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Despite significant progress, the treatment of estrogen receptor-positive (ER+) breast cancer remains clinically challenging due to reversible drug resistance and immune evasion. Drug resistance often arises as cells undergo a dynamic epithelial-to-mesenchymal transition (EMT), while elevated PD-L1 levels contribute to immune escape. While these phenotypic features can variably co-occur, the impact of co-occurrence on the availability of synergistic treatment strategies remains unknown. To investigate their interplay, we constructed an ER-EMT-PD-L1 gene regulatory network and simulated these networks as coupled ordinary differential equations with biologically informed parameters, to generate steady-state expression profiles. Our study revealed that the relevant overarching network generated antagonistic epithelial and mesenchymal modules, capable of producing monostable, bistable, and tristable dynamics. We further examined the link between phenotypes and immune evasion by quantifying average PD-L1 expression, and found that epithelial-sensitive states consistently exhibited low PD-L1. In contrast, hybrid- and mesenchymal-resistant states were associated with a non-linear, stepwise increase in PD-L1, highlighting a strong coupling between EMT, resistance, and immune evasion. Extending on these network-level insights, we further used a spatially explicit agent-based model seeded with GRN-derived phenotypes to probe tumor behavior under therapeutic pressure. Simulations revealed that sustained tumor expansion occurred only when resistance, motility, and immune evasion traits co-existed, and this requirement remained robust across GRN landscapes with differing stability. Plasticity and multistability increased the accessible phenotypic state-space and accelerated shifts toward high-fitness resistant states. We further identified combination therapies that significantly reduced phenotypic diversification and improved immune infiltration in silico. Taken together, our modeling work links regulatory dynamics with tumor-level adaptation and highlights strategies to reprogram resistant cell states toward sensitivity, which are difficult to infer from bulk or cross-sectional data alone. In addition, it provides a controllable in silico testbed to systematically evaluate candidate treatment combinations and their effects on tumor phenotypic transitions and spatial T cell access, thereby helping to prioritize experimental regimens for follow-up. 

Tumor growth and angiogenesis drive complex spatiotemporal variation in micro-environmental oxygen levels. Previous experimental studies have observed that cancer cells exposed to chronic hypoxia retained a phenotype characterized by enhanced migration and reduced proliferation, even after being shifted to normoxic conditions, a phenomenon which we refer to ashypoxic memory. However, because dynamic hypoxia and related hypoxic memory effects are challenging to measure experimentally, our understanding of their implications in tumor invasion is quite limited. Here, we propose a novel phenotype-structured partial differential equation modeling framework to elucidate the effects of hypoxic memory on tumor invasion along one spatial dimension in a cyclically varying hypoxic environment. We incorporated hypoxic memory by including time-dependent changes in hypoxic-to-normoxic phenotype transition rate upon continued exposure to hypoxic conditions. Our model simulations demonstrate that hypoxic memory significantly enhances tumor invasion without necessarily reducing tumor volume. This enhanced invasion was sensitive to the induction rate of hypoxic memory, but not the dilution rate. Further, shorter periods of cyclic hypoxia contributed to a more heterogeneous profile of hypoxic memory in the population, with the tumor front dominated by hypoxic cells that exhibited stronger memory. Overall, our model highlighted the complex interplay between hypoxic memory and cyclic hypoxia in shaping heterogeneous tumor invasion patterns. 

Cancer is heterogeneous and variability in drug sensitivity is widely documented across cancer types. Adaptive therapy is an emerging treatment strategy that leverages this heterogeneity to improve therapeutic outcomes. Current standard treatments eliminate a majority of drug-sensitive cells, leading to relapse by competitive release. Adaptive therapy retains some drug-sensitive cells, limiting resistant cell growth by ecological competition. This strategy has shown some early promise, but current methods largely assume cell phenotypes to remain constant, even though cell-state transitions could permit drug-sensitive and-resistant phenotypes to interchange and thus escape therapy. We address this gap using a deterministic model of population growth, in which sensitive and resistant cells grow under competition and undergo cell-state transitions. The model's steady-state behaviour and temporal dynamics identify optimal balances of competition and transitions suitable for effective adaptive versus constant dose therapy. Furthermore, under adaptive therapy, models with cell-state transitions show slower oscillations than those without, suggesting that the competition-transitions balance could impinge on population-level dynamical properties. Our analyses also identify key limitations of phenomenological models in therapy design and implementation, particularly with cell-state transitions. These findings elucidate the relevance of phenotypic plasticity for emerging cancer treatment strategies using population dynamics as an investigation framework. 

Phenotypic plasticity—the reversible switching of cell-states—is a central tenet of development, regeneration, and cancer progression. These transitions are governed by gene regulatory networks (GRNs), whose topological features strongly influence their dynamics. While toggle switches (mutually inhibitory feedback loops between two transcription factors) are a common motif observed for binary cell-fate decisions, GRNs across diverse contexts often exhibit a more general structure: two mutually inhibiting teams of nodes. Here, we investigate the teams of nodes as a potential topological design principle of GRNs. We first analyze GRNs from the Cell Collective database and introduce a metric, impurity, which quantifies the fraction of edges inconsistent with an idealized two-team architecture. Impurity correlates strongly with statistical properties of GRN phenotypic landscapes, highlighting its predictive value. To further probe this relationship, we simulate artificial two-team networks (TTNs) using both continuous (RACIPE) and discrete (Boolean) formalisms across varying impurity, density, and network size values. TTNs exhibit toggle-switch-like robustness under perturbations and enable accurate prediction of dynamical features such as inter-team correlations and steady-state entropy. Together, our findings establish the teams paradigm as a unifying principle linking GRN topology to dynamics, with broad implications for inferring coarse-grained network properties from high-throughput sequencing data. 

Stem cell differentiation during development is governed by the dynamics of the underlying gene regulatory networks (GRNs). Mutually inhibiting nodes/collection of nodes encompass the GRNs that govern differentiation to two distinct fates. However, the properties of GRNs that can allow differentiation into n-terminal phenotypes are poorly understood. In this study, we examine toggle-n networks, encompassing mutual inhibitions among multiple transcription factors (TFs), to derive generalized insights regarding the dynamics underlying differentiation into n-terminal phenotypes. We show through numerical and analytical methods that steady-state distributions of these networks involve co-expression of multiple cell state-specific TFs, indicating the presence of multi-potent hybrid phenotypes during multi-lineage differentiation. Furthermore, incorporating a case study of T-helper cell differentiation, we show that cytokine signalling and specific asymmetry of regulatory links can drive further directed differentiation of these hybrid phenotypes into particular cell states within our mathematical framework. 

The epithelial-to-mesenchymal transition (EMT) provides crucial insights into the metastatic process and possesses prognostic value within the cancer context. Here, we present COMET, an R package for inferring EMT trajectories and inter-state transition rates from single-cell RNA sequencing data. We describe steps for finding the optimal number of EMT genes for a specific context, estimating EMT-related trajectories, optimal fitting of continuous-timeMarkov chain to inferred trajectories, and estimating inter-state transition rates.