Modelling Malignant Progression with a Finite State Machine Supports a Two Checkpoint Theory of Cancer

Jackson, Robert

doi:10.7750/biodiscovery.2012.1.1

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…In a previous study we showed that the loss of the G1-S and/or SAC checkpoints were critical to the description of cancer [8]. This was consistent with Duesberg’s theory [9] which suggested that cancer is, in essence, a disease of chromosomal instability.…”

Section: Introductionsupporting

confidence: 85%

“…What they have in common is that all are involved in control of the cell cycle or its associated signalling pathways (including the apoptosis pathways). [ 8 , 25 – 27 ]Understanding the dynamics of tumour growth and the pharmacodynamics of anticancer drugs could be greatly assisted by quantitative descriptions of these processes.…”

Section: Discussionmentioning

confidence: 99%

“…Aneuploidy confers increased spontaneous cell loss, so that tumour cells can only survive and proliferate if they have a compensating growth advantage over competing normal cells. These changes in cancer are usually the result of mutations or changes in expression levels leading to over-ride of cell cycle checkpoints [ 8 , 10 – 12 ]. Modelling the cell cycle can therefore enable us to capture differences in dynamics between normal and cancer cells resulting from various mutations and associated phenotypes.…”

Section: Discussionmentioning

confidence: 99%

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Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy

et al. 2017

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The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways. It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells, a concept termed “cyclotherapy”. Quantitative approaches that describe these dysregulations can provide guidance in the design of novel or existing cancer therapies. We describe and illustrate this strategy via a mathematical model of the cell cycle that includes descriptions of the G1-S checkpoint and the spindle assembly checkpoint (SAC), the EGF signalling pathway and apoptosis. We incorporated sites of action of four drugs (palbociclib, gemcitabine, paclitaxel and actinomycin D) to illustrate potential applications of this approach. We show how drug effects on multiple cell populations can be simulated, facilitating simultaneous prediction of effects on normal and transformed cells. The consequences of aberrant signalling pathways or of altered expression of pro- or anti-apoptotic proteins can thus be compared. We suggest that this approach, particularly if used in conjunction with pharmacokinetic modelling, could be used to predict effects of specific oncogene expression patterns on drug response. The strategy could be used to search for synthetic lethality and optimise combination protocol designs.

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Section: Introductionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy

et al. 2017

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Modeling TGF-β in Early Stages of Cancer Tissue Dynamics

Ascolani

Lió

2014

PLoS ONE

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Recent works have highlighted a double role for the Transforming Growth Factor (-): it inhibits cancer in healthy cells and potentiates tumor progression during late stage of tumorigenicity, respectively; therefore it has been termed the “Jekyll and Hyde” of cancer or, alternatively, an “excellent servant but a bad master”. It remains unclear how this molecule could have the two opposite behaviours. In this work, we propose a - multi scale mathematical model at molecular, cellular and tissue scales. The multi scalar behaviours of the - are described by three coupled models built up together which can approximatively be related to distinct microscopic, mesoscopic, and macroscopic scales, respectively. We first model the dynamics of - at the single-cell level by taking into account the intracellular and extracellular balance and the autocrine and paracrine behaviour of -. Then we use the average estimates of the - from the first model to understand its dynamics in a model of duct breast tissue. Although the cellular model and the tissue model describe phenomena at different time scales, their cumulative dynamics explain the changes in the role of - in the progression from healthy to pre-tumoral to cancer. We estimate various parameters by using available gene expression datasets. Despite the fact that our model does not describe an explicit tissue geometry, it provides quantitative inference on the stage and progression of breast cancer tissue invasion that could be compared with epidemiological data in literature. Finally in the last model, we investigated the invasion of breast cancer cells in the bone niches and the subsequent disregulation of bone remodeling processes. The bone model provides an effective description of the bone dynamics in healthy and early stages cancer conditions and offers an evolutionary ecological perspective of the dynamics of the competition between cancer and healthy cells.

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Modelling Malignant Progression with a Finite State Machine Supports a Two Checkpoint Theory of Cancer

Cited by 2 publications

References 20 publications

Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy

Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy

Modeling TGF-β in Early Stages of Cancer Tissue Dynamics

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