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Data from PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models

Authors :
Carol J. Bult
Atul J. Butte
Helen Parkinson
Marcel Kool
Stefan M. Pfister
Frédéric Amant
S. John Weroha
Alana Welm
David M. Weinstock
Robert J. Wechsler-Reya
Emilie Vinolo
Livio Trusolino
Je Kyung Seong
Oscar M. Rueda
Daniel S. Peeper
James M. Olson
Steven B. Neuhauser
Enzo Medico
Jeremy Mason
K.C. Kent Lloyd
Michael T. Lewis
Tin O. Khor
Kristel Kemper
Jos Jonkers
Peter J. Houghton
Els Hermans
Melissa A. Haendel
Danielle Greenawalt
Neal C. Goodwin
Kristopher K. Frese
Stephane Ferretti
Yvonne A. Evrard
Olivier Duchamp
James H. Doroshow
Jonathan R. Dry
Heidi Dowst
Dominic A. Clark
Amanda L. Christie
Carlos Caldas
Annette T. Byrne
Matthew H. Brush
Alejandra Bruna
Andrea Bertotti
Debra M. Krupke
Dale A. Begley
Patrick Dunn
Jeffrey A. Wiser
Zhiping Gu
Sebastian Brabetz
Mark A. Murakami
Giorgio Inghirami
Theodore Goldstein
Nathalie Conte
Terrence F. Meehan
Publication Year :
2023
Publisher :
American Association for Cancer Research (AACR), 2023.

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.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........0373627dabe0ae72d83c81ddf8478d17
Full Text :
https://doi.org/10.1158/0008-5472.c.6510291.v1