Recent studies on gene molecular profiling using
cDNA microarray in a relatively small series of
breast cancer have identified biologically distinct groups with apparent clinical and prognostic relevance. The validation of such new taxonomies should be confirmed on larger series of cases prior to acceptance in clinical practice. The development of tissue microarray (TMA) technology provides methodology for high-throughput concomitant analyses of multiple
proteins on large numbers of archival tumour samples. In our study, we have used immunohistochemistry techniques applied to TMA preparations of 1,076 cases of invasive
breast cancer to study the combined
protein expression profiles of a large panel of well-characterized commercially available
biomarkers related to epithelial cell lineage, differentiation,
hormone and
growth factor receptors and gene products known to be altered in some forms of
breast cancer. Using hierarchical clustering methodology, 5 groups with distinct patterns of
protein expression were identified. A sixth group of only 4 cases was also identified but deemed too small for further detailed assessment. Further analysis of these clusters was performed using multiple layer perceptron (MLP)-artificial neural network (ANN) with a back propagation algorithm to identify key
biomarkers driving the membership of each group. We have identified 2 large groups by their expression of
luminal epithelial cell phenotypic characteristics,
hormone receptors positivity, absence of basal epithelial phenotype characteristics and lack of
c-erbB-2 protein overexpression. Two additional groups were characterized by high c-erbB-2 positivity and negative or weak
hormone receptors expression but showed differences in MUC1 and
E-cadherin expression. The final group was characterized by strong basal epithelial characteristics, p53 positivity, absent
hormone receptors and weak to low
luminal epithelial
cytokeratin expression. In addition, we have identified significant differences between clusters identified in this series with respect to established prognostic factors including tumour grade, size and histologic tumour type as well as differences in patient outcomes. The different
protein expression profiles identified in our study confirm the biologic heterogeneity of
breast cancer and demonstrate the clinical relevance of classification in this manner. These observations could form the basis of revision of existing traditional classification systems for
breast cancer.