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Molecular subtyping for clinically defined breast cancer subgroups

Overview of attention for article published in Breast Cancer Research, February 2015
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Title
Molecular subtyping for clinically defined breast cancer subgroups
Published in
Breast Cancer Research, February 2015
DOI 10.1186/s13058-015-0520-4
Pubmed ID
Authors

Xi Zhao, Einar Andreas Rødland, Robert Tibshirani, Sylvia Plevritis

Abstract

Breast cancer is commonly classified into intrinsic molecular subtypes. Standard gene centering is routinely done prior to molecular subtyping, but it can produce inaccurate classifications when the distribution of clinicopathological characteristics in the study cohort differs from that of the training cohort used to derive the classifier. We propose a subgroup-specific gene-centering method to perform molecular subtyping on a study cohort that has a skewed distribution of clinicopathological characteristics relative to the training cohort. On such a study cohort, we center each gene on a specified percentile, where the percentile is determined from a subgroup of the training cohort with clinicopathological characteristics similar to the study cohort. We demonstrate our method using the PAM50 classifier and its associated University of North Carolina (UNC) training cohort. We considered study cohorts with skewed clinicopathological characteristics, including subgroups composed of a single prototypic subtype of the UNC-PAM50 training cohort (n = 139), an external estrogen receptor (ER)-positive cohort (n = 48) and an external triple-negative cohort (n = 77). Subgroup-specific gene centering improved prediction performance with the accuracies between 77% and 100%, compared to accuracies between 17% and 33% from standard gene centering, when applied to the prototypic tumor subsets of the PAM50 training cohort. It reduced classification error rates on the ER-positive (11% versus 28%; P = 0.0389), the ER-negative (5% versus 41%; P < 0.0001) and the triple-negative (11% versus 56%; P = 0.1336) subgroups of the PAM50 training cohort. In addition, it produced higher accuracy for subtyping study cohorts composed of varying proportions of ER-positive versus ER-negative cases. Finally, it increased the percentage of assigned luminal subtypes on the external ER-positive cohort and basal-like subtype on the external triple-negative cohort. Gene centering is often necessary to accurately apply a molecular subtype classifier. Compared with standard gene centering, our proposed subgroup-specific gene centering produced more accurate molecular subtype assignments in a study cohort with skewed clinicopathological characteristics relative to the training cohort.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Mexico 1 <1%
Brazil 1 <1%
Unknown 121 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 19%
Researcher 21 17%
Student > Bachelor 12 10%
Student > Master 11 9%
Student > Doctoral Student 6 5%
Other 23 19%
Unknown 28 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 19%
Agricultural and Biological Sciences 20 16%
Medicine and Dentistry 19 15%
Computer Science 9 7%
Nursing and Health Professions 3 2%
Other 16 13%
Unknown 33 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 25 June 2019.
All research outputs
#17,285,668
of 25,373,627 outputs
Outputs from Breast Cancer Research
#1,534
of 2,052 outputs
Outputs of similar age
#163,311
of 270,174 outputs
Outputs of similar age from Breast Cancer Research
#38
of 49 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,052 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.2. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
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We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.