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Mouse Ovarian Cancer Models Recapitulate the Human Tumor Microenvironment and Patient Response to Treatment

  1. Author:
    Maniati, Eleni
    Berlato, Chiara
    Gopinathan, Ganga
    Heath, Owen
    Kotantaki, Panoraia
    Lakhani, Anissa
    McDermott, Jacqueline
    Pegrum, Colin
    Delaine-Smith, Robin M
    Pearce, Oliver M T
    Hirani, Priyanka
    Joy, Joash D
    Szabova,Ludmila
    Perets, Ruth
    Sansom, Owen J
    Drapkin, Ronny
    Bailey, Peter
    Balkwill, Frances R

  2. Author Address

    Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK., University College Hospital, UCLH Cellular Pathology, 11-20 Capper Street, London WC1E 6JA, UK., Center for Advanced Preclinical Research, Frederick National Laboratory for Cancer Research at the National Cancer Institute-Frederick, Frederick, MD, USA., Rambam Health Care Campus, Technion - Israel Institute of Technology, Haifa, Israel., Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Glasgow, G61 1BD, UK; Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Road, Glasgow, G61 1QH, UK., Penn Ovarian Cancer Research Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA., Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Glasgow, G61 1BD, UK; Department for Surgical Research, Universit 228;tsklinikum Erlangen, Erlangen, Germany., Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK. Electronic address: f.balkwill@qmul.ac.uk.,
    1. Year: 2020
    2. Date: Jan 14
  2. Journal: Cell reports
    1. 30
    2. 2
    3. Pages: 525-540.e7
  4. Type of Article: Article
  5. ISSN: 2211-1247
  1. Abstract:

    Although there are many prospective targets in the tumor microenvironment (TME) of high-grade serous ovarian cancer (HGSOC), pre-clinical testing is challenging, especially as there is limited information on the murine TME. Here, we characterize the TME of six orthotopic, transplantable syngeneic murine HGSOC lines established from genetic models and compare these to patient biopsies. We identify significant correlations between the transcriptome, host cell infiltrates, matrisome, vasculature, and tissue modulus of mouse and human TMEs, with several stromal and malignant targets in common. However, each model shows distinct differences and potential vulnerabilities that enabled us to test predictions about response to chemotherapy and an anti-IL-6 antibody. Using machine learning, the transcriptional profiles of the mouse tumors that differed in chemotherapy response are able to classify chemotherapy-sensitive and -refractory patient tumors. These models provide useful pre-clinical tools and may help identify subgroups of HGSOC patients who are most likely to respond to specific therapies. Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.

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External Sources

  1. View Full Text
  2. DOI: 10.1016/j.celrep.2019.12.034
  3. PMID: 31940494
  4. View record in Web of Science®
  5. WOS: 000507498100018
  6. PII : S2211-1247(19)31684-5

Library Notes

  1. Fiscal Year: FY2019-2020
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