Metabolomic Heterogeneity of Urogenital Tract Cancers Analyzed by Complementary Chromatographic Techniques Coupled with Mass Spectrometry.

BACKGROUND: In regard to urogenital tract cancer studies, an estimated 340,650 new cases and 58,360 deaths from genital system cancer and about 141,140 new cases and 29330 deaths from urinary system were projected to occur in the United States in 2012. The main drawbacks of currently available diagnostic tests constitute the low specificity, costliness and quite high invasiveness. OBJECTIVE: The main goal of this pilot study was to determine and compare urine metabolic fingerprints in urogenital tract cancer patients and healthy controls. METHOD: A comparative analysis of the metabolic profile of urine from 30 patients with cancer of the genitourinary system (bladder (n=10), kidney (n=10) and prostate (n=10)) and 30 healthy volunteers as a control group was provided by LC-TOF/MS and GCQqQ/ MS. The data analysis was performed by the use of U-Mann Whitney test or Student's t-test, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). RESULTS: As a result, 33, 43, and 22 compounds were identified as statistically significant in bladder, prostate and kidney cancer, respectively, compared to healthy groups. CONCLUSION: Diverse compounds such as purine, sugars, amino acids, nucleosides, organic acids which play a role in purine metabolism, in tricarboxylic acid cycle, in amino acid metabolism or in gut microbiota metabolism were identified. Only two metabolites namely glucocaffeic acid and lactic acid were found to be in common in studied three types of cancer.

Targeted metabolomics in bladder cancer: From analytical methods development and validation towards application to clinical samples.

Bladder cancer constitutes the ninth most common cancer worldwide and, despite continuous development of new diagnostic approaches, the thirteenth leading cause of global cancer mortality. In our previous untargeted urine metabolomic investigation, seventeen metabolites were found to be statistically differentiating bladder cancer patients and healthy volunteers. Therefore, the main goal of this study was to develop and validate an analytical method for simultaneous quantitative determination of those metabolites using reversed phase high-performance liquid chromatography coupled with triple quadrupole mass spectrometry technique (RP-HPLC-QQQ/MS). Different chromatographic conditions, as well as various sample treatment procedures were tested in order to provide the best separation and the lowest limit of quantification (LOQ) values for studied compounds. The validation was performed according to the Food and Drug Administration guidelines (FDA). The limit of determination (LOD) and the LOQ values were in the range of 0.21-10.51 ng/ml and 0.69-35.02 ng/ml, respectively. The concentration range of compounds was developed between 2.5 and 12500 ng/ml. Only one compound (trimethyllysine) showed a significant matrix effect (61%) and consequently low process efficiency (64%). Overall, developed method presented recovery and precision values within the ranges proposed by FDA guidelines. The optimized and validated method was applied to urine samples obtained from 40 patients with bladder cancer and 40 healthy volunteers matched according to ones of the most important risk factors for developing urinary bladder tumors, e.i. age, gender and BMI. Afterwards, statistical analysis was provided by the use of Student's t-test or U-Mann Whitney test. The developed method was sensitive, selective and reproducible to be applied for the quantification of metabolites in the investigation of urine samples. As a consequence, ten out of previously chosen seventeen compounds, participating in different metabolites' pathways (gut floral metabolism, RNA degradation, purine metabolism, etc.), were found to be statistically significantly different in the urine concentration (p < 0.05) between cancer and control groups.

How to model temporal changes in nontargeted metabolomics study? A Bayesian multilevel perspective.

Analysis of time series data addresses the question on mechanisms underlying normal physiology and its alteration under pathological conditions. However, adding time variable to high-dimension, collinear, noisy data is a challenge in terms of mining and analysis. Here, we used Bayesian multilevel modeling for time series metabolomics in vivo study to model different levels of random effects occurring as a consequence of hierarchical data structure. A multilevel linear model assuming different treatment effects with double exponential prior, considering major sources of variability and robustness to outliers was proposed and tested in terms of performance. The treatment effect for each metabolite was close to zero suggesting small if any effect of cancer on metabolomics profile change. The average difference in 964 signals for all metabolites varied by a factor ranging from 0.8 to 1.25. The inter-rat variability (expressed as a coefficient of variation) ranged from 3-30% across all metabolites with median around 10%, whereas the inter-occasion variability ranged from 0-30% with a median around 5%. Approximately 36% of metabolites contained outlying data points. The complex correlation structure between metabolite signals was revealed. We conclude that kinetics of metabolites can be modeled using tools accepted in pharmacokinetics type of studies.

Urine metabolic fingerprinting using LC-MS and GC-MS reveals metabolite changes in prostate cancer: A pilot study.

Prostate cancer (CaP) is a leading cause of cancer deaths in men worldwide. The alarming statistics, the currently applied biomarkers are still not enough specific and selective. In addition, pathogenesis of CaP development is not totally understood. Therefore, in the present work, metabolomics study related to urinary metabolic fingerprinting analyses has been performed in order to scrutinize potential biomarkers that could help in explaining the pathomechanism of the disease and be potentially useful in its diagnosis and prognosis. Urine samples from CaP patients and healthy volunteers were analyzed with the use of high performance liquid chromatography coupled with time of flight mass spectrometry detection (HPLC-TOF/MS) in positive and negative polarity as well as gas chromatography hyphenated with triple quadruple mass spectrometry detection (GC-QqQ/MS) in a scan mode. The obtained data sets were statistically analyzed using univariate and multivariate statistical analyses. The Principal Component Analysis (PCA) was used to check systems' stability and possible outliers, whereas Partial Least Squares Discriminant Analysis (PLS-DA) was performed for evaluation of quality of the model as well as its predictive ability using statistically significant metabolites. The subsequent identification of selected metabolites using NIST library and commonly available databases allows for creation of a list of putative biomarkers and related biochemical pathways they are involved in. The selected pathways, like urea and tricarboxylic acid cycle, amino acid and purine metabolism, can play crucial role in pathogenesis of prostate cancer disease.

GC/MS technique and AMDIS software application in identification of hydrophobic compounds of grasshoppers' abdominal secretion (Chorthippus spp.).

The automated mass spectral deconvolution and identification system (AMDIS) is a modern analytical tool, mostly used as a data processing method in environmental studies. The most attractive feature of that software is a fast, automatic data processing, which includes removal of interferences from the overlapping peaks and purification of the obtained mass spectra. The identification of analytes is based on their retention time and retention index and on comparison of the spectra obtained in GC/MS analysis with the spectra from the library of the National Institute of Standards and Technology (NIST). The main aim of the study reported was to elaborate and test a new data processing method with the use of AMDIS software for identification of lipidomic compounds present in the grasshopper's abdominal secretion. For the first time to the best of our knowledge, we have demonstrated the usage of AMDIS in a lipidomic study concerning a complex insect matrix. The samples processed with AMDIS software were analyzed with the use of GC/MS in order to determine the main fatty components of grasshoppers' abdominal secretion. The purification, concentration and fractionation of compounds present in a complex insect matrix were investigated with the use of liquid-liquid extraction as a pretreatment procedure. Moreover, a double-step derivatization process was carried out in order to obtain more volatile and stable derivatives of polar, non-volatile components of insects' secretion. This process, necessary for GC/MS analysis, was performed with the use of methoxyamine hydrochloride dissolved in pyridine and a mixture of bis-N-O-trimethylsilyl trifluoroacetamide (BSTFA) and chlorotrimethylsilane (TMCS). As a result, we obtained a fast, automatic method based on the use of AMDIS software, which enabled identification of 28 analytes, mainly fatty compounds. Moreover, 10 compounds out of 28 were determined to appear with 100% frequency in the tested samples, namely: seven fatty acids, one sterol, one organic acid and one alkaloid. The last part of our study was statistical analysis of average intensities of signals of compounds identified in grasshopper's abdominal secretion in order to differentiate insects collected at two distant locations in Poland: Starogard Gdanski and Lubianka meadows.

Liquid chromatography tandem mass spectrometry study of urinary nucleosides as potential cancer markers.

The aim of this work was a comprehensive analysis of metabolite profiles of nucleosides from biological samples obtained from patients with urogenital cancer disease as well as an interpretation of cancer-related patterns of the analyzed profiles. In our study we proposed a targeted approach that was focused on the simultaneous determination of twelve nucleosides from over a hundred urine samples. For analytes' quantification high performance liquid chromatography technique hyphenated with tripple quadrupole mass spectrometer was applied. The developed method was validated in terms of linearity, precision, accuracy as well as in terms of limit of quantification and limit of detection. The obtained, normalized data set was analyzed using univariate statistical analysis. The achieved results revealed statistically significant (p<0.05) differences between levels of five nucleosides determined in urine samples from cancer patients and healthy volunteers, namely: 6-methyladenosine, inosine, N-2-methylguanosine, 3-methyluridine and N,N-dimethylguanosine. Basing on the putative markers we built the discrimination models using partial least squares discriminant analysis as well as K-nearest neighbor method. The sensitivity and specificity of the markers calculated from the obtained models were in the range of 61.9-88.89% and 27.78-50%, respectively. The proposed procedure can be considered as a holistic approach for metabolites' analysis and includes clinical, analytical and bioinformatics sections.