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Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes

Overview of attention for article published in Cancer & Metabolism, June 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#43 of 204)
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

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1 blog
twitter
2 X users
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1 Facebook page

Citations

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57 Dimensions

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64 Mendeley
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Title
Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes
Published in
Cancer & Metabolism, June 2016
DOI 10.1186/s40170-016-0152-x
Pubmed ID
Authors

Tonje H. Haukaas, Leslie R. Euceda, Guro F. Giskeødegård, Santosh Lamichhane, Marit Krohn, Sandra Jernström, Miriam R. Aure, Ole C. Lingjærde, Ellen Schlichting, Øystein Garred, Eldri U. Due, Gordon B. Mills, Kristine K. Sahlberg, Anne-Lise Børresen-Dale, Tone F. Bathen, The Oslo Breast Cancer Consortium (OSBREAC)

Abstract

The heterogeneous biology of breast cancer leads to high diversity in prognosis and response to treatment, even for patients with similar clinical diagnosis, histology, and stage of disease. Identifying mechanisms contributing to this heterogeneity may reveal new cancer targets or clinically relevant subgroups for treatment stratification. In this study, we have merged metabolite, protein, and gene expression data from breast cancer patients to examine the heterogeneity at a molecular level. The study included primary tumor samples from 228 non-treated breast cancer patients. High-resolution magic-angle spinning magnetic resonance spectroscopy (HR MAS MRS) was performed to extract the tumors metabolic profiles further used for hierarchical cluster analysis resulting in three significantly different metabolic clusters (Mc1, Mc2, and Mc3). The clusters were further combined with gene and protein expression data. Our result revealed distinct differences in the metabolic profile of the three metabolic clusters. Among the most interesting differences, Mc1 had the highest levels of glycerophosphocholine (GPC) and phosphocholine (PCho), Mc2 had the highest levels of glucose, and Mc3 had the highest levels of lactate and alanine. Integrated pathway analysis of metabolite and gene expression data uncovered differences in glycolysis/gluconeogenesis and glycerophospholipid metabolism between the clusters. All three clusters had significant differences in the distribution of protein subtypes classified by the expression of breast cancer-related proteins. Genes related to collagens and extracellular matrix were downregulated in Mc1 and consequently upregulated in Mc2 and Mc3, underpinning the differences in protein subtypes within the metabolic clusters. Genetic subtypes were evenly distributed among the three metabolic clusters and could therefore contribute to additional explanation of breast cancer heterogeneity. Three naturally occurring metabolic clusters of breast cancer were detected among primary tumors from non-treated breast cancer patients. The clusters expressed differences in breast cancer-related protein as well as genes related to extracellular matrix and metabolic pathways known to be aberrant in cancer. Analyses of metabolic activity combined with gene and protein expression provide new information about the heterogeneity of breast tumors and, importantly, the metabolic differences infer that the clusters may be susceptible to different metabolically targeted drugs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 22%
Researcher 12 19%
Student > Master 10 16%
Student > Bachelor 5 8%
Student > Doctoral Student 3 5%
Other 6 9%
Unknown 14 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 27%
Agricultural and Biological Sciences 9 14%
Medicine and Dentistry 6 9%
Engineering 4 6%
Chemical Engineering 2 3%
Other 10 16%
Unknown 16 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 14 December 2016.
All research outputs
#3,639,825
of 22,880,230 outputs
Outputs from Cancer & Metabolism
#43
of 204 outputs
Outputs of similar age
#64,956
of 352,119 outputs
Outputs of similar age from Cancer & Metabolism
#2
of 5 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 204 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done well, scoring higher than 78% 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 352,119 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 81% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.