BME Seminar Series: Divya Ramn

Detecting Region of Recurrence of Brain Tumors by Tracking the Movement

Abstract

Brain tumor recurrence in humans has been shown to occur more frequently along white matter tracts. We have developed a computational model for migration of cancer cells that is influenced by the underlying brain fiber architecture measured from Magnetic Resonance Diffusion Tensor Imaging (MR-DTI). The model thus attempts to predict the location of human brain tumor recurrence following radiation treatment. Micrometer sized Super Paramagnetic Iron Oxide (MPIO) labeling has been used by others to detect single progenitor cells and macrophages, but not previously applied directly to monitor the migration patterns of native cancer cells. This study aims at validating the existing computational model by quantifying the migration of individual glioma cells that are dual-labeled with MPIO particles and GFP.

The distribution of migrating cells predicted from the computation model applied to a rat DTI data set showed that most simulated cancer cells remain close to a major fiber bundle. The fluorescence microscopy images show a glioma cell spread pattern that suggests far-field migration along the corpus callosum that is qualitatively in good agreement with the predictions of the computational model. MPIO-labeled cells were observed at large distances from the site of engraftment at 20-days post-injection, demonstrating that labeled CNS-1 cells are viable long-term and retain the ability to infiltrate. This work suggests that MPIO labeling can be used in future studies to track in vivo cell migration in real time with MRI, the goal being to quantify critical physiologic cell migration parameters such as in-vivo migration velocity, persistence in direction, and strength of affinity for fiber bundles. This knowledge can then be used to improve the computational migration model for clinical applications.