Cancer initiation with epistatic interactions between driver and passenger mutations.

@article{Bauer2014CancerIW,
  title={Cancer initiation with epistatic interactions between driver and passenger mutations.},
  author={Benedikt Bauer and Reiner Siebert and Arne Traulsen},
  journal={Journal of theoretical biology},
  year={2014},
  volume={358},
  pages={
          52-60
        }
}

Figures from this paper

Population dynamics with epistatic interactions and its applications to mathematical models of cancer
TLDR
The work described in this thesis is a theoretical analysis of systems with epistatic interactions in cancer initiation, and a recursive algorithm for the computation of the probability density functions of the different mutational pathways over time.
Passenger mutations can accelerate tumor suppressor gene inactivation in cancer evolution
TLDR
Using evolutionary computational models, it is demonstrated that in the context of tumour suppressor gene inactivation (and hence fitness valley crossing), the presence of passenger mutations can accelerate the rate of evolution by reducing overall population fitness and increasing the relative fitness of intermediate mutants in the Fitness valley crossing pathway.
Modeling the dynamics of chromosomal alteration progression in cervical cancer: A computational model
TLDR
A computational model is introduced to study the dynamics of deleterious and non-deleterious mutations as an outcome of tumor progression and reveals that multiple deleteriously mutations are more frequent in precursor lesions than in CC.
Modeling the dynamics of progression of chromosomal alterations in cervical cancer: a computational model
TLDR
A computational model is introduced to study the dynamic of deleterious and non-deleterious mutations as an outcome of tumor progression and reveals that multiple deleteriously mutations are more frequent in precursor lesions than in CC.
The role of deleterious passengers in cancer
TLDR
The results suggest a unique framework for understanding cancer progression as a balance between driver and passenger mutations and suggest that passengers could serve as a biomarker of response to mutagenic therapies.
A population genetics perspective on the determinants of intra-tumor heterogeneity.
Minimal models of invasion and clonal selection in cancer
TLDR
An analytical framework in which the spatial structure is coarse grained and the cancer treated as a continuously growing system with stochastic migration events concludes that the whole ensemble can undergo migration-driven exponential growth regardless of the dependence of size on time of individual lesions.
Influence of Mutation Frequency on Mutation Profile in Colon Cancer
TLDR
This research project compared types of mutations, genes targeted and specificity of gene targeting in high versus low mutation frequency tumors to support the hypothesis that there are qualitative and quantitative differences in the mutation spectrum of colorectal cancers based on the frequency of mutation in the individual tumors.
Ontogenic growth as the root of fundamental differences between childhood and adult cancer
TLDR
A stochastic model of stem cell proliferation that considers both tissue development and homeostasis and the disturbance of such a system by mutations is developed and explains unique properties of childhood disorders.
An exactly solvable, spatial model of mutation accumulation in cancer
TLDR
This work describes an exactly solvable mathematical model of a tumour featuring replication, mutation and local migration of cancer cells and predicts a quasi-exponential growth of large tumours even if different fragments of the tumour grow sub-exponentially due to nutrient and space limitations.
...
...

References

SHOWING 1-10 OF 70 REFERENCES
Genetic Progression and the Waiting Time to Cancer
TLDR
A new mathematical model is developed for the somatic evolution of colorectal cancers that predicts that the observed genetic diversity of cancer genomes can arise under a normal mutation rate if the average selective advantage per mutation is on the order of 1%.
Evolutionary dynamics of tumor progression with random fitness values.
Accumulation of driver and passenger mutations during tumor progression
TLDR
A mathematical model is provided that model tumors as a discrete time branching process that starts with a single driver mutation and proceeds as each new driver mutation leads to a slightly increased rate of clonal expansion, providing understanding of the heterogeneity in tumor sizes and development times that have been observed by epidemiologists and clinicians.
The effect of one additional driver mutation on tumor progression
TLDR
It is observed that early, small lesions need additional drivers, while late stage metastases are only marginally affected by them, which helps to explain why additional driver mutations are typically not detected in fast‐growing metastases.
Impact of deleterious passenger mutations on cancer progression
TLDR
From simulations, the approach combines evolutionary simulations of cancer progression with an analysis of cancer sequencing data finds that passengers accumulate and largely evade natural selection during progression, suggesting a unique framework for understanding cancer progression as a balance of driver and passenger mutations.
Evolutionary dynamics of tumor suppressor gene inactivation.
TLDR
Three different kinetic laws are found: in small, intermediate, and large populations, it takes, respectively, two, one, and zero rate-limiting steps to inactivate a TSG.
A waiting time problem arising from the study of multi-stage carcinogenesis
We consider the population genetics problem: How long does it take before some member of the population has m specified mutations? The case m = 2 is relevant to onset of cancer due to the
Population genetics of tumor suppressor genes.
Waiting Time Models of Cancer Progression
TLDR
The accumulation of mutations is described using conjunctive Bayesian networks, an exponential family of waiting time models in which the occurrence of mutations are constrained by a partial temporal order.
Modelling the evolution of genetic instability during tumour progression
TLDR
The model provides a framework for studying the evolution of genetic instability in tumour progression and highlights the central role of selection in shaping patterns of mutation in carcinogenesis.
...
...