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运用数学模型描绘癌细胞对周期性缺氧的反应。

Characterising Cancer Cell Responses to Cyclic Hypoxia Using Mathematical Modelling.

机构信息

Department of Mathematics, University College London, Gordon Street, London, 100190, UK.

Mathematical Institute, University of Oxford, Andrew Wiles Building, Woodstock Rd, Oxford, OX2 6GG, UK.

出版信息

Bull Math Biol. 2024 Nov 6;86(12):145. doi: 10.1007/s11538-024-01359-0.

DOI:10.1007/s11538-024-01359-0
PMID:39503769
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11541430/
Abstract

In vivo observations show that oxygen levels in tumours can fluctuate on fast and slow timescales. As a result, cancer cells can be periodically exposed to pathologically low oxygen levels; a phenomenon known as cyclic hypoxia. Yet, little is known about the response and adaptation of cancer cells to cyclic, rather than, constant hypoxia. Further, existing in vitro models of cyclic hypoxia fail to capture the complex and heterogeneous oxygen dynamics of tumours growing in vivo. Mathematical models can help to overcome current experimental limitations and, in so doing, offer new insights into the biology of tumour cyclic hypoxia by predicting cell responses to a wide range of cyclic dynamics. We develop an individual-based model to investigate how cell cycle progression and cell fate determination of cancer cells are altered following exposure to cyclic hypoxia. Our model can simulate standard in vitro experiments, such as clonogenic assays and cell cycle experiments, allowing for efficient screening of cell responses under a wide range of cyclic hypoxia conditions. Simulation results show that the same cell line can exhibit markedly different responses to cyclic hypoxia depending on the dynamics of the oxygen fluctuations. We also use our model to investigate the impact of changes to cell cycle checkpoint activation and damage repair on cell responses to cyclic hypoxia. Our simulations suggest that cyclic hypoxia can promote heterogeneity in cellular damage repair activity within vascular tumours.

摘要

体内观察表明,肿瘤中的氧气水平可以在快速和缓慢的时间尺度上波动。因此,癌细胞会周期性地暴露于病理性低氧水平下,这种现象被称为周期性缺氧。然而,人们对癌细胞对周期性缺氧而非持续缺氧的反应和适应知之甚少。此外,现有的周期性缺氧体外模型无法捕捉到体内生长的肿瘤中复杂和异质的氧气动力学。数学模型可以帮助克服当前的实验限制,并通过预测细胞对广泛的周期性动态的反应,为肿瘤周期性缺氧的生物学提供新的见解。我们开发了一种基于个体的模型,以研究癌细胞在暴露于周期性缺氧后如何改变细胞周期进程和细胞命运决定。我们的模型可以模拟标准的体外实验,如集落形成实验和细胞周期实验,从而能够在广泛的周期性缺氧条件下对细胞反应进行高效筛选。模拟结果表明,同一细胞系对周期性缺氧的反应可能因氧气波动的动态而异。我们还使用我们的模型来研究细胞周期检查点激活和损伤修复的变化对细胞对周期性缺氧反应的影响。我们的模拟表明,周期性缺氧可以促进血管肿瘤中细胞损伤修复活性的异质性。

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