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Characteristics of basal cytokeratin expression in breast cancer

Authors :
Andrew R. Green
Emad A. Rakha
Alaa T. Alshareeda
Daniele Soria
Christopher C. Nolan
Jonathan M. Garibaldi
Ian O. Ellis
Source :
Breast cancer research and treatment. 139(1)
Publication Year :
2013

Abstract

Breast cancer is recognised to be a heteroge- neous disease and the second most common cause of morbidity and mortality worldwide in women. Basal-like breast cancer (BLBC) is associated with aggressive char- acteristics including development of recurrent disease and reduced survival. BLBC has been defined in some studies as tumours lacking both oestrogen receptor and proges- terone receptor protein expression. Gene expression studies have shown that these tumours are also associated with expression of basal-type cytokeratins, the phenotypic pat- terns of basal cytokeratin expression in BLBC have not been widely studied. A well-characterised series of 995 invasive breast cancers with a long-term follow up were investigated using immunohistochemical staining for four basal cytokeratins (CK5, CK5/6, CK14 and CK17). The data were analysed using univariate and clustering analysis. As a result BLBC, as defined by negativity for ER and HER2 showed variable positivity for basal cytokeratin expression: 61.7 % CK5, 50.5 % CK5/6, 24.2 % CK14 and 23 % CK17. These characteristics were associated with poor outcome characteristics including high histological grade, mitosis, pleomorphism and tumour size (1.5 cm. CK5 positivity was more associated with ER - , PgR - ,T N and double ER - PgR - , than the other cytokeratins. Four different clusters of basal cytokeratin expression patterns were identified: (1) negativity for all basal cytokeratins, (2) CK5 ? /CK17 - , (3) CK5 - /CK17 ? and (4) CK5 ? /CK17 ? . These patterns of basal cytokeratin expression associated with differences in patient outcome, clusters 1 and 3 showed better outcomes than cluster 4 and 2, with cluster 2 having the poorest prognosis. In conclusion, four basal cytokeratin expression patterns were identified in human breast cancer using unsupervised clustering analysis and these patterns are associated with differences in patient outcome.

Details

ISSN :
15737217
Volume :
139
Issue :
1
Database :
OpenAIRE
Journal :
Breast cancer research and treatment
Accession number :
edsair.doi.dedup.....30ba29383bcec6fe3eb71269787971ec