On the impact of chemo-mechanically induced phenotypic transitions in gliomas

Abstract

Tumor microenvironment is a critical player in glioma progression and novel therapies for its targeting have been recently proposed. In particular, stress-alleviation strategies act on the tumor by reducing its stiffness, decreasing solid stresses and improving blood perfusion. However, these microenvironmental changes trigger chemo-mechanically induced cellular phenotypic transitions whose impact on therapy outcomes is not completely understood. In this work, we perform experiments to analyze the effects of mechanical compression on migration and proliferation of two glioma cell lines. From these experiments, we derive a mathematical model of glioma progression focusing on cellular phenotypic plasticity. The model reveals a trade-off between tumor infiltration and cellular content as a consequence of stress-alleviation approaches. We discuss how these findings can improve the current understanding of glioma/microenvironment interactions, and suggest strategies to improve therapeutic outcomes. 4/2513/25 14/25

Improving personalized tumor growth predictions using a Bayesian combination of mechanistic modeling and machine learning

“…The clinical needs led to the development of several mathematical models to support clinicians in the treatment of the disease 34 . As a first test case, we synthetically generate a dataset of glioma patients using a system of recently published 14,15 partial differential equations (PDEs). This complex mathematical model ('full model', in the following) provides a set of in silico patients, which represents our synthetic reality and serves as a benchmark to evaluate the performance of the proposed BaM 3 method.…”

“…Testing the method on synthetic glioma growth. The equations of the selected mathematical model 14,15 ('full model') describe the spatio-temporal dynamics of tumor cell density (c), oxygen concentration (n), and vascular density (v) in the context of glioma tumor growth. The full model includes the variation of cell motility and proliferation due to phenotypic plasticity of tumor cells induced by microenvironmental hypoxia [16][17][18][19][20] .…”

“…The full model includes the variation of cell motility and proliferation due to phenotypic plasticity of tumor cells induced by microenvironmental hypoxia [16][17][18][19][20] . It also accounts for oxygen consumption by tumor cells, formation of new vessels due to tumor angiogenesis, and vaso-occlusion by compression from tumor cells 15,21,22 . We generate N = 500 virtual patients by sampling the parameters of the full model from a uniform distribution over the available experimental range.…”

“…Mathematical models for glioma growth. The system variables are the density of glioma cells c(x, t), the concentration of oxygen n(x, t), and the density of functional vasculature v(x, t) 14,15 . For simplicity we consider a one-dimensional computational domain.…”

Bayesian combination of mechanistic modeling and machine learning (BaM3): improving personalized tumor growth predictions

“…The full model includes the variation of cell motility and proliferation due to phenotypic plasticity of tumor cells induced by microenvironmental hypoxia [16,17,18,19,20]. It also accounts for oxygen consumption by tumor cells, formation of new vessels due to tumor angiogenesis and vaso-occlusion by compression from tumor cells [21,22,23]. We generate a total of N = 675 virtual patients by sampling the parameters of the full model from a uniform distribution over the available experimental range.…”