癌症是一个重大的全球健康问题，肿瘤内异质性带来了治疗挑战。这种异质性主要源于体细胞进化，导致肿瘤内的遗传多样性，以及肿瘤细胞的表型可塑性导致可逆的表型变化。然而，这两个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，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胶质母细胞瘤（最具侵袭性的脑肿瘤）至关重要。我们认为体细胞进化改变了肿瘤细胞表型可塑性的调节，特别是对微环境变化的反应。我们使用一种新颖的、空间明确的模型来研究这一假设，该模型跟踪单个细胞的表型和遗传状态。我们假设细胞在迁移和增殖状态之间变化是由遗传和突变驱动的基因型以及细胞的微环境控制的。我们观察到肿瘤边缘的细胞进化为有利于迁移而不是增殖，反之亦然。值得注意的是，不同的遗传配置可能导致这种表型异质性模式。我们分析预测了进化过程的结果，表明它取决于肿瘤微环境。合成肿瘤表现出不同程度的遗传和表型异质性，我们证明这是治疗后肿瘤复发时间的预测因素。有趣的是，与遗传异质性不同，较高的表型异质性预示着较差的治疗结果。我们的研究为胶质母细胞瘤患者肿瘤复发的异质模式提供了新颖的解释。版权所有：© 2024 Syga 等人。这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

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.Copyright: © 2024 Syga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.