This research was supported by research grants RO1CA109545 and RO1CA086984 to M.S.S. by the National Institutes of Health/National Cancer Institute and by an award from the Leo and Ann Albert Charitable Trust (to M.S.S.).

All in vivo studies were approved by the University of Notre Dame Animal Care and Use Committee and used female C57/BL6J mice.
1. Murine Ovarian Cancer Cell Culture
<ol>
	<li>Make the ID8 murine ovarian cancer cell culture medium as follows: 1 L of Dulbecco&#39;s Modified Eagle Medium (DMEM) supplemented with 4% Fetal Bovine Serum (FBS), 1% Penicillin/Streptomycin, 5 &#181;g/ml Insulin, 5 &#181;g/ml Transferrin and 5 ng/ml Sodium Selenite.</li>
	<li>Transduce ID8 murine ovarian cancer cells13 to express Red Fluorescent Protein (RFP) using commercially procured lentiviral particles expressing RFP and a selection marker (blasticidin gene). Note that green fluorescent protein can be used, but provides a weaker fluorescence signal.
	<ol>
		<li>Immediately prior to transduction, culture cells to 50 - 70% confluence and add fresh medium (without penicillin/streptomycin) that includes 1 ml polybrene (5 &#181;g/ml final concentration).</li>
		<li>Gently pipette RFP-lentivirus (20 &#181;l) dropwise into the dish and incubate at 37 oC for 72 hr. Remove medium and incubate with fresh medium for 24 hr. Begin selection of transduced cells by adding blasticidin to the culture medium (5 &#181;g/ml) and monitor for red cells using fluorescence microscopy (Figure1).</li>
	</ol>
	</li>
	<li>Culture the RFP-tagged cells in ID8 medium by seeding approximately 106 cells into a tissue culture dish and visualizing daily using a light microscope until the cell monolayer is confluent.</li>
	<li>When confluent, harvest the cells using trypsin (0.25%, 3 min) and return the flask to the 37 oC incubator until the cells are detached.</li>
	<li>Neutralize the trypsin with 6 ml of ID8 culture medium containing serum. Pipette up and down against the bottom of the dish, then transfer to a 15 ml conical tube. Centrifuge at 1,200 RPM for 2 min, then aspirate the medium using a glass pipette.</li>
	<li>Wash the cells with Phosphate Buffered Saline (PBS) by gently resuspending in 6 ml warm PBS and centrifuging as in 1.5 above. Re-suspend in PBS to a final concentration of 5 x 106 cells/ml. Keep cells warm (37 oC) until injection.</li>
</ol>
2. Intra-peritoneal Injection of ID8 Cells and in vivo Imaging
<ol>
	<li>Inject each mouse with 2 mL of the warm ID8-PBS solution for a total of 10 million cells through intra-peritoneal (i.p.) injection. Allow tumor cells to grow in vivo for approximately 10 weeks with weekly live imaging.</li>
	<li>Immediately following injection and each week thereafter, weigh the mice and then anesthetize each mouse using isofluorane (2.5% flow rate in 0.5 L/min O2 flow). Note:&#160;The mice were weighed, not as a quantitative measure of tumor burden, but rather a more general measure of individual mouse physical progression throughout the study.&#160; Mice weights were compared with their own previous weight values. &#160;
	<ol>
		<li>Anesthetize mice by placing in an isofluorane chamber. Maintain constant anesthesia while scanning by positioning the mouse head in the nose-cone for exposure to 2.5% flow rate isofluorane throughout the procedure.</li>
	</ol>
	</li>
	<li>Use depilatory cream to remove the hair on the torso and abdomen of each mouse, as the black hair will cause attenuation of the fluorescence signal. Mice are bathed with warm water after application of the depilatory cream and dried with RT paper towels. &#160;</li>
	<li>While anesthetized, scan the entire body of each mouse using a small animal optical imaging system. Scan all mice in a cohort at each time point. Use the following two step protocol (Figure 2A):
	<ol>
		<li>In Step 1, to observe fluorescently labeled (RFP) tumor cells, execute multispectral acquisition to collect a total of 5 images using excitation filters of 440 nm, 460 nm, 480 nm, 520 nm and 540 nm, and an emission filter of 600 nm. Use the following acquisition parameters: a standard exposure of 15 sec, 2 x 2 binning, FOV of 160 mm, and fstop of 1.1.</li>
		<li>In Step 2, to observe the whole animal, acquire a reflectance image using an open excitation filter (white light), an open emission filter, a standard exposure of 0.2 sec with 2 x 2 binning and an FOV of 16 cm. &#160;Note: The actual imaging time is ~1 min.&#160;</li>
	</ol>
	</li>
</ol>
3. Mouse Dissection and Image Acquisition
<ol>
	<li>When live imaging shows the presence of extensive intra-peritoneal disease or animals exhibit accumulation of ascites (as evidenced by abdominal distension), loss or gain of body weight greater than 20%,&#160;lethargy or other signs of distress, euthanize each mouse using CO2 anesthesia followed by cervical dislocation.</li>
	<li>Cut the skin anteriorly along the ventral midline of the mouse, using pointed dissection scissors, from the top of the thighs to the bottom of the rib cage. Take extreme care to cut through only the skin layer (Figure 2Bi). Gently separate the skin from the peritoneal tissue, being sure to not puncture the peritoneal wall.</li>
	<li>Using pointed dissection scissors, cut laterally both along the bottom of the rib cage and the top of the thighs to create two flaps of skin (Figure 2B ii,iii).</li>
	<li>Place each mouse on its ventral side (peritoneal cavity facing downward) with the skin flaps pulled open and scan each mouse with its organs in situ using the small animal optical imaging system (Figure 3A,B).
	<ol>
		<li>Use the same image acquisition parameters as described in 2.4.1.-2.4.2.</li>
	</ol>
	</li>
	<li>Continue dissecting the mouse by extracting individual peritoneal organs including liver, omentum/pancreas, stomach, diaphragm, small intestine, large intestine, left and right peritoneum, ovaries, kidneys, mesentery and fat pad.
	<ol>
		<li>Observe, enumerate, and record visible tumors on each organ. Use a caliper to measure the diameter of each tumor.</li>
		<li>Designate tumors less than 2 mm in diameter as small, in between 2 and 5 mm as medium and greater than 5 mm as large.</li>
	</ol>
	</li>
	<li>Place the organs on the organ scanning template&#160;(Figure 4) that identifies a predetermined location for each organ. Use PBS to keep the organs moist.</li>
	<li>Scan each sheet of organs using the small animal optical imaging system (Figure 3C,D).
	<ol>
		<li>Use the same image acquisition parameters as described in step 2.4.</li>
	</ol>
	</li>
	<li>After scanning, place organs in 10% formaldehyde to enable subsequent processing for histology.</li>
</ol>
4. Quantification of Tumor Burden in the Fluorescence Images
<ol>
	<li>In order to reduce the amount of auto-fluorescence in each multispectral scan, first unmix the files using software available with the small animal imaging system.
	<ol>
		<li>Load first the RFP: In Vivo spectral file, followed by the Autofluor: In Vivo Red file in the Unmixed Images window and select Unmix.</li>
	</ol>
	</li>
	<li>Export the .bip files as 16 bit .tiff (unscaled) format .</li>
	<li>Use ImageJ software (ex vivo organs and then the full mouse body with organs in situ).
	<ol>
		<li>Use File &#62; Open to open the 16 bit .tiff files of the organ scanning template files in ImageJ.</li>
		<li>Under Image &#62; Adjust &#62; Brightness/Contrast, choose to Set the Minimum displayed value to 0 and the Maximum displayed value to +35353 and then under Image &#62; Lookup Tables, select Red Hot.&#160;Note: Various animal models will require different brightness levels, but it is important to keep the brightness consistent throughout a given study.</li>
		<li>Using the Freehand Selection tool, draw a free-form region of interest selection (ROI) around each organ and use the Measure tool (CTRL+M) to calculate the surface area of each organ; record the surface Area of each organ in a spreadsheet.</li>
		<li>With the ROI drawn around each organ, right click within the ROI and select Duplicate to include only the organ.</li>
		<li>While looking at the individual organ, under Image &#62; Adjust &#62; Threshold, choose to Set the threshold of the image to a Lower Threshold Level of + 1200 and an Upper Threshold Level of + 1700 to select only the most brightly fluorescing regions and check to select Dark Background; select OK.&#160;Note: The images may require manual manipulation of the threshold sliders in ImageJ so that the previously brightest areas are selected for the analysis step, as shown above.&#160; Different disease models will require different threshold constraints, but it is important to keep the threshold levels consistent throughout a given study.</li>
		<li>Under Analyze &#62; Analyze Particles, change the Size (pixels ^ 2) to 10-Infinity, choose to Show: Outlines, check to select only &#34;Display results&#34; and click OK to measure both the areas and raw integrated densities of these bright regions, deemed tumors.</li>
		<li>Record the values of the surface Area and the Raw Integrated Density of each tumor selection displayed in the Results table in the same spreadsheet containing the organ surface areas.</li>
		<li>Under Analyze &#62; Tools &#62; Calibration Bar, add a calibration scale bar to the images of the organs. Note: In this example, this was done by changing the Location to Upper Right, the Fill Color to Black, the Label Color to White, the Number of Labels to 5, the Decimal Places to 0, the Font Size to 12 and the Zoom Factor to 4.0 and enabling Bold Text.</li>
		<li>Under File &#62; Save As&#8230;, save the montage as a .Tiff and .Jpeg.</li>
		<li>Use File &#62; Open to open the .jpeg file of the organs and then under Insert &#62; Text Box, add organ labels by creating a text box below each of the three rows of organs.</li>
		<li>Use File &#62; Open to open each of the 16 bit .tiff files of the full body scans in ImageJ in order of mouse number.</li>
		<li>Under Image &#62; Adjust &#62; Brightness/Contrast, choose to Set the Minimum displayed value to 0 and the Maximum displayed value to + 35353 and then under Image &#62; Lookup Tables, select Red Hot.</li>
		<li>Under File &#62; Save As&#8230;, save the montage as a .Tiff and .Jpeg.</li>
	</ol>
	</li>
</ol>
5. Data Analysis
<ol>
	<li>Add the tumor selection areas and raw integrated densities, respectively, for each organ of each mouse and record the total organ tumor area for each mouse.</li>
	<li>Normalize the recorded area and raw integrated density values for the size of each organ by dividing the total tumor area and total raw integrated density values by the corresponding organ surface area (Table 1).</li>
	<li>Calculate and plot the average of both the normalized tumor surface areas and the normalized raw integrated density values (Figure 5A,B).</li>
	<li>Compare these average values to those obtained by visually counting the tumors that were visible to the naked eye during the inspection of each organ for metastatic lesions.</li>
</ol>

<ol>
	<li>Lengyel, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ovarian+cancer+development+and+metastasis.">Ovarian cancer development and metastasis.</a> American Journal of Pathology. 177 (3), 1053-1064 (2010).</li><li>Halkia, E., Spiliotis, J., Sugarbaker, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnosis+and+management+of+peritoneal+metastases+from+ovarian+cancer.">Diagnosis and management of peritoneal metastases from ovarian cancer.</a> Gastroenterology Research and Practice. 2012, 541842-541854 (2012).</li><li>Barbolina, M. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microenvironmental+regulation+of+ovarian+cancer+metastasis.">Microenvironmental regulation of ovarian cancer metastasis.</a> Cancer Treatment and Research. 149, 319-334 (2009).</li><li>Lengyel, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epithelial+ovarian+cancer+experimental+models.">Epithelial ovarian cancer experimental models.</a> Oncogene. 33 (28), 3619-3633 (2014).</li><li>Harter, P., duBois, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+surgery+in+ovarian+cancer+with+special+emphasis+on+cytoreductive+surgery+for+recurrence.">The role of surgery in ovarian cancer with special emphasis on cytoreductive surgery for recurrence.</a> Current Opinion in Oncology. 17 (5), 505-514 (2005).</li><li>Bristow, R. E., Puri, I., Chi, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytoreductive+surgery+for+recurrent+ovarian+cancer:+a+meta-analysis.">Cytoreductive surgery for recurrent ovarian cancer: a meta-analysis.</a> Gynecologic Oncology. 112 (1), 265-274 (2009).</li><li>Hoffman, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vivo+imaging+of+metastatic+cancer+with+fluorescent+proteins.">In vivo imaging of metastatic cancer with fluorescent proteins.</a> Cell Death and Differentiation. 9, 786-789 (2002).</li><li>Sweeney, T. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualizing+the+kinetics+of+tumor-cell+clearance+in+living+animals.">Visualizing the kinetics of tumor-cell clearance in living animals.</a> Proceedings of the National Academy of Science USA. 96, 12044-12049 (1999).</li><li>Chishima, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+invasion+and+micrometastasis+visualized+in+live+tissue+by+green+fluorescent+protein+expression.">Cancer invasion and micrometastasis visualized in live tissue by green fluorescent protein expression.</a> Cancer Research. 57, 2042-2047 (1997).</li><li>Bouvet, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time+optical+imaging+of+primary+tumor+growth+and+multiple+metastatic+events+in+a+pancreatic+cancer+orthotopic+model.">Real-time optical imaging of primary tumor growth and multiple metastatic events in a pancreatic cancer orthotopic model.</a> Cancer Research. 62, 1534-1540 (2002).</li><li>Hoffman, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Multiples+Uses+of+Fluorescent+Proteins+to+Visualize+Cancer+in+vivo.">The Multiples Uses of Fluorescent Proteins to Visualize Cancer in vivo.</a> Nature Reviews. 5, 796-806 (2005).</li><li>Roby, K. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+a+syngeneic+mouse+model+for+events+related+to+ovarian+cancer.">Development of a syngeneic mouse model for events related to ovarian cancer.</a> Carcinogenesis. 21 (4), 585-591 (2000).</li><li>Rampurwala, M., Ravoori, M. K., Wei, W., Johnson, V. E., Vikram, R., Kundra, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualization+and+quantification+of+intraperitoneal+tumors+by+in+vivo+computed+tomography+using+negative+contrast+enhancement+strategy+in+a+mouse+model+of+ovarian+cancer.">Visualization and quantification of intraperitoneal tumors by in vivo computed tomography using negative contrast enhancement strategy in a mouse model of ovarian cancer.</a> Translational Oncology. 2 (2), 96-106 (2009).</li><li>Kim, T. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antitumor+and+antivascular+effects+of+AVE8062+in+ovarian+carcinoma.">Antitumor and antivascular effects of AVE8062 in ovarian carcinoma.</a> Cancer Research. 67, 9337-9345 (2007).</li><li>Picchio, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advanced+ovarian+carcinoma:+usefulness+of+[(18)F]FDG-PET+in+combination+with+CT+for+lesion+detection+after+primary+treatment.">Advanced ovarian carcinoma: usefulness of [(18)F]FDG-PET in combination with CT for lesion detection after primary treatment.</a> Quarterly Journal of Nuclear Medicine. 47, 77-84 (2003).</li></ol>

This article is part of a special issue on Multimodal Pre-Clinical Imaging, sponsored by Bruker Biospin.

In contrast to studies using human ovarian cancer cells that must be conducted in immunocompromised mice, the protocol described above utilizes immunocompetent C57/Bl6 mice and syngeneic murine ovarian cancer cells. While this enables assessment of the potential role of immune infiltrates in tumor progression and metastasis, the presence of dark hair on the abdominal surface renders imaging less sensitive. Use of a depilatory to remove hair prior to imaging enhances image acquisition, but is time-consuming, particularly ...

Epithelial ovarian cancer (EOC) is the most common cause of death from gynecologic malignancy, with an estimated 21,290 new diagnoses in the U.S. in 2015 and an estimated 14,180 deaths1. The vast majority (&#62; 75%) of women are diagnosed with late-stage disease (stage III or IV) characterized by diffuse intra-peritoneal metastasis and poor prognosis. Disease recurrence in the peritoneal cavity following first-line chemotherapy is also common and represents a major cause of mortality2,3. EOC metastasizes by an unique mechanism involving both direct extension from the primary tumor to neighboring peritoneal organs as well as by dissociation or shedding of cells from the primary tumor surface as single cells or multi-cellular aggregates. Cells are shed into the peritoneal cavity, wherein they resist detachment-induced apoptosis4. Buildup of peritoneal ascites is common, as shed tumor cells block peritoneal lymphatic drainage and tumors produce growth factors that alter vascular permeability. A portion of shed tumor cells attach to the surface of peritoneal organs and structures including intestine, liver, omentum and mesentery, whereupon they anchor and proliferate to produce multiple widely disseminated secondary lesions3,5. Hematogenous metastasis is uncommon. Thus, clinical management commonly consists of cytoreductive surgery including &#34;optimal debulking&#34;, defined as resection of all visible tumor (no matter how small). Complete cytoreduction is associated with a significant increase in overall survival6,7 and is associated with the challenge of identification and removal of lesions &#60; 0.5 cm.
Small animal models have proven utility in ovarian cancer research in improving our understanding of disease progression as well in identification of prognostic biomarkers and testing of novel chemotherapies or combination therapy approaches. As the primary site of ovarian cancer incidence and metastasis is the peritoneal cavity, orthotopic models of EOC metastasis involve the analysis and characterization of intraperitoneal disease. Although there have been recent improvements in the ability to image tumor cells, even at the single cell level, there still exist significant difficulties in quantifying the metastatic tumor burden of EOC. These challenges arise due to the number, size and anatomic location of metastatic lesions. Furthermore there exists a need to label cancer cells to distinguish them from normal host cells. Previous studies have utilized antibody-based labeling protocols or transfection of tumor cells with luciferase8,9. Direct fluorescent labeling of cancer cells was first reported by Chishima and coworkers in 199710. Fluorescent labels do not require addition of exogenous substrate and provide exquisite tumor cell specificity, providing a more effective means to track cancer metastasis11,12.
Herein we describe an optical imaging method for quantitative analysis of metastatic disease using a syngeneic orthotopic xenograft model comprised of red fluorescent protein (RFP)-tagged murine ID8 ovarian cancer cells13 and immuno-competent C57/Bl6 mice. We demonstrate a novel method of relative tumor burden quantification combining in vivo and ex vivo imaging with removal of tissue auto-fluorescence. This approach has potential utility in studies designed to evaluate the effect of specific genetic, epigenetic or micro-environmental modifications and/or treatment modalities on organ-specific metastasis of ovarian cancer.

<table><tbody><tr><td>Dulbecco&#039;s Modified Eagle Medium</td><td>Corning</td><td>10-014-CM</td><td></td></tr><tr><td>Fetal bovine serum</td><td>Gibco</td><td>10437-028</td><td></td></tr><tr><td>penicillin/streptomycin</td><td></td><td></td><td></td></tr><tr><td>Insulin-transferrin-sodium selenite media supplement</td><td>Sigma</td><td>I-1884</td><td></td></tr><tr><td>Bruker Xtreme small animal imaging system</td><td>Bruker Corp.</td><td></td><td></td></tr><tr><td>Bruker Multispectral software</td><td>Bruker Corp</td><td></td><td></td></tr><tr><td>lentiviral particles with Red fluorescent protein</td><td>GenTarget, Inc.</td><td>LVP023</td><td></td></tr><tr><td>trypsin for cell culture</td><td>Corning</td><td>25-053-CI</td><td></td></tr><tr><td>PBS</td><td>Corning</td><td>21-040-CM</td><td></td></tr><tr><td>depilatory cream (such as Nair Hair Remover Lotion)</td><td>purchases from drugstore&amp;nbsp;</td><td>n/a</td><td></td></tr><tr><td>ImageJ software&amp;nbsp;</td><td>http://imagej.nih.gov/ij/&amp;nbsp;</td><td></td><td>free download</td></tr><tr><td>dissecting tools (forceps)</td><td>Roboz Surgical Instrument</td><td>&amp;nbsp;RS 5130</td><td></td></tr><tr><td>dissecting tools (Scissors)</td><td>Roboz Surgical Instrument</td><td>RS 5910</td><td></td></tr></tbody></table>

quantitation of intra-peritoneal ovarian cancer metastasis

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.

The metastatic mechanism of ovarian cancer is characterized by highly diffuse intra-peritoneal metastasis comprised of numerous lesions of varying size, including multiple small (&#60; 2mm) lesions. Thus, use of RFP-labelled tumor cells (Figure 1) and optical imaging provides an alternative method to manual counting and measurement of lesion size. The development of tumor burden over time can be determined by weekly weighing of mice and measurement of abdominal girth to g...

Watch this Scientific Journal Video about Quantitation of Intra-peritoneal Ovarian Cancer Metastasis at JoVE.com

Quantitation of Intra-peritoneal Ovarian Cancer Metastasis

Ovarian cancer metastasis is characterized by numerous diffuse intra-peritoneal lesions, such that accurate visual quantitation of tumor burden is challenging. Herein we describe a method for in situ and ex vivo quantitation of metastatic tumor burden using red fluorescent protein (RFP)-labeled tumor cells and optical imaging.

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 ...

quantitation-of-intra-peritoneal-ovarian-cancer-metastasis

Research

JoVE Journal

Medicine

10.8K Views.  University of Notre Dame. Ovarian cancer metastasis is characterized by numerous diffuse intra-peritoneal lesions, such that accurate visual quantitation of tumor burden is challenging. Herein we describe a method for in situ and ex vivo quantitation of metastatic tumor burden using red fluorescent protein (RFP)-labeled tumor cells and optical imaging.

Video: Quantitation of Intra-peritoneal Ovarian Cancer Metastasis

An Orthotopic Model of Serous Ovarian Cancer in Immunocompetent Mice for in vivo Tumor Imaging and Monitoring of Tumor Immune Responses

Background: Ovarian cancer is generally diagnosed at an advanced stage where the case/fatality ratio is high and thus remains the most lethal of all gynecologic malignancies among US women 1,2,3. Serous tumors are the most widespread forms of ovarian cancer and 4,5 the Tg-MISIIR-TAg transgenic represents the only mouse model that spontaneously develops this type of tumors. Tg-MISIIR-TAg mice express SV40 transforming region under control of the Mullerian Inhibitory Substance type II Receptor (MISIIR) gene promoter 6. Additional transgenic lines have been identified that express the SV40 TAg transgene, but do not develop ovarian tumors. Non-tumor prone mice exhibit typical lifespan for C57Bl/6 mice and are fertile. These mice can be used as syngeneic allograft recipients for tumor cells isolated from Tg-MISIIR-TAg-DR26 mice.
Objective: Although tumor imaging is possible 7, early detection of deep tumors is challenging in small living animals. To enable preclinical studies in an immunologically intact animal model for serous ovarian cancer, we describe a syngeneic mouse model for this type of ovarian cancer that permits in vivo imaging, studies of the tumor microenvironment and tumor immune responses.
Methods: We first derived a TAg+ mouse cancer cell line (MOV1) from a spontaneous ovarian tumor harvested in a 26 week-old DR26 Tg-MISIIR-TAg female. Then, we stably transduced MOV1 cells with TurboFP635 Lentivirus mammalian vector that encodes Katushka, a far-red mutant of the red fluorescent protein from sea anemone Entacmaea quadricolor with excitation/emission maxima at 588/635 nm 8,9,10. We orthotopically implanted MOV1Kat in the ovary 11,12,13,14 of non-tumor prone Tg-MISIIR-TAg female mice. Tumor progression was followed by in vivo optical imaging and tumor microenvironment was analyzed by immunohistochemistry.
Results: Orthotopically implanted MOV1Kat cells developed serous ovarian tumors. MOV1Kat tumors could be visualized by in vivo imaging up to three weeks after implantation (fig. 1) and were infiltrated with leukocytes, as observed in human ovarian cancers 15 (fig. 2).
Conclusions: We describe an orthotopic model of ovarian cancer suitable for in vivo imaging of early tumors due to the high pH-stability and photostability of Katushka in deep tissues. We propose the use of this novel syngeneic model of serous ovarian cancer for in vivo imaging studies and monitoring of tumor immune responses and immunotherapies.

An Orthotopic Model of Serous Ovarian Cancer in Immunocompetent Mice for in vivo Tumor Imaging and Monitoring of Tumor Immune Responses

To study in vivo tumor growth and tumor microenvironment, we used a syngeneic and orthotopic mouse model of ovarian cancer in immunocompetent animals. We transduced a mouse tumor cell line (MOV1) with Katushka fluorescent protein (MOV1KAT) and here we show its orthotopic implantation in ovary and in vivo imaging.

Background: Ovarian cancer is generally diagnosed at an advanced stage where the case/fatality ratio is high and thus remains the most lethal of all ...

Immunology and Infection

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting

Ovarian cancer represents the deadliest gynecologic malignancy. Most patients present at an advanced stage (FIGO stage III or IV), when local metastatic spread has already occurred. However, ovarian cancer has a unique pattern of metastatic spread, in that tumor implants are initially contained within the peritoneal cavity. This feature could enable, in principle, the complete resection of tumor implants with curative intent. Many of these metastatic lesions are microscopic, making them hard to identify and treat. Neutralizing such micrometastases is believed to be a major goal towards eliminating tumor recurrence and achieving long-term survival. Raman imaging with surface enhanced resonance Raman scattering nanoprobes can be used to delineate microscopic tumors with high sensitivity, due to their bright and bioorthogonal spectral signatures. Here, we describe the synthesis of two &#39;flavors&#39; of such nanoprobes: an antibody-functionalized one that targets the folate receptor &#8212; overexpressed in many ovarian cancers &#8212; and a non-targeted control nanoprobe, with&#160;distinct spectra. The nanoprobes are co-administered intraperitoneally to mouse models of metastatic human ovarian adenocarcinoma. All animal studies were approved by the Institutional Animal Care and Use Committee of Memorial Sloan Kettering Cancer Center. The peritoneal cavity of the animals is surgically exposed, washed, and scanned with a Raman microphotospectrometer. Subsequently, the Raman signatures of the two nanoprobes are decoupled using a Classical Least Squares fitting algorithm, and their respective scores divided to provide a ratiometric signal of folate-targeted over untargeted probes. In this way, microscopic metastases are visualized with high specificity. The main benefit of this approach is that the local application into the peritoneal cavity &#8212; which can be done conveniently during the surgical procedure &#8212; can tag tumors without subjecting the patient to systemic nanoparticle exposure. False positive signals stemming from non-specific binding of the nanoprobes onto visceral surfaces can be eliminated by following a ratiometric approach where targeted and non-targeted nanoprobes with distinct Raman signatures are applied as a mixture. The procedure is currently still limited by the lack of a commercial wide-field Raman imaging camera system, which once available will allow for the application of this technique in the operating theater.

Ovarian cancer forms metastases throughout the peritoneal cavity. Here, we present a protocol to make and use folate-receptor targeted surface-enhanced resonance Raman scattering nanoprobes that reveal these lesions with high specificity via ratiometric imaging. The nanoprobes are administered intraperitoneally to living mice, and the derived images correlate well with histology.

Ovarian cancer represents the deadliest gynecologic malignancy. Most patients present at an advanced stage (FIGO stage III or IV), when local metastatic ...

Cancer Research

In vitro Mesothelial Clearance Assay that Models the Early Steps of Ovarian Cancer Metastasis

Ovarian cancer is the fifth leading cause of cancer related deaths in the United States1. Despite a positive initial response to therapies, 70 to 90 percent of women with ovarian cancer develop new metastases, and the recurrence is often fatal2. It is, therefore, necessary to understand how secondary metastases arise in order to develop better treatments for intermediate and late stage ovarian cancer. Ovarian cancer metastasis occurs when malignant cells detach from the primary tumor site and disseminate throughout the peritoneal cavity. The disseminated cells can form multicellular clusters, or spheroids, that will either remain unattached, or implant onto organs within the peritoneal cavity3 (Figure 1, Movie 1). 
All of the organs within the peritoneal cavity are lined with a single, continuous, layer of mesothelial cells4-6 (Figure 2). However, mesothelial cells are absent from underneath peritoneal tumor masses, as revealed by electron micrograph studies of excised human tumor tissue sections3,5-7 (Figure 2). This suggests that mesothelial cells are excluded from underneath the tumor mass by an unknown process. 
Previous in vitro experiments demonstrated that primary ovarian cancer cells attach more efficiently to extracellular matrix than to mesothelial cells8, and more recent studies showed that primary peritoneal mesothelial cells actually provide a barrier to ovarian cancer cell adhesion and invasion (as compared to adhesion and invasion on substrates that were not covered with mesothelial cells)9,10. This would suggest that mesothelial cells act as a barrier against ovarian cancer metastasis. The cellular and molecular mechanisms by which ovarian cancer cells breach this barrier, and exclude the mesothelium have, until recently, remained unknown. 
Here we describe the methodology for an in vitro assay that models the interaction between ovarian cancer cell spheroids and mesothelial cells in vivo (Figure 3, Movie 2). Our protocol was adapted from previously described methods for analyzing ovarian tumor cell interactions with mesothelial monolayers8-16, and was first described in a report showing that ovarian tumor cells utilize an integrin &#x2013;dependent activation of myosin and traction force to promote the exclusion of the mesothelial cells from under a tumor spheroid17. This model takes advantage of time-lapse fluorescence microscopy to monitor the two cell populations in real time, providing spatial and temporal information on the interaction. The ovarian cancer cells express red fluorescent protein (RFP) while the mesothelial cells express green fluorescent protein (GFP). RFP-expressing ovarian cancer cell spheroids attach to the GFP-expressing mesothelial monolayer. The spheroids spread, invade, and force the mesothelial cells aside creating a hole in the monolayer. This hole is visualized as the negative space (black) in the GFP image. The area of the hole can then be measured to quantitatively analyze differences in clearance activity between control and experimental populations of ovarian cancer and/ or mesothelial cells. This assay requires only a small number of ovarian cancer cells (100 cells per spheroid X 20-30 spheroids per condition), so it is feasible to perform this assay using precious primary tumor cell samples. Furthermore, this assay can be easily adapted for high throughput screening.

In vitro Mesothelial Clearance Assay that Models the Early Steps of Ovarian Cancer Metastasis

The mesothelial clearance assay described here takes advantage of fluorescently labeled cells and time-lapse video microscopy to visualize and quantitatively measure the interactions of ovarian cancer multicellular spheroids and mesothelial cell monolayers. This assay models the early steps of ovarian cancer metastasis.

Ovarian cancer is the fifth leading cause of cancer related deaths in the United States1. Despite a positive initial response to therapies, 70 to 90 ...

An Orthotopic Mouse Model of Ovarian Cancer using Human Stroma to Promote Metastasis

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 &gt;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.

Orthotopic engraftment of ovarian cancer cells mixed with human stromal cells provides a mouse model that exhibits rapid, diffuse metastatic behavior that is characteristic of human ovarian cancer. This model also allows for the study of tumor cell and stromal cell interactions, as well as their role in tumor progression and metastasis.

Ovarian cancer is characterized by early, diffuse metastasis with 70% of women having metastatic disease at the time of diagnosis. While elegant ...

Assessment of Ovarian Cancer Spheroid Attachment and Invasion of Mesothelial Cells in Real Time

Ovarian cancers metastasize by shedding into the peritoneal fluid and dispersing to distal sites within the peritoneum. Monolayer cultures do not accurately model the behaviors of cancer cells within a nonadherent environment, as cancer cells inherently aggregate into multicellular structures which contribute to the metastatic process by attaching to and invading the peritoneal lining to form secondary tumors. To model this important stage of ovarian cancer metastasis, multicellular aggregates, or spheroids, can be generated from established ovarian cancer cell lines maintained under nonadherent conditions. To mimic the peritoneal microenvironment encountered by tumor cells in vivo, a spheroid-mesothelial co-culture model was established in which preformed spheroids are plated on top of a human mesothelial cell monolayer, formed over an extracellular matrix barrier. Methods were then developed using a real-time cell analyzer to conduct quantitative real time measurements of the invasive capacity of different ovarian cancer cell lines grown as spheroids. This approach allows for the continuous measurement of invasion over long periods of time, which has several advantages over traditional endpoint assays and more laborious real time microscopy image analyses. In short, this method enables a rapid, determination of factors which regulate the interactions between ovarian cancer spheroid cells invading through mesothelial and matrix barriers over time.

Ovarian cancer cell invasion into the mesothelial lining of the peritoneum is a dynamic process over time. Utilizing a real time analyzer, the invasive capacity of ovarian cancer cells in a spheroid-mesothelial cell co-culture model can be quantified over prolonged time periods, providing insights into factors regulating the metastatic process.

Ovarian cancers metastasize by shedding into the peritoneal fluid and dispersing to distal sites within the peritoneum. Monolayer cultures do not ...

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

Epithelial ovarian cancer is the most lethal gynecologic malignancy in the United States. Although patients initially respond to the current standard of care consisting of surgical debulking and combination chemotherapy consisting of platinum and taxane compounds, almost 90% of patients recur within a few years. In these patients the development of chemoresistant disease limits the efficacy of currently available chemotherapy agents and therefore contributes to the high mortality. To discover novel therapy options that can target recurrent disease, appropriate animal models that closely mimic the clinical profile of patients with recurrent ovarian cancer are required. The challenge in monitoring intra-peritoneal (i.p.) disease limits the use of i.p. models and thus most xenografts are established subcutaneously. We have developed a sensitive optical imaging platform that allows the detection and anatomical location of i.p. tumor mass. The platform includes the use of optical reporters that extend from the visible light range to near infrared, which in combination with 2-dimensional X-ray co-registration can provide anatomical location of molecular signals. Detection is significantly improved by the use of a rotation system that drives the animal to multiple angular positions for 360 degree imaging, allowing the identification of tumors that are not visible in single orientation. This platform provides a unique model to non-invasively monitor tumor growth and evaluate the efficacy of new therapies for the prevention or treatment of recurrent ovarian cancer.

We describe a non-invasive animal imaging platform that allows the detection, quantification, and monitoring of ovarian cancer growth and recurrence. This intra-peritoneal xenograft model mimics the clinical profile of patients with ovarian cancer.

Epithelial ovarian cancer is the most lethal gynecologic malignancy in the United States. Although patients initially respond to the current standard of ...

Establishing a 3D Ex Vivo Model for Ovarian Cancer-Omentum Interaction

An Ex Vivo Model of Ovarian Cancer Peritoneal Metastasis Using Human Omentum

Ovarian cancer is the deadliest gynecologic malignancy. The omentum plays a key role in providing a supportive microenvironment to metastatic ovarian cancer cells as well as immune modulatory signals that allow tumor tolerance. However, we have limited models that closely mimic the interaction between ovarian cancer cells and adipose-rich tissues. To further understand the cellular and molecular mechanisms by which the omentum provides a pro-tumoral microenvironment, we developed a unique 3D ex vivo model of cancer cell-omentum interaction. Using human omentum, we are able to grow ovarian cancer cells within this adipose-rich microenvironment and monitor the factors responsible for tumor growth and immune regulation. In addition to providing a platform for the study of this adipose-rich tumor microenvironment, the model provides an excellent platform for the development and evaluation of novel therapeutic approaches to target metastatic cancer cells in this niche. The proposed model is easy to generate, inexpensive, and applicable to translational investigations.

Author Spotlight: Advanced Ex Vivo Model for Investigating Cancer-Adipose Microenvironment Interaction 

This protocol describes the establishment of a three-dimensional (3D) ex vivo model of cancer cell-omentum interaction. The model provides a platform for elucidating pro-tumor mechanisms within the adipose niche and for testing novel therapies.

Ovarian cancer is the deadliest gynecologic malignancy. The omentum plays a key role in providing a supportive microenvironment to metastatic ovarian ...

In Vivo and Ex Vivo Approaches to Study Ovarian Cancer Metastatic Colonization of Milky Spot Structures in Peritoneal Adipose

High-grade serous ovarian cancer (HGSC), the cause of widespread peritoneal metastases, continues to have an extremely poor prognosis; fewer than 30% of women are alive 5 years after diagnosis. The omentum is a preferred site of HGSC metastasis formation. Despite the clinical importance of this microenvironment, the contribution of omental adipose tissue to ovarian cancer progression remains understudied. Omental adipose is unusual in that it contains structures known as milky spots, which are comprised of B, T, and NK cells, macrophages, and progenitor cells surrounding dense nests of vasculature. Milky spots play a key role in the physiologic functions of the omentum, which are required for peritoneal homeostasis. We have shown that milky spots also promote ovarian cancer metastatic colonization of peritoneal adipose, a key step in the development of peritoneal metastases. Here we describe the approaches we developed to evaluate and quantify milky spots in peritoneal adipose and study their functional contribution to ovarian cancer cell metastatic colonization of omental tissues both in vivo and ex vivo. These approaches are generalizable to additional mouse models and cell lines, thus enabling the study of ovarian cancer metastasis formation from initial localization of cells to milky spot structures to the development of widespread peritoneal metastases.

In Vivo and Ex Vivo Approaches to Study Ovarian Cancer Metastatic Colonization of Milky Spot Structures in Peritoneal Adipose

We outline a protocol that implements both in vivo and ex vivo approaches to study ovarian cancer colonization of peritoneal adipose tissues, particularly the omentum. Furthermore, we present a protocol to quantitate and analyze immune cell-structures in the omentum known as milky spots, which promote metastases of peritoneal adipose.

High-grade serous ovarian cancer (HGSC), the cause of widespread peritoneal metastases, continues to have an extremely poor prognosis; fewer than 30% of ...

Modeling the Early Steps of Ovarian Cancer Dissemination in an Organotypic Culture of the Human Peritoneal Cavity

The pattern of ovarian cancer metastasis is markedly different from that of most other epithelial tumors, because it rarely spreads hematogenously. Instead, ovarian cancer cells exfoliated from the primary tumor are carried by peritoneal fluid to metastatic sites within the peritoneal cavity. These sites, most notably the abdominal peritoneum and omentum, are organs covered by a mesothelium-lined surface. To investigate the processes of ovarian cancer dissemination, we assembled a complex three-dimensional culture system that reconstructs the lining of the peritoneal cavity in vitro. Primary human fibroblasts and mesothelial cells were isolated from human omentum. The fibroblasts were then mixed with extracellular matrix and covered with a layer of the primary human mesothelial cells to mimic the peritoneal and omental surfaces encountered by metastasizing ovarian cancer cells. The resulting organotypic model is, as shown, used to examine the early steps of ovarian cancer dissemination, including cancer cell adhesion, invasion, and proliferation. This model has been used in a number of studies to investigate the role of the microenvironment (cellular and acellular) in early ovarian cancer dissemination. It has also been successfully adapted to high throughput screening and used to identify and test inhibitors of ovarian cancer metastasis.

Here, we present a protocol to construct a three-dimensional in vitro model of the lining of the peritoneal cavity, composed of primary human mesothelial cells and fibroblasts layered with extracellular matrix, as a tool to investigate ovarian cancer cell adhesion, invasion, and proliferation.

The pattern of ovarian cancer metastasis is markedly different from that of most other epithelial tumors, because it rarely spreads hematogenously. ...

Mesothelial Clearance Assay: An In Vitro Technique to Quantify the Invasion Ability of Ovarian Cancer Spheroids into Mesothelial Cell Monolayers

Source: Davidowitz, R. A.&#160;et al. <a href="https://www.jove.com/t/3888 ">In Vitro Mesothelial Clearance Assay that Models the Early Steps of Ovarian Cancer Metastasis.</a> J. Vis. Exp.&#160;(2012)
This video describes a technique to quantitatively measure the interaction of ovarian cancer spheroids with mesothelial cells, using fluorescently labeled cells and time-lapse video microscopy. This in vitro assay models the interaction between metastasizing ovarian cancer cell spheroids and mesothelial cells lining the peritoneal cavity in vivo.

Quantitation of Intra-peritoneal Ovarian Cancer Metastasis

Samenvatting

Meer video's verkennen