Caleb A. Stine

Cellular invasion is a primary challenge to complete resection and treatment of glioblastoma, the most aggressive and deadly primary brain tumor. The brain tumor microenvironment actively stimulates glioma invasion through a multitude of cellular, chemical, and biophysical cues. We and others have shown elevated interstitial fluid flow at the tumor border is one such biophysical cue that directly stimulates invasion through tumor-intrinsic signaling and, in other tumor types, priming of cancer-associated stromal cells. It is currently unclear if interstitial flow similarly primes neuroglial cells to promote glioma cell dissemination and can be targeted for therapeutic purposes. Here, we show elevated interstitial flow upregulates expression of sphingosine-1-phosphate receptor 3 (S1PR3) in glial astrocytes and microglia, which drives glioma cell invasion via chemotaxis. Flow-induced expression of glial S1PR3 is tumor-independent and displays a biphasic relationship to fluid shear stress magnitude in vitro and flow rate in vivo . Inhibition of glial S1PR3 in a tissue engineered culture model and orthotopic mouse model abrogates flow-stimulated invasion, demonstrating a tumor-extrinsic approach to limiting glioblastoma progression. Given prior evidence of a pro-inflammatory role for glial S1PR3, identification of S1PR3 as a disease-agnostic marker of flow-stimulated glia may also have therapeutic implications across myriad neuropathologies.

Glioblastoma is characterized by aggressive infiltration into surrounding brain tissue, hindering complete surgical resection and contributing to poor patient outcomes. Identifying tumor-specific invasion patterns is essential for advancing our understanding of glioblastoma progression and improving surgical and radiotherapeutic strategies. Here, we leverage in vivo dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively quantify interstitial fluid velocity, direction, and diffusion within and around glioblastomas. We introduce a novel vector-based pathline analysis to trace downstream accumulation of fluid flow originating from the tumor core, providing a spatially explicit perspective on local flow patterns. We find that localized fluid transport metrics predict glioblastoma invasion and progression, offering a new framework to non-invasively identify high-risk regions and guide targeted treatment approaches. One sentence summary: Invasion and progression of glioblastoma can be predicted with interstitial fluid flow patterns via magnetic resonance imaging.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response.

Glioblastoma is an aggressive brain cancer characterized by diffuse infiltration. Infiltrated glioma cells persist in the brain post-resection where they interact with glial cells and experience interstitial fluid flow. We use patient-derived glioma stem cells and human glial cells (i.e., astrocytes and microglia) to create a four-component 3D model of this environment informed by resected patient tumors. We examine metrics for invasion, proliferation, and putative stemness in the context of glial cells, fluid forces, and chemotherapies. While the responses are heterogeneous across seven patient-derived lines, interstitial flow significantly increases glioma cell proliferation and stemness while glial cells affect invasion and stemness, potentially related to CCL2 expression and differential activation. In a screen of six drugs, we find in vitro expression of putative stemness marker CD71, but not viability at drug IC₅₀, to predict murine xenograft survival. We posit this patient-informed, infiltrative tumor model as a novel advance toward precision medicine in glioblastoma treatment.

Fluid flow and chemokine gradients play a large part in not only regulating homeostatic processes in the brain, but also in pathologic conditions by directing cell migration. Tumor cells in particular are superior at invading into the brain resulting in tumor recurrence. One mechanism that governs cellular invasion is autologous chemotaxis, whereby pericellular chemokine gradients form due to interstitial fluid flow (IFF) leading cells to migrate up the gradient. Glioma cells have been shown to specifically use CXCL12 to increase their invasion under heightened interstitial flow. Computational modeling of this gradient offers better insight into the extent of its development around single cells, yet very few conditions have been modelled. In this paper, a computational model is developed to investigate how a CXCL12 gradient may form around a tumor cell and what conditions are necessary to affect its formation. Through finite element analysis using COMSOL and coupled convection-diffusion/mass transport equations, we show that velocity (IFF magnitude) has the largest parametric effect on gradient formation, multidirectional fluid flow causes gradient formation in the direction of the resultant which is governed by IFF magnitude, common treatments and flow patterns have a spatiotemporal effect on pericellular gradients, exogenous background concentrations can abrogate the autologous effect depending on how close the cell is to the source, that there is a minimum distance away from the tumor border required for a single cell to establish an autologous gradient, and finally that the development of a gradient formation is highly dependent on specific cell morphology.

Fluid flow and chemokine gradients play a large part in not only regulating homeostatic processes in the brain, but also in pathologic conditions by directing cell migration. Tumor cells in particular are superior at invading into the brain resulting in tumor recurrence. One mechanism that governs cellular invasion is autologous chemotaxis, whereby pericellular chemokine gradients form due to interstitial fluid flow (IFF) leading cells to migrate up the gradient. Glioma cells have been shown to specifically use CXCL12 to increase their invasion under heightened interstitial flow. Computational modeling of this gradient offers better insight into the extent of its development around single cells, yet very few conditions have been modelled. In this paper, a computational model is developed to investigate how a CXCL12 gradient may form around a tumor cell and what conditions are necessary to affect its formation. Through finite element analysis using COMSOL and coupled convection-diffusion/mass transport equations, we show that velocity (IFF magnitude) has the largest parametric effect on gradient formation, multidirectional fluid flow causes gradient formation in the direction of the resultant which is governed by IFF magnitude, common treatments and flow patterns have a spatiotemporal effect on pericellular gradients, exogenous background concentrations can abrogate the autologous effect depending on how close the cell is to the source, that there is a minimal distance away from the tumor border required for a single cell to establish an autologous gradient, and finally that the development of a gradient formation is highly dependent on specific cell morphology.