Title: Glioblastoma Recurrence Modeling
Abstract:
Glioblastoma is the most lethal and prevalent form of cancer to the central nervous system. Its life expectancy is a few years, and in that time the tumor evolves pretty rapidly while modifying its microenvironment in the process, e.g. inducing hypoxia. When blasted with radiotherapy it recurs quickly to almost its original size. One major cause of this recurrence is within the hierarchical structure of the tumor. In particular, it has populations of stem cells and differentiated cells. Prior work with cell lineage models has shown that de-differentiation plays a key role in glioblastoma’s ability to recur, as well as the extent to which different sorts of radiotherapy scheduling can delay recurrence. In addition, analysis of such models demonstrates the salience of feedback on division in recurrence. The work we’re doing seeks to extend that by adding a protein/system with function similar to survivin, which is known to facilitate de-differentiation, improve cancer cell survival, and mitigate cellular division. Exploration of this model may provide insight into the efficacy of treatments targeting such a molecule.