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Genetic Progression and the Waiting Time to Cancer

Overview of attention for article published in PLoS Computational Biology, November 2007
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Title
Genetic Progression and the Waiting Time to Cancer
Published in
PLoS Computational Biology, November 2007
DOI 10.1371/journal.pcbi.0030225
Pubmed ID
Authors

Niko Beerenwinkel, Tibor Antal, David Dingli, Arne Traulsen, Kenneth W Kinzler, Victor E Velculescu, Bert Vogelstein, Martin A Nowak

Abstract

Cancer results from genetic alterations that disturb the normal cooperative behavior of cells. Recent high-throughput genomic studies of cancer cells have shown that the mutational landscape of cancer is complex and that individual cancers may evolve through mutations in as many as 20 different cancer-associated genes. We use data published by Sjöblom et al. (2006) to develop a new mathematical model for the somatic evolution of colorectal cancers. We employ the Wright-Fisher process for exploring the basic parameters of this evolutionary process and derive an analytical approximation for the expected waiting time to the cancer phenotype. Our results highlight the relative importance of selection over both the size of the cell population at risk and the mutation rate. The model 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%. Increased mutation rates due to genetic instability would allow even smaller selective advantages during tumorigenesis. The complexity of cancer progression can be understood as the result of multiple sequential mutations, each of which has a relatively small but positive effect on net cell growth.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 432 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 18 4%
France 5 1%
Germany 5 1%
Switzerland 3 <1%
Netherlands 2 <1%
United Kingdom 2 <1%
Canada 1 <1%
Spain 1 <1%
Nigeria 1 <1%
Other 0 0%
Unknown 394 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 111 26%
Researcher 92 21%
Student > Bachelor 34 8%
Student > Master 33 8%
Professor > Associate Professor 28 6%
Other 83 19%
Unknown 51 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 144 33%
Biochemistry, Genetics and Molecular Biology 63 15%
Medicine and Dentistry 55 13%
Computer Science 24 6%
Mathematics 23 5%
Other 61 14%
Unknown 62 14%