Cell migration simulator-based biomarkers for glioblastoma.

Area of Science:

• Biophysics
• Cancer Biology
• Computational Biology

Background:

• Glioblastoma is an aggressive brain tumor characterized by invasive cell migration and poor patient survival.
• The relationship between glioblastoma cell migration and transcriptomic information remains unclear.
• Developing patient-specific models is crucial for understanding and treating glioblastoma invasiveness.

Purpose of the Study:

• To develop a physics-based framework that integrates transcriptomic data to predict patient-specific glioblastoma cell migration.
• To identify key physical parameters governing glioblastoma cell motility.
• To establish a connection between molecular data and cellular mechanics for therapeutic targeting.

Main Methods:

• Applied a physics-based motor-clutch model and cell migration simulator (CMS) to parameterize glioblastoma cell migration.
• Reduced the CMS parameter space to three principal physical parameters: motor number, clutch number, and F-actin polymerization rate.
• Utilized patient-derived glioblastoma cell lines (N=13) across different subtypes and institutions.

Main Results:

• Identified optimal substrate stiffness (around 9.3 kPa) for glioblastoma cell motility and traction force.
• Found that glioblastoma cells maintain balanced motor/clutch ratios for effective migration.
• Mesenchymal (MES) glioblastoma cells exhibited higher actin polymerization rates and motility.
• The CMS predicted differential drug sensitivities among patients and identified 18 genes correlated with physical migration parameters.

Conclusions:

• A generalizable physics-based framework was established to parameterize glioblastoma patients.
• This framework connects cellular mechanics to clinical transcriptomic data.
• The approach holds potential for developing patient-specific anti-migratory therapeutic strategies against glioblastoma.