Epigenetic state inheritance drivers drug-tolerant persister-induced resistance in solid tumors: A stochastic agent-based model

TL;DR

A stochastic agent-based model of solid tumor evolution that couples macroscopic population dynamics with microscopic epigenetic state inheritance during the cell cycle is developed, providing a quantitative framework for dissecting DTP-driven resistance mechanisms and designing more effective, biologically informed therapeutic strategies.

Abstract

The efficacy of anti-cancer therapies is severely limited by the emergence of drug resistance. While genetic drivers are well-characterized, growing evidence suggests that non-genetic mechanisms, particularly those involving drug-tolerant persisters (DTPs), play a pivotal role in solid tumor relapse. To elucidate the evolutionary dynamics of DTP-induced resistance, we develop a stochastic agent-based model (ABM) of solid tumor evolution that couples macroscopic population dynamics with microscopic epigenetic state inheritance during the cell cycle. Our simulations accurately reproduce the temporal progression of relapse observed in experimental studies, capturing the dynamic transition from sensitive cells to DTPs, and ultimately to stable resistant phenotypes under prolonged therapy. By explicitly modeling the epigenetic plasticity of individual cells, our model bridges the gap between cellular heterogeneity and population-level tumor evolution. Furthermore, we performed \textit{in silico} clinical trials using virtual patient cohorts to evaluate therapeutic outcomes, demonstrating that optimized adaptive treatment strategies can significantly delay tumor relapse compared to standard dosing. This study provides a quantitative framework for dissecting DTP-driven resistance mechanisms and designing more effective, biologically informed therapeutic strategies.

Figures (14)

• Figure 1: Schematic of the multi-scale theoretical framework and the agent-based simulation flow.A The heterogeneous stem cell regeneration model. Tumor dynamics follow a G0 cell cycle structure where kinetic rates depend on the cell's epigenetic state $\boldsymbol{x} = (x_1, x_2, x_3)$. $Q(t, \boldsymbol{x})$ represents the density of G0 cells with state $\boldsymbol{x}$. The orange region denotes the proliferative phase (duration $\tau$). Cellular plasticity is mathematically captured by the inheritance probability kernel $p(\boldsymbol{x}, \boldsymbol{y})$, describing how epigenetic states are transmitted with variation from mother ($\boldsymbol{y})$ to daughter ($\boldsymbol{x}$). Colored circles within blue and orange regions represent cells with varying phenotypes. The lines with red bars denote inhibition, while blue arrows indicate promotion, and black arrows represent cell transition. B Flowchart of the agent-based stochastic simulation. The system is initialized with all cells in the resting phase. At each time step, the probabilities of cell fate decisions (division, death, differentiation) are computed for each agent based on its state $\boldsymbol{x}$. Detailed implementations are provided in Section \ref{['sec:ICBSS']}.
• Figure 2: Phenotypic transitions mapped on the epigenetic landscape. Before therapy, the population is dominated by DSCs (high $x_1$). Drug treatment suppresses $x_1$, selecting for DTPs (high $x_2$) which survive in a dormant state. Under continuous pressure, stochastic epigenetic drift eventually accesses the high-$x_3$ basin, leading to the outgrowth of proliferative DRCs.
• Figure 3: Functional dependence of kinetic rates on the stemness $x_2$. The proliferation rate $\beta_0(\boldsymbol{x})$ (red curve, normalized by $\bar{\beta}$) exhibits a unimodal profile, peaking at intermediate $x_2$ values (progenitor state) and decreasing at high $x_2$ (quiescent stem state). The differentiation rate $\kappa(\boldsymbol{x})$ (blue curve, normalized by $\kappa_0$) decreases monotonically with increasing stemness.
• Figure 4: Model calibration against in vivo data.A Fitting of macroscopic tumor volume dynamics. The model (solid lines) accurately captures both the unperturbed growth of Control tumors ($D = 0$) and the regression-relapse trajectory of osimertinib-treated tumors ($D = 1$). Experimental data (symbols, mean $\pm$SEM) derived from PC9 xenografts in Huz22JCI. B Validation of microscopic phenotypic heterogeneity. The simulated proportions of microscopic phenotypic heterogeneity. The simulated proportions of epigenetic states correspond to the relative expression levels observed in distinct tumor phases (Control, MRD, Regrowth), confirming the model's ability to reproduce drug-induced phenotypic plasticity. Experimental benchmarks derived from Huz22JCI and Aissa20Natcom.
• Figure 5: Intrindic tumor evolution driven by different competition parameter $\alpha$.A and B Temporal evolution of tumor subpopulations starting from a uniform epigenetic state distribution, with A biased competition ($\alpha=0.95$), and B balanced competition ($\alpha=1.0$). Subpopulations are color-coded: Total tumor cells (orange), DSCs (pale teal), DTPs (blue), DRCs (magenta), and other phenotypes (black). C and D Evolution of the epigenetic landscape $(x_1, x_3)$ at days $1$, $50$, $100$, and $150$, with C$\alpha = 0.95$, and D$\alpha=1.0$. Black dashed lines indicate the phenotypic threshold (see Table \ref{['tab-phenotype-epi']}), partitioning the state space into four distinct quadrants: I(Non-viable/Othersm black), II(DRCs, magenta), III(DTPs, blue), IV(DSCs, pale teal). Percentages denote the cell fraction within each quadrant.