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The genomic road to invasion—examining the similarities and differences in the genomes of associated oral pre-cancer and cancer samples

Overview of attention for article published in Genome Medicine, June 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Average Attention Score compared to outputs of the same age and source

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12 X users

Citations

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38 Dimensions

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41 Mendeley
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Title
The genomic road to invasion—examining the similarities and differences in the genomes of associated oral pre-cancer and cancer samples
Published in
Genome Medicine, June 2017
DOI 10.1186/s13073-017-0442-0
Pubmed ID
Authors

Henry M. Wood, Catherine Daly, Rebecca Chalkley, Burcu Senguven, Lisa Ross, Philip Egan, Preetha Chengot, Jennifer Graham, Neeraj Sethi, Thian K. Ong, Kenneth MacLennan, Pamela Rabbitts, Caroline Conway

Abstract

It is frequently assumed that pre-invasive lesions are simpler precursors of cancer and will contain a limited subset of the genomic changes seen in their associated invasive disease. Driver mutations are thought to occur early, but it is not known how many of these are present in pre-invasive lesions. These assumptions need to be tested with the increasing focus on both personalised cancer treatments and early detection methodologies. We examined genomic copy number changes in 256 pre-invasive and invasive samples from 69 oral cancer patients. Forty-eight samples from 16 patients were further examined using exome sequencing. Evidence of a shared ancestor of both dysplasia and carcinoma was seen in all but one patient. One-third of dysplasias showed independent copy number events. The remainder had a copy number pattern that was similar to or simpler than that of the carcinoma. All dysplasias examined contained somatic mutations absent in the related carcinoma. Previously observed copy number changes and TP53 mutations were very frequently observed, and almost always shared between dysplasia and carcinoma. Other gene changes were more sporadic. Pathway analysis confirmed that each patient's disease developed in a different way. Examining the numbers of shared mutations and the rate of accumulation of mutations showed evidence that all samples contain a population of sub-clones, with little evidence of selective advantage of a subset of these. These findings suggest that most of the genomic changes driving oral cancer occur in the pre-cancerous state by way of gradual random accumulation rather than a dramatic single event.

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X Demographics

The data shown below were collected from the profiles of 12 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 20%
Student > Ph. D. Student 6 15%
Researcher 5 12%
Professor > Associate Professor 3 7%
Student > Master 3 7%
Other 8 20%
Unknown 8 20%
Readers by discipline Count As %
Medicine and Dentistry 18 44%
Agricultural and Biological Sciences 3 7%
Biochemistry, Genetics and Molecular Biology 2 5%
Nursing and Health Professions 2 5%
Computer Science 2 5%
Other 3 7%
Unknown 11 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 29 July 2017.
All research outputs
#4,707,785
of 23,653,937 outputs
Outputs from Genome Medicine
#892
of 1,468 outputs
Outputs of similar age
#81,101
of 318,306 outputs
Outputs of similar age from Genome Medicine
#20
of 30 outputs
Altmetric has tracked 23,653,937 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.9. This one is in the 38th percentile – i.e., 38% 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 318,306 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.