Multiplexed MRM-based quantitation of candidate cancer biomarker proteins in undepleted and non-enriched human plasma

Andrew J. Percy, Andrew G. Chambers, Juncong Yang, Christoph H. Borchers

Research output: Contribution to journalArticlepeer-review

60 Citations (Scopus)

Abstract

An emerging approach for multiplexed targeted proteomics involves bottom-up LC-MRM-MS, with stable isotope-labeled internal standard peptides, to accurately quantitate panels of putative disease biomarkers in biofluids. In this paper, we used this approach to quantitate 27 candidate cancer-biomarker proteins in human plasma that had not been treated by immunoaffinity depletion or enrichment techniques. These proteins have been reported as biomarkers for a variety of human cancers, from laryngeal to ovarian, with breast cancer having the highest correlation. We implemented measures to minimize the analytical variability, improve the quantitative accuracy, and increase the feasibility and applicability of this MRM-based method. We have demonstrated excellent retention time reproducibility (median interday CV: 0.08%) and signal stability (median interday CV: 4.5% for the analytical platform and 6.1% for the bottom-up workflow) for the 27 biomarker proteins (represented by 57 interference-free peptides). The linear dynamic range for the MRM assays spanned four orders-of-magnitude, with 25 assays covering a 103-104 range in protein concentration. The lowest abundance quantifiable protein in our biomarker panel was insulin-like growth factor 1 (calculated concentration: 127 ng/mL). Overall, the analytical performance of this assay demonstrates high robustness and sensitivity, and provides the necessary throughput and multiplexing capabilities required to verify and validate cancer-associated protein biomarker panels in human plasma, prior to clinical use.

Original languageEnglish
Pages (from-to)2202-2215
Number of pages14
JournalProteomics
Volume13
Issue number14
DOIs
Publication statusPublished - Jul 2013
Externally publishedYes

Keywords

  • Biomedicine
  • Cancer biomarkers
  • MRM
  • MS
  • Plasma
  • Quantitative analysis

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