High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.

BACKGROUND For many years, basic and clinical researchers have taken advantage of the analytical sensitivity and specificity afforded by mass spectrometry in the measurement of proteins. Clinical laboratories are now beginning to deploy these work flows as well. For assays that use proteolysis to generate peptides for protein quantification and characterization, synthetic stable isotope–labeled internal standard peptides are of central importance. No general recommendations are currently available surrounding the use of peptides in protein mass spectrometric assays. CONTENT The Clinical Proteomic Tumor Analysis Consortium of the National Cancer Institute has collaborated with clinical laboratorians, peptide manufacturers, metrologists, representatives of the pharmaceutical industry, and other professionals to develop a consensus set of recommendations for peptide procurement, characterization, storage, and handling, as well as approaches to the interpretation of the data generated by mass spectrometric protein assays. Additionally, the importance of carefully characterized reference materials—in particular, peptide standards for the improved concordance of amino acid analysis methods across the industry—is highlighted. The alignment of practices around the use of peptides and the transparency of sample preparation protocols should allow for the harmonization of peptide and protein quantification in research and clinical care.

Multiple approaches for simplifying the serum proteome have been described. These techniques are generally developed across different laboratories, samples, mass spectrometry platforms, and analysis tools. Hence, comparing the available schemes is impossible from the existing literature because of confounding variables. We describe a head-to-head comparison of several serum fractionation schemes, including N-linked glycopeptide enrichment, cysteinyl-peptide enrichment, magnetic bead separation (C3, C8, and WCX), size fractionation, protein A/G depletion, and immunoaffinity column depletion of abundant serum proteins. Each technique was compared to results obtained from unfractionated human serum. The results show immunoaffinity subtraction is the most effective means for simplifying the serum proteome while maintaining reasonable sample throughput. The reported dataset is publicly available and provides a standard against which emergent technologies can be compared and evaluated for their contribution to serum-based biomarker discovery.