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Functional Heterogeneity of Genetically Defined Subclones in Acute Myeloid Leukemia.

Overview of attention for article published in Cancer Cell, March 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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19 tweeters
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1 research highlight platform

Readers on

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249 Mendeley
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Title
Functional Heterogeneity of Genetically Defined Subclones in Acute Myeloid Leukemia.
Published in
Cancer Cell, March 2014
DOI 10.1016/j.ccr.2014.01.031
Pubmed ID
Authors

Jeffery M. Klco, David H. Spencer, Christopher A. Miller, Malachi Griffith, Tamara L. Lamprecht, Michelle O’Laughlin, Catrina Fronick, Vincent Magrini, Ryan T. Demeter, Robert S. Fulton, William C. Eades, Daniel C. Link, Timothy A. Graubert, Matthew J. Walter, Elaine R. Mardis, John F. Dipersio, Richard K. Wilson, Timothy J. Ley, Klco JM, Spencer DH, Miller CA, Griffith M, Lamprecht TL, O'Laughlin M, Fronick C, Magrini V, Demeter RT, Fulton RS, Eades WC, Link DC, Graubert TA, Walter MJ, Mardis ER, Dipersio JF, Wilson RK, Ley TJ

Abstract

The relationships between clonal architecture and functional heterogeneity in acute myeloid leukemia (AML) samples are not yet clear. We used targeted sequencing to track AML subclones identified by whole-genome sequencing using a variety of experimental approaches. We found that virtually all AML subclones trafficked from the marrow to the peripheral blood, but some were enriched in specific cell populations. Subclones showed variable engraftment potential in immunodeficient mice. Xenografts were predominantly comprised of a single genetically defined subclone, but there was no predictable relationship between the engrafting subclone and the evolutionary hierarchy of the leukemia. These data demonstrate the importance of integrating genetic and functional data in studies of primary cancer samples, both in xenograft models and in patients.

Twitter Demographics

The data shown below were collected from the profiles of 19 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 249 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 5%
United Kingdom 5 2%
Switzerland 2 <1%
Austria 2 <1%
Germany 1 <1%
Australia 1 <1%
Italy 1 <1%
Denmark 1 <1%
China 1 <1%
Other 3 1%
Unknown 220 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 75 30%
Researcher 69 28%
Student > Master 22 9%
Student > Doctoral Student 22 9%
Student > Postgraduate 15 6%
Other 46 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 113 45%
Medicine and Dentistry 59 24%
Biochemistry, Genetics and Molecular Biology 46 18%
Unspecified 7 3%
Mathematics 7 3%
Other 17 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 18 May 2016.
All research outputs
#902,860
of 8,748,216 outputs
Outputs from Cancer Cell
#499
of 1,586 outputs
Outputs of similar age
#21,524
of 176,336 outputs
Outputs of similar age from Cancer Cell
#19
of 35 outputs
Altmetric has tracked 8,748,216 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,586 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.1. This one has gotten more attention than average, scoring higher than 68% 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 176,336 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.