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Disease Markers
Volume 2015 (2015), Article ID 179689, 6 pages
Research Article

Anti-MUC1 Antibody in Nipple Aspirate Fluids Correlates with Tumor Aggressiveness in Breast Cancer: A Feasibility Study

1Division of Surgical Oncology, Department of Surgery, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA 15213, USA
2Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA

Received 19 June 2015; Revised 27 October 2015; Accepted 11 November 2015

Academic Editor: Chih-Hung Ku

Copyright © 2015 Ebru Menekse et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Antibodies against MUC1 are found in circulation of breast cancer (BC) patients. We hypothesized that anti-MUC1 antibodies might be present in even a higher concentration in nipple aspirate fluid (NAF) and could be used to predict aggressiveness of BC. Serum and NAF samples were collected from high risk lesions, BC, and healthy contralateral breasts. ELISA was used to measure the amount of IgG, IgM, and IgA against a tumor-specific MUC1 peptide derived from the extracellular tandem repeat domain of MUC1. Tumor characteristics were recorded prospectively; 120 NAF samples were obtained from a total of 77 women in the study. There was no significant difference of anti-MUC1 antibody levels compared to BC with other lesions. Anti-MUC1 IgG level in NAF was higher in triple negative tumors (); serum anti-MUC1 IgG levels were significantly higher in patients with ER (−) tumor and recurrent disease (); NAF anti-MUC1 IgA levels were significantly higher in patients with LVI and Her2-neu (+) tumors (). These results show that NAF could be a reliable biomarker to predict tumor aggressiveness in BC. A larger study will be needed to confirm these data and to investigate the potential of anti-MUC1 antibodies in NAF and serum to predict disease outcome.