Epithelial ovarian cancer (OC) is a disease with high mortality due to vague early clinical symptoms. Benign ovarian cysts are common and accurate diagnosis remains a challenge because of the molecular heterogeneity of OC. We set out to investigate whether the disease diversity seen in ovarian cyst fluids and tumor tissue could be detected in plasma. Using existing mass spectrometry (MS)-based proteomics data, we constructed a selected reaction monitoring (SRM) assay targeting peptides from 177 cancer-related and classical proteins associated with OC. Plasma from benign, borderline, and malignant ovarian tumors were used to verify expression (n = 74). Unsupervised and supervised multivariate analyses were used for comparisons. The peptide signatures revealed by the supervised multivariate analysis contained 55 to 77 peptides each. The predictive (Q2) values were higher for benign vs. low-grade serous Q2 = 0.615, mucinous Q2 = 0.611, endometrioid Q2 = 0.428 and high-grade serous Q2 = 0.375 (stage I–II Q2 = 0.515; stage III Q2 = 0.43) OC compared to benign vs. all malignant Q2 = 0.226. With targeted SRM MS we constructed a multiplexed assay for simultaneous detection and relative quantification of 185 peptides from 177 proteins in only 20 µL of plasma. With the approach of histology-specific peptide patterns, derived from pre-selected proteins, we may be able to detect not only high-grade serous OC but also the less common OC subtypes.

Biomarkers for early detection of ovarian tumors are urgently needed. Tumors of the ovary grow within cysts and most are benign. Surgical sampling is the only way to ensure accurate diagnosis, but often leads to morbidity and loss of female hormones. The present study explored the deep proteome in well-defined sets of ovarian tumors, FIGO stage I, Type 1 (low-grade serous, mucinous, endometrioid; n = 9), Type 2 (high-grade serous; n = 9), and benign serous (n = 9) using TMT-LC-MS/MS. Data are available via ProteomeXchange with identifier PXD010939. We evaluated new bioinformatics tools in the discovery phase. This innovative selection process involved different normalizations, a combination of univariate statistics, and logistic model tree and naive Bayes tree classifiers. We identified 142 proteins by this combined approach. One biomarker panel and nine individual proteins were verified in cyst fluid and serum: transaldolase-1, fructose-bisphosphate aldolase A (ALDOA), transketolase, ceruloplasmin, mesothelin, clusterin, tenascin-XB, laminin subunit gamma-1, and mucin-16. Six of the proteins were found significant (p < .05) in cyst fluid while ALDOA was the only protein significant in serum. The biomarker panel achieved ROC AUC 0.96 and 0.57 respectively. We conclude that classification algorithms complement traditional statistical methods by selecting combinations that may be missed by standard univariate tests. Significance: In the discovery phase, we performed deep proteome analyses of well-defined histology subgroups of ovarian tumor cyst fluids, highly specified for stage and type (histology and grade). We present an original approach to selecting candidate biomarkers combining several normalization strategies, univariate statistics, and machine learning algorithms. The results from validation of selected proteins strengthen our prior proteomic and genomic data suggesting that cyst fluids are better than sera in early stage ovarian cancer diagnostics.

There is a great need for targeted protein assays with the capacity of sensitive measurements in complex samples such as plasma or serum, not the least for clinical purposes. Proteomics keeps generating hundreds of biomarker candidates that need to be transferred towards true clinical application through targeted verification studies and towards clinically applicable analysis formats. The immunoaffinity assay AFFIRM (AFFInity sRM) combines the sensitivity of recombinant single chain antibodies (scFv) for targeted protein enrichment with a specific mass spectrometry readout through selected reaction monitoring (SRM) in an automated workflow. Here we demonstrate a 100 times improved detection capacity of the assay down to pg/ml range through the use of oriented antibody immobilization to magnetic beads. This was achieved using biotin-tagged scFv coupled to streptavidin coated magnetic beads, or utilizing the FLAG tag for coupling to anti-FLAG antibody coated magnetic beads. An improved multiplexing capacity with an 11-plex setup was also demonstrated compared to a previous 3-plex setup, which is of great importance for the analysis of panels of biomarker targets.

Joint diseases are often characterized by inflammatory processes that result in pathological changes in joint tissues, including cartilage degradation and release of components into the synovial fluid. The complement system plays a central role in promoting the inflammation. Because several cartilage proteins are known to interact with complement, causing either activation or inhibition of the system, we aimed to investigate these interactions comprehensively. Bovine cartilage explants were cultured with IL-1 alpha to induce cartilage degradation, followed by incubation with human serum. Label-free selected reaction monitoring mass spectrometry was used to specifically quantify complement proteins interacting with the cartilage explant. In parallel, the time-dependent degradation of cartilage was detected using mass spectrometry analysis (liquid chromatography-tandem mass spectrometry). Complement proteins resulting from activation of the classical, alternative, and terminal pathways were detected on IL-1 alpha-stimulated cartilage at time points when clear alterations in extracellular matrix composition had occurred. Increased levels of the complement activation product C4d, as detected by ELISA in serum after incubation with IL-1 alpha-stimulated cartilage, confirmed the selected reaction monitoring results indicating complement activation. Further, typical activated (cleaved) C3 fragments were detected by Western blotting in extracts of IL-1 alpha-stimulated cartilage. No complement activation was triggered by cartilage cultured in the absence of IL-1 alpha. Components released from IL-1 alpha-stimulated cartilage during culture had an inhibitory effect on complement activation. These were released after a longer incubation period with IL-1 alpha and may represent a feedback reaction to cartilage-triggered complement activation observed after a shorter incubation period.

Background: Breast cancer is a complex and heterogeneous disease that is usually characterized by histological parameters such as tumor size, cellular arrangements/rearrangments, necrosis, nuclear grade and the mitotic index, leading to a set of around twenty subtypes. Together with clinical markers such as hormone receptor status, this classification has considerable prognostic value but there is a large variation in patient response to therapy. Gene expression profiling has provided molecular profiles characteristic of distinct subtypes of breast cancer that reflect the divergent cellular origins and degree of progression.Methods: Here we present a large-scale proteomic and transcriptomic profiling study of 477 sporadic and hereditary breast cancer tumors with matching mRNA expression analysis. Unsupervised hierarchal clustering was performed and selected proteins from large-scale tandem mass spectrometry (MS/MS) analysis were transferred into a highly multiplexed targeted selected reaction monitoring assay to classify tumors using a hierarchal cluster and support vector machine with leave one out cross-validation.Results: The subgroups formed upon unsupervised clustering agree very well with groups found at transcriptional level; however, the classifiers (genes or their respective protein products) differ almost entirely between the two datasets. In-depth analysis shows clear differences in pathways unique to each type, which may lie behind their different clinical outcomes. Targeted mass spectrometry analysis and supervised clustering correlate very well with subgroups determined by RNA classification and show convincing agreement with clinical parameters.Conclusions: This work demonstrates the merits of protein expression profiling for breast cancer stratification. These findings have important implications for the use of genomics and expression analysis for the prediction of protein expression, such as receptor status and drug target expression. The highly multiplexed MS assay is easily implemented in standard clinical chemistry practice, allowing rapid and cheap characterization of tumor tissue suitable for directing the choice of treatment.

BACKGROUND:Breast cancer is a very heterogeneous disease and some patients are cured by the surgical removal of the primary tumour whilst other patients suffer from metastasis and spreading of the disease, despite adjuvant therapy. A number of prognostic and treatment predictive factors have been identified such as tumour size, oestrogen (ER) and progesterone (PgR) receptor status, human epidermal growth factor receptor type 2 (HER2) status, histological grade, Ki67 and age. Lymph node involvement is also assessed during surgery to determine if the tumour has spread which requires dissection of the axilla and adjuvant treatment. The prognostic and treatment predictive factors assessing the nature of the tumour are all routinely based on the status of the primary tumour.RESULTS:We have analysed a unique tumour set of fourteen primary breast cancer tumours with matched synchronous axillary lymph node metastases and a set of nine primary tumours with, later developed, matched distant metastases from different sites in the body. We used a pairwise tumour analysis (from the same individual) since the difference between the same tumour-type in different patients was greater. Glycopeptide capture was used in this study to selectively isolate and quantify N-linked glycopeptides from tumours mixtures and the captured glycopeptides were subjected to label-free quantitative tandem mass spectrometry analysis. Differentially expressed proteins between primary tumours and matched lymph node metastasis and distant metastasis were identified. Two of the top hits, ATPIF1 and tubulin β-chain were validated by immunohistochemistry to be differentially regulated.CONCLUSIONS:We show that the expression of a large number of glycosylated proteins change between primary tumours and matched lymph node metastases and distant metastases, confirming that cancer cells undergo a molecular transformation during the spread to a secondary site. The proteins are part of important pathways such as cell adhesion, migration pathways and immune response giving insight into molecular changes needed for the tumour to spread. The large difference between primary tumours and lymph node and distant metastases also suggest that treatment should be based on the phenotype of the lymph node and distant metastases.

Disease and death caused by bacterial infections are global health problems. Effective bacterial strategies are required to promote survival and proliferation within a human host, and it is important to explore how this adaption occurs. However, the detection and quantification of bacterial virulence factors in complex biological samples are technically demanding challenges. These can be addressed by combining targeted affinity enrichment of antibodies with the sensitivity of liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM MS). However, many virulence factors have evolved properties that make specific detection by conventional antibodies difficult. We here present an antibody format that is particularly well suited for detection and analysis of immunoglobulin G (IgG)-binding virulence factors. As proof of concept, we have generated single chain fragment variable (scFv) antibodies that specifically target the IgG-binding surface proteins M1 and H of Streptococcus pyogenes. The binding ability of the developed scFv is demonstrated against both recombinant soluble protein M1 and H as well as the intact surface proteins on a wild-type S. pyogenes strain. Additionally, the capacity of the developed scFv antibodies to enrich their target proteins from both simple and complex backgrounds, thereby allowing for detection and quantification with LC-SRM MS, was demonstrated. We have established a workflow that allows for affinity enrichment of bacterial virulence factors.

Breast-cancer-derived cell lines are an important sample source for cancer proteomics and can be classified on the basis of transcriptomic analysis into subgroups corresponding to the molecular subtypes observed in mammary tumors. This study describes a tridimensional fractionation method that allows high sequence coverage and proteome-wide estimation of protein expression levels. This workflow has been used to conduct an in-depth quantitative proteomic survey of five breast cancer cell lines matching all major cancer subgroups and shows that despite their different classification, these cell lines display a very high level of similarity. A proteome-wide comparison with the RNA levels observed in the same samples showed very little to no correlation. Finally, we demonstrate that the proteomes of in vitro models of breast cancer display surprisingly little overlap with those of clinical samples.

Targeted measurements of low abundance proteins in complex mixtures are in high demand in many areas, not the least in clinical applications measuring biomarkers. We here present the novel platform AFFIRM (AFFInity sRM) that utilizes the power of antibody fragments (scFv) to efficiently enrich for target proteins from a complex background and the exquisite specificity of SRM-MS based detection. To demonstrate the ability of AFFIRM, three target proteins of interest were measured in a serum background in single-plexed and multiplexed experiments in a concentration range of 5-1000 ng/mL. Linear responses were demonstrated down to low ng/mL concentrations with high reproducibility. The platform allows for high throughput measurements in 96-well format, and all steps are amendable to automation and scale-up. We believe the use of recombinant antibody technology in combination with SRM MS analysis provides a powerful way to reach sensitivity, specificity, and reproducibility as well as the opportunity to build resources for fast on-demand implementation of novel assays.

Tumor progression and prognosis in breast cancer patients are difficult to assess using current clinical and laboratory parameters, where a pathological grading is indicative of tumor aggressiveness. This grading is based on assessments of nuclear grade, tubule formation, and mitotic rate. We report here the first protein signatures associated with histological grades of breast cancer, determined using a novel affinity proteomics approach. We profiled 52 breast cancer tissue samples by combining nine antibodies and label-free LC-MS/MS, which generated detailed quantified proteomic maps representing 1,388 proteins. The results showed that we could define in-depth molecular portraits of histologically graded breast cancer tumors. Consequently, a 49-plex candidate tissue protein signature was defined that discriminated between histological grades 1, 2, and 3 of breast cancer tumors with high accuracy. Highly biologically relevant proteins were identified, and the differentially expressed proteins indicated further support for the current hypothesis regarding remodeling of the tumor microenvironment during tumor progression. The protein signature was corroborated using meta-analysis of transcriptional profiling data from an independent patient cohort. In addition, the potential for using the markers to estimate the likelihood of long-term metastasis-free survival was also indicated. Taken together, these molecular portraits could pave the way for improved classification and prognostication of breast cancer.

Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation <0.15, peak width coefficient of variation <0.15, standard deviation of RT <0.15 min (9 s), and the RT drift <0.5min (30 s). The deleterious effect of a marginally performing LC-SID-MRM-MS system on the limit of quantification (LOQ) in targeted quantitative assays illustrates the use and need for a SSP to establish robust and reliable system performance. Use of a SSP helps to ensure that analyte quantification measurements can be replicated with good precision within and across multiple laboratories and should facilitate more widespread use of MRM-MS technology by the basic biomedical and clinical laboratory research communities.

Selected reaction monitoring (SRM) is emerging as a standard tool for high-throughput protein quantification. For reliable and reproducible SRM protein quantification it is essential that system performance is stable. We present here a quality control workflow that is based on repeated analysis of a standard sample to allow insight into the stability of the key properties of a SRM setup. This is supported by automated software to monitor system performance and display information like signal intensities and retention time stability over time, and alert upon deviations from expected metrics. Utilising the software to evaluate 407 repeated injections of a standard sample during half a year, outliers in relative peptide signal intensities and relative peptide fragment ratios are identified, indicating the need for instrument maintenance. We therefore believe that the software could be a vital and powerful tool for any lab regularly performing SRM, increasing the reliability and quality of the SRM platform.Biological significanceSelected reaction monitoring (SRM) mass spectrometry is becoming established as a standard technique for accurate protein quantification. However, to achieve the required quantification reproducibility of the liquid chromatography (LC)-SRM setup, system performance needs to be monitored over time. Here we introduce a workflow with associated software to enable automated monitoring of LC-SRM setups. We believe that usage of the presented concepts will further strengthen the role of SRM as a reliable tool for protein quantification.This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics. (C) 2013 Elsevier B.V. All rights reserved.

Several of the most significant bacterial pathogens in humans, including Streptococcus pyogenes, express surface proteins that bind IgG antibodies via their fragment crystallizable (Fc) region, and the dogma is that this protects the bacteria against phagocytic killing in blood. However, analysis of samples from a patient with invasive S. pyogenes infection revealed dramatic differences in the presence and orientation of IgG antibodies at the surface of bacteria from different sites. In the throat, IgG was mostly bound to the bacterial surface via Fc, whereas in the blood IgG was mostly bound via fragment antigen-binding (Fab). In infected and necrotic tissue, the Fc-binding proteins were removed from the bacterial surface. Further investigation showed that efficient bacterial IgGFc-binding occurs only in IgG-poor environments, such as saliva. As a consequence, the bacteria are protected against phagocytic killing, whereas in blood plasma where the concentration of IgG is high, the antibodies preferentially bind via Fab, facilitating opsonization and bacterial killing. IgG-poor environments represent the natural habitat for IgGFc-binding bacteria, and IgGFc-binding proteins may have evolved to execute their function in such environments. The lack of protection in plasma also helps to explain why cases of severe invasive infections with IgGFc-binding bacteria are so rare compared with superficial and uncomplicated infections.

Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.

Epithelial ovarian carcinoma has in general a poor prognosis since the vast majority of tumors are genomically unstable and clinically highly aggressive. This results in rapid progression of malignancy potential while still asymptomatic and thus in late diagnosis. It is therefore of critical importance to develop methods to diagnose epithelial ovarian carcinoma at its earliest developmental stage, that is, to differentiate between benign tissue and its early malignant transformed counterparts. Here we present a shotgun quantitative proteomic screen of benign and malignant epithelial ovarian tumors using iTRAQ technology with LC-MALDI-TOF/TOF and LC-ESI-QTOF MS/MS. Pathway analysis of the shotgun data pointed to the PI3K/Akt signaling pathway as a significant discriminatory pathway. Selected candidate proteins from the shotgun screen were further confirmed in 51 individual tissue samples of normal, benign, borderline or malignant origin using LC-MRM analysis. The MRM profile demonstrated significant differences between the four groups separating the normal tissue samples from all tumor groups as well as perfectly separating the benign and malignant tumors with a ROC-area of 1. This work demonstrates the utility of using a shotgun approach to filter out a signature of a few proteins only that discriminates between the different sample groups.

Little is known about the nature of post mortem degradation of proteins and peptides on a global level, the so-called degradome. This is especially true for nonneural tissues. Degradome properties in relation to sampling procedures on different tissues are of great importance for the studies of, for instance, post translational modifications and/or the establishment of clinical biobanks. Here, snap freezing of fresh (<2 min post mortem time) mouse liver and pancreas tissue is compared with rapid heat stabilization with regard to effects on the proteome (using two-dimensional differential in-gel electrophoresis) and peptidome (using label free liquid chromatography). We report several proteins and peptides that exhibit heightened degradation sensitivity, for instance superoxide dismutase in liver, and peptidyl-prolyl cis-trans isomerase and insulin C-peptides in pancreas. Tissue sampling based on snap freezing produces a greater amount of degradation products and lower levels of endogenous peptides than rapid heat stabilization. We also demonstrate that solely snap freezing related degradation can be attenuated by subsequent heat stabilization. We conclude that tissue sampling involving a rapid heat stabilization step is preferable to freezing with regard to proteomic and peptidomic sample quality.

Mass spectrometry is a method of choice for quantifying low-abundance proteins and peptides in many biological studies. Here, we describe a range of computational aspects of protein and peptide quantitation, including methods for finding and integrating mass spectrometric peptide peaks, and detecting interference to obtain a robust measure of the amount of proteins present in samples.

Nat. Biotechnol. 27, 633–641 (2009); published online 28 June 2009; corrected after print 9 September 2009 In the version of this article initially published, the following acknowledgment was inadvertently left out: “The UCSF CPTAC team gratefully acknowledges the support of the Canary Foundation for providing funds to purchase a 4000 QTRAP mass spectrometer.