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Luminal subtypes predict improved survival following central nervous system metastasis in patients with surgically managed metastatic breast carcinoma.

Wiens AL,Martin SE,Bertsch EC,Vance GH,Stohler RA,Cheng L,Badve S,Hattab EM

Abstract

Metastatic breast cancer to the central nervous system (CNS) is second only to lung cancer metastasis to the CNS in frequency. Patients with triple-negative primary breast cancer and those with human epidermal growth factor receptor 2 (HER2)-positive primary breast cancer are at an increased risk for metastasis. Very little is known about predictive or prognostic variables once patients develop CNS metastases. Currently, therapeutic options are limited, with surgery generally offered primarily to those with solitary lesions.
To determine the influence of molecular subtypes of metastatic breast cancer on survival from the time of CNS metastasis and to aid in the prognostic stratification of these patients.
We identified 59 cases of metastatic breast cancer to the CNS and analyzed them for various demographic and clinicopathologic parameters. Tumors were categorized into molecular subtypes using immunohistochemical methods: luminal A [estrogen receptor (ER⁺)/Ki67low], luminal B (ER⁺/Ki67 high), intrinsic HER2 (ER⁻/HER2⁺), and triple-negative. Survival after CNS metastasis for each group was plotted using a Kaplan-Meier curve, and multivariate analysis was performed.
Patients with metastases from luminal tumors had a statistically significant survival advantage when compared with those of the triple-negative phenotype. Importantly, survival among patients with luminal A and luminal B tumors was not significantly different. Similarly, patient's age, histologic grade, and number of lesions did not contribute to determining outcomes.
Estrogen receptor positivity (ie, luminal phenotype) of tumors appears to determine outcomes after development of metastases. In contrast, proliferation rate had little or no effect on the long-term survival. Understanding the biology of metastases can help stratify patients into prognostically meaningful categories and tailor treatment regimens for individual patients.

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