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A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer.


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Article

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Authors

Teschendorff, Andrew E 

Abstract

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.

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Keywords

Antineoplastic Agents, Breast Neoplasms, Cohort Studies, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Models, Statistical, Predictive Value of Tests, Prognosis, Proportional Hazards Models, Receptors, Estrogen, Regression Analysis, Reverse Transcriptase Polymerase Chain Reaction, Treatment Outcome

Journal Title

Breast Cancer Res

Conference Name

Journal ISSN

1465-5411
1465-542X

Volume Title

Publisher

Springer Science and Business Media LLC