A consensus prognostic gene expression classifier for ER positive breast cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2006-04

AUTHORS

Andrew E Teschendorff, Ali Naderi, Nuno L Barbosa-Morais, Sarah E Pinder, Ian O Ellis, Sam Aparicio, James D Brenton, Carlos Caldas

ABSTRACT

BACKGROUND: A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer. RESULTS: Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation. CONCLUSION: The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors. More... »

PAGES

r101

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/gb-2006-7-10-r101

DOI

http://dx.doi.org/10.1186/gb-2006-7-10-r101

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049083057

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17076897


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curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/gb-2006-7-10-r101'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/gb-2006-7-10-r101'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/gb-2006-7-10-r101'


 

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