Nimg-70. tracking cellular social behavior of patient-derived primary cell lines to gather new insights for better stratification of glioblastoma patients

Abstract Glioblastoma is one of the most frequent and aggressive brain tumors. The average life of these patients is on average less than 15 months, due to the proliferation and invasiveness of the tumor cells, which not only constitute the tumor mass but which also infiltrate the normal parenchyma brain to form new cancerous sites. Knowing the specific behavior of the cell types that make up the tumor of every single patient, in terms of volume, shape, motility, and cell-cell interaction could be informative for patients’ tumor stratification. Here we developed a protocol that integrates imaging and image processing algorithms, to accurately and quantitatively identify the morphological and behavioral characteristics of tumor cells cultured in vitro and track their movement over time. This approach enables quantifying important biological processes such as cell migration and cell growth, both of which play a major role in the development of diseases. The cell tracking method combines segmentation methods with spatiotemporal optimization to analyze microscopy images. The method also determines various information about cell movements such as movement paths, speed, and distance covered. Knowing the behavior of the malignant glioma cells of a specific patient, in terms of shape and motility (e.g. direction and speed of movement), also in relation to other cells could be informative in the context of dynamic precision medicine. In this context, we created a model that integrates the characteristics of each patient's tumor cells and provides a new way of stratifying patients that can be correlated with clinical data.