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Classifying cancer genome aberrations by their mutually exclusive effects on transcription

Overview of attention for article published in BMC Medical Genomics, December 2017
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  • Good Attention Score compared to outputs of the same age (68th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

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6 tweeters

Citations

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5 Dimensions

Readers on

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22 Mendeley
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Title
Classifying cancer genome aberrations by their mutually exclusive effects on transcription
Published in
BMC Medical Genomics, December 2017
DOI 10.1186/s12920-017-0303-0
Pubmed ID
Authors

Jonathan B. Dayton, Stephen R. Piccolo

Abstract

Malignant tumors are typically caused by a conglomeration of genomic aberrations-including point mutations, small insertions, small deletions, and large copy-number variations. In some cases, specific chemotherapies and targeted drug treatments are effective against tumors that harbor certain genomic aberrations. However, predictive aberrations (biomarkers) have not been identified for many tumor types and treatments. One way to address this problem is to examine the downstream, transcriptional effects of genomic aberrations and to identify characteristic patterns. Even though two tumors harbor different genomic aberrations, the transcriptional effects of those aberrations may be similar. These patterns could be used to inform treatment choices. We used data from 9300 tumors across 25 cancer types from The Cancer Genome Atlas. We used supervised machine learning to evaluate our ability to distinguish between tumors that had mutually exclusive genomic aberrations in specific genes. An ability to accurately distinguish between tumors with aberrations in these genes suggested that the genes have a relatively different downstream effect on transcription, and vice versa. We compared these findings against prior knowledge about signaling networks and drug responses. Our analysis recapitulates known relationships in cancer pathways and identifies gene pairs known to predict responses to the same treatments. For example, in lung adenocarcinomas, gene-expression profiles from tumors with somatic aberrations in EGFR or MET were negatively correlated with each other, in line with prior knowledge that MET amplification causes resistance to EGFR inhibition. In breast carcinomas, we observed high similarity between PTEN and PIK3CA, which play complementary roles in regulating cellular proliferation. In a pan-cancer analysis, we found that genomic aberrations in BRAF and VHL exhibit downstream effects that are clearly distinct from other genes. We show that transcriptional data offer promise as a way to group genomic aberrations according to their downstream effects, and these groupings recapitulate known relationships. Our approach shows potential to help pharmacologists and clinical trialists narrow the search space for candidate gene/drug associations, including for rare mutations, and for identifying potential drug-repurposing opportunities.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 18%
Student > Doctoral Student 3 14%
Other 3 14%
Researcher 3 14%
Student > Bachelor 3 14%
Other 3 14%
Unknown 3 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 36%
Agricultural and Biological Sciences 4 18%
Pharmacology, Toxicology and Pharmaceutical Science 2 9%
Computer Science 2 9%
Medicine and Dentistry 2 9%
Other 2 9%
Unknown 2 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 04 July 2018.
All research outputs
#3,588,262
of 13,183,063 outputs
Outputs from BMC Medical Genomics
#190
of 652 outputs
Outputs of similar age
#121,666
of 384,249 outputs
Outputs of similar age from BMC Medical Genomics
#7
of 39 outputs
Altmetric has tracked 13,183,063 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 652 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 70% 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 384,249 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 68% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.