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

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-08-09 eCollection Date: 2024-08-01 DOI:10.1371/journal.pcbi.1012003
Simon Syga, Harish P Jain, Marcus Krellner, Haralampos Hatzikirou, Andreas Deutsch
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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.

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表型可塑性的演变导致肿瘤异质性,对治疗产生影响。
癌症是一个重大的全球性健康问题,其治疗难题来自于肿瘤内的异质性。这种异质性主要源于体细胞的进化和肿瘤细胞的表型可塑性,前者导致肿瘤内部基因的多样性,后者导致肿瘤细胞表型的可逆变化。然而,这两种因素的相互作用尚未得到严格研究。在这里,我们研究了体细胞进化和表型可塑性之间的复杂关系,并明确关注细胞迁移和增殖之间的相互作用。这种表型可塑性对于最具侵袭性的脑肿瘤--胶质母细胞瘤至关重要。我们提出,体细胞进化改变了肿瘤细胞表型可塑性的调控,特别是对微环境变化的反应。我们使用一种新颖的空间显式模型来研究这一假设,该模型可追踪单个细胞的表型和遗传状态。我们假设细胞在遗传和突变驱动的基因型以及细胞微环境的控制下,在迁移和增殖状态之间变化。我们观察到,肿瘤边缘的细胞在进化过程中倾向于迁移而非增殖,反之亦然。值得注意的是,不同的基因配置会导致这种表型异质性模式。我们对进化过程的结果进行了分析预测,结果表明它取决于肿瘤微环境。合成肿瘤显示出不同程度的遗传和表型异质性,我们证明这些异质性是治疗后肿瘤复发时间的预测因素。有趣的是,与基因异质性不同,较高的表型异质性可预测较差的治疗效果。我们的研究为胶质母细胞瘤患者肿瘤复发的异质性模式提供了新的解释。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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