Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges, such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance. Various biochemical networks have been identified to contribute to phenotypic plasticity, but how plasticity emerges from the dynamics of these networks remains elusive. Here, we investigated the dynamics of various regulatory networks implicated in Epithelial-mesenchymal plasticity (EMP)-an important arm of phenotypic plasticity-through two different mathematical modelling frameworks: a discrete, parameter-independent framework (Boolean) and a continuous, parameteragnostic modelling framework (RACIPE). Results from either framework in terms of phenotypic distributions obtained from a given EMP network are qualitatively similar and suggest that these networks are multi-stable and can give rise to phenotypic plasticity. Neither method requires specific kinetic parameters, thus our results emphasize that EMP can emerge through these networks over a wide range of parameter sets, elucidating the importance of network topology in enabling phenotypic plasticity. Furthermore, we show that the ability to exhibit phenotypic plasticity correlates positively with the number of positive feedback loops in a given network. These results pave a way toward an unorthodox network topology-based approach to identify crucial links in a given EMP network that can reduce phenotypic plasticity and possibly inhibit metastasis-by reducing the number of positive feedback loops.

Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance. Various biochemical networks have been identified to contribute to phenotypic plasticity, but how plasticity emerges from the dynamics of these networks remains elusive. Here, we investigated the dynamics of various regulatory networks implicated in Epithelial-Mesenchymal Plasticity (EMP) − an important arm of phenotypic plasticity − through two different mathematical modelling frameworks: a discrete, parameter-independent framework (Boolean) and a continuous, parameter-agnostic modelling framework (RACIPE). Results from either framework in terms of phenotypic distributions obtained from a given EMP network are qualitatively similar and suggest that these networks are multi-stable and can give rise to phenotypic plasticity. Neither method requires specific kinetic parameters, thus our results emphasize that EMP can emerge through these networks over a wide range of parameter sets, elucidating the importance of network topology in enabling phenotypic plasticity. Furthermore, we show that the ability to exhibit phenotypic plasticity positively correlates with the number of positive feedback loops. These results pave a way towards an unorthodox network topology-based approach to identify crucial links in a given EMP network that can reduce phenotypic plasticity and possibly inhibit metastasis -by reducing the number of positive feedback loops.Boolean models, parameter independent and parameter agnostic models 1. Introduction 1 Metastasis, therapy resistance, and tumor relapse remain unsolved clinical challenges and a major 2 cause of cancer mortality [1]. During metastasis, cells navigate many bottlenecks: local invasion, 3 intravasation, survival in circulation in matrix-deprived conditions, extravasation, and eventually 4 colonization of the distant organ. Only a few (< 0.02%) cells survive this cascade of events and are 5 capable of initiating metastasis. Recent studies have identified phenotypic plasticity − the ability of 6 cells to reversibly switch phenotypes in response to their ever-changing environmental conditions − 7 as a hallmark of cancer metastasis [2]. Similarly, phenotypic plasticity enables a small proportion of 8 cancers cells to transiently acquire an adaptive drug-refractory phenotype which may contribute to 9 tumor relapse [3]. Therefore, identifying the mechanisms of phenotypic plasticity is essential for any 10 major breakthroughs in cancer treatment. 11Phenotypic plasticity is considered to be an adaptation strategy to survive in variable environ-12 mental conditions [4]. Recently, the contributions of phenotypic plasticity in driving metastasis and 13 therapy resistance have been realized more promi...

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic system for label-free enrichment of CTCs.

ABSTRACTWe collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic systems for label-free enrichment of CTCs.

Multi-stability is central to biological systems as it plays a key role in adaptation, evolvability, and differentiation. Multi-stability can be achieved by integrating positive feedback loops into the regulation. The simplest of such feedback loops are a) a mutual inhibition (MI) loop, b) a mutual activation (MA) loop, and, c) self-activation. While it is established that all three motifs can give rise to bistability, the characteristic differences in the bistability exhibited by each of these motifs is relatively less understood. Here, we use ODE-based simulations across a large ensemble of parameter sets and initial conditions to study the characteristics of the bistability of these motifs and their limits in the parameter space. We also examine the behavior of these motifs under mutual degradation and self-activation. Finally, we investigate the utility of these motifs for achieving coordinated expression through cyclic and parallel coupling. Through our analysis, we found that MI-based architectures offer robustness in maintaining distinct multi-stability and allow for coordination among multiple genes. This coordination paves way for understanding the naturally occurring gene regulatory network and may also help in the better design of robust and controllable synthetic networks.