Monitoring and modeling cancer cell
migration in the brain

Aims
White matter architecture can be measured non-invasively using MR Diffusion Tensor Imaging (DTI) and we have recently shown a significant correlation between the paths of fiber tracts leading from the primary tumor and the sites of tumor recurrence in humans. This result suggests a direct and unique utility for MR-DTI tractography in probing the mechanisms of cancer cell migration. Our objective in this research on human subjects on our 3T system is to utilize DTI to constrain a computational pseudo-random walk model of cancer cell migration to predict the location of future tumor recurrence (Figure A-C).

[A] Patient T1 weighted post-contrast image of a primary high grade glioma and the extent of radiation treatment (pink)	[B] Map of model-predicted cell concentrations (white=high) based on this patient's DTI data. Also shown is a modified treatment margin (green) based on the model	[C] 3-month follow-up image showing the location of the tumor recurrence (arrow) outside the treatment region and coinciding with our model predictions

Methods
We are currently recruiting patients for a prospective trail using our computational model to predict the location of tumor recurrence after surgery and radiation treatment. The initial results look very encouraging. However, patient data provides only organ-scale observations of cancer recurrence patterns whereas to advance scientific understanding of the effects of tissue architecture on cell migratory behavior more quantitative and direct measures of cell spread in animal models are needed (Figure D&E).

[D] Slice through 3D MR image set of excised rat brain obtained at 9.4T	[E] The principle DTI eigen-vectors in the region of the yellow box in [D] are color-coded based on orientation, giving directionality to the major white matter tracts

In vivo anatomical MR images of rat brains are being obtained at 3T just prior to engraftment with rat-native and GFP-labeled infiltrative glioma cells. A high-resolution ex-vivo rat brain DTI dataset has been acquired using our 9.4T scanner. The in vivo images from the engrafted rats will be registered to the ex vivo DTI data and the computational cell migration model will be run on these datasets. Histological analysis at 10-days post-trans-plantation will be used to directly monitor cell dispersion at the tissue level to compare with model predictions. Moreover, we propose to extend this animal model by labeling individual glioma cells with super-paramagnetic iron-oxide (SPIO) particles. The increased SPIO sensitivity at high field strength will enable us to visualize and track individual cells twice daily for 10 days post-implant. The knowledge gained from the SPIO-labeling experiments will enable us to provide our migration model with more physiologically-appropriate parameters for step size, persistence of step direction, fiber affinity, and probability of a cell leaving a fiber bundle once it has become associated with it.

Related Publications

1. Krishnan AP, Asher MI, Davis DD, Okunieff P, O’Dell WG
         Evidence that MR Diffusion Tensor Imaging (Tractography) Predicts the Natural History of
         Regional Progression in Patients Irradiated Conformally for Primary Brain Tumors
         Int. J. of Radiation Oncology, Biology, Physics, (August) 71(5):1553-1562, 2008

Related Presentions

1. Krishnan AP, Davis DD, Okunieff P, O'Dell WG
   Random walk model for predicting patterns of microscopic glioma spread using DTI: A prospective study
   17th ISMRM Scientific Meeting and Exhibition (#142 - Talk), Honolulu, Hawaii, April 2009
2. Krishnan AP, Davis DD, Okunieff P, O'Dell WG
   Random walk models based on DTI for determining the microscopic spread of gliomas
   5th IEEE International Symposium on Biomedical Imaging, Paris FR, June 2008
3. Krishnan AP, Davis DD, Okunieff P, O'Dell WG
   Preliminary results of the effects of higher order reconstruction on Diffusion MRI data on our predictive model for tumor recurrence
   16th Annual ISMRM Scientific Meeting (#5545 -Eposter), Toronto CA, May 2008
4. Krishnan AP, Asher MI, Fuller D, Davis DD, Okunieff P, O’Dell WG
   Evidence That Diffusion Tensor Imaging Predicts the Natural History of Regional Recurrence in Patients Irradiated Conformally for Primary Brain Tumors
   15th Annual ISMRM Scientific Meeting (#345 - Talk), Berlin GER, June 2007
5. Krishnan AP, Asher MI, Fuller D, Davis DD, Okunieff P, O’Dell WG
   Patterns of Brain Tumor Recurrence Predicted From DTI Tractography
   48th Annual AAPM Meeting (TU-C-330A-2), Orlando FL, July 2006