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Drug response in a genetically engineered mouse model of multiple myeloma is predictive of clinical efficacy

Overview of attention for article published in Blood, March 2012
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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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

blogs
1 blog
twitter
5 X users
patent
31 patents
wikipedia
1 Wikipedia page

Citations

dimensions_citation
172 Dimensions

Readers on

mendeley
125 Mendeley
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Title
Drug response in a genetically engineered mouse model of multiple myeloma is predictive of clinical efficacy
Published in
Blood, March 2012
DOI 10.1182/blood-2012-02-412783
Pubmed ID
Authors

Marta Chesi, Geoffrey M. Matthews, Victoria M. Garbitt, Stephen E. Palmer, Jake Shortt, Marcus Lefebure, A. Keith Stewart, Ricky W. Johnstone, P. Leif Bergsagel

Abstract

The attrition rate for anticancer drugs entering clinical trials is unacceptably high. For multiple myeloma (MM), we postulate that this is because of preclinical models that overemphasize the antiproliferative activity of drugs, and clinical trials performed in refractory end-stage patients. We validate the Vk*MYC transgenic mouse as a faithful model to predict single-agent drug activity in MM with a positive predictive value of 67% (4 of 6) for clinical activity, and a negative predictive value of 86% (6 of 7) for clinical inactivity. We identify 4 novel agents that should be prioritized for evaluation in clinical trials. Transplantation of Vk*MYC tumor cells into congenic mice selected for a more aggressive disease that models end-stage drug-resistant MM and responds only to combinations of drugs with single-agent activity in untreated Vk*MYC MM. We predict that combinations of standard agents, histone deacetylase inhibitors, bromodomain inhibitors, and hypoxia-activated prodrugs will demonstrate efficacy in the treatment of relapsed MM.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Turkey 1 <1%
Belgium 1 <1%
Australia 1 <1%
Unknown 121 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 25%
Student > Ph. D. Student 30 24%
Student > Master 12 10%
Student > Bachelor 11 9%
Other 9 7%
Other 13 10%
Unknown 19 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 23%
Medicine and Dentistry 28 22%
Biochemistry, Genetics and Molecular Biology 15 12%
Immunology and Microbiology 8 6%
Chemistry 6 5%
Other 14 11%
Unknown 25 20%
Attention Score in Context

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 05 March 2024.
All research outputs
#1,534,202
of 25,371,288 outputs
Outputs from Blood
#1,302
of 33,239 outputs
Outputs of similar age
#8,196
of 172,496 outputs
Outputs of similar age from Blood
#8
of 325 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 33,239 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done particularly well, scoring higher than 96% 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 172,496 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 95% of its contemporaries.
We're also able to compare this research output to 325 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.