Pathological features and prognosis of different molecular subtypes of breast cancer

Department of General Surgery, the First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, PR China.
Molecular Medicine Reports (Impact Factor: 1.48). 07/2012; 6(4):779-82. DOI: 10.3892/mmr.2012.981
Source: PubMed

ABSTRACT To examine the pathological features and prognosis of different molecular subtypes of breast cancer, the clinical data of 892 breast cancer patients were retrospectively analyzed and divided into four subtypes according to hormone receptor expression in breast cancer tissue: Her-2 overexpression, luminal A, luminal B and basal-like subtypes. The pathological data and prognosis of these subtypes were compared. Of the 892 breast cancer patients, there were 46 cases (5.2%) with Her-2 overexpression-type, 698 cases (78.3%) with luminal A-type, 38 cases (4.3%) with luminal B-type and 110 patients (12.2%) with basal-like-type. Immunohistochemistry was used to identify the progesterone and estrogen receptors in the tumor tissues. The χ2 test was used to verify the measurement data. The Cox proportional hazard regression model was used for the univariate and multivariate analyses. Results showed there was no statistical difference for lymphatic metastasis among the various molecular subtypes of breast cancer (P>0.05). The distant metastatic rate of patients with Her-2-type breast cancer was significantly higher compared to patients with the other three subtypes (P<0.05). The difference in local recurrence among molecular subtypes was not significantly significant (P>0.05). Lymph node metastasis, age and different molecular subtypes were found to have an impact on patient overall survival (OS) and disease-free survival (DFS). Her-2 overexpression-type breast cancer patients had the lowest 9-year DFS and 7-year OS compared to the other subtypes (P<0.05). Thus, Her-2-type was associated with the worst prognosis. In conlusion, the molecular typing of breast cancer has important clinical value in prognosis estimation and is expected to affect breast cancer treatment approaches.

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