Accurate quantification of cardiovascular biomarkers in serum using Protein Standard Absolute Quantification (PSAQ™) and selected reaction monitoring

Development of new biomarkers needs to be significantly accelerated to improve diagnostic, prognostic, and toxicity monitoring as well as therapeutic follow-up. Biomarker evaluation is the main bottleneck in this development process. Selected Reaction Monitoring (SRM) combined with stable isotope dilution has emerged as a promising option to speed this step, particularly because of its multiplexing capacities. However, analytical variabilities because of upstream sample handling or incomplete trypsin digestion still need to be resolved. In 2007, we developed the PSAQ™ method (Protein Standard Absolute Quantification), which uses full-length isotope-labeled protein standards to quantify target proteins. In the present study we used clinically validated cardiovascular biomarkers (LDH-B, CKMB, myoglobin, and troponin I) to demonstrate that the combination of PSAQ and SRM (PSAQ-SRM) allows highly accurate biomarker quantification in serum samples. A multiplex PSAQ-SRM assay was used to quantify these biomarkers in clinical samples from myocardial infarction patients. Good correlation between PSAQ-SRM and ELISA assay results was found and demonstrated the consistency between these analytical approaches. Thus, PSAQ-SRM has the capacity to improve both accuracy and reproducibility in protein analysis. This will be a major contribution to efficient biomarker development strategies.Introduction of new diagnostic assays in the clinical setting requires an operating pipeline to efficiently translate putative biomarkers into validated biomarkers. Despite the discovery platforms'' capacity to generate well populated lists of candidate biomarkers, very few proteins reach the patient bedside as fully fledged “FDA-approved” biomarkers. This is largely because of divergences between analytical needs and performances of the techniques available for candidate biomarker evaluation (1, 2).Candidate biomarker evaluation is a major process of the biomarker pipeline, positioned downstream of the biomarker discovery phase and necessary before clinical validation. Candidate evaluation aims to select, among hundreds of putative biomarkers, those of clinical relevance. Evaluation phase combines two steps which respectively consist in: (1) confirming a difference between physiological and pathological concentrations in biofluids (the so-called “qualification phase”) and (2) assessing the specificity of candidate biomarkers (the so-called “verification phase”) (1). Currently, because of its high throughput and high sensitivity, quantitative ELISA is the preferred assay format for studies evaluating biomarkers. However, as most candidates are likely to fail as relevant biomarkers, developing ELISA tests (with high quality antibodies) for all candidates is a financial burden for the diagnostics industry (3).Thus, there exists an urgent need to develop analytical methods capable of reliable candidate evaluation, at high throughput and reasonable cost. Selected Reaction Monitoring (SRM)¹ mass spectrometry combined with stable isotope dilution (SID-SRM) has shown promise as a solution to this technological hurdle (4, 5). MS analysis in SRM mode offers the unique possibility to specifically and simultaneously monitor the signatures of hundreds of target peptides generated by trypsin digestion of proteins. Combined with isotope-labeled quantification standards (6), SRM can provide quantitative data for each protein targeted (5).Recently, in an effort to demonstrate the potential of SID-SRM for candidate biomarker evaluation, a multilaboratory study was set up to assess its analytical performances and potential transferability (7). Exogenous proteins, seven in all, were added to unfractionated plasma samples. The spiked samples were analyzed by eight independent laboratories using SRM and isotope-labeled peptides as standards. The results obtained clearly demonstrated the capacity of SID-SRM to specifically and precisely quantify protein biomarkers in plasma. However, the results also revealed that the protein digestion rate was highly variable between laboratories. This variability had a significant effect on peptide recovery and on the accuracy of protein quantification. As suggested by the authors, this type of bias could be avoided if properly folded isotope-labeled protein standards were used as quantification standards (7, 8).In 2007, we developed the PSAQ™ (Protein Standard Absolute Quantification) method, which uses full-length isotope-labeled proteins as internal standards for absolute quantitative MS analysis. We demonstrated that, in contrast with peptide standards, adding isotope-labeled proteins before sample digestion enables accurate protein quantification, even for proteins resistant to trypsin digestion (9, 10). In addition, we, and others, have shown that this type of protein standard (“PSAQ standard”) also corrects for protein losses that may occur during sample handling prior to trypsin digestion and liquid chromatography (LC)-MS analysis (11–17). This latter feature is a particular advantage for MS analysis of blood biomarkers. Indeed, as plasma/serum are highly complex matrices and display a huge dynamic range, sample prefractionation must be performed to detect low-abundance protein biomarkers (4).In this study, we have tested a combination of the PSAQ strategy with SRM (PSAQ-SRM) for quantification of cardiovascular biomarkers in serum samples. Selected biomarkers include LDH-B, CKMB, myoglobin, and troponin I. For some of these validated biomarkers, a comparison of PSAQ-SRM data and ELISA results was performed on samples from patients having suffered myocardial infarction.     

Multiple Reaction Monitoring-based, Multiplexed, Absolute Quantitation of 45 Proteins in Human Plasma

Mass spectrometry-based multiple reaction monitoring (MRM) quantitation of proteins can dramatically impact the discovery and quantitation of biomarkers via rapid, targeted, multiplexed protein expression profiling of clinical samples. A mixture of 45 peptide standards, easily adaptable to common plasma proteomics work flows, was created to permit absolute quantitation of 45 endogenous proteins in human plasma trypsin digests. All experiments were performed on simple tryptic digests of human EDTA-plasma without prior affinity depletion or enrichment. Stable isotope-labeled standard peptides were added immediately following tryptic digestion because addition of stable isotope-labeled standard peptides prior to trypsin digestion was found to generate elevated and unpredictable results. Proteotypic tryptic peptides containing isotopically coded amino acids ([¹³C₆]Arg or [¹³C₆]Lys) were synthesized for all 45 proteins. Peptide purity was assessed by capillary zone electrophoresis, and the peptide quantity was determined by amino acid analysis. For maximum sensitivity and specificity, instrumental parameters were empirically determined to generate the most abundant precursor ions and y ion fragments. Concentrations of individual peptide standards in the mixture were optimized to approximate endogenous concentrations of analytes and to ensure the maximum linear dynamic range of the MRM assays. Excellent linear responses (r > 0.99) were obtained for 43 of the 45 proteins with attomole level limits of quantitation (<20% coefficient of variation) for 27 of the 45 proteins. Analytical precision for 44 of the 45 assays varied by <10%. LC-MRM/MS analyses performed on 3 different days on different batches of plasma trypsin digests resulted in coefficients of variation of <20% for 42 of the 45 assays. Concentrations for 39 of the 45 proteins are within a factor of 2 of reported literature values. This mixture of internal standards has many uses and can be applied to the characterization of trypsin digestion kinetics and plasma protein expression profiling because 31 of the 45 proteins are putative biomarkers of cardiovascular disease.MS is capable of sensitive and accurate protein quantitation based on the quantitation of proteolytic peptides as surrogates for the corresponding intact proteins. Over the past 10 years, MS-based protein quantitation based on the analysis of peptides (in other words, based on “bottom-up” proteomics) has had a profound impact on how biological problems can be addressed (1, 2). Although advances in MS instrumentation have contributed to the improvement of MS-based protein quantitation, the use of stable isotopes in quantitative work flows has arguably had the greatest impact in improving the quality and reproducibility of MS-based protein quantitation (3–5).The ongoing development of untargeted MS-based quantitation work flows has focused on increasingly exhaustive sample prefractionation methods, at both the protein and peptide levels, with the goal of detecting and quantifying entire proteomes (6). Although untargeted MS-based quantitation work flows have their utility, they are costly in terms of lengthy MS data acquisition and analysis times, and as a result, they are often limited to quantifying differences between small sample sets (n < 10). To facilitate rapid quantitation of larger, clinically relevant sample sets (n > 100) there is a need to both simplify sample preparation and reduce MS analysis time.Multiple reaction monitoring (MRM)¹ is a tandem MS (MS/MS) scan mode unique to triple quadrupole MS instrumentation that is capable of rapid, sensitive, and specific quantitation of analytes in highly complex sample matrices (7). MRM is a targeted approach that requires knowledge of the molecular weight of an analyte and its fragmentation behavior under CID. MRM is capable of highly reproducible concentration determination when stable isotope-labeled internal standards are included in work flows and has been used for decades for the quantitation of low molecular mass analytes (<1000 Da) in pharmaceutical, clinical, and environmental applications (7, 8).The combination of triple quadrupole MS instrumentation with nanoliter flow rate high performance LC and nanoelectrospray ionization provides the necessary sensitivity for detection and quantitation of biological molecules such as peptides in complex samples such as plasma by MRM. When combined with the use of isotopically labeled synthetic peptide standards, MRM analysis is capable of sensitive (attomole level) and absolute determination of peptide concentrations across a wide concentration scale spanning a dynamic range of 10³–10⁴ (1, 9–13).Several recent studies involving MRM-based analysis of plasma proteins have focused on increasing MRM detection sensitivity by fractionating plasma using either multidimensional liquid chromatography, affinity depletion of high abundance proteins (11, 14, 15), or affinity enrichment of low abundance peptides (16, 17). Anderson and Hunter (14) have shown that LC-MRM/MS analysis is capable of detecting 47 moderate to high abundance proteins in plasma without depletion even though ∼90% of the total protein by weight in trypsin-digested plasma can be attributed to 10 high abundance proteins (18).Relative abundance of a protein does not preclude its involvement in disease. In fact, 32 of the 47 plasma proteins detected by Anderson and Hunter (14) have been implicated as putative markers for cardiovascular disease. The ability to rapidly quantify proteins in a highly multiplexed manner using MRM and internal standard peptides expands the potential application of MRM quantitation beyond biomarker validation and into the field of biomarker discovery. Targeted, simultaneous quantitation of hundreds of proteins in a single analysis will enable rapid protein expression profiling of large (n > 100) clinically relevant sample sets in a manner similar to DNA microarray expression profiling. By allowing researchers to look at patterns of expression levels of a large number of proteins in a large number of samples (as opposed to looking at the expression levels of only a single protein), multiplexed MRM-based quantitation will allow the correlation of expression patterns with particular diseases. Once these characteristic patterns have been established, physicians will be able to use these protein expression patterns to diagnose diseases in the same way they currently use blood chemistry panels or comprehensive metabolic panels.When considering the clinical utility of MS-based assays, direct comparisons are often made to ELISA, which is considered the “gold standard” for protein quantitation in clinical samples. Attributes of ELISAs, such as “time to first result” (1–2 h (19)) and the ability to quantify 96 or 384 samples in parallel because of their microtiter plate-based format, are currently difficult to match with MS-based protein assays. However, MRM protein assays may surpass ELISA in the rapid development of clinically useful, multiplexed protein assays. The impact of multiplexed assays in the field of genomics has increased interest in multiplexed quantitation of many proteins in individual clinical samples (19). Development and characterization of MRM-based protein assays using isotopically labeled peptides is rapid and inexpensive compared with the time and cost associated with the generation and characterization of antibodies for ELISA development.In this study, we describe the creation of a customizable mixture of concentration-balanced stable isotope-labeled standard (SIS) peptides representing an initial panel of 45 human plasma proteins. We used this mixture of SIS peptides to develop a suite of multiplexed, rapid, and reproducible MRM-based assays for expression profiling of these 45 proteins in simple tryptic digests of whole plasma. Additionally we characterized the analytical performance of these MRM peptide assays with respect to their reproducibility, and we demonstrated their utility for absolute protein concentration determination.Multiplexed MRM quantitation of peptides for protein quantitation has the potential to replace iTRAQ or other isotope label and label-free quantitative proteomics approaches because the approach is much faster than these other methods (30–60 min per analysis compared with 4 days for LC-MALDI-based iTRAQ), has greater reproducibility (CV <5% versus iTRAQ CV >20%), and enables absolute quantitation (concentration and copy number versus only x-fold up- or down-regulated). Additionally MRM-based quantitation with SIS peptides does not “miss” peptides because the SIS peptide must be detected in every sample: this means that if an endogenous peptide is not observed then it is below the limit of detection.     

A Label-free Selected Reaction Monitoring Workflow Identifies a Subset of Pregnancy Specific Glycoproteins as Potential Predictive Markers of Early-onset Pre-eclampsia

Pre-eclampsia (PE) is a serious complication of pregnancy with potentially life threatening consequences for both mother and baby. Presently there is no test with the required performance to predict which healthy first-time mothers will go on to develop PE. The high specificity, sensitivity, and multiplexed nature of selected reaction monitoring holds great potential as a tool for the verification and validation of putative candidate biomarkersfor disease states. Realization of this potential involves establishing a high throughput, cost effective, reproducible sample preparation workflow. We have developed a semi-automated HPLC-based sample preparation workflow before a label-free selected reaction monitoring approach. This workflow has been applied to the search for novel predictive biomarkers for PE.To discover novel candidate biomarkers for PE, we used isobaric tagging to identify several potential biomarker proteins in plasma obtained at 15 weeks gestation from nulliparous women who later developed PE compared with pregnant women who remained healthy. Such a study generates a number of “candidate” biomarkers that require further testing in larger patient cohorts. As proof-of-principle, two of these proteins were taken forward for verification in a 100 women (58 PE, 42 controls) using label-free SRM. We obtained reproducible protein quantitation across the 100 samples and demonstrated significant changes in protein levels, even with as little as 20% change in protein concentration. The SRM data correlated with a commercial ELISA, suggesting that this is a robust workflow suitable for rapid, affordable, label-free verification of which candidate biomarkers should be taken forward for thorough investigation. A subset of pregnancy-specific glycoproteins (PSGs) had value as novel predictive markers for PE.The identification of clinically relevant plasma biomarkers with diagnostic and/or predictive value continues to challenge the proteomics field. Whereas once the biomarker pipeline was described as a two part discovery and validation process, there is increasing consensus that an intermediate step is required in which the proteins identified in the discovery phase are technically verified in 50 to 200 samples. This verification step identifies false positives from the discovery phase and allows prioritization of proteins to be taken into large-scale clinical validation studies (1). Although commercial ELISA kits may be used in this phase, these are unavailable for many proteins, are expensive, and may lack specificity. In addition, sample requirements may be too high to perform ELISA on all candidates, especially if many proteins are identified as potential markers by low powered, high penetration discovery workflows.Selected reaction monitoring (SRM)¹ mass spectrometry has great potential as an alternative verification method (2–6) as it can be multiplexed, customized, and is highly specific. This potential has not been exploited to date, largely because of technical issues developing a low-cost, reproducible workflow encompassing plasma and serum preparation and LC/MS analysis with the capability to measure protein levels reproducible in hundreds of samples. With traditional stable isotope dilution SRM (SID-SRM), the high cost of accurately quantified, purified stable isotope encoded peptides or proteins may be prohibitive for the verification of multiple peptides from many proteins. Label-free relatively quantitative methods are increasingly popular in discovery proteomics but to a much lesser extent in targeted SRM studies (7, 8).For any SRM method, sample preparation workflows must balance the extent of enrichment and fractionation to enable quantification of lower abundance proteins, against increased technical variability (which is influenced by the number of sample handling steps) and reduced multiplexed potential as a consequence of fractionating peptides from the protein of interest into several distinct fractions. It is also essential that the true technical variation in the workflow is quantitatively evaluated from freezer to MS analysis, rather than just the variation within the LC-SRM part of the experiment. As a paradigm for a label-free SRM assay, we developed our workflow and applied it to the verification of candidate biomarkers that indicate the risk of pre-eclampsia (PE).PE affects 2–8% of pregnancies, and is characterized by hypertension and proteinuria, which may progress to severe maternal complications or death (9). Because delivery of the infant is the only effective intervention, a third of babies are born premature and fetal or newborn mortality is increased three- to 10-fold (10). Its complex etiology involves abnormal placentation, an altered immune response and a sensitized maternal vascular endothelium (11). Prediction of the condition in early pregnancy would allow prevention strategies, such as low dose aspirin, to be targeted to high risk women. In first-time pregnant women, a group particularly at risk, biomarkers continue to fall short of a test that would be useful or cost effective in clinical practice (12–14). Better-performing novel biomarkers are required.The aim of this study was to identify candidate predictive biomarkers for PE and then develop a verification assay using mass spectrometry to determine whether these should be taken forward into more extensive and expensive validation studies. Initial discovery experiments were employed using a pooled sample iTRAQ approach using two different MS platforms to increase plasma proteome coverage. Among the set of proteins discovered, we then developed a label-free SRM assay for relative quantification of CXCL7 (Platelet basic protein; PBP) and members of the Pregnancy specific glycoprotein (PSG) family in a 100-sample set from the international SCreeningfOr Pregnancy Endpoints (SCOPE) study (www.scopestudy.net). Our workflow allowed the specificity and linearity of response for each peptide to be determined, along with true technical variability. Although absolute concentration and LOD/LOQ cannot be calculated using this approach, we aimed to test the hypothesis that a label-free SRM approach could provide a rapid, robust, and efficient screen of candidate plasma biomarkers.     

Proteomics of Microparticles with SILAC Quantification (PROMIS-Quan): A Novel Proteomic Method for Plasma Biomarker Quantification

Unbiased proteomic analysis of plasma samples holds the promise to reveal clinically invaluable disease biomarkers. However, the tremendous dynamic range of the plasma proteome has so far hampered the identification of such low abundant markers. To overcome this challenge we analyzed the plasma microparticle proteome, and reached an unprecedented depth of over 3000 plasma proteins in single runs. To add a quantitative dimension, we developed PROMIS-Quan—PROteomics of MIcroparticles with Super-Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) Quantification, a novel mass spectrometry-based technology for plasma microparticle proteome quantification. PROMIS-Quan enables a two-step relative and absolute SILAC quantification. First, plasma microparticle proteomes are quantified relative to a super-SILAC mix composed of cell lines from distinct origins. Next, the absolute amounts of selected proteins of interest are quantified relative to the super-SILAC mix. We applied PROMIS-Quan to prostate cancer and compared plasma microparticle samples of healthy individuals and prostate cancer patients. We identified in total 5374 plasma-microparticle proteins, and revealed a predictive signature of three proteins that were elevated in the patient-derived plasma microparticles. Finally, PROMIS-Quan enabled determination of the absolute quantitative changes in prostate specific antigen (PSA) upon treatment. We propose PROMIS-Quan as an innovative platform for biomarker discovery, validation, and quantification in both the biomedical research and in the clinical worlds.Biomarker discovery in plasma is one of the holy grails of the proteomic field toward the development of noninvasive diagnostic/prognostic tests (1). To achieve this goal, proteomics necessitates a comprehensive view of the plasma proteome, accurate proteome quantification, combined with relatively short analytical times to enable multiple sample comparisons. However, MS-based biomarker discovery is limited by the vast dynamic range of the plasma, over 11 orders of magnitude (2, 3), which leads to the masking of “tissue leakage” proteins that comprise of potential biomarkers by the core plasma proteins. Two main complementary strategies have been employed to reach identification of low abundance proteins: (i) Targeted proteomics, in which the MS identifies and quantifies only predetermined peptides, thereby circumventing the system''s inherent tendency to preferentially detect abundant proteins. This approach is utilized for validation of preselected candidate markers (4–6). (ii) Plasma fractionation, which biochemically reduces the complexity of the proteomes, and enables discovery of novel biomarkers (7, 8).Targeted MS analysis is dominated by the selected reaction monitoring approach, often in combination with antibody-based enrichment of proteins or peptides and stable isotope labeled standards for quantification (9). This approach benefits from the sensitivity and quantitative capabilities of the triple-quadrupole instruments. Its major limitation is that it relies on prior discovery of candidates within the plasma samples using extensive tissue/cell-line-based analysis and prediction of potential biomarkers. The fractionation strategy reduces both the complexity and the dynamic range of the plasma through depletion of the most abundant plasma proteins, and/or through extensive biochemical separation of proteins and peptides. Although these fractionation approaches enabled identification of thousands of plasma proteins (7), they dramatically reduce the throughput of the method, and thus, the applicability to clinical studies.A distinct fractionation approach involves the isolation of plasma microparticles and exosomes. Microparticles are large vesicles (100 nm–1 μm), which protrude directly from the plasma membrane, whereas exosomes are smaller (40–100 nm) and originate from endocytic compartments known as the multivesicular endosomes. These microvesicles are constitutively shed from all cell types into the blood, carrying a proteomic signature of their cells of origin (10). Microparticles mediate local and systemic communication in various conditions, in particularly in cancer, where they can promote metastasis, immune evasion of cancer cells and angiogenesis (10–13), but also in other conditions including autoimmune diseases (14) and cardiovascular disorders (15). Therefore, circulating plasma microparticle proteomics can reveal biomarkers of various diseases as the basis for further diagnostic test development.The profiling of plasma microparticle proteomes initiated by Jin et al. in 2005, with the analysis of 16 samples using two-dimensional (2D)-gels followed by matrix assisted laser desorption ionization- time of flight (MALDI-TOF) MS analysis, which resulted in the identification of 83 proteins (16). In the following years, low resolution MS analysis of plasma microparticles reached up to 229 plasma microparticle proteins and high resolution MS analysis reached 458 proteins (all without false discovery rate (FDR)¹ correction)(17, 18). The latest and most comprehensive study of plasma microparticles proteome profiling was published in 2012 by Ostergaard et al., who analyzed 12 samples on the LTQ Orbitrap XL mass spectrometer and identified 536 proteins in total, after 1% FDR correction (19). Other studies have profiled the proteomes of microparticles and exosomes derived from various body fluids other than plasma, including urine (20), saliva (21), cerebral spinal fluid (22), breast milk (23), amniotic fluid (24), seminal fluid (25), and more. However, despite the dramatic reduction of the dynamic range of the analytes, so far it has not yet provided sufficient depth for biomarker discovery. Nevertheless, it has a good prospective for discovering biomarkers. For example, biochemical analysis of breast cancer patient leukocytes-derived microparticles correlated between increased tumor size and increased levels of carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3), two well-known prognostic markers for colon and breast cancer, respectively (26).Combining all of the plasma proteomics approaches mentioned above, several prominent surveys of the human plasma proteome have been reported. The first large-scale collaborative study was conducted by the Human Proteome Organization (HuPO) group, which collectively identified 3020 proteins (7). These were later condensed to a list of 889 nonredundant proteins, after taking into account multiple hypotheses control with at least 95% confidence in protein identification (27). The Peptide Atlas team initially combined 91 studies, including the one conducted by HuPO, and altogether produced a list of 1929 proteins (28). Recently this team has elaborated their survey by assembling 127 studies (29) and reached the largest high-confidence list published so far of overall 3677 plasma proteins.In the current work we applied state of the art proteomics to study the microparticle proteome and developed the PROteomics of MIcroparticles with Super-SILAC Quantification (PROMIS-Quan) method, which combines deep plasma microparticle coverage of more than 3200 proteins in a single run, with dual-mode relative and absolute Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) quantification. We demonstrated its utilization on samples of prostate cancer patients, and calculated the absolute amount of PSA, a well-known prostate cancer biomarker.     

Reverse-Polynomial Dilution Calibration Methodology Extends Lower Limit of Quantification and Reduces Relative Residual Error in Targeted Peptide Measurements in Blood Plasma

Matrix effect is the alteration of an analyte''s concentration-signal response caused by co-existing ion components. With electrospray ionization (ESI), matrix effects are believed to be a function of the relative concentrations, ionization efficiency, and solvation energies of the analytes within the electrospray ionization droplet. For biological matrices such as plasma, the interactions between droplet components is immensely complex and the effect on analyte signal response not well elucidated. This study comprised of three sequential quantitative analyses: we investigated whether there is a generalizable correlation between the range of unique ions in a sample matrix (complexity); the amount of matrix components (concentration); and matrix effect, by comparing an E. coli digest matrix (∼2600 protein proteome) with phospholipid depleted human blood plasma, and unfractionated, nondepleted human plasma matrices (∼10⁷ proteome) for six human plasma peptide multiple reaction monitoring assays. Our data set demonstrated analyte-specific interactions with matrix complexity and concentration properties resulting in significant ion suppression for all peptides (p < 0.01), with nonuniform effects on the ion signals of the analytes and their stable-isotope analogs. These matrix effects were then assessed for translation into relative residual error and precision effects in a low concentration (∼0–250 ng/ml) range across no-matrix, complex matrix, and highly complex matrix, when a standard addition stable isotope dilution calibration method was used. Relative residual error (%) and precision (CV%) by stable isotope dilution were within <20%; however, error in phospholipid-depleted and nondepleted plasma matrices were significantly higher compared with no-matrix (p = 0.006). Finally a novel reverse-polynomial dilution calibration method with and without phospholipid-depletion was compared with stable isotope dilution for relative residual error and precision. Reverse-polynomial dilution techniques extend the Lower Limit of Quantification and reduce error (p = 0.005) in low-concentration plasma peptide assays and is broadly applicable for verification phase Tier 2 multiplexed multiple reaction monitoring assay development within the FDA-National Cancer Institute (NCI) biomarker development pipeline.Plasma is the overriding human medium sampled for established and novel protein biomarkers (1, 2). As of 2011, 1929 high-confidence proteins have been cataloged by the Human Plasma Proteome Project, with estimates that there are up to 10⁷ unique protein sequences in plasma that span a concentration range across 10 orders of magnitude (1, 3). 99% of the protein mass in plasma is made up of 22 proteins including Albumin, Fibrinogen, and a range of immunoglobulins, leaving more than 1900 known small proteins and essentially the entirety of the projected plasma proteome in the remaining 1% (4). It is these low-mass, low abundance proteins such as the Interleukins, C-Reactive Protein, and Carcinoma Antigen 125 (CA125), that are indicative of many important physiological and pathological processes, and proteomic scientists and clinicians have thus focused their efforts in qualitatively and quantitatively defining this fraction for novel biomarkers (4–6).The development of plasma biomarkers is a large-scale undertaking that spans discovery, verification, and validation phases in a multistage pipeline: Thousands of “discovered” differentiated proteins are evaluated for probability of effect, from which 10–100s of proteins are then selected for targeted quantification in verification phase to evaluate sensitivity and specificity for its intended indication (2, 7). Finally a panel of the strongest marker candidates is progressed to validation phase, and FDA-level validated quantitative assays are used to test the clinical utility of the biomarker panel. Liquid Chromatography coupled with Tandem Mass Spectrometry (LC-MS/MS)¹ is the most robust analytical method available for proteomic scientists in this pipeline, able to separate complex mixtures and specifically and sensitively identify and quantify its components (2, 7–10), The ability to ionize and evaporate the contents of a liquid sample (coupling LC to MS/MS) is the basis that allows this to happen (9). Electrospray Ionization (ESI) is the most widely used ionization apparatus in LC-MS/MS bioanalysis because of its ionization efficiency and stability and low chemical specificity (9, 10). Although these properties make ESI very robust, the complexity of biological matrices poses a significant challenge for LC-ESI-MS/MS-based quantitation; despite chromatography and nanospray technology, the ESI droplet of a plasma peptide-digest sample (given its immense range of unique protein/peptide sequences and concentrations) can contain an unknown multitude of co-eluting components that “compete” to dissolve from the droplet and reach gas phase, suppressing and varying the signal intensity responses for a given analyte concentration (9–13). These ionization competing elements can also go on to produce isobaric signals in the third quadrupole that interfere with an analyte''s transition signals (14). Termed “matrix effects,” these phenomena of complex sample matrices can significantly impede quantitative accuracy (15). For high-throughput clinical assays, matrix effects are controlled for by preparing calibration standards in the same biological matrix to mimic the conditions of the samples intended for study as per FDA bioanalytical method validation guidelines (16). The catch to this technique is that the signal from the endogenous analyte in the background matrix hinders accuracy when the nominal concentration is close to or below the endogenous signal (14, 17). There is a need for broadly applicable methods of controlling matrix effects and increasing accuracy in low concentration MRM peptide assays for nondepleted, unfractionated plasma that can be adopted for the highly multiplexed, high throughput, “Tier 2” MS assays required in verification phase of the biomarker development pipeline (2, 8). Several simple methods have independently demonstrated the ability to increase accuracy in various hyphenated-MS assays in complex matrices: “Reverse” curves utilize the stable-isotope analog not as an internal standard but as a surrogate calibration analyte to circumvent interference from the endogenous analyte signal and extend assay Lower Limit(s) of Quantification (LLOQ), and nonlinear calibration techniques have proven to more accurately reflect the concentration-MS detector response at the low and high end of concentration gradients (8, 14, 18–21). Specifically in the case of biological matrices, phospholipids are particularly deleterious ion suppressing elements because of their easily ionizable, polar, and hydrophobic moieties that can have complex interactions with co-eluting analytes as well as the chromatography stationary and mobile phases required for most other analytes (22–25). Combination solid-phase extraction (SPE) and phospholipid removal techniques have proved to effectively minimize ion suppression effects in ESI-MS assays (22–25).In this study, we investigated whether there is a generalizable linear correlation between the number of unique ions (complexity) in a biological sample matrix, the amount of ionizable matrix content (concentration), and matrix effects, for six human plasma peptides comparing serial dilutions of an Escherichia Coli (E. coli) peptide-digest against phospholipid-depleted and nondepleted unfractionated human plasma peptide-digest (highly complex) matrices. We examined the influence of matrix effects on relative residual error in a low-concentration (∼0–250 ng/ml) plasma peptide range, and compared the utility of a reverse-polynomial dilution (RPD) calibration method versus standard addition stable-isotope dilution (SID) in phospholipid-depleted and nondepleted unfractionated human plasma. A peptide-centric matrix effect is reported and the effect of the endogenous analyte signal on relative residual error in low-concentration (∼0–250 ng/ml) plasma peptide assays is established. A RPD calibration technique that extends LLOQ and reduces relative residual error in low-concentration plasma peptide MRM assays is presented.     

Low Mass Blood Peptides Discriminative of Inflammatory Bowel Disease (IBD) Severity: A Quantitative Proteomic Perspective

Breakdown of the protective gut barrier releases effector molecules and degradation products into the blood stream making serum and plasma ideal as a diagnostic medium. The enriched low mass proteome is unexplored as a source of differentiators for diagnosing and monitoring inflammatory bowel disease (IBD) activity, that is less invasive than colonoscopy. Differences in the enriched low mass plasma proteome (<25 kDa) were assessed by label-free quantitative mass-spectrometry. A panel of marker candidates were progressed to validation phase and “Tier-2” FDA-level validated quantitative assay. Proteins important in maintaining gut barrier function and homeostasis at the epithelial interface have been quantitated by multiple reaction monitoring in plasma and serum including both inflammatory; rheumatoid arthritis controls, and non-inflammatory healthy controls; ulcerative colitis (UC), and Crohn''s disease (CD) patients. Detection by immunoblot confirmed presence at the protein level in plasma. Correlation analysis and receiver operator characteristics were used to report the sensitivity and specificity. Peptides differentiating controls from IBD originate from secreted phosphoprotein 24 (SPP24, p = 0.000086, 0.009); whereas those in remission and healthy can be differentiated in UC by SPP24 (p = 0.00023, 0.001), α-1-microglobulin (AMBP, p = 0.006) and CD by SPP24 (p = 0.019, 0.05). UC and CD can be differentiated by Guanylin (GUC2A, p = 0.001), and Secretogranin-1 (CHGB p = 0.035). Active and quiescent disease can also be differentiated in UC and CD by CHGB (p ≤ 0.023) SPP24 (p ≤ 0.023) and AMBP (UC p = 0.046). Five peptides discriminating IBD activity and severity had very little-to-no correlation to erythrocyte sedimentation rate, C-reactive protein, white cell or platelet counts. Three of these peptides were found to be binding partners to SPP24 protein alongside other known matrix proteins. These proteins have the potential to improve diagnosis and evaluate IBD activity, reducing the need for more invasive techniques. Data are available via ProteomeXchange with identifier PXD002821.Inflammatory bowel disease (IBD)¹ is a life-long relapsing and remitting inflammatory disorder primarily affecting the gastrointestinal tract and can be subdivided into the main groups of Crohn''s disease (CD) and ulcerative colitis (UC) (1). Current treatment focuses on reducing and controlling inflammation. There is no cure and the majority of IBD patients remain under medical care and management for life. With increasing prevalence around the world, clinical assays that can provide accurate diagnosis, discrimination between CD and UC, and determination of disease activity are being sought to achieve effective treatment and management. The clinical presentations of both subtypes are similar and invasive diagnostic investigations, specifically colonoscopy and histopathological evaluation of the inflamed gut wall, remains the gold standard for diagnosis and assessment of activity (2–5). Current diagnostic antibody markers such as anti-saccharomyces cerevisiae antibody (ASCA) and peri-nuclear anti-neutrophil cytoplasmic antibody (P-ANCA) or combinations of genetic susceptibility markers and serological markers provide increased specificity (6–10). Despite this, acute phase proteins such as C-reactive protein (CRP), fecal calprotectin in addition to the erythrocyte sedimentation rate (ESR) and other clinical activity indicators are more typically used in practice to monitor disease progression in addition to colonoscopy (11). Unbiased discovery in patient plasma samples has the potential to capture both the reactive pathways that result in symptoms as well as identify novel causal proteins that may have initiated disease onset and the biological switch to autoimmune complications of IBD (12, 13). The regulation of homeostasis between the intestinal epithelial cells, mucosal surface, and the immune system that contribute to exacerbated inflamed response are less well characterized and would benefit from the posteriori knowledge of the global “omics” approach to explore emerging causal and reactive proteins and peptides for further validation. Discovery of new protein markers through proteomic technology has already expanded the knowledge of IBD (14–19) and can be used to improve the diagnostic accuracy, long-term management, and treatment of a host of different diseases (20, 21).We have specifically focused on the differential protein profiles of 1–25 kDa fraction between IBD and healthy human plasma samples. Such partitioning of proteins enabled powerful enrichment of low mass and poorly abundant proteins (22). Using a shotgun proteomic approach, this large scale survey of proteins has highlighted the increase in inflammatory and acute phase proteins that are known to plague the illness and in addition has revealed novel peptides and proteins that can be used to discriminate IBD from controls, and UC from CD. These proteins have been investigated further using accurate and sensitive quantitative techniques of multiple reaction monitoring (MRM) for low-concentration peptides (23) applicable to verification phase Tier 2 multiplexed MRM assay development within the FDA-National Cancer Institute (NCI) biomarker pipeline (24). The on-column amounts of each protein from this biomarker panel were evaluated for individual samples, and Western blots have also been used to confirm presence.     

Prolyl 4-Hydroxylation of ��-Fibrinogen: A NOVEL PROTEIN MODIFICATION REVEALED BY PLASMA PROTEOMICS*

Plasma proteome analysis requires sufficient power to compare numerous samples and detect changes in protein modification, because the protein content of human samples varies significantly among individuals, and many plasma proteins undergo changes in the bloodstream. A label-free proteomics platform developed in our laboratory, termed “Two-Dimensional Image Converted Analysis of Liquid chromatography and mass spectrometry (2DICAL),” is capable of these tasks. Here, we describe successful detection of novel prolyl hydroxylation of α-fibrinogen using 2DICAL, based on comparison of plasma samples of 38 pancreatic cancer patients and 39 healthy subjects. Using a newly generated monoclonal antibody 11A5, we confirmed the increase in prolyl-hydroxylated α-fibrinogen plasma levels and identified prolyl 4-hydroxylase A1 as a key enzyme for the modification. Competitive enzyme-linked immunosorbent assay of 685 blood samples revealed dynamic changes in prolyl-hydroxylated α-fibrinogen plasma level depending on clinical status. Prolyl-hydroxylated α-fibrinogen is presumably controlled by multiple biological mechanisms, which remain to be clarified in future studies.For comprehensive analysis of plasma proteins, it is necessary to compare a sufficient number of blood samples to avoid simple interindividual heterogeneity, because the protein content of human samples varies significantly among individuals. Also, the provision of sufficient power is needed to detect protein modification because many plasma proteins undergo changes in the bloodstream (1). Even though the proteomic technologies have advanced (2, 3), there remains room for improvement. Different isotope labeling and identification-based methods have been developed for quantitative proteomics technologies (4–6), but the number of samples that can be compared by the current isotope-labeling methods is limited, and identification-based proteomics is unable to capture information regarding unknown modifications.A label-free proteomics platform developed in our laboratory, termed “Two-Dimensional Image Converted Analysis of Liquid chromatography and mass spectrometry (2DICAL)² (7), simply compares the liquid chromatography and mass spectrometry (LC-MS) data and detects a protein modification by finding changes in the mass to charge ratio (m/z) and retention time (RT). Enhanced methods for accurate MS peak alignment across multiple LC runs have enabled the successful implementation of clinical studies requiring comparison of a large number of samples (8, 9). Using 2DICAL to analyze plasma samples of pancreatic cancer patients and healthy controls, novel prolyl hydroxylation of α-fibrinogen was successfully discovered.Fibrinogen and its modification has been investigated because of its clinical importance (10, 11). On the other hand, prolyl hydroxylation has attracted attention after the discovery of the hypoxia-inducible factor 1α (HIF1α) prolyl-hydroxylase and its role in switching of HIF1α functions (12). Prolyl hydroxylation in other proteins has been energetically sought, but only a few such proteins have been identified (13). Only one study has reported prolyl hydroxylation of fibrinogen at the β chain (14).Here, we report the detection of prolyl 4-hydroxylated α-fibrinogen by plasma proteome analysis, a protein modification that dynamically changes in plasma depending on the clinical status and is a candidate plasma biomarker.     

Identification of Differentially Expressed Proteins in Direct Expressed Prostatic Secretions of Men with Organ-confined Versus Extracapsular Prostate Cancer

Current protocols for the screening of prostate cancer cannot accurately discriminate clinically indolent tumors from more aggressive ones. One reliable indicator of outcome has been the determination of organ-confined versus nonorgan-confined disease but even this determination is often only made following prostatectomy. This underscores the need to explore alternate avenues to enhance outcome prediction of prostate cancer patients. Fluids that are proximal to the prostate, such as expressed prostatic secretions (EPS), are attractive sources of potential prostate cancer biomarkers as these fluids likely bathe the tumor. Direct-EPS samples from 16 individuals with extracapsular (n = 8) or organ-confined (n = 8) prostate cancer were used as a discovery cohort, and were analyzed in duplicate by a nine-step MudPIT on a LTQ-Orbitrap XL mass spectrometer. A total of 624 unique proteins were identified by at least two unique peptides with a 0.2% false discovery rate. A semiquantitative spectral counting algorithm identified 133 significantly differentially expressed proteins in the discovery cohort. Integrative data mining prioritized 14 candidates, including two known prostate cancer biomarkers: prostate-specific antigen and prostatic acid phosphatase, which were significantly elevated in the direct-EPS from the organ-confined cancer group. These and five other candidates (SFN, MME, PARK7, TIMP1, and TGM4) were verified by Western blotting in an independent set of direct-EPS from patients with biochemically recurrent disease (n = 5) versus patients with no evidence of recurrence upon follow-up (n = 10). Lastly, we performed proof-of-concept SRM-MS-based relative quantification of the five candidates using unpurified heavy isotope-labeled synthetic peptides spiked into pools of EPS-urines from men with extracapsular and organ-confined prostate tumors. This study represents the first efforts to define the direct-EPS proteome from two major subclasses of prostate cancer using shotgun proteomics and verification in EPS-urine by SRM-MS.Prostate cancer is the most common malignancy to affect men in the Western world, but only 15–20% of these men will present with aggressive, lethal disease (1, 2) whereas the majority of patients will die of other causes. Although the implementation of large-scale screening for prostate cancer using serum prostate-specific antigen (PSA) has dramatically improved early detection of disease, unnecessary biopsies and patient overtreatment are becoming increasingly evident (2, 3). Consequently, there has been a shift in emphasis away from detection of prostate cancer and toward identification of lethal disease. Currently, Gleason grading is considered to be one of the best outcome predictors; however, patients with Gleason 7 tumors are in the clinical “gray zone,” whereby the predictive ability of Gleason grading is mixed (4, 5). A recent study constructed a 157-gene signature based on the comparison of Gleason score ≤6 and ≥8 patients, and could show that their panel could predict lethality in the cohort of Gleason 7 patients (5). Nonetheless, the development and large-scale implementation of prognostic markers of prostate cancer has been hampered by numerous factors owing, in part, to the heterogeneous and multifocal nature of the disease (6). Although the widely used Gleason grading system attempts to control for heterogeneity of the glands and multifocality of cancerous lesions by summing the 2–3 most commonly observed histological patterns via inspection of multiple (typically 8–12) core biopsies, cancerous foci are still often missed (2, 6) providing only partial information that can lead to imprecise diagnoses and prognoses. Pathologic staging remains the gold standard for disease staging and risk assessment (7, 8); however, this process lacks timeliness in discriminating organ-confined from extracapsular disease. Indeed, one-third of individuals with nonorgan-confined disease are identified only after surgery (9). Furthermore, ∼35% of men treated with radical prostatectomy with curative intent subsequently develop biochemical recurrence (10–13) and the mean time from surgery to recurrence is 3.5 years (4). Significant risk factors for time to prostate-specific mortality following biochemical recurrence after radical prostatectomy are PSA doubling time, pathological Gleason score, and time from surgery to biochemical recurrence (4). Estimates place the percent of lethal cases at 20–25% of all patients that show biochemical recurrence, suggesting that nearly 75–80% of patients in this group may be overtreated (14).There is an emerging trend toward recruitment of men with perceived low-risk disease to an “active surveillance” monitoring approach. This is based on the supposition that most prostate cancers are slow growing, and that the more aggressive forms can be identified during a period of observation with little increased risk of death. Although a consensus may not exist for defining the disease stage where active surveillance is warranted, there is considerable agreement that men who have a PSA level less than 10 ng/ml, impalpable disease (clinical stage T1c) and only 1 biopsy core out of 12 or more that show Gleason 6 cancer are most likely to harbor indolent disease (15). Even so, these candidates for active surveillance will still contain individuals who will have disease progression and die from their cancer. Thus, despite efforts to recruit individuals to active surveillance protocols, overtreatment of prostate cancer is fueled by the lack of reliable means to accurately discriminate between men with clinically indolent prostate cancer from those with more aggressive disease (16, 17). This inability to accurately predict prostate cancer aggressiveness based solely on standard clinicopathologic features clearly underscores the need to explore the ability of additional biomarkers to enhance outcome prediction for men with prostate cancer. Furthermore, it is important to acknowledge that a single biomarker alone is unlikely to have sufficient prognostic power; rather, the integration of a panel of biomarkers hold the promise for improved prostate cancer detection and prognosis (2).Fluids that are proximal to the prostate are attractive sources of potential prostate cancer biomarkers (2, 18), as they house secreted proteins and sloughed cells that provide a presumably more comprehensive assessment of the organ and extent of disease. Further, fluids such as urine are clinically favorable for their ease of collection, the volume and frequency at which they can be obtained, and their adaptability to routine clinical assays. Prostate-proximal fluids include seminal fluid, semen, and expressed prostatic secretions (EPS)¹. Here, we focus on the analysis of EPS as our biological specimen, using direct-EPS samples for the discovery of candidate prognostic biomarkers and both direct-EPS and pooled EPS-urines derived from independent sets of patients for candidate biomarker verification. Direct-EPS is a prostatic fluid that is collected from patients undergoing prostatectomy by massaging the organ and expelling 0.5–1 ml of the fluid just prior to surgical removal. It was chosen as our discovery fluid as it is expected to house prostate-secreted proteins at a higher concentration and purity, and we have developed a workflow for the in-depth proteomic analysis of this fluid (19). Following discovery proteomics in 16 clinically stratified direct-EPS samples, verification studies were performed using independent sample sets of direct-EPS. Next, we focused our attention on the verification and quantitative analysis of candidate proteins in pooled EPS-urines. Before EPS-urine collection, men undergo digital rectal examination (DRE), often as part of a routine procedure, which causes direct-EPS to be expelled from the prostate and subsequently voided in urine. Because EPS-urine can be collected with substantial ease and in greater volumes and frequencies than direct-EPS, much attention has been paid to this fluid as a valuable resource of prostate cancer biomarkers amenable to routine clinical analysis. Following the recent FDA approval of the EPS-urine assay for prostate cancer gene 3 (PCA3), standardized clinical collection protocols will be widely implemented and easier access to this fluid is expected. Moreover, we have recently identified a number of prostate-enriched proteins in EPS-urine by comparing its proteome to a urine background (20).The present study used multidimensional protein identification technology (MudPIT) coupled with bioinformatics to first catalog and comparatively analyze the direct-EPS proteomes from a small cohort of patients with extracapsular versus organ-confined prostate cancers. A semiquantitative algorithm based on spectral counts (QSpec) (21) and an integrative data mining strategy led to the selection of a number of putative biomarkers that were verified by Western blotting in direct-EPS. Lastly, to demonstrate accurate quantitative measurements of verified candidates in EPS-urine, a pilot study utilizing SRM-MS was undertaken as a proof-of-concept.     

Candidate Serological Biomarkers for Cancer Identified from the Secretomes of 23 Cancer Cell Lines and the Human Protein Atlas

Although cancer cell secretome profiling is a promising strategy used to identify potential body fluid-accessible cancer biomarkers, questions remain regarding the depth to which the cancer cell secretome can be mined and the efficiency with which researchers can select useful candidates from the growing list of identified proteins. Therefore, we analyzed the secretomes of 23 human cancer cell lines derived from 11 cancer types using one-dimensional SDS-PAGE and nano-LC-MS/MS performed on an LTQ-Orbitrap mass spectrometer to generate a more comprehensive cancer cell secretome. A total of 31,180 proteins was detected, accounting for 4,584 non-redundant proteins, with an average of 1,300 proteins identified per cell line. Using protein secretion-predictive algorithms, 55.8% of the proteins appeared to be released or shed from cells. The identified proteins were selected as potential marker candidates according to three strategies: (i) proteins apparently secreted by one cancer type but not by others (cancer type-specific marker candidates), (ii) proteins released by most cancer cell lines (pan-cancer marker candidates), and (iii) proteins putatively linked to cancer-relevant pathways. We then examined protein expression profiles in the Human Protein Atlas to identify biomarker candidates that were simultaneously detected in the secretomes and highly expressed in cancer tissues. This analysis yielded 6–137 marker candidates selective for each tumor type and 94 potential pan-cancer markers. Among these, we selectively validated monocyte differentiation antigen CD14 (for liver cancer), stromal cell-derived factor 1 (for lung cancer), and cathepsin L1 and interferon-induced 17-kDa protein (for nasopharyngeal carcinoma) as potential serological cancer markers. In summary, the proteins identified from the secretomes of 23 cancer cell lines and the Human Protein Atlas represent a focused reservoir of potential cancer biomarkers.Cancer is a major cause of mortality worldwide, accounting for 10 million new cases and more than 6 million deaths per year. In developing countries, cancer is the second most common cause of death, accounting for 23–25% of the overall mortality rate (1). Notwithstanding improvements in diagnostic imaging technologies and medical treatments, the long term survival of most cancer patients is poor. Cancer therapy is often challenging because the majority of cancers are initially diagnosed in their advanced stages. For example, the 5-year survival rate for patients with HNC¹ is less than 50%. More than 50% of all HNC patients have advanced disease at the time of diagnosis (2, 3). Enormous effort has been devoted to screening and characterizing cancer markers for the early detection of cancer. Thus far, these markers include carcinoembryonic antigen, prostate-specific antigen, α-fetoprotein, CA 125, CA 15-3, and CA 19-9. Unfortunately, most biomarkers have limited specificity, sensitivity, or both (4). Thus, there is a growing consensus that marker panels, which are more sensitive and specific than individual markers, would increase the efficacy and accuracy of early stage cancer detection (4–8). The development of novel and useful biomarker panels is therefore an urgent need in the field of cancer management.Proteomics technology platforms are promising tools for the discovery of new cancer biomarkers (9). Over the past decade, serum and plasma have been the major targets of proteomics studies aimed at identifying potential cancer biomarkers (10–13). However, the progress of these studies has been hampered by the complex nature of serum/plasma samples and the large dynamic range between the concentrations of different proteins (14). As cancer biomarkers are likely to be present in low amounts in blood samples, the direct isolation of these markers from plasma and serum samples requires a labor-intensive process involving the depletion of abundant proteins and extensive protein fractionation prior to mass spectrometric analysis (15–18). Alternatively, the secretome, or group of proteins secreted by cancer cells (19), can be analyzed to identify circulating molecules present at elevated levels in serum or plasma samples from cancer patients. These proteins have the potential to act as cancer-derived marker candidates, which are distinct from host-responsive marker candidates. We, along with other groups, have demonstrated the efficacy of secretome-based strategies in a variety of cancer types, including NPC (20), breast cancer (21, 22), lung cancer (23, 24), CRC (25, 26), oral cancer (27), prostate cancer (28, 29), ovarian cancer (30), and Hodgkin lymphoma (31). In these studies, proteins secreted from cancer cells into serum-free media were resolved by one- or two-dimensional gels followed by in-gel tryptic digestion and analysis via MALDI-TOF MS or LC-MS/MS. Alternatively, the proteins were trypsin-digested in solution and analyzed by LC-MS/MS. In general, more proteins were detected in the secretome using the LC-MS/MS method than the MALDI-TOF MS method. Advanced protein separation and identification technologies have made it possible to detect more proteins in the secretomes of cancer cells, thereby facilitating the discovery of cancer biomarkers.Although the cancer cell secretomes of various tumor types have been individually analyzed by different groups using distinct protocols, few studies have used the same protocol to compare cancer cell secretomes derived from different tumor types. We previously assessed the secretomes of 21 cancer cell lines derived from 12 cancer types (i.e. consisting of 795 protein identities and 325 non-redundant proteins) by one-dimensional gel and MALDI-TOF MS (25). Our preliminary findings revealed that different cell lines have distinct secreted protein profiles and that several putative biomarkers, such as Mac-2BP (20, 26, 27, 29) and cathepsin D (21, 23, 32), present in the secretome of a given cancer cell type are commonly shared among different cancers. These observations suggest that an in-depth comparison of secretomes derived from different tumor types may identify marker candidates common to most cancers as well as markers for specific cancer types. As an increasing number of proteins are identified in the secretomes of various cancer cell lines, scientists are faced with the challenge of quickly and efficiently narrowing down the list to candidates with higher chances of success during validation testing with precious clinical specimens.In the present study, we applied one-dimensional SDS-PAGE in conjunction with nano-LC-MS/MS (GeLC-MS/MS) (33, 34) to analyze the conditioned media of 23 cancer cell lines derived from 11 cancer types, including NPC, breast cancer, bladder cancer, cervical cancer, CRC, epidermoid carcinoma, liver cancer, lung cancer, T cell lymphoma, oral cancer, and pancreatic cancer. Within this data set, 4,584 non-redundant proteins were identified from a total of 23 cell lines, yielding an average of ∼1,300 proteins per cell line. Potential marker candidates were identified via the comparative analysis of different cell line secretomes and by putative linkages to cancer-relevant pathways. The selected proteins were further compared with the HPA (35) to generate a focused data set of proteins that are secreted or released, cancer type-specific, and highly expressed in human cancer tissues. Finally, we selectively validated four proteins as potential serological cancer markers using blood samples from cancer patients.     

Discovery of Colorectal Cancer Biomarker Candidates by Membrane Proteomic Analysis and Subsequent Verification using Selected Reaction Monitoring (SRM) and Tissue Microarray (TMA) Analysis

Recent advances in quantitative proteomic technology have enabled the large-scale validation of biomarkers. We here performed a quantitative proteomic analysis of membrane fractions from colorectal cancer tissue to discover biomarker candidates, and then extensively validated the candidate proteins identified. A total of 5566 proteins were identified in six tissue samples, each of which was obtained from polyps and cancer with and without metastasis. GO cellular component analysis predicted that 3087 of these proteins were membrane proteins, whereas TMHMM algorithm predicted that 1567 proteins had a transmembrane domain. Differences were observed in the expression of 159 membrane proteins and 55 extracellular proteins between polyps and cancer without metastasis, while the expression of 32 membrane proteins and 17 extracellular proteins differed between cancer with and without metastasis. A total of 105 of these biomarker candidates were quantitated using selected (or multiple) reaction monitoring (SRM/MRM) with stable synthetic isotope-labeled peptides as an internal control. The results obtained revealed differences in the expression of 69 of these proteins, and this was subsequently verified in an independent set of patient samples (polyps (n = 10), cancer without metastasis (n = 10), cancer with metastasis (n = 10)). Significant differences were observed in the expression of 44 of these proteins, including ITGA5, GPRC5A, PDGFRB, and TFRC, which have already been shown to be overexpressed in colorectal cancer, as well as proteins with unknown function, such as C8orf55. The expression of C8orf55 was also shown to be high not only in colorectal cancer, but also in several cancer tissues using a multicancer tissue microarray, which included 1150 cores from 14 cancer tissues. This is the largest verification study of biomarker candidate membrane proteins to date; our methods for biomarker discovery and subsequent validation using SRM/MRM will contribute to the identification of useful biomarker candidates for various cancers. Data are available via ProteomeXchange with identifier PXD000851.Recent advances in proteomic technology have contributed to the identification of biomarkers for various diseases. Improvements in LC-MS technology have led to an increase in the number of proteins that have been identified. In addition, a stable isotopic labeling method using isobaric tag for relative and absolute quantitation (iTRAQ)¹ and stable isotope labeling by amino acids in cell culture has enabled the quantitative analysis of multiple samples (1, 2). Therefore, a large number of proteins have already been identified as biomarker candidates; however, only a few of these have been used in practical applications because most have not yet progressed to the validation stage, in which potential biomarker candidates are quantified on a large scale. The validation of biomarker candidates is generally accomplished using Western blotting and enzyme-linked immunosorbent assays (ELISA) if specific and well-characterized antibodies for these candidates are available. However, highly specific antibodies are not currently available for most novel biomarker candidate proteins, and it takes a significant amount of time and money to obtain these antibodies and optimize ELISA assay systems for many candidates; therefore, another validation assay system needs to be developed. Selected (or multiple) reaction monitoring (SRM or MRM) was previously shown to be a potentially effective method for the validation of biomarker candidates (3–5). The SRM/MRM assay can measure multiple targets at high sensitivity and throughput without antibodies; hence, it is useful for initial quantitative evaluations and the large-scale validation of biomarker candidates, which defines validation of hundreds of biomarker candidate proteins simultaneously.In addition to these technical improvements, the fractionation process also plays an important role in proteome analysis for biomarker discovery. This procedure very effectively analyzes the proteomes of specific cellular compartments or organelles in detail, which reduces sample complexity. The preparation of a membrane fraction was previously shown to be useful for identifying membrane proteins that are generally expressed at relatively low levels. Membrane proteins play critical roles in many biological functions, such as signal transduction, cell-cell interactions, and ion transport, account for ∼38% of all proteins encoded by the mammalian genome and more than one-third of biomarker candidates, and are also potential targets for drug therapy (6, 7). Therefore, membrane proteome analysis is important for biomarker discovery. However, difficulties have been associated with extracting and solubilizing membrane proteins and subsequent protease digestion. Many procedures have consequently been developed to improve the solubilization and digestion of membrane proteins (8–11), and a protocol using phase transfer surfactant (PTS) was shown to be suitable for membrane proteomics using LC-MS/MS (12, 13).The selection of a control group for comparisons is also important for identifying potential biomarkers. Tissue samples from cancer patients have been used in many studies to discover biomarker candidates by proteomic analysis. Previous studies, including our own, attempted to compare cancer tissues with matched normal tissue (14–17). However, marked differences have been reported in the histology, genetics, and proteomics of normal and cancer tissues, and many biomarker candidates have been identified, by making it difficult to narrow down more reliable candidates for further validation. Lazebnik recently emphasized that the features of malignant, but not benign tumors could be used as a hallmark of cancer (18), and also that premalignant lesions were more appropriate controls for cancer tissue than normal tissue for the identification of biomarker candidates involved in cancer progression. Moreover, comparisons of cancer with and without metastasis may also assist in the discovery of biomarker candidates involved in cancer metastasis. Therefore, the identification of biomarker candidates that can be used to diagnose and determine the prognosis of cancer should become more effective by comparing cancer tissues at different stages, including benign tumors.We performed a shotgun proteomic analysis of membrane fractions prepared from colorectal cancer tissue and benign polyps in the present study to identify biomarker candidates for the diagnosis and treatment of cancer. We identified a large number of biomarker candidate proteins associated with the progression of colon cancer by using membrane protein extraction with PTS followed by iTRAQ labeling. SRM/MRM confirmed the altered expression of these biomarker candidates, and these results were further verified using an independent set of tissue samples. A protein with uncharacterized function, C8orf55, was also validated with a tissue microarray that included various types of cancers.     

Identification of Novel Serological Biomarkers for Inflammatory Bowel Disease Using Escherichia coli Proteome Chip

Specific antimicrobial antibodies present in the sera of patients with inflammatory bowel disease (IBD) have been proven to be valuable serological biomarkers for diagnosis/prognosis of the disease. Herein we describe the use of a whole Escherichia coli proteome microarray as a novel high throughput proteomics approach to screen and identify new serological biomarkers for IBD. Each protein array, which contains 4,256 E. coli K12 proteins, was screened using individual serum from healthy controls (n = 39) and clinically well characterized patients with IBD (66 Crohn disease (CD) and 29 ulcerative colitis (UC)). Proteins that could be recognized by serum antibodies were visualized and quantified using Cy3-labeled goat anti-human antibodies. Surprisingly significance analysis of microarrays identified a total of 417 E. coli proteins that were differentially recognized by serum antibodies between healthy controls and CD or UC. Among those, 169 proteins were identified as highly immunogenic in healthy controls, 186 proteins were identified as highly immunogenic in CD, and only 19 were identified as highly immunogenic in UC. Using a supervised learning algorithm (k-top scoring pairs), we identified two sets of serum antibodies that were novel biomarkers for specifically distinguishing CD from healthy controls (accuracy, 86 ± 4%; p < 0.01) and CD from UC (accuracy, 80 ± 2%; p < 0.01), respectively. The Set 1 antibodies recognized three pairs of E. coli proteins: Era versus YbaN, YhgN versus FocA, and GabT versus YcdG, and the Set 2 antibodies recognized YidX versus FrvX. The specificity and sensitivity of Set 1 antibodies were 81 ± 5 and 89 ± 3%, respectively, whereas those of Set 2 antibodies were 84 ± 1 and 70 ± 6%, respectively. Serum antibodies identified for distinguishing healthy controls versus UC were only marginal because their accuracy, specificity, and sensitivity were 66 ± 5, 69 ± 5, and 61 ± 7%, respectively (p < 0.04). Taken together, we identified novel sets of serological biomarkers for diagnosis of CD versus healthy control and CD versus UC.Crohn disease (CD)¹ and ulcerative colitis (UC) are chronic, idiopathic, and clinically heterogeneous intestinal disorders collectively known as inflammatory bowel disease (IBD) (1, 2). Although the distinction between UC and CD would seem clear based on the combination of clinical, endoscopic, and radiological criteria, indeterminate colitis is present in up to 10 and 20% of adult and pediatric patients with isolated colitis, respectively (3, 4).Serological testing is a non-invasive method for diagnosing IBD and differentiating UC from CD (5–7). Several serological IBD biomarkers have been identified in the past decade, and some have been used in IBD clinics (5–7) (see the list below). Many of these antibodies are produced on intestinal exposure to normal commensal bacteria in genetically susceptible individuals. Although it is not known whether these antibodies are pathogenic or not, they are specific to patients with either CD or UC and may reflect a dysregulated immune inflammatory response to intestinal bacterial antigens (2, 8–10). Several experimental animal models of IBD have led to the theory that the pathogenesis of IBD is the result of an aberrant immune response to normal commensal bacteria in genetically susceptible individuals (11, 12). In fact, most of the major serological biomarkers being used in IBD clinics are antibodies to microbial antigens, including yeast oligomannose (anti-Saccharomyces cerevisiae (ASCA)), bacterial outer membrane porin C (OmpC), Pseudomonas fluorescens bacterial sequence I2 (anti-I2), and most recently bacterial flagellin (CBir 1) (5–7, 13). All of these antimicrobial antibodies show a preponderance in patients with CD. However, ASCA has been identified in up to 5% of patients with UC (13, 14).In comparison, perinuclear anti-neutrophil cytoplasmic antibody (pANCA) with perinuclear highlighting was first described in 1990. Although generally considered an autoantibody, the specific antigenic stimulation for pANCA production remains unclear. This autoantibody is present in up to 70% of patients with UC and in up to 20% of patients with CD (6, 10). Recently a panel of five new anti-glycan antibodies have been identified, including anti-chitobioside IgA, anti-laminaribioside IgG, anti-mannobioside IgG, and antibodies against two major chemically synthesized (Σ) oligomannose epitopes, Man α-1,3 Man α-1,2 Man (ΣMan3) and Man α-1,3 Man α-1,2 Man α-1,2 Man (ΣMan4) (5, 13, 15). These new biomarkers serve as valuable complimentary tools to the available serological biomarkers mentioned above. Collectively these antibodies are not generally present in either children or adults with non-IBD disease and may represent serological markers of intestinal inflammation specific to UC or CD.Although encouraging, none of the current commercially available biomarker tests/assays, including all of those mentioned above, can be used as stand alone tools in clinics and therefore are only recommended as an adjunct to endoscopy in diagnosis and prognosis of the disease (5, 7, 16). Therefore, additional specific and sensitive IBD biomarkers are needed as are prospective studies to assess the utility of current and newly identified biomarkers (5, 13). Proteomics technologies such as two-dimensional gel electrophoresis, various variations of mass spectrometry, and protein chip (array) technology are now proving to be powerful tools in biomarker discovery and are beginning to be utilized in IBD biomarker discovery (5, 17). These technologies enable robust and/or large scale and high throughput identification and analysis of differential protein expression when comparing disease with control. Blood-based (serum- or plasma-based) proteomics holds particular promises for biomarker discovery of various human diseases such as neurodegenerative diseases and cancers (18–20). Antigen microarrays are also powerful tools that allow high throughput serum analysis of aberrant immune responses in autoimmune diseases (21–23) as well as efficient discovery of biomarkers for infectious pathogens (24). Herein we describe the use of an Escherichia coli proteome microarray to characterize the differential immune response (serum anti-E. coli antibodies) among patients clinically classified as CD, UC, and healthy controls. We hypothesized that novel IBD-specific antimicrobial antibodies, particularly anti-E. coli antibodies, are present in IBD patients and are likely to be identified by screening the sera with E. coli protein arrays.     

Enhanced Interferon Signaling Pathway in Oral Cancer Revealed by Quantitative Proteome Analysis of Microdissected Specimens Using 16O/18O Labeling and Integrated Two-dimensional LC-ESI-MALDI Tandem MS

Oral squamous cell carcinoma (OSCC) remains one of the most common cancers worldwide, and the mortality rate of this disease has increased in recent years. No molecular markers are available to assist with the early detection and therapeutic evaluation of OSCC; thus, identification of differentially expressed proteins may assist with the detection of potential disease markers and shed light on the molecular mechanisms of OSCC pathogenesis. We performed a multidimensional ¹⁶O/¹⁸O proteomics analysis using an integrated ESI-ion trap and MALDI-TOF/TOF MS system and a computational data analysis pipeline to identify proteins that are differentially expressed in microdissected OSCC tumor cells relative to adjacent non-tumor epithelia. We identified 1233 unique proteins in microdissected oral squamous epithelia obtained from three pairs of OSCC specimens with a false discovery rate of <3%. Among these, 977 proteins were quantified between tumor and non-tumor cells. Our data revealed 80 dysregulated proteins (53 up-regulated and 27 down-regulated) when a 2.5-fold change was used as the threshold. Immunohistochemical staining and Western blot analyses were performed to confirm the overexpression of 12 up-regulated proteins in OSCC tissues. When the biological roles of 80 differentially expressed proteins were assessed via MetaCore™ analysis, the interferon (IFN) signaling pathway emerged as one of the most significantly altered pathways in OSCC. As many as 20% (10 of 53) of the up-regulated proteins belonged to the IFN-stimulated gene (ISG) family, including ubiquitin cross-reactive protein (UCRP)/ISG15. Using head-and-neck cancer tissue microarrays, we determined that UCRP is overexpressed in the majority of cheek and tongue cancers and in several cases of larynx cancer. In addition, we found that IFN-β stimulates UCRP expression in oral cancer cells and enhances their motility in vitro. Our findings shed new light on OSCC pathogenesis and provide a basis for the future development of novel biomarkers.Oral cancer is one of the most common cancers worldwide. In Taiwan, it remains the sixth most prevalent cancer overall and the fourth most common cancer to afflict males. Over the past 2 decades, the overall incidence and morbidity rates of patients with oral cancer have increased continuously. Epidemiological studies show that ∼50–70% of patients who undergo surgery for oral cancer die within 5 years (1–6). This poor prognosis predominantly reflects late stage presentation, secondary cancer occurrence, local recurrence, and metastasis (7) as well as the lack of suitable markers for cancer detection. Therefore, there is an urgent need to identify proteins that are dysregulated in patients with oral cancer. Such proteins would serve as a valuable resource to find markers for the early diagnosis and disease monitoring of patients with oral cancer.Oral cancer, a subtype of head-and-neck squamous cell carcinoma (HNSCC),¹ can form at various locations within the oral cavity, including the lips, tongue, buccal surfaces, gingiva, palate, floor of mouth, and oropharynx. Tongue and buccal cancers are the most common and most serious types of oral squamous cell carcinoma (OSCC) especially in southeast Asia (2, 8). Alcohol abuse, smoking, and betel nut chewing are the main risk factors for OSCC. Genome-wide approaches have revealed many epigenetic and genetic alterations in patients with OSCC, including several biochemical pathways (9–11). However, these studies have provided little information regarding alterations in the protein profiles of patients with OSCC. Recently state-of-the-art proteomics technologies have revealed alterations in protein abundance, posttranslational modification and turnover, and spatial and temporal distribution within tumor specimens. Using proteomics approaches, aberrantly expressed proteins have been identified in body fluids (12–14), frozen or paraffin-embedded tissues (15–18), and cultured cell lines (19–22). The fold changes in protein expression in samples from healthy and cancerous states as well as the roles of each protein in disease progression must be determined to identify potential candidates for biomarkers and therapeutic targets.Blood samples are often used in clinical studies because they are less invasive and more convenient than other types of bodily samples and can be analyzed using automatic and high throughput techniques. Unfortunately the extremely dynamic range of protein concentrations in serum and plasma impedes the direct discovery of potential biomarkers (23, 24). Proteins can be released into the blood from diseased tissues during cell death or via secretory pathways. To counteract this problem, serum and plasma biomarkers are sometimes identified by analyzing differential protein expression in tumors and adjacent normal tissues (25).Like many other types of solid tumors, OSCCs often contain heterogeneous cell populations. Laser capture microdissection (LCM) is a common technique used to dissect a particular tumor cell type from heterogeneous cell populations, thereby reducing the tissue complexity and facilitating the discovery of tumor-associated molecules in small samples (9, 26–28). Several laboratories have studied differential protein expression in microdissected tissue specimens from patients with head-and-neck cancer in efforts to discover novel tumor markers (15, 17, 29–31). However, the semiquantitative approaches used in these studies may have limited the number of potential markers identified as well as the reliability of the protein quantification. To minimize technical variations and improve the reliability of protein quantification, a variety of sophisticated stable isotope labeling techniques have been developed for MS-based proteomics analysis, including chemical (32, 33), metabolic (34, 35), and enzymatic (36–38) labeling techniques. Improvements in the quality and accuracy of quantitative proteomics analysis via such stable isotope labeling strategies have facilitated the discovery of potential tumor markers in malignancies such as OSCC/HNSCC (16, 39, 40).Here we describe a strategy consisting of LCM, ¹⁸O labeling, two-dimensional (2D) LC separation and an integrated ESI-MS/MS and MALDI-TOF/TOF MS (ESI-MALDI tandem MS) system. This strategy was used to identify differentially expressed proteins in OSCC cells microdissected from oral cancer tissue biopsies. A computational data analysis pipeline was also developed to calculate the relative abundances of ¹⁶O- and ¹⁸O-labeled peptides (similar to that described in a previous report (26)) and to assist with multidimensional protein identification and quantification. Using three pairs of OSCC specimens, we identified 1233 unique proteins with a false discovery rate less than 3%. Of these, we quantified 977 non-redundant proteins in which 80 proteins displayed ≥2.5-fold changes in expression in microdissected tumor cells versus non-tumor cells. We validated these results in 12 selected targets via immunohistochemical staining and Western blot analysis of OSCC tissues. Our findings reveal that the interferon (IFN) signaling pathway is significantly altered in OSCC lesions.     

Surfome Analysis as a Fast Track to Vaccine Discovery: IDENTIFICATION OF A NOVEL PROTECTIVE ANTIGEN FOR GROUP B STREPTOCOCCUS HYPERVIRULENT STRAIN COH1

Safe recombinant vaccines, based on a small number of antigenic proteins, are emerging as the most attractive, cost-effective solution against infectious diseases. In the present work, we confirmed previous data from our laboratory showing that whole viable bacterial cell treatment with proteases followed by the identification of released peptides by mass spectrometry is the method of choice for the rapid and reliable identification of vaccine candidates in Gram-positive bacteria. When applied to the Group B Streptococcus COH1 strain, 43 surface-associated proteins were identified, including all the protective antigens described in the literature as well as a new protective antigen, the cell wall-anchored protein SAN_1485 belonging to the serine-rich repeat protein family. This strategy overcomes the difficulties so far encountered in the identification of novel vaccine candidates and speeds up the entire vaccine discovery process by reducing the number of recombinant proteins to be tested in the animal model.Vaccination is the safest, most attractive, and cost-effective solution to combat infectious diseases (1). Unfortunately vaccines against several pathogens are not yet available, and this is largely because of the difficulties encountered in the identification of the few pathogen components capable of eliciting protective immune responses.Recently new genomics-based approaches have been described and shown to be very powerful for the discovery of vaccine candidates (2–4). However, these methods are labor-intensive and time-consuming in that the identification of the few protective antigens requires the screening of a large number of recombinant proteins in biological assays, which usually involve animal models. Therefore, the development of new strategies capable of substantially reducing the number of proteins to be tested would be highly desirable. Looking at the list of vaccines, either licensed or in advanced phase of development, that protect by eliciting antibody-mediated immunity, it appears that they include secreted toxins and/or highly expressed, surface-exposed molecules (5, 6). Hence the development of strategies capable of singling out this relatively small group of antigens from the plethora of pathogen components would substantially accelerate the vaccine discovery process.We have recently proposed a novel proteomics-based approach, which has allowed the identification of Group A Streptococcus (GAS)¹ proteins having domains protruding out of the bacterial surface (7). The approach is based on (i) the proteolytic treatment of bacteria under conditions that preserve cell viability and (ii) the analysis of the released peptides by mass spectrometry. The approach proved to be rapid and highly selective in that the large majority (>90%) of the identified proteins fell into the categories of cell wall proteins, lipoproteins, membrane proteins, and secreted proteins. Furthermore the method also allowed a semiquantitative evaluation of protein exposition and level of expression because, in general, the number of peptides identified from a given protein nicely correlates with the extent of its recognition by specific antibodies as judged by fluorescence-activated cell sorting analysis (7). Interestingly the list of surface-associated proteins included most of the published GAS protective antigens as well as new protective components such as the cell envelope proteinase Spy0416 (7), a protein attracting the interest of several laboratories for its important role in pathogenesis (8–10). To demonstrate that the proteomics-based approach represents a reliable and generally applicable strategy for the identification of vaccine components in Gram-positive bacteria, we have applied the same protocol to the Group B Streptococcus (GBS) for which a vaccine is not yet available on the market. GBS is a multiserotype Gram-positive opportunistic human pathogen that can lead to life-threatening infections in newborns and elderly adults (11–16).Here we show that on the surface of the hypervirulent GBS COH1 strain there are 43 major proteins belonging to the families of cell wall proteins, lipoproteins, and membrane proteins. As in the case of GAS (7), the proteins identified comprise all protective antigens so far described in the literature (6, 17,–26) as well as a new antigen, SAN_1485, which appears to be highly protective in the active maternal immunization mouse model. These data confirm the effectiveness of protease digestion coupled to mass spectrometry for the identification of surface-exposed antigens in Gram-positive bacteria and demonstrate the power of this technology for the rapid discovery of new vaccines.     

Serum Glycoprotein Biomarker Discovery and Qualification Pipeline Reveals Novel Diagnostic Biomarker Candidates for Esophageal Adenocarcinoma

We report an integrated pipeline for efficient serum glycoprotein biomarker candidate discovery and qualification that may be used to facilitate cancer diagnosis and management. The discovery phase used semi-automated lectin magnetic bead array (LeMBA)-coupled tandem mass spectrometry with a dedicated data-housing and analysis pipeline; GlycoSelector (http://glycoselector.di.uq.edu.au). The qualification phase used lectin magnetic bead array-multiple reaction monitoring-mass spectrometry incorporating an interactive web-interface, Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny), for univariate and multivariate statistical analysis. Relative quantitation was performed by referencing to a spiked-in glycoprotein, chicken ovalbumin. We applied this workflow to identify diagnostic biomarkers for esophageal adenocarcinoma (EAC), a life threatening malignancy with poor prognosis in the advanced setting. EAC develops from metaplastic condition Barrett''s esophagus (BE). Currently diagnosis and monitoring of at-risk patients is through endoscopy and biopsy, which is expensive and requires hospital admission. Hence there is a clinical need for a noninvasive diagnostic biomarker of EAC. In total 89 patient samples from healthy controls, and patients with BE or EAC were screened in discovery and qualification stages. Of the 246 glycoforms measured in the qualification stage, 40 glycoforms (as measured by lectin affinity) qualified as candidate serum markers. The top candidate for distinguishing healthy from BE patients'' group was Narcissus pseudonarcissus lectin (NPL)-reactive Apolipoprotein B-100 (p value = 0.0231; AUROC = 0.71); BE versus EAC, Aleuria aurantia lectin (AAL)-reactive complement component C9 (p value = 0.0001; AUROC = 0.85); healthy versus EAC, Erythroagglutinin Phaseolus vulgaris (EPHA)-reactive gelsolin (p value = 0.0014; AUROC = 0.80). A panel of 8 glycoforms showed an improved AUROC of 0.94 to discriminate EAC from BE. Two biomarker candidates were independently verified by lectin magnetic bead array-immunoblotting, confirming the validity of the relative quantitation approach. Thus, we have identified candidate biomarkers, which, following large-scale clinical evaluation, can be developed into diagnostic blood tests. A key feature of the pipeline is the potential for rapid translation of the candidate biomarkers to lectin-immunoassays.Biomarkers play a central role in health care by enabling accurate diagnosis and prognosis; hence there is extensive research on the identification and development of novel biomarkers. However, despite numerous biomarker publications over the years (1), only a handful of new cancer biomarkers have successfully completed the journey from discovery, qualification, to verification and validation (2–4). One possible way to overcome this challenge is to develop an integrated biomarker pipeline that facilitates the smooth and successful transition from discovery to validation (5–10). The first and foremost consideration in an integrated pipeline is the sample source. In general, most of the proteomics based workflows use tissues or proximal fluids during the discovery phase, with the goal of extending the findings to plasma. Although this approach avoid the high complexity serum/plasma proteome and the associated requisite multi-dimensional sample separation in discovery stages, it often leads to failure when the candidates are not detected in plasma because of the limited sensitivity of the available analytical methods, or the absence of candidates in the plasma (11). To overcome this pitfall, we have developed an integrated glycoprotein biomarker pipeline, which can simply and rapidly isolate glycosylated proteins from serum to enable high throughput analysis of differentially glycosylated proteins in discovery and qualification stages.The workflow utilizes naturally occurring glycan binding proteins, lectins, in a semi-automated high throughput workflow called lectin magnetic bead array-tandem mass spectrometry (LeMBA-MS/MS)¹ (12, 13). Although lectins have been well-utilized in glycobiology and biomarker discovery (14–17), the LeMBA-MS/MS workflow demonstrates several unique features. First, serum glycoproteins are isolated in a single-step using 20 individual lectin-coated magnetic beads in microplate format. Second, we have optimized the concentrations of salts and detergents for sample denaturation to avoid co-isolation of protein complexes without adversely affecting lectin pull-down efficiency. Third, a liquid handler is used for sample processing to facilitate high-throughput screening and increase reproducibility. In addition, we have optimized on-bead trypsin digestion and incorporated lectin-exclusion lists during nano-LC-MS/MS to identify nonglycosylated peptides from the isolated glycoproteins. With these innovations, LeMBA-MS/MS demonstrates nanomolar sensitivity and linearity, and applicability across species (12). Compared with existing single, serial or multi-lectin affinity chromatography (18, 19), LeMBA-MS/MS offers the capability to simultaneously screen 20 lectins in a semi-automated, high throughput manner. On the other hand, because LeMBA-MS/MS identifies the nonglycosylated peptides, it cannot be used for glycan site assignment and glycan structure elucidation (20–23). However, the main advantage of LeMBA, we believe, is as a part of an integrated translational biomarker pipeline leading to lectin immunoassays. The lack of glycan structure details is not critical for clinical translation, as exemplified by the alpha-fetoprotein-L3 (AFP-L3) test, which measures the Lens culinaris agglutinin (LCA) binding fraction of serum alpha-fetoprotein (24, 25), and has been approved by the U.S. Food and Drug Administration for detection of hepatocellular carcinoma.In this study, we report the extension of the glycoprotein biomarker pipeline to the qualification phase with LeMBA-MRM-MS, and introduce statistical analysis pipelines GlycoSelector (http://glycoselector.di.uq.edu.au/) and Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny) for the discovery and qualification phases, respectively. The utility of this integrated serum glycoprotein biomarker pipeline is demonstrated using esophageal adenocarcinoma (EAC) with unmet clinical need for an in vitro diagnostic test. EAC is a lethal malignancy of the lower esophagus with very poor 5-year survival rate of less than 25% (26). EAC is becoming increasingly common and its incidence is associated with the prevalent precursor metaplastic condition Barrett''s esophagus (BE), but with a low annual conversion rate of up to 1% (27). A common set of risk factors are described for BE and EAC, include gastroesophageal reflux disease (GERD), obesity, male gender, and smoking (28, 29). The current endoscopy-biopsy based diagnosis is invasive and costly, leading to an ineffective surveillance program. A blood test employing serum biomarkers that can distinguish patients with EAC from those with either BE or healthy tissue would, potentially, change the paradigm for the way in which BE and EAC are managed in the population (30). Serum glycan profiling studies have shown differential expression of glycan structures between healthy, BE, early dysplastic and EAC patients (31–35). However, diagnostic serum glycoproteins showing differential glycosylation hence differential lectin binding remain to be discovered, making it a suitable disease model for this study.     

Plasma Protein Pentosidine and Carboxymethyllysine, Biomarkers for Age-related Macular Degeneration

Age-related macular degeneration (AMD) causes severe vision loss in the elderly; early identification of AMD risk could help slow or prevent disease progression. Toward the discovery of AMD biomarkers, we quantified plasma protein N^ε-carboxymethyllysine (CML) and pentosidine from 58 AMD and 32 control donors. CML and pentosidine are advanced glycation end products that are abundant in Bruch membrane, the extracellular matrix separating the retinal pigment epithelium from the blood-bearing choriocapillaris. We measured CML and pentosidine by LC-MS/MS and LC-fluorometry, respectively, and found higher mean levels of CML (∼54%) and pentosidine (∼64%) in AMD (p < 0.0001) relative to normal controls. Plasma protein fructosyl-lysine, a marker of early glycation, was found by amino acid analysis to be in equal amounts in control and non-diabetic AMD donors, supporting an association between AMD and increased levels of CML and pentosidine independent of other diseases like diabetes. Carboxyethylpyrrole (CEP), an oxidative modification from docosahexaenoate-containing lipids and also abundant in AMD Bruch membrane, was elevated ∼86% in the AMD cohort, but autoantibody titers to CEP, CML, and pentosidine were not significantly increased. Compellingly higher mean levels of CML and pentosidine were present in AMD plasma protein over a broad age range. Receiver operating curves indicate that CML, CEP adducts, and pentosidine alone discriminated between AMD and control subjects with 78, 79, and 88% accuracy, respectively, whereas CML in combination with pentosidine provided ∼89% accuracy, and CEP plus pentosidine provided ∼92% accuracy. Pentosidine levels appeared slightly altered in AMD patients with hypertension and cardiovascular disease, indicating further studies are warranted. Overall this study supports the potential utility of plasma protein CML and pentosidine as biomarkers for assessing AMD risk and susceptibility, particularly in combination with CEP adducts and with concurrent analyses of fructosyl-lysine to detect confounding factors.Age-related macular degeneration (AMD)¹ is a progressive, multifactorial disease and a major cause of severe vision loss in the elderly (1). Deposition of debris (drusen) in the macular region of Bruch membrane, the extracellular matrix separating the choriocapillaris from the retinal pigment epithelium (RPE), is an early, hallmark risk factor of AMD. The disease can progress to advanced dry AMD (geographic atrophy), which is characterized by regional degeneration of photoreceptor and RPE cells, or to advanced wet AMD (choroidal neovascularization (CNV)), which is characterized by abnormal blood vessels growing from the choriocapillaris through Bruch membrane beneath the retina. CNV accounts for over 80% of debilitating vision loss in AMD; however, only 10–15% of AMD cases progress to CNV.There is growing consensus that AMD is an age-related inflammatory disease involving dysregulation of the complement system; however, triggers of the inflammatory response have yet to be well defined. Oxidative stress appears to be involved as smoking significantly increases the risk of AMD (2), antioxidant vitamins can selectively slow AMD progression (3), and a host of oxidative protein and DNA modifications have been detected at elevated levels in AMD Bruch membrane, drusen, retina, RPE, and plasma (4–11). Oxidative protein modifications like carboxyethylpyrrole (CEP) and N^ε-carboxymethyllysine (CML), both elevated in AMD Bruch membrane, stimulate neovascularization in vivo (12, 13), suggesting possible roles in CNV. Other studies have shown that mice immunized with CEP protein modifications develop an AMD-like phenotype (14). Accordingly oxidative modifications may be catalysts or triggers of AMD pathology (6).AMD has long been hypothesized to be a systemic disease (15) based in part on the presence of retinal drusen in patients with membranoproliferative glomerulonephritis type II (16) and systemic complement activation in AMD (17). Support for this hypothesis also comes from mounting evidence that advanced glycation end products (AGEs) may play a role in AMD (4, 5, 7, 18, 19). AGEs are a heterogeneous group of mostly oxidative modifications resulting from the Maillard nonenzymatic glycation reaction that have been associated with age-related diseases and diabetic complications (20, 21). In 1998, CML was the first AGE to be found in AMD Bruch membrane and drusen (4). Other AGEs have since been detected in AMD ocular tissues (5, 7, 18) and in Bruch membrane, drusen, RPE, and choroidal extracellular matrix from healthy eyes (6, 22). CML, a nonfluorescent AGE, and pentosidine, a fluorescent cross-linking AGE, increase with age in Bruch membrane (18, 23). Receptors for AGEs (RAGE and AGE-R1) appear elevated on RPE and photoreceptor cells in early and advanced dry AMD (7) especially in RPE overlying drusen-like deposits on Bruch membrane (19). AGE-R3, also known as galectin-3, is elevated in AMD Bruch membrane (24).Although AMD susceptibility genes now account for over 50% of AMD cases (25), many individuals with AMD risk genotypes may never develop advanced disease with severe vision loss. Nevertheless the prevalence of advanced AMD is increasing (26). Toward the discovery of better methods to detect those at risk for advanced AMD, we quantified CML and pentosidine in plasma proteins from AMD and control patients and compared their discriminatory accuracy with plasma CEP biomarkers. CEP biomarkers have been shown to enhance the AMD predictive accuracy of genomic AMD biomarkers (11). This report shows CML and pentosidine to be elevated in AMD plasma proteins and demonstrates their potential biomarker utility in assessing AMD risk and susceptibility especially in combination with CEP biomarkers.     

Identification of Tumor-associated Autoantigens for the Diagnosis of Colorectal Cancer in Serum Using High Density Protein Microarrays

There is a mounting evidence of the existence of autoantibodies associated to cancer progression. Antibodies are the target of choice for serum screening because of their stability and suitability for sensitive immunoassays. By using commercial protein microarrays containing 8000 human proteins, we examined 20 sera from colorectal cancer (CRC) patients and healthy subjects to identify autoantibody patterns and associated antigens. Forty-three proteins were differentially recognized by tumoral and reference sera (p value <0.04) in the protein microarrays. Five immunoreactive antigens, PIM1, MAPKAPK3, STK4, SRC, and FGFR4, showed the highest prevalence in cancer samples, whereas ACVR2B was more abundant in normal sera. Three of them, PIM1, MAPKAPK3, and ACVR2B, were used for further validation. A significant increase in the expression level of these antigens on CRC cell lines and colonic mucosa was confirmed by immunoblotting and immunohistochemistry on tissue microarrays. A diagnostic ELISA based on the combination of MAPKAPK3 and ACVR2B proteins yielded specificity and sensitivity values of 73.9 and 83.3% (area under the curve, 0.85), respectively, for CRC discrimination after using an independent sample set containing 94 sera representative of different stages of progression and control subjects. In summary, these studies confirmed the presence of specific autoantibodies for CRC and revealed new individual markers of disease (PIM1, MAPKAPK3, and ACVR2B) with the potential to diagnose CRC with higher specificity and sensitivity than previously reported serum biomarkers.Colorectal cancer (CRC)¹ is the second most prevalent cancer in the western world. The development of this disease takes decades and involves multiple genetic events. CRC remains a major cause of mortality in developed countries because most of the patients are diagnosed at advanced stages because of the reluctance to use highly invasive diagnostic tools like colonoscopy. Actually only a few proteins have been described as biomarkers in CRC (carcinoembryonic antigen (CEA), CA19.9, and CA125 (1–3)), although none of them is recommended for clinical screening (4). Proteomics analysis is actively used for the identification of new biomarkers. In previous studies, the use of two-dimensional DIGE and antibody microarrays allowed the identification of differentially expressed proteins in CRC tissue, including isoforms and post-translational modifications responsible for modifications in signaling pathways (5–8). Both approaches resulted in the identification of a collection of potential tumoral tissue biomarkers that is currently being investigated.However, the implementation of simpler, non-invasive methods for the early detection of CRC should be based on the identification of proteins or antibodies in serum or plasma (9–13). There is ample evidence of the existence of an immune response to cancer in humans as demonstrated by the presence of autoantibodies in cancer sera. Self-proteins (autoantigens) altered before or during tumor formation can elicit an immune response (13–19). These tumor-specific autoantibodies can be detected at early cancer stages and prior to cancer diagnosis revealing a great potential as biomarkers (14, 15, 20). Tumor proteins can be affected by specific point mutations, misfolding, overexpression, aberrant glycosylation, truncation, or aberrant degradation (e.g. p53, HER2, NY-ESO1, or MUC1 (16, 21–25)). In fact, a number of tumor-associated autoantigens (TAAs) were identified previously in different studies involving autoantibody screening in CRC (26–28).Several approaches have been used to identify TAAs in cancer, including natural protein arrays prepared with fractions obtained from two-dimensional LC separations of tumoral samples (29, 30) or protein extracts from cancer cells or tissue (9, 31) probed by Western blot with patient sera, cancer tissue peptide libraries expressed as cDNA expression libraries for serological screening (serological analysis of recombinant cDNA expression libraries (SEREX)) (22, 32), or peptides expressed on the surface of phages in combination with microarrays (17, 18, 33, 34). However, these approaches suffer from several drawbacks. In some cases TAAs have to be isolated and identified from the reactive protein lysate by LC-MS techniques, or in the phage display approach, the reactive TAA could be a mimotope without a corresponding linear amino acid sequence. Moreover, cDNA libraries might not be representative of the protein expression levels in tumors as there is a poor correspondence between mRNA and protein levels.Protein arrays provide a novel platform for the identification of both autoantibodies and their respective TAAs for diagnostic purposes in cancer serum patients. They present some advantages. Proteins printed on the microarray are known “a priori,” avoiding the need for later identifications and the discovery of mimotopes. There is no bias in protein selection as the proteins are printed at a similar concentration. This should result in a higher sensitivity for biomarker identification (13, 35, 36).In this study, we used commercially available high density protein microarrays for the identification of autoantibody signatures and tumor-associated antigens in colorectal cancer. We screened 20 CRC patient and control sera with protein microarrays containing 8000 human proteins to identify the CRC-associated autoantibody repertoire and the corresponding TAAs. Autoantibody profiles that discriminated the different types of CRC metastasis were identified. Moreover a set of TAAs showing increased or decreased expression in cancer cell lines and paired tumoral tissues was found. Finally an ELISA was set up to test the ability of the most immunoreactive proteins to detect colorectal adenocarcinoma. On the basis of the antibody response, combinations of three antigens, PIM1, MAPKAPK3, and ACVR2B, showed a great potential for diagnosis.     

Identification of Serum Biomarkers for Gastric Cancer Diagnosis Using a Human Proteome Microarray

We aimed to globally discover serum biomarkers for diagnosis of gastric cancer (GC). GC serum autoantibodies were discovered and validated using serum samples from independent patient cohorts encompassing 1,401 participants divided into three groups, i.e. healthy, GC patients, and GC-related disease group. To discover biomarkers for GC, the human proteome microarray was first applied to screen specific autoantibodies in a total of 87 serum samples from GC patients and healthy controls. Potential biomarkers were identified via a statistical analysis protocol. Targeted protein microarrays with only the potential biomarkers were constructed and used to validate the candidate biomarkers using 914 samples. To provide further validation, the abundance of autoantibodies specific to the biomarker candidates was analyzed using enzyme-linked immunosorbent assays. Receiver operating characteristic curves were generated to evaluate the diagnostic accuracy of the serum biomarkers. Finally, the efficacy of prognosis efficacy of the final four biomarkers was evaluated by analyzing the clinical records. The final panel of biomarkers consisting of COPS2, CTSF, NT5E, and TERF1 provides high diagnostic power, with 95% sensitivity and 92% specificity to differentiate GC patients from healthy individuals. Prognosis analysis showed that the panel could also serve as independent predictors of the overall GC patient survival. The panel of four serum biomarkers (COPS2, CTSF, NT5E, and TERF1) could serve as a noninvasive diagnostic index for GC, and the combination of them could potentially be used as a predictor of the overall GC survival rate.Gastric cancer (GC)¹ is the second leading cause of cancer-related deaths. A total of 952,000 new GC cases (6.8% of the total of the new cancer case) and 723,000 deaths (8.8% of the total new cancer case) occurred in 2012 (1). The highest mortality rates have been reported in East Asia, including China, Japan, and Korea (2–4), and ∼60% of new GC cases and deaths worldwide occur in this region. As GC has a 5-year survival rate of less than 15%, accurate diagnosis and prognostic assessment of patients are essential for optimizing therapeutic strategies, predicting the outcome of treatment, extending the survival period of patients, and potentially healing to reduce cancer mortality (5).A variety of serum antigen biomarkers has been used for GC diagnosis and prognosis, e.g. carcinoembryonic antigen, carbohydrate antibody 19-9 (CA19-9), carbohydrate antibody 72-4 (CA72-4), and carbohydrate antibody 50 (CA50); the protein levels of these antigens in serum are usually used as signatures indicating cancer risk. However, generally, these serum antigen biomarkers lack sufficient sensitivity and specificity (6–8).Autoantibodies against proteins that result from abnormal gene expression and gene mutation in patients with various tumors represent another type of serum biomarker (9–12). The corresponding antigens of the autoantibodies are usually recognized as tumor-specific antigens or tumor-associated antigens (13–16). There is particular interest in these autoantibodies due to the potential diagnostic and prognostic applications of the autoantibodies and their corresponding antigens. Indeed, there is a need to identify novel autoantibody-based biomarkers to improve the sensitivity and specificity for the diagnosis of GC.In this study, we used a human proteome microarray containing 16,368 proteins to discover and independently validate serum autoantibodies with potential for diagnosis and prognosis of GC in a set of 1,401 serum samples. The samples were from 537 GC patients, 550 healthy controls, and 314 individuals of GC-related diseases. Four autoantigen serum biomarkers, COP9 constitutive photomorphogenic homolog subunit 2 (COPS2), CTSF, ecto-5′-nucleotidase (NT5E), and telomeric repeat binding factor 1 (TERF1), were identified with a combined diagnostic sensitivity of 95% and specificity of 92%. Furthermore, our data suggested COPS2, CTSF, NT5E, and TERF1 could also serve as potential predictors for prognostic assessment.