
doi: 10.3791/53316-v , 10.3791/53316
pmid: 27500635
Epithelial ovarian cancer (EOC) is the leading cause of death from gynecologic malignancy in the United States. Mortality is due to diagnosis of 75% of women with late stage disease, when metastasis is already present. EOC is characterized by diffuse and widely disseminated intra-peritoneal metastasis. Cells shed from the primary tumor anchor in the mesothelium that lines the peritoneal cavity as well as in the omentum, resulting in multi-focal metastasis, often in the presence of peritoneal ascites. Efforts in our laboratory are directed at a more detailed understanding of factors that regulate EOC metastatic success. However, quantifying metastatic tumor burden represents a significant technical challenge due to the large number, small size and broad distribution of lesions throughout the peritoneum. Herein we describe a method for analysis of EOC metastasis using cells labeled with red fluorescent protein (RFP) coupled with in vivo multispectral imaging. Following intra-peritoneal injection of RFP-labelled tumor cells, mice are imaged weekly until time of sacrifice. At this time, the peritoneal cavity is surgically exposed and organs are imaged in situ. Dissected organs are then placed on a labeled transparent template and imaged ex vivo. Removal of tissue auto-fluorescence during image processing using multispectral unmixing enables accurate quantitation of relative tumor burden. This method has utility in a variety of applications including therapeutic studies to evaluate compounds that may inhibit metastasis and thereby improve overall survival.
Ovarian Neoplasms, Optical Imaging, Neoplasms, Experimental, Carcinoma, Ovarian Epithelial, Epithelium, Luminescent Proteins, Mice, Image Processing, Computer-Assisted, Animals, Humans, Female, Neoplasms, Glandular and Epithelial, Peritoneum, Omentum, Red Fluorescent Protein
Ovarian Neoplasms, Optical Imaging, Neoplasms, Experimental, Carcinoma, Ovarian Epithelial, Epithelium, Luminescent Proteins, Mice, Image Processing, Computer-Assisted, Animals, Humans, Female, Neoplasms, Glandular and Epithelial, Peritoneum, Omentum, Red Fluorescent Protein
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