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Despite advances in treatment, up to 30% of breast cancer patients experience disease recurrence accompanied by more aggressive disease and poorer prognosis. Treatment of breast cancer is complicated by the presence of multiple breast cancer subtypes, including: luminal, Her2 overexpressing, and aggressive basal-like breast cancers. Identifying new biomarkers specific to breast cancer subtypes could enhance the prediction of patient prognosis and contribute to improved treatment strategies. The microenvironment influences breast cancer progression through expression of growth factors, angiogenic factors and other soluble proteins. In particular, chemokine C-C ligand 2 (CCL2) regulates macrophage recruitment to primary tumors and signals to cancer cells to promote breast tumor progression. Here we employed a software-based approach to evaluate the prognostic significance of CCL2 protein expression in breast cancer subtypes in relation to its expression in the epithelium or stroma or in relation to fibroblast-specific protein 1 (Fsp1), a mesenchymal marker. Immunohistochemistry analysis of tissue microarrays revealed that CCL2 significantly correlated with Fsp1 expression in the stroma and tumor epithelium of invasive ductal carcinoma. In the overall cohort of invasive ductal carcinomas (n=427), CCL2 and Fsp1 expression in whole tissues, stroma and epithelium were inversely associated with cancer stage and tumor size. When factoring in molecular subtype, stromal CCL2 was observed to be most highly expressed in basal-like breast cancers. By Cox regression modeling, stromal CCL2, but not epithelial CCL2, expression was significantly associated with decreased recurrence-free survival. Furthermore, stromal CCL2 (HR=7.51 P=0.007) was associated with a greater hazard than cancer stage (HR=2.45, P=0.048) in multivariate analysis. These studies indicate that stromal CCL2 is associated with decreased recurrence-free survival in patients with basal-like breast cancer, with important implications on the use of stromal markers for predicting patient prognosis.