Interactive dynamics of matrix adhesion and reaction-diffusion predict diverse multiscale strategies of cancer cell invasion

Abstract

The metastasis of malignant epithelial tumors begins with the egress of transformed cells from the confines of their basement membrane to their surrounding collagenous stroma. Invasion can be morphologically diverse, ranging from dispersed mesenchymal cells to multicellular collectives. When breast cancer cells are cultured within basement membrane-like matrix (BM), or Type 1 collagen, or a combination of both, they exhibit collective-, dispersed mesenchymal-, and hybrid collective-dispersed (multiscale) invasion, respectively. In this paper, we asked how distinct these invasive modes are with respect to the cellular and microenvironmental cues that drive them. A rigorous computational exploration of invasion was performed within an experimentally motivated Cellular Potts-based modeling environment. The model comprises of adhesive interactions between cancer cells, BM- and collagen-like extracellular matrix (ECM), and reaction-diffusion-based remodeling of ECM. The model outputs were parameters cognate to dispersed- and collective- invasion. Input sweeps gave rise to a spatial output distribution that consisted of dispersed-, collective- and multiscale- invasion. K-means clustering of the output distribution followed by silhouette analysis revealed three optimal clusters: one signifying indolent invasion and two representing multiscale invasions, which we call collective-multiscale (CMI), and dispersed multiscale invasion (DMI), respectively. Constructing input-output mapped phenotypic spaces suggested that adhesion to BM- and collagen- matrix specify CMI and DMI respectively. Parameter perturbations confirmed these associations and revealed how the cellular phenotype may transition between the three states. Our systems-level analysis provides quantitative insights into how the diversity in matrix microenvironments may steer invasion into distinct phenotypic modes during metastasis.