Literature DB >> 26496815

Genetically-defined ovarian cancer mouse models.

Patrice J Morin1, Ashani T Weeraratna2.   

Abstract

Epithelial ovarian cancer (EOC), the deadliest of gynaecological cancers, is a disease that remains difficult to detect early and treat efficiently. A significant challenge for researchers in the field is that the aetiology of EOC and the molecular pathways important for its development are poorly understood. Moreover, precursor lesions have not been readily identifiable, making the mechanisms of EOC progression difficult to delineate. In order to address these issues, several genetically-defined ovarian mouse models have been generated in the past 15 years. However, because of the recent suggestion that most EOCs may not originate from the ovarian surface 'epithelium', but from other tissues of the female genital tract, some models may need to be re-evaluated within this new paradigm. In this review, we examine several genetically-defined EOC models and discuss how the new paradigm may explain some of the features of these models. A better understanding of the strengths and limitations of the current EOC mouse models will undoubtedly allow us to utilize these tools to better understand the biology of the disease and develop new approaches for EOC prevention, detection, and treatment.
Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Entities:  

Keywords:  Fallopian tube; intraepithelial carcinoma; ovarian cancer; transgenic mouse model

Mesh:

Substances:

Year:  2016        PMID: 26496815     DOI: 10.1002/path.4663

Source DB:  PubMed          Journal:  J Pathol        ISSN: 0022-3417            Impact factor:   7.996


  8 in total

Review 1.  Preclinical Models of Ovarian Cancer: Pathogenesis, Problems, and Implications for Prevention.

Authors:  Anthony N Karnezis; Kathleen R Cho
Journal:  Clin Obstet Gynecol       Date:  2017-12       Impact factor: 2.190

2.  Inactivation of TRP53, PTEN, RB1, and/or CDH1 in the ovarian surface epithelium induces ovarian cancer transformation and metastasis.

Authors:  Mingxin Shi; Allison E Whorton; Nikola Sekulovski; Marilène Paquet; James A MacLean; Yurong Song; Terry Van Dyke; Kanako Hayashi
Journal:  Biol Reprod       Date:  2020-04-24       Impact factor: 4.285

Review 3.  The tubal epigenome - An emerging target for ovarian cancer.

Authors:  Hunter D Reavis; Ronny Drapkin
Journal:  Pharmacol Ther       Date:  2020-03-18       Impact factor: 12.310

4.  [Analysis of orthologous lncRNAs in humans and mice and their species-specific epigenetic target genes].

Authors:  Yang-Yang Jiang; Hao Zhu; Hai Zhang
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2018-06-20

5.  Nanoparticle delivery of siRNA against TWIST to reduce drug resistance and tumor growth in ovarian cancer models.

Authors:  Cai M Roberts; Sophia Allaf Shahin; Wei Wen; James B Finlay; Juyao Dong; Ruining Wang; Thanh H Dellinger; Jeffrey I Zink; Fuyuhiko Tamanoi; Carlotta A Glackin
Journal:  Nanomedicine       Date:  2016-11-25       Impact factor: 5.307

6.  OvAd1, a Novel, Potent, and Selective Chimeric Oncolytic Virus Developed for Ovarian Cancer by 3D-Directed Evolution.

Authors:  Irene Kuhn; Maxine Bauzon; Nicola Green; Len Seymour; Kerry Fisher; Terry Hermiston
Journal:  Mol Ther Oncolytics       Date:  2016-12-14       Impact factor: 7.200

7.  Senescence induction dictates response to chemo- and immunotherapy in preclinical models of ovarian cancer.

Authors:  Stella V Paffenholz; Camilla Salvagno; Yu-Jui Ho; Matthew Limjoco; Timour Baslan; Sha Tian; Amanda Kulick; Elisa de Stanchina; John E Wilkinson; Francisco M Barriga; Dmitriy Zamarin; Juan R Cubillos-Ruiz; Josef Leibold; Scott W Lowe
Journal:  Proc Natl Acad Sci U S A       Date:  2022-02-01       Impact factor: 11.205

Review 8.  Ovarian Cancer Immunotherapy: Preclinical Models and Emerging Therapeutics.

Authors:  Curtis W McCloskey; Galaxia M Rodriguez; Kristianne J C Galpin; Barbara C Vanderhyden
Journal:  Cancers (Basel)       Date:  2018-07-26       Impact factor: 6.639

  8 in total

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