(58d) Computational Modeling of Cell Migration in Complex Chemokine Environments

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. Alteration in chemokine environments is expected during immunotherapy, emphasizing the importance of understanding cell migration in complex chemokine environments. Chemokines are a family of small proteins that mainly induce chemotaxis when binding to their respective receptors. The chemokine and chemokine receptor families are widely expressed in mammalian cells. A particular type of chemokine may bind to more than one type of receptor. Conversely, a particular type of receptor may also capture different types of chemokine. Besides, chemokine molecules may also dimerize, which is called the chemokine interactome [2]. These chemical interactions and the variety of chemokine and receptor families form a complex chemokine network that regulates diverse cell activities [3]. Since chemokines and chemokine receptors are expressed by both tumor cells and leukocytes, this signaling network can influence several activities, including leukocyte recruitment, angiogenesis, tumor growth, proliferation, and metastasis [1]. To understand the cell activities in the complex chemokine environment, an agent-based computational model can be used to complement the in vivo or in vitro experiments. We can also use this model to study the factors affecting cell migration and to predict cell migration during therapeutic intervention.

We built 2D & 3D agent-based models with Compucell3D (a cellular Potts lattice-based model) to simulate the physiological response, especially cell migration, of tumor and immune cells towards complex chemokine settings. We first developed a preliminary 2D agent-based model to simulate in vitro cell migration experiment. A single cell is placed at the center of the lattice, while chemokines are added at the edges and diffuse across the field. Several parameters, including chemical concentrations, diffusion coefficients, and chemotactic potential coefficients, were validated with cell migration experiments. This 2D model can help understand the mechanisms of cell chemotaxis, monomer-dimer equilibrium of certain chemokines, and competition between different pairs of chemokines and cognate receptors. We also used this model to demonstrate how cells react to complex chemokine environments. With this model, we confirmed larger chemotactic potential coefficient would lead to further displacement and observed how cells migrate in the existence of multiple sources of chemokines.

We also constructed a 3D model to simulate and predict an in vitro transwell experiment where cells have more realistic biomechanics of neighboring cells and tissue-mimic biomaterials. A group of moving agents mimics cells with random-walk, located above a layer of fixed agents representing collagen-coated transwell membrane. Apart from the parameters mentioned in the 2D model, an external potential energy term and a contact energy term are included with direct connection to published data. The randomized external potential energy allows cells to behave random-walk and drive cells to move through membranes in the negative control group (no chemotaxis). Smaller contact energy means greater adhesion between agents, which mimics the cell-matrix adhesion. We observed that a larger pore surface and smaller contact energy provide larger resistance making fewer cells move across the membrane during a certain amount of time.

In the future, we plan to include kinetics of receptor-ligand binding in our models to understand this chemokine-receptor signaling pathway and explore potential treatment with ligand or receptor inhibitors. Finally, we will use these mechanisms and physiological properties to build 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.

[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] Hughes, C.E. and R.J.B. Nibbs, A guide to chemokines and their receptors. The FEBS Journal, 2018. 285(16): p. 2944-2971.