Profiling signatures of ovarian cancer tumour suppression using 2D-DIGE and 2D-LC-MS/MS with tandem mass tagging

Targeting tumour markers in ovarian cancer treatment

Ovarian cancers (OC) are the most common, lethal, and stage-dependent cancers at the global level, specifically in female patients. Targeted therapies involve the administration of drugs that specifically target the alterations in tumour cells responsible for their growth, proliferation, and metastasis, with the aim of treating particular patients. Presently, within the realm of gynaecological malignancies, specifically in breast and OCs, there exist various prospective therapeutic targets encompassing tumour-intrinsic signalling pathways, angiogenesis, homologous-recombination deficit, hormone receptors, and immunologic components. Breast cancers are often detected in advanced stages, primarily due to the lack of a reliable screening method. However, various tumour markers have been extensively researched and employed to evaluate the condition, progression, and effectiveness of medication treatments for this ailment. The emergence of recent technological advancements in the domains of bioinformatics, genomics, proteomics, and metabolomics has facilitated the exploration and identification of hitherto unknown biomarkers. The primary objective of this comprehensive review is to meticulously investigate and analyze both established and emerging methodologies employed in the identification of tumour markers associated with OC.

DIGE Analysis Software and Protein Identification Approaches

Two-dimensional difference gel electrophoresis (2D-DIGE) is a high-resolution protein separation technique, with the excellent dynamic range obtained by fluorescent tag labeling of protein samples. Scanned images of 2D-DIGE gels show thousands of protein spots, each spot representing a single or a group of protein isoforms. By using commercially available software, each protein spot is defined by an outline, which is digitized and correlated with the quantity of proteins present in each spot. Software packages include DeCyder, SameSpots, and Dymension 3. In addition, proteins of interest can be excised from post-stained gels and identified with conventional mass spectrometric techniques. High-throughput mass spectrometry is performed using sophisticated instrumentation, including matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF), MALDI-TOF/TOF, and liquid chromatography tandem mass spectrometry (LC-MS/MS). Tandem MS (MALDI-TOF/TOF or LC-MS/MS) analyzes fragmented peptides, resulting in amino acid sequence information, which is especially useful when protein spots are low abundant or where a mixture of proteins is present.

Proteomic analysis reveals differentially abundant proteins probably involved in the virulence of amastigote and promastigote forms of Leishmania infantum

Owing to the importance and clinical diversity of Leishmania infantum, studying its virulence factors is promising for understanding the relationship between parasites and hosts. In the present study, differentially abundant proteins from strains with different degrees of virulence in promastigote and amastigote forms were compared using two quantitative proteomics techniques, differential gel electrophoresis and isobaric mass tag labeling, followed by identification by mass spectrometry. A total of 142 proteins were identified: 96 upregulated and 46 downregulated proteins in the most virulent strain compared to less virulent. The interaction between the proteins identified in each evolutionary form was predicted. The results showed that in the amastigote form of the most virulent strain, there was a large group of proteins related to glycolysis, heat shock, and ribosomal proteins, whereas in the promastigote form, the group consisted of stress response, heat shock, and ribosomal proteins. In addition, biological processes related to metabolic pathways, ribosomes, and oxidative phosphorylation were enriched in the most virulent strain (BH400). Finally, we noted several proteins previously found to play important roles in L. infantum infection, which showed increased abundance in the virulent strain, such as ribosomal proteins, HSP70, enolase, fructose 1,6-biphosphate aldolase, peroxidoxin, and tryparedoxin peroxidase, many of which interact with each other.

Proteomic analysis revealed common, unique and systemic signatures in gender-dependent hepatocarcinogenesis

Hepatocellular carcinoma (HCC) is the most common liver cancer and is highly malignant. Male prevalence and frequent activation of the Ras signaling pathway are distinct characteristics of HCC. However, the underlying mechanisms remain to be elucidated. By exploring Hras12V transgenic mice showing male-biased hepatocarcinogenesis, we performed a high-throughput comparative proteomic analysis based on tandem-mass-tag (TMT) labeling combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) on the tissue samples obtained from HCC (T) and their paired adjacent precancerous (P) of Hras12V transgenic male and female mice (Ras-Tg) and normal liver (W) of wild-type male and female mice (Non-Tg). The further validation and investigation were performed using quantitative real-time PCR and western blot. Totally, 5193 proteins were quantified, originating from 5733 identified proteins. Finally, 1344 differentially expressed proteins (DEPs) (quantified in all examined samples; |ratios| ≥ 1.5, p < 0.05) were selected for further analysis. Comparison within W, P, and T of males and females indicated that the number of DEPs in males was much higher than that in females. Bioinformatics analyses showed the common and unique cluster-enriched items between sexes, indicating the common and gender-disparate pathways towards HCC. Expression change pattern analysis revealed HCC positive/negative-correlated and ras oncogene positive/negative-correlated DEPs and pathways. In addition, it showed that the ras oncogene gradually and significantly reduced the responses to sex hormones from hepatocytes to hepatoma cells and therefore shrunk the gender disparity between males and females, which may contribute to the cause of the loss of HCC clinical responses to the therapeutic approaches targeting sex hormone pathways. Additionally, gender disparity in the expression levels of key enzymes involved in retinol metabolism and terpenoid backbone/steroid biosynthesis pathways may contribute to male prevalence in hepatocarcinogenesis. Further, the biomarkers, SAA2, Orm2, and Serpina1e, may be sex differences. In conclusion, common and unique DEPs and pathways toward HCC initiated by ras oncogene from sexually dimorphic hepatocytes provide valuable and novel insights into clinical investigation and practice.

Discovery of non-invasive biomarkers for the diagnosis of endometriosis

Background

Endometriosis is a common gynaecological disorder affecting 5-10% of women of reproductive age who often experience chronic pelvic pain and infertility. Definitive diagnosis is through laparoscopy, exposing patients to potentially serious complications, and is often delayed. Non-invasive biomarkers are urgently required to accelerate diagnosis and for triaging potential patients for surgery.

Methods

This retrospective case control biomarker discovery and validation study used quantitative 2D-difference gel electrophoresis and tandem mass tagging-liquid chromatography-tandem mass spectrometry for protein expression profiling of eutopic and ectopic endometrial tissue samples collected from 28 cases of endometriosis and 18 control patients undergoing surgery for investigation of chronic pelvic pain without endometriosis or prophylactic surgery. Samples were further sub-grouped by menstrual cycle phase. Selected differentially expressed candidate markers (LUM, CPM, TNC, TPM2 and PAEP) were verified by ELISA in a set of 87 serum samples collected from the same and additional women. Previously reported biomarkers (CA125, sICAM1, FST, VEGF, MCP1, MIF and IL1R2) were also validated and diagnostic performance of markers and combinations established.

Results

Cycle phase and endometriosis-associated proteomic changes were identified in eutopic tissue from over 1400 identified gene products, yielding potential biomarker candidates. Bioinformatics analysis revealed enrichment of adhesion/extracellular matrix proteins and progesterone signalling. The best single marker for discriminating endometriosis from controls remained CA125 (AUC = 0.63), with the best cross-validated multimarker models improving the AUC to 0.71-0.81, depending upon menstrual cycle phase and control group.

Conclusions

We have identified menstrual cycle- and endometriosis-associated protein changes linked to various cellular processes that are potential biomarkers and that provide insight into the biology of endometriosis. Our data indicate that the markers tested, whilst not useful alone, have improved diagnostic accuracy when used in combination and demonstrate menstrual cycle specificity. Tissue heterogeneity and blood contamination is likely to have hindered biomarker discovery, whilst a small sample size precludes accurate determination of performance by cycle phase. Independent validation of these biomarker panels in a larger cohort is however warranted, and if successful, they may have clinical utility in triaging patients for surgery.

Mass spectrometry-based proteomics techniques and their application in ovarian cancer research

Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008-2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. Main difficulties concern both complexity and heterogeneity of ovarian cancer and drawbacks of the mass spectrometry strategies. This review summarizes challenges, capabilities, and promises of the mass spectrometry-based proteomics techniques in ovarian cancer management.

DIGE Analysis Software and Protein Identification Approaches

DIGE is a high-resolution two-dimensional gel electrophoresis method, with excellent dynamic range obtained by fluorescent tag labeling of protein samples. Scanned images of DIGE gels show thousands of protein spots, each spot representing a single or a group of protein isoforms. By using commercially available software, each protein spot is defined by an outline, which is digitized and correlated with the quantity of proteins present in each spot. Software packages include DeCyder, SameSpots, and Dymension 3. In addition, proteins of interest can be excised from post-stained gels and identified with conventional mass spectrometry techniques. High-throughput mass spectrometry is performed using sophisticated instrumentation including matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF), MALDI-TOF/TOF, and liquid chromatography tandem mass spectrometry (LC-MS/MS). Tandem MS (MALDI-TOF/TOF or LC-MS/MS), analyzes fragmented peptides, resulting in amino acid sequence information, especially useful when protein spots are low abundant or where a mixture of proteins is present.

Effects of ErbB2 Overexpression on the Proteome and ErbB Ligand-specific Phosphosignaling in Mammary Luminal Epithelial Cells

Most breast cancers arise from luminal epithelial cells, and 25–30% of these tumors overexpress the ErbB2/HER2 receptor that correlates with disease progression and poor prognosis. The mechanisms of ErbB2 signaling and the effects of its overexpression are not fully understood. Herein, stable isotope labeling by amino acids in cell culture (SILAC), expression profiling, and phosphopeptide enrichment of a relevant, non-transformed, and immortalized human mammary luminal epithelial cell model were used to profile ErbB2-dependent differences in protein expression and phosphorylation events triggered via EGF receptor (EGF treatment) and ErbB3 (HRG1β treatment) in the context of ErbB2 overexpression. Bioinformatics analysis was used to infer changes in cellular processes and signaling events. We demonstrate the complexity of the responses to oncogene expression and growth factor signaling, and we identify protein changes relevant to ErbB2-dependent altered cellular phenotype, in particular cell cycle progression and hyper-proliferation, reduced adhesion, and enhanced motility. Moreover, we define a novel mechanism by which ErbB signaling suppresses basal interferon signaling that would promote the survival and proliferation of mammary luminal epithelial cells. Numerous novel sites of growth factor-regulated phosphorylation were identified that were enhanced by ErbB2 overexpression, and we putatively link these to altered cell behavior and also highlight the importance of performing parallel protein expression profiling alongside phosphoproteomic analysis.

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC-MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC-MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC-MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives.

The phenotype of a knockout mouse identifies flavin-containing monooxygenase 5 (FMO5) as a regulator of metabolic ageing

We report the production and metabolic phenotype of a mouse line in which the Fmo5 gene is disrupted. In comparison with wild-type (WT) mice, Fmo5(-/-) mice exhibit a lean phenotype, which is age-related, becoming apparent after 20 weeks of age. Despite greater food intake, Fmo5(-/-) mice weigh less, store less fat in white adipose tissue (WAT), have lower plasma glucose and cholesterol concentrations and enhanced whole-body energy expenditure, due mostly to increased resting energy expenditure, with no increase in physical activity. An increase in respiratory exchange ratio during the dark phase, the period in which the mice are active, indicates a switch from fat to carbohydrate oxidation. In comparison with WT mice, the rate of fatty acid oxidation in Fmo5(-/-) mice is higher in WAT, which would contribute to depletion of lipid stores in this tissue, and lower in skeletal muscle. Five proteins were down regulated in the liver of Fmo5(-/-) mice: aldolase B, ketohexokinase and cytosolic glycerol 3-phosphate dehydrogenase (GPD1) are involved in glucose or fructose metabolism and GPD1 also in production of glycerol 3-phosphate, a precursor of triglyceride biosynthesis; HMG-CoA synthase 1 is involved in cholesterol biosynthesis; and malic enzyme 1 catalyzes the oxidative decarboxylation of malate to pyruvate, in the process producing NADPH for use in lipid and cholesterol biosynthesis. Down regulation of these proteins provides a potential explanation for the reduced fat deposits and lower plasma cholesterol characteristic of Fmo5(-/-) mice. Our results indicate that disruption of the Fmo5 gene slows metabolic ageing via pleiotropic effects.

Employing TMT quantification in a shotgun-MS platform

The blood serum proteome may be an ideal source of disease biomarkers, although its complexity necessitates novel strategies to enrich and quantify lower-abundance proteins with biomarker utility. Herein, serum samples from pre-diagnosis pancreatic cancer cases and controls were compared using a workflow of immunodepletion, multi-lectin fractionation, and peptide tandem mass tag (TMT) labeling. Samples were then subjected to SCX and high pH reversed-phase fractionation and analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). The aim was the discovery of candidate serum biomarkers of pancreatic cancer, although the method is applicable to any comparative proteomic analysis of serum samples.

Discovery of serum biomarkers of ovarian cancer using complementary proteomic profiling strategies

Purpose

Ovarian cancer is a devastating disease and biomarkers for its early diagnosis are urgently required. Serum may be a valuable source of biomarkers that may be revealed by proteomic profiling. Herein, complementary serum protein profiling strategies were employed for discovery of biomarkers that could discriminate cases of malignant and benign ovarian cancer.

Experimental design

Identically collected and processed serum samples from 22 cases of invasive epithelial ovarian cancer, 45 benign ovarian neoplasms, and 64 healthy volunteers were subjected to immunodepletion and protein equalization coupled to 2D‐DIGE/MS and multidimensional fractionation coupled to SELDI‐TOF profiling with MS/MS for protein identification. Selected candidates were verified by ELISA in samples from malignant (n = 70) and benign (n = 89) cases and combined marker panels tested against serum CA125.

Results

Both profiling platforms were complementary in identifying biomarker candidates, four of which (A1AT, SLPI, APOA4, VDBP) significantly discriminated malignant from benign cases. However, no combination of markers was as good as CA125 for diagnostic accuracy. SLPI was further tested as an early marker using prediagnosis serum samples. While it rose in cases toward diagnosis, it did not discriminate prediagnosis cases from controls.

Conclusions and clinical relevance

The candidate biomarkers warrant further validation in independent sample sets.

Proteomic and bioinformatic analysis of differentially expressed proteins in denervated skeletal muscle

The aim of this study was to improve our understanding and the current treatment of denervation-induced skeletal muscle atrophy. We used isobaric tags for relative and absolute quantification (iTRAQ) coupled with two-dimensional liquid chromatography-tandem mass spectrometry (2D LC-MS/MS) to identify the differentially expressed proteins in the tibialis anterior (TA) muscle of rats at 1 and 4 weeks following sciatic nerve transection. A total of 110 proteins was differentially expressed and was further classified using terms from the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to unravel their molecular functions. Among the differentially expressed metabolic enzymes involved in glycolysis, Krebs cycle and oxidative phosphorylation, α- and β-enolase displayed an increased and decreased expression, respectively, which was further validated by western blot analysis and immunohistochemistry. These findings suggest that the enolase isozymic switch during denervation-induced muscle atrophy is the reverse of that occurring during muscle maturation. Notably, protein‑protein interaction analysis using the STRING database indicated that the protein expression of tumor necrosis factor receptor-associated factor-6 (TRAF6), muscle ring-finger protein 1 (MuRF1) and muscle atrophy F-box (MAFBx) was also upregulated during denervation‑induced skeletal muscle atrophy, which was confirmed by western blot analysis. TRAF6 knockdown experiments in L6 myotubes suggested that the decreased expression of TRAF6 attenuated glucocorticoid‑induced myotube atrophy. Therefore, we hypothesized that the upregulation of TRAF6 may be involved in the development of denervation‑induced muscle atrophy, at least in part, by regulating the expression of MAFBx and MuRF1 proteins. The data from the present study provide valuable insight into the molecular mechanisms regulating denervation-induced muscle atrophy.

Enhanced expression of IMPDH2 promotes metastasis and advanced tumor progression in patients with prostate cancer

PURPOSE

Our previous study showed the upregulation of inosine 5'-monophosphate dehydrogenase type II (IMPDH2) protein in human prostate cancer (PCa) tissues and sera compared to non-cancerous controls by proteomics and ELISA analyses. However, the clinical significance of IMPDH2 in PCa has not been fully elucidated. Thus, the aim of the current study was to investigate the associations of IMPDH2 upregulation with tumor progression in patients with PCa.

METHODS

IMPDH2 expression at mRNA and protein levels in human PCa and non-cancerous prostate tissues was respectively detected by qRT-PCR, Western blot and immunohistochemistry analyses, which was validated by microarray-based Taylor Data. Then, the association of IMPDH2 expression with clinicopathological features of PCa patients was statistically analyzed.

RESULTS

Compared with non-cancerous prostate tissues, IMPDH2 mRNA and protein expression levels were both significantly upregulated (at mRNA level: 9.22 ± 2.49 vs 5.06 ± 1.45, P < 0.01; at protein level by Western blot: 0.674 ± 0.029 vs 0.418 ± 0.140, P < 0.001; at protein level by immunohistochemistry: 4.97 ± 0.760 vs 3.32 ± 1.66, P < 0.001) in PCa tissues, which were consistent with our previous data. In addition, the enhanced expression of IMPDH2 in PCa tissues was significantly correlated with the advanced clinical stage (for our cohort: P < 0.001; for Taylor data: P = 0.002), the presence of metastasis (for our cohort: P < 0.001; for Taylor data: P = 0.012) and the higher Gleason score (for our cohort: P = 0.002; for Taylor data: P = 0.028).

CONCLUSIONS

These findings suggest for the first time that the enhanced expression of IMPDH2 may promote the tumor metastasis and the advanced tumor progression in patients with PCa.

Energy metabolism and changes in cellular composition in ovarian cancer

Ovarian cancer possesses metabolic properties typical for any malignancy as well as some specific characteristics. Most of the methodological approach to study metabolism and molecular composition of the living cells are suitable for ovarian cancer research, however, might require minor modifications. The chapter reviews various laboratory techniques adapted to study ovarian cancer.

Comparison of label-free and label-based strategies for proteome analysis of hepatoma cell lines

Within the past decade numerous methods for quantitative proteome analysis have been developed of which all exhibit particular advantages and disadvantages. Here, we present the results of a study aiming for a comprehensive comparison of ion-intensity based label-free proteomics and two label-based approaches using isobaric tags incorporated at the peptide and protein levels, respectively. As model system for our quantitative analysis we used the three hepatoma cell lines HepG2, Hep3B and SK-Hep-1. Four biological replicates of each cell line were quantitatively analyzed using an RPLC-MS/MS setup. Each quantification experiment was performed twice to determine technical variances of the different quantification techniques. We were able to show that the label-free approach by far outperforms both TMT methods regarding proteome coverage, as up to threefold more proteins were reproducibly identified in replicate measurements. Furthermore, we could demonstrate that all three methods show comparable reproducibility concerning protein quantification, but slightly differ in terms of accuracy. Here, label-free was found to be less accurate than both TMT approaches. It was also observed that the introduction of TMT labels at the protein level reduces the effect of underestimation of protein ratios, which is commonly monitored in case of TMT peptide labeling. Previously reported differences in protein expression between the particular cell lines were furthermore reproduced, which confirms the applicability of each investigated quantification method to study proteomic differences in such biological systems. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Proteomic profiling of ovarian cancer models using TMT-LC-MS/MS

Herein, we have utilized two cellular models of epithelial ovarian cancer (EOC), where transfer of normal chromosome 18 material into the EOC cell lines TOV-112D and TOV-21G induced in vitro and in vivo suppression of tumorigenic phenotype in derived hybrid clones. Two-dimensional-liquid chromatography tandem mass spectrometry (2D-LC-MS/MS) with tandem mass tagging (TMT) was then employed to profile the whole cell, secreted and crude membrane proteomes of the parental and hybrid cell models to identify differentially expressed proteins as potential markers of ovarian tumor suppression. Protein changes of interest were confirmed by immunoblotting in additional hybrid and revertant cell lines. This method afforded quantitative coverage of around 1,000 unique proteins and is applicable to the analysis of any cell model, tissue or biofluid.

An integrative proteomics and interaction network-based classifier for prostate cancer diagnosis

Aim

Early diagnosis of prostate cancer (PCa), which is a clinically heterogeneous-multifocal disease, is essential to improve the prognosis of patients. However, published PCa diagnostic markers share little overlap and are poorly validated using independent data. Therefore, we here developed an integrative proteomics and interaction network-based classifier by combining the differential protein expression with topological features of human protein interaction networks to enhance the ability of PCa diagnosis.

Methods and Results

By two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) coupled with MS using PCa and adjacent benign tissues of prostate, a total of 60 proteins with the differential expression in PCa tissues were identified as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and three hub proteins (PTEN, SFPQ and HDAC1) were chosen. After that, a PCa diagnostic classifier was constructed by support vector machine (SVM) modeling based on the microarray gene expression data of the genes which encode the hub proteins mentioned above. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.96∼90.18%) and area under ROC curve (approximating 1.0). Furthermore, the clinical significance of PTEN, SFPQ and HDAC1 proteins in PCa was validated by both ELISA and immunohistochemistry analyses. More interestingly, PTEN protein was identified as an independent prognostic marker for biochemical recurrence-free survival in PCa patients according to the multivariate analysis by Cox Regression.

Conclusions

Our data indicated that the integrative proteomics and interaction network-based classifier which combines the differential protein expression and topological features of human protein interaction network may be a powerful tool for the diagnosis of PCa. We also identified PTEN protein as a novel prognostic marker for biochemical recurrence-free survival in PCa patients.

Mass spectrometry-based proteomics in molecular diagnostics: discovery of cancer biomarkers using tissue culture

Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomics-based approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA) were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches.

IMAC/TiO(2) enrich for peptide modifications other than phosphorylation: implications for chromatographic choice and database searching in phosphoproteomics

Protein regulation by reversible phosphorylation is fundamental in nature, and large-scale phosphoproteomic analyses are becoming routine in proteomics laboratories. These analyses utilise phosphopeptide separation and enrichment techniques linked to LC-MS/MS. Herein, we report that IMAC and TiO(2) also enrich for non-phosphorylated modified peptides such as acetylated, deamidated and carbamylated peptides. Urea and digestion conditions commonly used in phosphoproteomic workflows are the likely sources of the induced modifications (deamidation and carbamylation) and can easily modify phosphopeptides. Including these variable modifications in database searches increased the total number of identified phosphopeptides by 15%. We also show that strong cation exchange fractionation provides poor resolution of phosphopeptides and actually enriches these alternatively modified peptides. By switching to reverse-phase chromatography, we show a significant improvement in the number of identified phosphopeptides. We recommend that the users of phosphopeptide enrichment strategies avoid using urea as a denaturant and that careful consideration is given to chromatographic conditions and the types of variable modifications used in database searches. Thus, the capacity of IMAC and TiO(2) to enrich phosphopeptides bearing modifications other than phosphorylation is a previously unappreciated property of these chromatographies with practical implications for the field of phosphoproteomics.

Global proteomics and metabolomics in cancer biomarker discovery

Chromatography and electrophoresis have been used for the last half-century to separate small and large molecules. Advances in MS instrumentation and techniques for sample introduction into the mass analyzer (i.e. matrix-assisted laser desorption/ionization and electrospray ionization), chromatography in all its formats and modes and two-dimensional gel electrophoresis, including two-dimensional difference gel electrophoresis, enabled the separation of complex biological mixtures, such as the proteome and the metabolome, in a biological sample. These advances have made it possible to identify compounds that can be used to discriminate between two samples taken from healthy and diseased individuals. The objective is to find proteins or metabolites that can be used as a clinical test for the early diagnosis, prognosis and monitoring of the disease and the outcome of therapy. In this manuscript, we present an overview of what has been achieved in the search for biomarkers, with emphasis on cancer, using separation science and MS.

A combination of serum leucine-rich α-2-glycoprotein 1, CA19-9 and interleukin-6 differentiate biliary tract cancer from benign biliary strictures

BACKGROUND

Biliary tract cancer (BTC) and benign biliary strictures can be difficult to differentiate using standard tumour markers such as serum carbohydrate antigen 19-9 (CA19-9) as they lack diagnostic accuracy.

METHODS

Two-dimensional difference gel electrophoresis and tandem mass spectrometry were used to profile immunodepleted serum samples collected from cases of BTC, primary sclerosing cholangitis (PSC), immunoglobulin G4-associated cholangitis and healthy volunteers. The serum levels of one candidate protein, leucine-rich α-2-glycoprotein (LRG1), were verified in individual samples using enzyme-linked immunosorbent assay and compared with serum levels of CA19-9, bilirubin, interleukin-6 (IL-6) and other inflammatory markers.

RESULTS

We report increased LRG1, CA19-9 and IL-6 levels in serum from patients with BTC compared with benign disease and healthy controls. Immunohistochemical analysis also demonstrated increased staining of LRG1 in BTC compared with cholangiocytes in benign biliary disease. The combination of receiver operating characteristic (ROC) curves for LRG1, CA19-9 and IL-6 demonstrated an area under the ROC curve of 0.98. In addition, raised LRG1 and CA19-9 were found to be independent predictors of BTC in the presence of elevated bilirubin, C-reactive protein and alkaline phosphatase.

CONCLUSION

These results suggest LRG1, CA19-9 and IL-6 as useful markers for the diagnosis of BTC, particularly in high-risk patients with PSC.