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Senescent Cells in Growing Tumors: Population Dynamics and Cancer Stem Cells

Overview of attention for article published in PLoS Computational Biology, January 2012
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Title
Senescent Cells in Growing Tumors: Population Dynamics and Cancer Stem Cells
Published in
PLoS Computational Biology, January 2012
DOI 10.1371/journal.pcbi.1002316
Pubmed ID
Authors

Caterina A. M. La Porta, Stefano Zapperi, James P. Sethna

Abstract

Tumors are defined by their intense proliferation, but sometimes cancer cells turn senescent and stop replicating. In the stochastic cancer model in which all cells are tumorigenic, senescence is seen as the result of random mutations, suggesting that it could represent a barrier to tumor growth. In the hierarchical cancer model a subset of the cells, the cancer stem cells, divide indefinitely while other cells eventually turn senescent. Here we formulate cancer growth in mathematical terms and obtain predictions for the evolution of senescence. We perform experiments in human melanoma cells which are compatible with the hierarchical model and show that senescence is a reversible process controlled by survivin. We conclude that enhancing senescence is unlikely to provide a useful therapeutic strategy to fight cancer, unless the cancer stem cells are specifically targeted.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
United Kingdom 2 2%
Portugal 1 <1%
Italy 1 <1%
South Africa 1 <1%
Japan 1 <1%
Spain 1 <1%
Unknown 100 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 30%
Student > Ph. D. Student 24 21%
Student > Master 12 11%
Student > Bachelor 10 9%
Professor 9 8%
Other 17 15%
Unknown 6 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 45%
Medicine and Dentistry 14 13%
Biochemistry, Genetics and Molecular Biology 12 11%
Physics and Astronomy 9 8%
Mathematics 5 4%
Other 12 11%
Unknown 10 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 12 October 2016.
All research outputs
#15,755,393
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#6,756
of 8,964 outputs
Outputs of similar age
#160,711
of 251,541 outputs
Outputs of similar age from PLoS Computational Biology
#61
of 118 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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 251,541 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.