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: Aleix Prat Aparicio 

PROVIDER: E-GEOD-15393 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress