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

Area of Science:

• Oncology
• Cancer Biology
• Translational Research

Background:

• The tumor microenvironment (TME) is a complex system crucial for high-grade serous ovarian cancer (HGSOC) progression.
• Pre-clinical testing of potential therapies for HGSOC is hindered by limited understanding of the murine TME.
• Developing accurate pre-clinical models is essential for advancing HGSOC treatment strategies.

Observation:

• Six orthotopic, transplantable syngeneic murine HGSOC lines were established and characterized.
• The murine TME was compared to human HGSOC patient biopsies.
• Correlations were identified between the transcriptome, host cell infiltrates, matrisome, vasculature, and tissue modulus in both mouse and human TMEs.

Findings:

• Significant correlations were observed between murine and human TMEs, revealing common stromal and malignant targets.
• Distinct differences and potential vulnerabilities were identified in each murine model.
• Machine learning analysis of transcriptional profiles from mouse models successfully classified chemotherapy-sensitive and -refractory patient tumors.

Implications:

• These characterized murine HGSOC models serve as valuable pre-clinical tools for therapeutic testing.
• The models can help predict patient responses to chemotherapy and targeted agents like anti-IL-6 antibodies.
• This research may facilitate the identification of HGSOC patient subgroups likely to benefit from specific therapies.