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BioMed Research International
Volume 2016 (2016), Article ID 1715053, 7 pages
Research Article

Tumor Take Rate Optimization for Colorectal Carcinoma Patient-Derived Xenograft Models

1Department of General, Vascular, Thoracic and Transplantation Surgery, University of Rostock, Schillingallee 35, 18055 Rostock, Germany
2Institute of Pathology, University of Rostock, Strempelstraße 14, 18055 Rostock, Germany

Received 18 February 2016; Revised 23 September 2016; Accepted 7 November 2016

Academic Editor: Andrea Vecchione

Copyright © 2016 Michael Gock et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. For development of individualized treatment on a routine basis, transfer of patients’ tumor tissue in a xenograft model (i.e., generation of patient-derived xenografts (PDX)) is desirable for molecular, biochemical, or functional analyses. Drawbacks are dissatisfactory tumor take rates, the necessity of fast tumor tissue processing, and extensive logistics demanding teamwork of surgeons, pathologists, and laboratory researchers. Methods. The take rates of ten colorectal cancer (CRC) tissue samples in immunodeficient mice were compared after direct cryopreservation and after a 24 h cooling period at 4°C prior to cryopreservation. Additionally, the effect of simultaneous Matrigel application on the take rates was investigated. Beside take rates, tumor growth characteristics and cell culture success were analyzed. Results. Tumor takes of CRC tissue samples were significantly improved after Matrigel application (8 versus 15 takes, ). As expected, they diminished furthermore after 24 h cooling. Application of Matrigel could counteract this decrease significantly (2 versus 7 takes, ). Cumulative take rate after cryopreservation was satisfactory (70%). Conclusion. Matrigel application after 24 h delay in tissue processing facilitates CRC PDX model development. These data help developing strategies for individualized tumor therapies in the context of multicenter clinical studies and for basic research on primary patient tumors.