A new cancer progression model: from synthetic tumors to real data and back

Intratumor heterogeneity (ITH) arises from the combined effects of genetic alterations, clonal interactions, and environmental constraints, and plays a central role in therapeutic resistance and disease progression. While ITH has been extensively documented in empirical tumor data, the scientific debate regarding the biological mechanisms underlying this heterogeneity remains complex, highlighting the need for cancer evolution models that are sufficiently flexible and sophisticated to reproduce the observed behaviors and to give insights on the unobserved ones. Here, we present a stochastic modelling framework for tumor evolution that integrates genotypic inheritance with phenotype driven functional traits and resource mediated competition. Mutational events are associated with functional capabilities such as altered proliferation, increased mutation rates, limit evasion potential or enhanced control over shared resources, allowing multiple genotypes to converge on similar phenotypes. The model explicitly tracks subclonal lineages while incorporating environmental constraints that modulate growth and competition.The framework is defined through a mathematically rigorous construction and is accompanied by an efficient simulation algorithm. To facilitate exploration and reproducibility, we provide an open-source graphical user interface that allows users to configure model parameters, run simulations, and inspect clonal genealogies and population dynamics without requiring direct interaction with the underlying code. Using this model, we illustrate how ecological feedbacks can shape clonal dynamics over time, supporting an interpretation in which early tumor growth is dominated by stochastic expansion, while later evolution increasingly reflects selection for traits that alleviate environmental constraints. Rather than constituting a new evolutionary paradigm, this behaviour demonstrates how well-documented biological patterns can emerge naturally from a unified stochastic and ecological description. Overall, our approach offers a flexible and extensible platform for investigating how chance, functional traits, and environmental interactions jointly govern tumor heterogeneity. Author summaryNot all cancerous cells are created equal: inside the same tumor, different populations of cells exist at the same time, fighting for the same resources and influencing the way the disease evolves and reacts to treatments. These groups of cells have different behaviour and abilities thanks to different genetic mutations, which might give them an advantage or bring their population to disappearance. We have built a mathematical model that mimics the evolution of a tumor over time, simulating a competition between its different populations of cells. Our simulated experiments show that tumors evolve in two distinct phases: at first, cells that grow and divide more quickly have an advantage. Once the space and nutrients are limited, cells that can survive with fewer resources have an advantage and can potentially take over the race. We use these simulations to argue that the evolution of a tumor doesnt depend on the shape of the space it expands in, but rather on the availability of nutrients.