Differential Exoprotease Activities Confer Tumor-Specific Serum Peptidome Patterns

Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA.
Journal of Clinical Investigation (Impact Factor: 13.22). 02/2006; 116(1):271-84. DOI: 10.1172/JCI26022
Source: PubMed


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.

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Available from: Josep Villanueva, Oct 07, 2015
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    • "Many current diagnostic tests depend on individual aspects of fractionated blood components: plasma, red blood cells (RBCs), WBCs and platelets. Clean, cell-free plasma is necessary for early cancer detection via blood-borne cancer biomarkers (Bunn 1997, Li et al 2002, Villanueva et al 2006). Leukocytes are required for several hematological tests as well as for DNA sequencing. "
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    ABSTRACT: Magnetic sorting using magnetic beads has become a routine methodology for the separation of key cell populations from biological suspensions. Due to the inherent ability of magnets to provide forces at a distance, magnetic cell manipulation is now a standardized process step in numerous processes in tissue engineering, medicine, and in fundamental biological research. Herein we review the current status of magnetic particles to enable isolation and separation of cells, with a strong focus on the fundamental governing physical phenomena, properties and syntheses of magnetic particles and on current applications of magnet-based cell separation in laboratory and clinical settings. We highlight the contribution of cell separation to biomedical research and medicine and detail modern cell-separation methods (both magnetic and non-magnetic). In addition to a review of the current state-of-the-art in magnet-based cell sorting, we discuss current challenges and available opportunities for further research, development and commercialization of magnetic particle-based cell-separation systems.
    Reports on Progress in Physics 12/2014; 78(1):016601. DOI:10.1088/0034-4885/78/1/016601 · 17.06 Impact Factor
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    • "Serum and plasma lipoprotein metabolism is regulated and controlled by the specific apolipoprotein (Apo-) constituents of the various lipoprotein classes such as ApoAI, ApoCI, ApoH (beta2 glycoprotein) and others. Several classes of apolipoprotein in serum or plasma have been discovered as putative breast cancer biomarkers using proteomic techniques including SELDI-TOF, MALDI-TOF/TOF, 2D-iTRAQ-LC-MS/MS, and 2D-LC MS/MS [19-21,30,34]. We observed that levels of ApoAI and ApoCI were significantly downregulated in breast cancer patients, while a peptide identified as a fragment of ApoH was significantly higher in BC. "
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    ABSTRACT: Introduction Serum profiling using proteomic techniques has great potential to detect biomarkers that might improve diagnosis and predict outcome for breast cancer patients (BC). This study used surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) to identify differentially expressed proteins in sera from BC and healthy volunteers (HV), with the goal of developing a new prognostic biomarker panel. Methods Training set serum samples from 99 BC and 51 HV subjects were applied to four adsorptive chip surfaces (anion-exchange, cation-exchange, hydrophobic, and metal affinity) and analyzed by time-of-flight MS. For validation, 100 independent BC serum samples and 70 HV samples were analyzed similarly. Cluster analysis of protein spectra was performed to identify protein patterns related to BC and HV groups. Univariate and multivariate statistical analyses were used to develop a protein panel to distinguish breast cancer sera from healthy sera, and its prognostic potential was evaluated. Results From 51 protein peaks that were significantly up- or downregulated in BC patients by univariate analysis, binary logistic regression yielded five protein peaks that together classified BC and HV with a receiver operating characteristic (ROC) area-under-the-curve value of 0.961. Validation on an independent patient cohort confirmed the five-protein parameter (ROC value 0.939). The five-protein parameter showed positive association with large tumor size (P = 0.018) and lymph node involvement (P = 0.016). By matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, immunoprecipitation and western blotting the proteins were identified as a fragment of apolipoprotein H (ApoH), ApoCI, complement C3a, transthyretin, and ApoAI. Kaplan-Meier analysis on 181 subjects after median follow-up of >5 years demonstrated that the panel significantly predicted disease-free survival (P = 0.005), its efficacy apparently greater in women with estrogen receptor (ER)-negative tumors (n = 50, P = 0.003) compared to ER-positive (n = 131, P = 0.161), although the influence of ER status needs to be confirmed after longer follow-up. Conclusions Protein mass profiling by MS has revealed five serum proteins which, in combination, can distinguish between serum from women with breast cancer and healthy control subjects with high sensitivity and specificity. The five-protein panel significantly predicts recurrence-free survival in women with ER-negative tumors and may have value in the management of these patients.
    Breast cancer research: BCR 06/2014; 16(3):R63. DOI:10.1186/bcr3676 · 5.49 Impact Factor
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    • "Furthermore, the progression of malignancy is also associated with the degradation of adhesion and cell-to-cell junction proteins and this may also be another source of endogenous peptides with diagnostic potential. It is well known that peptides play complex regulatory roles in many biological processes, such as intercellular signaling [4-9]. Current proteomic approaches to biomarker discovery, utilizing the power of mass spectrometry (MS), make it possible to delineate the endogenous peptidomes of various bodily fluids. "
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    ABSTRACT: Background Ovarian cancer (OvCa) is the most lethal gynecological malignancy. The emergence of high-throughput technologies, such as mass spectrometry, has allowed for a paradigm shift in the way we search for novel biomarkers. Urine-based peptidomic profiling is a novel approach that may result in the discovery of noninvasive biomarkers for diagnosing patients with OvCa. In this study, the peptidome of urine from 6 ovarian cancer patients and 6 healthy controls was deciphered. Results Urine samples underwent ultrafiltration and the filtrate was subjected to solid phase extraction, followed by fractionation using strong cation exchange chromatography. These fractions were analyzed using an Orbitrap mass spectrometer. Over 4600 unique endogenous urine peptides arising from 713 proteins were catalogued, representing the largest urine peptidome reported to date. Each specimen was processed in triplicate and reproducibility at the protein (69-76%) and peptide (58-63%) levels were noted. More importantly, over 3100 unique peptides were detected solely in OvCa specimens. One such promising biomarker was leucine-rich alpha-2-glycoprotein (LRG1), where multiple peptides were found in all urines from OvCa patients, but only one peptide was found in one healthy control urine sample. Conclusions Mining the urine peptidome may yield highly promising novel OvCa biomarkers.
    Clinical Proteomics 06/2014; 11(1):23. DOI:10.1186/1559-0275-11-23
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