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Abstract
Cancer Research
Ovarian cancer is characterized by early, diffuse metastasis with 70% of women having metastatic disease at the time of diagnosis. While elegant transgenic mouse models of ovarian cancer exist, these mice are expensive and take a long time to develop tumors. Intraperitoneal injection xenograft models lack human stroma and do not accurately model ovarian cancer metastasis. Even patient derived xenografts (PDX) do not fully recapitulate the human stromal microenvironment as serial PDX passages demonstrate significant loss of human stroma. The ability to easily model human ovarian cancer within a physiologically relevant stromal microenvironment is an unmet need. Here, the protocol presents an orthotopic ovarian cancer mouse model using human ovarian cancer cells combined with patient-derived carcinoma-associated mesenchymal stem cells (CA-MSCs). CA-MSCs are stromal progenitor cells, which drive the formation of the stromal microenvironment and support ovarian cancer growth and metastasis. This model develops early and diffuses metastasis mimicking clinical presentation. In this model, luciferase expressing ovarian cancer cells are mixed in a 1:1 ratio with CA-MSCs and injected into the ovarian bursa of NSG mice. Tumor growth and metastasis are followed serially over time using bioluminescence imaging. The resulting tumors grow aggressively and form abdominal metastases by 14 days post injection. Mice experienced significant decreases in body weight as a marker of systemic illness and increased disease burden. By day 30 post injection, mice met endpoint criteria of >10% body weight loss and necropsy confirmed intra-abdominal metastasis in 100% of mice and 60%-80% lung and parenchymal liver metastasis. Collectively, orthotopic engraftment of ovarian cancer cells and stroma cells generates tumors that closely mimic the early and diffuse metastatic behavior of human ovarian cancer. Furthermore, this model provides a tool to study the role of ovarian cancer cell: stroma cell interactions in metastatic progression.
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