Hou, Jay

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.