Cancer evolution: mathematical models and computational inference
Schwarz, Roland F
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Beerenwinkel, N., Schwarz, R. F., Gerstung, M., & Markowetz, F. (2014). Cancer evolution: mathematical models and computational inference. Systematic Biology, 64 e1-e25. https://doi.org/10.1093/sysbio/syu081
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.
Cancer, evolution, Cancer progression, Population genetics, Probabilistic graphical models
FM would like to acknowledge the support of The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited.
Cancer Research UK (C14303_do not transfer)
Cancer Research UK (CB4320)
External DOI: https://doi.org/10.1093/sysbio/syu081
This record's URL: https://www.repository.cam.ac.uk/handle/1810/246162
Attribution 2.0 UK: England & Wales
Licence URL: http://creativecommons.org/licenses/by/2.0/uk/
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