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

  • Preclinical study
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Abstract

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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Abbreviations

DFS:

Disease-free survival

DWD:

Distance weighted discrimination

ER:

Estrogen receptor

GEO:

Gene expression omnibus

HR:

Hazard ratio

IHC:

Immunohistochemistry

IPC:

Institut Paoli-Calmettes

MBC:

Medullary breast cancer

NCBI:

National cancer for biotechnology information

PR:

Progesterone receptor

SBR:

Scarff Bloom Richardson

SSP:

Single sample predictor

SVM:

Support vector machine

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Acknowledgments

Our study is supported by Institut Paoli-Calmettes, Inserm, Institut National du Cancer (Tr 2008), Association pour le Recherche contre le Cancer, Ligue Nationale contre le Cancer (label DB), and Fondation pour la Recherche Médicale (RS 2009).

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Authors and Affiliations

  1. Département d’Oncologie Moléculaire, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Institut Paoli-Calmettes, Marseille, France

    Renaud Sabatier, Pascal Finetti, Nathalie Cervera, Daniel Birnbaum & François Bertucci

  2. Département d’Oncologie Médicale, Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, 232 Bd. Sainte-Marguerite, Marseille Cedex 09, 13273, France

    Renaud Sabatier, Jean-Marc Extra, Patrice Viens & François Bertucci

  3. Département de Chirurgie, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille, France

    Eric Lambaudie

  4. Bureau d’Etudes Cliniques, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille, France

    Benjamin Esterni

  5. Centre d’Immunologie de Marseille-Luminy, Marseille, France

    Emilie Mamessier

  6. Département de Radiothérapie, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille, France

    Agnès Tallet

  7. Centre de Ressources Biologiques, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille, France

    Christian Chabannon

  8. Université de la Méditerranée, Marseille, France

    Christian Chabannon, Patrice Viens & François Bertucci

  9. Département d’Anatomopathologie, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, UMR891 Inserm, Marseille, France

    Jocelyne Jacquemier

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  1. Renaud Sabatier
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  2. Pascal Finetti
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  3. Nathalie Cervera
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  4. Eric Lambaudie
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  8. Christian Chabannon
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  9. Jean-Marc Extra
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  10. Jocelyne Jacquemier
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  11. Patrice Viens
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  12. Daniel Birnbaum
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  13. François Bertucci
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Correspondence to François Bertucci.

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10549_2010_897_MOESM6_ESM.tif

Supplementary Fig. 1. GSEA shows correlations between our SVM model-based classification of basal breast cancers and cell-type specific gene expression signatures of leucocytes. A Results of GSEA with the five tested signatures. NES, normalized enrichment score; FDR, false discovery rate. B Enrichment plots for the three significant signatures: B-cell, T-cell, and CD8+ T-cell (from left to right). (TIFF 1470 kb)

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Sabatier, R., Finetti, P., Cervera, N. et al. A gene expression signature identifies two prognostic subgroups of basal breast cancer. Breast Cancer Res Treat 126, 407–420 (2011). https://doi.org/10.1007/s10549-010-0897-9

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