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Predicting cancer type from tumour DNA signatures

Overview of attention for article published in Genome Medicine, November 2017
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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 (80th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

8 X users
3 patents


43 Dimensions

Readers on

111 Mendeley
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Predicting cancer type from tumour DNA signatures
Published in
Genome Medicine, November 2017
DOI 10.1186/s13073-017-0493-2
Pubmed ID

Kee Pang Soh, Ewa Szczurek, Thomas Sakoparnig, Niko Beerenwinkel


Establishing the cancer type and site of origin is important in determining the most appropriate course of treatment for cancer patients. Patients with cancer of unknown primary, where the site of origin cannot be established from an examination of the metastatic cancer cells, typically have poor survival. Here, we evaluate the potential and limitations of utilising gene alteration data from tumour DNA to identify cancer types. Using sequenced tumour DNA downloaded via the cBioPortal for Cancer Genomics, we collected the presence or absence of calls for gene alterations for 6640 tumour samples spanning 28 cancer types, as predictive features. We employed three machine-learning techniques, namely linear support vector machines with recursive feature selection, L 1-regularised logistic regression and random forest, to select a small subset of gene alterations that are most informative for cancer-type prediction. We then evaluated the predictive performance of the models in a comparative manner. We found the linear support vector machine to be the most predictive model of cancer type from gene alterations. Using only 100 somatic point-mutated genes for prediction, we achieved an overall accuracy of 49.4±0.4 % (95 % confidence interval). We observed a marked increase in the accuracy when copy number alterations are included as predictors. With a combination of somatic point mutations and copy number alterations, a mere 50 genes are enough to yield an overall accuracy of 77.7±0.3 %. A general cancer diagnostic tool that utilises either only somatic point mutations or only copy number alterations is not sufficient for distinguishing a broad range of cancer types. The combination of both gene alteration types can dramatically improve the performance.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 111 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 20%
Student > Ph. D. Student 15 14%
Student > Master 14 13%
Student > Bachelor 11 10%
Student > Doctoral Student 5 5%
Other 17 15%
Unknown 27 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 21%
Computer Science 19 17%
Agricultural and Biological Sciences 12 11%
Medicine and Dentistry 10 9%
Mathematics 4 4%
Other 10 9%
Unknown 33 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 20 May 2021.
All research outputs
of 23,009,818 outputs
Outputs from Genome Medicine
of 1,448 outputs
Outputs of similar age
of 438,539 outputs
Outputs of similar age from Genome Medicine
of 35 outputs
Altmetric has tracked 23,009,818 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,448 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 438,539 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 80% 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 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.