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Daniela Volpatto, Simone Pernice, Sandro Gepiro Contaldo, Roberta Sirovich, Francesca Cordero, Marco Beccuti (University of Turin, Turin, Italy)
We developed a stochastic simulator of tumor evolution, capable of generating large-scale synthetic datasets that preserve biologically meaningful correlations between genotype, phenotype, and clonal structure. However, the complexity of stochastic tumor evolution models, where each simulation produces vast amounts of data across multiple populations, each distinguished by unique mutational events, renders classical visualization techniques inadequate for interpretation. To address this challenge, we designed a ggplot compatible visualization inspired by classical cancer evolution pictures and muller-plots. This approach allows us to intuitively explore tumor heterogeneity, track clonal expansion, and assess evolutionary trajectories. By distinguishing populations based on genotype while allowing them to behave differently according to the functional effects of their mutations, we can visualize how distinct biological phenotypes drive divergent evolutionary pathways. This enables the comparison of stochastic realizations of the same scenario, revealing the impact of functional mutations on tumor progression.