par Haibe-Kains, Benjamin 
;Desmedt, Christine ;Loi, Sherene ;Culhane, Aedin C;Bontempi, Gianluca ;Quackenbush, John;Sotiriou, Christos
Référence Journal of the National Cancer Institute, 104, 4, page (311-325)
Publication Publié, 2012-02
;Desmedt, Christine ;Loi, Sherene ;Culhane, Aedin C;Bontempi, Gianluca ;Quackenbush, John;Sotiriou, Christos Référence Journal of the National Cancer Institute, 104, 4, page (311-325)
Publication Publié, 2012-02
Article révisé par les pairs
| Résumé : | Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets. |
