Abstract P1-06-05: Image driven biophysical mathematical modeling of multicellular breast tumor spheroids

Introduction: Conventional 2D monolayer cell culture methods have distinct weaknesses in mimicking in vivo tumors as they ignore major phenotypic physical tumor microenvironment (TME) factors. 3D multicellular tumor spheroid (MTS) systems have been developed to study cancer cell growth in the presence of such TME factors, including cell-cell and cell-extracellular matrix (ECM) mechanical interactions. However, the development of analysis methodologies for these complex culture systems has considerably lagged. We hypothesize that a coupled experimental-computational framework using a microscopy image-driven biophysical mathematical model of MTS systems can accurately estimate phenotypic growth and biophysical TME properties, revealing significantly more mechanistic information than traditional morphometric analysis methods. Materials and Methods: MDA-MB-231 triple-negative breast cancer cells were used to generate spheroids. A single 3D MTS embedded in Collagen I ECM was used as a 3D culture tumor model system as they exhibit cell-cell and cell-ECM interactions that mimic in vivo tumors. Fluorescent fiducial microspheres were placed in the ECM to track displacement induced by the MTS. Time-lapse fluorescence microscopy images were taken of the MTS and microspheres for 72 hours on an automated fluorescent microscope every 12-hours. Images were compiled using a customized fully-automated tiling/stitching/image processing software in MATLAB. Time-lapse images of the MTS and microspheres are used in conjunction with subsequent mathematical modeling analysis for biophysical parameter estimation. We assess MTS using a mathematical model based on two coupled partial differential equations describing growth/motility and mechanical equilibrium in response to cellular-induced forces. Parameters describing cell proliferation rate (k), cell diffusion (D), and cellular mechanical forces (λ) are estimated based on fitting acquired microscopy images to the model via an iterative least-squares parameter estimation. Experiments and model-based analyses were performed in triplicate to define the reproducibility of biophysical parameter estimation. Results and Discussion: In this preliminary study, our modeling framework is able to estimate biophysical parameters of cell diffusion, proliferation rate, and cellular mechanical force in a 3D breast cancer cell culture system of invading MTS embedded in ECM with coefficients of variation across all parameters less than 15% on average. The model predictions accurately represent changes in cellular density and ECM deformation induced by cellular traction forces throughout the observed time series, demonstrating that this model is capable of representing MTS/ECM growth behavior and parameterizing the driving biophysical properties. Conclusions: These results indicate that our imaging-driven mathematical modeling framework for estimating biophysical model parameters has potential to reliably characterize 3D MTS growth and invasion. With reproducibility established, we plan to extend the method to investigate these phenotypic biophysical TME properties in a range of culture and treatment condition interventions. These biophysical properties could be utilized in developing anti-neoplastic drug sensitivity assays to improve cancer therapy by enabling patient-specific estimation/prediction of response to agents and dosing conditions. Acknowledgements: NIH-NCI K25CA204599 and P30CA012197 Citation Format: Haley Brooke Johnson, Emily E. Fannin, Alexandra Thomas, Jared A Weis. Image driven biophysical mathematical modeling of multicellular breast tumor spheroids [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-06-05.