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Cancer Technologies

Tumor Microenvironment II

Interstitial transport to identify invading tumor cells and their environment in glioblastoma

Saturday, October 26, 2024
12:30 PM – 12:45 PM EST
Location: Room 336

Introduction:  In brain cancer, interstitial fluid flow is heightened at the borders of tumors as they interface with the surrounding brain. We have found that interstitial flow can drive tumor cell invasion in vitro and in vivo when increased exogenously. Here we aimed to determine if the nature of fluid flow and other MRI-measured transport metrics could indicate the invasive and progressive regions of glioma, and if there are changes to the microenvironment that correlate with these outcomes that might explain changes to invasion and transport.

Materials and

Methods:  Using DCE-MRI, we assessed the movement of contrast in implanted models of GL261 glioma in mice, using this to determine diffusion and velocity in and around the tumors. At Day 9 after implant, we assessed the patterns of glioma cells, and surrounding parenchymal cells (astrocytes, microglia, neurons, T cells) by immunohistochemistry to determine correlates with invasion and with interstitial transport. We also conducted a progression study in which GL261 tumors were implanted and imaged every 2-3 days over two weeks. Data was analyzed and flow patterns compared to progression patterns via in-house MATLAB code.

Results, Conclusions, and Discussions: We found that interstitial fluid velocity magnitude, diffusion, and amount of interstitial fluid flow tumor originating pathlines could predict the presence of invading tumor cells in the GL261 model. Similarly, these metrics correlated with regions of progression in implanted gliomas. We also determined that there are unique environmental changes in these regions including a proliferation of microglia and alignment of astrocytes that could be modulating these invasive patterns further. Overall, we have found that interstitial transport can be predictive or indicative of invasion in glioma models. Future work will move towards clinical implementation and further analysis in other models of glioma.

Acknowledgements (Optional):  We acknowledge Maosen Wang, Ph.D. for MRI assistance and funding from the NCI, NINDS, and Red Gates Foundation.