Modelling Glioblastoma Growth in Anisotropic Tissue
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2021
Författare
Berggren, Henrik
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Glioblastoma, or glioma grade IV is one of the most common types of brain cancer,
unfortunately, the cancer is highly aggressive with short survival times related to
the diffuse and invasive behaviour of the disease. In many cases, the cancer cells
spread to large areas of the brain resulting in difficulties for therapeutic methods
used to treat the disease. The mechanisms of glioblastoma spread and growth are
still poorly understood, hence this study aims to contribute to the subject by an
attempt to characterize tumour growth patterns observed in experimental studies.
With support in previous studies, we propose a novel modelling approach consisting
of a stochastic, agent-based mathematical model for simulations of glioblastoma
development. Anisotropic tissue properties of the brain, in terms of white matter
tracts and the brain vasculature, are taken into consideration and influence the
proliferation and migration of individual cells. The model is included in a framework
for parameter inference, Approximate Bayesian Computation, to identify tumour
characteristics from experimental data, originating from cell line-derived xenograft
mouse models.
By studying parameters of synthetic data, tumours generated by the model itself,
we show that model parameters are identifiable to a large extent. Specifically, the
method is successful in the task of estimating known tumour properties such as
the rate of migration and proliferation as well as the preference of migration in
relation to the brain tissue. Furthermore, we investigate three experimental data
sets in terms of coronal brain slices with respect to rates governing growth and
spread as well as a quantification of the influence from the microenvironment. We
present results showing a partial ability of the model to estimate parameters from
the experimental data. To improve the estimation of parameters there is a need for
an increased number of simulations to enhance the sampling density. Moreover, the
growth patterns possible to simulate are affected by the available medical data which
originates from different individuals compared to the experimental results. This
introduces sources of error in the task to characterize observed tumour development.
Beskrivning
Ämne/nyckelord
Glioblastoma, Glioma grade IV, Agent-based mathematical modelling, Approximate Bayesian Computation (ABC).