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Inferring the paths of somatic evolution in cancer

Misra, Navodit ; Szczurek, Ewa ; Vingron, Martin

In: Bioinformatics, 2014, vol. 30, no. 17, p. 2456-2463

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    Titre
    • Inferring the paths of somatic evolution in cancer
    Auteur
    • Misra, Navodit. Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany and Department of Biosystems Science and Engineering, ETH Zurich and Swiss Institute of Bioinformatics, CH-4058 Basel, Switzerland
    • Szczurek, Ewa. Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany and Department of Biosystems Science and Engineering, ETH Zurich and Swiss Institute of Bioinformatics, CH-4058 Basel, Switzerland
    • Vingron, Martin. Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany and Department of Biosystems Science and Engineering, ETH Zurich and Swiss Institute of Bioinformatics, CH-4058 Basel, Switzerland
    Type de document
    • Postprint
    Identifiant OAI-PMH
    • oai:doc.rero.ch:302157
    Summary
    Motivation: Cancer cell genomes acquire several genetic alterations during somatic evolution from a normal cell type. The relative order in which these mutations accumulate and contribute to cell fitness is affected by epistatic interactions. Inferring their evolutionary history is challenging because of the large number of mutations acquired by cancer cells as well as the presence of unknown epistatic interactions. Results: We developed Bayesian Mutation Landscape (BML), a probabilistic approach for reconstructing ancestral genotypes from tumor samples for much larger sets of genes than previously feasible. BML infers the likely sequence of mutation accumulation for any set of genes that is recurrently mutated in tumor samples. When applied to tumor samples from colorectal, glioblastoma, lung and ovarian cancer patients, BML identifies the diverse evolutionary scenarios involved in tumor initiation and progression in greater detail, but broadly in agreement with prior results. Availability and implementation: Source code and all datasets are freely available at bml.molgen.mpg.de Contact: misra@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online