Method
A fuzzy gene expression-based computational approach improves breast cancer prognostication
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* Corresponding author: Christos Sotiriou christos.sotiriou@bordet.be
- † Equal contributors
1 Functional Genomics and Translational Research Unit, Medical Oncology Department, Jules Bordet Institute, Boulevard de Waterloo, Brussels, 1000, Belgium
2 Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Boulevard du Triomphe, Brussels, 1050, Belgium
Genome Biology 2010, 11:R18 doi:10.1186/gb-2010-11-2-r18
Published: 15 February 2010Abstract
Early gene expression studies classified breast tumors into at least three clinically relevant subtypes. Although most current gene signatures are prognostic for estrogen receptor (ER) positive/human epidermal growth factor receptor 2 (HER2) negative breast cancers, few are informative for ER negative/HER2 negative and HER2 positive subtypes. Here we present Gene Expression Prognostic Index Using Subtypes (GENIUS), a fuzzy approach for prognostication that takes into account the molecular heterogeneity of breast cancer. In systematic evaluations, GENIUS significantly outperformed current gene signatures and clinical indices in the global population of patients.