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Model mimicking clinical profile of patients with ovarian cancer @ Yale School of Medicine

Reporter: Aviva Lev-Ari, PhD, RN

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

Natalia J. Sumi¹, Eydis Lima¹, John Pizzonia², Sean P. Orton³, Vinicius Craveiro¹, Wonduk Joo¹, Jennie C. Holmberg¹, Marta Gurrea¹, Yang Yang-Hartwich¹, Ayesha Alvero¹, Gil Mor¹

¹Department of Obstetrics, Gynecology and Reproductive Sciences, Reproductive Immunology Unit, Yale University School of Medicine, ²NatureMost Laboratories, ³Bruker Preclinical Imaging

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http://www.jove.com/video/51815/murine-model-for-non-invasive-imaging-to-detect-monitor-ovarian

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

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

Natalia J. Sumi, ¹ Eydis Lima, ¹ John Pizzonia, ² Sean P. Orton, ³ Vinicius Craveiro, ¹ Wonduk Joo, ¹Jennie C. Holmberg, ¹ Marta Gurrea, ¹ Yang Yang-Hartwich, ¹ Ayesha Alvero, ¹ and Gil Mor ¹

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Abstract

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.

Keywords: Cancer Biology, Issue 93, ovarian cancer, recurrence, in vivo imaging, tumor burden, cancer stem cells, chemotherapy

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SOURCE

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4353409/