U.S. Department of Energy

Pacific Northwest National Laboratory

Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues

TitleQuality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues
Publication TypeJournal Article
Year of Publication2017
AuthorsZhou J.Y, Chen L., Zhang B., Tian Y., Liu T., Thomas S.N, Chen L., Schnaubelt M., Boja E., Hiltke T., Kinsinger C.R, Rodriguez H., Davies S.R, Li S., Snider J.E, Erdmann-Gilmore P., Tabb D.L, Townsend R.R, Ellis M.J, Rodland K.D, Smith R.D, Carr S.A, Zhang Z., Chan D.W, Zhang H.
JournalJ. Proteome Res.
Abstract

Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating six 2D LC-MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.

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