A gene expression signature identifies two prognostic subgroups of basal breast cancer

Springer Science and Business Media LLC - Tập 126 - Trang 407-420 - 2010
Renaud Sabatier1,2, Pascal Finetti1, Nathalie Cervera1, Eric Lambaudie3, Benjamin Esterni4, Emilie Mamessier5, Agnès Tallet6, Christian Chabannon7,8, Jean-Marc Extra2, Jocelyne Jacquemier9, Patrice Viens2,8, Daniel Birnbaum1, François Bertucci1,2,8
1Département d’Oncologie Moléculaire, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Institut Paoli-Calmettes, Marseille, France
2Département d’Oncologie Médicale, Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille Cedex 09, France
3Département de Chirurgie, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille, France
4Bureau d’Etudes Cliniques, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille, France
5Centre d'Immunologie de Marseille-Luminy, Marseille, France
6Département de Radiothérapie, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille, France
7Centre de Ressources Biologiques, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille, France
8Université de la Méditerranée. Marseille, France
9Département d’Anatomopathologie, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille, France

Tóm tắt

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.

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Springer Science and Business Media LLC

Tập 126

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