Harnessing preclinical models for the interrogation of ovarian cancer

Abstract 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...
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Autor*in:	Tianyu Qin [verfasserIn] Junpeng Fan [verfasserIn] Funian Lu [verfasserIn] Li Zhang [verfasserIn] Chen Liu [verfasserIn] Qiyue Xiong [verfasserIn] Yang Zhao [verfasserIn] Gang Chen [verfasserIn] Chaoyang Sun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Ovarian cancer Preclinical models Patient-derived xenograft Patient-derived organoids Genetically engineered mouse models Personalised medicine

Übergeordnetes Werk:	In: Journal of Experimental & Clinical Cancer Research - BMC, 2008, 41(2022), 1, Seite 27
Übergeordnetes Werk:	volume:41 ; year:2022 ; number:1 ; pages:27

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13046-022-02486-z

Katalog-ID:	DOAJ084865997

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520			\|a Abstract 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.
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650		4	\|a Personalised medicine
653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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allfields	10.1186/s13046-022-02486-z doi (DE-627)DOAJ084865997 (DE-599)DOAJb4267ac11f184ed3952d09ff07905ecd DE-627 ger DE-627 rakwb eng RC254-282 Tianyu Qin verfasserin aut Harnessing preclinical models for the interrogation of ovarian cancer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Ovarian cancer Preclinical models Patient-derived xenograft Patient-derived organoids Genetically engineered mouse models Personalised medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Junpeng Fan verfasserin aut Funian Lu verfasserin aut Li Zhang verfasserin aut Chen Liu verfasserin aut Qiyue Xiong verfasserin aut Yang Zhao verfasserin aut Gang Chen verfasserin aut Chaoyang Sun verfasserin aut In Journal of Experimental & Clinical Cancer Research BMC, 2008 41(2022), 1, Seite 27 (DE-627)568921380 (DE-600)2430698-8 17569966 nnns volume:41 year:2022 number:1 pages:27 https://doi.org/10.1186/s13046-022-02486-z kostenfrei https://doaj.org/article/b4267ac11f184ed3952d09ff07905ecd kostenfrei https://doi.org/10.1186/s13046-022-02486-z kostenfrei https://doaj.org/toc/1756-9966 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 41 2022 1 27
spelling	10.1186/s13046-022-02486-z doi (DE-627)DOAJ084865997 (DE-599)DOAJb4267ac11f184ed3952d09ff07905ecd DE-627 ger DE-627 rakwb eng RC254-282 Tianyu Qin verfasserin aut Harnessing preclinical models for the interrogation of ovarian cancer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Ovarian cancer Preclinical models Patient-derived xenograft Patient-derived organoids Genetically engineered mouse models Personalised medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Junpeng Fan verfasserin aut Funian Lu verfasserin aut Li Zhang verfasserin aut Chen Liu verfasserin aut Qiyue Xiong verfasserin aut Yang Zhao verfasserin aut Gang Chen verfasserin aut Chaoyang Sun verfasserin aut In Journal of Experimental & Clinical Cancer Research BMC, 2008 41(2022), 1, Seite 27 (DE-627)568921380 (DE-600)2430698-8 17569966 nnns volume:41 year:2022 number:1 pages:27 https://doi.org/10.1186/s13046-022-02486-z kostenfrei https://doaj.org/article/b4267ac11f184ed3952d09ff07905ecd kostenfrei https://doi.org/10.1186/s13046-022-02486-z kostenfrei https://doaj.org/toc/1756-9966 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 41 2022 1 27
allfields_unstemmed	10.1186/s13046-022-02486-z doi (DE-627)DOAJ084865997 (DE-599)DOAJb4267ac11f184ed3952d09ff07905ecd DE-627 ger DE-627 rakwb eng RC254-282 Tianyu Qin verfasserin aut Harnessing preclinical models for the interrogation of ovarian cancer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Ovarian cancer Preclinical models Patient-derived xenograft Patient-derived organoids Genetically engineered mouse models Personalised medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Junpeng Fan verfasserin aut Funian Lu verfasserin aut Li Zhang verfasserin aut Chen Liu verfasserin aut Qiyue Xiong verfasserin aut Yang Zhao verfasserin aut Gang Chen verfasserin aut Chaoyang Sun verfasserin aut In Journal of Experimental & Clinical Cancer Research BMC, 2008 41(2022), 1, Seite 27 (DE-627)568921380 (DE-600)2430698-8 17569966 nnns volume:41 year:2022 number:1 pages:27 https://doi.org/10.1186/s13046-022-02486-z kostenfrei https://doaj.org/article/b4267ac11f184ed3952d09ff07905ecd kostenfrei https://doi.org/10.1186/s13046-022-02486-z kostenfrei https://doaj.org/toc/1756-9966 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 41 2022 1 27
allfieldsGer	10.1186/s13046-022-02486-z doi (DE-627)DOAJ084865997 (DE-599)DOAJb4267ac11f184ed3952d09ff07905ecd DE-627 ger DE-627 rakwb eng RC254-282 Tianyu Qin verfasserin aut Harnessing preclinical models for the interrogation of ovarian cancer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Ovarian cancer Preclinical models Patient-derived xenograft Patient-derived organoids Genetically engineered mouse models Personalised medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Junpeng Fan verfasserin aut Funian Lu verfasserin aut Li Zhang verfasserin aut Chen Liu verfasserin aut Qiyue Xiong verfasserin aut Yang Zhao verfasserin aut Gang Chen verfasserin aut Chaoyang Sun verfasserin aut In Journal of Experimental & Clinical Cancer Research BMC, 2008 41(2022), 1, Seite 27 (DE-627)568921380 (DE-600)2430698-8 17569966 nnns volume:41 year:2022 number:1 pages:27 https://doi.org/10.1186/s13046-022-02486-z kostenfrei https://doaj.org/article/b4267ac11f184ed3952d09ff07905ecd kostenfrei https://doi.org/10.1186/s13046-022-02486-z kostenfrei https://doaj.org/toc/1756-9966 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 41 2022 1 27
allfieldsSound	10.1186/s13046-022-02486-z doi (DE-627)DOAJ084865997 (DE-599)DOAJb4267ac11f184ed3952d09ff07905ecd DE-627 ger DE-627 rakwb eng RC254-282 Tianyu Qin verfasserin aut Harnessing preclinical models for the interrogation of ovarian cancer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Ovarian cancer Preclinical models Patient-derived xenograft Patient-derived organoids Genetically engineered mouse models Personalised medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Junpeng Fan verfasserin aut Funian Lu verfasserin aut Li Zhang verfasserin aut Chen Liu verfasserin aut Qiyue Xiong verfasserin aut Yang Zhao verfasserin aut Gang Chen verfasserin aut Chaoyang Sun verfasserin aut In Journal of Experimental & Clinical Cancer Research BMC, 2008 41(2022), 1, Seite 27 (DE-627)568921380 (DE-600)2430698-8 17569966 nnns volume:41 year:2022 number:1 pages:27 https://doi.org/10.1186/s13046-022-02486-z kostenfrei https://doaj.org/article/b4267ac11f184ed3952d09ff07905ecd kostenfrei https://doi.org/10.1186/s13046-022-02486-z kostenfrei https://doaj.org/toc/1756-9966 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 41 2022 1 27
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topic_facet	Ovarian cancer Preclinical models Patient-derived xenograft Patient-derived organoids Genetically engineered mouse models Personalised medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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container_title	Journal of Experimental & Clinical Cancer Research
authorswithroles_txt_mv	Tianyu Qin @@aut@@ Junpeng Fan @@aut@@ Funian Lu @@aut@@ Li Zhang @@aut@@ Chen Liu @@aut@@ Qiyue Xiong @@aut@@ Yang Zhao @@aut@@ Gang Chen @@aut@@ Chaoyang Sun @@aut@@
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callnumber-first	R - Medicine
author	Tianyu Qin
spellingShingle	Tianyu Qin misc RC254-282 misc Ovarian cancer misc Preclinical models misc Patient-derived xenograft misc Patient-derived organoids misc Genetically engineered mouse models misc Personalised medicine misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Harnessing preclinical models for the interrogation of ovarian cancer
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topic_title	RC254-282 Harnessing preclinical models for the interrogation of ovarian cancer Ovarian cancer Preclinical models Patient-derived xenograft Patient-derived organoids Genetically engineered mouse models Personalised medicine
topic	misc RC254-282 misc Ovarian cancer misc Preclinical models misc Patient-derived xenograft misc Patient-derived organoids misc Genetically engineered mouse models misc Personalised medicine misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc Ovarian cancer misc Preclinical models misc Patient-derived xenograft misc Patient-derived organoids misc Genetically engineered mouse models misc Personalised medicine misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Harnessing preclinical models for the interrogation of ovarian cancer
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title_sort	harnessing preclinical models for the interrogation of ovarian cancer
callnumber	RC254-282
title_auth	Harnessing preclinical models for the interrogation of ovarian cancer
abstract	Abstract 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.
abstractGer	Abstract 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.
abstract_unstemmed	Abstract 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.
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title_short	Harnessing preclinical models for the interrogation of ovarian cancer
url	https://doi.org/10.1186/s13046-022-02486-z https://doaj.org/article/b4267ac11f184ed3952d09ff07905ecd https://doaj.org/toc/1756-9966
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