Journal article : Review
Model genotype–phenotype mappings and the algorithmic structure of evolution
- Abstract:
- Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine approaches in the clinic increasingly rely on predictions of tumour response to one or more therapies; these predictions are complicated by the inevitable evolution of the tumour. Despite enormous amounts of data on the mutational status of cancers and numerous therapies developed in recent decades to target these mutations, many of these treatments fail after a time due to the development of resistance in the tumour. The emergence of these resistant phenotypes is not easily predicted from genomic data, since the relationship between genotypes and phenotypes, termed the genotype–phenotype (GP) mapping, is neither injective nor functional. We present a review of models of this mapping within a generalized evolutionary framework that takes into account the relation between genotype, phenotype, environment and fitness. Different modelling approaches are described and compared, and many evolutionary results are shown to be conserved across studies despite using different underlying model systems. In addition, several areas for future work that remain understudied are identified, including plasticity and bet-hedging. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.5MB, Terms of use)
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- Publisher copy:
- 10.1098/rsif.2019.0332
Authors
+ National Cancer Institute
More from this funder
- Funder identifier:
- 10.13039/100000054
- Grant:
- U01CA23238
- Publisher:
- The Royal Society
- Journal:
- Journal of the Royal Society Interface More from this journal
- Volume:
- 16
- Issue:
- 160
- Pages:
- 20190332
- Article number:
- 20190332
- Publication date:
- 2019-11-06
- Acceptance date:
- 2019-10-04
- DOI:
- EISSN:
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1742-5662
- ISSN:
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1742-5689
- Language:
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English
- Keywords:
- Subtype:
-
Review
- Pubs id:
-
1072539
- Local pid:
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pubs:1072539
- Source identifiers:
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3793607
- Deposit date:
-
2026-02-25
- ARK identifier:
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Terms of use
- Copyright date:
- 2019
- Licence:
- CC Attribution (CC BY)
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