Workflow Comparison for Label-Free, Quantitative Secretome Proteomics for Cancer Biomarker Discovery: Method Evaluation, Differential Analysis, and Verification in Serum

Abstract

The cancer cell secretome has emerged as an attractive subproteome for discovery of candidate blood-based biomarkers. To choose the best performing workflow, we assessed the performance of three first-dimension separation strategies prior to nanoLC-MS/MS analysis: (1) 1D gel electrophoresis (1DGE), (2) peptide SCX chromatography, and (3) tC2 protein reversed phase chromatography. 1DGE using 4-12% gradient gels outperformed the SCX and tC2 methods with respect to number of identified proteins (1092 vs 979 and 580, respectively), reproducibility of protein identification (80% vs 70% and 72%, respectively, assessed in biological N = 3). Reproducibility of protein quantitation based on spectral counting was similar for all 3 methods (CV: 26% vs 24% and 24%, respectively). As a proof-of-concept of secretome proteomics for blood-based biomarker discovery, the gradient 1DGE workflow was subsequently applied to identify IGF1R-signaling related proteins in the secretome of mouse embryonic fibroblasts transformed with human IGF1R (MEF/Toff/IGF1R). VEGF and osteopontin were differentially detected by LC-MS/MS and verified in secretomes by ELISA. Follow-up in serum of mice bearing MEF/Toff/IGF1R-induced tumors showed an increase of osteopontin levels paralleling tumor growth, and reduction in the serum of mice in which IGF1R expression was shut off and tumor regressed.