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Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes

Overview of attention for article published in Genome Medicine, April 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 (79th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

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13 X users
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1 Facebook page

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16 Dimensions

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27 Mendeley
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Title
Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes
Published in
Genome Medicine, April 2017
DOI 10.1186/s13073-017-0429-x
Pubmed ID
Authors

Mumtahena Rahman, Shelley M. MacNeil, David F. Jenkins, Gajendra Shrestha, Sydney R. Wyatt, Jasmine A. McQuerry, Stephen R. Piccolo, Laura M. Heiser, Joe W. Gray, W. Evan Johnson, Andrea H. Bild

Abstract

The growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns. Novel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines. Application of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways ("the survival phenotype") or the EGFR, KRAS (G12V), RAF1, and BAD pathways ("the growth phenotype"). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies. Gene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 37%
Student > Ph. D. Student 5 19%
Professor > Associate Professor 3 11%
Student > Master 3 11%
Other 1 4%
Other 0 0%
Unknown 5 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 30%
Biochemistry, Genetics and Molecular Biology 5 19%
Medicine and Dentistry 5 19%
Nursing and Health Professions 1 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 2 7%
Unknown 5 19%
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 12 August 2017.
All research outputs
#3,277,699
of 22,968,808 outputs
Outputs from Genome Medicine
#726
of 1,444 outputs
Outputs of similar age
#62,051
of 309,828 outputs
Outputs of similar age from Genome Medicine
#15
of 31 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,444 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 49th percentile – i.e., 49% 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 309,828 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 79% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.