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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.

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.

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.

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.