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Modeling Somatic Evolution in Tumorigenesis

Overview of attention for article published in PLoS Computational Biology, August 2006
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
15 news outlets
wikipedia
1 Wikipedia page

Citations

dimensions_citation
86 Dimensions

Readers on

mendeley
165 Mendeley
citeulike
8 CiteULike
connotea
3 Connotea
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Title
Modeling Somatic Evolution in Tumorigenesis
Published in
PLoS Computational Biology, August 2006
DOI 10.1371/journal.pcbi.0020108
Pubmed ID
Authors

Sabrina L Spencer, Ryan A Gerety, Kenneth J Pienta, Stephanie Forrest

Abstract

Tumorigenesis in humans is thought to be a multistep process where certain mutations confer a selective advantage, allowing lineages derived from the mutated cell to outcompete other cells. Although molecular cell biology has substantially advanced cancer research, our understanding of the evolutionary dynamics that govern tumorigenesis is limited. This paper analyzes the computational implications of cancer progression presented by Hanahan and Weinberg in The Hallmarks of Cancer. We model the complexities of tumor progression as a small set of underlying rules that govern the transformation of normal cells to tumor cells. The rules are implemented in a stochastic multistep model. The model predicts that (i) early-onset cancers proceed through a different sequence of mutation acquisition than late-onset cancers; (ii) tumor heterogeneity varies with acquisition of genetic instability, mutation pathway, and selective pressures during tumorigenesis; (iii) there exists an optimal initial telomere length which lowers cancer incidence and raises time of cancer onset; and (iv) the ability to initiate angiogenesis is an important stage-setting mutation, which is often exploited by other cells. The model offers insight into how the sequence of acquired mutations affects the timing and cellular makeup of the resulting tumor and how the cellular-level population dynamics drive neoplastic evolution.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 4%
United Kingdom 2 1%
Canada 2 1%
France 1 <1%
Italy 1 <1%
Switzerland 1 <1%
Germany 1 <1%
Nepal 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 148 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 45 27%
Student > Ph. D. Student 37 22%
Professor > Associate Professor 14 8%
Student > Master 13 8%
Student > Bachelor 9 5%
Other 27 16%
Unknown 20 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 68 41%
Medicine and Dentistry 18 11%
Biochemistry, Genetics and Molecular Biology 15 9%
Engineering 11 7%
Mathematics 9 5%
Other 19 12%
Unknown 25 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 123. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 February 2023.
All research outputs
#342,118
of 25,639,676 outputs
Outputs from PLoS Computational Biology
#233
of 9,021 outputs
Outputs of similar age
#454
of 90,964 outputs
Outputs of similar age from PLoS Computational Biology
#1
of 31 outputs
Altmetric has tracked 25,639,676 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,021 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.2. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 90,964 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.