Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy

Cancer is a significant global health issue, with treatment challenges arising from intratumor heterogeneity. This heterogeneity stems mainly from somatic evolution, causing genetic diversity within the tumor, and phenotypic plasticity of tumor cells leading to reversible phenotypic changes. However, the interplay of both factors has not been rigorously investigated. Here, we examine the complex relationship between somatic evolution and phenotypic plasticity, explicitly focusing on the interplay between cell migration and proliferation. This type of phenotypic plasticity is essential in glioblastoma, the most aggressive form of brain tumor. We propose that somatic evolution alters the regulation of phenotypic plasticity in tumor cells, specifically the reaction to changes in the microenvironment. We study this hypothesis using a novel, spatially explicit model that tracks individual cells' phenotypic and genetic states. We assume cells change between migratory and proliferative states controlled by inherited and mutation-driven genotypes and the cells' microenvironment. We observe that cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. Notably, different genetic configurations can result in this pattern of phenotypic heterogeneity. We analytically predict the outcome of the evolutionary process, showing that it depends on the tumor microenvironment. Synthetic tumors display varying levels of genetic and phenotypic heterogeneity, which we show are predictors of tumor recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. Our research offers a novel explanation for heterogeneous patterns of tumor recurrence in glioblastoma patients.

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Publication Details

Journal
PLoS Computational Biology
Published
2024-08-09
DOI
https://doi.org/10.1371/journal.pcbi.1012003
Citations
7
Primary Topic
Cellular Mechanics and Interactions
Type
article
Field-Weighted Citation Impact
2.28

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article

Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy

Simon Syga, Haralampos Hatzikirou, Harish P Jain, Marcus Krellner et al.
7 citations
PLoS Computational Biology
Cellular Mechanics and Interactions
2.28
article

Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy

Simon Syga, Haralampos Hatzikirou, Harish P Jain, Marcus Krellner, Andreas Deutsch
article en
7 citations

Abstract

Cancer is a significant global health issue, with treatment challenges arising from intratumor heterogeneity. This heterogeneity stems mainly from somatic evolution, causing genetic diversity within the tumor, and phenotypic plasticity of tumor cells leading to reversible phenotypic changes. However, the interplay of both factors has not been rigorously investigated. Here, we examine the complex relationship between somatic evolution and phenotypic plasticity, explicitly focusing on the interplay between cell migration and proliferation. This type of phenotypic plasticity is essential in glioblastoma, the most aggressive form of brain tumor. We propose that somatic evolution alters the regulation of phenotypic plasticity in tumor cells, specifically the reaction to changes in the microenvironment. We study this hypothesis using a novel, spatially explicit model that tracks individual cells' phenotypic and genetic states. We assume cells change between migratory and proliferative states controlled by inherited and mutation-driven genotypes and the cells' microenvironment. We observe that cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. Notably, different genetic configurations can result in this pattern of phenotypic heterogeneity. We analytically predict the outcome of the evolutionary process, showing that it depends on the tumor microenvironment. Synthetic tumors display varying levels of genetic and phenotypic heterogeneity, which we show are predictors of tumor recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. Our research offers a novel explanation for heterogeneous patterns of tumor recurrence in glioblastoma patients.

PLoS Computational BiologyVol. 20(8)
University of St Andrews (GB), Khalifa University of Science and Technology (AE), University of Oslo (NO), Technische Universität Dresden (DE)
Worldwide Cancer Research, European Commission, Volkswagen Foundation, Bundesministerium für Bildung und Forschung, Khalifa University of Science, Technology and Research, National Institutes of Health, European Research Council, H2020 Marie Skłodowska-Curie Actions
Openalex Percentile: Top 13%
Cellular Mechanics and Interactions
2.28
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