Literature DB >> 15692757

Significant differences in nipple aspirate fluid protein expression between healthy women and those with breast cancer demonstrated by time-of-flight mass spectrometry.

Timothy M Pawlik1, Herbert Fritsche, Kevin R Coombes, Lianchun Xiao, Savitri Krishnamurthy, Kelly K Hunt, Lajos Pusztai, Jeng-Neng Chen, Charlotte H Clarke, Banu Arun, Mien-Chie Hung, Henry M Kuerer.   

Abstract

New approaches are needed for the early detection of breast cancer. Proteomic profiling technologies, such as surface-enhanced laser desorption ionization mass spectrometry (SELDI-MS), may be able to identify tumor markers in biological fluids. The objective of this study was to determine whether there are differences in protein expression patterns in nipple aspirate fluid (NAF) from the cancerous and noncancerous breasts of patients with unilateral breast cancer and the breasts of healthy volunteers. Paired NAF samples were obtained from 23 women with stage I or II unilateral invasive breast carcinoma and five healthy female volunteers. Aliquots of the samples were applied to SELDI Protein-chip arrays (WCX2 and IMAC3-Cu++), and protein expression was analyzed using time-of-flight MS. A total of 463 distinct peaks were detected and analyzed. In breast cancer patients, no differences in protein expression were identified between the breast with the intact primary carcinoma and the contralateral noncancerous breast. Seventeen peaks were overexpressed in cancer-bearing breasts compared to breasts of healthy volunteers (p < 0.0005). When spectra from the nontumor-bearing breasts of breast cancer patients were compared with spectra from breasts of healthy volunteers, two peaks that were overexpressed in breast cancer patients and one peak that was underexpressed in breast cancer patients were detected (p < 0.0027). SELDI-MS was able to identify differences in the phenotypic proteomic profile of NAF samples obtained from patients with early-stage breast cancer and healthy women. Proteomic screening techniques such as SELDI-MS analysis of NAF may be useful for breast cancer screening and diagnosis.

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Year:  2005        PMID: 15692757     DOI: 10.1007/s10549-004-1710-4

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  24 in total

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Authors:  Yuliya V Karpievitch; Elizabeth G Hill; Adam J Smolka; Jeffrey S Morris; Kevin R Coombes; Keith A Baggerly; Jonas S Almeida
Journal:  Bioinformatics       Date:  2006-11-22       Impact factor: 6.937

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

Authors:  Edward R Sauter; Wade Davis; Wenyi Qin; Sarah Scanlon; Brian Mooney; Karen Bromert; William R Folk
Journal:  Biomark Med       Date:  2009-10       Impact factor: 2.851

Review 3.  Human body fluid proteome analysis.

Authors:  Shen Hu; Joseph A Loo; David T Wong
Journal:  Proteomics       Date:  2006-12       Impact factor: 3.984

4.  Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer.

Authors:  Monika Pietrowska; Lukasz Marczak; Joanna Polanska; Katarzyna Behrendt; Elzbieta Nowicka; Anna Walaszczyk; Aleksandra Chmura; Regina Deja; Maciej Stobiecki; Andrzej Polanski; Rafal Tarnawski; Piotr Widlak
Journal:  J Transl Med       Date:  2009-07-13       Impact factor: 5.531

5.  Metabolomic characterization of nipple aspirate fluid by (1)H NMR spectroscopy and GC-MS.

Authors:  Gregory D Tredwell; Jessica A Miller; H-H Sherry Chow; Patricia A Thompson; Hector C Keun
Journal:  J Proteome Res       Date:  2014-01-07       Impact factor: 4.466

6.  Protein Biomarkers for Breast Cancer Risk Are Specifically Correlated with Local Steroid Hormones in Nipple Aspirate Fluid.

Authors:  Ali Shidfar; Tolulope Fatokun; David Ivancic; Robert T Chatterton; Seema A Khan; Jun Wang
Journal:  Horm Cancer       Date:  2016-04-19       Impact factor: 3.869

7.  Comparison of tear protein levels in breast cancer patients and healthy controls using a de novo proteomic approach.

Authors:  Daniel Böhm; Ksenia Keller; Julia Pieter; Nils Boehm; Dominik Wolters; Wulf Siggelkow; Antje Lebrecht; Marcus Schmidt; Heinz Kölbl; Norbert Pfeiffer; Franz-Hermann Grus
Journal:  Oncol Rep       Date:  2012-06-01       Impact factor: 3.906

8.  An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++.

Authors:  Yuliya V Karpievitch; Elizabeth G Hill; Anthony P Leclerc; Alan R Dabney; Jonas S Almeida
Journal:  PLoS One       Date:  2009-09-18       Impact factor: 3.240

9.  Non-invasive proteomics-thinking about personalized breast cancer screening and treatment.

Authors:  Manuel Debald; Matthias Wolfgarten; Gisela Walgenbach-Brünagel; Walther Kuhn; Michael Braun
Journal:  EPMA J       Date:  2010-07-14       Impact factor: 6.543

10.  Paired ductal carcinoma in situ and invasive breast cancer lesions in the D-loop of the mitochondrial genome indicate a cancerization field effect.

Authors:  Andrea Maggrah; Kerry Robinson; Jennifer Creed; Roy Wittock; Ken Gehman; Teresa Gehman; Helen Brown; Andrew Harbottle; M Kent Froberg; Daniel Klein; Brian Reguly; Ryan Parr
Journal:  Biomed Res Int       Date:  2012-12-26       Impact factor: 3.411

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