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MMP11 expression in intratumoral inflammatory cells in breast cancer
Author(s) -
Eiro Noemi,
Cid Sandra,
Fernández Berta,
Fraile Maria,
Cernea Ana,
Sánchez Rosario,
Andicoechea Alejandro,
DeAndrés Galiana Enrique J,
González Luis O,
FernándezMuñiz Zulima,
FernándezMartínez Juan L,
Vizoso Francisco J
Publication year - 2019
Publication title -
histopathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.626
H-Index - 124
eISSN - 1365-2559
pISSN - 0309-0167
DOI - 10.1111/his.13956
Subject(s) - breast cancer , stromal cell , medicine , immunohistochemistry , cancer , oncology , proportional hazards model , metastasis , immune system , cancer research , breast carcinoma , pathology , immunology
Aims It is known that matrix metalloproteinase (MMP)‐11 has a role in tumour development and progression, and also that immune cells can influence cancer cells to increase their proliferative and invasive properties. The aim of the present study was to propose the evaluation of MMP11 expression by intratumoral mononuclear inflammatory cells (MICs) as a useful biological marker for breast cancer prognosis. Methods and results This study comprised 246 women with invasive breast carcinoma, and a long follow‐up period. Patients were stratified with regard to nodal status and to the development of metastatic disease. The median follow‐up period in patients without metastasis was 146 months and in patients with metastatic disease 31 months. MMP11 was determined by immunohistochemistry. For relapse‐free survival (RFS) and overall survival (OS) analysis we used the Cox’s univariate method. Cox’s regression model was used to examine the interactions between different prognostic factors in a multivariate analysis. Conclusions Our results showed that MMP11 expression by stromal cells was significantly associated with prognosis. MMP11 expression by cancer‐associated fibroblasts (CAFs) was associated with both shortened RFS and OS, but MMP11 expression by MICs showed a stronger association with both shortened RFS and OS, therefore being the most potent and independent factor to predict RFS and OS.