Metabonomic Profiling of Bladder Cancer - ACS Publications

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Metabonomic profiling of bladder cancer Eric Chun Yong Chan, Kishore Kumar Pasikanti, Yanjun Hong, Paul C Ho, Ratha Mahendran, Lata Raman Nee Mani, Edmund Chiong, and Kesavan Esuvaranathan J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr500966h • Publication Date (Web): 12 Nov 2014 Downloaded from http://pubs.acs.org on November 17, 2014

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Journal of Proteome Research

Metabonomic profiling of bladder cancer Eric Chun Yong Chan,1,* Kishore Kumar Pasikanti,1,# Yanjun Hong,1,# Paul C. Ho,1 Ratha Mahendran,2 Lata Raman Nee Mani,2 Edmund Chiong,2 and Kesavan Esuvaranathan 2,*

1 Department of Pharmacy, Faculty of Science, National University of Singapore, 18 Science Drive 4, Singapore 117543, 2 Department of Surgery, National University Health System, 5 Lower Kent Ridge Road, Singapore 119074.

*To whom correspondence should be addressed. Associate Professor Eric Chun Yong Chan Tel, +65 65166137; Fax, +65 67791554 Email: [email protected] Professor Kesavan Esuvaranathan Tel, +65 67724224; Fax, +65 67778427 Email: [email protected]

#

K.K.P. and Y.H. are joint first authors

This research was supported by the Singapore Ministry of Health’s National Medical Research Council under its Individual Research Grant scheme (R-176-000-119-213 to K.E., P.C.H. and E.C.Y.C.). K.K.P. was a recipient of the National University of Singapore President Graduate Fellowship.

1 ACS Paragon Plus Environment


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ABSTRACT Early diagnosis and life-long surveillance are clinically important to improve the long-term survival of bladder cancer patients. Currently, a noninvasive biomarker that is as sensitive and specific as cystoscopy in detecting bladder tumors is lacking. Metabonomics is a complementary approach for identifying perturbed metabolic pathways in bladder cancer. Significant progress has been made using modern metabonomic techniques to characterize and distinguish bladder cancer patients from control subjects, identify marker metabolites and shed insights on the disease biology and potential therapeutic targets. With its rapid development, metabonomics has the potential to impact the clinical management of bladder cancer patients in the future by revolutionizing the diagnosis and life-long surveillance strategies and stratifying patients for diagnostic, surgical and therapeutic clinical trials. In this review, introduction to metabonomics, typical metabonomic workflow and critical evaluation of metabonomic investigations in identifying biomarkers for the diagnosis of bladder cancer are presented. KEYWORDS: Bladder cancer, metabonomics, biomarker, LC/MS, GC/MS, NMR

INTRODUCTION 1

Bladder cancer (BC) is the second most common genitourinary malignant disease in the United States. The 2

incidence rate of BC is about four times greater among men than women. This trend has been attributed to 3

environmental and dietary exposures. Most notably, exposure to tobacco smoke and aromatic amines are the two most common environmental risk factors.

4-7

Among the aromatic amines, β-naphthylamine, 4-aminobiphenyl 5

and benzidines are strongly associated with BC. Other risk factors for BC include high levels of arsenic in drinking water,

6

polymorphisms.

heavy consumption of phenacetin containing analgesics

7

and N-acetyltransferase 2 gene

8

BC has been traditionally classified into two groups: non-muscle invasive and muscle-invasive urothelial 9

carcinoma. While more than 75% of patients are diagnosed with and treated for non-muscle invasive BC at the time of the initial evaluation, its recurrence rate is as high as 70%. Additionally, one third of the recurrent non-


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muscle invasive tumors can eventually progress to higher grade or stage BC. treated early, the 5-year survival rate of the patients approaches 90%.

11

10

If non-muscle invasive BC is

The second main variant, which accounts

for about 25% of BC, is an invasive tumor that arises de novo or derives from flat, high-grade carcinoma in-situ (CIS) lesions.

11

Despite radical cystectomy and debilitating systemic therapy, at least 50% of patients with CIS die 12

from metastases within 2 years of diagnosis. The 5 year survival rate of metastatic BC is as low as 6%.

In terms

of treatment, transurethral tumor resection and intravesical Bacille Calmette-Guerin (BCG) are generally recommended in non-muscle invasive BC

13

14

while radical cystectomy is advocated in invasive tumors.

this background, the early diagnosis and life-long surveillance of BC becomes clinically important.

In view of

15

BC may be diagnosed incidentally or through the presentation of symptoms such as hematuria, bladder irritation, dysuria, or urgency.

16

While microhematuria testing and urine cytology are the most basic and widely used tools

for the detection of BC, cystoscopic examination coupled to histopathologic evaluation of the biopsied tissue 9

remains the gold diagnostic standard. The schedule recommended by the European Organization for Research and Treatment of Cancer (EORTC) for patients with low- to intermediate-risk BC is cystoscopy at 3-month intervals for the first 2 years, 4-month intervals for the next 2 years and yearly thereafter.

17

Due to the need for the

life-long surveillance, the per-patient cost of BC management is highest among all other cancers.

18

Additionally,

cystoscopy is invasive and may be associated with poor patient compliance and a definite risk of morbidity. In a large cohort study, Hansen et al. showed that BC had the longest symptom-to-treatment delay (with a median of 19

4.5 months).

Delayed diagnosis in turn results in increased risk of death from BC, even for patients with low-risk

disease.

In recent years, numerous urine-based bladder tumor markers (UBBTMs) have been evaluated for the diagnosis of BC. These include the bladder tumor antigen (BTA), urinary nuclear matrix protein 22 (NMP22), fibrin degradation product (FDP), autocrine motility factor receptor, bladder cancer nuclear matrix protein (BCLA-4), cytokeratin 20 (CK20), telomerase, hyaluronic acid, hyaluronidase, ImmunoCyt, urinary bladder cancer (UBC) test, CYFRA 21-1, chemiluminescent hemoglobin, hemoglobin dipstick, urinary tissue polypeptidespecific (TPS)



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antigen, bladder cancer antigen (BCA), β-human chorionic gonadotropin, tissue polypeptide antigen (TPA), and microsatellite analysis.

9, 20, 21

Most notably, BTA, NMP22, ImmunoCyt, FDP and UroVysion assays have achieved

FDA approval for diagnostic purposes. Although, these molecular tests have significantly higher sensitivity than cytology, the UBBTMs yield poorer specificity and sensitivity compared to cystoscopy with median sensitivity well 9

9

below 90%. As noted by Mitra et al. median sensitivity % (range) of Cytology, BTA stat, BTA TRAK, NMP22 Bladder Cancer Test, NMP22 BladderChek, UBC-Rapid, HA–HAase, UroVysion, and ImmunoCyt are 34 (16– 100), 67 (34–91), 63 (17–100), 72 (31–100), 57 (47–85), 67 (21–84), 88 (83–91), 73 (13–87) and 81 (39–100), respectively. Vriesema et al. reported that 89% of patients prefer cystoscopy to noninvasive urinary screening when its sensitivity is lower than 90%.

22

Therefore, novel BC biomarkers for diagnosis and surveillance with

higher sensitivity are necessary.

Overview of metabonomics Metabolites are small organic molecules (below 2000 mass units) that are by- and end-products of complex pathways.

23

Metabonomics is defined as "the quantitative measurement of the dynamic multiparametric metabolic

response of living systems to pathophysiological stimuli or genetic modification".

24, 25

Several terms have been

used to describe the study of the metabonome, which include metabolomics, metabonomics, metabolic profiling, metabolic footprinting and metabolic fingerprinting, pharmacometabonomics and metabotyping (Table 1). The metabonome comprises endogenous metabolites and exogenous metabolites originating from microflora and environment.

23

Metabonomics versus other ‘omics’ Although the metabonome is certainly ‘complementary to other ‘omics’, such as genomics and proteomics, one needs to be mindful that gene or protein expression profiling provides a partial characterization of the pathophysiological status of a patient since single gene can have functional role in number of cellular processes by effecting considerable number of metabolic pathways. On the contrary, gene duplication for similar functions with varied substrate specificity and kinetic characteristics is also known. The complementary measurement of


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metabolites (such as sugars, fats, amino acids, vitamins and so forth) provides a direct and hence dynamic snapshot of the current physiological status of an individual. As there is no guaranteed quantitative correlation between mRNA expression and enzyme function, endogenous metabolites, downstream of both transcription and 26, 27

translation, are potentially better indicators of enzyme activity.

As metabolic fluxes result from the complex

interaction between the genome, transcriptome, proteome and the environment,

28, 29

the analysis of the

metabonome evolves as a complementary approach for identifying the perturbed metabolic pathways in a given pathology (Figure 1). In March 2006, FDA published the ‘Critical Path Opportunities Report and List’ in which metabonomics is highlighted to play a significant role in developing biomarkers, streamlining clinical trials and defining at-risk populations.

30

Clinical applications of metabonomics A number of diseases are studied using metabonomics, including cancer, disorders such as schizophrenia, disease,

41, 42

kidney disease

43

36-38

Alzheimer’s disease,

and diabetes.

44-47

38,39

31-35

central nervous system (CNS)

and Parkinson’s disease,

40

cardiovascular

Recently, Tenori et al. demonstrated in a large cohort clinical

study (500 women) that the time to progression, survival rate and treatment toxicity could be predicted based on pretreatment serum levels of metabolites (phenylalanine, glutamate and glucose) for a subset of patients who were HER2 positive for breast cancer.

48

In another large cohort study, using targeted profiling of 64 serum

metabolites it was shown that poor operative outcome of cardiac surgery could be accurately predicted.

49

Metabonomic investigations have shown that the levels of branched-chain and aromatic amino acid have significant association with type 2 diabetes as early as 12 years before the onset of the disease.

50, 51

Their 50

findings underscore the potential key role of amino acid metabolism in the early pathogenesis of diabetes. These diabetic biomarkers were validated in number of independent studies. 52

most widely probed disease using metabonomics approach.

51

In urology, prostate cancer is the

In the landmark study, Sreekumar et al. showed

that metabolic profiles of prostate cancer patients were distinct from healthy subjects and identified sarcosine to alanine ratio in urine sediments as modest but significant predictor of prostate cancer.

53

Recently, high resolution

magic angle spinning magnetic resonance spectroscopy (HR-MAS)-based metabonomics investigations identified



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spermine and citrate as metabolic biomarkers for assessing prostate cancer aggressiveness.

54

These studies not

only aimed to understand the pathogenesis of the disease, but also investigate possibility of disease diagnosis, prognosis and treatment. In addition, metabonomics has also shown potential applications in personalized medicine profiles.

55

in terms of personalizing therapies and guiding surgical interventions based on specific metabolic

56

Workflow in bladder cancer metabonomics Before discussing the specific metabonomic studies performed on BC, it is important to review the workflow in clinical metabonomics (Figure 2). Clinical metabolic phenotypes (metabotypes) may be altered due to age, 57

It is important to

58

and/or checklist

gender, diet, race, lifestyle, surgical intervention and underlying pathophysiological conditions. design BC metabonomic studies carefully based on Statistical Experimental Design (SED) criteria published by the US National Cancer Institute (NCI) on omics-based clinical trials. 59

should also be recruited to avoid confounding factors.

56

Symptomatic controls

In BC metabonomic investigations, baseline

characteristics such as stage and grade of tumor, hematuria (gross or micro), surgical interventions and smoking habit should be additionally taken into consideration during the study design.

60

Sample collection and storage Sample collection and storage conditions are important to ensure metabolic integrity during the period of analysis.

61

Urine is the most common specimen explored in BC metabonomics. Urine is expected to mirror

metabolic perturbations occurring in BC, as it directly contacts the BC lesion. Moreover, urine can be collected 62

non-invasively. Urine can be collected as spot, timed

or 24h samples.

63

Spot urine is collected during

consultation at the doctor’s office, while timed samples are needed to study time-related trends and catalogue metabolites with high diurnal variation. 24h urine collection is performed by collecting all urine which is passed within a 24h period. Although spot urine collection is convenient, its metabolic composition could be altered due to diet, circadian rhythms variations,

66

64

and lifestyle factors.

65

While collecting 24h or first-pass urine may help minimize such

it has been reported that metabolic variations due to pathology are more extensive than variations


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associated with sample collection or storage.

67

Dunn et al. demonstrated that there is no significant difference in

terms of metabolic profiles between samples that are frozen immediately compared to those stored initially at 4°C 68

for 24h.

Stability analyses demonstrated that urinary metabolites are stable up to 6 months (stored at -20 or 61,67, 69

80°C) and after multiple freeze-thaw cycles (up to 9 cycles).

On the other hand, Lauridsen et al. observed

that freeze-drying resulted in instability of selected urinary metabolites and the addition of preservative is not mandatory for urine that is stored at below -20°C.

61

Serum or plasma gives an “instantaneous” readout of the

metabolic state at the time of collection, and its composition directly reflects catabolic and anabolic processes occurring in the whole organism. Additionally, these matrices are less prone to be affected by exogenous factors. Serum is obtained by removing the naturally formed clot from the blood. Plasma is the supernatant fluid prepared by mixing blood with an anticoagulant followed by centrifugation at 4°C. A number of anticoagulants are available, including lithium heparin, citrate and potassium EDTA. Both citrate and EDTA can interfere with metabolic profiling, either by introducing interfering peaks, or obscuring the endogenous analyte. 70

heparin is preferred for general metabonomics analysis.

70

Therefore, lithium

Following collection, samples should be rapidly

processed, frozen and stored at -80°C. Tissue offers particular benefits over biofluids as direct study of tumor tissues gives information on metabolites perturbed within the solid tumor. However, there are some remarkable drawbacks of tissue such as invasive collection, limited availability and sample heterogeneity. For tissue samples, it is sufficient to snap-freeze tissues directly in liquid nitrogen post-collection. It is recommended that replicate aliquots of each clinical sample are stored to facilitate multiple analysis using different analytical platforms and circumvent metabolic degradation due to repeated freeze-thaw cycles.

Instrument platforms Metabonomic profiling of endogenous metabolites is challenging as there is no one analytical tool that can 28, 71

capture the entire metabolic space in systems biology.

Commonly employed analytical platforms include

nuclear magnetic resonance spectroscopy (NMR), gas chromatography mass spectrometry (GC/MS) and liquid chromatography mass spectrometry (LC/MS).

71



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NMR. NMR is advantageous in providing detailed structural information of metabolites.

29, 71, 72

NMR analysis is

nondiscriminating, nondestructive, highly reproducible and requires minimal sample preparation.

73

In addition,

analysis of intact tissue samples is possible using high-resolution magic angle spinning NMR spectroscopy (HR74

MAS NMR). Despite these advantages, NMR sensitivity is lower compared to MS techniques. GC/MS. GC/MS offers high sensitivity, chromatographic peak resolution and reproducibility. 78

impact (EI) spectral libraries facilitates the identification of diagnostic biomarkers

75

65, 76, 77

Its electron

while the EI ionization mode

circumvents matrix effects observed in LC/MS. Matrix effects refers to ion suppression and ion enhancement effects observed in LC/MS analysis due to co-eluting residual matrix components affecting the ionization efficiency and reproducibility of target analytes.

79, 80

Recent advances such as comprehensive two-dimensional

gas chromatography time-of-flight mass spectrometry (GC×GC/TOFMS) increases the metabolic space coverage significantly. Nevertheless, GC/MS is relatively low throughput due to the time-consuming sample preparation 65

steps such as chemical derivatization of metabolites.

LC/MS. LC/MS offers good sensitivity and dynamic range with minimal sample preparation.

72, 81, 82

Nevertheless,

the identification of marker metabolites remains challenging due to matrix effects associated with electrospray ionization (ESI) in LC/MS.

79, 80, 83

In summary, each analytical platform has its strengths and limitations. It is recommended that scientists explore 84

multiple platforms to expand the metabolic space coverage in metabonomics.

Further discussion on these

71, 72, 82, 85

analytical platforms can be found in several reviews.

Sample preparation To avoid analytical bias, all clinical samples including quality controls (QC) should be randomized prior to sample preparation. QC, prepared by mixing aliquots of all or selected samples, are typically co-analyzed to validate the method.

86

In GC/MS, sample preparation for urine and tissue samples requires distinct extraction processes.

Urine is pretreated with urease to remove excess urea, a chromatographic interference.

65, 86

Subsequently,

termination of urease activity and extraction of urinary metabolites is performed using organic solvents such as methanol.

86

Tissues are usually extracted using organic solvent mixtures directly without urease pretreatment.

65, 87


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Chemical derivatization is subsequently performed on extracts derived from all biological matrices. Oximation using methoxylamine hydrochloride followed by trimethylsilyl (TMS) derivatization is the most widely used protocol.

65

As derivatization efficiency of TMS derivatives is sensitive to hydrolytic effects of moisture, thorough

drying of extracts is critical. In NMR spectroscopy, it is necessary to remove catalytically active proteins using filtration or solvent extraction when preparing serum and plasma samples so as to avoid quantitative changes in metabolites.

88

As chemical shifts of metabolites may be affected by urine pH and salt contents, it is a common

practice to dilute urine samples using highly concentrated buffers such as 100 mM sodium phosphate buffer (pH 89

7.4 in D2O).

Due to low protein content, reference compound TSP (deuterated sodium salt of 3-

trimethylsilylpropionic acid) can be used for NMR analysis of urine samples

89

For HR-MAS NMR study of tissue

samples, the best practice is to place the sample into rotor inserts post-sample collection so as to minimize sample handling.

88

In LC/MS, the method most commonly used for the analysis of urine is the “dilute-and-shoot”

strategy with typical dilution factors between 1:1 and 1:10 with purified water.

90

Sample preparation of plasma and

serum usually involves protein precipitation with organic solvents such as acetonitrile, methanol and ethanol.

91

For tissue samples, homogenization is usually performed at low temperatures followed by extraction using organic solvents.

92

Sample analysis In GC/MS analysis, derivatized metabolites are introduced into a heated injector where rapid vaporization and mixing with the carrier helium gas occur followed by chromatographic separation and MS detection.

65

Typically,

fused silica capillary column with non-polar stationary phase coupled with gradient oven temperature 65 1

programming is used for the GC separation of metabolites.

H-NMR is typically applied in metabonomics.

73, 93

For structural identification of important marker metabolites, two-dimensional NMR correlation methods may be explored.

93

For samples dissolved in deuterated water, solvent suppression is minimal and pre-saturation with a

weak RF field from the transmitter is sufficient.

73

However, in urine analysis, the strong water signal causes

radiation damping, thus requiring water suppression through pre-saturation or excitation sculpting.

73

Proteins and

lipids can mask the metabolite peaks due their high concentrations and broad peaks. Signal from macromolecules



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can be suppressed using a Carr–Purcell Meiboom–Gill (CPMG) pulse train to enhance the detection of endogenous metabolites.

73

Chromatographic resolution of metabolites is of utmost importance in LC/MS analysis

due to ion suppression or enhancement effects caused by co-eluting metabolites. As such, ultra-performance liquid chromatography (UPLC) is widely adopted to enhance chromatographic resolution of metabolites while quadrupole time-of-flight MS (QTOFMS) aids metabolite identification by acquiring highly resolved and mass 94

accurate MS/MS spectra.

Due to the chemical diversity of metabolites, samples are analyzed in both positive

and negative ESI modes.

Data preprocessing Prior to chemometric analysis, it is necessary to preprocess the multivariate data and generate a numerical matrix comprising metabolite levels (variables) and clinical samples (observations).

58, 95

Typically, data preprocessing

involves noise reduction, baseline correction, peak deconvolution and chemical shift binning in NMR or retention time (RT) alignment in GC/MS and LC/MS, followed by normalization, centering, scaling and transformation. number of software tools such as MZmine,

97

98

XCMS,

metAlign,

99

100

MathDAMP,

and MSFACTs

101

96

A

can be used

to automate MS data preprocessing. Alternatively, data preprocessing can be automated in-house using custom programming scripts in MATLAB (Mathworks, Natick, MA, USA) and R (open source GNU project).

Prediction model building and validation Data preprocessing yields complex multivariate datasets with large number of variables and observations. In addition, noise, colinearities, and missing data make it challenging to generate interpretable models.

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Therefore,

chemometric analysis has become an integral part of the metabonomic workflow. Chemometric techniques can be broadly classified into unsupervised and supervised approaches.

103

In unsupervised approaches class

membership (or biological relevant classification) information is not provided to the classification algorithm. In contrast, prior class information is provided in a supervised model that allows dominant variances in the dataset to drive the models.

58

Typically, unsupervised principal component analysis (PCA) is performed as a first step to

visualize grouping trends and outliers.

58

Subsequent to PCA, a supervised analysis technique such as partial


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least squares discriminant analysis (PLS-DA) or orthogonal PLS-DA (OPLS-DA) is utilized to build a predictive model and derive a list of potential biomarkers that characterize the pathology.

58, 104

While PCA and PLS-DA are

arguably most widely used chemometric techniques in metabonomics, cluster analysis, support vector machines and neural networks can also be employed.

105

Chemometric software tools such as MetaboAnalyst

106

(public

domain) or SIMCA-P (Umetrics, Umeå, Sweden) provide easy user interface and offer powerful data visualization tools. In clinical metabonomics, good class separation observed in chemometric analysis could be due to chance correlation arising from imbalances in baseline characteristics of the study subjects and analytical variations. addition, multivariate models have an innate tendency to over-fit models to data.

108

107

In

Therefore, chemometric model

must be validated before it can be used to identify marker metabolites or build predictive models.

107

Various

strategies can be employed to check the data over fitting. Internal cross-validation test such as permutation tests, leave-one-out method and receiver operating characteristic (ROC) analysis are commonly utilized.

109

For external

validation, a random subset of samples (training set) is used to build a prediction model and the remaining clinical samples (external validation set) is subjected to a classification test based on the model.

Marker metabolite discovery and structure validation Chemometric analyses yield potential marker metabolites that are responsible for class separation (e.g. cancer versus control). Shortlisted marker metabolites can be further validated based on univariate statistical t-tests and quality of metabolite data.

86

If several metabolites are under investigation, cut-off for p-values should be adjusted

according to multiple hypothesis testing corrections such as Bonferroni correction. 110

metabolites are determined using metabolite libraries such as Golm, 113

MassBank

116

in NMR. In addition, HMDB

METLIN, (MMCD)

114

and LipidMaps

108

111

Fiehn

Putative identities of these and NIST

112

in GC/MS,

115

in LC/MS, and MetaMadison Metabolomics Consortium Database

117

is a unique database comprising EI, MS/MS and NMR spectra.

Although such databases are available, metabolite identification remains challenging as structural similarities of metabolites may result in multiple empirical hits. To refine the process, additional features such as RI in GC/MS, accurate mass and MS/MS information in LC/MS, and biological relevance of the metabolites are leveraged to



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further refine the structural possibilities. Finally, it is important to confirm the identities of the important marker metabolites using analytical standards.

Metabolic pathway analysis Several software tools are available to provide functional and biological interpretation of metabonomic results. These tools are broadly classified under two applications: (1) mapping and visualization and (2) enrichment analysis.

118

Mapping and visualization software provides graphical representation of the relationship among

enzymes, metabolites and reactions, and the interconnectivity between perturbed pathways. include the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, 120

(SMPDB),

123

(VANTED),

121

MetaCyc,

Reactome,

122

125

These tools

Small Molecule Pathway Database

Visualization and Analysis of Networks containing Experimental Data

Interactive Pathways explorer (iPath),

System (IPAVS),

119

118

124

Integrated Pathway resources, Analysis and Visualization

Metabolic Pathway Analysis (MetPA),

126

and MetScape.

127

On the other hand, enrichment

analysis provides functional interpretation by performing statistical tests on the concentration levels of metabolites to determine whether a set of biomarkers is enriched within a particular pathway. The enrichment analysis tools include Metabolite Set Enrichment Analysis (MSEA), Integrated Molecular Pathway Level Analysis (IMPaLA)

128

130

Metabolite Pathway Enrichment Analysis (MPEA), and Metabolites Biological Role (MBRole).

129

131

Metabotyping of bladder cancer A number of global metabonomic studies have been conducted to characterize BC using NMR, GC/MS and LC/MS. In this section, the key features and findings of these studies are discussed (Table 2).

NMR metabotyping of bladder cancer 1

Srivastava et al. utilized H NMR to probe the metabolic perturbations in non-muscle invasive BC.

132

The study

examined fasted first-pass urine collected from 33 BC patients, 33 benign controls (urinary tract infection/bladder stone) and 37 healthy controls. The level of taurine was significantly increased in BC patients compared to controls, while the levels of citrate and hippuric acid showed marked decrease in BC. The stages of BC could not


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be differentiated based on the altered metabolite indices, and the sensitivity and specificity of the prediction model 1

were not evaluated nor validated. Cao et al. performed H NMR-based metabonomic analysis on serum collected from BC patients, calculi patients (similar symptom of hematuria as BC patients) and healthy subjects.

133

OPLS-

DA showed that BC patients could be distinctly separated from both calculi patients and healthy controls. Interestingly, distinct metabolic profiles were associated with low-grade versus high-grade BC, as well as preversus post-transurethral resected cohorts. All the OPLS-DA models were validated using permutation test with 2

2

R and Q values below 0.5. The serum levels of isoleucine, leucine, tyrosine, lactate, glycine and citrate were found to be lower in BC, while the levels of lipids and glucose showed elevation. Serum-based metabonomics on 1

BC was also performed by another group using H NMR, with the aim of probing and grading BC.

134

Both low-

grade (n=36) and high-grade (n = 32) BC samples as well as healthy controls (n = 32) were included in the study. Importantly, a new batch of suspected BC patients (n = 106) was used for external validation through a doublebind study. OPLS-DA-derived model was able to discriminate BC and controls, as well as low-grade BC and highgrade BC, with high sensitivity and specificity. Six metabolites with an extremely significant difference (p < 0.001) were found to play an utmost role for differentiate among each group. In addition to urine and serum, bladder 135

tissue was profiled using metabonomics based on HR-MAS NMR.

Twenty-six patients with benign disease and

33 patients with BC were included for HR-MAS NMR analysis. PCA and PLS-DA models clearly differentiated 2

2

benign patients from BC patients with good predictivity (R = 0.93 and Q = 0.86). Fifteen metabolites were found to be altered in BC. The perturbed metabolic signatures derived from HR-MAS NMR were cross-validated via targeted GC/MS analysis of the same tissue samples (10 benign and 20 BC). Measured metabolites included glycolytic/TCA cycle intermediates namely alanine, glutamate, glutamine, valine, isoleucine, phenylalanine and tyrosine. Consistent with the HR-MAS NMR findings, the GC/MS-measured amino acids were significantly elevated in BC compared to benign tissues. The strength of this study lies in the cross-validation of biomarkers for diagnosis using an alternative analytical platform.

GC/MS metabotyping of bladder cancer



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Page 14 of 41

As volatile organic compounds in urine are potentially detectable, it was not unexpected that dogs could be 136

trained to identify BC patients based on urine odor.

Taking a step further, Jobu et al. explored GC/MS-based 137

metabonomic analysis of urine odor to characterize BC.

Nine BC patients and 7 healthy controls were recruited

where urine from patients was collected 3-day before and 3-7 day post-surgery. PCA separated 7 of the 2

2

preoperative BC patients from the healthy controls but the R and Q values of the PCA model were not reported. Surprisingly, only 12 metabolites were identified from the urine odor where 5 were reported as potential diagnostic biomarkers of BC. Although the study demonstrated the potential to screen BC by analyzing urine odor, the accuracy of the prediction model was relatively low. Using GC/TOFMS, our group profiled urine samples collected from 24 BC patients and 51 non-BC controls.

109

The OPLS-DA model demonstrated that BC patients could be

clearly distinguished from the controls with 100% sensitivity as compared to 33% sensitivity achieved by urinary cytology. An external validation set comprising 6 BC patients and 5 non-BC controls was further tested to evaluate the predictive ability of the OPLS-DA model where relatively good sensitivity (92%) and specificity (80%) were achieved. While a larger patient cohort is needed for validation, our work established surprisingly robust proof-of-principle for GC/TOFMS-based metabonomics in staging and grading bladder tumors (supplementary Figure 1). GC×GC/TOFMS is noted for its superior performance in terms of metabolic space coverage, screening 138-140

of marker metabolites and robustness in model prediction. GC×GC/TOFMS-based metabonomics in profiling BC.

59

In addition to GC/TOFMS, our laboratory applied

The urinary metabotypes derived from 38 BC patients

and 61 non-BC controls were used to build the prediction model. Based on an external validation set comprising 7 BC patients and 10 non-BC subjects, the OPLS-DA model demonstrated 100% specificity and 71% sensitivity in differentiating BC patients from non-BC subjects. Additionally, our model revealed 46 metabolites that characterize the disease. Cross-validation via LC/MS further confirmed the perturbation of the tryptophanquinolinic metabolic axis in BC and suggested the potential roles of the kynurenine metabolic pathway in the malignancy and therapy of BC.

LC/MS metabotyping of bladder cancer


Page 15 of 41

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Issaq et al. utilized LC/MS to examine the urinary metabonome of BC patients versus healthy controls.

141

Urine

collected from 48 healthy individuals and 41 BC patients were analyzed using reversed-phase liquid chromatography QTOFMS (RPLC/QTOFMS). OPLS-DA differentiated BC patients from controls with 100% sensitivity and 100% specificity. However, the authors did not further investigate nor report any potential diagnostic biomarkers related to BC. Although RPLC is the most widely used platform in metabonomic studies, highly polar metabolites in urine which is predominantly hydrophilic would not be retained by RPLC.

142

To

circumvent the technical deficiency, Huang et al. carried out a similar study utilizing both hydrophilic interaction chromatography (HILIC) and RPLC.

143

Twenty-seven BC patients and 32 healthy volunteers were enrolled in this

study. The HILIC and RPLC methods detected 703 and 417 compounds respectively. Data derived from the two sets of chromatographic experiments were combined to build the prediction model. An external validation set comprising 3 BC and 3 non-BC controls was tested to evaluate the predictive ability of the model. The PLS-DA model correctly predicted BC patients and controls with 100% sensitivity and specificity. Fourteen metabolic variables were identified in discriminating the BC patients from controls. Carnitine C9:1 and component I (an unidentified ion) were further combined as a biomarker pattern for diagnosis, yielding sensitivity and specificity of 92.6% and 96.9% respectively for all BC patients, and 90.5% and 96.9% for low-grade BC patients. While the possible role of carnitine C9:1 in the pathogenesis of BC was elaborated, the identity and biological function of componentwere not reported. In a subsequent study, urinary metabonomics was performed to profile both BC and 144

kidney cancer (KC) using both HILIC and RPLC coupled to MS.

Urine samples from 19 BC patients, 25 KC

patients, and 24 healthy controls were collected and analyzed. The OPLS-DA model classified BC, KC and healthy controls with 100% sensitivity and specificity. An external validation set comprising 4 BC patients and 5 healthy controls were predicted accurately. With the largest number of patients to date (138 BC and 121 healthy controls), Xing et al. carried out another urinary metabonomics studies on BC using HPLC/QTOFMS.

145

Fifty-two

patients with hematuria were included as controls to check the possible confounding effects of benign hematuria. The BC group could be clearly distinguished from the control groups with sensitivity and specificity of 91.3% and 92.5%, respectively. Their model also showed potential in differentiation between muscle-invasive and nonmuscle invasive BC. Twelve potential biomarkers were identified and many of them were involved in glycolysis



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Page 16 of 41

and betaoxidation. Interestingly, the metabonomic profile was also shown to correlate well with cancer-specific survival time. In order to search for biomarkers of BC recurrence, Juliana et al. performed urinary metabonomics 146

on BC using both LC/MS and capillary electrophoresis (CE)/MS.

Only BC patients were included in the study.

All the patients were classified based on recurrence as well as their risk according to tumor grade and stage, namely: low risk-stable; low risk-recurrent; high risk-stable and high risk-recurrent. A total of 27 metabolite features were identified to be significantly different between each group, which could be useful in the clinic in diagnosis and prognostics of BC. Of further interest is a study by Putluri et al., who applied LC/MS to profile a total of 58 pathologically evaluated BC, benign adjacent and healthy control bladder tissues.

147

The metabolites

were separated using both RP and aqueous normal phase chromatography coupled with QTOFMS employing both positive and negative ESI. Fifty-five metabolites were found altered in BC as compared to the adjacent benign tissues. An external validation set of 50 bladder tissues (13 BC, 13 benign adjacent and 24 controls) was used to examine the BC-associated metabolites using selected reaction monitoring experiments. The experiments yielded 35 metabolites that exhibited significant changes in BC. Importantly, the metabolic signature demonstrated potential in distinguishing non-muscle from muscle-invasive BC. To understand possible altered biochemical processes involved in BC development and progression, the authors further examined the BC148

associated metabolites using oncoming concept maps (OCM).

The enriched concepts include altered utilization

of amino acids and related aromatic counterparts (i.e. tyrosine and tryptophan), and metabolism of fatty acids, lactic acid, intermediates of tricarboxylic acid cycle, electron transport, as well as CYP450-dependent xenobiotic metabolism and methylation.

Perturbed metabolic pathways in bladder cancer An in-depth analysis and consolidation of metabonomics-derived marker metabolites was performed in this review to characterize the BC pathology. The perturbed metabolic pathways derived from the KEGG database were analyzed using MetaboAnalyst

106

and the results are discussed in this section and summarized in supplementary

Table 1 and supplementary Figure 2. Consistently perturbed metabolites in 2 or more studies are listed in Table 3.


Page 17 of 41

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BC marker metabolites that are not documented in KEGG nor found to be associated with any specific biological pathways are summarized in supplementary Table 2.

Glutathione (GSH) metabolism 3

Elevated GSH level was reported in BC tissues and cell lines via both metabonomic and non-metabonomic studies.

149, 150

Cancer cells are known to survive under oxidative stress associated with reactive oxygen species, 151-153

oncogenic transformation and alterations in metabolic activity.

Oxidative stress results in elevation of GSH

and overexpression of antioxidant enzymes such as glutathione peroxidase, glutathione reductase and glutathione-S transferase.

154, 155

With respect to cancer, GSH metabolism plays both protective and pathogenic

functions. While GSH is involved in the detoxification of carcinogens, its elevation in tumors may promote the resistance of cancer cells to chemotherapy via its conjugation with the pharmacologically active drug or its metabolites.

156, 157

Glycolysis Lactate, an important end product of glycolysis, was found to be elevated in BC tissue

135

and urine,

59

indicating

higher glycolysis rate in BC. The upregulation of glycolysis, resulting in increased glucose consumption, is an universal phenomenon of cancers

158

and termed the ‘Warburg effect’.

159,160

Gatenby and Gillies proposed that the

upregulation of glycolysis is an adaptation of pre-malignant lesions to intermittent hypoxia, but requires evolution to the resultant proliferative and invasive phenotypes where resistant to acid-induced cell toxicity is also observed.

158

Purine and pyrimidine metabolism Purine and pyrimidine metabolism have been found to be perturbed in BC, with the upregulation of guanine, hypoxanthine,

147

cytidine monophosphate,

147

147

thymine,

uracil,

147

59, 109

uridine

and pseudouridine.

59

147

Nucleosides,

particularly modified nucleosides (e.g. pseudouridine), are elevated and suggested as potential biomarkers in various cancers.

161-164

Such elevation in the levels of nucleosides has been postulated to be the result of



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increased DNA synthesis associated with enhanced cell cycle activity in cancer. excreted in urine as they cannot be recycled as nucleosides.

168

Page 18 of 41

165-167

Modified nucleosides are

Thus, the levels of modified nucleosides in urine

reflect oxidative DNA damage and RNA turnover in the body.

Tryptophan metabolism 59

The upregulation of tryptophan metabolism in BC was observed with increased levels of anthranilic acid, acetyl-anthranilic acid,

59

kynurenine,

147

3-hydroxykynurenine

147

N-

and malonate.134 Possible roles of excessive 169

tryptophan metabolites in BC had been extensively reviewed by Chung et al.

The proposed underlying

mechanisms include autoxidation, interaction with nitrite or transition metals to form reactive intermediates, binding as ligands to aryl hydrocarbon receptor (AHR) that plays a role in carcinogenesis.

169

Notably, Opitz et al.

demonstrated that tryptophan-2,3-dioxygenase (TDO)-derived kynurenine suppresses antitumor immune responses and promotes tumor-cell survival through AHR, that in turn suggests TDO as a potential cancer therapeutic target.

170

Carnitine species Up or downregulation in carnitine species including carnitine, carnitine C9:1,

143, 144

carnitine C10:1,

dimethylheptanoylcarnitine,

143

143

carnitine C10:3,

isovalerylcarnitine,

145

144

145 147

,

carnitine C8:1,

isobutyryl carnitine,

143, 144, 145

147

carnitine C9:0,

acetylcarnitine,

143

144

2,6-

glutarylcarnitine145 and decanoylcarnitine145 has been

reported in BC. The carnitine system plays a central role in lipid metabolism by facilitating the entry of long-chain fatty acids into mitochondria for their utilization in energy-generating processes and removal of short-chain and medium-chain fatty acids from mitochondria that accumulate as a result of metabolism.

171

It has been postulated 3

that dysregulation of lipid metabolism provides an environment which is beneficial to development of BC tumor.

Additionally, altered fatty acid transportation, fatty acid β-oxidation, or energy metabolism might partially explain why BC patients are prone to lethargy.

3

Citrate


Page 19 of 41

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Significant decrease in citrate concentration was consistently observed in urine and serum of BC patients. 132, 133

59, 109,

One possible explanation is the active uptake of citrate from extra-cellular medium into the tumor cell.

172

Due to its importance in lipid biosynthesis which is crucial for tumor proliferation, citrate is imported into cytoplasm from within mitochondria through citrate transport protein as well as from extracellular medium by different membrane transporters.

173

In cytoplasm, citrate is converted to acetyl-CoA which is subsequently utilized for de

novo fatty acid and sterol synthesis.

173

Therefore, the decrease in citrate levels in urine or serum, which is in

immediate vicinity of tumor, may illustrate its increased utilization in lipogenesis for the rapid proliferation of tumor cells.

3

Taurine Taurine was generally found to be elevated in BC patients as compared to benign and healthy controls. 144,147

132, 135,

Being the most abundant single amino acid in leukocytes, taurine is required for the development and

survival of mammalian cells.

174

It serves as an anti-oxidant,

175

free-radical scavenger

174

176

and osmolyte

against

oxidative stress-induced cell injury and hypoxia-induced cell swelling. Taurine inactivates hypochlorous acid, which is a strong oxidant and cytotoxic agent, by forming stable taurine chloramine (Tau-Cl). In turn, Tau-Cl downregulates immunological responses via production of proinflammatory cytokines, leading to tumor progression.

174

Hippuric acid Downregulation of hippuric acid was generally observed in BC patients.

132, 143, 144, 147

Hippuric acid is an acyl

glycine derived by the conjugation of benzoic acid with glycine in a reaction catalyzed by glycine Naceyltransferase.

177, 178

The excretion of hippuric acid is associated with several possible sources, including

environmentally-derived toxic aromatic compounds (e.g. benzoic acid, toluene), oxidative stressors (e.g. tobacco smoke), diet (particularly phenol containing products) and intestinal microflora.

179-180, 181

Limitations of current metabonomic studies on bladder cancer



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Page 20 of 41

In spite of the progress, several intrinsic limitations associated with BC metabonomics still exist. For instance, the number of patients recruited in each BC metabonomic study was relatively inadequate. This in turn rendered the translation of the findings to clinical application challenging. Although external validation was performed in most of current studies, the validation cohorts were relatively small. To overcome these challenges, it becomes important for clinician scientists to develop multi-center collaborative studies, both nationally and internationally, to increase the clinical sample size.

182

Notably, almost all the studies did not use quantitative methods to validate the

important marker metabolites.

183

This might be due to the fact that marker metabolite quantification is difficult and

time-consuming, especially if the standard is not commercially available. In addition, there is relatively little overlap in terms of marker metabolites among the individual studies. This seemingly peculiar observation in the field of metabonomics might be associated with several reasons. Metabolic profile is highly dynamic and sensitive to many environmental conditions, such as variations in storage conditions.

184

Additionally, the metabolic

signatures obtained by different analytical platforms may not necessarily be the same due to different metabolic space coverage. As such, it is common to derive different results in metabonomics studies when the same 185

samples are analyzed by different analytical platforms.

Furthermore, other internal (such as age, gender,

genetics) and external (such as diet, lifestyle, drug therapy) factors also contribute to the variations in the global metabolic profiles.

185

Therefore, the standardization of the metabonomic experiment (such as subject selection,

sample handling, preparation and analysis) is pertinent to ensure good quality data. Whereas the list of marker metabolites generated by BC metabonomic studies is useful in deciphering the biological processes in BC, further evaluation and validation studies are required to translate the putative biomarkers to clinical practice.

Conclusions and future perspectives Significant progress has been made in BC metabonomic research using various techniques such as NMR, GC/MS and LC/MS. Unlike targeted biomarker studies, unique BC metabotypes that are defined by a complex network of perturbed metabolic pathways can be detected via metabonomics. The metabolic deregulation underscores key pathological processes that are essential for rapid BC cell proliferation and its modification of the paracrine environment such as the immune system. The risk of BC has been associated with environmental


Page 21 of 41

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3

factors such as diets, smoking,

186, 187

color dyes

188

and industrial chemicals.

189

A metagenomic sequencing study

further reported the interaction of bacteria-specific metabolites with disease-relevant protein complexes that are highly expressed in cells and tissues associated with BC

190

191

among genetic, gut microbial and environment factors,

. As metabotypes are the outcomes of the interactions

a metabonome-wide association study (MWAS)

192

can

be explored in the future to elucidate the complex interactions of lifestyle, environment and genes in the etiology and progression of BC. Such a novel study may in turn lead to the discovery of new therapeutic targets for BC and the stratification of patients for future diagnostic and therapeutic clinical trials. With the rapid development of metabonomics, this technology can be potentially explored for the holistic clinical management of BC patients in the future. Database containing the urinary metabotypes derived from thousands of BC patients would facilitate the clinical diagnosis, prognosis and life-long surveillance of the disease. This may in turn improve patients’ compliance, reduce cost of surveillance and improve the long-term survival of BC patients. To further reduce the medical cost, selected urinary marker metabolites may be validated in terms of their clinical robustness and adopted as a test kit for the rapid diagnosis of BC (Figure 2). Recently, Balog et al.

193

reported the application of

rapid evaporation ionization mass spectrometry (REIMS) technique for the intraoperative analysis of tumor specimens in vivo where a tissue identification platform constituting the spectral database and the chemometricbased algorithm was tested during clinical surgical intervention. Their findings confirmed that the REIMS intelligent knife (REIMS-iKnife) differentiated accurately between distinct histological and histopathological tissue types whereby the malignant tissues yielded chemical characteristics specific to their histopathological subtypes. Such a metabonomics-inspired technology is potentially applicable for the surgical intervention of BC where tumor diagnostics, “on-table” decision-making and oncological outcomes may be significantly augmented. Additionally, near-real-time identification of positive resection margins could help improve the functional outcomes of the bladder by mitigating surgical trauma and reducing the removal of healthy tissues.



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Page 22 of 41

Competing interests All the authors do not have any competing interest or industry affiliation that is associated with the clinical studies presented in the current manuscript. The authors (E.C.Y.C., Y.H., P.C.H., R.M., L.R.N.M., E.C. and K.E.) work for the National University of Singapore (NUS) and the National University Health System (NUHS). Both NUS and NUHS are funded by the Singapore Government. K.K.P. graduated from NUS and joined the GlaxoSmithKline (GSK) Neurology Research Unit recently as a Drug Metabolism Pharmacokinetics (DMPK) scientist. K.P.P. declared that there is no competing interest with regards to the current manuscript.

Supporting Information Available : This material is available free of charge via the internet at

http://pubs.acs.org. Supplementary Table 1 Metabolic pathways and respective marker metabolites perturbed in bladder cancer Supplementary Table 2 Bladder cancer marker metabolites that are not documented within KEGG database nor associated with any specific metabolic pathways Supplementary Figure 1 OPLS-DA scores plot showing clustering of bladder cancer (BC) samples according to classification of tumors. Tumors were classified according World Health Organization (WHO)/International Society of Urological Pathology (ISUP) classification criteria. PUNLMP, papillary urothelial neoplasm of low malignant potential; Ta, noninvasive papillary tumors confined to the mucosa; T1, invasive tumors that invade the submucosa (lamina propria). Low grade and high grade are represented as LG and HG respectively. Non bladder cancer sample is represented as non-BC. Supplementary Figure 2 Bladder cancer metabolic pathway analysis and visualization using MetaboAnalyst. The metabolic pathways are arranged according to the scores from enrichment analysis (y axis) and from topology analysis (x axis), and displayed as circles based on their individual P values (intensity of color) and pathway impact values (size of circle).


Page 23 of 41

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Figure Legends

Figure 1 Schematic showing the relationships between genomics, transcriptomics, proteomics and metabonomics. DNA, Deoxyribonucleic acid; RNA, Ribonucleic acid; ncRNA, non-coding RNA.

Figure 2 Overview of workflow in clinical metabonomic study.



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Page 24 of 41

Table 1 Various terminologies used in study of metabonome Terminology

Scope or definition

Metabonomics

quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification.

Metabolomics

Unbiased qualitative, and quantitative study of all metabolites in an organism.

Metabolic profiling

35, 36

56

quantitative study of metabolites related to a metabolic pathway or a specific chemical class.

Metabolic fingerprinting

194

global, rapid and high-throughput sample classification through pattern recognition.

195

Metabolic footprinting/

The strategy of analyzing the phenotypes of cells or tissues by

Exometabolomics

profiling the metabolites that they excrete or fail to take up from their surroundings.

Pharmacometabonomics

196

the prediction of the effects of a drug on the basis of a mathematical model of pre-dose metabolite profiles.

55


Page 25 of 41

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Table 2 Global metabonomic studies that characterize human bladder cancer

Technique 1

132

H NMR

Matrix Urine

Statistical Performance Not reported

Strength •

Offered an insight of cancer cell metabolism including cellular volume regulation and cell proliferation

Limitation • • •

• 1

H NMR

133

Serum

R and Q values of the PLS-DA models were below 0.5

•

1

H NMR

134

Serum

Discriminate 95% of BC from healthy control with 96% sensitivity and 94% specificity; differentiate 98% cases of low-grade from high-grade with 97% sensitivity and 99% specificity Differentiated benign patients from BC with good 2 2 predictivity (R = 0.93 and Q = 0.86) Predicted BC with accuracy of 77.8%

•

Discriminated BC from controls with 100% sensitivity and 100% specificity for the training set, and 92% sensitivity and 80% specificity for the validation set Discriminated BC from controls with 100% sensitivity and 100% specificity for the training set, and with 71% sensitivity and 100% specificity for the validation set

•

1

H HR-MAS 135 NMR

GC/MS

137

GC/TOFMS

GC×GC 59 /TOFMS

Tissue

Urine odor 109

Urine

Urine

2

2

•

•

• •

•

RPLC

Urine

Differentiated

BC

Differentiated low- and highgrade BC, as well as pre- and post-transurethral resection groups of BC patients Demonstrated potential in distinguishing between lowgrade and high-grade BC

•

Included only non-muscle invasive BC patients Limited metabonome coverage No evaluation of the sensitivity and specificity of the prediction model Failed to differentiate stages of BC 2 2 Relatively low R and Q values

Cross-validated potential diagnostic biomarkers using an alternative GC/MS analytical platform

Exhibited potential in the staging and grading of bladder tumors The diagnostic power of the test was not overemphasized by studying highly selected populations Comprehensive coverage of the metabolites Two external validation sets were used to evaluate the predictive ability of the model and further confirm the perturbed metabolic pathway An alternative LC/MS/MS platform was used to validate the perturbed metabolic pathway

from

• • • •

Limited sample size Limited metabonome coverage Low accuracy Included only non-muscle invasive BC patients

•

Included only non-muscle invasive BC patients

•

No

investigation

on

potential



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/QQQTOFMS 141 (ESI+) RPLC, HILIC /QTOFMS 143 (ESI+)

Urine

healthy controls with 100% sensitivity and 100% specificity Predicted BC and healthy controls with 100% sensitivity and 100% specificity

metabolic pathway perturbations related to bladder tumorigenesis •

•

RPLC, HILIC /QTOFMS 144 (ESI+)

Urine

Classified BC, KC and healthy controls with 100% sensitivity and 100% specificity

•

•

RPLC /QTOFMS + 145 (ESI )

Urine

Diagnose BC with a sensitivity and specificity of 91.3% and 92.5%, respectively

• •

•

• RPLC/QTOFMS + (ESI ), 146 CE/TOFMS

Urine

RPLC,ANPLC /QTOF,QQQMS 147 (ESI+, ESI-)

Tissue Urine

The quality parameters for the OPLS-DA models were 2 high for R (close to 1) and 2 satisfactory for Q (ranging from 0.242 to 0.744) Detect BC in urine with an overall accuracy between 67% and 72% in 4 independent validation sets

Page 26 of 41

•

•

•

•

Two complementary chromatographic techniques were adopted to increase metabolic space coverage Early diagnosis ability for lowgrade BC patients using identified biomarker pattern with 90.5% sensitivity and 96.9% specificity Two complementary chromatographic techniques were adopted to increase metabolic space coverage Included KC in the control set and demonstrated ability to discriminate BC and KC Employed the largest sample size to date Patients with benign hematuria were included in the control group to exclude the possible confounding effects of hematuria Demonstrated potential in distinguishing non-muscle from muscle invasive BC Demonstrated potential in prediction of BC survival Revealed potential biomarkers that are specific to the stage/grade and recurrence of BC Two complementary chromatographic techniques were adopted to increase metabolic space coverage Potential diagnositic biomarkers were further confirmed and quantified using: external validation sets; different sample matrix; more accurate tandem MS analysis Demonstrated potential in distinguishing non-muscle from muscle invasive BC


Page 27 of 41

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Table 3 Bladder cancer marker metabolites that are consistently perturbed in 2 or more studies Metabolites

Direction of change & biofluid utilized 133,135, 147

Phenylalanine 106, 134 144 Valine , 133,135, 147 Tyrosine

133,147

Isoleucine/Leucine 135, 147 Creatine 59, 109 2-Butenedioic acid 59, 109, 132, 133 Citrate 135, 147

Alanine 59, 109 Glycerol 33, 73 Melibiose 59, 133-135 Lactate 96, 98, 107,147

Taurine 59, 109 Ribitol 33, 73 Gluconic acid 106, 107 Phenylacetylglutamine 59, 109 Uridine 143, 144 Carnitine C9:1 132, 143, 144, 147 Hippuric acid 134, 135 Glutamine 131,147 Histidine 145 147 Carnitine ,

− in serum; + in tissue & urine + in tissue, serum & urine − in serum; + in tissue & urine − in serum; + in tissue & urine + in tissue & urine − in urine − in urine & serum +/− in tissue; − in urine − in urine + in urine +/− in serum; + in urine & tissue +/− in urine & tissue − in urine − in urine − in urine + in urine − in urine +/− in urine; + in tissue + in tissue & serum + in tissue, serum & urine + in tissue & urine

(+) refers to elevated levels in BC patients compared to benign or healthy controls (−) refers to decreased levels in BC patients compared to benign or healthy controls



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REFERENCES

1. Siegel, R.; Naishadham, D.; Jemal, A., Cancer statistics, 2012. CA Cancer J. Clin. 2012, 62, (1), 10-29. 2. Jemal, A.; Siegel, R.; Ward, E.; Hao, Y.; Xu, J.; Thun, M. J., Cancer statistics, 2009. CA Cancer J. Clin. 2009, 59, (4), 225-49. 3. Mitra, A. P.; Cote, R. J., Molecular pathogenesis and diagnostics of bladder cancer. Annu. Rev. Pathol. 2009, 4, 251-85. 4. Kaufman, D. S.; Shipley, W. U.; Feldman, A. S., Bladder cancer. Lancet 2009, 374, (9685), 239-49. 5. Ruder, A. M.; Fine, L. J.; Sundin, D. S., National estimates of occupational exposure to animal bladder tumorigens. J. Occup. Med. 1990, 32, (9), 797-805. 6. Pelucchi, C.; Bosetti, C.; Negri, E.; Malvezzi, M.; La Vecchia, C., Mechanisms of disease: The epidemiology of bladder cancer. Nat. Clin. Pract. Urol. 2006, 3, (6), 327-40. 7. Castelao, J. E.; Yuan, J. M.; Gago-Dominguez, M.; Yu, M. C.; Ross, R. K., Non-steroidal anti-inflammatory drugs and bladder cancer prevention. Br. J. Cancer 2000, 82, (7), 1364-9. 8. Marcus, P. M.; Vineis, P.; Rothman, N., NAT2 slow acetylation and bladder cancer risk: a meta-analysis of 22 case-control studies conducted in the general population. Pharmacogenetics 2000, 10, (2), 115-22. 9. Mitra, A. P.; Cote, R. J., Molecular screening for bladder cancer: progress and potential. Nat. Rev. Urol. 2010, 7, (1), 11-20. 10. Hurle, R.; Losa, A.; Manzetti, A.; Lembo, A., Upper urinary tract tumors developing after treatment of superficial bladder cancer: 7-year follow-up of 591 consecutive patients. Urology 1999, 53, (6), 1144-8. 11. Wu, X. R., Urothelial tumorigenesis: a tale of divergent pathways. Nat. Rev. Cancer 2005, 5, (9), 713-25. 12. Steinberg, G. D.; Trump, D. L.; Cummings, K. B., Metastatic bladder cancer. Natural history, clinical course, and consideration for treatment. The Urologic clinics of North America 1992, 19, (4), 735-46. 13. Alexandroff, A. B.; Jackson, A. M.; O'Donnell, M. A.; James, K., BCG immunotherapy of bladder cancer: 20 years on. Lancet 1999, 353, (9165), 1689-94. 14. Sharma, S.; Ksheersagar, P.; Sharma, P., Diagnosis and treatment of bladder cancer. Am. Fam. Physician 2009, 80, (7), 717-23. 15. Cookson, M. S.; Herr, H. W.; Zhang, Z. F.; Soloway, S.; Sogani, P. C.; Fair, W. R., The treated natural history of high risk superficial bladder cancer: 15-year outcome. J. Urol. 1997, 158, (1), 62-7. 16. Babjuk, M.; Oosterlinck, W.; Sylvester, R.; Kaasinen, E.; Bohle, A.; Palou-Redorta, J., EAU guidelines on non-muscle-invasive urothelial carcinoma of the bladder. Eur. Urol. 2008, 54, (2), 303-14. 17. Lokeshwar, V. B.; Habuchi, T.; Grossman, H. B.; Murphy, W. M.; Hautmann, S. H.; Hemstreet, G. P., 3rd; Bono, A. V.; Getzenberg, R. H.; Goebell, P.; Schmitz-Drager, B. J.; Schalken, J. A.; Fradet, Y.; Marberger, M.; Messing, E.; Droller, M. J., Bladder tumor markers beyond cytology: International Consensus Panel on bladder tumor markers. Urology 2005, 66, (6 Suppl 1), 35-63. 18. James, A. C.; Gore, J. L., The costs of non-muscle invasive bladder cancer. The Urologic clinics of North America 2013, 40, (2), 261-9. 19. Hansen, R. P.; Vedsted, P.; Sokolowski, I.; Sondergaard, J.; Olesen, F., Time intervals from first symptom to treatment of cancer: a cohort study of 2,212 newly diagnosed cancer patients. BMC Health Serv. Res. 2011, 11, 284. 20. Knowles, M. A., Molecular pathogenesis of bladder cancer. Int. J. Clin. Oncol. 2008, 13, (4), 287-97. 21. Lacy, S.; Lopez-Beltran, A.; MacLennan, G. T.; Foster, S. R.; Montironi, R.; Cheng, L., Molecular pathogenesis of urothelial carcinoma: the clinical utility of emerging new biomarkers and future molecular classification of bladder cancer. Anal. Quant. Cytol. Histol. 2009, 31, (1), 5-16. 22. Vriesema, J. L.; Poucki, M. H.; Kiemeney, L. A.; Witjes, J. A., Patient opinion of urinary tests versus flexible urethrocystoscopy in follow-up examination for superficial bladder cancer: a utility analysis. Urology 2000, 56, (5), 793-7.


Page 29 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


23. Wishart, D. S.; Jewison, T.; Guo, A. C.; Wilson, M.; Knox, C.; Liu, Y.; Djoumbou, Y.; Mandal, R.; Aziat, F.; Dong, E.; Bouatra, S.; Sinelnikov, I.; Arndt, D.; Xia, J.; Liu, P.; Yallou, F.; Bjorndahl, T.; Perez-Pineiro, R.; Eisner, R.; Allen, F.; Neveu, V.; Greiner, R.; Scalbert, A., HMDB 3.0--The Human Metabolome Database in 2013. Nucleic Acids Res. 2013, 41, (Database issue), D801-7. 24. Nicholson, J. K.; Lindon, J. C.; Holmes, E., 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29, (11), 1181-9. 25. Fiehn, O., Metabolomics--the link between genotypes and phenotypes. Plant Mol. Biol. 2002, 48, (1-2), 155-71. 26. ter Kuile, B. H.; Westerhoff, H. V., Transcriptome meets metabolome: hierarchical and metabolic regulation of the glycolytic pathway. FEBS Lett 2001, 500, (3), 169-71. 27. Griffin, J. L.; Shockcor, J. P., Metabolic profiles of cancer cells. Nat Rev Cancer 2004, 4, (7), 551-61. 28. Kaddurah-Daouk, R.; Kristal, B. S.; Weinshilboum, R. M., Metabolomics: a global biochemical approach to drug response and disease. Annu. Rev. Pharmacol. Toxicol. 2008, 48, 653-83. 29. Martin, F. P.; Dumas, M. E.; Wang, Y.; Legido-Quigley, C.; Yap, I. K.; Tang, H.; Zirah, S.; Murphy, G. M.; Cloarec, O.; Lindon, J. C.; Sprenger, N.; Fay, L. B.; Kochhar, S.; van Bladeren, P.; Holmes, E.; Nicholson, J. K., A topdown systems biology view of microbiome-mammalian metabolic interactions in a mouse model. Mol. Syst. Biol. 2007, 3, 112. 30. Schnackenberg, L. K.; Beger, R. D., Metabolomic biomarkers: their role in the critical path. Drug Discovery Today: Technol. 2007, 4, (1), 13-16. 31. Kind, T.; Tolstikov, V.; Fiehn, O.; Weiss, R. H., A comprehensive urinary metabolomic approach for identifying kidney cancerr. Analytical biochemistry 2007, 363, (2), 185-95. 32. Yang, Y.; Li, C.; Nie, X.; Feng, X.; Chen, W.; Yue, Y.; Tang, H.; Deng, F., Metabonomic studies of human hepatocellular carcinoma using high-resolution magic-angle spinning 1H NMR spectroscopy in conjunction with multivariate data analysis. Journal of proteome research 2007, 6, (7), 2605-14. 33. Bathen, T. F.; Jensen, L. R.; Sitter, B.; Fjösne, H. E.; Halgunset, J.; Axelson, D. E.; Gribbestad, I. S.; Lundgren, S., MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status. Breast cancer research and treatment 2007, 104, (2), 181-9. 34. Serkova, N. J.; Gamito, E. J.; Jones, R. H.; O'Donnell, C.; Brown, J. L.; Green, S.; Sullivan, H.; Hedlund, T.; Crawford, E. D., The metabolites citrate, myo-inositol, and spermine are potential age-independent markers of prostate cancer in human expressed prostatic secretions. The Prostate 2008, 68, (6), 620-8. 35. Denkert, C.; Budczies, J.; Kind, T.; Weichert, W.; Tablack, P.; Sehouli, J.; Niesporek, S.; Könsgen, D.; Dietel, M.; Fiehn, O., Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer research 2006, 66, (22), 10795-804. 36. Michell, A.; Mosedale, D.; Grainger, D.; Barker, R., Metabolomic analysis of urine and serum in Parkinson's disease. METABOLOMICS 2008, 4, 191-201. 37. Holmes, E.; Tsang, T. M.; Huang, J. T. J.; Leweke, F. M.; Koethe, D.; Gerth, C. W.; Nolden, B. M.; Gross, S.; Schreiber, D.; Nicholson, J. K.; Bahn, S., Metabolic profiling of CSF: evidence that early intervention may impact on disease progression and outcome in schizophrenia. PLoS medicine 2006, 3, (8), e327. 38. Wood, P. L., Mass Spectrometry Strategies for Clinical Metabolomics and Lipidomics in Psychiatry, Neurology, and Neuro-Oncology. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 2013. 39. Greenberg, N.; Grassano, A.; Thambisetty, M.; Lovestone, S.; Legido-Quigley, C., A proposed metabolic strategy for monitoring disease progression in Alzheimer's disease. Electrophoresis 2009, 30, (7), 1235-9. 40. Ahmed, S. S.; Santosh, W.; Kumar, S.; Christlet, H. T. T., Metabolic profiling of Parkinson's disease: evidence of biomarker from gene expression analysis and rapid neural network detection. Journal of biomedical science 2009, 16, 63.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 41

41. Heather, L. C.; Wang, X.; West, J. A.; Griffin, J. L., A practical guide to metabolomic profiling as a discovery tool for human heart disease. Journal of molecular and cellular cardiology 2013, 55, 2-11. 42. Sabatine, M. S.; Liu, E.; Morrow, D. A.; Heller, E.; McCarroll, R.; Wiegand, R.; Berriz, G. F.; Roth, F. P.; Gerszten, R. E., Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation 2005, 112, (25), 3868-75. 43. Weiss, R. H.; Kim, K., Metabolomics in the study of kidney diseases. Nature reviews. Nephrology 2012, 8, (1), 22-33. 44. Zeng, M.; Che, Z.; Liang, Y.; S., M., GC-Plasma Metabolic Profiling of Type 2 Diabetes Mellitus. Dis Markers 2009, 69, 941-948. 45. Yang, J.; Xu, G.; Hong, Q.; Liebich, H. M.; Lutz, K.; Schmülling, R. M.; Wahl, H. G., Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles. Journal of chromatography. B, Analytical technologies in the biomedical and life sciences 2004, 813, (1-2), 53-8. 46. Yi, L.-Z.; He, J.; Liang, Y.-Z.; Yuan, D.-L.; Chau, F.-T., Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS-LDA. FEBS letters 2006, 580, (30), 6837-45. 47. Yuan, K.; Kong, H.; Guan, Y.; Yang, J.; Xu, G., A GC-based metabonomics investigation of type 2 diabetes by organic acids metabolic profile. Journal of chromatography. B, Analytical technologies in the biomedical and life sciences 2007, 850, (1-2), 236-40. 48. Tenori, L.; Oakman, C.; Claudino, W. M.; Bernini, P.; Cappadona, S.; Nepi, S.; Biganzoli, L.; Arbushites, M. C.; Luchinat, C.; Bertini, I.; Di Leo, A., Exploration of serum metabolomic profiles and outcomes in women with metastatic breast cancer: a pilot study. Molecular oncology 2012, 6, (4), 437-44. 49. Shah, A. A.; Craig, D. M.; Sebek, J. K.; Haynes, C.; Stevens, R. C.; Muehlbauer, M. J.; Granger, C. B.; Hauser, E. R.; Newby, L. K.; Newgard, C. B.; Kraus, W. E.; Hughes, G. C.; Shah, S. H., Metabolic profiles predict adverse events after coronary artery bypass grafting. The Journal of thoracic and cardiovascular surgery 2012, 143, (4), 873-8. 50. Wang, T. J.; Larson, M. G.; Vasan, R. S.; Cheng, S.; Rhee, E. P.; McCabe, E.; Lewis, G. D.; Fox, C. S.; Jacques, P. F.; Fernandez, C.; O'Donnell, C. J.; Carr, S. A.; Mootha, V. K.; Florez, J. C.; Souza, A.; Melander, O.; Clish, C. B.; Gerszten, R. E., Metabolite profiles and the risk of developing diabetes. Nat Med 2011, 17, (4), 448453. 51. Roberts, Lee D.; Gerszten, Robert E., Toward New Biomarkers of Cardiometabolic Diseases. Cell metabolism 2013, 18, (1), 43-50. 52. Trock, B. J., Application of metabolomics to prostate cancer. Urologic oncology 2011, 29, (5), 572-81. 53. Sreekumar, A.; Poisson, L. M.; Rajendiran, T. M.; Khan, A. P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R. J.; Li, Y.; Nyati, M. K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G. S.; Ghosh, D.; Pennathur, S.; Alexander, D. C.; Berger, A.; Shuster, J. R.; Wei, J. T.; Varambally, S.; Beecher, C.; Chinnaiyan, A. M., Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 2009, 457, (7231), 910-4. 54. Giskeodegard, G. F.; Bertilsson, H.; Selnaes, K. M.; Wright, A. J.; Bathen, T. F.; Viset, T.; Halgunset, J.; Angelsen, A.; Gribbestad, I. S.; Tessem, M. B., Spermine and citrate as metabolic biomarkers for assessing prostate cancer aggressiveness. PloS one 2013, 8, (4), e62375. 55. Clayton, T. A.; Lindon, J. C.; Cloarec, O.; Antti, H.; Charuel, C.; Hanton, G.; Provost, J. P.; Le Net, J. L.; Baker, D.; Walley, R. J.; Everett, J. R.; Nicholson, J. K., Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 2006, 440, (7087), 1073-7. 56. Nicholson, J. K.; Holmes, E.; Kinross, J. M.; Darzi, A. W.; Takats, Z.; Lindon, J. C., Metabolic phenotyping in clinical and surgical environments. Nature 2012, 491, (7424), 384-92. 57. Assfalg, M.; Bertini, I.; Colangiuli, D.; Luchinat, C.; Schafer, H.; Schutz, B.; Spraul, M., Evidence of different metabolic phenotypes in humans. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, (5), 1420-4. 58. Trygg, J.; Holmes, E.; Lundstedt, T., Chemometrics in metabonomics. J. Proteome Res. 2007, 6, (2), 46979. 30 ACS Paragon Plus Environment

Page 31 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


59. Pasikanti, K. K.; Esuvaranathan, K.; Hong, Y.; Ho, P. C.; Mahendran, R.; Raman Nee Mani, L.; Chiong, E.; Chan, E. C. Y., Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass spectrometry. J. Proteome Res. 2013, 12, (9), 3865-3873. 60. Lotan, Y.; Shariat, S. F.; Schmitz-Drager, B. J.; Sanchez-Carbayo, M.; Jankevicius, F.; Racioppi, M.; Minner, S. J.; Stohr, B.; Bassi, P. F.; Grossman, H. B., Considerations on implementing diagnostic markers into clinical decision making in bladder cancer. Urologic oncology 2010, 28, (4), 441-8. 61. Lauridsen, M.; Hansen, S. H.; Jaroszewski, J. W.; Cornett, C., Human urine as test material in 1H NMRbased metabonomics: recommendations for sample preparation and storage. Anal. Chem. 2007, 79, (3), 1181-6. 62. Garcia-Villalba, R.; Carrasco-Pancorbo, A.; Nevedomskaya, E.; Mayboroda, O. A.; Deelder, A. M.; SeguraCarretero, A.; Fernandez-Gutierrez, A., Exploratory analysis of human urine by LC-ESI-TOF MS after high intake of olive oil: understanding the metabolism of polyphenols. Anal. Bioanal. Chem. 2010, 398, (1), 463-75. 63. Fernández-Peralbo, M. A.; Luque de Castro, M. D., Preparation of urine samples prior to targeted or untargeted metabolomics mass-spectrometry analysis. Trends Anal. Chem. 2012, 41, (0), 75-85. 64. Wang, X.; Lv, H.; Zhang, G.; Sun, W.; Zhou, D.; Jiao, G.; Yu, Y., Development and validation of a ultra performance LC-ESI/MS method for analysis of metabolic phenotypes of healthy men in day and night urine samples. J. Sep. Sci. 2008, 31, (16-17), 2994-3001. 65. Pasikanti, K. K.; Ho, P. C.; Chan, E. C., Gas chromatography/mass spectrometry in metabolic profiling of biological fluids. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2008, 871, (2), 202-11. 66. Walsh, M. C.; Brennan, L.; Malthouse, J. P.; Roche, H. M.; Gibney, M. J., Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans. Am. J. Clin. Nutr. 2006, 84, (3), 531-9. 67. Gika, H. G.; Theodoridis, G. A.; Wilson, I. D., Liquid chromatography and ultra-performance liquid chromatography-mass spectrometry fingerprinting of human urine: sample stability under different handling and storage conditions for metabonomics studies. Journal of chromatography. A 2008, 1189, (1-2), 314-22. 68. Dunn, W. B.; Broadhurst, D.; Ellis, D. I.; Brown, M.; Halsall, A.; O'Hagan, S.; Spasic, I.; Tseng, A.; Kell, D. B., A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. Int. J. Epidemiol. 2008, 37 Suppl 1, i23-30. 69. Pasikanti, K. K.; Ho, P. C.; Chan, E. C., Development and validation of a gas chromatography/mass spectrometry metabonomic platform for the global profiling of urinary metabolites. Rapid Commun. Mass Spectrom. 2008, 22, (19), 2984-92. 70. Dunn, W. B.; Broadhurst, D.; Begley, P.; Zelena, E.; Francis-McIntyre, S.; Anderson, N.; Brown, M.; Knowles, J. D.; Halsall, A.; Haselden, J. N., Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature protocols 2011, 6, (7), 1060-1083. 71. Xiayan, L.; Legido-Quigley, C., Advances in separation science applied to metabonomics. Electrophoresis 2008, 29, (18), 3724-36. 72. Lenz, E. M.; Wilson, I. D., Analytical strategies in metabonomics. J. Proteome Res. 2007, 6, (2), 443-58. 73. Beckonert, O.; Keun, H. C.; Ebbels, T. M.; Bundy, J.; Holmes, E.; Lindon, J. C.; Nicholson, J. K., Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2007, 2, (11), 2692-703. 74. Tkac, I.; Henry, P. G.; Zacharoff, L.; Wedel, M.; Gong, W.; Deelchand, D. K.; Li, T.; Dubinsky, J. M., Homeostatic adaptations in brain energy metabolism in mouse models of Huntington disease. J. Cereb. Blood Flow Metab. 2012, 32, (11), 1977-88. 75. Atherton, H. J.; Bailey, N. J.; Zhang, W.; Taylor, J.; Major, H.; Shockcor, J.; Clarke, K.; Griffin, J. L., A combined 1H-NMR spectroscopy- and mass spectrometry-based metabolomic study of the PPAR-alpha null mutant mouse defines profound systemic changes in metabolism linked to the metabolic syndrome. Physiol. Genomics 2006, 27, (2), 178-86.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 41

76. Allwood, J. W.; Erban, A.; de Koning, S.; Dunn, W. B.; Luedemann, A.; Lommen, A.; Kay, L.; Loscher, R.; Kopka, J.; Goodacre, R., Inter-laboratory reproducibility of fast gas chromatography-electron impact-time of flight mass spectrometry (GC-EI-TOF/MS) based plant metabolomics. Metabolomics 2009, 5, (4), 479-496. 77. Koek, M. M.; Muilwijk, B.; van der Werf, M. J.; Hankemeier, T., Microbial metabolomics with gas chromatography/mass spectrometry. Anal. Chem. 2006, 78, (4), 1272-81. 78. Want, E. J.; Nordstrom, A.; Morita, H.; Siuzdak, G., From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics. J. Proteome Res. 2007, 6, (2), 459-68. 79. Bergeron, A.; Garofolo, F., Importance of matrix effects in LC-MS/MS bioanalysis. Bioanalysis 2013, 5, (19), 2331-2. 80. Halket, J. M.; Waterman, D.; Przyborowska, A. M.; Patel, R. K.; Fraser, P. D.; Bramley, P. M., Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS. J. Exp. Bot. 2005, 56, (410), 219-43. 81. Metz, T. O.; Zhang, Q.; Page, J. S.; Shen, Y.; Callister, S. J.; Jacobs, J. M.; Smith, R. D., The future of liquid chromatography-mass spectrometry (LC-MS) in metabolic profiling and metabolomic studies for biomarker discovery. Biomarkers Med. 2007, 1, (1), 159-185. 82. Dunn, W. B.; Ellis, D. I., Metabolomics: current analytical platforms and methodologies. Trends Anal. Chem. 2005, 24. 83. Chambers, E.; Wagrowski-Diehl, D. M.; Lu, Z.; Mazzeo, J. R., Systematic and comprehensive strategy for reducing matrix effects in LC/MS/MS analyses. Journal of chromatography. B, Analytical technologies in the biomedical and life sciences 2007, 852, (1-2), 22-34. 84. Wishart, D. S.; Lewis, M. J.; Morrissey, J. A.; Flegel, M. D.; Jeroncic, K.; Xiong, Y.; Cheng, D.; Eisner, R.; Gautam, B.; Tzur, D.; Sawhney, S.; Bamforth, F.; Greiner, R.; Li, L., The human cerebrospinal fluid metabolome. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2008, 871, (2), 164-73. 85. Dunn, W. B.; Bailey, N. J. C.; Johnson, H. E., Measuring the metabolome: current analytical technologies. The Analyst 2005, 130, (5), 606-25. 86. Chan, E. C.; Pasikanti, K. K.; Nicholson, J. K., Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry. Nat. Protoc. 2011, 6, (10), 1483-99. 87. Chan, E. C.; Koh, P. K.; Mal, M.; Cheah, P. Y.; Eu, K. W.; Backshall, A.; Cavill, R.; Nicholson, J. K.; Keun, H. C., Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res. 2009, 8, (1), 352-61. 88. Emwas, A.-H. M.; Salek, R. M.; Griffin, J. L.; Merzaban, J., NMR-based metabolomics in human disease diagnosis: applications, limitations, and recommendations. Metabolomics 2013, 9, (5), 1048-1072. 89. Beckonert, O.; Keun, H. C.; Ebbels, T. M.; Bundy, J.; Holmes, E.; Lindon, J. C.; Nicholson, J. K., Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nature protocols 2007, 2, (11), 2692-703. 90. Want, E. J.; Wilson, I. D.; Gika, H.; Theodoridis, G.; Plumb, R. S.; Shockcor, J.; Holmes, E.; Nicholson, J. K., Global metabolic profiling procedures for urine using UPLC–MS. Nature protocols 2010, 5, (6), 1005-1018. 91. Theodoridis, G.; Gika, H. G.; Wilson, I. D., LC-MS-based methodology for global metabolite profiling in metabonomics/metabolomics. TrAC Trends in Analytical Chemistry 2008, 27, (3), 251-260. 92. Vuckovic, D., Current trends and challenges in sample preparation for global metabolomics using liquid chromatography–mass spectrometry. Analytical and bioanalytical chemistry 2012, 403, (6), 1523-1548. 93. Kim, H. K.; Choi, Y. H.; Verpoorte, R., NMR-based metabolomic analysis of plants. Nat. Protoc. 2010, 5, (3), 536-49. 94. Zhou, B.; Xiao, J. F.; Tuli, L.; Ressom, H. W., LC-MS-based metabolomics. Mol. Biosyst. 2012, 8, (2), 47081.


Page 33 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


95. Jonsson, P.; Johansson, A. I.; Gullberg, J.; Trygg, J.; A, J.; Grung, B.; Marklund, S.; Sjostrom, M.; Antti, H.; Moritz, T., High-throughput data analysis for detecting and identifying differences between samples in GC/MSbased metabolomic analyses. Anal. Chem. 2005, 77, (17), 5635-42. 96. Goodacre, R.; Broadhurst, D.; Smilde, A. K.; Kristal, B. S.; Baker, J. D.; Beger, R.; Bessant, C.; Connor, S.; Calmani, G.; Craig, A.; Ebbels, T.; Kell, D. B.; Manetti, C.; Newton, J.; Paternostro, G.; Somorjai, R.; Sjostrom, M.; Trygg, J.; Wulfert, F., Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 2007, 3, (3), 231-241. 97. Katajamaa, M.; Miettinen, J.; Oresic, M., MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 2006, 22, (5), 634-6. 98. Smith, C. A.; Want, E. J.; O'Maille, G.; Abagyan, R.; Siuzdak, G., XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 2006, 78, (3), 779-87. 99. Lommen, A., MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal. Chem. 2009, 81, (8), 3079-86. 100. Baran, R.; Kochi, H.; Saito, N.; Suematsu, M.; Soga, T.; Nishioka, T.; Robert, M.; Tomita, M., MathDAMP: a package for differential analysis of metabolite profiles. BMC Bioinf. 2006, 7, 530. 101. Duran, A. L.; Yang, J.; Wang, L.; Sumner, L. W., Metabolomics spectral formatting, alignment and conversion tools (MSFACTs). Bioinformatics 2003, 19, (17), 2283-93. 102. Eriksson, L.; Umetrics AB., Multi- and megavariate data analysis. 2nd rev. and enl. ed.; Umetrics AB;: Umeå, Sweden;, 2006. 103. Heather, L. C.; Wang, X.; West, J. A.; Griffin, J. L., A practical guide to metabolomic profiling as a discovery tool for human heart disease. J. Mol. Cell Cardiol. 2013, 55, 2-11. 104. Wiklund, S.; Johansson, E.; Sjostrom, L.; Mellerowicz, E. J.; Edlund, U.; Shockcor, J. P.; Gottfries, J.; Moritz, T.; Trygg, J., Visualization of GC/TOF-MS-Based Metabolomics Data for Identification of Biochemically Interesting Compounds Using OPLS Class Models. Anal. Chem. 2008, 80, (1), 115-122. 105. Madsen, R.; Lundstedt, T.; Trygg, J., Chemometrics in metabolomics--a review in human disease diagnosis. Anal. Chim. Acta 2010, 659, (1-2), 23-33. 106. Xia, J.; Mandal, R.; Sinelnikov, I. V.; Broadhurst, D.; Wishart, D. S., MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 2012, 40, (W1), W127-W133. 107. Mahadevan, S.; Shah, S. L.; Marrie, T. J.; Slupsky, C. M., Analysis of metabolomic data using support vector machines. Anal. Chem. 2008, 80, (19), 7562-70. 108. Broadhurst, D.; Kell, D., Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2006, 2, (4), 171-196. 109. Pasikanti, K. K.; Esuvaranathan, K.; Ho, P. C.; Mahendran, R.; Kamaraj, R.; Wu, Q. H.; Chiong, E.; Chan, E. C. Y., Noninvasive urinary metabonomic diagnosis of human bladder cancer. J. Proteome Res. 2010, 9, (6), 29882995. 110. Kopka, J.; Schauer, N.; Krueger, S.; Birkemeyer, C.; Usadel, B.; Bergmuller, E.; Dormann, P.; Weckwerth, W.; Gibon, Y.; Stitt, M.; Willmitzer, L.; Fernie, A. R.; Steinhauser, D., [email protected]: the Golm Metabolome Database. Bioinformatics 2005, 21, (8), 1635-8. 111. Kind, T.; Wohlgemuth, G.; Lee do, Y.; Lu, Y.; Palazoglu, M.; Shahbaz, S.; Fiehn, O., FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 2009, 81, (24), 10038-48. 112. Ausloos, P.; Clifton, C. L.; Lias, S. G.; Mikaya, A. I.; Stein, S. E.; Tchekhovskoi, D. V.; Sparkman, O. D.; Zaikin, V.; Zhu, D., The critical evaluation of a comprehensive mass spectral library. J. Am. Soc. Mass Spectrom. 1999, 10, (4), 287-99. 113. Tautenhahn, R.; Cho, K.; Uritboonthai, W.; Zhu, Z.; Patti, G. J.; Siuzdak, G., An accelerated workflow for untargeted metabolomics using the METLIN database. Nat. Biotechnol. 2012, 30, (9), 826-8.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 41

114. Horai, H.; Arita, M.; Kanaya, S.; Nihei, Y.; Ikeda, T.; Suwa, K.; Ojima, Y.; Tanaka, K.; Tanaka, S.; Aoshima, K.; Oda, Y.; Kakazu, Y.; Kusano, M.; Tohge, T.; Matsuda, F.; Sawada, Y.; Hirai, M. Y.; Nakanishi, H.; Ikeda, K.; Akimoto, N.; Maoka, T.; Takahashi, H.; Ara, T.; Sakurai, N.; Suzuki, H.; Shibata, D.; Neumann, S.; Iida, T.; Tanaka, K.; Funatsu, K.; Matsuura, F.; Soga, T.; Taguchi, R.; Saito, K.; Nishioka, T., MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass. Spectrom. 2010, 45, (7), 703-14. 115. Sud, M.; Fahy, E.; Cotter, D.; Brown, A.; Dennis, E. A.; Glass, C. K.; Merrill, A. H., Jr.; Murphy, R. C.; Raetz, C. R.; Russell, D. W.; Subramaniam, S., LMSD: LIPID MAPS structure database. Nucleic Acids Res. 2007, 35, (Database issue), D527-32. 116. Cui, Q.; Lewis, I. A.; Hegeman, A. D.; Anderson, M. E.; Li, J.; Schulte, C. F.; Westler, W. M.; Eghbalnia, H. R.; Sussman, M. R.; Markley, J. L., Metabolite identification via the Madison Metabolomics Consortium Database. Nat. Biotechnol. 2008, 26, (2), 162-4. 117. Wishart, D. S.; Tzur, D.; Knox, C.; Eisner, R.; Guo, A. C.; Young, N.; Cheng, D.; Jewell, K.; Arndt, D.; Sawhney, S.; Fung, C.; Nikolai, L.; Lewis, M.; Coutouly, M. A.; Forsythe, I.; Tang, P.; Shrivastava, S.; Jeroncic, K.; Stothard, P.; Amegbey, G.; Block, D.; Hau, D. D.; Wagner, J.; Miniaci, J.; Clements, M.; Gebremedhin, M.; Guo, N.; Zhang, Y.; Duggan, G. E.; Macinnis, G. D.; Weljie, A. M.; Dowlatabadi, R.; Bamforth, F.; Clive, D.; Greiner, R.; Li, L.; Marrie, T.; Sykes, B. D.; Vogel, H. J.; Querengesser, L., HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007, 35, (Database issue), D521-6. 118. Chagoyen, M.; Pazos, F., Tools for the functional interpretation of metabolomic experiments. Brief. Bioinform. 2012. 119. Mendes, P., Emerging bioinformatics for the metabolome. Brief Bioinform. 2002, 3, (2), 134-45. 120. Frolkis, A.; Knox, C.; Lim, E.; Jewison, T.; Law, V.; Hau, D. D.; Liu, P.; Gautam, B.; Ly, S.; Guo, A. C.; Xia, J.; Liang, Y.; Shrivastava, S.; Wishart, D. S., SMPDB: The Small Molecule Pathway Database. Nucleic Acids Res. 2010, 38, (Database issue), D480-7. 121. Caspi, R.; Altman, T.; Dale, J. M.; Dreher, K.; Fulcher, C. A.; Gilham, F.; Kaipa, P.; Karthikeyan, A. S.; Kothari, A.; Krummenacker, M.; Latendresse, M.; Mueller, L. A.; Paley, S.; Popescu, L.; Pujar, A.; Shearer, A. G.; Zhang, P.; Karp, P. D., The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic acids research 2010, 38, (Database issue), D473-9. 122. Croft, D.; O'Kelly, G.; Wu, G.; Haw, R.; Gillespie, M.; Matthews, L.; Caudy, M.; Garapati, P.; Gopinath, G.; Jassal, B.; Jupe, S.; Kalatskaya, I.; Mahajan, S.; May, B.; Ndegwa, N.; Schmidt, E.; Shamovsky, V.; Yung, C.; Birney, E.; Hermjakob, H.; D'Eustachio, P.; Stein, L., Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011, 39, (Database issue), D691-7. 123. Junker, B. H.; Klukas, C.; Schreiber, F., VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinf. 2006, 7, 109. 124. Letunic, I.; Yamada, T.; Kanehisa, M.; Bork, P., iPath: interactive exploration of biochemical pathways and networks. Trends Biochem. Sci. 2008, 33, (3), 101-3. 125. Sreenivasaiah, P. K.; Rani, S.; Cayetano, J.; Arul, N.; Kim do, H., IPAVS: Integrated Pathway Resources, Analysis and Visualization System. Nucleic Acids Res. 2012, 40, (Database issue), D803-8. 126. Xia, J.; Wishart, D. S., MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 2010, 26, (18), 2342-4. 127. Karnovsky, A.; Weymouth, T.; Hull, T.; Tarcea, V. G.; Scardoni, G.; Laudanna, C.; Sartor, M. A.; Stringer, K. A.; Jagadish, H. V.; Burant, C.; Athey, B.; Omenn, G. S., Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 2012, 28, (3), 373-80. 128. Xia, J.; Wishart, D. S., MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 2010, 38, (Web Server issue), W71-7. 129. Kankainen, M.; Gopalacharyulu, P.; Holm, L.; Oresic, M., MPEA--metabolite pathway enrichment analysis. Bioinformatics 2011, 27, (13), 1878-9. 130. Kamburov, A.; Cavill, R.; Ebbels, T. M.; Herwig, R.; Keun, H. C., Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics 2011, 27, (20), 2917-8. 34 ACS Paragon Plus Environment

Page 35 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


131. Chagoyen, M.; Pazos, F., MBRole: enrichment analysis of metabolomic data. Bioinformatics 2011, 27, (5), 730-1. 132. Srivastava, S.; Roy, R.; Singh, S.; Kumar, P.; Dalela, D.; Sankhwar, S. N.; Goel, A.; Sonkar, A. A., Taurine–a possible fingerprint biomarker in non-muscle invasive bladder cancer: A pilot study by^{1} H NMR spectroscopy. Cancer Biomarkers 2010, 6, (1), 11-20. 133. Cao, M.; Zhao, L.; Chen, H.; Xue, W.; Lin, D., NMR-based metabolomic analysis of human bladder cancer. Anal. Sci. 2012, 28, (5), 451-456. 134. Bansal, N.; Gupta, A.; Mitash, N.; Shakya, P. S.; Mandhani, A.; Mahdi, A. A.; Sankhwar, S. N.; Mandal, S. K., Low-and High-Grade Bladder Cancer Determination via Human Serum-Based Metabolomics Approach. Journal of proteome research 2013, 12, (12), 5839-5850. 135. Tripathi, P.; Somashekar, B. S.; Ponnusamy, M.; Gursky, A.; Dailey, S.; Kunju, P.; Lee, C. T.; Chinnaiyan, A. M.; Rajendiran, T. M.; Ramamoorthy, A., HR-MAS NMR Tissue Metabolomic Signatures Cross-Validated by Mass Spectrometry Distinguish Bladder Cancer from Benign Disease. J. Proteome Res. 2013. 136. Willis, C. M.; Church, S. M.; Guest, C. M.; Cook, W. A.; McCarthy, N.; Bransbury, A. J.; Church, M. R.; Church, J. C., Olfactory detection of human bladder cancer by dogs: proof of principle study. BMJ 2004, 329, (7468), 712. 137. Jobu, K.; Sun, C.; Yoshioka, S.; Yokota, J.; Onogawa, M.; Kawada, C.; Inoue, K.; Shuin, T.; Sendo, T.; Miyamura, M., Metabolomics study on the biochemical profiles of odor elements in urine of human with bladder cancer. Biol. Pharm. Bull 2012, 35, (4), 639-642. 138. Mondello, L.; Tranchida, P. Q.; Dugo, P.; Dugo, G., Comprehensive two‐dimensional gas chromatography‐mass spectrometry: A review. Mass Spectrom. Rev. 2008, 27, (2), 101-124. 139. Li, X.; Xu, Z.; Lu, X.; Yang, X.; Yin, P.; Kong, H.; Yu, Y.; Xu, G., Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus. Anal. Chim. Acta 2009, 633, (2), 257-262. 140. Pierce, K. M.; Hoggard, J. C.; Mohler, R. E.; Synovec, R. E., Recent advancements in comprehensive twodimensional separations with chemometrics. Journal of chromatography. A 2008, 1184, (1), 341-352. 141. Issaq, H. J.; Nativ, O.; Waybright, T.; Luke, B.; Veenstra, T. D.; Issaq, E. J.; Kravstov, A.; Mullerad, M., Detection of bladder cancer in human urine by metabolomic profiling using high performance liquid chromatography/mass spectrometry. J. Urol. 2008, 179, (6), 2422-2426. 142. Cubbon, S.; Bradbury, T.; Wilson, J.; Thomas-Oates, J., Hydrophilic interaction chromatography for mass spectrometric metabonomic studies of urine. Anal. Chem. 2007, 79, (23), 8911-8918. 143. Huang, Z.; Lin, L.; Gao, Y.; Chen, Y.; Yan, X.; Xing, J.; Hang, W., Bladder cancer determination via two urinary metabolites: a biomarker pattern approach. Mol. Cell. Proteomics 2011, 10, (10). 144. Huang, Z.; Chen, Y.; Hang, W.; Gao, Y.; Lin, L.; Li, D. Y.; Xing, J.; Yan, X., Holistic metabonomic profiling of urine affords potential early diagnosis for bladder and kidney cancers. Metabolomics 2013, 9, (1), 119-129. 145. Jin, X.; Yun, S. J.; Jeong, P.; Kim, I. Y.; Kim, W.-J.; Park, S., Diagnosis of bladder cancer and prediction of survival by urinary metabolomics. Oncotarget 2014, 5, (6), 1635. 146. Alberice, J. V.; Amaral, A. F.; Armitage, E. G.; Lorente, J. A.; Algaba, F.; Carrilho, E.; Márquez, M.; García, A.; Malats, N.; Barbas, C., Searching for urine biomarkers of bladder cancer recurrence using a liquid chromatography–mass spectrometry and capillary electrophoresis–mass spectrometry metabolomics approach. Journal of Chromatography A 2013, 1318, 163-170. 147. Putluri, N.; Shojaie, A.; Vasu, V. T.; Vareed, S. K.; Nalluri, S.; Putluri, V.; Thangjam, G. S.; Panzitt, K.; Tallman, C. T.; Butler, C., Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression. Cancer Res. 2011, 71, (24), 7376-7386. 148. Rhodes, D. R.; Kalyana-Sundaram, S.; Tomlins, S. A.; Mahavisno, V.; Kasper, N.; Varambally, R.; Barrette, T. R.; Ghosh, D.; Varambally, S.; Chinnaiyan, A. M., Molecular concepts analysis links tumors, pathways, mechanisms, and drugs. Neoplasia 2007, 9, (5), 443.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 41

149. Pendyala, L.; Velagapudi, S.; Toth, K.; Zdanowicz, J.; Glaves, D.; Slocum, H.; Perez, R.; Huben, R.; Creaven, P. J.; Raghavan, D., Translational studies of glutathione in bladder cancer cell lines and human specimens. Clin. Cancer Res. 1997, 3, (5), 793-798. 150. Giralt, M.; Lafuente, A.; Pujol, F.; Mallol, J., Enhanced glutathione S-transferase activity and glutathione content in human bladder cancer. Followup study: influence of smoking. J. Urol. 1993, 149, (6), 1452-4. 151. Toyokuni, S.; Okamoto, K.; Yodoi, J.; Hiai, H., Persistent oxidative stress in cancer. FEBS letters 1995, 358, (1), 1-3. 152. Hileman, E. O.; Liu, J.; Albitar, M.; Keating, M. J.; Huang, P., Intrinsic oxidative stress in cancer cells: a biochemical basis for therapeutic selectivity. Cancer Chemother. Pharmacol. 2004, 53, (3), 209-219. 153. Behrend, L.; Henderson, G.; Zwacka, R., Reactive oxygen species in oncogenic transformation. Biochem. Soc. Trans. 2003, 31, (Pt 6), 1441. 154. Meister, A.; Anderson, M. E., Glutathione. Annu. Rev. Biochem. 1983, 52, (1), 711-760. 155. Pelicano, H.; Carney, D.; Huang, P., ROS stress in cancer cells and therapeutic implications. Drug Resist. Updates 2004, 7, (2), 97-110. 156. Zhang, K.; Mack, P.; Wong, K., Glutathione-related mechanisms in cellular resistance to anticancer drugs. Int. J. Oncol. 1998, 12, (4), 871-953. 157. Shen, H.; Kauvar, L.; Tew, K. D., Importance of glutathione and associated enzymes in drug response. Oncol. Res. 1996, 9, (6-7), 295-302. 158. Gatenby, R. A.; Gillies, R. J., Why do cancers have high aerobic glycolysis? Nat. Rev. Cancer 2004, 4, (11), 891-899. 159. Warburg, O. H.; Biologie, K.-W.-I. f., Über den Stoffwechsel der Tumoren. J. Springer: 1926. 160. Semenza, G. L.; Artemov, D.; Bedi, A.; Bhujwalla, Z.; Chiles, K.; Feldser, D.; Laughner, E.; Ravi, R.; Simons, J.; Taghavi, P. In ‘The metabolism of tumours’: 70 years later, The Tumour Microenvironment: Causes and Consequences of Hypoxia and Acidity: Novartis Foundation Symposium 240, 2001; Wiley Online Library: 2001; pp 251-264. 161. Zheng, Y.-F.; Kong, H.-W.; Xiong, J.-H.; Lv, S.; Xu, G.-W., Clinical significance and prognostic value of urinary nucleosides in breast cancer patients. Clin. Biochem. 2005, 38, (1), 24-30. 162. La, S.; Cho, J.; Kim, J.-H.; Kim, K.-R., Capillary electrophoretic profiling and pattern recognition analysis of urinary nucleosides from thyroid cancer patients. Anal. Chim. Acta 2003, 486, (2), 171-182. 163. Feng, B.; ZHENG, M. H.; ZHENG, Y. F.; LU, A. G.; LI, J. W.; WANG, M. L.; MA, J. J.; XU, G. W.; LIU, B. Y.; ZHU, Z. G., Normal and modified urinary nucleosides represent novel biomarkers for colorectal cancer diagnosis and surgery monitoring. J. Gastroenterol. Hepatol. 2005, 20, (12), 1913-1919. 164. Zhao, R.; Xu, G.; Yue, B.; Liebich, H.; Zhang, Y., Artificial neural network classification based on capillary electrophoresis of urinary nucleosides for the clinical diagnosis of tumors. Journal of chromatography. A 1998, 828, (1), 489-496. 165. Schramm, G.; Surmann, E.-M.; Wiesberg, S.; Oswald, M.; Reinelt, G.; Eils, R.; König, R., Analyzing the regulation of metabolic pathways in human breast cancer. BMC Med. Genomics 2010, 3, (1), 39. 166. Denkert, C.; Budczies, J.; Weichert, W.; Wohlgemuth, G.; Scholz, M.; Kind, T.; Niesporek, S.; Noske, A.; Buckendahl, A.; Dietel, M., Metabolite profiling of human colon carcinoma–deregulation of TCA cycle and amino acid turnover. Mol. Cancer 2008, 7, (1), 72. 167. Wu, H.; Xue, R.; Dong, L.; Liu, T.; Deng, C.; Zeng, H.; Shen, X., Metabolomic profiling of human urine in hepatocellular carcinoma patients using gas chromatography/mass spectrometry. Anal. Chim. Acta 2009, 648, (1), 98-104. 168. Nakano, K.; Nakao, T.; Schram, K. H.; Hammargren, W. M.; McClure, T. D.; Katz, M.; Petersen, E., Urinary excretion of modified nucleosides as biological marker of RNA turnover in patients with cancer and AIDS. Clinica chimica acta 1993, 218, (2), 169-183. 169. Chung, K. T.; Gadupudi, G. S., Possible roles of excess tryptophan metabolites in cancer. Environ. Mol. Mutagen. 2011, 52, (2), 81-104. 36 ACS Paragon Plus Environment

Page 37 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


170. Opitz, C. A.; Litzenburger, U. M.; Sahm, F.; Ott, M.; Tritschler, I.; Trump, S.; Schumacher, T.; Jestaedt, L.; Schrenk, D.; Weller, M., An endogenous tumour-promoting ligand of the human aryl hydrocarbon receptor. Nature 2011, 478, (7368), 197-203. 171. Peluso, G.; Nicolai, R.; Reda, E.; Benatti, P.; Barbarisi, A.; Calvani, M., Cancer and anticancer therapy‐ induced modifications on metabolism mediated by carnitine system. J. Cell. Physiol. 2000, 182, (3), 339-350. 172. Anghileri, L. J.; Crone-Escanye, M.-C.; Thouvenot, P.; Brunotte, F.; Robert, J., Mechanisms of gallium-67 accumulation by tumors: Role of cell membrane permeability. J. Nucl. Med. 1988, 29, (5), 663. 173. Mycielska, M. E.; Patel, A.; Rizaner, N.; Mazurek, M. P.; Keun, H.; Patel, A.; Ganapathy, V.; Djamgoz, M., Citrate transport and metabolism in mammalian cells. Bioessays 2009, 31, (1), 10-20. 174. Rosado, J. O.; Salvador, M.; Bonatto, D., Importance of the trans-sulfuration pathway in cancer prevention and promotion. Mol. Cell. Biochem. 2007, 301, (1-2), 1-12. 175. Redmond, H. P.; Stapleton, P.; Neary, P.; Bouchier-Hayes, D., Immunonutrition: the role of taurine. Nutrition 1998, 14, (7), 599-604. 176. Griffin, J. L.; Shockcor, J. P., Metabolic profiles of cancer cells. Nat. Rev. Cancer 2004, 4, (7), 551-561. 177. Delaney, J.; Neville, W. A.; Swain, A.; Miles, A.; Leonard, M. S.; Waterfield, C. J., Phenylacetylglycine, a putative biomarker of phospholipidosis: its origins and relevance to phospholipid accumulation using amiodarone treated rats as a model. Biomarkers 2004, 9, (3), 271-290. 178. Kasuya, F.; Yamaoka, Y.; Osawa, E.; Igarashi, K.; Fukui, M., Difference of the liver and kidney in glycine conjugation of ortho-substituted benzoic acids. Chem.-Biol. Interact. 2000, 125, (1), 39-50. 179. Carrola, J.; Rocha, C. u. M.; Barros, A. n. S.; Gil, A. M.; Goodfellow, B. J.; Carreira, I. M.; Bernardo, J. o.; Gomes, A.; Sousa, V.; Carvalho, L., Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of urine. J. Proteome Res. 2010, 10, (1), 221-230. 180. De Vries, A.; Alexander, B.; Quamo, Y., Studies on amino acid metabolism. III. Plasma glycine concentration and hippuric acid formation following the ingestion of benzoate. J. Clin. Invest. 1948, 27, (5), 665. 181. Duydu, Y.; Süzen, S.; Erdem, N.; Uysal, H.; Vural, N., Validation of hippuric acid as a biomarker of toluene exposure. Bull. Environ. Contam. Toxicol. 1999, 63, (1), 1-8. 182. He, J. C.; Chuang, P. Y.; Ma'Ayan, A.; Iyengar, R., Systems biology of kidney diseases. Kidney Int. 2012, 81, (1), 22-39. 183. Xia, J.; Broadhurst, D. I.; Wilson, M.; Wishart, D. S., Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics 2013, 9, (2), 280-299. 184. Monteiro, M.; Carvalho, M.; Bastos, M.; Guedes de Pinho, P., Metabolomics analysis for biomarker discovery: advances and challenges. Curr. Med. Chem. 2013, 20, (2), 257-271. 185. Weiss, R. H.; Kim, K., Metabolomics in the study of kidney diseases. Nat. Rev. Nephrol. 2012, 8, (1), 2233. 186. Alberg, A. J.; Kouzis, A.; Genkinger, J. M.; Gallicchio, L.; Burke, A. E.; Hoffman, S. C.; Diener-West, M.; Helzlsouer, K. J.; Comstock, G. W., A prospective cohort study of bladder cancer risk in relation to active cigarette smoking and household exposure to secondhand cigarette smoke. Am. J. Epidemiol. 2007, 165, (6), 660-6. 187. Augustine, A.; Hebert, J. R.; Kabat, G. C.; Wynder, E. L., Bladder cancer in relation to cigarette smoking. Cancer Res. 1988, 48, (15), 4405-8. 188. Koutros, S.; Silverman, D. T.; Baris, D.; Zahm, S. H.; Morton, L. M.; Colt, J. S.; Hein, D. W.; Moore, L. E.; Johnson, A.; Schwenn, M.; Cherala, S.; Schned, A.; Doll, M. A.; Rothman, N.; Karagas, M. R., Hair dye use and risk of bladder cancer in the New England bladder cancer study. Int. J. Cancer 2011, 129, (12), 2894-904. 189. Anton-Culver, H.; Lee-Feldstein, A.; Taylor, T. H., Occupation and bladder cancer risk. Am. J. Epidemiol. 1992, 136, (1), 89-94. 190. Jacobsen, U. P.; Nielsen, H. B.; Hildebrand, F.; Raes, J.; Sicheritz-Ponten, T.; Kouskoumvekaki, I.; Panagiotou, G., The chemical interactome space between the human host and the genetically defined gut metabotypes. ISME J. 2013, 7, (4), 730-42. 37 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 41

191. Nicholson, J. K.; Holmes, E.; Kinross, J.; Burcelin, R.; Gibson, G.; Jia, W.; Pettersson, S., Host-gut microbiota metabolic interactions. Science 2012, 336, (6086), 1262-7. 192. Holmes, E.; Loo, R. L.; Stamler, J.; Bictash, M.; Yap, I. K.; Chan, Q.; Ebbels, T.; De Iorio, M.; Brown, I. J.; Veselkov, K. A.; Daviglus, M. L.; Kesteloot, H.; Ueshima, H.; Zhao, L.; Nicholson, J. K.; Elliott, P., Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 2008, 453, (7193), 396-400. 193. Balog, J.; Sasi-Szabo, L.; Kinross, J.; Lewis, M. R.; Muirhead, L. J.; Veselkov, K.; Mirnezami, R.; Dezso, B.; Damjanovich, L.; Darzi, A.; Nicholson, J. K.; Takats, Z., Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci. Transl. Med. 2013, 5, (194), 194ra93. 194. Dettmer, K.; Aronov, P. A.; Hammock, B. D., Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 2007, 26, (1), 51-78. 195. Garcia-Perez, I.; Vallejo, M.; Garcia, A.; Legido-Quigley, C.; Barbas, C., Metabolic fingerprinting with capillary electrophoresis. Journal of chromatography. A 2008, 1204, (2), 130-9. 196. Kell, D. B.; Brown, M.; Davey, H. M.; Dunn, W. B.; Spasic, I.; Oliver, S. G., Metabolic footprinting and systems biology: the medium is the message. Nat. Rev. Microbiol. 2005, 3, (7), 557-65.


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Figure 1

DNA/gene

mRNA

Protein

Metabolite

ncRNA

Genomics

Transcriptomics

Proteomics

ACS Paragon Plus Environment

Metabonomics


Figure 2

NMR GC/MS

Study design

Sample collection and storage

Sample preparation

LC/MS 2e+007

1.8e+007

Sample analysis

1.6e+007

1.4e+007

2e+007

1.2e+007

1.8e+007

1e+007

1.6e+007

8e+006

1.4e+007

6e+006

1.2e+007

4e+006

1e+007

2e+006

8e+006

0 Time (s)

995

1000

1005

1010

1015

1020

1025

1030 6e+0061035

1040

4e+006

2e+006

0 Time (s)

995

1000

1005

1010

1015

1020

1025

1030

1035

1040

Peak alignment

Samples

Metabolites

Data matrix

Data pre-processing

Biomarker detection kit Metabolite identification

BC BC_Predict H H_Predict

5

toPS[1]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Page 40 of 41

0

-5

Statistical tests -6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

tPS[1]

PCA, PLS-DA

SIMCA-P+ 12.0.1 - 2010-04-28 07:45:11 (UTC+8)

Clinical application

Metabolite ACS Paragon Plus Environment

Metabolic pathway elucidation

marker discovery and structure validation

Chemometric analysis

Page 41 of 41

Graphical Abstract

Clinical Workflow

Current Studies

2e+007

1.8e+007

1.6e+007

1.4e+007

1.2e+007

1e+007

8e+006

6e+006

4e+006

2e+006

0 Time (s)

995

1000

1005

1010

1015

1020

1025

1030

1035

1040

BC BC_Predict H H_Predict

5

toPS[1]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42


0

-5

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

tPS[1] SIMCA-P+ 12.0.1 - 2010-04-28 07:45:11 (UTC+8)

Perturbed Pathways

Future Perspectives

?

Diagnostic & Surgical Surveillance of Prognostic Intervention Recurrence Screening

ACS Paragon Plus Environment

Metabonomic Profiling of Bladder Cancer - ACS Publications

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