Speaker
Description
More than 50% of cancer patients undergo radiotherapy. Nowadays, treatment plannings are based on the linear-quadratic model, that predicts the survival fraction of cancer cells but does not include any temporal component. Moreover, it is usually not valide for large doses (that are used in new radiotherapy modalities such as mini-beam). It is therefore of the utmost importance to develop models that can describe the temporal response to irradiation for a large range of doses.
In this work we combine time-lapse fluorescence microscopy experiments (to follow the cell density of F98 glioma cells under varying radiation doses and initial cell densities) and mathematical modeling, in order to characterize the temporal response of a cancerous cell population to single-dose radiation. The model (based on ordinary differential equations) reproduces very well all the experimental data, with only three free parameters (and four others that are fixed). It allows us to have access and to follow the evolution of different cell populations after irradiation, in particular, the senescent and repaired cell populations. Therefore, surviving fractions could also be estimated. Our model allows us to analyze and quantify an inhibition effect (or cohort effect) of the dead and senescent cell populations on the regrowth of the repaired one (billoir2025). With the same model, we are also able to model mini-beam experiments with a spatially-structured irradiation.
Bibliography
(Billoir2025) Billoir M., Crepin D., Plaszczynski S., Grammaticos B., Seksek O., Badoual M. (2025) The temporal response of a glioma cell population to irradiation: modelling the effect of dose and cell density, R Soc Open Sci.. 2025;12:241917
