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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Induction of prolonged survival of CD4+ T lymphocytes by intermittent IL-2 therapy in HIV-infected patients.

HIV infection leads to decreases in the number of CD4 T lymphocytes and an increased risk for opportunistic infections and neoplasms. The administration of intermittent cycles of IL-2 to HIV-infected patients can lead to profound increases (often greater than 100%) in CD4 cell number and percentage. Using in vivo labeling with 2H-glucose and BrdU, we have been able to demonstrate that, although therapy with IL-2 leads to high levels of proliferation of CD4 as well as CD8 lymphocytes, it is a remarkable preferential increase in survival of CD4 cells (with half-lives that can exceed 3 years) that is critical to the sustained expansion of these cells. This increased survival was time-dependent: the median half-life, as determined by semiempirical modeling, of labeled CD4 cells in 6 patients increased from 1.7 weeks following an early IL-2 cycle to 28.7 weeks following a later cycle, while CD8 cells showed no change in the median half-life. Examination of lymphocyte subsets demonstrated that phenotypically naive (CD27+CD45RO-) as well as central memory (CD27+CD45RO+) CD4 cells were preferentially expanded, suggesting that IL-2 can help maintain cells important for host defense against new antigens as well as for long-term memory to opportunistic pathogens.

Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE): advances and perspectives.

The recent trend in science is to assay as many biological molecules as possible within a single experiment. This trend is evident in proteomics where the aim is to characterize thousands of proteins within cells, tissues, and organisms. While advances in mass spectrometry have been critical, developments made in two-dimensional PAGE (2D-PAGE) have also played a major role in enabling proteomics. In this review, we discuss and highlight the advances made in 2D-PAGE over the past 25 years that have made it a foundational tool in proteomic research.

Detection of bladder cancer in human urine by metabolomic profiling using high performance liquid chromatography/mass spectrometry.

PURPOSE: The current use of cystoscopy for screening and detecting bladder cancer is invasive and expansive. Various urine based biomarkers have been used for this purpose with limited success. Metabolomics, ie metabonomics, is the quantitative measurement of the metabolic response to pathophysiological stimuli. This analysis provides a metabolite pattern that can be characteristic of various benign and malignant conditions. We evaluated high performance liquid chromatography coupled online with a mass spectrometer metabolomic approach to differentiate urine samples from healthy individuals and patients with bladder cancer. MATERIALS AND METHODS: Urine specimens were collected from 48 healthy individuals and 41 patients with transitional cell carcinoma, and stored at -80C. Samples were analyzed using an Agilent 1100 Series high performance liquid chromatography system (Agilent Technologies, Santa Clara, California) coupled online with a hybrid triple-quad time-of-flight QSTAR XL mass spectrometer. At the time of analysis samples were thawed and centrifuged. The resulting total ion chromatograms of each sample were submitted for statistical analysis. For data interpretation in this study 2 statistical methods were used, that is principal component analysis and orthogonal partial least square-discriminate analysis. RESULTS: Using positive ionization mass spectrometry orthogonal partial least square-discriminate analysis correctly predicted 48 of 48 healthy and 41 of 41 bladder cancer urine samples, while principal component analysis, which is an unsupervised profiling statistical method, confirmed these results and correctly predicted 46 of 48 healthy and 40 of 41 bladder cancer urine samples. CONCLUSIONS: The results of this proof of concept study in a relatively small number of subjects indicate that metabolomics using high performance liquid chromatography-mass spectrometry has the potential to become a noninvasive early detection test for bladder cancer.

Identification of a highly effective rapamycin schedule that markedly reduces the size, multiplicity, and phenotypic progression of tobacco carcinogen-induced murine lung tumors.

PURPOSE: Human and murine preneoplastic lung lesions induced by tobacco exposure are characterized by increased activation of the Akt/mammalian target of rapamycin (mTOR) pathway, suggesting a role for this pathway in lung cancer development. To test this, we did studies with rapamycin, an inhibitor of mTOR, in A/J mice that had been exposed to the tobacco-specific carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). EXPERIMENTAL DESIGN: Tumorigenesis was induced by i.p. injection of NNK, and rapamycin was administered 1 or 26 weeks after NNK administration. Biomarkers associated with mTOR inhibition were assessed in lung and/or surrogate tissues using immunohistochemistry and immunoblotting. Rapamycin levels were measured using mass spectroscopy. RESULTS: Rapamycin was administered on a daily (5 of 7 days) regimen beginning 26 weeks after NNK decreased tumor size, proliferative rate, and mTOR activity. Multiplicity was not affected. Comparing this regimen with an every-other-day (qod) regimen revealed that rapamycin levels were better maintained with qod administration, reaching a nadir of 16.4 ng/mL, a level relevant in humans. When begun 1 week after NNK, this regimen was well tolerated and decreased tumor multiplicity by 90%. Tumors that did develop showed decreased phenotypic progression and a 74% decrease in size that correlated with decreased proliferation and inhibition of mTOR. CONCLUSIONS: Tobacco carcinogen-induced lung tumors in A/J mice are dependent upon mTOR activity because rapamycin markedly reduced the development and growth of tumors. Combined with the Food and Drug Administration approval of rapamycin and broad clinical experience, these studies provide a rationale to assess rapamycin in trials with smokers at high risk to develop lung cancer.

Clinical and pharmaceutical applications of packed-column supercritical fluid chromatography.

Packed-column supercritical fluid chromatography (pSFC) is a fast separation technique that combines the properties of HPLC and GC. pSFC with carbon dioxide as the mobile phase and packed silica column as the stationary phase possesses the properties of normal phase mechanism; however, the addition of modifiers to the mobile phase allows the separation of relatively polar compounds. In spite of its many positive attributes, pSFC has not been widely used in areas such as proteomics, where methods such as HPLC dominate. Packed column SFC has been extensively used in clinical and pharmaceutical laboratories, especially for separation of nonpolar and chiral drugs. This review will discuss recently published applications of pSFC, with a specific focus on its advantages and limitations for the analysis of pharmaceuticals with varying chemical properties.