Serum peptidomics is a special form of functional proteomics. The small number of blood proteins that are the source of most prominent peptides in human serum serve as a substrate pool for commonly occurring and/or cancer-derived proteases. Exoprotease activities in particular, when superimposed on the ex vivo coagulation and complement degradation pathways, contribute to generation of not only cancer-specific but also "cancer type"-specific serum peptides. Following development of a unique, semiautomated serum peptide profiling platform and after completing investigations to eliminate common experimental bias, we have now studied possible effects of gender and age on serum peptidomes of 200 healthy men and women, ages 20 -80, and of 60 patients (30 men and 30 women) with metastatic thyroid carcinomas. Extensive MALDI-TOF MS and data analysis suggested negligible contributions of both age and gender to the serum peptidome patterns except that healthy men and women under 35 years, but not older individuals, could be distinguished with ϳ70% accuracy. Considering the more advanced age of most patients, this finding is unlikely to interfere with peptidomics analysis of most cancers. By examining patient samples and age/gender-matched controls followed by variability analysis of either demographic or disease (versus control) groups, we could conclusively rule out demographic bias. An optimized, 12-peptide ion thyroid cancer signature was then developed, enabling classification of an independent validation set with 95% sensitivity and 95% specificity (binomial confidence intervals, 75.1-99.9%). Ten of these peptides had previously been assigned to signature patterns of other solid tumor cancers. One of the two newly discovered peptides was dehydro-Ala 3 -fibrinopeptide A. As we expand this study to include hundreds of thyroid cancer patients, the peptide signature will be adjusted, further validated, and then evaluated in a clinical setting used either independently or in combination with existing markers.

One form of functional proteomics entails profiling of genuine activities, as opposed to surrogates of activity or active "states," in a complex biological matrix: for example, tracking enzyme-catalyzed changes, in real time, ranging from simple modifications to complex anabolic or catabolic reactions. Here we present a test to compare defined exoprotease activities within individual proteomes of two or more groups of biological samples. It tracks degradation of artificial substrates, under strictly controlled conditions, using semiautomated MALDI-TOF mass spectrometric analysis of the resulting patterns. Each fragment is quantitated by comparison with double labeled, non-degradable internal standards (all-D-amino acid peptides) spiked into the samples at the same time as the substrates to reflect adsorptive and processingrelated losses. The full array of metabolites is then quantitated (coefficients of variation of 6.3-14.3% over five replicates) and subjected to multivariate statistical analysis. Using this approach, we tested serum samples of 48 metastatic thyroid cancer patients and 48 healthy controls, with selected peptide substrates taken from earlier standard peptidomics screens (i.e. the "discovery" phase), and obtained class predictions with 94% sensitivity and 90% specificity without prior feature selection (24 features). The test all but eliminates reproducibility problems related to sample collection, storage, and handling as well as to possible variability in endogenous peptide precursor levels because of hemostatic alterations in cancer patients.

An efficient means for the identification of prognostic and predictive biomarkers is essential in today's cancer management. A new approach toward biomarker discovery has therefore been proposed, where pathways instead of individual proteins would be monitored and targeted. Recently, the 'secretome', a biological fluid that may be enriched with secreted and/or shed proteins from adjacent disease-relevant cancer cells, has been targeted for biomarker discovery. We describe a novel method for secretome analysis using "stacking gels", label-free relative quantitation, and pathway analysis. The protocol presented here increases the throughput of secretome analysis by approximately 1 order of magnitude compared to earlier methodologies. In the first application, six cancer cell lines from three different tissues were studied. The global secretome data sets obtained were analyzed using pathway analysis software to attempt integrating the experimental findings into a cellular signaling context. This suggested that several secretome proteins might be interconnected with intracellular canonical pathways. This, in turn, may eventually allow the use of secretomes for discovery of pathway-based biomarkers. When this strategy was applied to two breast cancer cell lines, it appeared that the IGF signaling and the plasminogen activating system may be differentially regulated in invasive breast cancer, but this remains speculative until it is verified in a clinical setting. In summary, the methodology proposed optimizes cell culture with sample fractionation and LC-MS to obtain the highest yield from cultured cell secretomes, with a focus on rational biomarker discovery through putative linkage with cancer relevant pathways.

Blood is a convenient source of biomarkers. Readily obtainable, it immerses most tissues in the body and is therefore likely to contain cell-derived proteins and peptides that may provide information about various biological processes. Serum proteome and peptidome profiling--using mass spectrometry (MS), for example--may thus show a functional correlate of biological events and disorders. To this end, serum peptides must be enriched and interfering substances removed: a step that should be automated to a degree, reproducible and free of bias if it is to generate a test with any future diagnostic potential. The current protocol allows simultaneous analysis of large numbers of peptides using reversed-phase, magnetic particle-assisted sample processing with a matrix-assisted laser desorption/ionization-time of flight MS readout. It may be used for diagnostic or predictive purposes, specifically as an in vitro readout of proteolytic activities with qualitative and quantitative product analysis, and enables profiling of 96 samples in less than 27 h.

Reversible protein phosphorylation is a key regulatory process in all living cells. Deregulation of modification control mechanisms, especially in the case of tyrosine, may lead to malignant transformation and disease. Phosphotyrosine (p-Tyr) accounts for only 0.05% of the total cellular phospho-amino acid content, yet plays an unusually prominent role in eukaryotic signaling, development, and growth. Tracking temporal and positional p-Tyr changes across the cellular proteome, i.e. tyrosine phosphoproteomics, is therefore tremendously valuable. Here, we describe and evaluate a prototype antibody (Ab) microarray platform to monitor changes in protein Tyr phosphorylation. Availability permitting, a virtually unlimited number of Abs, each recognizing a specific cellular protein, may be arrayed on a chip, incubated with total cell or tissue extracts or with biological fluids, and then probed with a fluorescently labeled p-Tyr-specific monoclonal Ab, PY-KD1, specifically generated for this assay as part of the current study. The optimized protocol allowed detection of changes in the Tyr phosphorylation state of selected proteins using submicrogram to low nanogram of total protein extract, amounts that may conceivably be obtained from a thousand to a hundred thousand cells, or less, depending on the cell or tissue type. The assay platform was evaluated by assessing changes in a rationally selected subset of the Tyr phosphoproteome of BcrAbl-expressing cells treated with a specific inhibitor, Gleevec, and of epidermal growth factor (EGF)-treated HeLa cells. The results, ratiometric rather than strictly quantitative in nature, conformed with previous identifications of several Bcr-Abl and EGF receptor targets, and associated proteins, as detected by exhaustive mass spectrometric analyses. The Ab microarray method described here offers advantages of low sample and reagent consumption, scalability, detection multiplexing, and potential compatibility with microfluidic devices and automation. The system may hold particular promise for dissecting signaling pathways, molecular classification of tumors, and profiling of novel target-cancer drugs.