1. Identification of Stage-Specific Breast Markers Using Quantitative Proteomics
- Author
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Shaheed, Sadr-ul, Rustogi, Nitin, Scally, Andrew, Wilson, Julie, Thygesen, Helene, Loizidou, Maria A., Hadjisavvas, Andreas, Hanby, Andrew, Speirs, Valerie, Loadman, Paul, Linforth, Richard, Kyriacou, Kyriacos, and Sutton, Chris W.
- Abstract
Matched healthy and diseased tissues from breast cancer patients were analyzed by quantitative proteomics. By comparing proteomic profiles of fibroadenoma (benign tumors, three patients), DCIS (noninvasive cancer, three patients), and invasive ductal carcinoma (four patients), we identified protein alterations that correlated with breast cancer progression. Three 8-plex iTRAQ experiments generated an average of 826 protein identifications, of which 402 were common. After excluding those originating from blood, 59 proteins were significantly changed in tumor compared with normal tissues, with the majority associated with invasive carcinomas. Bioinformatics analysis identified relationships between proteins in this subset including roles in redox regulation, lipid transport, protein folding, and proteasomal degradation, with a substantial number increased in expression due to Myc oncogene activation. Three target proteins, cofilin-1 and p23 (increased in invasive carcinoma) and membrane copper amine oxidase 3 (decreased in invasive carcinoma), were subjected to further validation. All three were observed in phenotype-specific breast cancer cell lines, normal (nontransformed) breast cell lines, and primary breast epithelial cells by Western blotting, but only cofilin-1 and p23 were detected by multiple reaction monitoring mass spectrometry analysis. All three proteins were detected by both analytical approaches in matched tissue biopsies emulating the response observed with proteomics analysis. Tissue microarray analysis (361 patients) indicated cofilin-1 staining positively correlating with tumor grade and p23 staining with ER positive status; both therefore merit further investigation as potential biomarkers.
- Published
- 2013
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