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The influence of methylation status on a stochastic model of MGMT dynamics in glioblastoma: phenotypic selection can occur with and without a downshift in promoter methylation status

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posted on 21.01.2022, 17:40 authored by Marc SturrockMarc Sturrock, Ayoub Lasri DoukkaliAyoub Lasri Doukkali
Glioblastoma originates in the brain and is one of the most aggressive cancer types. Glioblastoma represents 15% of all brain tumours, with a median survival of 15 months. Although the current standard of care for such a tumour (the Stupp protocol) has shown positive results for the prognosis of patients, O-6-methylguanine-DNA methyltransferase (MGMT) driven drug resistance has been an issue of increasing concern and hence requires innovative approaches. In addition to the well established drug resistance factors such as tumour location and blood brain barriers, it is also important to understand how the genetic and epigenetic dynamics of the glioblastoma cells can play a role. One important aspect of this is the study of methylation status of MGMT following administration of temozolomide. In this paper, we extend our previously published model that simulated MGMT expression in glioblastoma cells to incorporate the promoter methylation status of MGMT. This methylation status has clinical significance and is used as a marker for patient outcomes. Using this model, we investigate the causative relationship between temozolomide treatment and the methylation status of the MGMT promoter in a population of cells. In addition by constraining the model to relevant biological data using Approximate Bayesian Computation, we were able to identify parameter regimes that yield different possible modes of resistances, namely, phenotypic selection of MGMT, a downshift in the methylation status of the MGMT promoter or both simultaneously. We analysed each of the parameter sets associated with the different modes of resistance, presenting representative solutions as well as discovering some similarities between them as well as unique requirements for each of them. Finally, we used them to devise optimal strategies for inhibiting MGMT expression with the aim of minimising live glioblastoma cell numbers.

Funding

European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ITN initiative (Grant No. 766069)

History

Comments

The original article is available at https://www.sciencedirect.com/

Published Citation

Lasri A, Sturrock M. The influence of methylation status on a stochastic model of MGMT dynamics in glioblastoma: phenotypic selection can occur with and without a downshift in promoter methylation status. J Theor Biol. 2021;521:110662

Publication Date

6 March 2021

PubMed ID

33684406

Department/Unit

  • Physiology and Medical Physics

Research Area

  • Cancer

Publisher

Elsevier BV

Version

  • Published Version (Version of Record)