Format

Send to

Choose Destination
J Theor Biol. 2017 Dec 21;435:78-97. doi: 10.1016/j.jtbi.2017.08.022. Epub 2017 Sep 21.

Spatial vs. non-spatial eco-evolutionary dynamics in a tumor growth model.

Author information

1
Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands.
2
Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, USA; Department of Integrated Mathematical Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
3
Department of Integrated Mathematical Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
4
Department of Integrated Mathematical Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA; Department of Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
5
Department of Genitourinary Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
6
Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands; Delft Institute of Applied Mathematics, Technical University Delft, Delft, The Netherlands. Electronic address: k.stankova@maastrichtuniversity.nl.

Abstract

Metastatic prostate cancer is initially treated with androgen deprivation therapy (ADT). However, resistance typically develops in about 1 year - a clinical condition termed metastatic castrate-resistant prostate cancer (mCRPC). We develop and investigate a spatial game (agent based continuous space) of mCRPC that considers three distinct cancer cell types: (1) those dependent on exogenous testosterone (T+), (2) those with increased CYP17A expression that produce testosterone and provide it to the environment as a public good (TP), and (3) those independent of testosterone (T-). The interactions within and between cancer cell types can be represented by a 3 × 3 matrix. Based on the known biology of this cancer there are 22 potential matrices that give roughly three major outcomes depending upon the absence (good prognosis), near absence or high frequency (poor prognosis) of T- cells at the evolutionarily stable strategy (ESS). When just two cell types coexist the spatial game faithfully reproduces the ESS of the corresponding matrix game. With three cell types divergences occur, in some cases just two strategies coexist in the spatial game even as a non-spatial matrix game supports all three. Discrepancies between the spatial game and non-spatial ESS happen because different cell types become more or less clumped in the spatial game - leading to non-random assortative interactions between cell types. Three key spatial scales influence the distribution and abundance of cell types in the spatial game: i. Increasing the radius at which cells interact with each other can lead to higher clumping of each type, ii. Increasing the radius at which cells experience limits to population growth can cause densely packed tumor clusters in space, iii. Increasing the dispersal radius of daughter cells promotes increased mixing of cell types. To our knowledge the effects of these spatial scales on eco-evolutionary dynamics have not been explored in cancer models. The fact that cancer interactions are spatially explicit and that our spatial game of mCRPC provides in general different outcomes than the non-spatial game might suggest that non-spatial models are insufficient for capturing key elements of tumorigenesis.

KEYWORDS:

Evolutionary game theory; Non-spatial game; Prostate cancer; Spatial game

PMID:
28870617
DOI:
10.1016/j.jtbi.2017.08.022
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center