| Literature DB >> 29092942 |
Terrence F Meehan1, Nathalie Conte2, Theodore Goldstein3, Giorgio Inghirami4, Mark A Murakami5, Sebastian Brabetz6,7, Zhiping Gu8, Jeffrey A Wiser8, Patrick Dunn8, Dale A Begley9, Debra M Krupke9, Andrea Bertotti10, Alejandra Bruna11, Matthew H Brush12, Annette T Byrne13, Carlos Caldas11, Amanda L Christie5, Dominic A Clark2, Heidi Dowst14, Jonathan R Dry15, James H Doroshow16, Olivier Duchamp17, Yvonne A Evrard18, Stephane Ferretti19, Kristopher K Frese20, Neal C Goodwin21, Danielle Greenawalt22, Melissa A Haendel12, Els Hermans23, Peter J Houghton24, Jos Jonkers25, Kristel Kemper25, Tin O Khor26, Michael T Lewis27, K C Kent Lloyd28, Jeremy Mason2, Enzo Medico10, Steven B Neuhauser9, James M Olson29, Daniel S Peeper25, Oscar M Rueda11, Je Kyung Seong30, Livio Trusolino10, Emilie Vinolo31, Robert J Wechsler-Reya32, David M Weinstock5, Alana Welm33, S John Weroha34, Frédéric Amant25,35, Stefan M Pfister6,7,36, Marcel Kool6,7, Helen Parkinson2, Atul J Butte3, Carol J Bult9.
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. Cancer Res; 77(21); e62-66. ©2017 AACR. ©2017 American Association for Cancer Research.Entities:
Mesh:
Year: 2017 PMID: 29092942 PMCID: PMC5738926 DOI: 10.1158/0008-5472.CAN-17-0582
Source DB: PubMed Journal: Cancer Res ISSN: 0008-5472 Impact factor: 12.701