Abstract 2446: Stochastic calibration of an agent-based tumor growth model using time-resolved microscopy data

Like every biological process, tumor development is not purely deterministic, as it is subject to random perturbations from the environment. Moreover, mathematical and computational tumor growth models are subject to uncertainties in the parameters, which may arise from experimental variations or intratumor and intrapatient heterogeneities. Tumor growth models are typically deterministic representations of growth, neglecting the aforementioned uncertainties. However, carcinogenesis must be modeled as a stochastic process to generate more informative predictions of the tumor microenvironment, which is particularly important when describing the action of various therapies. The model developed here aims to reflect the stochastic proliferation and differentiation of tumor cells. We present, for the first time, a methodology for performing a stochastic calibration of an agent-based tumor growth model. The agent-based model reproduces the interactions among different tumor cell phenotypes (e.g., proliferative and dead cells). The cell movement is driven by the balance of a variety of forces (e.g., cell-cell adhesion and repulsion) according to Newton’s second law. Transitions among cell phenotypes are defined by rules that depend on glucose concentration and time spent in each phase of cell cycle. The model captures the phenomena at the cell scale, tracking the velocity, position, and confluence, as well as the glucose concentration in the microenvironment. The experiments were done using a HER2+ breast cancer line (BT-474). Cells were seeded at a density of 3.2×104, 4.0×104 and 4.8×104 cells/well on a 96-well tissue culture plate, with four glucose levels (0, 2, 5 and 10 mM), and four replicates for each combination of initial density and glucose levels. The cells were incubated in the IncuCyte live cell imaging system (Essen BioScience, USA). Multiple images were acquired and stitched together automatically by this system with a 4× objective to obtain whole well images for each well. IncuCyte Cytotox Red Reagents (Essen BioScience, USA), a highly sensitive cyanine nucleic acid dye was added into the medium to estimate cell death. The wells were imaged every 2 hours and cell segmentation was performed in Matlab (The Mathworks, Inc., USA). Segmented images from phase-contrast and fluorescent images were employed to determine the confluence of the live cells. The dead and live cells from our agent-based model were calibrated using a Bayesian framework. The likelihood used during the calibration process considers the stochasticity from our model and the variance in the in vitro experiments. Preliminary efforts indicate the average difference between the calibrated model and the experimental data is below 5.6±4.5%. The results of the proposed model suggest that the computational framework developed can correctly calibrate the agent-based model while considering the stochasticity of the model and data. Citation Format: Ernesto A. Lima, Danial Faghihi, Russel Philley, Jianchen Yang, Jack Virostko, Thomas E. Yankeelov. Stochastic calibration of an agent-based tumor growth model using time-resolved microscopy data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2446.