(721c) Computational Modeling of Cell Migration in Complex Chemokine Microenvironments

Cell migration in tumor microenvironments plays a crucial role 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 suppressing tumor growth and metastasis using receptor or ligand inhibitors to block the signaling pathways. Another is to alter the chemokine expression in some hidden cancer cells at an early stage to recruit more immune cells. Chemokines are a family of small proteins that mainly induce chemotaxis when binding to their respective receptors. Apart from binding reactions, chemokine molecules may also homo- or heterodimerize, which is called the chemokine interactome [2]. A study on pancreatic cancer migration towards chemokine CXCL12 observed a biphasic concentration-dependent manner of migration [3]. One hypothesis for biphasic concentration-dependent migration is the dimerization of chemokines and competitive ligand-receptor binding. 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. One popular method to study chemotaxis is the transwell migration assay. 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 tuning parameters, the model can be a general platform for the simulation of a variety of cell lines and chemokines.

Here, we developed a 3D agent-based model with Compucell3D (a cellular Potts lattice-based model) to simulate the physiological response and identify the effects of random and directed cell migration in response to chemokines. To simulate the dynamics of the transwell cell migration assay, the model shows a 3D slice space of the transwell device with 200 moving agents. 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 cells with Brownian motion, located above a solid plane representing a collagen-coated transwell membrane. The solid plane contains randomly distributed pores that simulate the realistic structure of the transwell membrane with the same level of pore density. Chemokines are initiated from the bottom half 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 membranes in the negative control group without chemokines. Smaller contact energy between cells and membrane mimics the stronger cell-collagen adhesion.

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, and HPAFII) [3]. Our simulated results also predicted variations in cell migration with cell density and pore density of the membrane in the negative control groups.

Next, we are extending the model to investigate the effects of chemokine concentrations and diffusion. The model will consider the dimerization of chemokines at a threshold concentration to simulate the biphasic concentration-dependent migration of different cell lines. We also plan to include receptors in our models to understand this chemokine-receptor signaling pathway and explore potential treatment with ligand or receptor inhibitors. By integrating experimental data and our ABM, we aim to elucidate the mechanisms that induce the biphasic concentration-dependent manner. 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 is 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. 10:379.
2. Von Hundelshausen, P., et al., Chemokine interactome mapping enables tailored intervention in acute and chronic inflammation. Science Translational Medicine, 2017. 9(384): eaah6650.
3. Roy, I., et al., CXCL12 chemokine expression suppresses human pancreatic cancer growth and metastasis. PLoS One, 2014. 9(3): e90400.
4. Hughes, C. E. and R. J. B. Nibbs, A guide to chemokines and their receptors. FEBS Journal, 2018. 285(16): 2944-2971.