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Frequent mutations in acetylation and ubiquitination sites suggest novel driver mechanisms of cancer

Overview of attention for article published in Genome Medicine, May 2016
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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 (87th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
Frequent mutations in acetylation and ubiquitination sites suggest novel driver mechanisms of cancer
Published in
Genome Medicine, May 2016
DOI 10.1186/s13073-016-0311-2
Pubmed ID
Authors

Soumil Narayan, Gary D. Bader, Jüri Reimand

Abstract

Discovery of cancer drivers is a major goal of cancer research. Driver genes and pathways are often predicted using mutation frequency, assuming that statistically significant recurrence of specific somatic mutations across independent samples indicates their importance in cancer. However, many mutations, including known cancer drivers, are not observed at high frequency. Fortunately, abundant information is available about functional "active sites" in proteins that can be integrated with mutations to predict cancer driver genes, even based on low frequency mutations. Further, considering active site information predicts detailed biochemical mechanisms impacted by the mutations. Post-translational modifications (PTMs) are active sites that are regulatory switches in proteins and pathways. We analyzed acetylation and ubiquitination, two important PTM types often involved in chromatin organization and protein degradation, to find proteins that are significantly affected by tumor somatic mutations. We performed computational analyses of acetylation and ubiquitination sites in a pan-cancer dataset of 3200 tumor samples from The Cancer Genome Atlas (TCGA). These analyses were targeted at different levels of biological organization including individual genes, pathway annotated gene sets, and protein-protein interaction networks. Acetylation and ubiquitination site mutations are enriched in cancer with significantly stronger evolutionary conservation and accumulation in protein domains. Gene-focused analysis with the ActiveDriver method reveals significant co-occurrences of acetylation and ubiquitination PTMs and mutation hotspots in known oncoproteins (TP53, AKT1, IDH1) and highlights candidate cancer driver genes with PTM-related mechanisms (e.g. several histone proteins and the splicing factor SF3B1). Pathway analysis shows that PTM mutations in acetylation and ubiquitination sites accumulate in cancer-related processes such as cell cycle, apoptosis, chromatin regulation, and metabolism. Integrated mutation analysis of clinical information and protein interaction networks suggests that many PTM-specific mutations associate with decreased patient survival. Mutation analysis of acetylation and ubiquitination PTM sites reveals their importance in cancer. As PTM networks are increasingly mapped and related enzymes are often druggable, deeper investigation of specific associated mutations may lead to the discovery of treatment-relevant cellular mechanisms.

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X Demographics

The data shown below were collected from the profiles of 25 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 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Germany 1 1%
Canada 1 1%
Unknown 85 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 26%
Researcher 18 20%
Student > Master 10 11%
Student > Bachelor 7 8%
Other 5 6%
Other 16 18%
Unknown 9 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 32 36%
Agricultural and Biological Sciences 29 33%
Computer Science 5 6%
Business, Management and Accounting 2 2%
Medicine and Dentistry 2 2%
Other 7 8%
Unknown 11 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 26 May 2016.
All research outputs
#2,610,588
of 25,374,917 outputs
Outputs from Genome Medicine
#597
of 1,585 outputs
Outputs of similar age
#42,124
of 326,212 outputs
Outputs of similar age from Genome Medicine
#26
of 41 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one has gotten more attention than average, scoring higher than 62% 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 326,212 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 87% of its contemporaries.
We're also able to compare this research output to 41 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.