Dataset Information


Building Prognostic Models for Breast Cancer Patients Using Clinical Variables and Gene Expression Signatures

ABSTRACT: Our findings indicate that the integration of expression signatures and clinicopathological factors can better determine the individual risk of recurrence for newly diagnosed patients with lymph-node negative ER-positive breast cancer. Models incorporating other variables yet to be discovered will be needed to obtain robust prognostic models for ER-negative and HER2-positive breast cancer patients. A large data set was created by combining five different publicly available microarray datasets of node-negative breast cancer patients treated with local therapy only. The microarray gene expression data was combined using the batch effect adjustment by the Distance Weighted Discrimination method.

ORGANISM(S): Homo sapiens  

SUBMITTER: Yufeng Liu   Charles Perou  Cheng FAN  Charles M Perou  Joel S Parker  Aleix Prat Aparicio 

PROVIDER: E-GEOD-15393 | ArrayExpress | 2010-10-25



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