Quantifying the Morphology and Mechanisms of Cancer Progression in 3D in-vitro environments: Integrating Experiments and Multiscale Models

Author:

Dimitriou Nikolaos M.ORCID,Flores-Torres Salvador,Kinsella Joseph Matthew,Mitsis Georgios D.

Abstract

AbstractMathematical models of cancer growth have become increasingly more accurate both in the space and time domains. However, the limited amount of data typically available has resulted in a larger number of qualitative rather than quantitative studies. In the present study, we provide an integrated experimental-computational framework for the quantification of the morphological characteristics and the mechanistic modelling of cancer progression in 3D environments. The proposed framework allows for the calibration of multiscale, spatiotemporal models of cancer growth using state-of-the-art 3D cell culture data, and their validation based on the resulting experimental morphological patterns. Its implementation enabled us to pursue two goals; first, the quantitative description of the morphology of cancer progression in 3D cultures, and second, the relation of tumour morphology with underlying biophysical mechanisms that govern cancer growth and migration. We applied this framework to the study of the spatiotemporal progression of Triple Negative Breast Cancer cells cultured in Matrigel scaffolds, and validated the hypothesis of chemotactic migration using a multiscale Keller-Segel model. The results revealed transient, non-random spatial distributions of cancer cells that consist of clustered, and dispersion patterns. The proposed model was able to describe the general characteristics of the experimental observations and suggests that cancer cells exhibited chemotactic migration and accumulation, as well as random motion during the examined time period of development. To our knowledge, this is the first time that a multiscale model is used to quantify the relationship between the spatial patterns and the underlying mechanisms of cancer growth in 3D environments.

Publisher

Cold Spring Harbor Laboratory

Reference61 articles.

1. Abler D , Büchler P , Rockne RC . Towards Model-Based Characterization of Biomechanical Tumor Growth Phenotypes. In: Bebis G , Benos T , Chen K , Jahn K , Lima E , editors. Mathematical and Computational Oncology Cham: Springer International Publishing; 2019. p. 75–86.

2. Multi-process dynamic modeling of tumor-specific evolution

3. Multiprocess Dynamic Modeling of Tumor Evolution with Bayesian Tumor-Specific Predictions

4. Adams, B M , Bohnhoff, W J , Dalbey, K R , Ebeida, M S , Eddy, J P , Eldred, M S , Frye, J R , Geraci, G , Hooper, R W , Hough, P D , Hu, K T , Jakeman, J D , Khalil, M , Maupin, K A , Monschke, J A , Ridgway, E M , Rushdi, A A , Stephens, J A , Swiler, L JG . Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.8 Reference Manual. Sandia Technical Report SAND2014-5015; 2014.

5. Single-cell-based models in biology and medicine

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