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SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution

Overview of attention for article published in PLoS Computational Biology, August 2014
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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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
2 blogs
twitter
36 tweeters
googleplus
1 Google+ user

Readers on

mendeley
232 Mendeley
citeulike
6 CiteULike
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Title
SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003665
Pubmed ID
Authors

Christopher A. Miller, Brian S. White, Nathan D. Dees, Malachi Griffith, John S. Welch, Obi L. Griffith, Ravi Vij, Michael H. Tomasson, Timothy A. Graubert, Matthew J. Walter, Matthew J. Ellis, William Schierding, John F. DiPersio, Timothy J. Ley, Elaine R. Mardis, Richard K. Wilson, Li Ding, Miller CA, White BS, Dees ND, Griffith M, Welch JS, Griffith OL, Vij R, Tomasson MH, Graubert TA, Walter MJ, Ellis MJ, Schierding W, DiPersio JF, Ley TJ, Mardis ER, Wilson RK, Ding L, Niko Beerenwinkel

Abstract

The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy.

Twitter Demographics

The data shown below were collected from the profiles of 36 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 232 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 16 7%
Italy 5 2%
Germany 4 2%
United Kingdom 4 2%
Canada 3 1%
China 2 <1%
Australia 1 <1%
France 1 <1%
Denmark 1 <1%
Other 8 3%
Unknown 187 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 76 33%
Student > Ph. D. Student 68 29%
Student > Doctoral Student 18 8%
Student > Master 17 7%
Student > Bachelor 16 7%
Other 37 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 121 52%
Biochemistry, Genetics and Molecular Biology 36 16%
Medicine and Dentistry 30 13%
Computer Science 29 13%
Mathematics 6 3%
Other 10 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 33. 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 27 April 2016.
All research outputs
#259,618
of 8,035,375 outputs
Outputs from PLoS Computational Biology
#416
of 3,959 outputs
Outputs of similar age
#7,552
of 180,698 outputs
Outputs of similar age from PLoS Computational Biology
#11
of 136 outputs
Altmetric has tracked 8,035,375 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,959 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.1. This one has done well, scoring higher than 89% 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 180,698 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 95% of its contemporaries.
We're also able to compare this research output to 136 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 91% of its contemporaries.