Cell migration simulator-based biomarkers for glioblastoma

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.

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.

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.