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TumorTracer: a method to identify the tissue of origin from the somatic mutations of a tumor specimen

Overview of attention for article published in BMC Medical Genomics, October 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#1 of 787)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
15 news outlets
blogs
3 blogs
twitter
22 tweeters
patent
1 patent
facebook
1 Facebook page
reddit
1 Redditor

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
85 Mendeley
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Title
TumorTracer: a method to identify the tissue of origin from the somatic mutations of a tumor specimen
Published in
BMC Medical Genomics, October 2015
DOI 10.1186/s12920-015-0130-0
Pubmed ID
Authors

Andrea Marion Marquard, Nicolai Juul Birkbak, Cecilia Engel Thomas, Francesco Favero, Marcin Krzystanek, Celine Lefebvre, Charles Ferté, Mariam Jamal-Hanjani, Gareth A. Wilson, Seema Shafi, Charles Swanton, Fabrice André, Zoltan Szallasi, Aron Charles Eklund

Abstract

A substantial proportion of cancer cases present with a metastatic tumor and require further testing to determine the primary site; many of these are never fully diagnosed and remain cancer of unknown primary origin (CUP). It has been previously demonstrated that the somatic point mutations detected in a tumor can be used to identify its site of origin with limited accuracy. We hypothesized that higher accuracy could be achieved by a classification algorithm based on the following feature sets: 1) the number of nonsynonymous point mutations in a set of 232 specific cancer-associated genes, 2) frequencies of the 96 classes of single-nucleotide substitution determined by the flanking bases, and 3) copy number profiles, if available. We used publicly available somatic mutation data from the COSMIC database to train random forest classifiers to distinguish among those tissues of origin for which sufficient data was available. We selected feature sets using cross-validation and then derived two final classifiers (with or without copy number profiles) using 80 % of the available tumors. We evaluated the accuracy using the remaining 20 %. For further validation, we assessed accuracy of the without-copy-number classifier on three independent data sets: 1669 newly available public tumors of various types, a cohort of 91 breast metastases, and a set of 24 specimens from 9 lung cancer patients subjected to multiregion sequencing. The cross-validation accuracy was highest when all three types of information were used. On the left-out COSMIC data not used for training, we achieved a classification accuracy of 85 % across 6 primary sites (with copy numbers), and 69 % across 10 primary sites (without copy numbers). Importantly, a derived confidence score could distinguish tumors that could be identified with 95 % accuracy (32 %/75 % of tumors with/without copy numbers) from those that were less certain. Accuracy in the independent data sets was 46 %, 53 % and 89 % respectively, similar to the accuracy expected from the training data. Identification of primary site from point mutation and/or copy number data may be accurate enough to aid clinical diagnosis of cancers of unknown primary origin.

Twitter Demographics

The data shown below were collected from the profiles of 22 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 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
United Kingdom 3 4%
Denmark 2 2%
Canada 1 1%
Russia 1 1%
Germany 1 1%
Unknown 74 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 27%
Student > Ph. D. Student 18 21%
Student > Master 14 16%
Student > Bachelor 6 7%
Lecturer > Senior Lecturer 3 4%
Other 12 14%
Unknown 9 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 31%
Biochemistry, Genetics and Molecular Biology 21 25%
Medicine and Dentistry 13 15%
Computer Science 8 9%
Arts and Humanities 1 1%
Other 6 7%
Unknown 10 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 146. 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 24 January 2019.
All research outputs
#120,378
of 14,826,751 outputs
Outputs from BMC Medical Genomics
#1
of 787 outputs
Outputs of similar age
#2,867
of 249,024 outputs
Outputs of similar age from BMC Medical Genomics
#1
of 19 outputs
Altmetric has tracked 14,826,751 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 787 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 99% 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 249,024 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 98% of its contemporaries.
We're also able to compare this research output to 19 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 94% of its contemporaries.