Identification of prognostically relevant and reproducible subsets of endometrial adenocarcinoma based on clustering analysis of immunostaining data

摘要

Panels of immunomarkers can provide greater information than single markers, but the problem of how to optimally interpret data from multiple immunomarkers is unresolved. We examined the expression profile of 12 immunomarkers in 200 endometrial carcinomas using a tissue microarray. The outcomes of groups of patients were analyzed by using the Kaplan–Meier method, using the log-rank statistic for comparison of survival curves. Correlation between clustering results and traditional prognosticators of endometrial carcinoma was examined by either Fisher's exact test or 2-test. Multivariate analysis was performed using a proportional hazards method (Cox regression modeling). Seven of the 12 immunomarkers studied showed prognostic significance in univariate analysis (PP=0.06). These eight markers were used in unsupervised hierarchical clustering of the cases, and resulted in identification of three cluster groups. There was a statistically significant difference in patient survival between these cluster groups (P=0.0001). The prognostic significance of the cluster groups was independent of tumor stage and patient age on multivariate analysis (P=0.014), but was not of independent significance when either tumor grade or cell type was added to the model. The cluster group designation was strongly correlated with tumor grade, stage, and cell type (P=0.79, concordance rate=89.6%). Unsupervised hierarchical clustering of immunostaining data identifies prognostically relevant subsets of endometrial adenocarcinoma. Such analysis is reproducible, showing less interobserver variability than histopathological assessment of tumor cell type or grade.