(106d) Agent-Based Modeling As a Digital Twin for the Transwell Migration Assay

Cell migration in tumor microenvironments is crucial in disease progression and treatment efficacy. In recent decades, research on the active expression and regulatory effects of chemokines in cancer and immune cells has made the chemokine system an emerging target of immunotherapy [1]. One potential therapy is to alter the chemokine expression in cancer cells to recruit more immune cells. Chemokines are a family of small proteins that primarily induce chemotaxis when binding to their corresponding receptors. Apart from binding reactions, chemokine molecules can also homo- or heterodimerize [2]. A study on pancreatic cancer migration towards chemokine CXCL12 observed a biphasic-concentration dependence [3]. Researchers hypothesized that dimerization and competitive binding cause the biphasic manner. These chemical interactions and the variety of chemokine and receptor families form a complex chemokine network that regulates diverse cell activities [4]. Alteration in chemokine environments is expected during immunotherapy, emphasizing the importance of understanding cell migration in complex chemokine environments. A popular method to study chemotaxis is the transwell migration assay, in which cells are plated above the membrane in the upper insert and attracted by the chemoattractants deployed in the bottom well. To complement the in vitro experiments, we developed an agent-based computational model to study factors affecting cell migration and responses to therapeutic intervention. By adjusting parameters, the model can serve as a general platform for the simulation of a variety of cell lines and chemokines, building towards more complex conditions.

Here, we developed a 3D agent-based model with Compucell3D (a cellular Potts lattice-based model) to simulate the effects of random and directed cell migration in response to chemokines. To accommodate various cell lines, we categorized targeted cell lines into 6 groups based on their size and adhesion to the membrane. The model shows a 3D column space of the transwell device with 400 moving agents to simulate the dynamics of the transwell cell migration assay. With periodic boundary conditions applied to vertical surfaces of the domain, the model can simulate in vitro transwell experiments where cells have realistic biomechanics of neighboring cells and tissue-mimic biomaterials. The group of moving agents mimics migrating cells initially located above a solid plane that represents a collagen-coated transwell membrane. The solid plane contains randomly distributed pores that match the structure of the collagen-coated membrane with the same level of pore density. Chemokines are initiated from the bottom half of the assay below the membrane and can diffuse upwards to generate a concentration gradient. Several parameters, including chemical concentrations, diffusion coefficients, chemotactic potential coefficient, an external potential energy term, and a contact energy term are included with a direct connection to published data. The randomized external potential energy simulates the intrinsic Brownian motion of cells and drives cells to move through the membrane in the negative control group without chemokines. Smaller contact energy between cells and the membrane mimics stronger cell-collagen adhesion. The chemotactic energy term and heterogeneous chemical field regulate directional chemotaxis.

We observed that larger external potential energy can induce more cells to migrate through the membrane. Thus, we calibrate this energy term with negative control group data from different cell lines (e.g., Panc1, MiaPaCa2, HCT116, U937, and THP-1) [3]. Our simulated results also predicted variations in cell migration with cell density and pore density of the membrane in the negative control groups. We employed various methods to generate Brownian motion and analyzed the resulting trajectories with their velocity profiles and mean squared displacements (MSD). Under the external force field with uniform intensity, these methods affected cell persistence, average velocity, and diffusivity.

Next, we are extending the model to investigate the effects of chemokine concentrations and diffusion. We also plan to include receptors in our models to understand the chemokine-receptor signaling pathway, dimerization, and potential treatments with ligand or receptor inhibitors. By integrating experimental data and our ABM, we aim to elucidate the mechanism that induces the biphasic-concentration-dependent manner of cell migration. In the future, we will implement these validated mechanisms and physiological properties to new agent-based models to simulate cancer pathology and therapy inside the body, considering cells, chemokines, and tissue microenvironments.

Acknowledgments:

This work was supported by the National Institutes of Health grant R35GM133763 and the University at Buffalo. MBD was supported in part by R01 CA226279.

Disclosures:

MBD has ownership and financial interests in Protein Foundry, LLC and Xlock Biosciences, LLC.

References:

1. Mollica Poeta, V., et al., Chemokines and Chemokine Receptors: New Targets for Cancer Immunotherapy. Frontiers in Immunology, 2019. 10.
2. Von Hundelshausen, P., et al., Chemokine interactome mapping enables tailored intervention in acute and chronic inflammation. Science Translational Medicine, 2017. 9(384): p. eaah6650.
3. Roy, I., et al., CXCL12 chemokine expression suppresses human pancreatic cancer growth and metastasis. PLoS One, 2014. 9(3): p. e90400.
4. Hughes, C.E. and R.J.B. Nibbs, A guide to chemokines and their receptors. The FEBS Journal, 2018. 285(16): p. 2944-2971.