A fuzzy gene expression-based computational approach improves breast cancer prognostication.
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.
Functional Genomics and Translational Research Unit, Medical Oncology Department, Jules Bordet Institute, Boulevard de Waterloo, Brussels, 1000, Belgium.
Benjamin
B
Haibe-Kains
Christine
C
Desmedt
Françoise
F
Rothé
Martine
M
Piccart
Christos
C
Sotiriou
Gianluca
G
Bontempi
D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D005260:Female; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000235:genetics; D011960:Receptors, Estrogen / Q000235:genetics; D012306:Risk
D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; C508053:ERBB2 protein, human