Abstract
Abstract Tumour progression consists of various stages including migration to the surrounding tissues, leading to metastasis [1]. In this work, we investigate the multiscale quantitative characteristics of the spatial organization and migration of cancer in 3D cultures. For this study, we combined 3D cell culture experiments of Triple Negative Breast Cancer (TNBC) cells, hybrid spatiotemporal models, and longitudinal RNA-sequencing data analysis. The experiments included two experimental conditions; simple growth experiments, and experiments of growth in presence of the migrastatic drug Paclitaxel. The spatial distributions of the cells enabled us to calibrate and validate a hybrid Keller-Segel model, incorporated into a novel computational framework capable of interpreting the relation between morphological patterns and the underlying mechanisms of cancer growth [2]. The RNA-seq data included different time-points with and without treatment. The results suggested that cancer cells exhibited biased movement towards the bottom of the space, a movement that was inhibited in the presence of Paclitaxel. The calibrated model was able to describe the overall characteristics of the experimental observations, and suggested that cancer cells exhibited chemotactic migration and cell accumulation, as well as random motion throughout the period of development. The spatial pattern analysis revealed transient, non-random spatial distributions of cancer cells that consisted of clustered patterns across a wide range of neighbourhood distances, as well as dispersion for larger distances. The RNA-seq data exhibited coherence with the chemotactic migration hypothesis of the mathematical model, indicating significant under-representation of the Gene Ontology (GO) terms related to chemotactic migration in presence of the migrastatic drug. Overall, this study provided an insightful quantitative characterization of the spatiotemporal organization and progression of TNBC cells in 3D cultures. We anticipate that these developments will enable us to expand our studies to more realistic conditions, including the introduction of heterogeneic cell populations.
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