A fuzzy gene expression-based computational approach improves breast cancer prognostication

被引:42
|
作者
Haibe-Kains, Benjamin [1 ,2 ]
Desmedt, Christine [1 ]
Rothe, Francoise [1 ]
Piccart, Martine [1 ]
Sotiriou, Christos [1 ]
Bontempi, Gianluca [2 ]
机构
[1] Inst Jules Bordet, Dept Med Oncol, Funct Genom & Translat Res Unit, B-1000 Brussels, Belgium
[2] Univ Libre Bruxelles, Dept Comp Sci, Machine Learning Grp, B-1050 Brussels, Belgium
来源
GENOME BIOLOGY | 2010年 / 11卷 / 02期
关键词
MOLECULAR PORTRAITS; ADJUVANT THERAPY; SIGNATURE; VALIDATION; PROGNOSIS; METASTASIS; SURVIVAL; CLASSIFICATION; CHEMOTHERAPY; PROFILES;
D O I
10.1186/gb-2010-11-2-r18
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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.
引用
收藏
页数:18
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