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Predicting variability in glioblastoma proliferation and spread using stochastic mathematical models and 3-D-cultures of patient-derived neurospheres.

  • Full or part time
    Dr B Murphy
    Dr M Sturrock
  • Application Deadline
    Monday, December 02, 2019
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

Brain tumours are the biggest cancer killer of adults under 40. Brain tumours reduce life expectancy by an average of 20 years, the highest of any cancer. Survival rates have improved little in over 40 years. The most common and aggressive primary brain tumour is glioblastoma (GBM). Despite intense effort to combat GBM with surgery, radiation and temozolomide (TMZ) chemotherapy, 90-95% of patients succumb to the disease within 5 years of diagnosis and nearly all patients experience disease recurrence, usually within 6-8 months of treatment onset (Stupp et al. 2009). New, better, more-personalised treatments are urgently required.

The extensive molecular heterogeneity found between GBM tumours contributes significantly to the limited effectiveness of current therapies and the difficulty in developing new efficacious treatment regimes. The theory that a ‘one-size-fits-all’ approach to treating this disease is not valid. Instead, as our laboratories and others have published, a more personalized approach is necessary to select the most appropriate drugs for a given patient (Paul et al. 2010; Murphy et al. 2013; Weyhenmeyer et al. 2016). Diagnostic tools that can predict case-specifically if and which treatment (Murphy et al. 2013; Weyhenmeyer et al. 2016) is likely to be beneficial for a specific subset of GBM patients are therefore of high interest, both for innovative clinical trials design (enrolment of patients which are likely to respond) and to optimise/personalise treatment. As we and others have highlighted, for this to work, reliable in silico models of the disease that can accurately predict treatment responsiveness need to be developed (Weyhenmeyer et al. 2016).

Such novel approaches are pursued in the field of mathematical modelling (Sturrock et al. 2015; Neal et al. 2013; Jackson et al. 2015). However, one major weakness of existing studies is that they do not capture variability in rates of glioma growth and spread. Hence, the primary objective of this project is to develop stochastic mathematical models of glioma growth that capture and quantify the variability in glioma growth and spread in vivo. Furthermore, model predictions of tumour growth under current and novel treatment conditions will be validated using spatio-temporal imaging data of 3-D-cultures of patient-derived neurospheres grown under these same treatment conditions. Our project holds tremendous potential to develop clinically relevant tools that can identify patients that will likely benefit from currently approved and novel treatment options. This would be an excellent step-forward for patients as it will spare them toxic treatments that hold no overall benefit to them, as well as helping to preselect patients for clinical trials.

The aims of this research address one of the most significant problems in the field of brain cancer, and we are confident that our research can make a significant contribution towards improving GBM treatment and patient survival in the future.

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