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dc.contributor.authorTeschendorff, Andrew Een
dc.contributor.authorCaldas, Carlosen
dc.date.accessioned2011-06-17T14:27:16Z
dc.date.available2011-06-17T14:27:16Z
dc.date.issued2008-08-28en
dc.identifier.citationBreast Cancer Research 2008, 10:R73
dc.identifier.issn1465-5411
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/238197
dc.description.abstractAbstract Introduction Patients with primary operable oestrogen receptor (ER) negative (-) breast cancer account for about 30% of all cases and generally have a worse prognosis than ER-positive (+) patients. Nevertheless, a significant proportion of ER- cases have favourable outcomes and could potentially benefit from a less aggressive course of therapy. However, identification of such patients with a good prognosis remains difficult and at present is only possible through examining histopathological factors. Methods Building on a previously identified seven-gene prognostic immune response module for ER- breast cancer, we developed a novel statistical tool based on Mixture Discriminant Analysis in order to build a classifier that could accurately identify ER- patients with a good prognosis. Results We report the construction of a seven-gene expression classifier that accurately predicts, across a training cohort of 183 ER- tumours and six independent test cohorts (a total of 469 ER- tumours), ER- patients of good prognosis (in test sets, average predictive value = 94% [range 85 to 100%], average hazard ratio = 0.15 [range 0.07 to 0.36] p < 0.000001) independently of lymph node status and treatment. Conclusions This seven-gene classifier could be used in a polymerase chain reaction-based clinical assay to identify ER- patients with a good prognosis, who may therefore benefit from less aggressive treatment regimens.
dc.languageEnglishen
dc.language.isoen
dc.titleA robust classifier of high predictive value to identify good prognosis patients in ER negative breast cancer.en
dc.typeArticle
dc.date.updated2011-06-17T14:27:16Z
dc.description.versionRIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.en
dc.rights.holderTeschendorff et al.; licensee BioMed Central Ltd.
prism.publicationDate2008en
dcterms.dateAccepted2008-08-28en
rioxxterms.versionofrecord10.1186/bcr2138en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2008-08-28en
dc.contributor.orcidCaldas, Carlos [0000-0003-3547-1489]
dc.identifier.eissn1465-542X
rioxxterms.typeJournal Article/Reviewen


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