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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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 5%
United Kingdom 2 2%
Portugal 1 <1%
Italy 1 <1%
South Africa 1 <1%
Japan 1 <1%
Spain 1 <1%
Unknown 99 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 30%
Student > Ph. D. Student 24 22%
Student > Master 12 11%
Student > Bachelor 9 8%
Professor 9 8%
Other 18 16%
Unknown 6 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 49 44%
Medicine and Dentistry 14 13%
Biochemistry, Genetics and Molecular Biology 13 12%
Physics and Astronomy 8 7%
Mathematics 5 5%
Other 12 11%
Unknown 10 9%