title: Cancer evolution: mathematical models and computational inference.
abstract: Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB20RE, United Kingdom Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB20RE, United Kingdom niko.beerenwinkel@bsse.ethz.ch.
authors.forename: Niko
authors.initials: N
authors.lastname: Beerenwinkel
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB20RE, United Kingdom.
authors.forename: Roland F
authors.initials: RF
authors.lastname: Schwarz
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB20RE, United Kingdom.
authors.forename: Moritz
authors.initials: M
authors.lastname: Gerstung
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB20RE, United Kingdom.
authors.forename: Florian
authors.initials: F
authors.lastname: Markowetz
mesh_terms: D005075:Biological Evolution; D006801:Humans; D008954:Models, Biological; D009154:Mutation; D009369:Neoplasms; D010802:Phylogeny
keywords: Cancer; cancer progression; evolution; population genetics; probabilistic graphical models