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Microenvironmental Variables Must Influence Intrinsic Phenotypic Parameters of Cancer Stem Cells to Affect Tumourigenicity

Overview of attention for article published in PLoS Computational Biology, January 2014
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Title
Microenvironmental Variables Must Influence Intrinsic Phenotypic Parameters of Cancer Stem Cells to Affect Tumourigenicity
Published in
PLoS Computational Biology, January 2014
DOI 10.1371/journal.pcbi.1003433
Pubmed ID
Authors

Jacob G. Scott, Anita B. Hjelmeland, Prakash Chinnaiyan, Alexander R. A. Anderson, David Basanta

Abstract

Since the discovery of tumour initiating cells (TICs) in solid tumours, studies focussing on their role in cancer initiation and progression have abounded. The biological interrogation of these cells continues to yield volumes of information on their pro-tumourigenic behaviour, but actionable generalised conclusions have been scarce. Further, new information suggesting a dependence of tumour composition and growth on the microenvironment has yet to be studied theoretically. To address this point, we created a hybrid, discrete/continuous computational cellular automaton model of a generalised stem-cell driven tissue with a simple microenvironment. Using the model we explored the phenotypic traits inherent to the tumour initiating cells and the effect of the microenvironment on tissue growth. We identify the regions in phenotype parameter space where TICs are able to cause a disruption in homeostasis, leading to tissue overgrowth and tumour maintenance. As our parameters and model are non-specific, they could apply to any tissue TIC and do not assume specific genetic mutations. Targeting these phenotypic traits could represent a generalizable therapeutic strategy across cancer types. Further, we find that the microenvironmental variable does not strongly affect the outcomes, suggesting a need for direct feedback from the microenvironment onto stem-cell behaviour in future modelling endeavours.

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Geographical breakdown

Country Count As %
United Kingdom 3 5%
India 1 2%
France 1 2%
Ukraine 1 2%
United States 1 2%
Unknown 57 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 28%
Researcher 16 25%
Student > Master 9 14%
Student > Bachelor 6 9%
Student > Doctoral Student 2 3%
Other 5 8%
Unknown 8 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 27%
Biochemistry, Genetics and Molecular Biology 9 14%
Physics and Astronomy 6 9%
Mathematics 5 8%
Computer Science 5 8%
Other 11 17%
Unknown 11 17%