Publication Date:
2020
Description:
〈p〉Publication date: 14 January 2020〈/p〉
〈p〉〈b〉Source:〈/b〉 Cell Reports, Volume 30, Issue 2〈/p〉
〈p〉Author(s): Eleni Maniati, Chiara Berlato, Ganga Gopinathan, Owen Heath, Panoraia Kotantaki, Anissa Lakhani, Jacqueline McDermott, Colin Pegrum, Robin M. Delaine-Smith, Oliver M.T. Pearce, Priyanka Hirani, Joash D. Joy, Ludmila Szabova, Ruth Perets, Owen J. Sansom, Ronny Drapkin, Peter Bailey, Frances R. Balkwill〈/p〉
〈h5〉Summary〈/h5〉
〈div〉〈p〉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.〈/p〉〈/div〉
〈h5〉Graphical Abstract〈/h5〉
〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S2211124719316845-fx1.jpg" width="375" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉
Electronic ISSN:
2211-1247
Topics:
Biology
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