Identification of a ββ-casein-like peptide in breast nipple aspirate fluid that is associated with breast cancer. (October 2009)

• Record Type: Journal Article
• Authors: Sauter, Edward R; Davis, Wade; Qin, Wenyi; Scanlon, Sarah; Mooney, Brian; Bromert, Karen; Folk, William R
• Is Part Of: Biomarkers in medicine. Volume 3:Number 5(2009)
• Page Start: 577
• Page End: 588
• Publication Date: 2009-10
• Subjects: biomarker -- breast cancer -- early detection -- nipple aspirate fluid -- proteomics; Biochemical markers -- Periodicals; 610.28
• DOI: 10.2217/bmm.09.46
• Languages: English
• ISSNs: 1752-0363

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 ββ-casein-like peptide.Conclusion: Differential nipple aspirate fluid proteomic expression exists between women with/without breast cancer. The most discriminating protein identified is a ββ-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.