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Cross-species DNA copy number analyses identifies multiple 1q21-q23 subtype-specific driver genes for breast cancer

Overview of attention for article published in Breast Cancer Research and Treatment, June 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

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2 X users
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3 patents

Citations

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39 Dimensions

Readers on

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48 Mendeley
Title
Cross-species DNA copy number analyses identifies multiple 1q21-q23 subtype-specific driver genes for breast cancer
Published in
Breast Cancer Research and Treatment, June 2015
DOI 10.1007/s10549-015-3476-2
Pubmed ID
Authors

Grace O. Silva, Xiaping He, Joel S. Parker, Michael L. Gatza, Lisa A. Carey, Jack P. Hou, Stacy L. Moulder, Paul K. Marcom, Jian Ma, Jeffrey M. Rosen, Charles M. Perou

Abstract

A large number of DNA copy number alterations (CNAs) exist in human breast cancers, and thus characterizing the most frequent CNAs is key to advancing therapeutics because it is likely that these regions contain breast tumor 'drivers' (i.e., cancer causal genes). This study aims to characterize the genomic landscape of breast cancer CNAs and identify potential subtype-specific drivers using a large set of human breast tumors and genetically engineered mouse (GEM) mammary tumors. Using a novel method called SWITCHplus, we identified subtype-specific DNA CNAs occurring at a 15 % or greater frequency, which excluded many well-known breast cancer-related drivers such as amplification of ERBB2, and deletions of TP53 and RB1. A comparison of CNAs between mouse and human breast tumors identified regions with shared subtype-specific CNAs. Additional criteria that included gene expression-to-copy number correlation, a DawnRank network analysis, and RNA interference functional studies highlighted candidate driver genes that fulfilled these multiple criteria. Numerous regions of shared CNAs were observed between human breast tumors and GEM mammary tumor models that shared similar gene expression features. Specifically, we identified chromosome 1q21-23 as a Basal-like subtype-enriched region with multiple potential driver genes including PI4KB, SHC1, and NCSTN. This step-wise computational approach based on a cross-species comparison is applicable to any tumor type for which sufficient human and model system DNA copy number data exist, and in this instance, highlights that a single region of amplification may in fact harbor multiple driver genes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 47 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 21%
Researcher 7 15%
Professor > Associate Professor 6 13%
Student > Bachelor 4 8%
Student > Master 3 6%
Other 6 13%
Unknown 12 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 31%
Medicine and Dentistry 10 21%
Biochemistry, Genetics and Molecular Biology 4 8%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Engineering 2 4%
Other 2 4%
Unknown 13 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 June 2023.
All research outputs
#3,283,022
of 24,089,711 outputs
Outputs from Breast Cancer Research and Treatment
#479
of 4,821 outputs
Outputs of similar age
#40,274
of 267,955 outputs
Outputs of similar age from Breast Cancer Research and Treatment
#7
of 76 outputs
Altmetric has tracked 24,089,711 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,821 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done well, scoring higher than 89% 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 267,955 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 84% of its contemporaries.
We're also able to compare this research output to 76 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 92% of its contemporaries.