(295a) Multi-Scale Agent-Based Modeling of Cancer Cell Chemotaxis within a Microfluidic Assay: Investigating the Role of Receptor Dynamics and Ligand Isoforms

Chemotaxis is proposed to be a key
mechanism that allows cancer cells to invade surrounding tissue and
vasculature, and also to extravasate into distant
locations leading to metastasis. The signaling axis comprised of the chemokine CXCL12 and its receptor CXCR4 has been implicated
as a major chemotactic proponent in breast cancer and
more than 20 other cancers. Inhibition of CXCR4 has not resulted in clinically
successful cancer therapy, prompting the search for other targets. A second
receptor, CXCR7, is also present in the breast tumor environment and can
scavenge CXCL12. Understanding how CXCR7 can shape ligand
gradients on a molecular level, and influence cancer cell chemotaxis
within the tissue scale, is unclear. In addition, CXCL12 exists as multiple isoforms, yet studies probing its role in cancer have only
focused on one isoform. These isoforms
have varying affinities for cell surface receptors and glycosaminoglycans,
as well as experimental device surfaces, ultimately changing the presentation
of the ligand to the receptor.  These complex interactions are overlooked
in standard non-physiologic cell culture models and confound our understanding of
chemotactic gradient formation, sensing, and its role
in cancer.

In order to investigate the molecular mechanisms that
regulate gradient formation and cancer cell responses to these gradients, we
constructed a data-driven multi-scale agent-based model that simulates cancer
cell chemotaxis within a microfluidic
assay. First, we use in vitro data to
construct an ordinary differential model to describe ligand
and receptor binding, internalization, recycling and degradation for both receptors.
Second, we take these single-cell equations and embed them within an
agent-based model describing how cells interact and chemotax
within a microfluidic device. Thus the molecular
scale behavior drives the cellular level decisions, which ultimately dictate
the overall organization of the cells in the device. We build and validate the
model based on data in Torisawa et al. (Integrative Biology 2:680-686, 2010). By
performing uncertainty and sensitivity analyses with Latin Hypercube Sampling
and Partial Rank Correlation Coefficients, we then identify and analyze
processes that have the greatest influence on chemotaxis.
We find that ligand and receptor dynamics play a
large role in shaping gradients and thus modulating cell behavior. By
incorporating the partitioning of CXCL12 from its soluble state to its glycosaminoglycan-, surface- and receptor-bound states in
the molecular scale of our model, we can also investigate how different CXCL12 isoforms affect the gradient, concentration, and cell
behavior. Overall, our model is able to provide information on how molecular-
and cellular-scale events determine cellular organization in a microfluidic device, and can be extended to explore cell
behavior in tissue in response to CXCL12.