Molecular Classification of Breast Carcinomas Using Tissue... : Diagnostic Molecular Pathology (original) (raw)

Original Articles

Callagy, Grace M.D.; Cattaneo, Elena M.D.; Daigo, Yataro M.D., Ph.D.; Happerfield, Lisa M.L.S.O.; Bobrow, Lynda G. M.D.; Pharoah, Paul D. P. M.D., Ph.D.; Caldas, Carlos M.D.

From the Cancer Genomics Program, Department of Oncology, University of Cambridge, Hutchison/MRC Research Centre, Cambridge, CB2 2XZ (G.C., E.C., Y.D., P.D.P.P., C.C.), Division of Histopathology (G.C., L.H., L.G.B.), the Cambridge Breast Unit (G.C., L.G.B., C.C), Addenbrooke's Hospital, Hills Road, Cambridge, and Strangeways Research Laboratories, Cambridge, United Kingdom (P.D.P.P.).

Sources of support: Grants from the Breast Cancer Campaign and Cancer Research UK; Medical Research Council Clinical Training Fellowship and Sackler Studentship to GC; Fondazione Europa Donna Fellowship to EC; and Uehara Memorial Foundation Postdoctoral Research Fellowship to YD.

Address correspondence and reprint requests to Dr Carlos Caldas, Cancer Genomics Program, Department of Oncology, University of Cambridge, Hutchinson/MRC Research Centre, Cambridge CB2 2XZ, United Kingdom (e-mail: [email protected]).

Abstract

The histopathologic classification of breast cancer stratifies tumors based on tumor grade, stage, and type. Despite an overall correlation with survival, this classification is poorly predictive and tumors with identical grade and stage can have markedly contrasting outcomes. Recently, breast carcinomas have been classified by their gene expression profiles on frozen material. The validation of such a classification on formalin-fixed paraffin-embedded tumor archives linked to clinical information in a high-throughput fashion would have a major impact on clinical practice. The authors tested the ability of tumor tissue microarrays (TMAs) to sub-classify breast cancers using a TMA containing 107 breast cancers. The pattern of expression of 13 different protein biomarkers was assessed by immunohistochemistry and the multidimensional data was analyzed using an unsupervised two-dimensional clustering algorithm. This revealed distinct tumor clusters which divided into two main groups correlating with tumor grade (P <0.001) and nodal status (P = 0.04). None of the protein biomarkers tested could individually identify these groups. The biological significance of this classification is supported by its similarity with one derived from gene expression microarray analysis. Thus, molecular profiling of breast cancer using a limited number of protein biomarkers in TMAs can sub-classify tumors into clinically and biologically relevant subgroups.