A large, consistent plasma proteomics data set from prospectively collected breast cancer patient and healthy volunteer samples

Authors

Catherine P. Riley, Bindley Bioscience Center, Purdue University, West Lafayette, IN
Xiang Zhang, Department of Chemistry, University of Louisville, Louisville, KY
Harikrishna Nakshatri, Department of Surgery, Indiana University School of Medicine, Indianapolis, IN
Bryan Schneider, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
Fred E. Regnier, Bindley Bioscience Center, Purdue University, West Lafayette, IN
Jiri Adamec, University of Nebraska-LincolnFollow
Charles Buck, Bindley Bioscience Center, Purdue University, West Lafayette, IN,Follow

Date of this Version

2011

Citation

Riley et al. Journal of Translational Medicine 2011, 9:80

Comments

This document is published under a Creative Commons/Open Access license.

Abstract

Background: Variability of plasma sample collection and of proteomics technology platforms has been detrimental to generation of large proteomic profile datasets from human biospecimens.

Methods: We carried out a clinical trial-like protocol to standardize collection of plasma from 204 healthy and 216 breast cancer patient volunteers. The breast cancer patients provided follow up samples at 3 month intervals. We generated proteomics profiles from these samples with a stable and reproducible platform for differential proteomics that employs a highly consistent nanofabricated ChipCube™ chromatography system for peptide detection and quantification with fast, single dimension mass spectrometry (LC-MS). Protein identification is achieved with subsequent LC-MS/MS analysis employing the same ChipCube™ chromatography system.

Results: With this consistent platform, over 800 LC-MS plasma proteomic profiles from prospectively collected samples of 420 individuals were obtained. Using a web-based data analysis pipeline for LC-MS profiling data, analyses of all peptide peaks from these plasma LC-MS profiles reveals an average coefficient of variability of less than 15%. Protein identification of peptide peaks of interest has been achieved with subsequent LC-MS/MS analyses and by referring to a spectral library created from about 150 discrete LC-MS/MS runs. Verification of peptide quantity and identity is demonstrated with several Multiple Reaction Monitoring analyses. These plasma proteomic profiles are publicly available through ProteomeCommons.

Conclusion: From a large prospective cohort of healthy and breast cancer patient volunteers and using a nanofabricated chromatography system, a consistent LC-MS proteomics dataset has been generated that includes more than 800 discrete human plasma profiles. This large proteomics dataset provides an important resource in support of breast cancer biomarker discovery and validation efforts.