Spatial invasion dynamics on random and unstructured meshes: Implications for heterogeneous tumor populations

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• 作者：V.S.K. Manem^a ; M. Kohandel^a ; ^b ; ^{kohandel@uwaterloo.ca" class="auth_mail} ; N.L. Komarova^c ; S. Sivaloganathan^a ; ^b
• 关键词：Math oncology ; Random meshes ; Cell migration ; Cellular automata ; Evolutionary modeling
• 刊名：Journal of Theoretical Biology
• 出版年：21 May, 2014
• 年：2014
• 卷：349
• 期：Complete
• 页码：66-73
• 全文大小：2043 K

文摘

In this work we discuss a spatial evolutionary model for a heterogeneous cancer cell population. We consider the gain-of-function mutations that not only change the fitness potential of the mutant phenotypes against normal background cells but may also increase the relative motility of the mutant cells. The spatial modeling is implemented as a stochastic evolutionary system on a structured grid (a lattice, with random neighborhoods, which is not necessarily bi-directional) or on a two-dimensional unstructured mesh, i.e. a bi-directional graph with random numbers of neighbors. We present a computational approach to investigate the fixation probability of mutants in these spatial models. Additionally, we examine the effect of the migration potential on the spatial dynamics of mutants on unstructured meshes. Our results suggest that the probability of fixation is negatively correlated with the width of the distribution of the neighborhood size. Also, the fixation probability increases given a migration potential for mutants. We find that the fixation probability (of advantaged, disadvantaged and neutral mutants) on unstructured meshes is relatively smaller than the corresponding results on regular grids. More importantly, in the case of neutral mutants the introduction of a migration potential has a critical effect on the fixation probability and increases this by orders of magnitude. Further, we examine the effect of boundaries and as intuitively expected, the fixation probability is smaller on the boundary of regular grids when compared to its value in the bulk. Based on these computational results, we speculate on possible better therapeutic strategies that may delay tumor progression to some extent.