[Webinar] How to better optimize an oncology drug discovery program
Register for this webinar discussing how conventional and innovative integrated technological skills should be incorporated into an oncology program.Read more
Terrence F Meehan, Nathalie Conte, Theodore Goldstein, Giorgio Inghirami, Mark A Murakami, Sebastian Brabetz, Zhiping Gu, Jeffrey A Wiser, Patrick Dunn, Dale A Begley, Debra M Krupke, Andrea Bertotti, Alejandra Bruna, Matthew H Brush, Annette T Byrne, Carlos Caldas, Amanda L Christie, Dominic A Clark, Heidi Dowst, Jonathan R Dry, James H Doroshow, Olivier Duchamp, Yvonne A Evrard, Stephane Ferretti, Kristopher K Frese, Neal C Goodwin, Danielle Greenawalt, Melissa A Haendel, Els Hermans, Peter J Houghton, Jos Jonkers, Kristel Kemper, Tin O Khor, Michael T Lewis, K C Kent Lloyd , Jeremy Mason, Enzo Medico, Steven B Neuhauser, James M Olson, Daniel S Peeper, Oscar M Rueda, Je Kyung Seong, Livio Trusolino, Emilie Vinolo, Robert J Wechsler-Reya, David M Weinstock, Alana Welm, S John Weroha, Frédéric Amant , Stefan M Pfister, Marcel Kool, Helen Parkinson, Atul J Butte, Carol J Bult
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. Cancer Res; 77(21); e62-66. ©2017 AACR.
Ref: Cancer Res. 2017 Nov 1;77(21):e62-e66. doi: 10.1158/0008-5472.CAN-17-0582.