Pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. We identified and characterized a patient derived xenograft model with metastatic potential, melanoma xenograft 425362-245-T. In this study we performed a 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 cm3 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-425362-245-T) 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: Melanoma (PDMR-425362-245-T)
Table 1: Penetrance and location of pathological confirmed metastatic lesion(s).
# animals in group
# animals that displayed metastasis in MRI and confirmed by Pathology
Pathology confirmation of MRI (primary imaging site)
Other confirmed Location (s)
Mouse ID: MRI with pathology confirmation of metastasis
(6 mice were EU due to xenograft size prior to observation of metastasis)
1512, 1516, 1518, 1520
Kidney, Liver, Pancreas
1506, 1508, 1509, 1510, 1511, 1515, 1517, 1519, 1521, 1523
PET/CT Characterization of the primary tumor: Baseline PET (SOP attached) were performed when tumor reached an approximate 200 mm3. Average SUVmax values (n=5) were calculated; [18F]FDG: 2.7 ± 0.5 and [18F]FLT: 2.0 ± 0.3.
Melanoma PDMR-425362-245-T model can be challenging due to the rapid growth of the xenograft and regrowth. Metastases was well observed on T2 MRI imaging allowing non-invasive evaluation in treatment trials.
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- Frederick National Laboratory for Cancer Research – Special Thanks to Joseph D. Kalen, PhD, Lilia V. Ileva, MS, Lisa A Riffle, Nimit Patel, Keita Saito, PhD, Yvonne Evrard, PhD, Elijah Edmondson, DVM, PhD, Jessica Phillips, Simone Difilippantonio, PhD, Chelsea Sanders, Amy James, Lia Thang, 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 Institute, National Institutes of Health, under Contract No. HHSN261201500003I. 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.