McMahon, Mariah

Abstract Background Glioblastoma is the most aggressive malignant brain tumor with poor survival due to its invasive nature driven by cell migration, with unclear linkage to transcriptomic information. The aim of this study was to develop a physics-based framework connecting to transcriptomics to predict patient-specific glioblastoma cell migration. Methods and Results We applied a physics-based motor-clutch model, a cell migration simulator (CMS), to parameterize the migration of glioblastoma cells and define physical biomarkers on a patient-by-patient basis. We reduced the 11-dimensional parameter space of the CMS into 3 principal physical parameters that govern cell migration: motor number—describing myosin II activity, clutch number—describing adhesion level, and F-actin polymerization rate. Experimentally, we found that glioblastoma patient-derived (xenograft) cell lines across mesenchymal (MES), proneural, and classical subtypes and 2 institutions (N = 13 patients) had optimal motility and traction force on stiffnesses around 9.3 kPa, with otherwise heterogeneous and uncorrelated motility, traction, and F-actin flow. By contrast, with the CMS parameterization, we found that glioblastoma cells consistently had balanced motor/clutch ratios to enable effective migration and that MES cells had higher actin polymerization rates resulting in higher motility. The CMS also predicted differential sensitivity to cytoskeletal drugs between patients. Finally, we identified 18 genes that correlated with the physical parameters, suggesting transcriptomic data alone could potentially predict the mechanics and speed of glioblastoma cell migration. Conclusions We describe a general physics-based framework for parameterizing individual glioblastoma patients and connecting to clinical transcriptomic data that can potentially be used to develop patient-specific anti-migratory therapeutic strategies.

Abstract Glioblastoma is the most aggressive malignant brain tumor with the poor survival due to its invasive nature driven by cell migration. There is still no effective treatment targeting glioma cell migration based on their transcriptomic information, due to the lack of understanding in the links to the fundamental mechanics of cell migration. In the previous studies, we developed a physics-based motor-clutch model, the cell migration simulator (CMS), to identify the basic mechanisms of cell migration in various cell types. In this study, we found that glioblastoma patient-derived (xenograft) (PD(X)) cell lines across mesenchymal (MES), proneural (PN), classical (CL) subtypes and two institutions (N = 13 patients) had optimal motility and traction force on stiffnesses around 9.3kPa, with otherwise heterogeneous and uncorrelated motility, traction, and F-actin flow. We applied the CMS as a consistent framework to describe the physical parameters of glioblastoma patient cell migration. Using single cell migration and force generation data on compliant 2D surfaces, we found all glioma PD(X) lines (11 patients) had balanced myosin/adhesion ratios resulting in effective migration. Mesenchymal (MES) lines had enhanced F-actin polymerization resulting in higher motility. Adherent PDX lines had increased myosin-adhesion amount resulting in higher traction force. The CMS also predicted the differential cell migration under cytoskeletal drugs between glioma subtypes. Finally, we found 18 genes in actin cytoskeleton and focal adhesion pathways significantly correlated with the glioma physical parameters, which were being used to estimate the physical parameters of a larger cohort (66 patients) and to strategize targeting their cell migration. Overall, we described a consistent physics-based framework for parameterizing individual glioblastoma patients, connecting to clinical transcriptomic data, and predicting drug effects, that can potentially be used to develop subtype and patient-specific anti-migratory therapeutic strategies.

Abstract Glioblastoma remains a deadly cancer driven in part by invasion of tumor cells into the brain. Transcriptomic analyses have identified distinct molecular subtypes, but mechanistic differences that account for clinical differences are not clear. Here, we show that, as predicted by the motor-clutch model of cell migration, mesenchymal glioma cells are more spread, generate larger traction forces, and migrate faster in brain tissue compared to proneural cells. Despite their rapid migration and comparable proliferation rates in vitro, mice with mesenchymal tumors survive longer than those with proneural tumors. This improved survival correlated with an immune response in the mesenchymal tumors, including T cell-mediated. Consistently, inducing mesenchymal tumors in immunodeficient mice resulted in shorter survival supporting a protective immune role in mesenchymal tumors. Thus, mesenchymal tumors have aggressive migration, but are immunologically ‘hot’ which suppresses net proliferation. These two features counteract each other and may explain the lack of a strong survival difference between subtypes clinically, while also opening up new opportunities for subtype-specific therapies. Significant Statement This study highlights new mechanical and immunological insights into glioblastoma molecular subtypes using an integrated modeling-genome engineering strategy, which can potentially facilitate glioblastoma subtype-specific therapeutic strategies.

Abstract In Glioblastoma (GBM), tumor spreading is driven by tumor cells’ ability to infiltrate healthy brain parenchyma, which prevents complete surgical resection and contributes to tumor recurrence. GBM molecular subtypes, classical, proneural and mesenchymal, were shown to strongly correlate with specific genetic alterations (Mesenchymal: NF1; Classical: EGFRVIII; Proneural: PDGFRA). Here we tested the hypothesis that a key mechanistic difference between GBM molecular subtypes is that proneural cells are slow migrating and mesenchymal cells are fast migrating. Using Sleeping Beauty transposon system, immune-competent murine brain tumors were induced by SV40-LgT antigen in combination with either NRASG12V (NRAS) or PDGFB (PDGF) overexpression. Cross-species transcriptomic analysis revealed NRAS and PDGF-driven tumors correlate with human mesenchymal and proneural GBM, respectively. Similar to human GBM, CD44 expression was higher in NRAS tumors and, consistent with migration simulations of varying CD44 levels, ex vivo brain slice live imaging showed NRAS tumors cells migrate faster than PDGF tumors cells (random motility coefficient = 30µm2/hr vs. 2.5µm2/hr, p < 0.001). Consistent with CD44 function as an adhesion molecule, migration phenotype was independent of the tumor microenvironment. NRAS and human PDX/MES tumor cells were found to migrate faster and have larger cell spread area than PDGF and human PDX/PN tumors cells, respectively, in healthy mouse brain slices. Furthermore, traction force microscopy revealed NRAS tumor cells generate larger traction forces than PDGF tumors cells which further supports our theoretical mechanism driving glioma migration. Despite increased migration, NRAS cohort had better survival than PDGF which was attributed to enhanced antitumoral immune response in NRAS tumors, consistent with increased immune cell infiltration found in human mesenchymal GBM. Overall our work identified a potentially actionable difference in migration mechanics between GBM subtypes and establishes an integrated biophysical modeling and experimental approach to mechanically parameterize and simulate distinct molecular subtypes in preclinical models of cancer.

Significance Glioblastoma, the most common and lethal of primary brain tumors, can diffusely infiltrate brain. Because the components of the cytoskeleton that drive tumor invasion are nonredundant, they have been attractive targets for blocking glioblastoma dispersion. However, we find that while targeting one of these—the molecular motor myosin IIA—effectively blocks glioblastoma invasion, it enhances tumor proliferation and lethality. In this study, we have found that myosin IIA functions as a tumor suppressor by reducing signaling of two oncogenes—ERK1/2 and NF-κB—in a manner regulated by the mechanics of the tumor and surrounding brain. Our results also argue that effective treatment of glioblastoma will require inhibiting targets that drive both invasion and proliferation.