Kinome expression profiling and prognosis of basal breast cancers

Molecular Cancer

Research

Emilie Mamessier¹  Patrice Viens²  Pascal Finetti³  Nathalie Cervera³  Daniel Birnbaum³  Stéphane Raynaud³  Jocelyne Jacquemier⁴  Renaud Sabatier⁵  François Bertucci⁶  Eric Lambaudie⁷ 

[1] Centre d'Immunologie Marseille-Luminy, Parc Scientifique & Technologique de Luminy - Case 906, F13288, Marseille cedex 09, France;Department of Medical Oncology, Institut Paoli-Calmettes, 232 Bd. Ste-Marguerite, 13273, Marseille Cedex 09, France;Université de la Méditerranée, 58 Bd Charles Livon, 13284, Marseille Cedex 07, France;Department of Molecular Oncology, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Institut Paoli-Calmettes, 27 bd Leï Roure, 13009, Marseille, France;Department of Molecular Oncology, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Institut Paoli-Calmettes, 27 bd Leï Roure, 13009, Marseille, France;Department of Biopathology, Institut Paoli-Calmettes, 232 Bd. Ste-Marguerite, 13273, Marseille Cedex 09, France;Department of Molecular Oncology, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Institut Paoli-Calmettes, 27 bd Leï Roure, 13009, Marseille, France;Department of Medical Oncology, Institut Paoli-Calmettes, 232 Bd. Ste-Marguerite, 13273, Marseille Cedex 09, France;Department of Molecular Oncology, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Institut Paoli-Calmettes, 27 bd Leï Roure, 13009, Marseille, France;Department of Medical Oncology, Institut Paoli-Calmettes, 232 Bd. Ste-Marguerite, 13273, Marseille Cedex 09, France;Université de la Méditerranée, 58 Bd Charles Livon, 13284, Marseille Cedex 07, France;Department of Surgery, Institut Paoli-Calmettes, 232 Bd. Ste-Marguerite, 13273, Marseille Cedex 09, France;

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

DOI  :  10.1186/1476-4598-10-86

 received in 2011-04-28, accepted in 2011-07-21,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundBasal 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.MethodsDNA 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.ResultsA 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).ConclusionsBased 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.

【 授权许可】

Unknown   
© Sabatier et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.