Recent studies have established distinctive serum polypeptide patterns through mass spectrometry (MS) that reportedly correlate with clinically relevant outcomes. Wider acceptance of these signatures as valid biomarkers for disease may follow sequence characterization of the components and elucidation of the mechanisms by which they are generated. Using a highly optimized peptide extraction and matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS-based approach, we now show that a limited subset of serum peptides (a signature) provides accurate class discrimination between patients with 3 types of solid tumors and controls without cancer. Targeted sequence identification of 61 signature peptides revealed that they fall into several tight clusters and that most are generated by exopeptidase activities that confer cancer type-specific differences superimposed on the proteolytic events of the ex vivo coagulation and complement degradation pathways. This small but robust set of marker peptides then enabled highly accurate class prediction for an external validation set of prostate cancer samples. In sum, this study provides a direct link between peptide marker profiles of disease and differential protease activity, and the patterns we describe may have clinical utility as surrogate markers for detection and classification of cancer. Our findings also have important implications for future peptide biomarker discovery efforts.

Human serum contains a complex array of proteolytically derived peptides (serum peptidome) that may provide a correlate of biological events occurring in the entire organism; for instance, as a diagnostic for solid tumors (Petricoin, E. F.; Ardekani, A. M.; Hitt, B. A.; Levine, P. J.; Fusaro, V. A.; Steinberg, S. M.; Mills, G. B.; Simone, C.; Fishman, D. A.; Kohn, E. C.; Liotta, L. Lancet 2002, 359, 572-577). Here, we describe a novel, automated technology platform for the simultaneous measurement of serum peptides that is simple, scalable, and generates highly reproducible patterns. Peptides are captured and concentrated using reversed-phase (RP) batch processing in a magnetic particle-based format, automated on a liquid handling robot, and followed by a MALDI TOF mass spectrometric readout. The protocol is based on a detailed investigation of serum handling, RP ligand and eluant selection, small-volume robotics design, an optimized spectral acquisition program, and consistent peak extraction plus binning across a study set. The improved sensitivity and resolution allowed detection of 400 polypeptides (0.8-15-kDa range) in a single droplet (approximately 50 microL) of serum, and almost 2000 unique peptides in larger sample sets, which can then be analyzed using common microarray data analysis software. A pilot study indicated that sera from brain tumor patients can be distinguished from controls based on a pattern of 274 peptide masses. This, in turn, served to create a learning algorithm that correctly predicted 96.4% of the samples as either normal or diseased.

Abstract'Molecular signatures' are the qualitative and quantitative patterns of groups of biomolecules (e.g., mRNA, proteins, peptides, or metabolites) in a cell, tissue, biological fluid, or an entire organism. To apply this concept to biomarker discovery, the measurements should ideally be noninvasive and performed in a single read-out. We have therefore developed a peptidomics platform that couples magnetics-based, automated solid-phase extraction of small peptides with a high-resolution MALDI-TOF mass spectrometric readout (Villanueva, J.; Philip, J.; Entenberg, D.; Chaparro, C. A.; Tanwar, M. K.; Holland, E. C.; Tempst, P. Anal. Chem. 2004Chem. , 76, 1560Chem. -1570. Since hundreds of peptides can be detected in microliter volumes of serum, it allows to search for disease signatures, for instance in the presence of cancer. We have now evaluated, optimized, and standardized a number of clinical and analytical chemistry variables that are major sources of bias; ranging from blood collection and clotting, to serum storage and handling, automated peptide extraction, crystallization, spectral acquisition, and signal processing. In addition, proper alignment of spectra and user-friendly visualization tools are essential for meaningful, certifiable data mining. We introduce a minimal entropy algorithm, 'Entropycal', that simplifies alignment and subsequent statistical analysis and increases the percentage of the highly distinguishing spectral information being retained after feature selection of the datasets. Using the improved analytical platform and tools, and a commercial statistics program, we found that sera from thyroid cancer patients can be distinguished from healthy controls based on an array of 98 discriminant peptides. With adequate technological and computational methods in place, and using rigorously standardized conditions, potential sources of patient related bias (e.g., gender, age, genetics, environmental, dietary, and other factors) may now be addressed.

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.

A challenge in achieving optimal management of cancer is the discovery of secreted biomarkers that represent useful surrogates for the disease and could be measured noninvasively. Because of the problems encountered in the proteomic interrogation of plasma, secretomes have been proposed as an alternative source of tumor markers that might be enriched with secreted proteins relevant to the disease. However, secretome analysis faces analytical challenges that interfere with the search for true secreted tumor biomarkers. Here, we have addressed two of the main challenges of secretome analysis in comparative discovery proteomics. First, we carried out a kinetics experiment whereby secretomes and lysates of tumor cells were analyzed to monitor cellular viability during secretome production. Interestingly, the proteomic signal of a group of secreted proteins correlated well with the apoptosis induced by serum starvation and could be used as an internal cell viability marker. We then addressed a second challenge relating to contamination of serum proteins in secretomes caused by the required use of serum for tumor cell culture. The comparative proteomic analysis between cell lines labeled with SILAC showed a number of false positives coming from serum and that several proteins are both in serum and being secreted from tumor cells. A thorough study of secretome methodology revealed that under optimized experimental conditions there is a substantial fraction of proteins secreted through unconventional secretion in secretomes. Finally, we showed that some of the nuclear proteins detected in secretomes change their cellular localization in breast tumors, explaining their presence in secretomes and suggesting that tumor cells use unconventional secretion during tumorigenesis. The unconventional secretion of proteins into the extracellular space exposes a new layer of genome post-translational regulation and reveals an untapped source of potential tumor biomarkers and drug targets. Molecular & Cellular

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.

Senescence, a terminal cell proliferation arrest, can be triggered by oncogenes. Oncogene-induced senescence is classically considered a tumor defense barrier. However, several findings show that, under certain circumstances, senescent cells may favor tumor progression because of their secretory phenotype. Here, we show that the expression in different breast epithelial cell lines of p95HER2, a constitutively active fragment of the tyrosine kinase receptor HER2, results in either increased proliferation or senescence. In senescent cells, p95HER2 elicits a secretome enriched in proteases, cytokines, and growth factors. This secretory phenotype is not a mere consequence of the senescence status and requires continuous HER2 signaling to be maintained. Underscoring the functional relevance of the p95HER2-induced senescence secretome, we show that p95HER2-induced senescent cells promote metastasis in vivo in a non-cell-autonomous manner. Cancer Res; 73(1); 450-8. Ó2012 AACR.