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Harnessing preclinical models for the interrogation of ovarian cancer
Ovarian cancer (OC) is a heterogeneous malignancy with various etiology, histopathology, and biological feature. Despite accumulating understanding of OC in the post-genomic era, the preclinical knowledge still undergoes limited translation from bench to beside, and the prognosis of ovarian cancer h...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9479310/ https://www.ncbi.nlm.nih.gov/pubmed/36114548 http://dx.doi.org/10.1186/s13046-022-02486-z |
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author | Qin, Tianyu Fan, Junpeng Lu, Funian Zhang, Li Liu, Chen Xiong, Qiyue Zhao, Yang Chen, Gang Sun, Chaoyang |
author_facet | Qin, Tianyu Fan, Junpeng Lu, Funian Zhang, Li Liu, Chen Xiong, Qiyue Zhao, Yang Chen, Gang Sun, Chaoyang |
author_sort | Qin, Tianyu |
collection | PubMed |
description | Ovarian cancer (OC) is a heterogeneous malignancy with various etiology, histopathology, and biological feature. Despite accumulating understanding of OC in the post-genomic era, the preclinical knowledge still undergoes limited translation from bench to beside, and the prognosis of ovarian cancer has remained dismal over the past 30 years. Henceforth, reliable preclinical model systems are warranted to bridge the gap between laboratory experiments and clinical practice. In this review, we discuss the status quo of ovarian cancer preclinical models which includes conventional cell line models, patient-derived xenografts (PDXs), patient-derived organoids (PDOs), patient-derived explants (PDEs), and genetically engineered mouse models (GEMMs). Each model has its own strengths and drawbacks. We focus on the potentials and challenges of using these valuable tools, either alone or in combination, to interrogate critical issues with OC. |
format | Online Article Text |
id | pubmed-9479310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-94793102022-09-17 Harnessing preclinical models for the interrogation of ovarian cancer Qin, Tianyu Fan, Junpeng Lu, Funian Zhang, Li Liu, Chen Xiong, Qiyue Zhao, Yang Chen, Gang Sun, Chaoyang J Exp Clin Cancer Res Review Ovarian cancer (OC) is a heterogeneous malignancy with various etiology, histopathology, and biological feature. Despite accumulating understanding of OC in the post-genomic era, the preclinical knowledge still undergoes limited translation from bench to beside, and the prognosis of ovarian cancer has remained dismal over the past 30 years. Henceforth, reliable preclinical model systems are warranted to bridge the gap between laboratory experiments and clinical practice. In this review, we discuss the status quo of ovarian cancer preclinical models which includes conventional cell line models, patient-derived xenografts (PDXs), patient-derived organoids (PDOs), patient-derived explants (PDEs), and genetically engineered mouse models (GEMMs). Each model has its own strengths and drawbacks. We focus on the potentials and challenges of using these valuable tools, either alone or in combination, to interrogate critical issues with OC. BioMed Central 2022-09-16 /pmc/articles/PMC9479310/ /pubmed/36114548 http://dx.doi.org/10.1186/s13046-022-02486-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Qin, Tianyu Fan, Junpeng Lu, Funian Zhang, Li Liu, Chen Xiong, Qiyue Zhao, Yang Chen, Gang Sun, Chaoyang Harnessing preclinical models for the interrogation of ovarian cancer |
title | Harnessing preclinical models for the interrogation of ovarian cancer |
title_full | Harnessing preclinical models for the interrogation of ovarian cancer |
title_fullStr | Harnessing preclinical models for the interrogation of ovarian cancer |
title_full_unstemmed | Harnessing preclinical models for the interrogation of ovarian cancer |
title_short | Harnessing preclinical models for the interrogation of ovarian cancer |
title_sort | harnessing preclinical models for the interrogation of ovarian cancer |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9479310/ https://www.ncbi.nlm.nih.gov/pubmed/36114548 http://dx.doi.org/10.1186/s13046-022-02486-z |
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