This work develops a spatial, agent-based model to examine how physical stresses drive non-genetic adaptation in cancer tissues. The authors aim to clarify how electrical, mechanical, and hypoxic cues combine in three-dimensional tissue contexts to influence phenotype switching, memory inheritance across cell division, and resulting population structure. The model purposefully focuses on phenotype changes that occur without genetic mutations and on how history-dependent transitions shape tissue-level heterogeneity.
The model represents a 3D tumor tissue with individual cell agents and includes the following integrated components: vascular oxygen supply; a globally imposed electric field; mechanically mediated crowding and compression cues; rules for phenotype transitions; cell growth, mitosis, and death; and inheritance of adaptive memory across cell division. These components were coupled to allow interactions among oxygen availability, mechanical constraints, electrical forcing, and history-dependent phenotype dynamics within a spatially explicit tumor structure.
Simulated tumors in the model exhibit a reproducible three-stage trajectory. The first stage is the onset of necrosis, consistent with oxygen depletion and stress in central regions. The second stage is a transient collapse of live mass, where the viable cell population declines. The third stage is a partial regrowth of live cells accompanied by a progressive accumulation of cells that have undergone adaptive, non-genetic phenotype changes. This emergent trajectory reflects the coupled effects of local microenvironmental conditions and memory-bearing phenotype transitions.
Continuous electrical stimulation in the simulations produced a dose-dependent reduction in live tumor mass while markedly increasing the fraction of adapted cells. In other words, higher levels of sustained electrical forcing tended to decrease total viable mass but enrich the population for cells that had adopted adaptive phenotypes. The final necrotic burden, however, showed comparatively limited changes across stimulation doses, indicating that electrical forcing altered the balance between viability and adaptation more than it changed gross necrosis in the modeled tissue.
When the applied electric field was delivered in pulsed protocols, the model showed that both the amplitude of the field and the temporal schedule of pulses jointly influenced outcomes. Specifically, the combination of field strength and timing affected memory phenomena, the degree of phenotypic diversification within the population, and the capacity for growth recovery after stress. These results indicate that temporal patterning of electrical stress—beyond simple cumulative dose—can shape adaptive dynamics in spatially structured tumor tissue.
Mechanical conditions in the tissue strongly conditioned the simulated response to electrical forcing. Mechanics both shaped adaptive capacity and were themselves reshaped by adaptation in the model. Local oxygen availability was integrated through vascular supply and contributed to necrosis onset and spatial patterning of phenotypes. The coupling of oxygen gradients, mechanical constraints, and electrical forcing produced distinct tissue-level patterns of phenotypic heterogeneity, demonstrating that these physical factors interact nonlinearly to determine adaptive outcomes.
The simulations suggest that the history and protocol of physical stressors—magnitude and temporal pattern—are important determinants of non-genetic adaptive dynamics in spatially organized tumor models. By linking oxygen, mechanics, electric fields, and memory-dependent phenotype transitions, the model generates diverse population structures and growth trajectories. The authors present these findings as conceptual and mechanistic insights into how physical microenvironmental factors can steer non-genetic adaptation and phenotypic heterogeneity in tumors.
The source is a preprint and presents a computational model; the report focuses on simulated behavior rather than experimental validation. Specific numerical parameter values, quantitative metrics, or experimental corroboration beyond the reported simulated behaviors were not detailed in the abstract.
The authors have provided code and/or data associated with the model on a public repository; the source includes a GitHub link for the Electromechanical Adaptation Modeling project. The preprint declares no competing interests and places the work in the public domain.