Design Principles of Phenotypic Robustness and Plasticity in Gene Regulatory Networks underlying Cancer Metastasis
Metastasis – the process of cancer cells leaving the primary tumor and colonizing multiple organs – remains a major cause of cancer mortality. However, it is a highly inefficient process with only 0.01% of disseminated cells eventually succeeding in colonization. The reason for such high rates of attrition is inability of disseminated cancer cells to constantly adapt to various obstacles such as rapidly changing biochemical environments, anoikis and immune attack. Successful metastasis requires disseminating cells to strike a balance between two contrasting dynamical features: phenotypic plasticity and phenotypic robustness. Phenotypic plasticity is the ability of cells to switch among different phenotypes reversibly, while robustness is their ability to retain a phenotype against intrinsic or extrinsic fluctuations they face. For example, successful colonization is often achieved by disseminating cells acquiring an epithelial phenotype upon reaching a secondary organ, and then maintaining it despite fluctuating microenvironments at metastatic site. While individual molecules driving robustness and plasticity have been reported, how cells maintain this delicate balance remains an open question. Here, we have investigated the dynamics and design principles of regulatory networks that can govern these properties, taking the Epithelial Mesenchymal Plasticity (EMP) – a key component of metastasis – as a case study. First, to understand the emergent dynamics of EMP networks of varying sizes and densities, we employed two simulation formalisms: RACIPE (a continuous, parameter agnostic approach) and Boolean modeling (a discrete, parameter independent formalism). These networks enabled epithelial (E) and mesenchymal (M) phenotypes as predominant ones, alongside the less frequent hybrid E/M ones. Using these two formalisms, we found that phenotypic frequency distributions obtained from EMP networks are more robust to structural perturbations (mutations that change the nature of edge connecting two nodes) and dynamical perturbations (mutations that effect the strength of connection between two nodes), as compared to their randomized counterparts. Similarly, we observed that EMP networks have a higher tendency to allow for phenotypic plasticity than randomized networks, indicating that they may have evolved to facilitate both phenotypic robustness and plasticity. Second, focusing on the design principles of EMP networks, we demonstrated that both phenotypic robustness and plasticity are supported by a larger number of positive feedback loops and a smaller number of negative feedback loops embedded in these networks. Importantly, we noted that these feedback loops intertwine to manifest themselves as two well-defined “teams” of nodes. These “teams” allow for the co-expression of specific genes that characterize the dominant E and M phenotypes. Also, the “teams” structure confers robustness to multiple structural and dynamical perturbations specifically for E and M phenotypes while allowing for hybrid E/M phenotypes to be more plastic relatively. Finally, we identify two network topology metrics – team strength and the fraction of positive feedback loops – that can explain the extent of plasticity and robustness emergent from EMP networks. These metrics allow us to identify the single-edge perturbations capable of significantly altering these networks' plasticity and/or robustness. Together, our analysis elucidates that phenotypic plasticity and robustness in cancer are emergent properties of the topology of EMP regulatory networks and present a platform to isolate specific network perturbations to curtail these dynamical features, thereby potentially impacting metastasis.