Evaluation of immunohistochemical markers in non-small cell lung cancer by unsupervised hierarchical clustering analysis: a tissue microarray study of 284 cases and 18 markers.

ABSTRACT

This study has investigated a panel of immunomarkers in non-small cell lung carcinoma (NSCLC). Unsupervised hierarchical clustering analysis was used to investigate the possibility of identifying different subgroups in NSCLC based on their molecular expression profile rather than morphological features. A tissue microarray consisting of 284 cases of NSCLC was constructed. Immunohistochemistry was used to detect the presence of 18 biomarkers including synaptophysin, chromogranin, bombesin, NSE, GFI1, ASH-1, p53, p63, p21, p27, E2F-1, cyclin D1, Bcl-2, TTF-1, CEA, HER2/neu, cytokeratin 5/6, and pancytokeratin. Univariate analysis of all 18 markers for prognostic significance was performed. Immunohistochemical scoring data for NSCLC were analysed by unsupervised hierarchical clustering analysis. Kaplan-Meier survival curves were plotted for the different cluster groups of lung tumours identified by this method. Analysis of the three different World Health Organization (WHO) subtypes (adenocarcinoma, squamous cell carcinoma, large cell carcinoma) of NSCLC individually showed that different markers were significant in different subtypes. For example, p53 and p63 were significant for squamous cell carcinoma (p = 0.007 and p = 0.03, respectively), whereas cyclin D1 and HER2/neu were significant prognostic markers for adenocarcinoma (p = 0.025 and p = 0.015, respectively). These markers were not significant prognostic predictors for NSCLC as a group. Hierarchical clustering analysis of NSCLC produced four separate cluster groups, although the vast majority of cases were found in two cluster groups, one dominated by squamous cell carcinoma and the other by adenocarcinoma. The clinical outcomes of cases from the four cluster groups were not significantly different. Prognostic indicators vary between different morphological subtypes of NSCLC. Unsupervised hierarchical clustering analysis, based on an extended immunoprofile, identifies two main cluster groups corresponding to adenocarcinoma and squamous cell carcinoma; cases of large cell carcinomas are assigned to one of these two groups based on their molecular phenotype.