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Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations

Overview of attention for article published in Biology Direct, August 2016
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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 (83rd percentile)

Mentioned by

1 blog
1 tweeter
1 Q&A thread


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44 Mendeley
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Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations
Published in
Biology Direct, August 2016
DOI 10.1186/s13062-016-0143-4
Pubmed ID

Tommaso Lorenzi, Rebecca H. Chisholm, Jean Clairambault


A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies. To elucidate the roles of phenotypic plasticity and selection pressures in tumour relapse, we present here a phenotype-structured model of evolutionary dynamics in a cancer cell population which is exposed to the action of a cytotoxic drug. The analytical tractability of our model allows us to investigate how the phenotype distribution, the level of phenotypic heterogeneity, and the size of the cell population are shaped by the strength of natural selection, the rate of random epimutations, the intensity of the competition for limited resources between cells, and the drug dose in use. Our analytical results clarify the conditions for the successful adaptation of cancer cells faced with environmental changes. Furthermore, the results of our analyses demonstrate that the same cell population exposed to different concentrations of the same cytotoxic drug can take different evolutionary trajectories, which culminate in the selection of phenotypic variants characterised by different levels of drug tolerance. This suggests that the response of cancer cells to cytotoxic agents is more complex than a simple binary outcome, i.e., extinction of sensitive cells and selection of highly resistant cells. Also, our mathematical results formalise the idea that the use of cytotoxic agents at high doses can act as a double-edged sword by promoting the outgrowth of drug resistant cellular clones. Overall, our theoretical work offers a formal basis for the development of anti-cancer therapeutic protocols that go beyond the 'maximum-tolerated-dose paradigm', as they may be more effective than traditional protocols at keeping the size of cancer cell populations under control while avoiding the expansion of drug tolerant clones. This article was reviewed by Angela Pisco, Sébastien Benzekry and Heiko Enderling.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 5%
Unknown 42 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 30%
Researcher 11 25%
Student > Bachelor 7 16%
Student > Master 4 9%
Student > Doctoral Student 3 7%
Other 2 5%
Unknown 4 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 25%
Mathematics 9 20%
Biochemistry, Genetics and Molecular Biology 6 14%
Computer Science 3 7%
Immunology and Microbiology 2 5%
Other 7 16%
Unknown 6 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 22 November 2019.
All research outputs
of 21,242,513 outputs
Outputs from Biology Direct
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Outputs of similar age
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Outputs of similar age from Biology Direct
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Altmetric has tracked 21,242,513 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 575 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one has done well, scoring higher than 78% 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 286,887 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 83% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them