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dc.contributor.authorBeerenwinkel, Nikoen
dc.contributor.authorSchwarz, Roland Fen
dc.contributor.authorGerstung, Moritzen
dc.contributor.authorMarkowetz, Florianen
dc.date.accessioned2014-10-13T13:40:45Z
dc.date.available2014-10-13T13:40:45Z
dc.date.issued2014-10-07en
dc.identifier.citationSystematic Biology (2015) 64 (1): e1-e25. DOI: 10.1093/sysbio/syu081en
dc.identifier.issn1063-5157
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/246162
dc.description.abstractCancer 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.
dc.description.sponsorshipFM would like to acknowledge the support of The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited.
dc.languageEnglishen
dc.language.isoenen
dc.publisherOxford Journals
dc.rightsAttribution 2.0 UK: England & Wales*
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/*
dc.subjectCanceren
dc.subjectevolutionen
dc.subjectCancer progressionen
dc.subjectPopulation geneticsen
dc.subjectProbabilistic graphical modelsen
dc.titleCancer evolution: mathematical models and computational inferenceen
dc.typeArticle
dc.description.versionThis is the final published version. It first appeared at http://sysbio.oxfordjournals.org/content/early/2014/10/07/sysbio.syu081.short?rss=1.en
prism.endingPagee25
prism.publicationDate2014en
prism.publicationNameSystematic Biologyen
prism.startingPagee1
prism.volume64en
dc.rioxxterms.funderCancer Research UK
rioxxterms.versionofrecord10.1093/sysbio/syu081en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2014-10-07en
dc.contributor.orcidMarkowetz, Florian [0000-0002-2784-5308]
dc.identifier.eissn1076-836X
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idCancer Research UK (C14303_do not transfer)
pubs.funder-project-idCancer Research UK (CB4320)


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Attribution 2.0 UK: England & Wales
Except where otherwise noted, this item's licence is described as Attribution 2.0 UK: England & Wales