MYC-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis.

Metabolic profiling of cancer cells has recently been established as a promising tool for the development of therapies and identification of cancer biomarkers. Here we characterized the metabolomic profile of human breast tumors and uncovered intrinsic metabolite signatures in these tumors using an untargeted discovery approach and validation of key metabolites. The oncometabolite 2-hydroxyglutarate (2HG) accumulated at high levels in a subset of tumors and human breast cancer cell lines. We discovered an association between increased 2HG levels and MYC pathway activation in breast cancer, and further corroborated this relationship using MYC overexpression and knockdown in human mammary epithelial and breast cancer cells. Further analyses revealed globally increased DNA methylation in 2HG-high tumors and identified a tumor subtype with high tissue 2HG and a distinct DNA methylation pattern that was associated with poor prognosis and occurred with higher frequency in African-American patients. Tumors of this subtype had a stem cell-like transcriptional signature and tended to overexpress glutaminase, suggestive of a functional relationship between glutamine and 2HG metabolism in breast cancer. Accordingly, 13C-labeled glutamine was incorporated into 2HG in cells with aberrant 2HG accumulation, whereas pharmacologic and siRNA-mediated glutaminase inhibition reduced 2HG levels. Our findings implicate 2HG as a candidate breast cancer oncometabolite associated with MYC activation and poor prognosis.

Prospective evaluation of serum sarcosine and risk of prostate cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

Metabolomic profiling has identified, sarcosine, a derivative of the amino acid glycine, as an important metabolite involved in the etiology or natural history of prostate cancer. We examined the association between serum sarcosine levels and risk of prostate cancer in 1122 cases (813 non-aggressive and 309 aggressive) and 1112 controls in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Sarcosine was quantified using high-throughput liquid chromatography-mass spectrometry. A significantly increased risk of prostate cancer was observed with increasing levels of sarcosine (odds ratio [OR] for the highest quartile of exposure [Q4] versus the lowest quartile [Q1] = 1.30, 95% confidence interval [CI]: 1.02, 1.65; P-trend 0.03). When stratified by disease aggressiveness, we observed a stronger association for non-aggressive cases (OR for Q4 versus Q1 = 1.44, 95% CI: 1.11, 1.88; P-trend 0.006) but no association for aggressive prostate cancer (OR for Q4 versus Q1 = 1.03, 95% CI: 0.73, 1.47; P-trend 0.89). Although not statistically significant, temporal analyses showed a stronger association between sarcosine and prostate cancer for serum collected closer to diagnosis, suggesting that sarcosine may be an early biomarker of disease. Interestingly, the association between sarcosine and prostate cancer risk was stronger among men with diabetes (OR = 2.66, 95% CI: 1.04, 6.84) compared with those without reported diabetes (OR = 1.23, 95% CI: 0.95-1.59, P-interaction = 0.01). This study found that elevated levels of serum sarcosine are associated with an increased prostate cancer risk and evidence to suggest that sarcosine may be an early biomarker for this disease.

Fractionation of peptides by strong cation-exchange liquid chromatography.

Chromatography in all its formats plays an important role in proteomics research. The search for protein biomarkers for different diseases has been an active area of research. The dynamic concentration range and the large number of proteins in a proteome require the development of multidimensional separation strategies to allow for the identification of the largest number of proteins. Strong cation-exchange chromatography (SCX) has been used extensively for the fractionation of proteins and peptides. This chapter provides a detailed description for the SCX fractionation of complex proteome samples.

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.

Is sarcosine a biomarker for prostate cancer?

Sarcosine was suggested in a letter to Nature in 2009 as a biomarker for prostate cancer. This communication reviews what has been accomplished to date to determine whether sarcosine is or is not a biomarker for prostate cancer that can replace prostate-specific antigen tests.

An Efficient Stochastic Search for Bayesian Variable Selection with High-Dimensional Correlated Predictors.

We present a Bayesian variable selection method for the setting in which the number of independent variables or predictors in a particular dataset is much larger than the available sample size. While most existing methods allow some degree of correlations among predictors but do not consider these correlations for variable selection, our method accounts for correlations among the predictors in variable selection. Our correlation-based stochastic search (CBS) method, the hybrid-CBS algorithm, extends a popular search algorithm for high-dimensional data, the stochastic search variable selection (SSVS) method. Similar to SSVS, we search the space of all possible models using variable addition, deletion or swap moves. However, our moves through the model space are designed to accommodate correlations among the variables. We describe our approach for continuous, binary, ordinal, and count outcome data. The impact of choices of prior distributions and hyper-parameters is assessed in simulation studies. We also examined performance of variable selection and prediction as the correlation structure of the predictors varies. We found that the hybrid-CBS resulted in lower prediction errors and better identified the true outcome associated predictors than SSVS when predictors were moderately to highly correlated. We illustrate the method on data from a proteomic profiling study of melanoma, a skin cancer.

A reproducible and high-throughput HPLC/MS method to separate sarcosine from α- and ß-alanine and to quantify sarcosine in human serum and urine.

While sarcosine was recently identified as a potential urine biomarker for prostate cancer, further studies have cast doubt on its utility to diagnose this condition. The inconsistent results may be due to the fact that alanine and sarcosine coelute on an HPLC reversed-phase column and the mass spectrometer cannot differentiate between the two isomers, since the same parent/product ions are generally used to measure them. In this study, we developed a high-throughput liquid chromatography-mass spectrometry (LC-MS) method that resolves sarcosine from alanine isomers, allowing its accurate quantification in human serum and urine. Assay reproducibility was determined using the coefficient of variation (CV) and intraclass correlation coefficient (ICC) in serum aliquots from 10 subjects and urine aliquots from 20 subjects across multiple analytic runs. Paired serum/urine samples from 42 subjects were used to evaluate sarcosine serum/urine correlation. Both urine and serum assays gave high sensitivity (limit of quantitation of 5 ng/mL) and reproducibility (serum assay, intra- and interassay CVs < 3% and ICCs > 99%; urine assay, intra-assay CV = 7.7% and ICC = 98.2% and interassay CV = 12.3% and ICC = 94.2%). In conclusion, this high-throughput LC-MS method is able to resolve sarcosine from α- and ß-alanine and is useful for quantifying sarcosine in serum and urine samples.

Proteomic biomarker discovery: it's more than just mass spectrometry.

The previous decade witnessed an enormous number of studies with the singular goal of identifying protein biomarkers for diseases such as cancer. A large majority of these studies have focused on comparative studies of serum or plasma obtained from disease-affected and control patients. In these studies, proteins identified in the samples using MS were compared with the hope that differences between samples would reveal useful biomarkers. Unfortunately, finding clinically relevant biomarkers has often been elusive and frustrating. As with most research efforts, both successes and failures, much has been learned about what strategies work and which do not. Part of the problem can be attributed to underestimating the effort required to discover novel biomarkers and depending too heavily on MS analysis of peripheral blood samples. Fortunately, the future for biomarker discovery still appears bright. MS technology continues to increase in sensitivity, throughput, and accuracy while novel types of samples and clever experimental designs coupled with innovative bioinformatics will make this vision of routine biomarker discovery a reality. To achieve ultimate success is going to require concomitant application of a number of different technologies, all providing the information necessary for discovering and validating clinically useful biomarkers.

Quantitation of free and total bisphenol A in human urine using liquid chromatography-tandem mass spectrometry.

Bisphenol A (BPA) is employed in the synthesis of polycarbonate plastics and epoxy resins and is widely used in consumer products including as a coating for the inside of almost all food and beverage containers and thermal-imaging paper. Bisphenol A is considered to have important health implications because it possesses weak estrogenic activity and can leach from storage containers resulting in its consumption by both humans and animals. It is metabolized in the body and excreted into urine as a glucuronide derivative. In this report, we present an accurate, selective, sensitive, and reproducible high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS) method for the quantitation of BPA in human urine, which is not prone to exogenous contamination. BPA-glucuronide is hydrolyzed enzymatically, extracted with toluene, derivatized with dansyl chloride, and the BPA-(dansyl)(2) derivative is analyzed using reversed-phase HPLC/MS/MS. Calibration was linear to 50 ng/mL with a limit of quantitation of 50 pg/mL and a limit of detection of 5 pg/mL.

Cancer biomarker discovery: Opportunities and pitfalls in analytical methods.

Many diseases result in specific and characteristic changes in the chemical and biochemical profiles of biological fluids and tissues prior to development of clinical symptoms. These changes are often useful diagnostic and prognostic biomarkers. Identifying biomarkers that can be used for the early detection of cancer will result in more efficient treatments, reduction in suffering, and lower mortality rates. An ideal screening test should be non-invasive with high sensitivity and specificity. Proteomic and metabolomic analyses of biological samples can reveal changes in abundance levels of metabolites and proteins that when validated and confirmed through clinical trials can function as clinical tests for early detection, diagnosis, monitoring disease progression, and predicting therapeutic response. While the past decade has seen great advancements in proteomics and metabolomics research producing potential biomarkers for cancer, most of the identified biomarkers have failed to replace existing clinical tests. To become a clinically approved test, a potential biomarker should be confirmed and validated using hundreds of specimens and should be reproducible, specific, and sensitive. A search of the scientific and medical literature indicates that many studies report the discovery of potential biomarkers without proper validation and/or they do not meet the above criteria. In this manuscript, we will discuss the successes and the pitfalls of biomarker research and comment on study and experimental design, which in most cases is lacking, resulting in suboptimal biomarkers.

Comparison of fluorescence, laser-induced fluorescence, and ultraviolet absorbance detection for measuring HPLC fractionated protein/peptide mixtures.

Proteomics is the study of all proteins in a biological sample. High-pressure liquid chromatography coupled online with mass spectrometry (HPLC/MS) is currently the method of choice for proteomic analysis. Proteins are extracted, separated at the protein or peptide level (after enzymatic digestion), and fractions are analyzed by HPLC/MS. Detection during off-line fractionation is generally conducted using UV-vis, which is not sensitive enough to distinguish fractions having the largest concentration of proteins/peptides and should not be combined prior to HPLC/MS. To overcome this deficiency, we utilize fluorescence or UV-laser induced fluorescence (UV-LIF) detection for measuring proteins/peptides during the off-line fractionation. Fluorescence detection allows low-abundance proteins/peptides that contain aromatic amino acids to be measured. In this study, peptide/protein samples fractionated using ion-exchange chromatography were detected using UV absorbance, fluorescence, and UV-LIF. The results indicated that fluorescence and UV-LIF were able to detect the lower abundance proteins/peptides to give a more representative chromatogram, allowing the analyst to decide which fractions should be combined prior to HPLC/tandem mass spectrometry (MS/MS) analysis.

Metabolic profiling for the detection of bladder cancer.

The development and progression of many human diseases often result in changes in gene expression and protein and metabolite concentrations. Changes at the protein and metabolite level often are detectable in biological fluids and tissues before the appearance of clinical symptoms, rendering them useful diagnostic and prognostic biomarkers. As with many conditions, the discovery of a sensitive and specific urinary biomarker for bladder cancer would save lives and reduce the suffering due to this condition. A number of potential urinary protein biomarkers for bladder cancer have been identified, but they lack the sensitivity and specificity required to replace cystoscopy and histopathology. We discuss the use of mass spectrometry and nuclear magnetic resonance spectroscopy for the detection of metabolites in biological samples, comment on their advantages and limitations, and discuss recently published work in urine metabolic profiling for bladder cancer detection.

Quantitation of nanoparticles in serum matrix by capillary electrophoresis.

Sensitive and fast analytical techniques are needed to determine the concentration of nanoparticles in biological samples (e.g., blood and tissues) for biodistribution and toxicity studies. This chapter describes a method for the use of capillary zone electrophoresis (CZE) and micellar electrokinetic chromatography (MEKC) for the quantitation of fullerene nanoparticles in human serum matrix. Data on the fullerene-based nanoparticle carboxyfullerene (C3 fullerene) in human serum is presented as an example.

Comparison of strong cation exchange and SDS-PAGE fractionation for analysis of multiprotein complexes.

Affinity purification of protein complexes followed by identification using liquid chromatography/mass spectrometry (LC-MS/MS) is a robust method to study the fundamental process of protein interaction. Although affinity isolation reduces the complexity of the sample, fractionation prior to LC-MS/MS analysis is still necessary to maximize protein coverage. In this study, we compared the protein coverage obtained via LC-MS/MS analysis of protein complexes prefractionated using two commonly employed methods, SDS-PAGE and strong cation exchange chromatography (SCX). The two complexes analyzed focused on the nuclear proteins Bmi-1 and GATA3 that were expressed within the cells at low and high levels, respectively. Prefractionation of the complexes at the peptide level using SCX consistently resulted in the identification of approximately 3-fold more proteins compared to separation at the protein level using SDS-PAGE. The increase in the number of identified proteins was especially pronounced for the Bmi-1 complex, where the target protein was expressed at a low level. The data show that prefractionation of affinity isolated protein complexes using SCX prior to LC-MS/MS analysis significantly increases the number of identified proteins and individual protein coverage, particularly for target proteins expressed at low levels.

Optimized method for computing (18)O/(16)O ratios of differentially stable-isotope labeled peptides in the context of postdigestion (18)O exchange/labeling.

Differential (18)O/(16)O stable isotope labeling of peptides that relies on enzyme-catalyzed oxygen exchange at their carboxyl termini in the presence of H(2)(18)O has been widely used for relative quantitation of peptides/proteins. The role of tryptic proteolysis in bottom-up shotgun proteomics and low reagent costs have made trypsin-catalyzed (18)O postdigestion exchange a convenient and affordable stable isotope labeling approach. However, it is known that trypsin-catalyzed (18)O exchange at the carboxyl terminus is in many instances inhomogeneous/incomplete. The extent of the (18)O exchange/incorporation fluctuates from peptide to peptide mostly due to variable enzyme-substrate affinity. Thus, accurate calculation and interpretation of peptide ratios are analytically complicated and in some regard deficient. Therefore, a computational approach capable of improved measurement of actual (18)O incorporation for each differentially labeled peptide pair is needed. In this regard, we have developed an algorithmic method that relies on the trapezoidal rule to integrate peak intensities of all detected isotopic species across a particular peptide ion over the retention time, which fits the isotopic manifold to Poisson distributions. Optimal values for manifold fitting were calculated and then (18)O/(16)O ratios derived via evolutionary programming. The algorithm is tested using trypsin-catalyzed (18)O postdigestion exchange to differentially label bovine serum albumin (BSA) at a priori determined ratios. Both accuracy and precision are improved utilizing this rigorous mathematical approach. We further demonstrate the effectiveness of this method to accurately calculate (18)O/(16)O ratios in a large scale proteomic quantitation of detergent resistant membrane microdomains (DRMMs) isolated from cells expressing wild-type HIV-1 Gag and its nonmyristylated mutant.

Combined blood/tissue analysis for cancer biomarker discovery: application to renal cell carcinoma.

A method that relies on subtractive tissue-directed shot-gun proteomics to identify tumor proteins in the blood of a patient newly diagnosed with cancer is described. To avoid analytical and statistical biases caused by physiologic variability of protein expression in the human population, this method was applied on clinical specimens obtained from a single patient diagnosed with nonmetastatic renal cell carcinoma (RCC). The proteomes extracted from tumor, normal adjacent tissue and preoperative plasma were analyzed using 2D-liquid chromatography-mass spectrometry (LC-MS). The lists of identified proteins were filtered to discover proteins that (i) were found in the tumor but not normal tissue, (ii) were identified in matching plasma, and (iii) whose spectral count was higher in tumor tissue than plasma. These filtering criteria resulted in identification of eight tumor proteins in the blood. Subsequent Western-blot analysis confirmed the presence of cadherin-5, cadherin-11, DEAD-box protein-23, and pyruvate kinase in the blood of the patient in the study as well as in the blood of four other patients diagnosed with RCC. These results demonstrate the utility of a combined blood/tissue analysis strategy that permits the detection of tumor proteins in the blood of a patient diagnosed with RCC.

Rapamycin protects mice from staphylococcal enterotoxin B-induced toxic shock and blocks cytokine release in vitro and in vivo.

Staphylococcal enterotoxins are potent activators for human T cells and cause lethal toxic shock. Rapamycin, an immunosuppressant, was tested for its ability to inhibit staphylococcal enterotoxin B (SEB)-induced activation of human peripheral blood mononuclear cells (PBMC) in vitro and toxin-mediated shock in mice. Stimulation of PMBC by SEB was effectively blocked by rapamycin as evidenced by the inhibition of tumor necrosis factor alpha (TNF-alpha), interleukin 1beta (IL-1beta), IL-6, IL-2, gamma interferon (IFN-gamma), monocyte chemoattractant protein 1 (MCP-1), macrophage inflammatory protein 1alpha (MIP-1alpha), MIP-1beta, and T-cell proliferation. In vivo, rapamycin protected 100% of mice from lethal shock, even when administered 24 h after intranasal SEB challenge. The serum levels of MCP-1 and IL-6, after intranasal exposure to SEB, were significantly reduced in mice given rapamycin versus controls. Additionally, rapamycin diminished the weight loss and temperature fluctuations elicited by SEB.

Optimization of protein solubilization for the analysis of the CD14 human monocyte membrane proteome using LC-MS/MS.

Proteomic profiling of membrane proteins is of vital importance in the search for disease biomarkers and drug development. However, the slow pace in this field has resulted mainly from the difficulty to analyze membrane proteins by mass spectrometry (MS). The objective of this investigation was to explore and optimize solubilization of membrane proteins for shotgun membrane proteomics of the CD14 human monocytes by examining different systems that rely on: i) an organic solvent (methanol) ii) an acid-labile detergent 3-[3-(1,1-bisalkyloxyethyl)pyridin-1-yl]propane-1-sulfonate (PPS), iii) a combination of both agents (methanol+PPS). Solubilization efficiency of different buffers was first compared using bacteriorhodopsin as a model membrane protein. Selected approaches were then applied on a membrane subproteome isolated from a highly enriched human monocyte population that was approximately 98% positive for CD14 expression as determined by FACS analysis. A methanol-based buffer yielded 194 proteins of which 93 (48%) were mapped as integral membrane proteins. The combination of methanol and acid-cleavable detergent gave similar results; 203 identified proteins of which 93 (46%) were mapped integral membrane proteins. However, employing PPS 216 proteins were identified of which 75 (35%) were mapped as integral membrane proteins. These results indicate that methanol alone or in combination with PPS yielded significantly higher membrane protein identification/enrichment than the PPS alone.

Analytical and statistical approaches to metabolomics research.

Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern-recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.

Electrophoresis and liquid chromatography/tandem mass spectrometry in disease biomarker discovery.

The search for disease markers is not new; however, with the emergence of new technologies such as nano-HPLC and electrospray ionization and time of flight mass spectrometry, the search has intensified considerably. Genomic, proteomic and metabolomic technologies are being used to search for novel disease markers. In this manuscript emphasis will be on different HPLC and MS methods that are used to search for metabolites and proteins that can be used for the discovery of novel, sensitive and specific disease biomarkers. Definitions of terms such as sensitivity, specificity, and protein profiles will be given. Methods used for effective fractionation, separation and quantitation of proteins and peptides using HPLC/MS will be discussed and examples are presented. A brief discussion of electrophoretic procedures used for protein fractionation and biomarker discovery is also included.