Open Access Open Badges Research

Kinome expression profiling and prognosis of basal breast cancers

Renaud Sabatier12, Pascal Finetti1, Emilie Mamessier3, Stéphane Raynaud1, Nathalie Cervera1, Eric Lambaudie4, Jocelyne Jacquemier15, Patrice Viens26, Daniel Birnbaum1 and François Bertucci126*

Author Affiliations

1 Department of Molecular Oncology, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Institut Paoli-Calmettes, 27 bd Leï Roure, 13009 Marseille, France

2 Department of Medical Oncology, Institut Paoli-Calmettes, 232 Bd. Ste-Marguerite, 13273 Marseille Cedex 09, France

3 Centre d'Immunologie Marseille-Luminy, Parc Scientifique & Technologique de Luminy - Case 906 - F13288 Marseille cedex 09, France

4 Department of Surgery, Institut Paoli-Calmettes, 232 Bd. Ste-Marguerite, 13273 Marseille Cedex 09, France

5 Department of Biopathology, Institut Paoli-Calmettes, 232 Bd. Ste-Marguerite, 13273 Marseille Cedex 09, France

6 Université de la Méditerranée, 58 Bd Charles Livon, 13284 Marseille Cedex 07, France

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Molecular Cancer 2011, 10:86  doi:10.1186/1476-4598-10-86

Published: 21 July 2011



Basal breast cancers (BCs) represent ~15% of BCs. Although overall poor, prognosis is heterogeneous. Identification of good- versus poor-prognosis patients is difficult or impossible using the standard histoclinical features and the recently defined prognostic gene expression signatures (GES). Kinases are often activated or overexpressed in cancers, and constitute targets for successful therapies. We sought to define a prognostic model of basal BCs based on kinome expression profiling.


DNA microarray-based gene expression and histoclinical data of 2515 early BCs from thirteen datasets were collected. We searched for a kinome-based GES associated with disease-free survival (DFS) in basal BCs of the learning set using a metagene-based approach. The signature was then tested in basal tumors of the independent validation set.


A total of 591 samples were basal. We identified a 28-kinase metagene associated with DFS in the learning set (N = 73). This metagene was associated with immune response and particularly cytotoxic T-cell response. On multivariate analysis, a metagene-based predictor outperformed the classical prognostic factors, both in the learning and the validation (N = 518) sets, independently of the lymphocyte infiltrate. In the validation set, patients whose tumors overexpressed the metagene had a 78% 5-year DFS versus 54% for other patients (p = 1.62E-4, log-rank test).


Based on kinome expression, we identified a predictor that separated basal BCs into two subgroups of different prognosis. Tumors associated with higher activation of cytotoxic tumor-infiltrative lymphocytes harbored a better prognosis. Such classification should help tailor the treatment and develop new therapies based on immune response manipulation.

breast cancer; basal-like; gene expression profiling; prognosis; immune response