Original Article

Modern Pathology (2007) 20, 1156–1165; doi:10.1038/modpathol.3800950; published online 24 August 2007

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

Abdulmohsen Alkushi1,*, Blaise A Clarke2,*, Majid Akbari3, Nikita Makretsov2, Peter Lim4, Dianne Miller5, Anthony Magliocco3, Andrew Coldman6, Matt van de Rijn7, David Huntsman2, Robin Parker3 and C Blake Gilks2

  1. 1Department of Pathology, King Fahad National Guard Hospital, Riyadh, Saudi Arabia
  2. 2Genetic Pathology Evaluation Centre of the Prostate Research Centre, Department of Pathology, Vancouver General Hospital and British Columbia Cancer Agency, Vancouver, BC, Canada
  3. 3Department of Pathology, University of Calgary, Calgary, AB, Canada
  4. 4Department of Radiation Oncology, British Columbia Cancer Agency, Vancouver, BC, Canada
  5. 5Department of Gynecologic Oncology and Gynecology Tumour Group, British Columbia Cancer Agency, Vancouver, BC, Canada
  6. 6Population and Preventive Oncology, British Columbia Cancer Agency, Vancouver, BC, Canada
  7. 7Department of Pathology, Stanford University Medical Center, Stanford, CA, USA

Correspondence: Dr CB Gilks, MD, FRCPC, Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Room 1259, 1st Floor JPPN, 855 West 12th Avenue, Vancouver, BC, Canada V5Z 1M9. E-mail: Blake.Gilks@vch.ca

*These authors contributed equally to this work.

Received 7 March 2007; Revised 29 May 2007; Accepted 5 June 2007; Published online 24 August 2007.

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Abstract

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 chi2-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 (P<0.05) and 1 marker showed a trend toward significance (P=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<0.0001 for each). Interlaboratory reproducibility of subclassification of endometrial adenocarcinoma by hierarchical clustering analysis was verified by showing highly reproducible assignment of individual cases to specific cluster groups when the immunostaining was performed, interpreted, and clustered in a second laboratory (kappa=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.

Keywords:

adenocarcinoma, clustering analysis, endometrial cancer, immunohistochemistry, reproducibility, subclassification