Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma.

Prognostically relevant cluster groups, based on gene expression profiles, have been recently identified for breast cancers, lung cancers, and lymphoma. Our aim was to determine whether hierarchical clustering analysis of multiple immunomarkers (protein expression profiles) improves prognostication in patients with invasive breast cancer. A cohort of 438 sequential cases of invasive breast cancer with median follow-up of 15.4 years was selected for tissue microarray construction. A total of 31 biomarkers were tested by immunohistochemistry on these tissue arrays. The prognostic significance of individual markers was assessed by using Kaplan-Meier survival estimates and log-rank tests. Seventeen of 31 markers showed prognostic significance in univariate analysis (P < or = 0.05) and 4 markers showed a trend toward significance (P < or = 0.2). Unsupervised hierarchical clustering analysis was done by using these 21 immunomarkers, and this resulted in identification of three cluster groups with significant differences in clinical outcome. chi2 analysis showed that expression of 11 markers significantly correlated with membership in one of the three cluster groups. Unsupervised hierarchical clustering analysis with this set of 11 markers reproduced the same three prognostically significant cluster groups identified by using the larger set of markers. These cluster groups were of prognostic significance independent of lymph node metastasis, tumor size, and tumor grade in multivariate analysis (P=0.0001). The cluster groups were as powerful a prognostic indicator as lymph node status. This work demonstrates that hierarchical clustering of immunostaining data by using multiple markers can group breast cancers into classes with clinical relevance and is superior to the use of individual prognostic markers.

Stromal CD10 expression in invasive breast carcinoma correlates with poor prognosis, estrogen receptor negativity, and high grade.

CD10 is a zinc-dependent peptidase (metalloproteinase), which degrades a variety of bioactive peptides. Earlier studies suggested that CD10 expression in tumor stroma is associated with biological aggressiveness of the tumor. To date, only one study has addressed the clinical significance of stromal CD10 expression in invasive carcinoma of the breast. The aim of this confirmatory study is to evaluate stromal CD10 expression in breast carcinoma and to examine associations between CD10, clinicopathological variables, and patient outcome. Tissue microarrays, containing 438 cases of invasive breast carcinoma and 15 cases of ductal carcinoma in situ with 15 years median follow-up time, were assembled. CD10 expression was assessed by immunohistochemistry and scored as negative, weak and strong. Nonparametric correlational tests, univariate and multivariate survival analyses were performed. Stromal CD10 was preferentially expressed in invasive compared to noninvasive breast cancers (P=0.003). There were correlations between stromal CD10 expression and higher tumor grade (P=0.01) and estrogen receptor (ER) negative status (P=0.002). There was no correlation between CD10 and lymph node status, tumor size, histological subtype, progesterone receptors, and Her2 status. Stromal CD 10 expression was associated with decreased long-term disease-specific and overall survival in the entire cohort (P<0.01), and in lymph node negative (P<0.05), but not lymph node positive subset of patients. It approached prognostic significance in multivariate analysis (P=0.06) when lymph node status, tumor size, ER and Her2 were considered in the same model; and was associated with a relative risk of death of 2.8, compared to relative risk of 2.4 for lymph node positive status. Thus, stromal CD10 expression in invasive carcinoma of the breast is associated with ER negativity, higher tumor grade and decreased survival and constitutes a potential prognostic marker and a target for development of novel therapies.