P12.34.A TRACKING INDIVIDUAL AND COLLECTIVE CELLULAR DIFFUSION IN PATIENT-DERIVED PRIMARY GBM CELL LINES: EXPLORING CELL DIFFUSION PROPERTIES AS A POTENTIAL VULNERABILITY IN GLIOBLASTOMA

Abstract BACKGROUND Glioblastoma (GBM) is one of the most aggressive and deadly brain tumors, with a median survival of ~14 months. Its poor prognosis is largely due to its ability to infiltrate healthy brain tissue both as single cells and in collectives. A deeper understanding of both individual and collective GBM cell motility could reveal new insights for patient-specific therapies. We applied live-cell imaging and mathematical modeling to characterize migration in patient-derived GBM cells, aiming to identify stratification markers and therapeutic vulnerabilities. MATERIAL AND METHODS We developed Single-Cell Behavior Live Imaging (ScBLI), an approach integrating live imaging with computational analysis to study the dynamics of GBM and healthy astrocyte (HA) cultures. We analyzed 21 GBM cell lines and one HA culture. Using TrackMate and Cellpose for segmentation and tracking, we extracted data on trajectories, velocities, distances, and morphological features (e.g., volume, circularity). Delta scaling was applied to classify trajectories as subdiffusive, Brownian, or Levy-like. Functional assays evaluated responses to migration stimuli and therapeutics. We also assessed collective migration behavior and integrated these data with single-cell analyses. All findings were correlated with clinical outcomes and transcriptomic profiling. RESULTS We tracked 100 cells per sample, analyzing 2200 trajectories. Each culture contained three distinct migratory subpopulations based on metrics such as displacement, velocity, confinement ratio, and cellular organization. GBM samples were grouped into three behavioral clusters. Delta scaling values (0.28-0.80) identified corresponding migration modes. Higher delta values indicated more structured, Levy-like migration and were associated with shorter patient survival, suggesting a relationship between organized motility and increased invasiveness. Transcriptomic analysis revealed differential gene expression signatures among the clusters, potentially linked to migration phenotypes. CONCLUSION Our study presents a robust framework to characterize GBM migration dynamics across patient-derived cultures. By integrating individual and collective motility data with clinical and transcriptomic information, we identified three distinct behavioral profiles with prognostic value. Delta scaling proved to be an effective metric for stratifying tumors based on migratory efficiency and aggressiveness. These insights highlight how structured motility may facilitate tumor spread and serve as a functional biomarker. Moreover, the identification of migration-associated gene signatures opens avenues for the development of targeted therapies aimed at disrupting invasive behavior. Ultimately, our findings support the use of dynamic phenotyping in guiding personalized treatment strategies for GBM patients.