Aug 2008 Molecular & cellular proteomics : MCP

Protein profiling of human breast tumor cells identifies novel biomarkers associated with molecular subtypes.

Auteurs

Gonçalves A, Charafe-Jauffret E, Bertucci F, Audebert S, Toiron Y, Esterni B, Monville F, Tarpin C, Jacquemier J, Houvenaeghel G, Chabannon C, Extra JM, Viens P, Borg JP, Birnbaum D

Résumé

Molecular subtypes of breast cancer with relevant biological and clinical features have been defined recently, notably ERBB2-overexpressing, basal-like, and luminal-like subtypes. To investigate the ability of mass spectrometry-based proteomics technologies to analyze the molecular complexity of human breast cancer, we performed a SELDI-TOF MS-based protein profiling of human breast cell lines (BCLs). Triton-soluble proteins from 27 BCLs were incubated with ProteinChip arrays and subjected to SELDI analysis. Unsupervised global hierarchical clustering spontaneously discriminated two groups of BCLs corresponding to « luminal-like » cell lines and to « basal-like » cell lines, respectively. These groups of BCLs were also different in terms of estrogen receptor status as well as expression of epidermal growth factor receptor and other basal markers. Supervised analysis revealed various protein biomarkers with differential expression in basal-like versus luminal-like cell lines. We identified two of them as a carboxyl terminus-truncated form of ubiquitin and S100A9. In a small series of frozen human breast tumors, we confirmed that carboxyl terminus-truncated ubiquitin is observed in primary breast samples, and our results suggest its higher expression in luminal-like tumors. S100A9 up-regulation was found as part of the transcriptionally defined basal-like cluster in DNA microarrays analysis of human tumors. S100A9 association with basal subtypes as well as its poor prognosis value was demonstrated on a series of 547 tumor samples from early breast cancer deposited in a tissue microarray. Our study shows the potential of integrated genomics and proteomics profiling to improve molecular knowledge of complex tumor phenotypes and identify biomarkers with valuable diagnostic or prognostic values.

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