Characterising Cancer Cell Responses to Cyclic Hypoxia Using Mathematical Modelling

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.

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Publication Details

Journal
Bulletin of Mathematical Biology
Published
2024-11-06
DOI
https://doi.org/10.1007/s11538-024-01359-0
Citations
5
Primary Topic
Cancer, Hypoxia, and Metabolism
Type
article
Field-Weighted Citation Impact
1.43

Funders

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article

Characterising Cancer Cell Responses to Cyclic Hypoxia Using Mathematical Modelling

Philip K. Maini, Giulia L. Celora, Joe Pitt‐Francis, Helen M. Byrne et al.
5 citations
Bulletin of Mathematical Biology
Cancer, Hypoxia, and Metabolism
1.43
article

Characterising Cancer Cell Responses to Cyclic Hypoxia Using Mathematical Modelling

Philip K. Maini, Giulia L. Celora, Joe Pitt‐Francis, Helen M. Byrne, Ruby Nixson
article en
5 citations

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.

Bulletin of Mathematical BiologyVol. 86(12)
University of Oxford (GB), Ludwig Cancer Research (GB), The London College (GB), University College London (GB)
Medical Research Council, Engineering and Physical Sciences Research Council
Good health and well-being
Openalex Percentile: Top 17%
Cancer, Hypoxia, and Metabolism
1.43
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