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This article is part of the supplement: Breast Cancer Research 2008

Poster presentation

Identification and definition of novel clinical phenotypes of breast cancer through consensus derived from automated clustering methods

AR Green1, JM Garibaldi1, D Soria1, F Ambrogi2, G Ball3, PJG Lisboa4, TA Etchells4, P Boracchi2, E Biganzoli2, RD Macmillan5, RW Blamey5, DG Powe5, EA Rakha5 and IO Ellis1

Author Affiliations

1 University of Nottingham, UK

2 University of Milan, Italy

3 Nottingham Trent University, Nottingham, UK

4 Liverpool John Moores University, Liverpool, UK

5 Nottingham University Hospitals NHS Trust, Nottingham, UK

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Breast Cancer Research 2008, 10(Suppl 2):P69  doi:10.1186/bcr1953

The electronic version of this article is the complete one and can be found online at: http://breast-cancer-research.com/content/10/S2/P69


Published:13 May 2008

© 2008 BioMed Central Ltd

Background

Breast cancer is a heterogeneous disease for which several forms have recently been identified on the basis of their gene expression characteristics [1]. We have previously demonstrated that protein expression characteristics can be used to identify comparable classes [2]. In the present study we extend this approach using improved biostatistical methods to confirm the validity of such an approach and to further define the key criteria for class membership.

Methods

Expression of 25 proteins, with known relevance to breast cancer, have been assessed in a series of 1,076 patients. This large dataset has been examined by four alternative computational data clustering techniques. Concordance between techniques was used to elucidate core classes of patients that could be well characterised.

Results

A total of 663/1,076 (62%) patients were assigned to six different core classes, while 413 (38%) patients were of indeterminate or mixed class. Three of these core classes correspond to well known clinical phenotypes (luminal A, luminal B and HER2). Two classes correspond to the well known basal phenotype, but exhibit a novel differentiation into two subgroups. The last class appears to characterise a novel luminal subgroup.

Conclusion

The present study serves to confirm that key clinical phenotypes of breast cancer can be identified. It has identified that both the luminal and basal breast cancer phenotypes appear to be heterogeneous and contain distinct subgroups. Of importance is the observation that only 62% of breast cancer cases in this cohort have been assigned to the determined phenotypes, while the remaining 38% of cases express mixed or indeterminate characteristics. This latter observation, although previously recognised, has not been emphasised in the past. It has important clinical implications should either cDNA expression or protein expression assays be used for stratification of patients into treatment groups either in clinical trails or for routine clinical management. The clinical phenotypes determined in this study are a new luminal group, luminal N, the new basal subgroups, basal X and basal Y, and the previously well-established luminal A, luminal B and HER2 groups.

Acknowledgements

Funded by Breast Cancer Campaign (2005Nov08), BIOPATTERN FP6 Network of Excellence (FP6-IST-508803) and the BIOPTRAIN FP6 Marie-Curie EST Fellowship (FP6-007597).

References

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