Identification of a β-casein-like peptide in breast nipple aspirate fluid that is associated with breast cancer

Author:

Sauter Edward R1,Davis Wade23,Qin Wenyi1,Scanlon Sarah4,Mooney Brian45,Bromert Karen4,Folk William R4

Affiliation:

1. Department of Surgery, University of North Dakota School of Medicine & Health Sciences, 501 N. Columbia Road, Stop 9037, Grand Forks, ND 58201, USA.

2. Department of Health Management & Informatics, University of Missouri, USA

3. Department of Statistics, University of Missouri, USA

4. Department of Biochemistry, University of Missouri, USA

5. The Charles W Gehrke Proteomics Center, University of Missouri, USA

Abstract

Aims: Nipple aspirate fluid was collected prospectively from women scheduled for diagnostic breast surgery in order to determine protein masses associated with breast cancer, subsets of women with a unique proteomic profile and a breast cancer predictive model. Materials & methods: Breast nipple aspirate fluid was collected preoperatively in 163 breasts from 125 women and analyzed for changes in cell morphology and by SELDI-TOF mass spectrometry over approximately a 44 kDa range (1.5–45 kDa) using IMAC30, CM10 and Q10 ProteinChips. Results: Considering all samples, 16 protein masses were associated with the presence of cancer, the most discriminating being 3592, 6570/6580 and 15870 Da. Excluding women with pathologic nipple discharge or those with a papilloma identified an additional protein of 6383 Da. The best cancer detection models included Breast Imaging Reporting and Data System, age, and either the 4262 (best sensitivity: >87%) or 3592 (best specificity: >94%) peak. MALDI-TOF mass spectrometry demonstrated the 3592 peak, which was most discriminating in many of our cancer prediction models, to be a β-casein-like peptide. Conclusion: Differential nipple aspirate fluid proteomic expression exists between women with/without breast cancer. The most discriminating protein identified is a β-casein-like peptide not previously described. Combining proteomic and clinical information, which are available before surgery, optimizes the prediction of which women have breast cancer.

Publisher

Future Medicine Ltd

Subject

Biochemistry (medical),Clinical Biochemistry,Drug Discovery

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