Cargando…
ZeOncoTest: Refining and Automating the Zebrafish Xenograft Model for Drug Discovery in Cancer
The xenograft of human cancer cells in model animals is a powerful tool for understanding tumor progression and metastatic potential. Mice represent a validated host, but their use is limited by the elevated experimental costs and low throughput. To overcome these restrictions, zebrafish larvae migh...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7169390/ https://www.ncbi.nlm.nih.gov/pubmed/31878274 http://dx.doi.org/10.3390/ph13010001 |
_version_ | 1783523779367927808 |
---|---|
author | Cornet, Carles Dyballa, Sylvia Terriente, Javier Di Giacomo, Valeria |
author_facet | Cornet, Carles Dyballa, Sylvia Terriente, Javier Di Giacomo, Valeria |
author_sort | Cornet, Carles |
collection | PubMed |
description | The xenograft of human cancer cells in model animals is a powerful tool for understanding tumor progression and metastatic potential. Mice represent a validated host, but their use is limited by the elevated experimental costs and low throughput. To overcome these restrictions, zebrafish larvae might represent a valuable alternative. Their small size and transparency allow the tracking of transplanted cells. Therefore, tumor growth and early steps of metastasis, which are difficult to evaluate in mice, can be addressed. In spite of its advantages, the use of this model has been hindered by lack of experimental homogeneity and validation. Considering these facts, the aim of our work was to standardize, automate, and validate a zebrafish larvae xenograft assay with increased translatability and higher drug screening throughput. The ZeOncoTest reliability is based on the optimization of different experimental parameters, such as cell labeling, injection site, automated individual sample image acquisition, and analysis. This workflow implementation finally allows a higher precision and experimental throughput increase, when compared to previous reports. The approach was validated with the breast cancer cell line MDA-MB-231, the colorectal cancer cells HCT116, and the prostate cancer cells PC3; and known drugs, respectively RKI-1447, Docetaxel, and Mitoxantrone. The results recapitulate growth and invasion for all tested tumor cells, along with expected efficacy of the compounds. Finally, the methodology has proven useful for understanding specific drugs mode of action. The insights gained bring a step further for zebrafish larvae xenografts to enter the regulated preclinical drug discovery path. |
format | Online Article Text |
id | pubmed-7169390 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-71693902020-04-20 ZeOncoTest: Refining and Automating the Zebrafish Xenograft Model for Drug Discovery in Cancer Cornet, Carles Dyballa, Sylvia Terriente, Javier Di Giacomo, Valeria Pharmaceuticals (Basel) Article The xenograft of human cancer cells in model animals is a powerful tool for understanding tumor progression and metastatic potential. Mice represent a validated host, but their use is limited by the elevated experimental costs and low throughput. To overcome these restrictions, zebrafish larvae might represent a valuable alternative. Their small size and transparency allow the tracking of transplanted cells. Therefore, tumor growth and early steps of metastasis, which are difficult to evaluate in mice, can be addressed. In spite of its advantages, the use of this model has been hindered by lack of experimental homogeneity and validation. Considering these facts, the aim of our work was to standardize, automate, and validate a zebrafish larvae xenograft assay with increased translatability and higher drug screening throughput. The ZeOncoTest reliability is based on the optimization of different experimental parameters, such as cell labeling, injection site, automated individual sample image acquisition, and analysis. This workflow implementation finally allows a higher precision and experimental throughput increase, when compared to previous reports. The approach was validated with the breast cancer cell line MDA-MB-231, the colorectal cancer cells HCT116, and the prostate cancer cells PC3; and known drugs, respectively RKI-1447, Docetaxel, and Mitoxantrone. The results recapitulate growth and invasion for all tested tumor cells, along with expected efficacy of the compounds. Finally, the methodology has proven useful for understanding specific drugs mode of action. The insights gained bring a step further for zebrafish larvae xenografts to enter the regulated preclinical drug discovery path. MDPI 2019-12-24 /pmc/articles/PMC7169390/ /pubmed/31878274 http://dx.doi.org/10.3390/ph13010001 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cornet, Carles Dyballa, Sylvia Terriente, Javier Di Giacomo, Valeria ZeOncoTest: Refining and Automating the Zebrafish Xenograft Model for Drug Discovery in Cancer |
title | ZeOncoTest: Refining and Automating the Zebrafish Xenograft Model for Drug Discovery in Cancer |
title_full | ZeOncoTest: Refining and Automating the Zebrafish Xenograft Model for Drug Discovery in Cancer |
title_fullStr | ZeOncoTest: Refining and Automating the Zebrafish Xenograft Model for Drug Discovery in Cancer |
title_full_unstemmed | ZeOncoTest: Refining and Automating the Zebrafish Xenograft Model for Drug Discovery in Cancer |
title_short | ZeOncoTest: Refining and Automating the Zebrafish Xenograft Model for Drug Discovery in Cancer |
title_sort | zeoncotest: refining and automating the zebrafish xenograft model for drug discovery in cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7169390/ https://www.ncbi.nlm.nih.gov/pubmed/31878274 http://dx.doi.org/10.3390/ph13010001 |
work_keys_str_mv | AT cornetcarles zeoncotestrefiningandautomatingthezebrafishxenograftmodelfordrugdiscoveryincancer AT dyballasylvia zeoncotestrefiningandautomatingthezebrafishxenograftmodelfordrugdiscoveryincancer AT terrientejavier zeoncotestrefiningandautomatingthezebrafishxenograftmodelfordrugdiscoveryincancer AT digiacomovaleria zeoncotestrefiningandautomatingthezebrafishxenograftmodelfordrugdiscoveryincancer |