p53 expression in breast cancer predicts tumors with low probability of non-sentinel nodes infiltration.

Abstract

AIM: Several predictive tools of non-sentinel lymph nodes neoplastic involvement when a positive sentinel lymph node is found have been described. However, molecular factors have been rarely evaluated to build these tools. The aim of this study was to establish which factors predicted non-sentinel lymph nodes infiltration in our setting, including some molecular factors. MATERIAL AND METHODS: We carried out a retrospective review of 161 patients with breast cancer and a positive sentinel lymph node who had undergone axillary lymph node dissection, none of whom had received neoadjuvant treatment. Features evaluated as predictive factors for non-sentinel node positivity were: menopausal status, tumor size, histological subtype, histological grade, lymphovascular invasion, extracapsular invasion, Ki67 index, hormonal receptors, CerbB2 and p53 expression, size of sentinel lymph node metastases and number of sentinel lymph nodes affected. RESULTS: Tumor size (P = 0.001), size of sentinel lymph node metastases (P = 0.001), lobular invasive carcinoma (P = 0.05) and lymphovascular invasion (P = 0.006) were significantly associated with non-sentinel lymph node positivity. Tumor p53 positive expression was strongly associated with non-sentinel lymph node negativity (P = 0.000). In multivariate analysis, all these factors but tumor size maintained their significance. The discrimination power of the model calculated by the area under the receiver-operator curve was 0.811 (95% confidence interval, 0.741-0.880). CONCLUSION: p53 expression in breast cancer was highly predictive of non-sentinel lymph node negativity in our study. New studies should evaluate if it would be useful to add p53 expression to other existing predictive tools.This research was funded by theinternal resources of the departments involved (Breast Functional Unit, Obstetrics and Gynecology Department, Nuclear Medicine Department and Pathology Departmen

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