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

Jian Ying 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

13 Scopus citations


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
Issue number12
StatePublished - Dec 1 2017
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the National Institutes of Health under grants and contracts of the National Cancer Institute, Clinical Proteomics Tumor Analysis Consortium (U24CA160036 and U24CA210985).

Publisher Copyright:
© 2017 American Chemical Society.


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


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