PMCE: efficient inference of expressive models of cancer evolution with high prognostic power
This work addresses the need for better prognostic tools in cancer research by enabling more expressive modeling of evolutionary paths, though it is incremental as it builds on existing methods for inference.
The authors tackled the problem of inferring cancer evolutionary models from cross-sectional data, introducing PMCE, which outperforms state-of-the-art methods in accuracy and robustness on simulations and identified a highly significant correlation between predicted evolutionary paths and overall survival in 7 tumor types from TCGA data.
Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods cannot infer logical formulas connecting events to represent alternative evolutionary routes or convergent evolution. Results: We introduce PMCE, an expressive framework that leverages mutational profiles from cross-sectional sequencing data to infer probabilistic graphical models of cancer evolution including arbitrary logical formulas, and which outperforms the state-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 to identify a highly significant correlation between the predicted evolutionary paths and the overall survival in 7 tumor types, proving that our approach can effectively stratify cancer patients in reliable risk groups. Availability: PMCE is freely available at https://github.com/BIMIB-DISCo/PMCE, in addition to the code to replicate all the analyses presented in the manuscript. Contacts: daniele.ramazzotti@unimib.it, alex.graudenzi@ibfm.cnr.it.