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

Overview of attention for article published in Cancer Research, October 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

1 blog
25 tweeters
1 Google+ user
1 research highlight platform


44 Dimensions

Readers on

119 Mendeley
2 CiteULike
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PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models
Published in
Cancer Research, October 2017
DOI 10.1158/0008-5472.can-17-0582
Pubmed ID

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.

Twitter Demographics

The data shown below were collected from the profiles of 25 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 119 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 31%
Student > Ph. D. Student 25 21%
Other 10 8%
Student > Doctoral Student 6 5%
Student > Master 5 4%
Other 16 13%
Unknown 20 17%
Readers by discipline Count As %
Medicine and Dentistry 22 18%
Biochemistry, Genetics and Molecular Biology 20 17%
Agricultural and Biological Sciences 20 17%
Computer Science 7 6%
Pharmacology, Toxicology and Pharmaceutical Science 5 4%
Other 21 18%
Unknown 24 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 25. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 April 2018.
All research outputs
of 15,157,504 outputs
Outputs from Cancer Research
of 14,291 outputs
Outputs of similar age
of 320,939 outputs
Outputs of similar age from Cancer Research
of 150 outputs
Altmetric has tracked 15,157,504 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,291 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 320,939 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.