↓ Skip to main content

Functional Heterogeneity of Genetically Defined Subclones in Acute Myeloid Leukemia

Overview of attention for article published in Cancer Cell, March 2014
Altmetric Badge

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 (88th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

Mentioned by

twitter
19 tweeters
f1000
1 research highlight platform

Citations

dimensions_citation
170 Dimensions

Readers on

mendeley
295 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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

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 295 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 4%
United Kingdom 5 2%
Switzerland 2 <1%
Austria 2 <1%
Denmark 1 <1%
Italy 1 <1%
China 1 <1%
Australia 1 <1%
Korea, Republic of 1 <1%
Other 3 1%
Unknown 266 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 87 29%
Researcher 77 26%
Student > Master 27 9%
Student > Doctoral Student 24 8%
Student > Bachelor 15 5%
Other 65 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 126 43%
Medicine and Dentistry 67 23%
Biochemistry, Genetics and Molecular Biology 54 18%
Unspecified 19 6%
Mathematics 7 2%
Other 22 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
#1,044,733
of 11,229,218 outputs
Outputs from Cancer Cell
#635
of 1,968 outputs
Outputs of similar age
#21,894
of 182,845 outputs
Outputs of similar age from Cancer Cell
#18
of 37 outputs
Altmetric has tracked 11,229,218 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,968 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.5. This one has gotten more attention than average, scoring higher than 67% 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 182,845 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 88% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.