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Pancreatic cancer is a lethal disease that is difficult to diagnose at early stages when curable treatments are effective. Biomarkers that can improve current pancreatic cancer detection would have great value in improving patient management and survival rate. A large scale quantitative proteomics study was performed to search for the plasma protein alterations associated with pancreatic cancer. The enormous complexity of the plasma proteome and the vast dynamic range of protein concentration therein present major challenges for quantitative global profiling of plasma. To address these challenges, multi-dimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than thirteen hundred proteins total were identified in plasma across 8-orders of magnitude in protein concentration. Differential proteins associated with pancreatic cancer were identified, and their relationship with the proteome of pancreatic tissue and pancreatic juice from our previous studies was discussed. A subgroup of differentially expressed proteins was selected for biomarker testing using an independent cohort of plasma and serum samples from well-diagnosed patients with pancreatic cancer, chronic pancreatitis and non-pancreatic disease controls. Using ELISA methodology, the performance of each of these protein candidates was benchmarked against CA19-9, the current gold standard for a pancreatic cancer blood test. A composite marker of TIMP1 and ICAM1 demonstrate significantly better performance than CA19-9 in distinguishing pancreatic cancer from the non-pancreatic disease controls and chronic pancreatitis controls. In addition, protein AZGP1 was identified as a biomarker candidate for chronic pancreatitis. The discovery and technical challenges associated with plasma-based quantitative proteomics are discussed and may benefit the development of plasma proteomics technology in general. The protein candidates identified in this study provide a biomarker candidate pool for future investigations.

We describe and demonstrate the proteomics computational toolkit provided in the open-source msInspect software distribution. The toolkit includes modules written in Java and in the R statistical programming language to aid the rapid development of proteomics software applications. It contains tools for processing and manipulating standard MS data files, including signal processing of LC-MS data and parsing of MS/MS search results, as well as for modeling proteomics data structures, creating charts, and other common tasks. We present this toolkit's capability to rapidly develop new computational tools by presenting an example application, Qurate, a graphical tool for manually curating isotopically labeled peptide quantitative events.

Several modes of inference are currently used in practice to evaluate the confidence of putative peptide identifications resulting from database scoring algorithms such as Mascot, SEQUEST, or X!Tandem. The approaches include parametric methods, such as classic PeptideProphet, and distribution free methods, such as methods based on reverse or decoy databases. Because of its parametric nature, classic PeptideProphet, although highly robust, was not highly flexible and was difficult to apply to new search algorithms or classification scores. While commonly applied, the decoy approach has not yet been fully formalized and standardized. And, although they are distribution-free, they like other approaches are not free of assumptions. Recent manuscripts by Kall et al., Choi and Nesvizhskii, and Choi et al. help advance these methods, specifically by formalizing an alternative formulation of decoy databases approaches and extending the PeptideProphet methods to make explicit use of decoy databases, respectively. Taken together with standardized decoy database methods, and expectation scores computed by search engines like Tandem, there exist at least four different modes of inference used to assign confidence levels to individual peptides or groups of peptides. We overview and compare the assumptions of each of these approaches and summarize some interpretation issues. We also discuss some suggestions, which may make the use of decoy databases more computationally efficient in practice.

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.

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.

Isotopic labeling of cysteine residues with acrylamide was previously utilized for relative quantitation of proteins by MALDI-TOF. Here, we explored and compared the application of deuterated and 13C isotopes of acrylamide for quantitative proteomic analysis using LC−MS/MS and high-resolution FTICR mass spectrometry. The method was applied to human serum samples that were immunodepleted of abundant proteins. Our results show reliable quantitation of proteins across an abundance range that spans 5 orders of magnitude based on ion intensities and known protein concentration in plasma. The use of 13C isotope of acrylamide had a slightly greater advantage relative to deuterated acrylamide, because of shifts in elution of deuterated acrylamide relative to its corresponding nondeuterated compound by reversed-phase chromatography. Overall, the use of acrylamide for differentially labeling intact proteins in complex mixtures, in combination with LC−MS/MS provides a robust method for quantitative analysis of compl...

Glycosylation plays an important role in epithelial cancers, including pancreatic ductal adenocarcinoma. However, little is known about the glycoproteome of the human pancreas or its alterations associated with pancreatic tumorigenesis. Using quantitative glycoproteomics approach, we investigated protein N-glycosylation in pancreatic tumor tissue in comparison with normal pancreas and chronic pancreatitis tissue. The study lead to the discovery of a roster of glycoproteins with aberrant N-glycosylation level associated with pancreatic cancer, including mucin-5AC (MUC5AC), carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), insulin-like growth factor binding protein (IGFBP3), and galectin-3-binding protein (LGALS3BP). Pathway analysis of cancer-associated aberrant glycoproteins revealed an emerging phenomenon that increased activity of N-glycosylation was implicated in several pancreatic cancer pathways, including TGF-β, TNF, NF-kappa-B, and TFEB-related lysosomal changes. In addition, the study provided evidence that specific N-glycosylation sites within certain individual proteins can have significantly altered glycosylation occupancy in pancreatic cancer, reflecting the complexity of the molecular mechanisms underlying cancer-associated glycosylation events.

In an attempt to identify prostate cancer biomarkers with greater diagnostic and prognostic capabilities, we have developed an integrative proteomic discovery workflow focused on N-linked glycoproteins that refines the target selection process. In this work, hydrazide-based chemistry was used to identify N-linked glycopeptides from 22Rv1 prostate cancer cells cultured in vitro, which were compared with glycopeptides identified from explanted 22Rv1 murine tumor xenografts. One hundred and four human glycoproteins were identified in the former analysis and 75 in the latter, with 40 proteins overlapping between data sets. Of the 40 overlapping proteins, 80% have multiple literature references to the neoplastic process and ∼40% to prostatic neoplasms. These include a number of well-known prostate cancer-associated biomarkers, such as prostate-specific membrane antigen (PSMA). By integrating gene expression data and available literature, we identified members of the overlap data set that deserve consideration as potential prostate cancer biomarkers. Specifically, the identification of the extracellular domain of protein tyrosine phosphatase receptor type F (PTPRF) was of particular interest due to the direct involvement of PTPRF in the control of β-catenin signaling, as well as dramatically elevated gene expression levels in the prostate compared to other tissues. In this investigation, we demonstrate that the PTPRF E-subunit is more abundant in human prostate tumor tissue compared to normal control and also detectable in murine plasma by immunoblot and ELISA. Specifically, PTPRF distinguishes between animals xenografted with the 22Rv1 cells and control animals as early as 14 days after implantation. This result suggests that the ectodomain of PTPRF has the potential to function as a novel plasma or tissue-based biomarker for prostate cancer. The workflow described adds to the literature of potential biomarker candidates for prostate cancer and demonstrates a pathway to developing new diagnostic assays.

We present new algorithms and a software implementation for assigning confidence to peptide sequence assignments obtained through classic accurate mass and retention time (AMT) matching techniques, as well as methods for integrating these assignments with standard proteomics workflows. The algorithms are intended to increase the number of peptides and proteins identified (and, when applicable, quantitated by isotopic labeling) among related proteomics experiments that use high-resolution mass spectrometry instrumentation. The motivations for our extensions include the need to exploit high-resolution data to support highly complex proteomics experiments, especially those involving extensive off-line fractionation, to which recent label-free workflows might not easily generalize.

The recent advance in technology for mass spectrometry-based targeted protein quantification has opened new avenues for a broad range of proteomic applications in clinical research. The major breakthroughs are highlighted by the capability of using a “universal” approach to perform quantitative assays for a wide spectrum of proteins with minimum restrictions, and the ease of assembling multiplex detections in a single measurement. The quantitative approach relies on the use of synthetic stable isotope labeled peptides or proteins, which precisely mimic their endogenous counterparts and act as internal standards to quantify the corresponding candidate proteins. This report reviews recently developed platform technologies for emerging applications of clinical proteomics and biomarker development.

We have applied an in-depth quantitative proteomic approach, combining isotopic labeling extensive intact protein separation and mass spectrometry, for high confidence identification of protein changes in plasmas from a mouse model of breast cancer. We hypothesized that a wide spectrum of proteins may be up-regulated in plasma with tumor development and that comparisons with proteins expressed in human breast cancer cell lines may identify a subset of up-regulated proteins in common with proteins expressed in breast cancer cell lines that may represent candidate biomarkers for breast cancer. Plasma from PyMT transgenic tumor-bearing mice and matched controls were obtained at two time points during tumor growth. A total of 133 proteins were found to be increased by 1.5-fold or greater at one or both time points. A comparison of this set of proteins with published findings from proteomic analysis of human breast cancer cell lines yielded 49 proteins with increased levels in mouse plasma that were identified in breast cancer cell lines. Pathway analysis comparing the subset of up-regulated proteins known to be expressed in breast cancer cell lines with other up-regulated proteins indicated a cancer related function for the former and a host-response function for the latter. We conclude that integration of proteomic findings from mouse models of breast cancer and from human breast cancer cell lines may help identify a subset of proteins released by breast cancer cells into the circulation and that occur at increased levels in breast cancer.

In-depth analysis of the serum and plasma proteomes by mass spectrometry is challenged by the vast dynamic range of protein abundance and substantial complexity. There is merit in reducing complexity through fractionation to facilitate mass spectrometry analysis of low-abundance proteins. However, fractionation reduces throughput and has the potential of diluting individual proteins or inducing their loss. Here, we have investigated the contribution of extensive fractionation of intact proteins to depth of analysis. Pooled serum depleted of abundant proteins was fractionated by an orthogonal two-dimensional system consisting of anion-exchange and reversed-phase chromatography. The resulting protein fractions were aliquotted; one aliquot was analyzed by shotgun LC-MS/MS, and another was further resolved into protein bands in a third dimension using SDS-PAGE. Individual gel bands were excised and subjected to in situ digestion and mass spectrometry. We demonstrate that increased fractionation results in increased depth of analysis based on total number of proteins identified in serum and based on representation in individual fractions of specific proteins identified in gel bands following a third-dimension SDS gel analysis. An intact protein analysis system (IPAS) based on a two-dimensional plasma fractionation schema was implemented that resulted in identification of 1662 proteins with high confidence with representation of protein isoforms that differed in their chromatographic mobility. Further increase in depth of analysis was accomplished by repeat analysis of aliquots from the same set of two-dimensional fractions resulting in overall identification of 2254 proteins. We conclude that substantial depth of analysis of proteins from milliliter quantities of serum or plasma and detection of isoforms are achieved with depletion of abundant proteins followed by two-dimensional protein fractionation and MS analysis of individual fractions.

We describe an integrated suite of algorithms and software for general accurate mass and time (AMT) tagging data analysis of mass spectrometry data. The AMT approach combines identifications from liquid chromatography (LC) tandem mass spectrometry (MS/MS) data with peptide accurate mass and retention time locations from high-resolution LC-MS data. Our workflow includes the traditional AMT approach, in which MS/MS identifications are located in external databases, as well as methods based on more recent hybrid instruments such as the LTQ-FT or Orbitrap, where MS/MS identifications are embedded with the MS data. We demonstrate our AMT workflow's utility for general data synthesis by combining data from two dissimilar biospecimens. Specifically, we demonstrate its use relevant to serum biomarker discovery by identifying which peptides sequenced by MS/MS analysis of tumor tissue may also be present in the plasma of tumor-bearing and control mice. The analysis workflow, referred to as msInspect/AMT, extends and combines existing open-source platforms for LC-MS/MS (CPAS) and LC-MS (msInspect) data analysis and is available in an unrestricted open-source distribution.

Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Because the Mass Deviance metric is derived from the natural distribution of peptide masses, it is machine- and proteome-independent and enables assessment of feature detection tools in the absence of completely annotated data sets. We validate the use of Mass Deviance with a second, independent metric that is based on isotopic distributions, demonstrating that we can use Mass Deviance to identify aberrant features with high accuracy. We then demonstrate the use of independent metrics in tandem as a robust way to evaluate the performance of peptide feature detection algorithms. This work is done on complex LC-MS profiles of Saccharomyces cerevisiae which present a significant challenge to peptide feature detection algorithms.

The open-source Computational Proteomics Analysis System (CPAS) contains an entire data analysis and management pipeline for Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) proteomics, including experiment annotation, protein database searching and sequence management, and mining LC-MS/MS peptide and protein identifications. CPAS architecture and features, such as a general experiment annotation component, installation software, and data security management, make it useful for collaborative projects across geographical locations and for proteomics laboratories without substantial computational support.