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  The invasion of de-differentiating cancer cells into hierarchical tissues

Zhou, D., Luo, Y., Dingli, D., & Traulsen, A. (2019). The invasion of de-differentiating cancer cells into hierarchical tissues. PLoS Computational Biology, 15(7): e1007167. doi:10.1371/journal.pcbi.1007167.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-66E0-D Version Permalink: http://hdl.handle.net/21.11116/0000-0004-48F3-9
Genre: Journal Article

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Zhou, Da1, Author              
Luo, Yue, Author
Dingli, David, Author
Traulsen, Arne1, Author              
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1Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445641              

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 Abstract: Many fast renewing tissues are characterized by a hierarchical cellular architecture, with tissue specific stem cells at the root of the cellular hierarchy and differentiating into a whole range of specialized cells. There is increasing evidence that tumors are structured in a very similar way, mirroring the hierarchical structure of the host tissue. In some tissues, differentiated cells can also revert to the stem cell phenotype, which increases the risk that cells that have already acquired mutations lead to long lasting clones in the tissue. Recently, the modelling community has paid special attention to the consequences of de-differentiation on cellular hierarchies. However, the adaptive significance of de-differentiation is still poorly understood and thus it is unclear under which circumstances de-differentiating cells will invade a tissue. To address this, we developed mathematical models to investigate how de-differentiation could be selected as an adaptive mechanism in the context of cellular hierarchies. We consider the cases of stepwise and jumpwise de-differentiation in this study. Our results show that the emergence of de-differentiation is driven by the combination of the properties of the cellular hierarchy and the de-differentiation pattern and derive thresholds for which de-differentiation is expected to emerge.

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Language(s): eng - English
 Dates: 2019-02-252019-06-072019-07-01
 Publication Status: Published online
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 Identifiers: DOI: 10.1371/journal.pcbi.1007167
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 15 (7) Sequence Number: e1007167 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1