title: Proteomic analysis of nipple aspirate fluid using SELDI-TOF-MS.
abstract: Proteomic analysis of body fluids, including breast nipple aspirate fluid (NAF), holds promise to aid in early cancer detection. We conducted a prospective trial that collected NAF from women scheduled for diagnostic breast surgery to determine 1) the consistency of proteomic results, 2) protein masses associated with breast cancer, 3) subsets of women with a unique proteomic profile and 4) a breast cancer predictive model. NAF was collected preoperatively in 114 women and analyzed by SELDI-TOF mass spectrometry over a 3-50 kDa range using H4, NP and SAX ProteinChips. For all 3 chips, the same protein peaks were detected over 90% of the time in duplicate samples. The overall coefficient of variation was < or = 0.17% for each chip for the internal standard and < or = 0.29% for the unknown proteins. Seven candidate protein ion masses frequently expressed in NAF were identified. Three (5,200-H4, p=.04, 11,880-H4, p=.07 and 13,880 Da-SAX, p=.03) were differentially expressed in women with/without breast cancer. Protein expression differed between women with/without pathologic nipple discharge (PND), but the 5,200, 11,880 and 13,880 proteins remained associated with breast cancer even if PND samples were excluded. Subset analysis identified differences in expression between benign disease and DCIS and between DCIS and invasive cancer for the 5,200 and 33,400 Da proteins. The best cancer detection model included age, parity and the 11,880 Da protein, and excluded women with PND. 1) NAF proteomic analysis using SELDI-TOF is reproducible with the same sample set across different platforms, 2) differential proteomic expression exists between women/without breast cancer and 3) combining proteomic and clinical information that are available before surgery optimizes the prediction of which women have breast cancer.
authors.affiliation: Department of Surgery, University of Missouri-Columbia, Columbia, MO 65212, USA.
authors.forename: Edward R
authors.initials: ER
authors.lastname: Sauter
authors.forename: Sumei
authors.initials: S
authors.lastname: Shan
authors.forename: John E
authors.initials: JE
authors.lastname: Hewett
authors.forename: Paul
authors.initials: P
authors.lastname: Speckman
authors.forename: Garrett C
authors.initials: GC
authors.lastname: Du Bois
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000175:diagnosis* / Q000378:metabolism; D005260:Female; D006801:Humans; D008875:Middle Aged; D015999:Multivariate Analysis; D009558:Nipples / Q000378:metabolism*; D040081:Protein Array Analysis / Q000379:methods*; D040901:Proteomics / Q000379:methods; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*