Tmod-16. biomathematical model of proneural tumors suggests best candidates for pdgf-inhibitor therapies

Platelet Derived Growth Factor (PDGF) is often over-expressed in gliomas, where it can drive tumor growth via autocrine stimulation of PDGF receptor (PDGFR) expressing glioma cells and via paracrine stimulation of non-neoplastic oligodendrocyte progenitor cells (OPCs), which also express PDGFRa. To date, the use of PDGF inhibitors has remained largely unsuccessful at improving patient outcomes in glioblastoma; however, this may be due to inadequate targeting of these agents to the best candidates. In particular, these therapies have been given in the recurrent setting, when tumors that had been predominantly comprised by OPCs and OPC-like glioma cells (e.g., proneural subtype) have transformed to a mesenchymal phenotype with fewer OPC-like cells, thereby precluding the opportunity to target OPC-like cells with these agents. Using a mathematical model of PDGF-driven glioma, we explore which patients might receive the greatest benefit from PDGF-targeted therapies. The results of our mathematical model show that tumors with higher levels PDGF signaling recruit more OPCs and grow faster, resulting in larger but less infiltrating tumors. By incorporating different treatment simulations in our model, we show that PDGF inhibition results in decreased OPC recruitment, which leads to slower growing, but more diffusely infiltrating tumors. This suggests that PDGF inhibitors may be most effective at treating patients with more rapidly proliferating, less infiltrative tumors which show a...
Source: Neuro-Oncology - Category: Cancer & Oncology Authors: Tags: TUMOR MODELS Source Type: research