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Poor-Prognosis Estrogen Receptor–Positive Breast Cancer Identified by Histopathologic Subclassification

Authors' Affiliations: ¹ Translational Oncology, Sydney West Area Health Service; ² Westmead Institute for Cancer Research; ³ Westmead Millennium Institute, Westmead, New South Wales, Australia; ⁴ University of Sydney, Sydney, New South Wales, Australia; ⁵ School of Medical Sciences and ⁶ St. Vincent's Clinical School, University of New South Wales; Sydney, New South Wales, Australia; ⁷ Department of Anatomical Pathology, St. Vincent's Hospital, Darlinghurst, New South Wales, Australia; ⁸ Yorkshire Cancer Research and Liz Dawn Pathology and Translational Sciences Centre, St. James's University Hospital, Leeds, United Kingdom; ⁹ Department of Radiation Oncology, Royal North Shore Hospital, St, Leonards, New South Wales, Australia; ¹⁰ Department of Histopathology, Nottingham City Hospital NHS Trust, Nottingham, United Kingdom; and ¹¹ Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas

Requests for reprints: Rosemary L. Balleine, Medical Oncology, Westmead Hospital, P. O. Box 533, Wentworthville, New South Wales 2145, Australia. Phone: 61-2-98458086; Fax: 61-2-98459102; E-mail: rosemary_balleine{at}wmi.usyd.edu.au.

Purpose: Identification of biologically and clinically distinct breast cancer subtypes could improve prognostic assessment of primary tumors. The characteristics of "molecular" breast cancer subtypes suggest that routinely assessed histopathologic features in combination with limited biomarkers may provide an informative classification for routine use.

Experimental Design: Hierarchical cluster analysis based on components of histopathologic grade (tubule formation, nuclear pleomorphism, and mitotic score), expression of ER, cytokeratin 5/6, and HER2 amplification identified four breast cancer subgroups in a cohort of 270 cases. Cluster subgroup membership was compared with observed and Adjuvant! Online predicted 10-year survival. Survival characteristics were confirmed in an independent cohort of 300 cases assigned to cluster subgroups using a decision tree model.

Results: Four distinct breast cancer cluster subgroups (A-D) were identified that were analogous to molecular tumor types and showed a significant association with survival in both the original and validation cohorts (P < 0.001). There was a striking difference between survival for patients in cluster subgroups A and B with ER⁺ breast cancer (P < 0.001). Outcome for all tumor types was well estimated by Adjuvant! Online, with the exception of cluster B ER⁺ cancers where Adjuvant! Online was too optimistic.

Conclusions: Breast cancer subclassification based on readily accessible pathologic features could improve prognostic assessment of ER⁺ breast cancer.