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Development and verification of the PAM50-based Prosigna breast cancer gene signature assay

Overview of attention for article published in BMC Medical Genomics, August 2015
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Title
Development and verification of the PAM50-based Prosigna breast cancer gene signature assay
Published in
BMC Medical Genomics, August 2015
DOI 10.1186/s12920-015-0129-6
Pubmed ID
Authors

Brett Wallden, James Storhoff, Torsten Nielsen, Naeem Dowidar, Carl Schaper, Sean Ferree, Shuzhen Liu, Samuel Leung, Gary Geiss, Jacqueline Snider, Tammi Vickery, Sherri R. Davies, Elaine R. Mardis, Michael Gnant, Ivana Sestak, Matthew J. Ellis, Charles M. Perou, Philip S. Bernard, Joel S. Parker

Abstract

The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories. 514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies. The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online. The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 436 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 <1%
Unknown 435 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 71 16%
Researcher 64 15%
Student > Master 53 12%
Student > Bachelor 47 11%
Other 26 6%
Other 70 16%
Unknown 105 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 94 22%
Medicine and Dentistry 93 21%
Agricultural and Biological Sciences 45 10%
Computer Science 18 4%
Pharmacology, Toxicology and Pharmaceutical Science 11 3%
Other 50 11%
Unknown 125 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 October 2015.
All research outputs
#16,722,190
of 25,374,917 outputs
Outputs from BMC Medical Genomics
#1,194
of 2,444 outputs
Outputs of similar age
#157,502
of 277,653 outputs
Outputs of similar age from BMC Medical Genomics
#31
of 60 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,444 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 46th percentile – i.e., 46% 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 277,653 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.