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Pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are exist are resource intensive because determination of the presence of metastatic disease, metastatic metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological extensive pathological examination. We identified and characterized a patient derived xenograft model xenograft model with metastatic potential, adenocarcinoma pancreas xenograft 521955-158-R4. In this study we performed a detailed imaging detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 cmmm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size. The imaging characteristics of this model (PDMR-521955-158-R4), which is available from the National Cancer Institute Patient-Derived Models Repository (https://pdmr.cancer.gov/), is highly favorable for preclinical research studies of metastatic disease when used in conjunction with non-contrast T2 weighted MRI. Results: Adenocarcinoma pancreas (PDMR-521955-158-R4) Table 1: Penetrance and location of pathological confirmed metastatic lesion(s).
Percent penetrance with respect to the average time-to-metastasis for non-resected (time from implant: 10.7 ± 1.8 weeks) and resected (time from tumor resection: 5.3 ± 1.1 weeks) cohorts. PET/CT Characterization of the primary tumor: Baseline PET (SOP attached) were performed when tumor reached an approximate 200 mm3. Average SUVmax values (n=6) were calculated; [18F]FDG: 2.3 ± 0.8 and [18F]FLT: 2.4 ± 0.4. Conclusion: Good metastatic model with 40% penetrance un-resected and 70% with planned early resection. Metastases are well observed on T2 MRI imaging allowing non-invasive evaluation in treatment trials. |
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Frederick National Laboratory for Cancer Research – Special Thanks to Joseph D. Kalen, PhD, Lilia V. Ileva, MS, Lisa A Riffle, Nimit L Patel, MS, Keita Saito, PhD, Yvonne Evrard, PhD, Elijah Edmondson, DVM, PhD, Justin Smith, Simone Difilippantonio, PhD, Chelsea Sanders, Ulrike Wagner, Yanling Liu, PhD, John B. Freymann, and Justin Kirby.
Division of Cancer Therapeutics and Diagnosis/National Cancer Institute - James L. Tatum, MD, Paula M Jacobs, PhD, Melinda G. Hollingshead, DVM, and James H. Doroshow, MD
PixelMed Publishing – Special Thanks to David A. Clunie, MD
University of Arkansas for Medical Sciences – Special Thanks to Kirk E. Smith
- This project has been funded in whole or in part with Federal funds from the National Cancer Institution, National Institutes of Health, under Contract No. Number HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
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