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Patient-derived xenograft models of breast cancer and their predictive power

Overview of attention for article published in Breast Cancer Research, February 2015
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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 (89th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

blogs
1 blog
twitter
4 tweeters
facebook
1 Facebook page
q&a
1 Q&A thread

Citations

dimensions_citation
200 Dimensions

Readers on

mendeley
368 Mendeley
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Title
Patient-derived xenograft models of breast cancer and their predictive power
Published in
Breast Cancer Research, February 2015
DOI 10.1186/s13058-015-0523-1
Pubmed ID
Authors

James R Whittle, Michael T Lewis, Geoffrey J Lindeman, Jane E Visvader

Abstract

Despite advances in the treatment of patients with early and metastatic breast cancer, mortality remains high due to intrinsic or acquired resistance to therapy. Increased understanding of the genomic landscape through massively parallel sequencing has revealed somatic mutations common to specific subtypes of breast cancer, provided new prognostic and predictive markers, and highlighted potential therapeutic targets. Evaluating new targets using established cell lines is limited by the inexact correlation between responsiveness observed in cell lines versus that elicited in the patient. Patient-derived xenografts (PDXs) generated from fresh tumor specimens recapitulate the diversity of breast cancer and reflect histopathology, tumor behavior, and the metastatic properties of the original tumor. The high degree of genomic preservation evident across primary tumors and their matching PDXs over serial passaging validate them as important preclinical tools. Indeed, there is accumulating evidence that PDXs can recapitulate treatment responses of the parental tumor. The finding that tumor engraftment is an independent and poor prognostic indicator of patient outcome represents the first step towards personalized medicine. Here we review the utility of breast cancer PDX models to study the clonal evolution of tumors and to evaluate novel therapies and drug resistance.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 368 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 <1%
Spain 2 <1%
France 2 <1%
Portugal 1 <1%
Netherlands 1 <1%
Canada 1 <1%
China 1 <1%
Malaysia 1 <1%
United States 1 <1%
Other 0 0%
Unknown 356 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 79 21%
Student > Ph. D. Student 72 20%
Student > Master 37 10%
Student > Bachelor 37 10%
Student > Doctoral Student 23 6%
Other 62 17%
Unknown 58 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 90 24%
Biochemistry, Genetics and Molecular Biology 79 21%
Medicine and Dentistry 54 15%
Pharmacology, Toxicology and Pharmaceutical Science 14 4%
Immunology and Microbiology 13 4%
Other 43 12%
Unknown 75 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 30 October 2017.
All research outputs
#1,117,220
of 12,786,466 outputs
Outputs from Breast Cancer Research
#151
of 1,448 outputs
Outputs of similar age
#28,594
of 277,482 outputs
Outputs of similar age from Breast Cancer Research
#2
of 6 outputs
Altmetric has tracked 12,786,466 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,448 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.0. This one has done well, scoring higher than 89% 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 277,482 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.