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Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas

Overview of attention for article published in Genome Medicine, July 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

1 news outlet
3 tweeters

Readers on

33 Mendeley
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Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas
Published in
Genome Medicine, July 2015
DOI 10.1186/s13073-015-0187-6
Pubmed ID

Chulin Sha, Sharon Barrans, Matthew A. Care, David Cunningham, Reuben M. Tooze, Andrew Jack, David R. Westhead


Classifiers based on molecular criteria such as gene expression signatures have been developed to distinguish Burkitt lymphoma and diffuse large B cell lymphoma, which help to explore the intermediate cases where traditional diagnosis is difficult. Transfer of these research classifiers into a clinical setting is challenging because there are competing classifiers in the literature based on different methodology and gene sets with no clear best choice; classifiers based on one expression measurement platform may not transfer effectively to another; and, classifiers developed using fresh frozen samples may not work effectively with the commonly used and more convenient formalin fixed paraffin-embedded samples used in routine diagnosis. Here we thoroughly compared two published high profile classifiers developed on data from different Affymetrix array platforms and fresh-frozen tissue, examining their transferability and concordance. Based on this analysis, a new Burkitt and diffuse large B cell lymphoma classifier (BDC) was developed and employed on Illumina DASL data from our own paraffin-embedded samples, allowing comparison with the diagnosis made in a central haematopathology laboratory and evaluation of clinical relevance. We show that both previous classifiers can be recapitulated using very much smaller gene sets than originally employed, and that the classification result is closely dependent on the Burkitt lymphoma criteria applied in the training set. The BDC classification on our data exhibits high agreement (~95 %) with the original diagnosis. A simple outcome comparison in the patients presenting intermediate features on conventional criteria suggests that the cases classified as Burkitt lymphoma by BDC have worse response to standard diffuse large B cell lymphoma treatment than those classified as diffuse large B cell lymphoma. In this study, we comprehensively investigate two previous Burkitt lymphoma molecular classifiers, and implement a new gene expression classifier, BDC, that works effectively on paraffin-embedded samples and provides useful information for treatment decisions. The classifier is available as a free software package under the GNU public licence within the R statistical software environment through the link http://www.bioinformatics.leeds.ac.uk/labpages/softwares/ or on github https://github.com/Sharlene/BDC.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Canada 1 3%
Unknown 31 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 39%
Student > Ph. D. Student 5 15%
Student > Postgraduate 4 12%
Other 2 6%
Student > Bachelor 2 6%
Other 3 9%
Unknown 4 12%
Readers by discipline Count As %
Medicine and Dentistry 8 24%
Biochemistry, Genetics and Molecular Biology 8 24%
Agricultural and Biological Sciences 8 24%
Computer Science 4 12%
Social Sciences 1 3%
Other 1 3%
Unknown 3 9%

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 February 2019.
All research outputs
of 14,346,625 outputs
Outputs from Genome Medicine
of 1,003 outputs
Outputs of similar age
of 233,116 outputs
Outputs of similar age from Genome Medicine
of 6 outputs
Altmetric has tracked 14,346,625 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,003 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.7. This one has gotten more attention than average, scoring higher than 56% 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 233,116 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 86% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.