Diffuse low-grade gliomas are slowly growing tumors that mainly affect adults around 40 years old and are incurable. After tens of years, they transform inexorably into more aggressive forms, jeopardizing the patient’s life. Mathematical modeling could help clinicians to have a better understanding of the underlying biological process involved in the evolution of these tumors and their response to treatments.
In my team in IJCLab (Orsay), we develop complementary methods of image analysis, statistical physics and mathematical modeling, to make bridges between the cellular and the tumor scale. I will present two examples of this multiscale approach.
The first example is the effect of radiotherapy on low-grade gliomas: we designed a model of evolution of these tumors, based on a PDE that describes the evolution of the cell density and the effect of radiotherapy. This model is used to fit clinical data (MRI scans), and to predict the regrowth time after radiotherapy. In parallel, in order to have a better understanding of the effect of irradiation at the cellular level, we performed in vitro experiments and we modelled the temporal evolution of a cellular population at different doses, with a simple compartmental model.
A second example is the origin of low grade gliomas: Oligodendrocyte precursor cells (OPCs) are precursor cells that are strongly suspected to be the “cell-of-origin” of gliomas. We developped an agent-based model that reproduces the dynamics of normal OPCs in the brain. I will show that an increase in the proliferation coefficient of new cells is sufficient to trigger the growth of a tumor that has low-grade glioma features. Our model represents a possible scenario of transformation of a normal tissue into a glioma.