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Cancer evolution: mathematical models and computational inference.


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Authors

Beerenwinkel, Niko 
Schwarz, Roland F 
Gerstung, Moritz 

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.

Description

Keywords

Cancer, cancer progression, evolution, population genetics, probabilistic graphical models, Biological Evolution, Humans, Models, Biological, Mutation, Neoplasms, Phylogeny

Journal Title

Syst Biol

Conference Name

Journal ISSN

1063-5157
1076-836X

Volume Title

64

Publisher

Oxford University Press (OUP)
Sponsorship
Cancer Research UK (C14303/A17197)
Cancer Research UK (CB4320)
FM would like to acknowledge the support of The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited.