Computational Trials: Unraveling Motility Phenotypes, Progression Patterns, and Treatment Options for Glioblastoma Multiforme

Abstract

Glioblastoma multiforme is a malignant brain tumor with poor prognosis and high morbidity due to its invasiveness. Hypoxia-driven motility and concentration-driven motility are two mechanisms of glioblastoma multiforme invasion in the brain. The use of anti-angiogenic drugs has uncovered new progression patterns of glioblastoma multiforme associated with significant differences in overall survival. Here, we apply a mathematical model of glioblastoma multiforme growth and invasion in humans and design computational trials using agents that target angiogenesis, tumor replication rates, or motility. The findings link highly-dispersive, moderately-dispersive, and hypoxia-driven tumors to the patterns observed in glioblastoma multiforme treated by anti-angiogenesis, consisting of progression by Expanding FLAIR, Expanding FLAIR + Necrosis, and Expanding Necrosis, respectively. Furthermore, replication rate-reducing strategies (e.g. Tumor Treating Fields) appear to be effective in highly-dispersive and moderately-dispersive tumors but not in hypoxia-driven tumors. The latter may respond to motility-reducing agents. In a population computational trial, with all three phenotypes, a correlation was observed between the efficacy of the rate-reducing agent and the prolongation of overall survival times. This research highlights the potential applications of computational trials and supports new hypotheses on glioblastoma multiforme phenotypes and treatment options.

Single Cell Mathematical Model Successfully Replicates Key Features of GBM: Go-Or-Grow Is Not Necessary

“…The authors have recently reported a system of partial differential equations (PDE) that models GBM at the scale of MRI; the equations model replication, brain invasion, angiogenesis, and hypoxia [4, 5]. Here, we modify the system of equations to eliminate the GoG phenotype, thus reducing the system to a single glioma cell model; the equations are shown in Table 1, and the parameter values and units are displayed in Table 2.…”

“…necrosis, enhancing ring and edema, but also reproduces specific tumor progression patterns and their associated average patient survival times [4, 5]. This model incorporates the GoG phenotype by including two specific cell types, Invasive (I) cells and Proliferative (P) cells, which can switch back and forth between one phenotype or another depending on the nutrient environment in the brain.…”

“…As suggested by their names, I cells move throughout the brain but do not divide, and P cells divide but do not move; under the assumption of the GoG phenotype, a single tumor cell will never move and divide at the same time. The equations in [4, 5] also include an angiogenic term.…”

Mathematical Modeling in Radiation Oncology

“…Mathematical models have also been used to perform "computational trials" which interrogate the impact that varying biological parameters may have in determining outcomes for a given treatment regimen (Raman et al 2016). In particular, Raman et al use a mathematical model of glioblastoma growth and response to treatment to quantify motility phenotypes, patterns of progression, and treatment scenarios for various in silico patients that are hypothetically treated.…”

Intraoperative Fluorescence-Guided Resection of High-Grade Glyoblastoma Using 5-Aminolevulinic Acid: A Meta-Analysis

“…от окружающей мозговой ткани, тотальная резек-ция опухоли представляет собой сложную задачу для нейрохирурга [5,6]. К настоящему времени для достижения максимальной степени резекции ГБМ разработаны многочисленные техники и ме-тодики: интраоперационная МРТ и МСКТ, ультра-сонография, а также флуоресцентная навигация с 5-аминолевулиновой кислотой (5-АЛА-ФН).…”