Your browser doesn't support javascript.
loading
PMCE: efficient inference of expressive models of cancer evolution with high prognostic power.
Angaroni, Fabrizio; Chen, Kevin; Damiani, Chiara; Caravagna, Giulio; Graudenzi, Alex; Ramazzotti, Daniele.
Afiliação
  • Angaroni F; Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan 20125, Italy.
  • Chen K; Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
  • Damiani C; Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan 20126, Italy.
  • Caravagna G; Sysbio Centre for Systems Biology, Milan 20100, Italy.
  • Graudenzi A; Department of Mathematics and Geosciences, University of Trieste, Trieste 34128, Italy.
  • Ramazzotti D; Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan 20054, Italy.
Bioinformatics ; 38(3): 754-762, 2022 01 12.
Article em En | MEDLINE | ID: mdl-34647978
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 can not 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 AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article