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A mathematical model of tumour self-seeding reveals secondary metastatic deposits as drivers of primary tumour growth

Overview of attention for article published in Journal of The Royal Society Interface, May 2013
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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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

blogs
2 blogs
twitter
17 X users
facebook
1 Facebook page
googleplus
3 Google+ users

Citations

dimensions_citation
50 Dimensions

Readers on

mendeley
61 Mendeley
citeulike
1 CiteULike
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Title
A mathematical model of tumour self-seeding reveals secondary metastatic deposits as drivers of primary tumour growth
Published in
Journal of The Royal Society Interface, May 2013
DOI 10.1098/rsif.2013.0011
Pubmed ID
Authors

Jacob G. Scott, David Basanta, Alexander R. A. Anderson, Philip Gerlee

Abstract

Two models of circulating tumour cell (CTC) dynamics have been proposed to explain the phenomenon of tumour 'self-seeding', whereby CTCs repopulate the primary tumour and accelerate growth: primary seeding, where cells from a primary tumour shed into the vasculature and return back to the primary themselves; and secondary seeding, where cells from the primary first metastasize into a secondary tissue and form microscopic secondary deposits, which then shed cells into the vasculature returning to the primary. These two models are difficult to distinguish experimentally, yet the differences between them is of great importance to both our understanding of the metastatic process and also for designing methods of intervention. Therefore, we developed a mathematical model to test the relative likelihood of these two phenomena in the subset of tumours whose shed CTCs first encounter the lung capillary bed, and show that secondary seeding is several orders of magnitude more likely than primary seeding. We suggest how this difference could affect tumour evolution, progression and therapy, and propose several possible methods of experimental validation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 8%
United Kingdom 1 2%
Portugal 1 2%
France 1 2%
Unknown 53 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 23%
Student > Ph. D. Student 10 16%
Professor 6 10%
Student > Master 5 8%
Student > Bachelor 4 7%
Other 12 20%
Unknown 10 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 21%
Medicine and Dentistry 11 18%
Mathematics 6 10%
Biochemistry, Genetics and Molecular Biology 5 8%
Engineering 5 8%
Other 8 13%
Unknown 13 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 10 October 2020.
All research outputs
#1,414,753
of 25,639,676 outputs
Outputs from Journal of The Royal Society Interface
#628
of 3,320 outputs
Outputs of similar age
#11,054
of 205,481 outputs
Outputs of similar age from Journal of The Royal Society Interface
#5
of 39 outputs
Altmetric has tracked 25,639,676 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 3,320 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.7. This one has done well, scoring higher than 81% 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 205,481 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 94% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.