Open access
Date
2015-01Type
- Journal Article
ETH Bibliography
yes
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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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000095953Publication status
publishedExternal links
Journal / series
Systematic BiologyVolume
Pages / Article No.
Publisher
Oxford University PressSubject
Cancer; Cancer progression; Evolution; Population genetics; Probabilistic graphical modelsOrganisational unit
03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
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ETH Bibliography
yes
Altmetrics