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Generation of an algorithm based on minimal gene sets to clinically subtype triple negative breast cancer patients

Overview of attention for article published in BMC Cancer, February 2016
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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 (77th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

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3 tweeters
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1 patent

Citations

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28 Dimensions

Readers on

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57 Mendeley
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Title
Generation of an algorithm based on minimal gene sets to clinically subtype triple negative breast cancer patients
Published in
BMC Cancer, February 2016
DOI 10.1186/s12885-016-2198-0
Pubmed ID
Authors

Brian Z. Ring, David R. Hout, Stephan W. Morris, Kasey Lawrence, Brock L. Schweitzer, Daniel B. Bailey, Brian D. Lehmann, Jennifer A. Pietenpol, Robert S. Seitz

Abstract

Recently, a gene expression algorithm, TNBCtype, was developed that can divide triple-negative breast cancer (TNBC) into molecularly-defined subtypes. The algorithm has potential to provide predictive value for TNBC subtype-specific response to various treatments. TNBCtype used in a retrospective analysis of neoadjuvant clinical trial data of TNBC patients demonstrated that TNBC subtype and pathological complete response to neoadjuvant chemotherapy were significantly associated. Herein we describe an expression algorithm reduced to 101 genes with the power to subtype TNBC tumors similar to the original 2188-gene expression algorithm and predict patient outcomes. The new classification model was built using the same expression data sets used for the original TNBCtype algorithm. Gene set enrichment followed by shrunken centroid analysis were used for feature reduction, then elastic-net regularized linear modeling was used to identify genes for a centroid model classifying all subtypes, comprised of 101 genes. The predictive capability of both this new "lean" algorithm and the original 2188-gene model were applied to an independent clinical trial cohort of 139 TNBC patients treated initially with neoadjuvant doxorubicin/cyclophosphamide and then randomized to receive either paclitaxel or ixabepilone to determine association of pathologic complete response within the subtypes. The new 101-gene expression model reproduced the classification provided by the 2188-gene algorithm and was highly concordant in the same set of seven TNBC cohorts used to generate the TNBCtype algorithm (87 %), as well as in the independent clinical trial cohort (88 %), when cases with significant correlations to multiple subtypes were excluded. Clinical responses to both neoadjuvant treatment arms, found BL2 to be significantly associated with poor response (Odds Ratio (OR) =0.12, p =0.03 for the 2188-gene model; OR = 0.23, p < 0.03 for the 101-gene model). Additionally, while the BL1 subtype trended towards significance in the 2188-gene model (OR = 1.91, p = 0.14), the 101-gene model demonstrated significant association with improved response in patients with the BL1 subtype (OR = 3.59, p = 0.02). These results demonstrate that a model using small gene sets can recapitulate the TNBC subtypes identified by the original 2188-gene model and in the case of standard chemotherapy, the ability to predict therapeutic response.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 2%
Unknown 56 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 21%
Student > Master 10 18%
Researcher 8 14%
Student > Bachelor 7 12%
Student > Postgraduate 3 5%
Other 8 14%
Unknown 9 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 18%
Medicine and Dentistry 8 14%
Agricultural and Biological Sciences 7 12%
Computer Science 5 9%
Psychology 3 5%
Other 11 19%
Unknown 13 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 January 2018.
All research outputs
#2,135,354
of 12,376,381 outputs
Outputs from BMC Cancer
#521
of 4,523 outputs
Outputs of similar age
#63,845
of 284,825 outputs
Outputs of similar age from BMC Cancer
#23
of 183 outputs
Altmetric has tracked 12,376,381 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,523 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 87% 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 284,825 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 77% of its contemporaries.
We're also able to compare this research output to 183 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.