Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models.

PDX-MI: Minimal information for patient-derived tumor xenograft models

Inghirami, Giorgio;Bertotti, Andrea;Medico, Enzo;Trusolino, Livio;
2017-01-01

Abstract

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models.
2017
77
21
e62
e66
http://cancerres.aacrjournals.org//content/canres/77/21/e62.full.pdf
Animals; Databases as Topic; Disease Models, Animal; Humans; Mice; Patients; Xenograft Model Antitumor Assays; Neoplasms; Oncology; Cancer Research
Meehan, Terrence F.; Conte, Nathalie; Goldstein, Theodore; Inghirami, Giorgio; Murakami, Mark A.; Brabetz, Sebastian; Zhiping, Gu; Wiser, Jeffrey A.; Dunn, Patrick; Begley, Dale A.; Krupke, Debra M.; Bertotti, Andrea; Bruna, Alejandra; Brush, Matthew H.; Byrne, Annette T.; Caldas, Carlos; Christie, Amanda L.; Clark, Dominic A.; Dowst, Heidi; Dry, Jonathan R.; Doroshow, James H.; Duchamp, Olivier; Evrard, Yvonne A.; Ferretti, Stephane; Frese, Kristopher K.; Goodwin, Neal C.; Greenawalt, Danielle; Haendel, Melissa A.; Hermans, Els; Houghton, Peter J.; Jonkers, Jos; Kemper, Kristel; Khor, Tin O.; Lewis, Michael T.; Lloyd, K. C. Kent; Mason, Jeremy; Medico, Enzo; Neuhauser, Steven B.; Olson, James M.; Peeper, Daniel S.; Rueda, Oscar M.; Seong, Je Kyung; Trusolino, Livio; Vinolo, Emilie; Wechsler-Reya, Robert J.; Weinstock, David M.; Welm, Alana; Weroha, S. John; Amant, Frã©dã©ric; Pfister, Stefan M.; Kool, Marcel; Parkinson, Helen; Butte, Atul J.; Bult, Carol J.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1658615
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