A gene expression signature identifies two prognostic subgroups of basal breast cancer.
Prognosis of basal breast cancers is poor but heterogeneous. Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (P = 9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR = 0.52, P = 3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative. Ontology analysis revealed effective immune response (IR), enhanced tumor cell apoptosis, elevated levels of metastasis-inhibiting factors and low levels of metastasis-promoting factors in the good-prognosis subgroup, and a more developed cell migration system in the poor-prognosis subgroup. In conclusion, based on this 368-gene SVM model derived from an MBC signature, basal breast cancers were classified in two prognostic subgroups, suggesting that MBC and basal breast cancers share similar molecular alterations associated with aggressiveness. This signature could help define the prognosis, adapt the systemic treatment, and identify new therapeutic targets.
Sabatier
Renaud
R
Département d'Oncologie Moléculaire, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Institut Paoli-Calmettes, Marseille, France.
Finetti
Pascal
P
Cervera
Nathalie
N
Lambaudie
Eric
E
Esterni
Benjamin
B
Mamessier
Emilie
E
Tallet
Agnès
A
Chabannon
Christian
C
Extra
Jean-Marc
JM
Jacquemier
Jocelyne
J
Viens
Patrice
P
Birnbaum
Daniel
D
Bertucci
François
F
D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D018270:Carcinoma, Ductal, Breast / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D018275:Carcinoma, Lobular / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D018276:Carcinoma, Medullary / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D003198:Computer Simulation; D018572:Disease-Free Survival; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D019394:Ki-67 Antigen / Q000378:metabolism; D008207:Lymphatic Metastasis; D008875:Middle Aged; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism; D047368:Tumor Burden; D016159:Tumor Suppressor Protein p53 / Q000378:metabolism
D019394:Ki-67 Antigen; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D016159:Tumor Suppressor Protein p53