Citations 2
Promoting validation and cross-phylogenetic integration in model organism research
Cheng, Keith C. , Burdine, Rebecca D. , Dickinson, Mary E. , Lloyd, K. C. Kent , MacRae, Calum A. , Morrison, John H., O’Connor, David H. , Rogers, Crystal D. , Sánchez, Susan , Simpson, J. , Talbot, William S. , Wallace, Douglas C. , Bellen, Hugo J , O'Connor, David H , Ekker, Stephen C. , Lin, Alex Y. , Lutz, Cathleen M. , Postlethwait, John H. , Weimer, Jill M. , Bellen, Hugo J.
Disease Models & Mechanisms | September 20, 2022 | Volume: 15 | Issue: 9 | Pages: No page available
ABSTRACT Model organism (MO) research provides a basic understanding of biology and disease due to the evolutionary conservation of the molecular and cellular language of life. MOs have been used to identify and understand the function of orthologous genes, proteins, cells and tissues involved in biological processes, to develop and evaluate techniques and methods, and to perform whole-organism-based chemical screens to test drug efficacy and toxicity. However, a growing richness of datasets and the rising power of computation raise an important question: How do we maximize the value of MOs? In-depth discussions in over 50 virtual presentations organized by the National Institutes of Health across more than 10 weeks yielded important suggestions for improving the rigor, validation, reproducibility and translatability of MO research. The effort clarified challenges and opportunities for developing and integrating tools and resources. Maintenance of critical existing infrastructure and the implementation of suggested improvements will play important roles in maintaining productivity and facilitating the validation of animal models of human biology and disease.
PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models
Meehan, Terrence F. , Goldstein, Theodore C. , Murakami, Mark A. , Brabetz, Sebastian , Gu, Zhiping , Wiser, Jeffrey A., Dunn, Patrick , Begley, Dale A. , Krupke, Debra M. , Bertotti, Andrea , Bruna, Alejandra , Brush, Matthew , Byrne, Annette T. , Caldas, Carlos , Christie, Amanda L., Clark, Dominic A. , Dowst, Heidi , Dry, Jonathan R. , Doroshow, James H. , Duchamp, Olivier, Ferretti, Stéphane , Frese, Kristopher K. , Goodwin, Neal , Greenawalt, Danielle M., Haendel, Melissa , Hermans, Els , Houghton, Peter J. , Jonkers, Jos , Kemper, Kristel , Khor, Tin Oo, Lloyd, K. C. Kent , Mason, Jeremy , Médico, Enzo , Olson, James M. , Peeper, Daniel S. , Seong, Je Kyung , Trusolino, Livio , Vinolo, Emilie, Wechsler‐Reya, Robert J. , Weinstock, David M. , Welm, Alana L. , Weroha, S. John , Pfister, Stefan M. , Kool, Marcel , Parkinson, Helen , Butte, Atul J. , Bult, Carol J. , Haendel, Melissa , Caldas, Carlos , Pfister, Stefan M , Conte, Nathalie , Inghirami, Giorgio , Evrard, Yvonne A. , Lewis, Michael T. , Neuhauser, Steven B. , Rueda, Oscar M. , Amant, Frédéric
Cancer Research | November 1, 2017 | Volume: 77 | Issue: 21 | Pages: e62-e66
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.