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Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches
High-grade serous epithelial ovarian cancer (HGSC) is the most aggressive subtype of epithelial ovarian cancer. The identification of germline and somatic mutations along with genomic information unveiled by The Cancer Genome Atlas (TCGA) and other studies has laid the foundation for establishing pr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7370285/ https://www.ncbi.nlm.nih.gov/pubmed/32645943 http://dx.doi.org/10.3390/ijms21134806 |
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author | Zakarya, Razia Howell, Viive M. Colvin, Emily K. |
author_facet | Zakarya, Razia Howell, Viive M. Colvin, Emily K. |
author_sort | Zakarya, Razia |
collection | PubMed |
description | High-grade serous epithelial ovarian cancer (HGSC) is the most aggressive subtype of epithelial ovarian cancer. The identification of germline and somatic mutations along with genomic information unveiled by The Cancer Genome Atlas (TCGA) and other studies has laid the foundation for establishing preclinical models with high fidelity to the molecular features of HGSC. Notwithstanding such progress, the field of HGSC research still lacks a model that is both robust and widely accessible. In this review, we discuss the recent advancements and utility of HGSC genetically engineered mouse models (GEMMs) to date. Further analysis and critique on alternative approaches to modelling HGSC considers technological advancements in somatic gene editing and modelling prototypic organs, capable of tumorigenesis, on a chip. |
format | Online Article Text |
id | pubmed-7370285 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73702852020-08-07 Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches Zakarya, Razia Howell, Viive M. Colvin, Emily K. Int J Mol Sci Review High-grade serous epithelial ovarian cancer (HGSC) is the most aggressive subtype of epithelial ovarian cancer. The identification of germline and somatic mutations along with genomic information unveiled by The Cancer Genome Atlas (TCGA) and other studies has laid the foundation for establishing preclinical models with high fidelity to the molecular features of HGSC. Notwithstanding such progress, the field of HGSC research still lacks a model that is both robust and widely accessible. In this review, we discuss the recent advancements and utility of HGSC genetically engineered mouse models (GEMMs) to date. Further analysis and critique on alternative approaches to modelling HGSC considers technological advancements in somatic gene editing and modelling prototypic organs, capable of tumorigenesis, on a chip. MDPI 2020-07-07 /pmc/articles/PMC7370285/ /pubmed/32645943 http://dx.doi.org/10.3390/ijms21134806 Text en © 2020 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 | Review Zakarya, Razia Howell, Viive M. Colvin, Emily K. Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches |
title | Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches |
title_full | Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches |
title_fullStr | Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches |
title_full_unstemmed | Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches |
title_short | Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches |
title_sort | modelling epithelial ovarian cancer in mice: classical and emerging approaches |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7370285/ https://www.ncbi.nlm.nih.gov/pubmed/32645943 http://dx.doi.org/10.3390/ijms21134806 |
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