2 research outputs found
Hypoxia-resistance heterogeneity in tumours: the impact of geometrical characterization of environmental niches and evolutionary trade-offs. A mathematical approach
In the study of cancer evolution and therapeutic strategies, scientific
evidence shows that a key dynamics lies in the tumor-environment interaction.
In particular, oxygen concentration plays a central role in the determination
of the phenotypic heterogeneity of cancer cell populations, whose qualitative
and geometric characteristics are predominant factors in the occurrence of
relapses and failure of eradication. We propose a mathematical model able to
describe the eco-evolutionary spatial dynamics of tumour cells in their
adaptation to hypoxic microenvironments. As a main novelty with respect to the
existing literature, we combine a phenotypic indicator reflecting the
experimentally-observed metabolic trade-off between the hypoxia-resistance
ability and the proliferative potential with a 2d geometric domain, without the
constraint of radial symmetry. The model is settled in the mathematical
framework of phenotype-structured population dynamics and it is formulated in
terms of systems of coupled non-linear integro-differential equations. The
computational outcomes demonstrate that hypoxia-induced selection results in a
geometric characterization of phenotypic-defined tumour niches that impact on
tumour aggressiveness and invasive ability. Furthermore, results show how the
knowledge of environmental characteristics provides a predictive advantage on
tumour mass development in terms of size, shape, and composition
Hypoxia-related radiotherapy resistance in tumours: treatment efficacy investigation in an eco-evolutionary perspective
In the study of therapeutic strategies for the treatment of cancer,
eco-evolutionary dynamics are of particular interest, since characteristics of
the tumour population, interaction with the environment and effects of the
treatment, influence the geometric and epigenetic characterization of the
tumour with direct consequences on the efficacy of the therapy and possible
relapses. In particular, when considering radiotherapy, oxygen concentration
plays a central role both in determining the effectiveness of the treatment and
the selective pressure due to hypoxia. We propose a mathematical model, settled
in the framework of epigenetically-structured population dynamics and
formulated in terms of systems of coupled non-linear integro-differential
equations, that aims to catch these phenomena and to provide a predictive tool
for the tumour mass evolution and therapeutic effects. The outcomes of the
simulations show how the model is able to explain the impact of environmental
selection and therapies on the evolution of the mass, motivating observed
dynamics such as relapses and therapeutic failures. Furthermore it offers a
first hint for the development of therapies which can be adapted to overcome
problems of resistance and relapses