Journal
CELL REPORTS
Volume 30, Issue 2, Pages 525-+Publisher
CELL PRESS
DOI: 10.1016/j.celrep.2019.12.034
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Funding
- Cancer Research UK [A16354, A25714, A27947, A17196]
- ERC [ERC322566]
- BBSRC CASE studentship [BIDS3000032485]
- Wellcome Trust Research Training Fellowship [20118/Z/16/Z]
- National Cancer Institute grant [P50 CA228991]
- Honorable Tina Brozman Foundation
- Dr. Miriam and Sheldon G. Adelson Medical Research Foundation
- Claneil Foundation
- Basser Center for BRCA
- European Commission [PCIG13-GA-2013-618174]
- National Cancer Institute, National Institutes of Health [HHSN261200800001E]
- BCI/WHRI Flow Cytometry Core Facility
- NATIONAL CANCER INSTITUTE [ZIGBC011735] Funding Source: NIH RePORTER
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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.
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