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Motivation: Driver (epi)genomic alterations underlie the positive selectionof cancer subpopulations, which promotes drug resistance and relapse. Eventhough substantial heterogeneity is witnessed in most cancer types, mutationaccumulation patterns can be regularly found and can be exploited toreconstruct predictive models of cancer evolution. Yet, available methodscannot infer logical formulas connecting events to represent alternativeevolutionary routes or convergent evolution. Results: We introduce PMCE, anexpressive framework that leverages mutational profiles from cross-sectionalsequencing data to infer probabilistic graphical models of cancer evolutionincluding arbitrary logical formulas, and which outperforms thestate-of-the-art in terms of accuracy and robustness to noise, on simulations.The application of PMCE to 7866 samples from the TCGA database allows us toidentify a highly significant correlation between the predicted evolutionarypaths and the overall survival in 7 tumor types, proving that our approach caneffectively stratify cancer patients in reliable risk groups. Availability:PMCE is freely available at https://github.com/BIMIB-DISCo/PMCE, in addition tothe code to replicate all the analyses presented in the manuscript. Contacts:daniele.ramazzotti@unimib.it, alex.graudenzi@ibfm.cnr.it.

PMCE: efficient inference of expressive models of cancer evolution with high prognostic power