis. Leukemia 2019, 33, 333–347. [Google Scholar] [CrossRef]
  104. Mandelbaum, J.; Shestopalov, I.A.; Henderson, R.E.; Chau, N.G.; Knoechel, B.; Wick, M.J.; Zon, L.I. Zebrafish Blastomere Screen Identifies Retinoic Acid Suppression of MYB in Adenoid Cystic Carcinoma. J. Exp. Med. 2018, 215, 2673–2685. [Google Scholar] [CrossRef]
  105. Xu, M.; Ye, Y.; Ye, Z.; Xu, S.; Liu, W.; Xu, J.; Zhang, Y.; Liu, Q.; Huang, Z.; Zhang, W. Human BCR/ABL1 Induces Chronic Myeloid Leukemia-like Disease in Zebrafish. Haematologica 2020, 105, 674. [Google Scholar] [CrossRef]
  106. Somasagara, R.R.; Huang, X.; Xu, C.; Haider, J.; Serody, J.S.; Armistead, P.M.; Leung, T.C. Targeted Therapy of Human Leukemia Xenografts in Immunodeficient Zebrafish. Sci. Rep. 2021, 11, 5715. [Google Scholar] [CrossRef] [PubMed]
  107. Rosenbluh, J.; Nijhawan, D.; Cox, A.G.; Li, X.; Neal, J.T.; Schafer, E.J.; Zack, T.I.; Wang, X.; Tsherniak, A.; Schinzel, A.C.; et al. β-Catenin Driven Cancers Require a YAP1 Transcriptional Complex for Survival and Tumorigenesis. Cell 2012, 151, 1457. [Google Scholar] [CrossRef] [PubMed]
  108. Meng, Y.; Du, M.; Gu, D.; Li, C.; Li, S.; Zhang, Q.; Ben, S.; Zhu, Q.; Xin, J.; Zhang, Z.; et al. Genome-Wide Association Analyses Identify CATSPERE as a Mediator of Colorectal Cancer Susceptibility and Progression. Cancer Res. 2022, 82, 986–997. [Google Scholar] [CrossRef]
  109. Maradonna, F.; Fontana, C.M.; Sella, F.; Giommi, C.; Facchinello, N.; Rampazzo, C.; Caichiolo, M.; Hoseinifar, S.H.; Dalla Valle, L.; Van Doan, H.; et al. A Zebrafish HCT116 Xenograft Model to Predict Anandamide Outcomes on Colorectal Cancer. Cell Death Dis. 2022, 13, 1069. [Google Scholar] [CrossRef] [PubMed]
  110. Noonan, H.R.; Metelo, A.M.; Kamei, C.N.; Peterson, R.T.; Drummond, I.A.; Iliopoulos, O. Loss of Vhl in the Zebrafish Pronephros Recapitulates Early Stages of Human Clear Cell Renal Cell Carcinoma. Dis. Models Mech. 2016, 9, 873–884. [Google Scholar] [CrossRef]
  111. Luo, J.; Lu, C.; Feng, M.; Dai, L.; Wang, M.; Qiu, Y.; Zheng, H.; Liu, Y.; Li, L.; Tang, B.; et al. Cooperation between Liver-Specific Mutations of Pten and Tp53 Genetically Induces Hepatocarcinogenesis in Zebrafish. J. Exp. Clin. Cancer Res. 2021, 40, 262. [Google Scholar] [CrossRef] [PubMed]
  112. Kaufman, C.K.; Mosimann, C.; Fan, Z.P.; Yang, S.; Thomas, A.J.; Ablain, J.; Tan, J.L.; Fogley, R.D.; Van Rooijen, E.; Hagedorn, E.J.; et al. A Zebrafish Melanoma Model Reveals Emergence of Neural Crest Identity during Melanoma Initiation. Science 2016, 351, aad2197. [Google Scholar] [CrossRef]
  113. van der Ent, W.; Burrello, C.; Teunisse, A.F.A.S.; Ksander, B.R.; van der Velden, P.A.; Jager, M.J.; Jochemsen, A.G.; Snaar-Jagalska, B.E. Modeling of Human Uveal Melanoma in Zebrafish Xenograft Embryos. Investig. Ophthalmol. Vis. Sci. 2014, 55, 6612–6622. [Google Scholar] [CrossRef]
  114. Travnickova, J.; Wojciechowska, S.; Khamseh, A.; Gautier, P.; Brown, D.V.; Lefevre, T.; Brombin, A.; Ewing, A.; Capper, A.; Spitzer, M.; et al. Zebrafish MITF-Low Melanoma Subtype Models Reveal Transcriptional Subclusters and MITF-Independent Residual Disease. Cancer Res. 2019, 79, 5769–5784. [Google Scholar] [CrossRef]
  115. Santoriello, C.; Gennaro, E.; Anelli, V.; Distel, M.; Kelly, A.; Köster, R.W.; Hurlstone, A.; Mione, M. Kita Driven Expression of Oncogenic HRAS Leads to Early Onset and Highly Penetrant Melanoma in Zebrafish. PLoS ONE 2010, 5, e15170. [Google Scholar] [CrossRef] [PubMed]
  116. Gjini, E.; Mansour, M.R.; Sander, J.D.; Moritz, N.; Nguyen, A.T.; Kesarsing, M.; Gans, E.; He, S.; Chen, S.; Ko, M.; et al. A Zebrafish Model of Myelodysplastic Syndrome Produced through Tet2 Genomic Editing. Mol. Cell. Biol. 2015, 35, 789. [Google Scholar] [CrossRef] [PubMed]
  117. Liu, W.; Wu, M.; Huang, Z.; Lian, J.; Chen, J.; Wang, T.; Leung, A.Y.H.; Liao, Y.; Zhang, Z.; Liu, Q.; et al. C-Myb Hyperactivity Leads to Myeloid and Lymphoid Malignancies in Zebrafish. Leukemia 2017, 31, 222. [Google Scholar] [CrossRef] [PubMed]
  118. Nissim, S.; Leshchiner, I.; Mancias, J.D.; Greenblatt, M.B.; Maertens, O.; Cassa, C.A.; Rosenfeld, J.A.; Cox, A.G.; Hedgepeth, J.; Wucherpfennig, J.I.; et al. Mutations in RABL3 Alter KRAS Prenylation and Are Associated with Hereditary Pancreatic Cancer. Nat. Genet. 2019, 51, 1308. [Google Scholar] [CrossRef] [PubMed]
  119. Oh, S.; Park, J.T. Zebrafish Model of KRAS-Initiated Pancreatic Endocrine Tumor. Anim. Cells Syst. 2019, 23, 209. [Google Scholar] [CrossRef] [PubMed]
  120. Barroso, M.T.; Costa, B.; de Almeida, C.R.; Martin, M.C.; Couto, N.; Carvalho, T.; Fior, R. Establishment of Pancreatobiliary Cancer Zebrafish Avatars for Chemotherapy Screening. Cells 2021, 10, 2077. [Google Scholar] [CrossRef] [PubMed]
  121. Brandt, Z.J.; North, P.N.; Link, B.A. Somatic Mutations of Lats2 Cause Peripheral Nerve Sheath Tumors in Zebrafish. Cells 2019, 8, 972. [Google Scholar] [CrossRef]
  122. Kochat, V.; Raman, A.T.; Landers, S.M.; Tang, M.; Schulz, J.; Terranova, C.; Landry, J.P. Enhancer Reprogramming in PRC2-Deficient Malignant Peripheral Nerve Sheath Tumors Induces a Targetable de-Differentiated State. Acta Neuropathol. 2021, 142, 565–591. [Google Scholar] [CrossRef]
  123. Asnaghi, L.; White, D.T.; Key, N.; Choi, J.; Mahale, A.; Alkatan, H.; Edward, D.P.; Elkhamary, S.M.; Al-Mesfer, S.; Maktabi, A.; et al. ACVR1C/SMAD2 Signaling Promotes Invasion and Growth in Retinoblastoma. Oncogene 2019, 38, 2056. [Google Scholar] [CrossRef]
  124. Storer, N.Y.; White, R.M.; Uong, A.; Price, E.; Nielsen, G.P.; Langenau, D.M.; Zon, L.I. Zebrafish Rhabdomyosarcoma Reflects the Developmental Stage of Oncogene Expression during Myogenesis. Development 2013, 140, 3040–3050. [Google Scholar] [CrossRef]
  125. Kendall, G.C.; Watson, S.; Xu, L.; Lavigne, C.A.; Murchison, W.; Rakheja, D.; Skapek, S.X.; Tirode, F.; Delattre, O.; Amatruda, J.F. PAX3-FOXO1 Transgenic Zebrafish Models Identify HES3 as a Mediator of Rhabdomyosarcoma Tumorigenesis. eLife 2018, 7, e33800. [Google Scholar] [CrossRef]
  126. Balci, T.B.; Prykhozhij, S.V.; Teh, E.M.; Da’as, S.I.; Mcbride, E.; Liwski, R.; Chute, I.C.; Leger, D.; Lewis, S.M.; Berman, J.N. A Transgenic Zebrafish Model Expressing KIT-D816V Recapitulates Features of Aggressive Systemic Mastocytosis. Br. J. Haematol. 2014, 167, 48–61. [Google Scholar] [CrossRef] [PubMed]
  127. Oliveira, M.L.; Veloso, A.; Garcia, E.G.; Iyer, S.; Pereira, C.; Barreto, V.M.; Langenau, D.M.; Barata, J.T. Mutant IL7R Collaborates with MYC to Induce T-Cell Acute Lymphoblastic Leukemia. Leukemia 2022, 36, 1533. [Google Scholar] [CrossRef] [PubMed]
  128. Jiang, J.; Wang, J.; Yue, M.; Cai, X.; Wang, T.; Wu, C.; Su, H.; Wang, Y.; Han, M.; Zhang, Y.; et al. Direct Phosphorylation and Stabilization of MYC by Aurora B Kinase Promote T-Cell Leukemogenesis. Cancer Cell 2020, 37, 200. [Google Scholar] [CrossRef]
  129. Garcia, E.G.; Veloso, A.; Oliveira, M.L.; Allen, J.R.; Loontiens, S.; Brunson, D.; Do, D.; Yan, C.; Morris, R.; Iyer, S.; et al. PRL3 Enhances T-Cell Acute Lymphoblastic Leukemia Growth through Suppressing T-Cell Signaling Pathways and Apoptosis. Leukemia 2021, 35, 679–690. [Google Scholar] [CrossRef] [PubMed]
  130. Kindermann, B.; Valkova, C.; Krämer, A.; Perner, B.; Engelmann, C.; Behrendt, L.; Kritsch, D.; Jungnickel, B.; Kehlenbach, R.H.; Oswald, F.; et al. The Nuclear Pore Proteins Nup88/214 and T-Cell Acute Lymphatic Leukemia–Associated NUP214 Fusion Proteins Regulate Notch Signaling. J. Biol. Chem. 2019, 294, 11741. [Google Scholar] [CrossRef]
  131. Basten, S.G.; Davis, E.E.; Gillis, A.J.M.; van Rooijen, E.; Stoop, H.; Babala, N.; Logister, I.; Heath, Z.G.; Jonges, T.N.; Katsanis, N.; et al. Mutations in LRRC50 Predispose Zebrafish and Humans to Seminomas. PLoS Genet. 2013, 9, e1003384. [Google Scholar] [CrossRef] [PubMed]
  132. Anelli, V.; Villefranc, J.A.; Chhangawala, S.; Martinez-Mcfaline, R.; Riva, E.; Nguyen, A.; Verma, A.; Bareja, R.; Chen, Z.; Scognamiglio, T.; et al. Oncogenic BRAF Disrupts Thyroid Morphogenesis and Function via Twist Expression. eLife 2017, 6, e20728. [Google Scholar] [CrossRef] [PubMed]
  133. Pan, Z.; Xu, T.; Bao, L.; Hu, X.; Jin, T.; Chen, J.; Chen, J.; Qian, Y.; Lu, X.; Li, L.; et al. CREB3L1 Promotes Tumor Growth and Metastasis of Anaplastic Thyroid Carcinoma by Remodeling the Tumor Microenvironment. Mol. Cancer 2022, 21, 190. [Google Scholar] [CrossRef]
  134. Curtin, S.C.; Minino, A.M.; Anderson, R.N. Declines in Cancer Death Rates among Children and Adolescents in the United States, 1999–2014; Data Brief, no 257; National Center for Health Statistics: Hyattsville, MD, USA, 2016. [Google Scholar]
  135. Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer Statistics, 2018. CA Cancer J. Clin. 2018, 68, 7–30. [Google Scholar] [CrossRef]
  136. Langova, V.; Vales, K.; Horka, P.; Horacek, J. The Role of Zebrafish and Laboratory Rodents in Schizophrenia Research. Front. Psychiatry 2020, 11, 703. [Google Scholar] [CrossRef] [PubMed]
  137. Ronneberger, O.; Liu, K.; Rath, M.; Rue, D.; Mueller, T.; Skibbe, H.; Drayer, B.; Schmidt, T.; Filippi, A.; Nitschke, R.; et al. ViBE-Z: A Framework for 3D Virtual Colocalization Analysis in Zebrafish Larval Brains. Nat. Methods 2012, 9, 735–742. [Google Scholar] [CrossRef] [PubMed]
  138. Friedrich, R.W.; Korsching, S.I. Combinatorial and Chemotopic Odorant Coding in the Zebrafish Olfactory Bulb Visualized by Optical Imaging. Neuron 1997, 18, 737–752. [Google Scholar] [CrossRef] [PubMed]
  139. Miyasaka, N.; Morimoto, K.; Tsubokawa, T.; Higashijima, S.I.; Okamoto, H.; Yoshihara, Y. From the Olfactory Bulb to Higher Brain Centers: Genetic Visualization of Secondary Olfactory Pathways in Zebrafish. J. Neurosci. 2009, 29, 4756–4767. [Google Scholar] [CrossRef] [PubMed]
  140. Teles, M.C.; Cardoso, S.D.; Oliveira, R.F. Social Plasticity Relies on Different Neuroplasticity Mechanisms across the Brain Social Decision-Making Network in Zebrafish. Front. Behav. Neurosci. 2016, 10, 16. [Google Scholar] [CrossRef] [PubMed]
  141. Stednitz, S.J.; McDermott, E.M.; Ncube, D.; Tallafuss, A.; Eisen, J.S.; Washbourne, P. Forebrain Control of Behaviorally Driven Social Orienting in Zebrafish. Curr. Biol. 2018, 28, 2445–2451.e3. [Google Scholar] [CrossRef]
  142. Lin, Q.; Jesuthasan, S. Masking of a Circadian Behavior in Larval Zebrafish Involves the Thalamo-Habenula Pathway. Sci. Rep. 2017, 7, 4104. [Google Scholar] [CrossRef]
  143. Redila, V.; Kinzel, C.; Jo, Y.S.; Puryear, C.B.; Mizumori, S.J.Y. A Role for the Lateral Dorsal Tegmentum in Memory and Decision Neural Circuitry. Neurobiol. Learn. Mem. 2015, 117, 93–108. [Google Scholar] [CrossRef]
  144. Lodge, D.J.; Grace, A.A. The Laterodorsal Tegmentum Is Essential for Burst Firing of Ventral Tegmental Area Dopamine Neurons. Proc. Natl. Acad. Sci. USA 2006, 103, 5167–5172. [Google Scholar] [CrossRef]
  145. Moens, C.B.; Prince, V.E. Constructing the Hindbrain: Insights from the Zebrafish. Dev. Dyn. 2002, 224, 1–17. [Google Scholar] [CrossRef]
  146. Oxtoby, E.; Jowett, T. Cloning of the Zebrafish Krox-20 Gene (Krx-20) and Its Expression during Hindbrain Development. Nucleic Acids Res. 1993, 21, 1087–1095. [Google Scholar] [CrossRef] [PubMed]
  147. Köster, R.W.; Fraser, S.E. Direct Imaging of in Vivo Neuronal Migration in the Developing Cerebellum. Curr. Biol. 2001, 11, 1858–1863. [Google Scholar] [CrossRef]
  148. Lalonde, R.; Botez, M.I. The Cerebellum and Learning Processes in Animals. Brain Res. Rev. 1990, 15, 325–332. [Google Scholar] [CrossRef] [PubMed]
  149. Rodríguez, F.; Durán, E.; Gómez, A.; Ocaña, F.M.; Álvarez, E.; Jiménez-Moya, F.; Broglio, C.; Salas, C. Cognitive and Emotional Functions of the Teleost Fish Cerebellum. Brain Res. Bull. 2005, 66, 365–370. [Google Scholar] [CrossRef]
  150. Nakajima, R.; Hagihara, H.; Miyakawa, T. Similarities of Developmental Gene Expression Changes in the Brain between Human and Experimental Animals: Rhesus Monkey, Mouse, Zebrafish, and Drosophila. Mol. Brain 2021, 14, 135. [Google Scholar] [CrossRef]
  151. Barreiro-Iglesias, A.; Mysiak, K.S.; Adrio, F.; Rodicio, M.C.; Becker, C.G.; Becker, T.; Anadón, R. Distribution of Glycinergic Neurons in the Brain of Glycine Transporter-2 Transgenic Tg(Glyt2:Gfp) Adult Zebrafish: Relationship to Brain-Spinal Descending Systems. J. Comp. Neurol. 2013, 521, 389–425. [Google Scholar] [CrossRef] [PubMed]
  152. Mueller, T.; Vernier, P.; Wullimann, M.F. The Adult Central Nervous Cholinergic System of a Neurogenetic Model Animal, the Zebrafish Danio Rerio. Brain Res. 2004, 1011, 156–169. [Google Scholar] [CrossRef]
  153. Filippi, A.; Mueller, T.; Driever, W. Vglut2 and Gad Expression Reveal Distinct Patterns of Dual GABAergic versus Glutamatergic Cotransmitter Phenotypes of Dopaminergic and Noradrenergic Neurons in the Zebrafish Brain. J. Comp. Neurol. 2014, 522, 2019–2037. [Google Scholar] [CrossRef]
  154. Higashijima, S.I.; Mandel, G.; Fetcho, J.R. Distribution of Prospective Glutamatergic, Glycinergic, and GABAergic Neurons in Embryonic and Larval Zebrafish. J. Comp. Neurol. 2004, 480, 1–18. [Google Scholar] [CrossRef]
  155. Schweitzer, J.; Löhr, H.; Filippi, A.; Driever, W. Dopaminergic and Noradrenergic Circuit Development in Zebrafish. Dev. Neurobiol. 2012, 72, 256–268. [Google Scholar] [CrossRef]
  156. Kaslin, J.; Panula, P. Comparative Anatomy of the Histaminergic and Other Aminergic Systems in Zebrafish (Danio rerio). J. Comp. Neurol. 2001, 440, 342–377. [Google Scholar] [CrossRef] [PubMed]
  157. Tay, T.L.; Ronneberger, O.; Ryu, S.; Nitschke, R.; Driever, W. Comprehensive Catecholaminergic Projectome Analysis Reveals Single-Neuron Integration of Zebrafish Ascending and Descending Dopaminergic Systems. Nat. Commun. 2011, 2, 171. [Google Scholar] [CrossRef] [PubMed]
  158. McLean, D.L.; Fetcho, J.R. Ontogeny and Innervation Patterns of Dopaminergic, Noradrenergic, and Serotonergic Neurons in Larval Zebrafish. J. Comp. Neurol. 2004, 480, 38–56. [Google Scholar] [CrossRef] [PubMed]
  159. Lillesaar, C.; Stigloher, C.; Tannhäuser, B.; Wullimann, M.F.; Bally-Cuif, L. Axonal Projections Originating from Raphe Serotonergic Neurons in the Developing and Adult Zebrafish, Danio Rerio, Using Transgenics to Visualize Raphe-Specific Pet1 Expression. J. Comp. Neurol. 2009, 512, 158–182. [Google Scholar] [CrossRef] [PubMed]
  160. Biran, J.; Blechman, J.; Wircer, E.; Levkowitz, G. Development and Function of the Zebrafish Neuroendocrine System. In Model Animals in Neuroendocrinology: From Worm to Mouse to Man; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2018; pp. 101–131. [Google Scholar] [CrossRef]
  161. Ziv, L.; Muto, A.; Schoonheim, P.J.; Meijsing, S.H.; Strasser, D.; Ingraham, H.A.; Schaaf, M.J.M.; Yamamoto, K.R.; Baier, H. An Affective Disorder in Zebrafish with Mutation of the Glucocorticoid Receptor. Mol. Psychiatry 2012, 18, 681–691. [Google Scholar] [CrossRef]
  162. Piato, A.L.; Capiotti, K.M.; Tamborski, A.R.; Oses, J.P.; Barcellos, L.J.G.; Bogo, M.R.; Lara, D.R.; Vianna, M.R.; Bonan, C.D. Unpredictable Chronic Stress Model in Zebrafish (Danio Rerio): Behavioral and Physiological Responses. Prog. Neuropsychopharmacol. Biol. Psychiatry 2011, 35, 561–567. [Google Scholar] [CrossRef] [PubMed]
  163. Griffiths, B.; Schoonheim, P.J.; Ziv, L.; Voelker, L.; Baier, H.; Gahtan, E. A Zebrafish Model of Glucocorticoid Resistance Shows Serotonergic Modulation of the Stress Response. Front. Behav. Neurosci. 2012, 6, 68. [Google Scholar] [CrossRef] [PubMed]
  164. Haynes, E.M.; Ulland, T.K.; Eliceiri, K.W. A Model of Discovery: The Role of Imaging Established and Emerging Non-Mammalian Models in Neuroscience. Front. Mol. Neurosci. 2022, 15, 153. [Google Scholar] [CrossRef]
  165. Shin, J.; Padmanabhan, A.; De Groh, E.D.; Lee, J.S.; Haidar, S.; Dahlberg, S.; Guo, F.; He, S.; Wolman, M.A.; Granato, M.; et al. Zebrafish Neurofibromatosis Type 1 Genes Have Redundant Functions in Tumorigenesis and Embryonic Development. DMM Dis. Models Mech. 2012, 5, 881–894. [Google Scholar] [CrossRef]
  166. Graeber, M.B.; Scheithauer, B.W.; Kreutzberg, G.W. Microglia in Brain Tumors. Glia 2002, 40, 252–259. [Google Scholar] [CrossRef]
  167. Chia, K.; Mazzolini, J.; Mione, M.; Sieger, D. Tumor Initiating Cells Induce Cxcr4-Mediated Infiltration of pro-Tumoral Macrophages into the Brain. eLife 2018, 7, e31918. [Google Scholar] [CrossRef] [PubMed]
  168. Gualano, F.M.; Hassoun, P.; Carter, C.L.; Hanson, D. Embryonal Tumor with Multilayered Rosettes: Post-treatment Maturation and Implications for Future Therapy. Cancer Rep. 2023, 6, e1812. [Google Scholar] [CrossRef] [PubMed]
  169. Malchenko, S.; Sredni, S.T.; Boyineni, J.; Bi, Y.; Margaryan, N.V.; Guda, M.R.; Kostenko, Y.; Tomita, T.; Davuluri, R.V.; Velpula, K.; et al. Characterization of Brain Tumor Initiating Cells Isolated from an Animal Model of CNS Primitive Neuroectodermal Tumors. Oncotarget 2018, 9, 13733. [Google Scholar] [CrossRef]
  170. Louis, D.N.; Perry, A.; Reifenberger, G.; von Deimling, A.; Figarella-Branger, D.; Cavenee, W.K.; Ohgaki, H.; Wiestler, O.D.; Kleihues, P.; Ellison, D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A Summary. Acta Neuropathol. 2016, 131, 803–820. [Google Scholar] [CrossRef]
  171. Louis, D.N.; Ohgaki, H.; Wiestler, O.D.; Cavenee, W.K.; Burger, P.C.; Jouvet, A.; Scheithauer, B.W.; Kleihues, P. The 2007 WHO Classification of Tumours of the Central Nervous System. Acta Neuropathol. 2007, 114, 97–109. [Google Scholar] [CrossRef]
  172. Schultz, L.E.; Haltom, J.A.; Almeida, M.P.; Wierson, W.A.; Solin, S.L.; Weiss, T.J.; Helmer, J.A.; Sandquist, E.J.; Shive, H.R.; McGrail, M. Epigenetic Regulators Rbbp4 and Hdac1 Are Overexpressed in a Zebrafish Model of RB1 Embryonal Brain Tumor, and Are Required for Neural Progenitor Survival and Proliferation. DMM Dis. Models Mech. 2018, 11, dmm034124. [Google Scholar] [CrossRef] [PubMed]
  173. Modzelewska, K.; Boer, E.F.; Mosbruger, T.L.; Picard, D.; Anderson, D.; Miles, R.R.; Kroll, M.; Oslund, W.; Pysher, T.J.; Schiffman, J.D.; et al. MEK Inhibitors Reverse Growth of Embryonal Brain Tumors Derived from Oligoneural Precursor Cells. Cell Rep. 2016, 17, 1255–1264. [Google Scholar] [CrossRef]
  174. Lam, S.H.; Chua, H.L.; Gong, Z.; Lam, T.J.; Sin, Y.M. Development and Maturation of the Immune System in Zebrafish, Danio Rerio: A Gene Expression Profiling, in Situ Hybridization and Immunological Study. Dev. Comp. Immunol. 2004, 28, 9–28. [Google Scholar] [CrossRef]
  175. Almstedt, E.; Rosen, E.; Gloger, M.; Stockgard, R.; Hekmati, N.; Koltowska, K.; Krona, C.; Nelander, S. Real-Time Evaluation of Glioblastoma Growth in Patient-Specific Zebrafish Xenografts. Neuro Oncol. 2022, 24, 726. [Google Scholar] [CrossRef]
  176. Hamilton, L.; Astell, K.R.; Velikova, G.; Sieger, D. A Zebrafish Live Imaging Model Reveals Differential Responses of Microglia Toward Glioblastoma Cells In Vivo. Zebrafish 2016, 13, 523. [Google Scholar] [CrossRef]
  177. Pudelko, L.; Rouhi, P.; Sanjiv, K.; Gad, H.; Kalderén, C.; Höglund, A.; Squatrito, M.; Schuhmacher, A.J.; Edwards, S.; Hägerstrand, D.; et al. Glioblastoma and Glioblastoma Stem Cells Are Dependent on Functional MTH1. Oncotarget 2017, 8, 84671. [Google Scholar] [CrossRef] [PubMed]
  178. Yan, C.; Yang, Q.; Do, D.; Brunson, D.C.; Langenau, D.M. Adult Immune Compromised Zebrafish for Xenograft Cell Transplantation Studies. eBioMedicine 2019, 47, 24. [Google Scholar] [CrossRef] [PubMed]
  179. Yan, C.; Brunson, D.C.; Tang, Q.; Do, D.; Iftimia, N.A.; Moore, J.C.; Hayes, M.N.; Welker, A.M.; Garcia, E.G.; Dubash, T.D.; et al. Visualizing Engrafted Human Cancer and Therapy Responses in Immunodeficient Zebrafish. Cell 2019, 177, 1903–1914.e14. [Google Scholar] [CrossRef] [PubMed]
  180. Yan, C.; Do, D.; Yang, Q.; Brunson, D.C.; Rawls, J.F.; Langenau, D.M. Single-Cell Imaging of Human Cancer Xenografts Using Adult Immunodeficient Zebrafish. Nat. Protoc. 2020, 15, 3105–3128. [Google Scholar] [CrossRef] [PubMed]
  181. Idilli, A.I.; Cusanelli, E.; Pagani, F.; Berardinelli, F.; Bernabé, M.; Cayuela, M.L.; Poliani, P.L.; Mione, M.C. Expression of Tert Prevents ALT in Zebrafish Brain Tumors. Front. Cell Dev. Biol. 2020, 8, 514067. [Google Scholar] [CrossRef] [PubMed]
  182. Gronseth, E.; Gupta, A.; Koceja, C.; Kumar, S.; Kutty, R.G.; Rarick, K.; Wang, L.; Ramchandran, R. Astrocytes Influence Medulloblastoma Phenotypes and CD133 Surface Expression. PLoS ONE 2020, 15, e0235852. [Google Scholar] [CrossRef] [PubMed]
  183. Yuan, M.; White, D.; Resar, L.; Bar, E.; Groves, M.; Cohen, A.; Jackson, E.; Bynum, J.; Rubens, J.; Mumm, J.; et al. Conditional Reprogramming Culture Conditions Facilitate Growth of Lower-Grade Glioma Models. Neuro Oncol. 2021, 23, 770–782. [Google Scholar] [CrossRef] [PubMed]
  184. Gatzweiler, C.; Ridinger, J.; Herter, S.; Gerloff, X.F.; Elharouni, D.; Berker, Y.; Imle, R.; Schmitt, L.; Kreth, S.; Stainczyk, S.; et al. Functional Therapeutic Target Validation Using Pediatric Zebrafish Xenograft Models. Cancers 2022, 14, 849. [Google Scholar] [CrossRef]
  185. Zhu, F.; Nair, R.R.; Fisher, E.M.C.; Cunningham, T.J. Humanising the Mouse Genome Piece by Piece. Nat. Commun. 2019, 10, 1845. [Google Scholar] [CrossRef]
  186. Rajan, V.; Melong, N.; Wong, W.H.; King, B.; Spencer Tong, R.; Mahajan, N.; Gaston, D.; Lund, T.; Rittenberg, D.; Dellaire, G.; et al. Humanized Zebrafish Enhance Human Hematopoietic Stem Cell Survival and Promote Acute Myeloid Leukemia Clonal Diversity. Haematologica 2020, 105, 2391–2399. [Google Scholar] [CrossRef]
  187. Häberlein, F.; Mingardo, E.; Merten, N.; Schulze Köhling, N.K.; Reinoß, P.; Simon, K.; Japp, A.; Nagarajan, B.; Schrage, R.; Pegurier, C.; et al. Humanized Zebrafish as a Tractable Tool for in Vivo Evaluation of Pro-Myelinating Drugs. Cell Chem. Biol. 2022, 29, 1541–1555. [Google Scholar] [CrossRef] [PubMed]
  188. Al-Hamaly, M.A.; Turner, L.T.; Rivera-Martinez, A.; Rodriguez, A.; Blackburn, J.S. Zebrafish Cancer Avatars: A Translational Platform for Analyzing Tumor Heterogeneity and Predicting Patient Outcomes. Int. J. Mol. Sci. 2023, 24, 2288. [Google Scholar] [CrossRef] [PubMed]
  189. Hoffman, L.M.; Van Zanten, S.E.M.V.; Colditz, N.; Baugh, J.; Chaney, B.; Hoffmann, M.; Lane, A.; Fuller, C.; Miles, L.; Hawkins, C.; et al. Clinical, Radiologic, Pathologic, and Molecular Characteristics of Long-Term Survivors of Diffuse Intrinsic Pontine Glioma (DIPG): A Collaborative Report from the International and European Society for Pediatric Oncology DIPG Registries. J. Clin. Oncol. 2018, 36, 1963. [Google Scholar] [CrossRef] [PubMed]
  190. Packer, R.J. Brain Tumors in Children. Arch. Neurol. 1999, 56, 421–425. [Google Scholar] [CrossRef] [PubMed]
  191. ’t Hart, E.; Bianco, J.; Besse, H.C.; Chin Joe Kie, L.A.; Cornet, L.; Eikelenboom, K.L.; van den Broek, T.J.M.; Derieppe, M.; Su, Y.; Hoving, E.W.; et al. Towards Standardisation of a Diffuse Midline Glioma Patient-Derived Xenograft Mouse Model Based on Suspension Matrices for Preclinical Research. Biomedicines 2023, 11, 527. [Google Scholar] [CrossRef]
  192. Pelka, K.E.; Henn, K.; Keck, A.; Sapel, B.; Braunbeck, T. Size Does Matter–Determination of the Critical Molecular Size for the Uptake of Chemicals across the Chorion of Zebrafish (Danio rerio) Embryos. Aquat. Toxicol. 2017, 185, 1–10. [Google Scholar] [CrossRef]
Cancers 16 01361 g001
Figure 1. Depiction of conserved organ systems in humans and zebrafish.
Figure 1. Depiction of conserved organ systems in humans and zebrafish.
Cancers 16 01361 g001
Cancers 16 01361 g002
Figure 2. Comparison of anatomy between human and zebrafish brain. Image is adapted from the review article by Haynes et al., 2022 [164].
Figure 2. Comparison of anatomy between human and zebrafish brain. Image is adapted from the review article by Haynes et al., 2022 [164].
Cancers 16 01361 g002
Cancers 16 01361 g003
Figure 3. Anti-cancer drug screening using zebrafish xenograft model.
Figure 3. Anti-cancer drug screening using zebrafish xenograft model.
Cancers 16 01361 g003
Cancers 16 01361 g004
Figure 4. Developmental defects found in drug toxicity. (A) Wild-type control 4 dpf embryo. (B–F) Different developmental defects in 4 dpf embryos due to drug toxicity. PC—pericardium, YS—yolk sac, PCE—pericardial edema, YSE—yolk sac edema, SC—spinal curvature, YSN—yolk sac necrosis. Scale bar = 1.5 mm.
Figure 4. Developmental defects found in drug toxicity. (A) Wild-type control 4 dpf embryo. (B–F) Different developmental defects in 4 dpf embryos due to drug toxicity. PC—pericardium, YS—yolk sac, PCE—pericardial edema, YSE—yolk sac edema, SC—spinal curvature, YSN—yolk sac necrosis. Scale bar = 1.5 mm.
Cancers 16 01361 g004
Table 1. Comparison between zebrafish and other childhood cancer models.
Table 1. Comparison between zebrafish and other childhood cancer models.
ModelCostAdvantagesDisadvantagesDrug Screening Throughput
Cell cultureLowImmortality, rapid growth, robustness, ease of genetic modifications, maintenance, and storageLong-term culture can result in the development of cells that are genetically distinct from primary tumor cells. There are no tumor microenvironments.Very High
OrganoidsHighSimilar tumor heterogeneity, characteristics, and microenvironments to human systems.Technically difficult to develop, costly, and variable in growth.High
RodentsVery HighReplicate tumor microenvironment, genetic alterations, and pharmacodynamics as closely as possible to humans. Time-consuming; lack immune interactions. PDX models primarily depend on tissue integrity; highly variable in nature.Low
ZebrafishIntermediateExternal fertilization, large number of offspring, ease of transplantation, high efficiency in genetic manipulation, rapid tumor engraftment, and the development of tumors with histopathology similar to that of humans. Cell tracking in embryos and larvae is very easy due to their transparent bodies. Difficulties in accurately measuring drug concentration in larval plasma, hindering drug absorption, distribution, metabolism, and excretion (ADME) studies, along with a lack of adaptive immune system in larvae, restrict the direct prediction of clinical dosage from zebrafish to humans.High
DrosophilaLowShort lifespan, large number of progenies, ease of genetic manipulation, drug screening, dissection of complex tissues.Tumors can grow for a limited time and thus are not suitable for long-term studies. Angiogenesis and adaptive immunity cannot be studied. A few organ-specific tumors, such as pancreas, liver, and lung tumors, cannot be studied due to a lack of homologous organs.High
C. elegansLowConserve signaling pathways, genetic similarities, ease of genetic manipulation, transparent body.They do not develop cancers in a similar way to humans. They lack adaptive immunity, angiogenesis, and organ systems comparable to those of humans.High
Table 2. Pediatric non-CNS cancer models in zebrafish.
Table 2. Pediatric non-CNS cancer models in zebrafish.
CancerStudied Gene/Cell Line/DrugMethodZebrafish Line Used/DevelopedReference
Acute lymphoid leukemiaBAG1RS4;11 cell xenograftTg(fli1:GFP)[94]
Ext1 and Ext 2 Morpholino-based knockdownTg(Tp1bglob:eGFP)[102]
MYCTransgenicTg(rag2:hMYC), Tg(lck:eGFP), Tg(hMYC;GFP)[103]
Adenoid Cystic CarcinomaMYBTransgenicZebrafish blastomeres, Tg(c-myb-GFP), Tg(c-myb:GFP; lyz:dsRed), Tg(c-myb:GFP; mpeg1:mCherry)[104]
Chronic Myeloid LeukemiaBCR/ABL1TransgenicWT (AB), Tg(lyz:DsRed), Tg(hsp70:p210BCR/ABL1)[105]
ABL inhibitor imatinib, MEK inhibitor U0126, cytarabine, azacitidine, and arsenic trioxideK562, CD34+ HPSC, MV4-11 and MOLM-13 cell xenograftprkdc−/− in casper background (SCID zebrafish)[106]
IntestineYES1, YAP1Morpholino-based knockdown, dasatinib treatmentWT, Tg(fabp2:RFP)as200, axin1tm213[107]
CATSPERETransgenicWT (AB), Tg(ifabp:DsRed-P2A-CATSPERE; CATSPERE), Tg(ifabp:EGFP;WT), p53−/− [108]
HCT116, anandamide XenograftWT (Tübingen), Tg(fli1:EGFP), Tg(mpeg1:EGFP)[109]
Renal cell carcinomaVHLTransgenicTg(ATPase1.a1A4:GFP), Tg(vhlhu2117+/−), Tg(ATPase1.a1A4:GFP:vhl−/−)[110]
LiverTulp3CRISPR knockoutWT (AB, TL), Tg(wt1b:EGFP), homozygous mutant-tulpm/m[74]
Pten and Tp53 CRISPR knockoutWT (AB), Tg(fabp10:Cas9-mCherry);ptena−/−, Tg(fabp10:Cas9-mCherry); ptenb−/−, Tg(fabp10:Cas9-mCherry); tp53−/−, Tg(fabp10:Cas9-mCherry)[111]
MelanomaBRAFTransgenicWT (AB), Tg(crestin:CreERt2;crystallin:YFP), Tg(−3.5ubi:loxP-GFP-loxP-mCherry), Tg(p53/BRAF/Na/MiniCoopR/crestin:EGFP)[112]
92.1 and Mel270 cell line XenograftTg(fli1:eGFP)[113]
MITF, BRAFTransgenicmitfavc7, tp53M214K, Tg(mitfa-BRAFV600E), Tg(mitfa:BRAFV600E); mitfavc7; p53M214K)[114]
kita promoter, HRAS TransgenicWT (AB), tg(UAS:GFP), tg(5XUAS:eGFP-HRASV12)io6, mitfaw2/w2, p53zdf1/zdf1, tg(mitfa:Gal4VP16;UAS:mCherry); Et(kita:GalTA4,UAS:mCherry)hzm1[115]
Myelodysplastic syndromeTET2TransgenicWT (AB), tet2m/m, Tg(c-myb-GFP), Tg(cd41-GFP)[116]
c-mybTransgenicWT (AB), Tg(c-myb:gfp), Tg(c-mybhkz3), Tg(rag2:dsRed), Tg(lyz:dsRed) [117]
Pancreatic cancerRabl3, KRASCRISPR transgenicWT, tp53−/−, Tg(tp53−/−;rabl3-TR52), Tg(rabl3-TR41/+), Tg(rabl3-TR41/41)[118]
KRASTransgenicTg(ubb:Lox-NucmCherry-stop-Lox-GFP::KRASG12D), Tg(elastase3I:CRE;cryaa:Venus), Tg(ela3I-CRE; LSL-KRASG12D)[119]
Panc-1 cellsXenograftTg(fli1:eGFP), Nacre (mitfa−/−)[120]
Peripheral nerve sheath tumor (PNST)lats1 and lats2 CRISPR knockoutWT (AB)
lats2mw87/mw87		[121]
Suz12Morpholino-based knockdownnf1a+/−:nf1b−/−:p53e7/e7[122]
RetinoblastomaACVR1C, SMADY79-GFP cell xenograftWT (AB)[123]
RhabdomyosarcomaPax3TransgenicTg(pax3a:EGFP), pax3a−/− and pax3b−/− [100]
HES3, her3CRISPR knockouther3 null mutants (her3nch1, her3nc2, her3nch3)[101]
kRASTransgenicWT (AB), Tg(myf5:GFP;mylz2:mCherry), Tg(cdh15:GFP), Tg(mylz2:mCherry), Tg(cdh15:KRASG12D), Tg(mylz2:KRASG12D) [124]
PAX3-FOXO1, HES3TransgenicWT (AB), WIK, TL, AB/TL, tp53M214K, Tg(BetaActin:GFP2A:PAX3FOXO1), Tg(CMV:GFP2A:PAX3FOXO1), Tg(ubi:GFP2A:PAX3FOXO1) [125]
Systemic mastocytosis KITTransgenicTg(actb2:KITD816V:2AeGFP)[126]
T-cell acute lymphoid leukemiaptch1CRISPR knockoutTg(ptch1mutant)
Tg (rag2-notch1aICD)		[96]
IL7RTransgenicCG1, Tg(rag2:RFP), Tg(rag2:IL7Rmut2), Tg(rag2:IL7Rmut2-tdTomato)[127]
AURKB and MycTransgenicWT, Tg(rag2:AURKB;rag2:mCherry), Tg(rag2:Loxp-dsRED2-Loxp-EGFP-Myc;hsp70:Cre), Tg(rag2:EGFP;rag2:Myc), Tg(rag2:EGFP;rag2:MycS67A), Tg(rag2:EGFP;rag2:Myc;rag2:AURKB)[128]
prl3 and MycTransgenicCG1, Tg(rag2:GFP;rag2:Myc), Tg(rag2:prl3;rag2:mCherry)[129]
NUP88/Nup214Morpholino-based knockdownWT (AB, Tübingen)[130]
Lrrc50ENU mutagenesis, Morpholino-based knockdownWT, lrrc50hu255h (+/−)[131]
Thyroid cancerBRAFTransgenicTg(TdTomato-pA),Tg(BRAFV600E-pA:TdTomato-pA)[132]
CREB3L18505C cell xenograftWT[133]
Table 3. Childhood CNS cancer models in zebrafish.
Table 3. Childhood CNS cancer models in zebrafish.
CancerStudied Gene/Cell Line/DrugMethodZebrafish Line Used/DevelopedReference
Glioblastomanf1TransgenicTg(nf1a+/–; nf1b–/–; p53e7/e7)[165]
MTH1, TH588 and TH1579 (MTH1 inhibitors)GBM #18-CMV-LUC cell xenograftWild-type (TL)[177]
Microglial response toward GBM cellsU87 and U251 cell xenograftTg(mpeg1:EGFP), irf8−/−[176]
Role of tert in telomere stabilityTransgenicTg(10xUAS:tert),
Tg(10xUAS:terc), 		[181]
Cxcr4-mediated infiltration of pro-tumoral macrophagesTransgenicTg(NBT:∆LexPR-lexOP-pA; mpeg1:EGFP), Tg(mpeg1:mCherry; p2ry12:p2ry12-GFP),
cxcr4b−/−		[167]
Embryonal tumors (previously classified as CNS-PNETs)rb1, rbbp4, and hdac1TransgenicTg(H2A.F/Z-GFP), rb1Δ7/Δ7[172]
SOX10 and OLIG2Transgenic, Tumor allograft from Tg(mitfaw2; p53M214K; Tg(sox10:mCherry-NRASWT) fish into mitfw2 fishTg(mitfaw2; p53M214K; Tg(sox10:mCherry-NRASWT), mitfw2[173]
MedulloblastomaCD133Daoy cell xenograftTg(flk:mCherry); Absolut+/+ (ednrbl−/−
mitfa−/−)		[182]
Pilocytic astrocytomaNF1JHH-NF1-PA1 cell xenograftWild-type (AB)[183]
Rhabdoid tumorSMARCB, PRKCD, DDR2INF_R_1288_r1, INF_R_1467_r1, and INF_R_359_r3 cell xenograft Wild-type (AB)[184]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Roy, D.; Subramaniam, B.; Chong, W.C.; Bornhorst, M.; Packer, R.J.; Nazarian, J. Zebrafish—A Suitable Model for Rapid Translation of Effective Therapies for Pediatric Cancers. Cancers 2024, 16, 1361. https://doi.org/10.3390/cancers16071361

AMA Style

Roy D, Subramaniam B, Chong WC, Bornhorst M, Packer RJ, Nazarian J. Zebrafish—A Suitable Model for Rapid Translation of Effective Therapies for Pediatric Cancers. Cancers. 2024; 16(7):1361. https://doi.org/10.3390/cancers16071361

Chicago/Turabian Style

Roy, Debasish, Bavani Subramaniam, Wai Chin Chong, Miriam Bornhorst, Roger J. Packer, and Javad Nazarian. 2024. "Zebrafish—A Suitable Model for Rapid Translation of Effective Therapies for Pediatric Cancers" Cancers 16, no. 7: 1361. https://doi.org/10.3390/cancers16071361

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop