Hierarchical Clustering Analysis of Tissue Microarray Immunostaining Data Identifies Prognostically Significant Groups of Breast Carcinoma

Abstract

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 < 0.05) and 4 markers showed a trend toward significance (P < 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. 2 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.

Hierarchical cluster analysis of myoepithelial/basal cell markers in adenoid cystic carcinoma and polymorphous low-grade adenocarcinoma

“…In recent years, this statistical tool has been applied to analyze immunohistochemical results on Formalinfixed paraffin-embedded tissues using multiple antibodies, and has demonstrated that tumors clustered into groups that correlated to their site of origin, 23 phenotype, 24,25 biologic potential, 26 and prognosis. [27][28][29] Our goal was to identify a cluster of markers that can be used in combination in a diagnostic immunohistochemical panel for differentiating adenoid cystic carcinoma from polymorphous low-grade adenocarcinoma. In this study, 47 tumors were immunostained with primary antibodies against six proteins, and their expressions were subclassified into three categories (negative, focal, and diffuse expression), generating a dataset comprising of 846 (47 Â 6 Â 3) individual observations.…”

MDM2 protein expression is a negative prognostic marker in breast carcinoma

“…Microarray blocks were constructed using a manual arrayer (Beecher Instruments, Inc., Silver Springs, MD, USA) as previously described. [13][14][15] Sections (4 mm thick) were cut from the array blocks. Antigen retrieval was performed by steaming for 30 min in citrate buffer (pH 6.0).…”

A quantifier-based fuzzy classification system for breast cancer patients

“…Moreover, an alternative approach to gene expression profiling is to use established robust laboratory technology, such as immunocytochemistry on formalin fixed paraffin embedded patient tumour samples. We and others have applied protein biomarker panels, with known relevance to breast cancer, to large numbers of cases using tissue microarrays, exploring the existence and clinical significance of distinct breast cancer classes through clustering approaches [13][14][15][16].…”