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

Jianying Zhou, Lijun Chen, Bai Zhang, Yuan Tian, Tao Liu, Stefani N Thomas, Li Chan, Michael Schnaubelt, Emily Boja, Tara Hiltke, Christopher R. Kinsinger, Henry Rodriguez, Sherri R. Davies, Shunqiang Li, Jacqueline E. Snider, Petra Erdmann-Gilmore, David L. Tabb, R. Reid Townsend, Matthew J. Ellis, Karin D. RodlandRichard D. Smith, Steven A. Carr, Zhen Zhang, Daniel W. Chan, Hui Zhang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)4523-4530
Number of pages8
JournalJournal of proteome research
Volume16
Issue number12
DOIs
StatePublished - Dec 1 2017

Keywords

  • Cancer Biology and Disease Human Proteome Project
  • clinical proteomics
  • iTRAQ
  • quantification
  • tumor tissues

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

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