The invasion of de-differentiating cancer cells into hierarchical tissues

Da Zhou, Yue Luo, David M Dingli, Arne Traulsen

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Many fast renewing tissues are characterized by a hierarchical cellular architecture, with tissue specific stem cells at the root of the cellular hierarchy, 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 mutant cells lead to long lasting clones in the tissue. However, 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 is selected as an adaptive mechanism in the context of cellular hierarchies. We derive thresholds for which de-differentiation is expected to emerge, and it is shown that the selection of de-differentiation is a result of the combination of the properties of cellular hierarchy and de-differentiation patterns. Our results suggest that de-differentiation is most likely to be favored provided stem cells having the largest effective self-renewal rate. Moreover, jumpwise de-differentiation provides a wider range of favorable conditions than stepwise de-differentiation. Finally, the effect of de-differentiation on the redistribution of self-renewal and differentiation probabilities also greatly influences the selection for de-differentiation.

Original languageEnglish (US)
Article numbere1007167
JournalPLoS computational biology
Volume15
Issue number7
DOIs
StatePublished - Jul 1 2019

Fingerprint

Invasion
cancer
Cancer
Cells
Tissue
Cell
Stem cells
stem cells
Neoplasms
Stem Cells
cells
Renewal
neoplasm cells
tissues
tissue
stem
Tumors
Theoretical Models
mathematical models
Clone Cells

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

The invasion of de-differentiating cancer cells into hierarchical tissues. / Zhou, Da; Luo, Yue; Dingli, David M; Traulsen, Arne.

In: PLoS computational biology, Vol. 15, No. 7, e1007167, 01.07.2019.

Research output: Contribution to journalArticle

@article{486767a9b22f4f1f872123bd3c6394d3,
title = "The invasion of de-differentiating cancer cells into hierarchical tissues",
abstract = "Many fast renewing tissues are characterized by a hierarchical cellular architecture, with tissue specific stem cells at the root of the cellular hierarchy, 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 mutant cells lead to long lasting clones in the tissue. However, 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 is selected as an adaptive mechanism in the context of cellular hierarchies. We derive thresholds for which de-differentiation is expected to emerge, and it is shown that the selection of de-differentiation is a result of the combination of the properties of cellular hierarchy and de-differentiation patterns. Our results suggest that de-differentiation is most likely to be favored provided stem cells having the largest effective self-renewal rate. Moreover, jumpwise de-differentiation provides a wider range of favorable conditions than stepwise de-differentiation. Finally, the effect of de-differentiation on the redistribution of self-renewal and differentiation probabilities also greatly influences the selection for de-differentiation.",
author = "Da Zhou and Yue Luo and Dingli, {David M} and Arne Traulsen",
year = "2019",
month = "7",
day = "1",
doi = "10.1371/journal.pcbi.1007167",
language = "English (US)",
volume = "15",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "7",

}

TY - JOUR

T1 - The invasion of de-differentiating cancer cells into hierarchical tissues

AU - Zhou, Da

AU - Luo, Yue

AU - Dingli, David M

AU - Traulsen, Arne

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Many fast renewing tissues are characterized by a hierarchical cellular architecture, with tissue specific stem cells at the root of the cellular hierarchy, 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 mutant cells lead to long lasting clones in the tissue. However, 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 is selected as an adaptive mechanism in the context of cellular hierarchies. We derive thresholds for which de-differentiation is expected to emerge, and it is shown that the selection of de-differentiation is a result of the combination of the properties of cellular hierarchy and de-differentiation patterns. Our results suggest that de-differentiation is most likely to be favored provided stem cells having the largest effective self-renewal rate. Moreover, jumpwise de-differentiation provides a wider range of favorable conditions than stepwise de-differentiation. Finally, the effect of de-differentiation on the redistribution of self-renewal and differentiation probabilities also greatly influences the selection for de-differentiation.

AB - Many fast renewing tissues are characterized by a hierarchical cellular architecture, with tissue specific stem cells at the root of the cellular hierarchy, 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 mutant cells lead to long lasting clones in the tissue. However, 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 is selected as an adaptive mechanism in the context of cellular hierarchies. We derive thresholds for which de-differentiation is expected to emerge, and it is shown that the selection of de-differentiation is a result of the combination of the properties of cellular hierarchy and de-differentiation patterns. Our results suggest that de-differentiation is most likely to be favored provided stem cells having the largest effective self-renewal rate. Moreover, jumpwise de-differentiation provides a wider range of favorable conditions than stepwise de-differentiation. Finally, the effect of de-differentiation on the redistribution of self-renewal and differentiation probabilities also greatly influences the selection for de-differentiation.

UR - http://www.scopus.com/inward/record.url?scp=85069888129&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069888129&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1007167

DO - 10.1371/journal.pcbi.1007167

M3 - Article

C2 - 31260442

AN - SCOPUS:85069888129

VL - 15

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 7

M1 - e1007167

ER -