Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zakarya, Razia, Howell, Viive M., Colvin, Emily K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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
_version_ 1783560959297585152
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
work_keys_str_mv AT zakaryarazia modellingepithelialovariancancerinmiceclassicalandemergingapproaches
AT howellviivem modellingepithelialovariancancerinmiceclassicalandemergingapproaches
AT colvinemilyk modellingepithelialovariancancerinmiceclassicalandemergingapproaches