A refined molecular taxonomy of breast cancer.
Fiche publication
Date publication
mars 2012
Journal
Oncogene
Auteurs
Membres identifiés du Cancéropôle Est :
Pr FEUGEAS Jean-Paul, Pr BIBEAU Frédéric
Tous les auteurs :
Guedj M, Marisa L, de Reynies A, Orsetti B, Schiappa R, Bibeau F, MacGrogan G, Lerebours F, Finetti P, Longy M, Bertheau P, Bertrand F, Bonnet F, Martin AL, Feugeas JP, Bièche I, Lehmann-Che J, Lidereau R, Birnbaum D, Bertucci F, de Thé H, Theillet C
Lien Pubmed
Résumé
The current histoclinical breast cancer classification is simple but imprecise. Several molecular classifications of breast cancers based on expression profiling have been proposed as alternatives. However, their reliability and clinical utility have been repeatedly questioned, notably because most of them were derived from relatively small initial patient populations. We analyzed the transcriptomes of 537 breast tumors using three unsupervised classification methods. A core subset of 355 tumors was assigned to six clusters by all three methods. These six subgroups overlapped with previously defined molecular classes of breast cancer, but also showed important differences, notably the absence of an ERBB2 subgroup and the division of the large luminal ER+ group into four subgroups, two of them being highly proliferative. Of the six subgroups, four were ER+/PR+/AR+, one was ER-/PR-/AR+ and one was triple negative (AR-/ER-/PR-). ERBB2-amplified tumors were split between the ER-/PR-/AR+ subgroup and the highly proliferative ER+ LumC subgroup. Importantly, each of these six molecular subgroups showed specific copy-number alterations. Gene expression changes were correlated to specific signaling pathways. Each of these six subgroups showed very significant differences in tumor grade, metastatic sites, relapse-free survival or response to chemotherapy. All these findings were validated on large external datasets including more than 3000 tumors. Our data thus indicate that these six molecular subgroups represent well-defined clinico-biological entities of breast cancer. Their identification should facilitate the detection of novel prognostic factors or therapeutical targets in breast cancer.
Mots clés
Biomarkers, Tumor, genetics, Breast Neoplasms, classification, Cluster Analysis, Databases, Genetic, Female, Gene Expression Profiling, Humans, Prognosis, Reproducibility of Results, Signal Transduction, Survival Analysis, Transcriptome, Treatment Outcome
Référence
Oncogene. 2012 Mar 1;31(9):1196-206