Search

Recently, the rapid development and application of mass spectrometry (MS)-based technologies have markedly improved the comprehensive proteomic characterization of global proteome and protein post-translational modifications (PTMs). However, the current conventional approach for global proteomic analysis is often carried out separately from PTM analysis. In our study, we developed an integrated workflow for multiplex analysis of global, glyco-, and phospho-proteomics using breast cancer patient-derived xenograft (PDX) tumor samples. Our approach included the following steps: trypsin-digested tumor samples were enriched for phosphopeptides through immobilized metal ion affinity chromatography (IMAC), followed by enrichment of glycopeptides through mixed anion exchange (MAX) method, and then the flow-through peptides were analyzed for global proteomics. Our workflow demonstrated an increased identification of peptides and associated proteins in global proteome, as compared to those using the peptides without PTM depletion. In addition to global proteome, the workflow identified phosphopeptides and glycopeptides from the PTM enrichment. We also found a subset of glycans with unique distribution profiles in the IMAC flow-through, as compared to those enriched directly using the MAX method. Our integrated workflow provided an effective platform for simultaneous global proteomic and PTM analysis of biospecimens.

Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating six 2D LC-MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.

Ovarian cancer is the deadliest gynecologic malignancy in the United States with most patients diagnosed in the advanced stage of the disease. Platinum-based antineoplastic therapeutics is indispensable to treating advanced ovarian serous carcinoma. However, patients have heterogeneous responses to platinum drugs, and it is difficult to predict these interindividual differences before administering medication. In this study, we investigated the tumor proteomic profiles and clinical characteristics of 130 ovarian serous carcinoma patients analyzed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), predicted the platinum drug response using supervised machine learning methods, and evaluated our prediction models through leave-one-out cross-validation. Our data-driven feature selection approach indicated that tumor proteomics profiles contain information for predicting binarized platinum response (P < 0.0001). We further built a least absolute shrinkage and selection operator (LASSO)-Cox proportional hazards model that stratified patients into early relapse and late relapse groups (P = 0.00013). The top proteomic features indicative of platinum response were involved in ATP synthesis pathways and Ran GTPase binding. Overall, we demonstrated that proteomic profiles of ovarian serous carcinoma patients predicted platinum drug responses as well as provided insights into the biological processes influencing the efficacy of platinum-based therapeutics. Our analytical approach is also extensible to predicting response to other antineoplastic agents or treatment modalities for both ovarian and other cancers.

The NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) employed a pair of reference xenograft proteomes for initial platform validation and ongoing quality control of its data collection for The Cancer Genome Atlas (TCGA) tumors. These two xenografts, representing basal and luminal-B human breast cancer, were fractionated and analyzed on six mass spectrometers in a total of 46 replicates divided between iTRAQ and label-free technologies, spanning a total of 1095 LC-MS/MS experiments. These data represent a unique opportunity to evaluate the stability of proteomic differentiation by mass spectrometry over many months of time for individual instruments or across instruments running dissimilar workflows. We evaluated iTRAQ reporter ions, label-free spectral counts, and label-free extracted ion chromatograms as strategies for data interpretation (source code is available from http://homepages.uc.edu/~wang2x7/Research.htm ). From these assessments, we found that differential genes from a single replicate were confirmed by other replicates on the same instrument from 61 to 93% of the time. When comparing across different instruments and quantitative technologies, using multiple replicates, differential genes were reproduced by other data sets from 67 to 99% of the time. Projecting gene differences to biological pathways and networks increased the degree of similarity. These overlaps send an encouraging message about the maturity of technologies for proteomic differentiation.

Protein glycosylation is one of the most common protein modifications, and the quantitative analysis of glycoproteins has the potential to reveal biological functions and their association with disease. However, the high throughput accurate quantification of glycoproteins is technically challenging due to the scarcity of robust assays to detect and quantify glycoproteins. Here we describe the development of multiplexed targeted MS assays to quantify N-linked glycosite-containing peptides in serum using parallel reaction monitoring (PRM). Each assay was characterized by its performance metrics and criteria established by the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (NCI CPTAC) to facilitate the widespread adoption of the assays in studies designed to confidently detect changes in the relative abundance of these analytes. An in-house developed software program, MRMPlus, was used to compute assay performance parameters including specificity, precision, and repeatability. We show that 43 selected N-linked glycosite-containing peptides identified in prostate cancer tissue studies carried out in our group were detected in the sera of prostate cancer patients within the quantitative range of the developed PRM assays. A total of 41 of these formerly N-linked glycosite-containing peptides (corresponding to 37 proteins) were reproducibly quantified based on their relative peak area ratios in human serum during PRM assay development, with 4 proteins showing differential significance in serum from nonaggressive (NAG) vs aggressive (AG) prostate cancer patient serum (n = 50, NAG vs AG). The data demonstrate that the assays can be used for the high throughput and reproducible quantification of a panel of formerly N-linked glycosite-containing peptides. The developed assays can also be used for the quantification of formerly N-linked glycosite-containing peptides in human serum irrespective of disease state.

Post-translational modifications of proteins can have a major role in disease initiation and progression. Incredible efforts have recently been made to study the regulation of glycoproteins for disease prognosis and diagnosis. It is essential to elucidate glycans and intact glycoproteins to understand the role of glycosylation in diseases. Sialylated N-glycans play crucial roles in physiological and pathological processes; however, it is laborious to study sialylated glycoproteins due to the labile nature of sialic acid residues. In this study, an integrated platform is developed for the analysis of intact glycoproteins and glycans using a chemoenzymatic approach for immobilization and derivatization of sialic acids. N-Glycans, deglycosylated proteins, and intact glycoproteins from heart tissues of wild type (WT) and transverse aortic constriction (TAC) mouse models were analyzed. We identified 291 unique glycopeptides from 195 glycoproteins; the comparative studies between WT and TAC mice indicate the overexpression of extracellular proteins for heart matrix remodeling and the down-regulation of proteins associated with energy metabolism in cardiac hypertrophy. The integrated platform is a powerful tool for the analysis of glycans and glycoproteins in the discovery of potential cardiac hypertrophy biomarkers.

Glycosylation is one of the most common protein modifications. Each glycoprotein can be glycosylated at multiple glycosites, and each glycosites can be modified by different glycans. Due to this heterogeneity of glycosylation, it has proven difficult to study the structure-function relationship of specific glycans and their affected glycoproteins. Here, we report a novel method for rapid and quantitative identification of glycoproteins containing specific glycans. Lectin affinity isolations are followed by chemical immobilization of the captured glycopeptides, allowing the identification of glycoproteins containing specific glycans by subsequent mass spectrometry. The application of the method should be useful to facilitate our understanding of how changes in glycan associate with diseases, and to discover novel glycoproteins with certain glycans that could serve as biomarkers or therapeutic targets.

Anterior gradient 2 (AGR2) is a secreted, cancer-associated protein in many types of epithelial cancer cells. We developed a highly sensitive targeted mass spectrometric assay for quantification of AGR2 in urine and serum. Digested peptides from clinical samples were processed by PRISM (high pressure and high resolution separations coupled with intelligent selection and multiplexing), which incorporates high pH reversed-phase liquid chromatography (LC) separations to fractionate and select target fractions for follow-on LC-selected reaction monitoring (LC-SRM) analyses. The PRISM-SRM assay for AGR2 showed a reproducibility of <10% CV and limit of quantification (LOQ) values of ∼130 pg/mL in serum and ∼10 pg per 100 μg of total protein mass in urine, respectively. A good correlation (R(2) = 0.91) was observed for the measurable AGR2 concentrations in urine between SRM and enzyme-linked immunosorbent assay (ELISA). On the basis of an initial cohort of 37 subjects, urinary AGR2/PSA concentration ratios showed a significant difference (P = 0.026) between noncancer and cancer. Large clinical cohort studies are needed for the validation of AGR2 as a useful diagnostic biomarker for prostate cancer. Our work validated the approach of identifying candidate secreted protein biomarkers through genomics and measurement by targeted proteomics, especially for proteins where no immunoassays are available.

Protein glycosylation has long been recognized as one of the most common post-translational modifications. Most membrane proteins and extracellular proteins are N-linked glycosylated, and they account for the majority of current clinical diagnostic markers or therapeutic targets. Quantitative proteomic analysis of detectable N-linked glycoproteins from cells or tissues using mass spectrometry has the potential to provide biological basis for disease development and identify disease associated glycoproteins. However, the information of low abundance but important peptides is lost due to the lack of MS/MS fragmentation or low quality of MS/MS spectra for low abundance peptides. Here, we show the feasibility of formerly N-glycopeptide identification and quantification at MS1 level using genomic N-glycosite prediction (GenoGlyco) coupled with stable isotopic labeling and accurate mass matching. The GenoGlyco Analyzer software uses accurate precursor masses of detected N-deglycopeptide peaks to match them to N-linked deglycopeptides that are predicted from genes expressed in the cells. This method results in more robust glycopeptide identification compared to MS/MS-based identification. Our results showed that over three times the quantity of N-deglycopeptide assignments from the same mass spectrometry data could be produced in ovarian cancer cell lines compared to a MS/MS fragmentation method. Furthermore, the method was also applied to N-deglycopeptide analysis of ovarian tumors using the identified deglycopeptides from the two ovarian cell lines as heavy standards. We show that the described method has a great potential in the analysis of detectable N-glycoproteins from cells and tissues.

Cytological examination of cells from bronchoalveolar lavage (BAL) is commonly used for the diagnosis of lung cancer. Proteins released from lung cancer cells into BAL may serve as biomarkers for cancer detection. In this study, N-glycoproteins in eight cases of BAL fluid, as well as eight lung adenocarcinoma tissues and eight tumor-matched normal lung tissues, were analyzed using the solid-phase extraction of N-glycoprotein (SPEG), iTRAQ labeling, and liquid chromatography tandem mass spectrometry (LC-MS/MS). Of 80 glycoproteins found in BAL specimens, 32 were identified in both cancer BAL and cancer tissues, with levels of 25 glycoproteins showing at least a 2-fold difference between cancer and benign BAL. Among them, eight glycoproteins showed greater than 2-fold elevations in cancer BAL, including Neutrophil elastase (NE), Integrin alpha-M, Cullin-4B, Napsin A, lysosome-associated membrane protein 2 (LAMP2), Cathepsin D, BPI fold-containing family B member 2, and Neutrophil gelatinase-associated lipocalin. The levels of Napsin A in cancer BAL were further verified in independently collected 39 BAL specimens using an ELISA assay. Our study demonstrates that potential protein biomarkers in BAL fluid can be detected and quantified.

Over the past few years, mass spectrometry has emerged as a technology to complement and potentially replace standard immunoassays in routine clinical core laboratories. Application of mass spectrometry to protein and peptide measurement can provide advantages including high sensitivity, the ability to multiplex analytes, and high specificity at the amino acid sequence level. In our previous study, we demonstrated excellent reproducibility of mass spectrometry-selective reaction monitoring (MS-SRM) assays when applying standardized standard operating procedures (SOPs) to measure synthetic peptides in a complex sample, as lack of reproducibility has been a frequent criticism leveled at the use of mass spectrometers in the clinical laboratory compared to immunoassays. Furthermore, an important caveat of SRM-based assays for proteins is that many low-abundance analytes require some type of enrichment before detection with MS. This adds a level of complexity to the procedure and the potential for irreproducibility increases, especially across different laboratories with different operators. The purpose of this study was to test the interlaboratory reproducibility of SRM assays with various upfront enrichment strategies and different types of clinical samples (representing real-world body fluids commonly encountered in routine clinical laboratories). Three different, previously published enrichment strategies for low-abundance analytes and a no-enrichment strategy for high-abundance analytes were tested across four different laboratories using different liquid chromatography-SRM (LC-SRM) platforms and previously developed SOPs. The results demonstrated that these assays were indeed reproducible with coefficients of variation of less than 30% for the measurement of important clinical proteins across all four laboratories in real world samples.

Aberrant glycosylation is a fundamental characteristic of progression of diseases such as cancer. Therefore, characterization of glycosylation patterns of proteins from disease tissues may identify changes specific to the disease development and improve diagnostic performance. Thus, analysis strategies with sufficient sensitivity for evaluation of glycosylation patterns in clinical specimens are needed. Here, we describe an analytical strategy for detection and verification of glycosylation patterns. It is based on a two-phase platform including a pattern discovery phase to identify the glycosylation changes using high-density lectin microarrays and a verification phase by developing lectin-based immunosorbent assays using the identified lectins. We evaluated the analytical performance of the platform using the glycoprotein standard and found that the lectin microarray could detect specific bindings of glycoprotein to lectins at the nanogram level and the lectin-based immunosorbent assay could be used for verification of protein glycosylation. We then applied the approach to the analysis of glycosylation patterns of two glycoproteins, which are highly expressed in prostate cancer in our prior studies, prostate specific antigen (PSA) and membrane metallo-endopeptidase (MME), from aggressive (AC) and nonaggressive prostate cancer (NAC) tissues. The observed differences in glycosylation patterns of PSA and MME may represent a significant clinical importance and could be used to develop multiplex assays for diagnosis of aggressive prostate cancer.

Antibody-overlay lectin microarray (ALM) has been used for targeted glycan profiling to identify disease-related protein glycoforms. In this context, high sensitivity is desired because it allows for the identification of disease-related glycoforms that are often present at low concentrations. We describe a new tyramide signal amplification (TSA) for the antibody-overlay lectin microarray procedure for sensitive profiling of glycosylation patterns. We demonstrate that TSA increased the sensitivity of the microarray over 100 times for glycan profiling using the model protein prostate specific antigen (PSA). The glycan profile of PSA enriched from LNCAP cells, obtained at a subnanogram level with the aid of TSA, was consistent with the previous reports. We also established the glycan profile of prostate specific membrane antigen (PSMA) using the TSA and ALM. Thus, the TSA for antibody-overlay lectin microarray is a sensitive, rapid, comprehensive, and high-throughput method for targeted glycan profiling and can potentially be used for the identification of disease-related protein glycoforms.

Aberrant protein glycosylation has been shown to be associated with disease progression and can be potentially useful as a biomarker if disease-specific glycosylation can be identified. However, high-throughput quantitative analysis of protein glycosylation derived from clinical specimens presents technical challenges due to the typically high complexity of biological samples. In this study, a mass spectrometry-based analytical method was developed to measure different glycosylated forms of glycoproteins from complex biological samples by coupling glycopeptide extraction strategy for specific glycosylation with selected reaction monitoring (SRM). Using this method, we monitored glycosylated and sialylated prostate-specific antigen (PSA) in prostate cancer and noncancer tissues. Results of this study demonstrated that the relative abundance of glycosylated PSA isoforms were not correlated with total PSA protein levels measured in the same prostate cancer tissue samples by clinical immunoassay. Furthermore, the sialylated PSA was differentially distributed in cancer and noncancer tissues. These data suggest that differently glycosylated isoforms of glycoproteins can be quantitatively analyzed and may provide unique information for clinically relevant studies.

Mass spectrometry (MS) is an attractive alternative to quantification of proteins by immunoassays, particularly for protein biomarkers of clinical relevance. Reliable quantification requires that the MS-based assays are robust, selective, and reproducible. Thus, the development of standardized protocols is essential to introduce MS into clinical research laboratories. The aim of this study was to establish a complete workflow for assessing the transferability and reproducibility of selected reaction monitoring (SRM) assays between clinical research laboratories. Four independent laboratories in North America, using identical triple-quadrupole mass spectrometers (Quantum Ultra, Thermo), were provided with standard protocols and instrumentation settings to analyze unknown samples and internal standards in a digested plasma matrix to quantify 51 peptides from 39 human proteins using a multiplexed SRM assay. The interlaboratory coefficient of variation (CV) was less than 10% for 25 of 39 peptides quantified (12 peptides were not quantified based upon hydrophobicity) and exhibited CVs less than 20% for the remaining peptides. In this report, we demonstrate that previously developed research platforms for SRM assays can be improved and optimized for deployment in clinical research environments.

Currently, serum prostate-specific antigen (PSA) is used for the early detection of prostate cancer despite its low specificity in the range of 4-10 ng/mL. Because aberrant glycosylation is a fundamental characteristic of tumor genesis, the objective of this study was to investigate whether changes in PSA glycosylation may be used to improve the cancer specificity of PSA. We developed five lectin immunosorbant assays to analyze the glycosylation patterns of PSA in serum. Each assay sandwiches serum PSA between a PSA monoclonal antibody and a biotinylated lectin and then tags the biotin complex using a streptavidin SULFO TAG for electrochemiluminescence detection. Low limits of detection (0.04-1.35 ng/mL), good reproducibility (%CVs < 10%), and direct analysis of PSA glycosylation in sera suggest these assays may have a potential role in improving PSA's cancer specificity. Clinical performance was evaluated in 52 human subjects (26 cancer and 26 noncancer). ROC analysis showed that the total SNA assay (AUC = 0.71) appeared to perform better than percent free PSA (AUC = 0.54) in its diagnostic gray zone between 10 and 20% in a subset of 21 subjects. A separate study of 16 additional subjects showed similar findings.

Specimen collection is an integral component of clinical research. Specimens from subjects with various stages of cancers or other conditions, as well as those without disease, are critical tools in the hunt for biomarkers, predictors, or tests that will detect serious diseases earlier or more readily than currently possible. Analytic methodologies evolve quickly. Access to high-quality specimens, collected and handled in standardized ways that minimize potential bias or confounding factors, is key to the "bench to bedside" aim of translational research. It is essential that standard operating procedures, "the how" of creating the repositories, be defined prospectively when designing clinical trials. Small differences in the processing or handling of a specimen can have dramatic effects in analytical reliability and reproducibility, especially when multiplex methods are used. A representative working group, Standard Operating Procedures Internal Working Group (SOPIWG), comprised of members from across Early Detection Research Network (EDRN) was formed to develop standard operating procedures (SOPs) for various types of specimens collected and managed for our biomarker discovery and validation work. This report presents our consensus on SOPs for the collection, processing, handling, and storage of serum and plasma for biomarker discovery and validation.

Ku antigen is composed of 70 and 82 kDa subunits (Ku70 and Ku80, respectively) that together bind with high affinity to ends of double-stranded DNA and other DNA structures in vitro. When bound to DNA, the Ku 70/80 heterodimer enhances the kinase activity of the catalytic subunit of the DNA-dependent protein kinase, DNA-PKcs. Ku and DNA-PKcs are required for V(D)J recombination and DNA double-strand break repair in vivo and may also play a role in regulation of transcription. Ku is phosphorylated by DNA-PKcs in vitro, and cells that lack DNA-PKcs are deficient in Ku phosphorylation in vitro, suggesting that Ku may be a physiological target for DNA-PK. Here we have identified the sites of DNA-PK phosphorylation in human Ku protein. We find that Ku70 is phosphorylated at a single serine residue, serine 6, located in the putative transcriptional activation domain, and Ku80 is phosphorylated at serines 577 and 580 and at threonine 715. Interestingly, none of the phosphorylation sites identified in Ku correspond to the serine-glutamine consensus for DNA-PK phosphorylation, consistent with previous reports that DNA-PK can recognize additional phosphorylation motifs.

Steady-state fluorescence and circular dichroism (CD) were used to examine the unfolding in denaturants of recombinant cytochrome c peroxidase [CCP(MI)] and horseradish peroxidase (HRP) in their ferric forms. CCP(MI) unfolds in urea and in guanidine hydrochloride (GdHCl) at pH 7.0, while HRP loses its secondary structure only in the presence of GdHCl. CCP(MI) unfolds in urea by two distinct steps as monitored by fluorescence, but the loss of its secondary structure as monitored by UV/CD occurs in a single step between 3.4 and 5 M urea and 1.5 and 2.5 M GdHCl. The localized changes detected by fluorescence involve the CCP(MI) heme cavity since the Soret maximum red-shifts from 408 to 416 nm, and the heme CD changes examined in urea are biphasic. The polypeptide of HRP also loses secondary structure in a single step between 1.2 and 2.7 M GdHCl as monitored by UV/CD, and a fluorescence-monitored transition involving conformational change in the Trp117-containing loop occurs above 4 M GdHCl. Free energies of denaturation extrapolated to 0 M denaturant (delta Gd,aq) of approximately 6 and approximately 4 kcal/mol were calculated for CCP(MI) and HRP, respectively, from the UV/CD data. The refolding mechanisms of the two peroxidases differ since heme capture in CCP(MI) is synchronous with refolding while apoHRP captures heme after refolding. Thus, the denatured form of apoHRP does not recognize heme and has to correctly refold prior to heme capture. The half-life for unfolding of native HRP in 6 M GdHCl is slow (519 s) compared to that for CCP(MI) (14.3 s), indicating that HRP is kinetically much more stable than CCP(MI). Treatment with EDTA and DTT greatly destabilizes HRP, and unfolding in 4 M GdHCl occurs with t1/2 = 0.42 s.