emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery
TRANSCRIPT
Accepted Manuscript
Title: Emerging Field of Metabolomics: Big Promise forCancer Biomarker Identification and Drug Discovery
Author: Seema Patel Shadab Ahmed
PII: S0731-7085(14)00626-8DOI: http://dx.doi.org/doi:10.1016/j.jpba.2014.12.020Reference: PBA 9858
To appear in: Journal of Pharmaceutical and Biomedical Analysis
Received date: 25-9-2014Revised date: 7-12-2014Accepted date: 14-12-2014
Please cite this article as: S. Patel, S. Ahmed, Emerging Field ofMetabolomics: Big Promise for Cancer Biomarker Identification and DrugDiscovery, Journal of Pharmaceutical and Biomedical Analysis (2014),http://dx.doi.org/10.1016/j.jpba.2014.12.020
This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers thatapply to the journal pertain.
Page 2 of 31
Accep
ted
Man
uscr
ipt
2
Highlights3
Mass spectrometry, Nuclear magnetic resonance and chemometrics have enabled cancer biomarker 4
discovery.5
Metabolomics can non-invasively identify biomarkers for diagnosis, prognosis and treatment of cancer6
All major types of cancers and their biomarkers didcovered by metabolomics have been discussed.7
This review sheds light on the pitfalls and potentials of metabolomics with respect to oncology.8
910
Page 3 of 31
Accep
ted
Man
uscr
ipt
3
Emerging Field of Metabolomics: Big Promise for Cancer Biomarker Identification and Drug Discovery10111213141516171819
Seema Patela* and Shadab Ahmedb2021
aBioinformatics and Medical Informatics Research Center, San Diego State University, San Diego 92182, USA22bInstitute of Bioinformatics and Biotechnology, Savitribai Phule Pune University, Pune 411007, India23
24252627282930313233
Short running title: Metabolomics for cancer target discovery3435
Key words: Metabolomics, cancer, biomarker, nuclear magnetic resonance, mass spectrometry36373839404142434445
*Corresponding author and address for correspondence:464748
Dr. Seema Patel49Bioinformatics and Medical Informatics Research Center50San Diego State University515500 Campanile Dr San Diego, CA 9218252Email: [email protected]
545556575859606162
63
64
65
Page 4 of 31
Accep
ted
Man
uscr
ipt
4
Abstract66
Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease.67
Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as 68
downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic 69
portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the 70
end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of 71
metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. 72
Metabolites are highly informative about the functional status of the biological system, owing to their proximity 73
to organismal phenotypes. Scores of publications have reported about high-throughput data generation by74
cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated 75
statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic 76
data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid acycle, choline 77
and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the 78
future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors 79
scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer 80
databases to compile this review to pique the interest of researchers towards cancer metabolomics.81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
Page 5 of 31
Accep
ted
Man
uscr
ipt
5
Introduction96
Cancer is characterized by uncontrolled proliferation of cells and eventual transformation into 97
malignancy. It is a leading cause of death worldwide, and going by its alarming spread, it can amplify mortality 98
rate if not restrained. Cancer can develop in any part of the body, chiefly prostate gland, lungs, colon, breast, 99
brain, cervix, kidney, blood, liver, pancreas, ovary, uterus, skin, mouth, thyroids and testis. It is generally 100
triggered by xenobiotics viz. heterocyclic amines, genetic aberrations (microsatellite instability, somatic copy 101
number alterations), radiations, virus (hepatitis B virus, Epstein-Barr virus, papillomavirus) etc; however, many 102
unknown causes are still to be identified. The predominant mechanisms for cancer are impairment of DNA 103
repair pathways, the conversion of normal genes into oncogenes and the malfunction of tumour suppressor gene.104
Many gold standard clinical diagnostic and surveillance tests exist for screening cancer viz. biopsy, blood test, 105
blood cell count, image-based tests (computerized tomography (CT) scan, magnetic resonance imaging (MRI)),106
chest X-ray, liver function test, bone marrow test, mammography, colonoscopy and cystoscopy. Still, in some 107
cases cancer goes undetected (benign grade I tumour are low in sensitivity) and finally when identified108
(malignant stage), it becomes too late to treat. Further, the invasive procedures combined with high cost render109
it challenging diagnostic procedure. It necessitates superior detection strategies to improve prevent malignant 110
cancers. Identifying specific biomarkers to detect cancer at the onset to ensure patient survival appears very 111
promising.112
A cancer biomarker can be a metabolite (secreted by tumour, metabolic pathway or process) that may be 113
employed to diagnose cancer, predict patient response towards therapies and monitor recurrence. Though 114
proteins are the key markers, they can be as diverse as molecular, biochemical, physiological or anatomical [1].115
Markers can be employed for diagnosis (to identify early stage), prognosis (assess the lethality) and prediction 116
(of patient’s response to treatment) of cancer. The markers can be detected in body fluids (blood, urine, serum, 117
stool, saliva), or tissues (tissue samples or biopsies of the cancer). Recently, it has been shown that cancer 118
volatile organic compounds (VOC) markers can be detected in breath odour [2]. However, detecting the 119
markers is an intricate process. 120
Omic technologies have contributed vastly in this pursuit. Genomics, transcriptomics and proteomics 121
have been around since quite a long time now and have immensely aided to cancer research. The latest in this 122
league is metabolomics, the study of metabolites, which seems competent in cancer biomarker discovery [3]. 123
Among genome, transcriptome, proteome and metabolome, the latter is the closest representative of the 124
phenotype[4]. Metabolome being the end-product and most stable among all four functional levels mirrors the 125
Page 6 of 31
Accep
ted
Man
uscr
ipt
6
activities of cell. Exploring the cancer metabolome seems to be an effective way to understand the phenotypic 126
changes associated with cancer. Screening biomarkers by recruiting an array of analytical techniques is being 127
emphasized[5]. Rather than a single metabolite, a pattern is believed to be more indicative of cancer status.128
Metabolomic approach makes it feasible to detect an array of metabolites in a single assay. The principal 129
analytical tools recruited for metabolome analysis are mass spectrometry (MS) and nuclear magnetic resonance 130
spectroscopy (NMR). MS can be coupled with a separation technique such as gas chromatography (GC-MS),131
liquid chromatography (LC-MS) or capillary electrophoresis (CE-MS) depending on the application. LC-MS 132
can be further ramified into high performance (HPLS-MS) or ultra performance (UPLC-MS). Also, MS can be 133
performed using different mass analysers (e.g. quadrupole (Q), matrix-assisted laser desorption/ionization 134
(MALDI), time of flight (TOF), magnetic sector, electrostatic) for the separation of ions depending on the type 135
of experiment. Variations of NMR include 1H-NMR and high resolution magic angle spinning (HR-MAS-136
NMR). Different approaches in metabolomics include (i) fingerprinting (the global screening for all detectable 137
metabolites from within the system under investigation) (ii) footprinting (analysis of metabolites from the 138
environment around the system under investigation which reveals information about metabolic exchange) (iii) 139
metabolome profiling (screening of a particular class of chemicals) (iv) flux analysis ( tracing of one compound, 140
usually isotope labelled carbon, through a particular pathway or set of pathways to determine the fate of the 141
compound) (v) target analysis (comparison of one or a few closely related target metabolites whose 142
concentrations may change depending on the experimental conditions). Data collection entails pre-processing 143
and subsequent statistical clustering. Univariate analysis is often deficient in discriminating metabolites between 144
complex systems. So, multivariate analysis such as principle component analysis (PCA), partial least square145
discriminant analysis (PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), and 146
hierarchical clustering analysis (HCA) are used for complex data interpretation and subsequent visualization. 147
XCMS is a popular tool for processing MS data for metabolite profiling using nonlinear peak alignment, 148
matching, and identification[6]. MZmine 2 is a modular framework for processing, visualizing, and analyzing 149
MS-based molecular profile data[7]. SIMCA-P is implemented for multivariate analysis by principle component 150
analysis (PCA), partial least square (PLS) and orthogonal partial least square (OPLS) discriminant analysis[8]. 151
MetaboAnalyst is a web-based pipeline for metabolomic data processing, statistical analysis and subsequent 152
functional interpretation[9]. There exist a host of other tools to facilitate data analysis of spectrometric dataset.153
Many reviews shedding light on various aspects of this topic have been published in recent times.154
According to Spratlin et al[5], metabolome can serve as diagnostic and pharmacodynamic tool in oncology.155
Page 7 of 31
Accep
ted
Man
uscr
ipt
7
Gupta and Chawla[10] reported that metabolomics expedite our comprehension of tumour biology by156
identifying diagnostic, prognostic and therapeutic targets. Abbassi-Ghadi et al.[11] reviewed the metabolomic 157
biomarkers in human oesophago-gastric cancer. Metabolites of glycolysis, tricarboxylic acid (TCA) cycle, 158
anaerobic respiration and protein/lipid metabolism were found to be significantly different between cancer and 159
control. Lactate and fumarate were the most commonly recognised cellular respiration biomarkers of cancer.160
Valine, glutamine and glutamate were the most commonly identified amino acid biomarkers, while saturated 161
and unsaturated free fatty acids, ketones and aldehydes and triacylglycerides were lipid metabolite 162
biomarkers[11]. Zhang et al.[12] reviewed how metabolomics can identify new prognostic and predictive 163
markers and discover new targets for future therapeutic interventions in breast cancer. Leung et al.[13] reviewed 164
the potential of MS for identification of novel diagnostic markers for ovarian cancer at the earliest to prolong 165
patient survival. Putri et al. [14] have discussed the latest applications of metabolomics in diverse fields, 166
including medical science. Wang et al.[15] reviewed the metabolomic techniques for hepatocellular carcinoma 167
biomarker discovery. Aboud and Weiss[16] reviewed several standard techniques for metabolite identification168
and drawbacks in the marker discovery. Vermeersch and Styczynski[17] reviewed role of metabolomics in 169
promoting cancer research. Khunger et al.[18] have discussed fundamental principles of cancer screening and 170
impediments in developing novel biomarkers. Struck-Lewicka et al.[19] reviewed on targeted and untargeted171
metabolomics of nucleosides (the RNA metabolites well known as potential cancer markers) and their analogues 172
(cis-diol metabolites) using HPLC-MS and discussed challenges in the pursuit. Zhang et al.[20] highlighted the 173
possible contribution of metabolomics to colorectal cancer diagnostic marker discovery. Armitage and 174
Barbas[21] discussed the metabolomic techniques employed for cancer biomarker discovery. Lu et al.[22]175
described LC-MS and GC-MS as analytical platforms for the comprehensive analysis of cellular metabolites, 176
polar as well as non-polar. Suzuki et al.[23] reviewed various MS techniques viz. LC-MS, GC-MS, CE-MS for 177
metabaolome-based biomarker discovery. Di Gangi et al.[24] reviewed the analytical technologies applied in 178
pancreatic cancer study and summarized the associated metabolites. Diagnostic marker potential of pteridines 179
(key cofactors in cell metabolism) in cancer, using HPLC and CE was reviewed [25]. 180
Overview on existing markers181
It is an established fact that cancer arises due to perturbation in metabolism, but the mechanisms 182
underlying the alterations leading to the malignancy is still poorly understood. Metabolism generates ROS, 183
which cause genetic mutations. Activated oncogenes and loss of tumour suppressors modify metabolism and 184
provoke aerobic glycolysis (Warburg effect). Together with glutamine, glucose via glycolysis supplies the 185
Page 8 of 31
Accep
ted
Man
uscr
ipt
8
carbon skeletons, NADPH, and ATP to build new cancer cells, which persist in hypoxia. Excessive caloric 186
intake is associated with an increased risk for cancers, while caloric restriction is protective, perhaps through 187
clearance of mitochondria or mitophagy, thereby reducing oxidative stress. Hence, the links between 188
metabolism and cancer are multifaceted, spanning from the low incidence of cancer in large mammals with low 189
specific metabolic rates to altered cancer cell metabolism resulting from mutated enzymes or cancer 190
genes[26,27]. Bio-signature in the form of metabolite markers might throw light on this aspect and could solve 191
the puzzle.192
Many gene and protein-based biomarkers (more than 20 tumour markers are currently in use as per 193
National Cancer Institute) have been successfully used for cancer detection [28]. Markers detected in blood or 194
urine are α-fetoprotein (AFP), BCR-ABL, BRCA1/BRCA2, KIT, β-2-microglobulin (B2M), human chorionic 195
gonadotropin (HCG), bladder tumour antigen (BTA), CA 15-3, CA 19-9, CA 27-29, CA 125, calcitonin, 196
carcinoembryonic antigen (CEA), chromogranin A, immunoglobulins, free light chains, inhibin, lactate 197
dehydrogenase (LDH), neuron-specific enolase (NSE), NMP22, prostate-specific antigen (PSA), 198
thyroglobulin, soluble mesothelin-related peptides (SMRP), S-100 and prostatic acid phosphatase (PAP). 199
Markers detected in cancer tissue are anaplastic lymphoma kinase (ALK), BRAF oncogene (BRAFV600E), 200
epidermal growth factor receptor (EGFR), HER2 (or HER2/neu, erbB-2, or EGFR2), hormone receptors, 201
KRAS, S-100, Bmi-1, fibroblast activation protein (FAP) etc. A number of other markers are garnering 202
significant attention. Table 1. presents the list of conventional and emerging cancer markers.203
Roles of biomarkers in cancer therapy204
It is well known that cancer metabolism differs from that of normal tissue. Cancer cells rely on anaerobic 205
metabolism as the source for energy, even under physiological oxygen levels. Metabolomics could reveal novel206
cancer biomarkers that might expand our current understanding of the multi-factorial disease. The metabolome 207
consists of both endogenous (catabolised or anabolised by the biological system itself) and exogenous (extra-208
organism or extracellular) components. The best studied feature of cancer metabolism is central carbon 209
metabolism (CCM) and the relationship between glycolysis, the tricarboxylic acid (TCA) cycle and oxidative 210
phosphorylation. The major roles played by markers in facilitating cancer treatment are diagnosis, prognosis and 211
treatment. In several cancers, symptoms are subtle and nonspecific, which causes cancer to advance without 212
being detected. Early diagnosis of most cancer is life-saving, as observed in case of ovary, colon, kidney, gastric213
cancer (mostly for localised tumours) [29–32]. Cancer biomarkers can also be useful in establishing a specific 214
diagnosis, irrespective of tumours being of primary or secondary (metastatic) origin. Markers can predict the215
Page 9 of 31
Accep
ted
Man
uscr
ipt
9
aggressiveness of an identified cancer as well as its likelihood of responding to the given treatment [33]. Serum 216
metabolomic profile could also be used for tumour staging (grading based on tumour size). Early identification 217
of metastases could be recommended for surgery, whereas more disseminated disease could be subjected to 218
palliative chemotherapy (when treatment is inadequate for major survival advantage)[34]. Markers can also be 219
used to determine the most effective treatment regime for a particular patient, paving the path for development 220
of personalized cancer care. Distinct genetic makeup of each patient results indifferent degree rate of drug 221
metabolism, requiring different drug dosing. So, metabolomics can provide knowledge regarding 222
pharmacodynamics and pharmacokinetics. Tumour cells undergoing apoptosis release specific cellular 223
components viz. cytochromes, nucleosomes, cleaved cytokeratin-18, E-cadherin that could be considered as 224
metrics of therapeutic efficacy. Also, aggressiveness of cancer and recurrence risks after surgical treatment 225
could be tracked. Halama et al. [35] suggested that metabolomics can be harnessed for finding specific 226
biomarkers of apoptosis for monitoring treatment efficacy (theranostics). Fig. 1. shows the potential drug targets 227
in cancer and role of metabolomics in diagnosis, prognosis and therapy.228
Possible markers revealed by metabolomics229
Head and neck230
Head and neck cancer usually develop in the squamous cells lining the inner mucosal surfaces of mouth, 231
salivary glands, tongue, nose, sinuses, throat, lymph nodes in the neck and larynx. Tiziani et al.[36] analyzed 232
blood samples of oral cancer patients and control using NMR. PCA and PLS-DA on the data showed a clear 233
discrimination between metabolites of the two groups. Pan et al.[37] exposed four brain cancer cell lines to 234
cisplatin drug and subjected to 1H-NMR. The result shed light on relation of glycosylated UDP compounds 235
(uridine diphospho-N-acetylglucosamine and uridine diphospho-N-acetylgalactosamine) to cancer cell death 236
following chemotherapeutic treatment. The glycosylated UDP compounds serve as substrates for glycosylation 237
of proteins and lipids, and thus they play considerable role in proliferation and malignant transformation.238
Tripathi et al.[38] employed 1H-NMR for metabolite profiling of head and neck squamous cell carcinoma. PCA 239
applied on the data showed a clear distinction between the cancerous and control (normal human oral 240
keratinocytes) tissue. Cancer cells exhibited significantly altered levels of various metabolites that clearly 241
revealed perturbation in metabolic events viz. Warburg effect, oxidative phosphorylation, energy metabolism, 242
TCA cycle, glutaminolysis, and hexosamine pathway, osmo-regulatory and antioxidant mechanism. Also, 243
significant alterations in the ratios of phosphatidylcholine/lysophosphatidylcholine and 244
phosphocholine/glycerophosphocholine, and elevated arachidonic acid level observed in cancer cells indicated 245
Page 10 of 31
Accep
ted
Man
uscr
ipt
10
an altered membrane choline phospholipid metabolism. Significant increase in the activity of phospholipase A2246
observed in all the cancer cells confirmed an altered membrane choline phospholipid metabolism, which 247
projected cytosolic phospholipase A2 as a potential therapeutic target for anti-cancer therapy of head and neck 248
cancer. Nakamizo et al.[39] used GC-MS for metabolomic profiling of pre-operative cerebrospinal fluid samples 249
from patients with glioma. A total of 61 metabolites were identified, among which citric and isocitric acid levels 250
were significantly higher in the glioblastoma (the aggressive form of brain tumour whereas glioma is often a 251
benign primary brain tumour) samples than in the grades I, II, III glioma samples. Also, the lactic acid and 2-252
aminopimelic acid levels were relatively higher in the glioblastoma samples than in the grades I and II glioma 253
samples. The levels of the citric, isocitric and lactic acids in the cerebrospinal fluid were significantly higher in 254
grade I, II and III gliomas with mutant isocitrate dehydrogenase as compared to wild-type isocitrate 255
dehydrogenase. Yonezawa et al.[40] performed GC-MS of serum and tissue samples from squamous cell 256
carcinoma of the head and neck (HNSCC) patients. Metabolites related to the glycolytic pathway, such as 257
glucose were higher while some amino acids were lower in the sera of patients. However, the levels of many 258
metabolites related to the glycolytic pathway were lower in the tumour tissues than in non-tumourous tissues, 259
and the levels of several amino acids, such as valine, thyrosine, serine and methionine, were significantly higher.260
Lungs261
Lung cancer is among the leading causes of mortality worldwide. Gao et al.[41] investigated the 262
metabolic profile of patients with benign and malignant pulmonary nodules using a combination of GC-MS and 263
LC-MS. Chemometric analysis of the profiles by OPLS-DA resulted in 63 differential metabolites. Among 264
them, 48 metabolites were detected in both malignant and benign nodules, which imply common inflammatory265
pathways shared between the two groups. Fourteen metabolites viz. N-succinyl-2,6-diaminopimelate, 266
deoxycholic acid glycine conjugate, octanoylcarnitine, lysophosphatidylcholines, lysophosphatidylcholines, 267
phosphatidylcholines, phosphatidylserine and cholesteryl acetate distinguished malignant from benign nodules.268
Wu et al.[42] recruited a dansylated LC-MS to analyze variations of metabolites in pulmonary tumour and 269
normal tissues and reported abundance of dipeptides in the former. Wen et al.[43] used a combination of GC-270
MS and LC-MS to investigate the metabolic signatures in the plasma of stage-I human lung adenocarcinoma 271
patients and healthy controls. The OPLS-DA models reported 37 discriminatory metabolites between the two 272
groups. The fluctuated metabolites in human lung adenocarcinoma consisted of amino acids, lipids, fatty acids 273
and glutaminolysis (reactions by which glutamine is converted into glutamate, aspartate, CO2, pyruvate, lactate, 274
alanine and citrate). Also, significant decrease in the sulfate conjugate of sex hormones viz. testosterone, 275
Page 11 of 31
Accep
ted
Man
uscr
ipt
11
androsterone, pregnenolone were observed in the plasma of patients. The development of resistance towards276
epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors remains a major hurdle in lung cancer277
therapy. Serizawa et al.[44] employed CE-TOF-MS to screen biomarkers for the early detection of resistance to 278
these inhibitors, using PC-9ER cells (drug erlotinib-resistant non-small-cell lung cancer cell). Enhanced 279
glutamine metabolism was observed in the cancer cells that can be explained by the multiple pathways for 280
bioenergetics and biosynthesis in proliferating cells. Surged glutamine metabolism might be considered as 281
marker to predict patient response to EGFR- tyrosine kinase inhibitors. Li et al.[45] performed UPLC-Q-TOF 282
MS on serum of lung cancer patient. Data clustering by PLS-DA resulted in differential metabolites involved 283
with the perturbation of lipid metabolism, including choline, free fatty acids, lysophosphatidylcholines.284
Breast285
Breast cancer is a heterogeneous disease with different subtypes and it develops through multi-step 286
carcinogenesis. Lefort et al.[46] used 1H-NMR to profile the effects of drugs and concluded that treatment of 287
cancer cells (MDA-MB-231 and MCF-7) with dichloroacetate (a pyruvate dehydrogenase kinase inhibitor) and 288
allopurinol (xanthine oxidase/dehydrogenase inhibitor) results in more pronounced changes in metabolites found 289
in extracellular medium than intracellular pools. An elevation in phosphocholine, total choline and 290
phosphocholine/glycerophosphocholine level was observed in cancer cells compared to control. Wei et al.[47]291
used LC-MS and 1H-NMR to generate serum metabolite profile and categorize breast cancer patients as 292
responders and non-responders to therapy. The concentrations of four metabolites, threonine, isoleucine, 293
glutamine, linolenic acid were significantly different in patients and controls when comparing response to 294
chemotherapy. The metabolites correctly identified 80% of the patients whose tumours did not show complete 295
response to chemotherapy. Shen et al.[48] analyzed plasma samples of breast cancer patients and controls. 296
Results showed that metabolomic profiles are influenced by race and tumour receptor status. Also, the levels of297
many amino acids were lower in the patients compared to controls, suggesting the rapid utilization of amino 298
acids in breast tumour metabolism. Metabolites related to fatty acid β-oxidation were significantly higher in 299
breast cancer patients than controls, signifying fast lipid metabolism. Mimmi et al.[49] used LC-ESI-MS and 300
1H-NMR for quantification of choline, phosphocholine and glycerophosphocholine in breast carcinoma tissues 301
and controls. These metabolites were found in abundance in the cancer tissues, as characteristics of other 302
cancers. Budczies et al.[50] investigated estrogen receptor positive (ER+) and estrogen receptor negative (ER-) 303
breast cancer tissues using GC-TOF-MS and detected 19 altered metabolites, which included β-alanine, 2-304
hydroyglutarate, glutamate, xanthine and glutamine. Beta-alanine demonstrated the strongest variation between 305
Page 12 of 31
Accep
ted
Man
uscr
ipt
12
ER- and ER+ breast cancer. The findings corroborated the current view of ER+ breast cancer being different 306
from ER-, thus necessitating unique treatment strategies. Further, the metabolomics analysis revealed that ER-307
subtype is the favoured target for glutaminase inhibitors. Jobard et al.[51] conducted 1H-NMR to monitor308
metabolite fluctuation in the serum of advanced metastatic breast cancer compared to localized early stage 309
tumour. The 9 significant discriminatory metabolites were histidine, acetoacetate, glycerol, pyruvate, 310
glycoproteins (N-acetyl), mannose, glutamate and phenylalanine. Corona et al.[52] performed MS-based serum 311
metabolomic profiling of elderly breast cancer. Further, ANOVA was used to identify the significant 312
metabolites from amino acids, acylcarnitines, sphingo- and glycerol-phospolipid class. In frail patients, the 313
perturbations of serine, tryptophan, hydroxyproline, histidine, its derivate 3-methyl-hystidine, cystine, and β-314
aminoisobutyric acid was observed. Also, in this phenotype, decrease lowered level of glycerol- and sphingo-315
phospholipid metabolites were observed. Qiu et al.[53] used GC-TOF-MS to analyze colorectal cancer biopsies.316
Differential metabolites were identified and evaluated as potential prognostic markers. A panel of 15 317
significantly altered metabolites was identified, which demonstrates the ability to predict the rate of recurrence 318
and survival for patients after surgery and chemotherapy. 319
Oesophagus and stomach320
Stomach cancers are a leading cause of mortality worldwide. Xu et al.[54] studied plasma of oesophageal 321
squamous cell carcinoma patients using LC-MS. Clustering with PCA and PLS-DA revealed the significant 322
plasma metabolites in the patients to be lysophosphatidylcholines, fatty acids, L-carnitine, acylcarnitines, 323
organic acids, and a sterol metabolite. Also, variation in metabolites between pre- and post-treatment patients 324
was observed. Zhang et al.[55] used 1H-NMR coupled to UHPLC for characterizing the metabolic turbulence325
caused by oesophageal cancer. OPLS-DA identified 19 metabolites as potential biomarkers. The pathways 326
undergoing perturbation in cancer patients were lipid metabolism, amino acid metabolism, glycolysis, 327
ketogenesis, TCA cycle and energy metabolism. Wang et al.[56] investigated the metabolites in human 328
oesophageal cancer tissues and normal esophageal mucosa using 1H-NMR followed by PCA, PLS-DA and 329
OPLS-DA. Down-regulation of glucose, adenosine monophosphate (AMP), NAD and up-regulation of formate 330
yet again confirmed the great energy necessity by oesophageal cancer. The amplification in acetate, short-chain 331
fatty acid and gamma aminobutyric acid (GABA) in oesophageal cancer tissue pointed towards fatty acids 332
metabolism required for cellular membrane formation. Other altered metabolites in oesophageal cancer were 333
associated with choline metabolic pathway.334
335
Page 13 of 31
Accep
ted
Man
uscr
ipt
13
336
Bile duct, pancreas and liver337
Cancers of bile duct, pancreas and liver are very challenging to treat. Wen et al.[57] subjected the bile 338
collected from biliary cancer patients to NMR followed by OPLS-DA. Results showed good distinction between 339
cancer and benign groups. The diagnostic ability of this method proved better than the conventional markers 340
(CEA, CA19-9 and bile cytology).341
He et al.[58] used HR-MAS-NMR to profile xenograft pancreatic cancer (SW1990 cell implanted in 342
nude mice) before and after radiotherapy. PCA selected metabolites of normal pancreas, pancreatic tumour343
tissues, and radiation- treated pancreatic tumour tissues were compared. The levels of choline, taurine, alanine, 344
isoleucine, leucine, valine, lactate and glutamic acid in the pancreatic cancer group increased, whereas,345
phosphocholine, glycerophosphocholine, and betaine levels were found to be decreased. The ratio of 346
phosphocholine to creatine, and glycerophosphocholine to creatine showed remarkable decrease in the 347
pancreatic cancer group. PCA results showed that the levels of choline and betaine were decreased with the 348
increased radiation dose, whereas, the level of acetic acid was dramatically increased. Yabushita et al.[59] used 349
transgenic rats bearing pancreatic ductal adenocarcinoma and control transgenic rats with normal pancreatic 350
tissues for metabolomic analysis of serum and pancreatic tissue by non-targeted as well as targeted GC-MS and 351
by microarray-based transcriptomic analysis. Results showed that the decreased serum levels of palmitoleic acid 352
in rats with pancreatic ductal adenocarcinoma was due to the decrement in pancreatic tissue and suggested 353
deeper investigation into biomarker potential of palmitoleic acid. Kobayashi et al.[60] analyzed the sera from 354
pancreatic cancer patients and healthy volunteers by GC-MS followed by statistical analyses. The diagnostic 355
model constructed seemed promising for improving the prognosis of pancreatic cancer via its early detection 356
and accurate discrimination from chronic pancreatitis. Leichtle et al.[61] analyzed serum samples of pancreatic 357
carcinoma patients, pancreatitis patients and controls using MS and statistics. The result indicated that multi-358
marker models are better than conventional marker CA19-9 (carbohydrate antigen 19-9) in differentiating 359
between the different groups. Ritchie et al.[62] employed a non-targeted metabolomics approach based on flow-360
injection Fourier transform ion cyclotron resonance mass spectrometry (FI-FTICR-MS) to generate 361
comprehensive metabolomic profiles of sera from pancreas cancer patients and control. The metabolome of 362
pancreas cancer patients was significantly altered with decrease in metabolites like 36-carbon ultra long-chain 363
fatty acids, multiple-choline-related systems including phosphatidylcholines, lysophosphatidylcholines and 364
sphingomyelins, as well as vinyl ether-containing plasmalogen ethanolamines. Metabolite biomarkers PC-594365
Page 14 of 31
Accep
ted
Man
uscr
ipt
14
(an ultra-long-chain fatty acid) appeared promising in diagnosis of candidates at higher risk of developing366
pancreas cancer. 367
Xiao et al.[63] compared metabolite levels in sera of hepatocellular carcinoma and cirrhosis patients by 368
UPLC-Q-TOF-MS. After statistical analysis, the discriminating candidate biomarkers were found to be369
glycholic acid, glycodeoxycholic acid, 3β, 6β-dihydroxy-5β-cholan-24-oic acid, oleoyl carnitine and the peptide 370
Phe-Phe. Chen et al.[64] used UHPLC-QQQ-MRM-MS-based pseudo-targeted method to discover serum 371
biomarkers for patients with hepatocellular carcinoma. The metabolite lysophosphatidylcholine, medium-chain 372
acylcarnitines and branched-chain amino acid levels were found decreased; whereas the metabolite long-chain 373
acylcarnitines and aromatic amino acid levels were found elevated in the patients. Beyoglu et al.[65] combined 374
transcriptomics and GC-MS datasets to examine energy metabolism in tumours and non-tumorous tissues from 375
the liver of same hepatocellular carcinoma patients. The tumour was characterized by about 2-fold depletion of 376
glucose, glycerol 3- and 2-phosphate, malate, alanine, myo-inositol, and linoleic acid. Data were consistent with 377
a metabolic remodelling involving a 4-fold increase in glycolysis over mitochondrial oxidative phosphorylation.378
Liu et al.[8] used UPLC-Q-MS characterize metabolites in hepatitis B virus-related hepatocellular carcinoma. 379
Clustering identified 14 discriminating metabolites viz. L-phenylalanine, glycerophosphocholine, 380
lysophosphatidylcholines, lysophosphatidylethanolamines and chenodeoxycholic acid glycine conjugate, β-381
sitosterol, quinaldic acid, arachidyl carnitine, tetradecanal, and oleamide. Zhang et al. [66] used UPLC-Q-TOF-382
HDMS for metabolomic profiling of glycocholic acid from urine samples of hepaocellular carcinoma patients. A383
metabolite network followed by biochemical analyses of urine sample revealed that glycocholic acid expression 384
gets upregulated hepatocellular carcinoma. Pathway analysis suggested the modulation of multiple vital 385
physiological pathways, including primary bile acid biosynthesis, secondary bile acid biosynthesis, metabolic 386
pathways, and bile secretion. The in silico network generation enhanced the interpretation and understanding of 387
mechanisms for glycocholic acid. Clarke et al.[67] monitored the changes in the expression of hepatocellular 388
carcinoma-related genes and metabolite profiles in non-alcoholic fatty liver disease progression. Results 389
indicated an overlap in the pathogenesis of non-alcoholic fatty liver disease and hepatocellular carcinoma where 390
several classes of oncogenic genes and metabolites were altered in the former. Wnt signalling and several 391
metabolites were different, which indicated that the perturbed genes and metabolites could be mediators in the 392
transition from non-alcoholic steatohepatitis to hepatocellular carcinoma. Bowers et al.[68] conducted 393
metabolomic profling of serum samples from patients with hepatitis C virus and the virus with hepatocellular 394
carcinoma using HPLC-MS. Application of PLS-DA on the data revealed discriminatory metabolites. Further 395
Page 15 of 31
Accep
ted
Man
uscr
ipt
15
the combined application of NMR and LC-MS in followed by random forest statistical analysis could find the 396
differential metabolites. Perturbations in the synthesis of ketone bodies, citrate cycle, phospholipid metabolism, 397
sphingolipid metabolism, fatty acid oxidation, amino acid catabolism and bile acid metabolism was observed in 398
hepatocellular carcinoma patients as compared to controls[69].399
Kidney and bladder400
Bladder cancer is the most common malignancy of the urinary system. Pasikanti et al.[70] conducted 401
GC-GC-TOF-MS for metabotyping of urine from bladder cancer and control. OPLS-DA resulted in 46 402
metabolites associated with the cancer. Alteration of the tryptophan-quinolinic acid level in the patient urine 403
suggested the potential roles of kynurenine (metabolite of tryptophan) in the malignancy and possible 404
therapeutic strategy. Tripathi et al.[71] subjected tissue samples from bladder cancer patient and control to HR-405
MAS-NMR and subsequent PLS-DA. Fifteen metabolites showed significant difference between the types of 406
tissues. Alberice et al.[72] conducted LC-MS and CE-TOF-MS on urine samples from urothelial bladder cancer 407
patients. Twenty-seven discriminatory metabolites were obtained which included histidine, phenylalanine, 408
tyrosine and tryptophan. Perturbed level of urinary nucleosides are considered hallmark of urinogenital cancer. 409
In this regard, the nucleosides were quantified using CE-MS and their significance assessed by PCA and PLS-410
DA [73]. LC-QQQ-MS of urogenital cancer patient urine sample revealed 5 nucleosides makers viz. 6-411
methyladenosine, inosine, N-2-methylguanosine, 3-methyluridine and N,N-dimethylguanosine [74].412
Pterins are nitrogen heterocylic compounds with 2-amino-4-hydroxypiridine core, characterized to play 413
role as cofactors in cell metabolism. Possible biomarker roles of pterin compounds in bladder cancer urine 414
samples were investigated using reverse-phase HPLC. Four pterins viz. neopterin, pterin-6-carboxylic acid, 415
biopterin and isoxanthopterin were detected in higher level in cancer patients. [75]. Though the elevation in 416
concentration of these metabolites was not statistically-significant, it might pave way for further verification of 417
the efficacy.418
Ovary, uterus and cervix419
Gynecologic cancers develop in female reproductive system and can be cervical, ovarian, uterine, vaginal 420
and vulvar. Zhang et al.[76] performed UPLC-Q-TOF-MS on urine samples from epithelial ovarian cancer421
patients, including nucleotide metabolism (pseudouridine, N4-acetylcytidine), histidine metabolism (L-histidine, 422
imidazol-5-yl-pyruvate), tryptophan metabolism (3-indolelactic acid) and mucin metabolism (3'-sialyllactose 423
and 3-sialyl-N-acetyllactosamine). The metabolites N4-acetylcytidine, pseudouridine, urate-3-ribonucleoside, 424
and succinic acid differed significantly between pre-operation and post-operation group and hold promise as 425
Page 16 of 31
Accep
ted
Man
uscr
ipt
16
biomarkers. Vettukattil et al.[77] measured metabolic differences between ovarian serous carcinoma effusions 426
(common manifestation of advanced cancer) obtained pre- and post-chemotherapy. HR-NMR followed by PCA 427
and PLS-DA showed elevated levels of ketones (aceto-acetate and β-hydroxybutyrate) and lactate in the 428
carcinoma.429
Chen et al.[78] used LC-MS followed by multivariate statistics to identify potential biomarkers in uterine 430
cervix cancer. Nine potential biomarkers associated with the cancer were identified viz. proline betaine, 431
pyridylacetic acid, urocanic acid, 1-methylguanine, uric acid, methylxanthine, tryptophan, theophylline, and 432
carnitine.433
Prostate434
Prostate cancer is the second leading cause of cancer death in men. Zhou et al[79] employed ESI-MS and 435
chemometric tools PCA and HCA to identify lipid biomarkers in plasma of prostate cancer patients. It was 436
reported that three out of 13 lipid classes, phosphatidylethanolamine, ether-linked phosphatidylethanolamine 437
and ether-linked phosphatidylcholine possessed biomarker potential. Raina et al.[80] performed 1H-NMR on 438
prostate tissue extracts from transgenic adenocarcinoma of the mouse prostate (TRAMP) model. It showed that 439
inositol hexaphosphate (IP6) significantly decreased glucose metabolism and membrane phospholipid synthesis, 440
in addition to raising myoinositol levels in the prostate. Oral IP6 supplement blocked growth and angiogenesis 441
of prostate cancer in the TRAMP model, depriving the tumour of energy. The inhibitory potential of IP6 in 442
towards prostate cancer could be explored further. Giskeodegrad et al.[81] performed metabolic profiling of 443
prostate cancer tissue by aid of HR-MAS-NMR. Data analysis by PLS, PLS-DA and absolute quantification 444
(LCModel) were used to predict cancer aggressiveness. High grade cancer tissue differed from low grade cancer445
tissue in lower level of spermine and citrate. McDunn et al.[82] analyzed malignant and normal prostate tissue446
using GC-MS and UHPLC-MS/MS. HCA identified the metabolites associated with cancer aggressiveness. 447
Prostate tumours had significantly altered metabolite profiles compared to cancer-free prostate tissues. The 448
discriminatory metabolites were associated with cell growth, energetics, stress, and loss of prostate-specific 449
biochemistry. Aggressive prostate tumours could be further categorized by metabolites viz. NAD+ and 450
kynurenine. 451
Colon452
Colorectal cancer is one of the most prevalent cancers and a major cause of morbidity and mortality.453
Effective screening can facilitate timely detection and result in a better clinical outcome. Jimenez et al.[83]454
applied HR-MAS-NMR to analyze metabolites in tumour samples and adjacent mucosal tissues excised from 455
Page 17 of 31
Accep
ted
Man
uscr
ipt
17
colorectal cancer patients. OPLS-DA of metabolic profiles showed biochemical discrimination between the two 456
types of tissues. Taurine, isoglutamine, choline, lactate, phenylalanine, tyrosine (increased concentrations in 457
tumour tissue) together with lipids and triglycerides (decreased concentrations in tumour tissue) were the most 458
discriminant metabolites between the two groups. Also, the metabolic profiling could distinguish between 459
tumours of different T- and N-stages (T stage has greater weight than the N stage and the former affects survival 460
more significantly) according to tumour node metastasis (TNM) stage classification. It was observed that 461
adjacent mucosa (10 cm from the tumour margin) harbours unique metabolites for telling apart from T- to N-462
stage and predicting 5-year survival, more reliably than tumours. Holst et al.[84] compared glycosylation 463
profiles of colorectal tumour tissues and corresponding control tissues of patients. Using MALDI-TOF-MS and 464
2D-LC-MS/MS, enzymatically-released and 2-aminobenzoic acid-labelled glycans from glycosphingolipids 465
were characterized. PCA and PLS-DA revealed significant differences between tumour and corresponding 466
control tissues. Anomalous glycosylations in colorectal tumours were observed. The obtained discriminating 467
glycans (various degrees of fucosylation, acetylation, sulfation sialylation etc.) can play role as biomarkers to 468
improve diagnostics and therapy. Tan et al.[85] profiled serum metabolites from newly diagnosed colorectal 469
carcinoma patients as well as healthy subjects using GC-TOF-MS and UPLC-Q-TOF-MS. A distinct metabolic 470
signature involving TCA cycle, urea cycle, glutamine, fatty acids, and gut flora metabolism was observed in the 471
patients. Differential metabolites were identified with OPLS-DA. 2-hydroxybutyrate, 2-oxobutyrate and 2-472
aminobutyrate level was higher in the patients that indicated higher ROS generation in the patients. 5-473
hydroxytryptamine, N-acetyl-5-hydroxytryptamine, indoxyl, and indoxyl sulfate were found significantly 474
lowered in the patients. Phua et al.[86] conducted metabolomic analysis of faeces to detect colorectal cancer. 475
GC-TOF-MS based metabolic profiling of the samples from patients and healthy subjects showed significant 476
distinction. OPLS-DA revealed the discriminatory marker metabolites to be fructose, linoleic acid and nicotinic 477
acid. Manna et al.[87] performed metabolic profiling of colon tumour and adjacent tissues as well as urine 478
samples from cancerous mice and patients. The discriminatory metabolites were found to be proline, threonine, 479
glutamic acid, arginine, N1-acetylspermidine, xanthine, uracil, betaine, symmetric dimethylarginine and480
asymmetric dimethylarginine. These altered metabolites were detected to be the products of polyamine 481
metabolism, nucleic acid metabolism and methylation.482
Leukaemia483
Wang et al.[88] used UPLC/Q-TOF-MS to study the effects of an acridone derivative-based anti-tumour484
compound on human immature T-cell line (CCRF-CEM). PCA and OPLS-DA identified distinct metabolites 485
Page 18 of 31
Accep
ted
Man
uscr
ipt
18
involved in glutathione (GSH) and glycerophospholipid metabolism pathways. Glutathione level and the 486
reduced/oxidized glutathione ratio were significantly decreased in the treated cells, while L-cysteinyl-glycine 487
and glutamate were greatly increased. In glycerophospholipid metabolism, cell membrane components 488
phosphatidylcholines were decreased in the treated cells, while the oxidative products lysophosphatidylcholines 489
were significantly increased. The ROC and lipid peroxidation product malondialdehyde (MDA) were notably 490
increased, accompanied with decrease of mitochondrial trans-membrane potential, release of cytochrome C and 491
activation of caspase-3, indicating oxidative stress-mediated apoptosis in the leukaemia cells. Acute myeloid 492
leukaemia is a fatal form of cancer. Wang et al.[89] analyzed the serum metabolites in acute myeloid leukaemia 493
patients and healthy controls using 1H-NMR spectroscopy in conjunction with multivariate data analysis. 494
Significant metabolite differences were observed due to perturbation in glycolysis/gluconeogenesis, TCA cycle, 495
biosynthesis of proteins and lipoproteins, and metabolism of fatty acids and cell membrane components, 496
especially choline and its phosphorylated derivatives.497
Myeloma498
Lodi et al.[90] performed NMR analysis of blood serum and urine samples from multiple myeloma 499
patients and detected metabolites associated with diagnosis, post-treatment remission and disease progression. 500
Carnitine and acetylcarnitine emerged as novel biomarkers of diagnosis and relapse. Puchades-Carrasco et 501
al.[91] used 1H-NMR for metabolic profiling of multiple myeloma patient serum. Patient at diagnosis exhibited 502
higher levels of isoleucine, arginine, acetate, phenylalanine, tyrosine and lower levels of 3-hydroxybutyrate, 503
lysine, glutamine, and some lipids compared with the control set. Serum of patients at remission displayed a 504
metabolic profile different from that in diagnosis stage, indicating patient had responded to drug.505
Melanoma506
Abaffy et al.[92] used an untargeted approach to compare volatile metabolomic signature of melanoma507
and matched control non-neoplastic skin from the same patient. VOC from tissues were extracted and analyzed 508
by GC-MS. XCMS and MetaboAnalyst were used to find differentially expressed metabolic features. The 509
analysis revealed increased levels of lauric acid and palmitic acid in melanoma, assumed to be due to enhanced510
de novo lipid synthesis (a characteristic of cancer, caused due to increased oxidative stress). Increased oxidative 511
stress is likely to cause the spike in lauric acid. Feng et al.[93] employed HR-MAS-NMR as well 1H-NMR, 512
followed by PCA to investiagte secondary metastatic B16-F10 melanoma in C57BL/6J mouse liver. Absolute 513
concentrations of glutamate, creatine, fumarate and cholesterol were elevated in the melanoma group as 514
compared to control, while the absolute concentrations of succinate, glycine, glucose, and the family of linear 515
Page 19 of 31
Accep
ted
Man
uscr
ipt
19
lipids including long chain fatty acids, total choline and acyl glycerol were decreased. The ratio of 516
glycerophosphocholine to phosphocholine is increased by about 1.5-fold in the melanoma group, while the 517
estimate of absolute concentration of total choline is lower in melanoma mice. Linear lipid levels were518
decreased by β-oxidation in the melanoma group, which contributes to an increase in the synthesis of cholesterol519
and also serves as energy source for TCA cycle. These findings suggest a correlation between lipid oxidation, 520
TCA cycle and the hypoxia-inducible factors signal pathway in tumour metastases. Fedele et al[94] subjected 521
B16F10 melanoma tumour cells to 1H-NMR for metabolite profiling. The result showed the relationship 522
between the metabolites involved in energy metabolism, apoptosis and proliferation. Among the metabolites, 523
lactate, aspartate, glycerol, lipids, alanine, myo-inositol, phosphocholine, choline, acetate, creatine and taurine 524
showed differential expression. Choline and creatine were shown to be closely related with tumour progression. 525
From the above studies, it can be inferred that metabolomics is effective non-invasive technique for melanoma 526
biomarker discovery.527
Hurdles and future directions528
Metabolic phenotyping has furnished crucial biomarker findings, but replicating them with different 529
sample sets is cumbersome, often futile due to un-uniformity in analytical and clinical protocols used in the 530
studies [4,95]. The same metabolites can also be high in some non-cancerous conditions such as thyroid disease, 531
rheumatoid arthritis, inflammatory bowel disease, pancreatitis and liver diseases. So, markers might tend to 532
mislead diagnosis, prognosis and therapy. The Cancer Genome Atlas (TCGA) is a database of cancer genomics 533
and proteomics, a resource to understand tumour initiation and progression. However, metabolomic information 534
has not been incorporated into it. Tang et al.[96] suggest the addition of metabolomic profiles to the public 535
TCGA. This dataset might provide insight on tumour biology. Strengthened with metabolic flux analyses 536
(isotope tracers and mathematical modelling) metabolomics can serve as an excellent tool for biomarker 537
discovery. Cross metabotyping through different analytical devices could validate result. This strategy has been 538
adopted in order to obtain high-fidelity markers. Integration of two highly sensitive and complementary539
metabolomics platforms could enable a comprehensive metabolic profiling and assist in discrimination 540
malignant from benign tumours[41].541
Conclusions 542
Robust, low-cost and non-invasive biomarkers to facilitate screening, surveillance and therapy 543
monitoring are scanty, necessitating the development of more effective modalities. In this regard, metabolomics 544
seems highly promising. Biomarkers detected by analytical platforms give an insight into cancer biology and545
Page 20 of 31
Accep
ted
Man
uscr
ipt
20
tapped properly, metabolomics might open new avenues for diagnostics and drug discovery. It relies on the 546
analysis of the cancer metabolome to identify marker metabolites. By identifying the key metabolic pathways 547
which elaborated the significant metabolites, potential targets for cancer therapy could be developed. However, 548
the issues with the specificity and sensitivity for their detection must be dealt with, in order to unleash full 549
potential of metabolomics. The appreciation of metabolomics in the realm of cancer research domain has just 550
started, tremendous amount of work remains to be done by thwarting bottlenecks.551
Conflict of interest statement552
The authors declare there is no conflict of interest in submission of this manuscript to this journal.553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
Page 21 of 31
Accep
ted
Man
uscr
ipt
21
References576
[1] P.M. Winter, S.D. Caruthers, A. Kassner, T.D. Harris, L.K. Chinen, J.S. Allen, et al., Molecular Imaging 577of Angiogenesis in Nascent Vx-2 Rabbit Tumors Using a Novel {alpha}{nu}{beta}3-targeted 578Nanoparticle and 1.5 Tesla Magnetic Resonance Imaging, Cancer Res. 63 (2003) 5838–5843. 579http://cancerres.aacrjournals.org/content/63/18/5838.long (accessed September 16, 2014).580
[2] M. Westhoff, P. Litterst, L. Freitag, W. Urfer, S. Bader, J.-I. Baumbach, Ion mobility spectrometry for 581the detection of volatile organic compounds in exhaled breath of patients with lung cancer: results of a 582pilot study., Thorax. 64 (2009) 744–8. doi:10.1136/thx.2008.099465.583
[3] C. Denkert, E. Bucher, M. Hilvo, R. Salek, M. Orešič, J. Griffin, et al., Metabolomics of human breast 584cancer: new approaches for tumor typing and biomarker discovery., Genome Med. 4 (2012) 37. 585doi:10.1186/gm336.586
[4] E. Holmes, I.D. Wilson, J.K. Nicholson, Metabolic phenotyping in health and disease., Cell. 134 (2008) 587714–7. doi:10.1016/j.cell.2008.08.026.588
[5] J.L. Spratlin, N.J. Serkova, S.G. Eckhardt, Clinical applications of metabolomics in oncology: a review., 589Clin. Cancer Res. 15 (2009) 431–40. doi:10.1158/1078-0432.CCR-08-1059.590
[6] C.A. Smith, E.J. Want, G. O’Maille, R. Abagyan, G. Siuzdak, XCMS: processing mass spectrometry 591data for metabolite profiling using nonlinear peak alignment, matching, and identification., Anal. Chem. 59278 (2006) 779–87. doi:10.1021/ac051437y.593
[7] T. Pluskal, S. Castillo, A. Villar-Briones, M. Oresic, MZmine 2: modular framework for processing, 594visualizing, and analyzing mass spectrometry-based molecular profile data., BMC Bioinformatics. 11 595(2010) 395. doi:10.1186/1471-2105-11-395.596
[8] S.-Y. Liu, R.-L. Zhang, H. Kang, Z.-J. Fan, Z. Du, Human liver tissue metabolic profiling research on 597hepatitis B virus-related hepatocellular carcinoma., World J. Gastroenterol. 19 (2013) 3423–32. 598doi:10.3748/wjg.v19.i22.3423.599
[9] J. Xia, R. Mandal, I. V Sinelnikov, D. Broadhurst, D.S. Wishart, MetaboAnalyst 2.0--a comprehensive 600server for metabolomic data analysis., Nucleic Acids Res. 40 (2012) W127–33. doi:10.1093/nar/gks374.601
[10] S. Gupta, K. Chawla, Oncometabolomics in cancer research., Expert Rev. Proteomics. 10 (2013) 325–60236. doi:10.1586/14789450.2013.828947.603
[11] N. Abbassi-Ghadi, S. Kumar, J. Huang, R. Goldin, Z. Takats, G.B. Hanna, Metabolomic profiling of 604oesophago-gastric cancer: a systematic review., Eur. J. Cancer. 49 (2013) 3625–37. 605doi:10.1016/j.ejca.2013.07.004.606
[12] A. Zhang, H. Sun, S. Qiu, X. Wang, Metabolomics in noninvasive breast cancer., Clin. Chim. Acta. 424 607(2013) 3–7. doi:10.1016/j.cca.2013.05.003.608
[13] F. Leung, N. Musrap, E.P. Diamandis, V. Kulasingam, Advances in mass spectrometry-based 609technologies to direct personalized medicine in ovarian cancer, Transl. Proteomics. 1 (2013) 74–86. 610doi:10.1016/j.trprot.2013.08.001.611
[14] S.P. Putri, Y. Nakayama, F. Matsuda, T. Uchikata, S. Kobayashi, A. Matsubara, et al., Current 612metabolomics: practical applications., J. Biosci. Bioeng. 115 (2013) 579–89. 613doi:10.1016/j.jbiosc.2012.12.007.614
[15] X. Wang, A. Zhang, H. Sun, Power of metabolomics in diagnosis and biomarker discovery of 615hepatocellular carcinoma., Hepatology. 57 (2013) 2072–7. doi:10.1002/hep.26130.616
Page 22 of 31
Accep
ted
Man
uscr
ipt
22
[16] O.A. Aboud, R.H. Weiss, New opportunities from the cancer metabolome., Clin. Chem. 59 (2013) 138–61746. doi:10.1373/clinchem.2012.184598.618
[17] K.A. Vermeersch, M.P. Styczynski, Applications of metabolomics in cancer research., J. Carcinog. 12 619(2013) 9. doi:10.4103/1477-3163.113622.620
[18] M. Khunger, U. Kumar, H.K. Roy, A.K. Tiwari, Dysplasia and cancer screening in 21st century., 621APMIS. 122 (2014) 674–82. doi:10.1111/apm.12283.622
[19] W. Struck-Lewicka, R. Kaliszan, M.J. Markuszewski, Analysis of urinary nucleosides as potential 623cancer markers determined using LC-MS technique., J. Pharm. Biomed. Anal. (2014). 624doi:10.1016/j.jpba.2014.04.022.625
[20] A. Zhang, H. Sun, G. Yan, P. Wang, Y. Han, X. Wang, Metabolomics in diagnosis and biomarker 626discovery of colorectal cancer., Cancer Lett. 345 (2014) 17–20. doi:10.1016/j.canlet.2013.11.011.627
[21] E.G. Armitage, C. Barbas, Metabolomics in cancer biomarker discovery: current trends and future 628perspectives., J. Pharm. Biomed. Anal. 87 (2014) 1–11. doi:10.1016/j.jpba.2013.08.041.629
[22] W. Lu, S. Zhang, X. Teng, E. Melamud, M.A. Lazar, E. White, et al., LC-MS and GC-MS based 630metabolomics platform for cancer research, Cancer Metab. 2 (2014) P41. doi:10.1186/2049-3002-2-S1-631P41.632
[23] M. Suzuki, S. Nishiumi, A. Matsubara, T. Azuma, M. Yoshida, Metabolome analysis for discovering 633biomarkers of gastroenterological cancer., J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci. 966 634(2014) 59–69. doi:10.1016/j.jchromb.2014.02.042.635
[24] I.M. Di Gangi, U. Vrhovsek, V. Pazienza, F. Mattivi, Analytical metabolomics-based approaches to 636pancreatic cancer, TrAC Trends Anal. Chem. 55 (2014) 94–116. doi:10.1016/j.trac.2013.12.006.637
[25] P. Kośliński, R. Bujak, E. Daghir, M.J. Markuszewski, Metabolic profiling of pteridines for 638determination of potential biomarkers in cancer diseases., Electrophoresis. 32 (2011) 2044–54. 639doi:10.1002/elps.201000664.640
[26] J.W. Locasale, L.C. Cantley, Altered metabolism in cancer., BMC Biol. 8 (2010) 88. doi:10.1186/1741-6417007-8-88.642
[27] C. V Dang, Links between metabolism and cancer., Genes Dev. 26 (2012) 877–90. 643doi:10.1101/gad.189365.112.644
[28] Tumor Markers - National Cancer Institute, (n.d.). 645http://www.cancer.gov/cancertopics/factsheet/detection/tumor-markers (accessed November 08, 2014).646
[29] A.M. Lutz, J.K. Willmann, C.W. Drescher, P. Ray, F. V Cochran, N. Urban, et al., Early diagnosis of 647ovarian carcinoma: is a solution in sight?, Radiology. 259 (2011) 329–45. doi:10.1148/radiol.11090563.648
[30] M.G. Keane, G.J. Johnson, Early diagnosis improves survival in colorectal cancer., Practitioner. 256 649(n.d.) 15–8, 2. http://www.ncbi.nlm.nih.gov/pubmed/22988701 (accessed September 16, 2014).650
[31] G. Lewis, A.P. Maxwell, Early diagnosis improves survival in kidney cancer., Practitioner. 256 (2012) 65113–6, 2. http://www.ncbi.nlm.nih.gov/pubmed/22497103 (accessed September 16, 2014).652
[32] A.D. Hopper, Early endoscopy improves survival in gastric cancer., Practitioner. 258 (n.d.) 23–7, 2. 653http://www.ncbi.nlm.nih.gov/pubmed/25211790 (accessed September 16, 2014).654
Page 23 of 31
Accep
ted
Man
uscr
ipt
23
[33] C.O. Madu, Y. Lu, Novel diagnostic biomarkers for prostate cancer., J. Cancer. 1 (2010) 150–77. 655http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2962426&tool=pmcentrez&rendertype=abst656ract (accessed September 15, 2014).657
[34] F. Farshidfar, A.M. Weljie, K. Kopciuk, W.D. Buie, A. Maclean, E. Dixon, et al., Serum metabolomic 658profile as a means to distinguish stage of colorectal cancer., Genome Med. 4 (2012) 42. 659doi:10.1186/gm341.660
[35] A. Halama, N. Riesen, G. Möller, M. Hrabě de Angelis, J. Adamski, Identification of biomarkers for 661apoptosis in cancer cell lines using metabolomics: tools for individualized medicine., J. Intern. Med. 274 662(2013) 425–39. doi:10.1111/joim.12117.663
[36] S. Tiziani, V. Lopes, U.L. Günther, Early stage diagnosis of oral cancer using 1H NMR-based 664metabolomics., Neoplasia. 11 (2009) 269–76, 4p following 269. 665http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2647729&tool=pmcentrez&rendertype=abst666ract (accessed September 15, 2014).667
[37] X. Pan, M. Wilson, L. Mirbahai, C. McConville, T.N. Arvanitis, J.L. Griffin, et al., In vitro 668metabonomic study detects increases in UDP-GlcNAc and UDP-GalNAc, as early phase markers of 669cisplatin treatment response in brain tumor cells., J. Proteome Res. 10 (2011) 3493–500. 670doi:10.1021/pr200114v.671
[38] P. Tripathi, P. Kamarajan, B.S. Somashekar, N. MacKinnon, A.M. Chinnaiyan, Y.L. Kapila, et al., 672Delineating metabolic signatures of head and neck squamous cell carcinoma: phospholipase A2, a 673potential therapeutic target., Int. J. Biochem. Cell Biol. 44 (2012) 1852–61. 674doi:10.1016/j.biocel.2012.06.025.675
[39] S. Nakamizo, T. Sasayama, M. Shinohara, Y. Irino, S. Nishiumi, M. Nishihara, et al., GC/MS-based 676metabolomic analysis of cerebrospinal fluid (CSF) from glioma patients., J. Neurooncol. 113 (2013) 65–67774. doi:10.1007/s11060-013-1090-x.678
[40] K. Yonezawa, S. Nishiumii, J. Kitamoto-Matsuda, T. Fujita, K. Morimoto, D. Yamashita, et al., Serum 679and tissue metabolomics of head and neck cancer., Cancer Genomics Proteomics. 10 (n.d.) 233–8. 680http://www.ncbi.nlm.nih.gov/pubmed/24136976 (accessed September 15, 2014).681
[41] L. Gao, Z. Wen, C. Wu, T. Wen, C.N. Ong, Metabolic profiling of plasma from benign and malignant 682pulmonary nodules patients using mass spectrometry-based metabolomics., Metabolites. 3 (2013) 539–68351. doi:10.3390/metabo3030539.684
[42] M. Wu, Y. Xu, W.L. Fitch, M. Zheng, R.E. Merritt, J.B. Shrager, et al., Liquid chromatography/mass 685spectrometry methods for measuring dipeptide abundance in non-small-cell lung cancer., Rapid 686Commun. Mass Spectrom. 27 (2013) 2091–8. doi:10.1002/rcm.6656.687
[43] T. Wen, L. Gao, Z. Wen, C. Wu, C.S. Tan, W.Z. Toh, et al., Exploratory investigation of plasma 688metabolomics in human lung adenocarcinoma., Mol. Biosyst. 9 (2013) 2370–8. 689doi:10.1039/c3mb70138g.690
[44] M. Serizawa, M. Kusuhara, V. Zangiacomi, K. Urakami, M. Watanabe, T. Takahashi, et al., 691Identification of metabolic signatures associated with erlotinib resistance of non-small cell lung cancer 692cells., Anticancer Res. 34 (2014) 2779–87. http://www.ncbi.nlm.nih.gov/pubmed/24922639 (accessed 693September 15, 2014).694
[45] Y. Li, X. Song, X. Zhao, L. Zou, G. Xu, Serum metabolic profiling study of lung cancer using ultra high 695performance liquid chromatography/quadrupole time-of-flight mass spectrometry., J. Chromatogr. B. 696Analyt. Technol. Biomed. Life Sci. 966 (2014) 147–53. doi:10.1016/j.jchromb.2014.04.047.697
Page 24 of 31
Accep
ted
Man
uscr
ipt
24
[46] N. Lefort, A. Brown, V. Lloyd, R. Ouellette, M. Touaibia, A.S. Culf, et al., 1H NMR metabolomics 698analysis of the effect of dichloroacetate and allopurinol on breast cancers., J. Pharm. Biomed. Anal. 93 699(2014) 77–85. doi:10.1016/j.jpba.2013.08.017.700
[47] S. Wei, L. Liu, J. Zhang, J. Bowers, G.A.N. Gowda, H. Seeger, et al., Metabolomics approach for 701predicting response to neoadjuvant chemotherapy for breast cancer., Mol. Oncol. 7 (2013) 297–307. 702doi:10.1016/j.molonc.2012.10.003.703
[48] J. Shen, L. Yan, S. Liu, C.B. Ambrosone, H. Zhao, Plasma metabolomic profiles in breast cancer 704patients and healthy controls: by race and tumor receptor subtypes., Transl. Oncol. 6 (2013) 757–65. 705http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3890711&tool=pmcentrez&rendertype=abst706ract (accessed September 15, 2014).707
[49] M.C. Mimmi, N. Finato, G. Pizzolato, C.A. Beltrami, F. Fogolari, A. Corazza, et al., Absolute 708quantification of choline-related biomarkers in breast cancer biopsies by liquid chromatography 709electrospray ionization mass spectrometry., Anal. Cell. Pathol. (Amst). 36 (2013) 71–83. 710doi:10.3233/ACP-130082.711
[50] J. Budczies, S.F. Brockmöller, B.M. Müller, D.K. Barupal, C. Richter-Ehrenstein, A. Kleine-Tebbe, et 712al., Comparative metabolomics of estrogen receptor positive and estrogen receptor negative breast 713cancer: alterations in glutamine and beta-alanine metabolism., J. Proteomics. 94 (2013) 279–88. 714doi:10.1016/j.jprot.2013.10.002.715
[51] E. Jobard, C. Pontoizeau, B.J. Blaise, T. Bachelot, B. Elena-Herrmann, O. Trédan, A serum nuclear 716magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer., Cancer 717Lett. 343 (2014) 33–41. doi:10.1016/j.canlet.2013.09.011.718
[52] G. Corona, J. Polesel, L. Fratino, G. Miolo, F. Rizzolio, D. Crivellari, et al., Metabolomics biomarkers 719of frailty in elderly breast cancer patients., J. Cell. Physiol. 229 (2014) 898–902. doi:10.1002/jcp.24520.720
[53] Y. Qiu, G. Cai, B. Zhou, D. Li, A. Zhao, G. Xie, et al., A distinct metabolic signature of human 721colorectal cancer with prognostic potential., Clin. Cancer Res. 20 (2014) 2136–46. doi:10.1158/1078-7220432.CCR-13-1939.723
[54] J. Xu, Y. Chen, R. Zhang, Y. Song, J. Cao, N. Bi, et al., Global and targeted metabolomics of 724esophageal squamous cell carcinoma discovers potential diagnostic and therapeutic biomarkers., Mol. 725Cell. Proteomics. 12 (2013) 1306–18. doi:10.1074/mcp.M112.022830.726
[55] X. Zhang, L. Xu, J. Shen, B. Cao, T. Cheng, T. Zhao, et al., Metabolic signatures of esophageal cancer: 727NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum., Biochim. 728Biophys. Acta. 1832 (2013) 1207–16. doi:10.1016/j.bbadis.2013.03.009.729
[56] L. Wang, J. Chen, L. Chen, P. Deng, Q. Bu, P. Xiang, et al., 1H-NMR based metabonomic profiling of 730human esophageal cancer tissue., Mol. Cancer. 12 (2013) 25. doi:10.1186/1476-4598-12-25.731
[57] H. Wen, S.S. Yoo, J. Kang, H.G. Kim, J.-S. Park, S. Jeong, et al., A new NMR-based metabolomics 732approach for the diagnosis of biliary tract cancer., J. Hepatol. 52 (2010) 228–33. 733doi:10.1016/j.jhep.2009.11.002.734
[58] X.-H. He, W.-T. Li, Y.-J. Gu, B.-F. Yang, H.-W. Deng, Y.-H. Yu, et al., Metabonomic studies of 735pancreatic cancer response to radiotherapy in a mouse xenograft model using magnetic resonance 736spectroscopy and principal components analysis., World J. Gastroenterol. 19 (2013) 4200–8. 737doi:10.3748/wjg.v19.i26.4200.738
[59] S. Yabushita, K. Fukamachi, H. Tanaka, T. Fukuda, K. Sumida, Y. Deguchi, et al., Metabolomic and 739transcriptomic profiling of human K-ras oncogene transgenic rats with pancreatic ductal 740adenocarcinomas., Carcinogenesis. 34 (2013) 1251–9. doi:10.1093/carcin/bgt053.741
Page 25 of 31
Accep
ted
Man
uscr
ipt
25
[60] T. Kobayashi, S. Nishiumi, A. Ikeda, T. Yoshie, A. Sakai, A. Matsubara, et al., A novel serum 742metabolomics-based diagnostic approach to pancreatic cancer., Cancer Epidemiol. Biomarkers Prev. 22 743(2013) 571–9. doi:10.1158/1055-9965.EPI-12-1033.744
[61] A.B. Leichtle, U. Ceglarek, P. Weinert, C.T. Nakas, J.-M. Nuoffer, J. Kase, et al., Pancreatic carcinoma, 745pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma., 746Metabolomics. 9 (2013) 677–687. doi:10.1007/s11306-012-0476-7.747
[62] S.A. Ritchie, H. Akita, I. Takemasa, H. Eguchi, E. Pastural, H. Nagano, et al., Metabolic system 748alterations in pancreatic cancer patient serum: potential for early detection., BMC Cancer. 13 (2013) 749416. doi:10.1186/1471-2407-13-416.750
[63] J.F. Xiao, R.S. Varghese, B. Zhou, M.R. Nezami Ranjbar, Y. Zhao, T.-H. Tsai, et al., LC-MS based 751serum metabolomics for identification of hepatocellular carcinoma biomarkers in Egyptian cohort., J. 752Proteome Res. 11 (2012) 5914–23. doi:10.1021/pr300673x.753
[64] S. Chen, H. Kong, X. Lu, Y. Li, P. Yin, Z. Zeng, et al., Pseudotargeted metabolomics method and its 754application in serum biomarker discovery for hepatocellular carcinoma based on ultra high-performance 755liquid chromatography/triple quadrupole mass spectrometry., Anal. Chem. 85 (2013) 8326–33. 756doi:10.1021/ac4016787.757
[65] D. Beyoğlu, S. Imbeaud, O. Maurhofer, P. Bioulac-Sage, J. Zucman-Rossi, J.-F. Dufour, et al., Tissue 758metabolomics of hepatocellular carcinoma: tumor energy metabolism and the role of transcriptomic 759classification., Hepatology. 58 (2013) 229–38. doi:10.1002/hep.26350.760
[66] A. Zhang, H. Sun, G. Yan, Y. Han, Y. Ye, X. Wang, Urinary metabolic profiling identifies a key role 761for glycocholic acid in human liver cancer by ultra-performance liquid-chromatography coupled with 762high-definition mass spectrometry., Clin. Chim. Acta. 418 (2013) 86–90. doi:10.1016/j.cca.2012.12.024.763
[67] J.D. Clarke, P. Novak, A.D. Lake, P. Shipkova, N. Aranibar, D. Robertson, et al., Characterization of 764hepatocellular carcinoma related genes and metabolites in human nonalcoholic fatty liver disease., Dig. 765Dis. Sci. 59 (2014) 365–74. doi:10.1007/s10620-013-2873-9.766
[68] J. Bowers, E. Hughes, N. Skill, M. Maluccio, D. Raftery, Detection of hepatocellular carcinoma in 767hepatitis C patients: biomarker discovery by LC-MS., J. Chromatogr. B. Analyt. Technol. Biomed. Life 768Sci. 966 (2014) 154–62. doi:10.1016/j.jchromb.2014.02.043.769
[69] Y. Liu, Z. Hong, G. Tan, X. Dong, G. Yang, L. Zhao, et al., NMR and LC/MS-based global 770metabolomics to identify serum biomarkers differentiating hepatocellular carcinoma from liver 771cirrhosis., Int. J. Cancer. 135 (2014) 658–68. doi:10.1002/ijc.28706.772
[70] K.K. Pasikanti, K. Esuvaranathan, Y. Hong, P.C. Ho, R. Mahendran, L. Raman Nee Mani, et al., 773Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass 774spectrometry., J. Proteome Res. 12 (2013) 3865–73. doi:10.1021/pr4000448.775
[71] P. Tripathi, B.S. Somashekar, M. Ponnusamy, A. Gursky, S. Dailey, P. Kunju, et al., HR-MAS NMR 776tissue metabolomic signatures cross-validated by mass spectrometry distinguish bladder cancer from 777benign disease., J. Proteome Res. 12 (2013) 3519–28. doi:10.1021/pr4004135.778
[72] J.V. Alberice, A.F.S. Amaral, E.G. Armitage, J.A. Lorente, F. Algaba, E. Carrilho, et al., Searching for 779urine biomarkers of bladder cancer recurrence using a liquid chromatography-mass spectrometry and 780capillary electrophoresis-mass spectrometry metabolomics approach., J. Chromatogr. A. 1318 (2013) 781163–70. doi:10.1016/j.chroma.2013.10.002.782
[73] E. Szymańska, M.J. Markuszewski, M. Markuszewski, R. Kaliszan, Altered levels of nucleoside 783metabolite profiles in urogenital tract cancer measured by capillary electrophoresis., J. Pharm. Biomed. 784Anal. 53 (2010) 1305–12. doi:10.1016/j.jpba.2010.07.031.785
Page 26 of 31
Accep
ted
Man
uscr
ipt
26
[74] W. Struck, D. Siluk, A. Yumba-Mpanga, M. Markuszewski, R. Kaliszan, M.J. Markuszewski, Liquid 786chromatography tandem mass spectrometry study of urinary nucleosides as potential cancer markers., J. 787Chromatogr. A. 1283 (2013) 122–31. doi:10.1016/j.chroma.2013.01.111.788
[75] P. Kośliński, P. Jarzemski, M.J. Markuszewski, R. Kaliszan, Determination of pterins in urine by HPLC 789with UV and fluorescent detection using different types of chromatographic stationary phases (HILIC, 790RP C8, RP C18)., J. Pharm. Biomed. Anal. 91 (2014) 37–45. doi:10.1016/j.jpba.2013.12.012.791
[76] T. Zhang, X. Wu, C. Ke, M. Yin, Z. Li, L. Fan, et al., Identification of potential biomarkers for ovarian 792cancer by urinary metabolomic profiling., J. Proteome Res. 12 (2013) 505–12. doi:10.1021/pr3009572.793
[77] R. Vettukattil, T.E. Hetland, V.A. Flørenes, J. Kærn, B. Davidson, T.F. Bathen, Proton magnetic 794resonance metabolomic characterization of ovarian serous carcinoma effusions: chemotherapy-related 795effects and comparison with malignant mesothelioma and breast carcinoma., Hum. Pathol. 44 (2013) 7961859–66. doi:10.1016/j.humpath.2013.02.009.797
[78] Y. Chen, J. Xu, R. Zhang, G. Shen, Y. Song, J. Sun, et al., Assessment of data pre-processing methods 798for LC-MS/MS-based metabolomics of uterine cervix cancer., Analyst. 138 (2013) 2669–77. 799doi:10.1039/c3an36818a.800
[79] X. Zhou, J. Mao, J. Ai, Y. Deng, M.R. Roth, C. Pound, et al., Identification of plasma lipid biomarkers 801for prostate cancer by lipidomics and bioinformatics., PLoS One. 7 (2012) e48889. 802doi:10.1371/journal.pone.0048889.803
[80] K. Raina, K. Ravichandran, S. Rajamanickam, K.M. Huber, N.J. Serkova, R. Agarwal, Inositol 804hexaphosphate inhibits tumor growth, vascularity, and metabolism in TRAMP mice: a multiparametric 805magnetic resonance study., Cancer Prev. Res. (Phila). 6 (2013) 40–50. doi:10.1158/1940-6207.CAPR-80612-0387.807
[81] G.F. Giskeødegård, H. Bertilsson, K.M. Selnæs, A.J. Wright, T.F. Bathen, T. Viset, et al., Spermine and 808citrate as metabolic biomarkers for assessing prostate cancer aggressiveness., PLoS One. 8 (2013) 809e62375. doi:10.1371/journal.pone.0062375.810
[82] J.E. McDunn, Z. Li, K.-P. Adam, B.P. Neri, R.L. Wolfert, M. V Milburn, et al., Metabolomic signatures 811of aggressive prostate cancer., Prostate. 73 (2013) 1547–60. doi:10.1002/pros.22704.812
[83] B. Jiménez, R. Mirnezami, J. Kinross, O. Cloarec, H.C. Keun, E. Holmes, et al., 1H HR-MAS NMR 813spectroscopy of tumor-induced local metabolic “field-effects” enables colorectal cancer staging and 814prognostication., J. Proteome Res. 12 (2013) 959–68. doi:10.1021/pr3010106.815
[84] S. Holst, K. Stavenhagen, C.I.A. Balog, C.A.M. Koeleman, L.M. McDonnell, O.A. Mayboroda, et al., 816Investigations on aberrant glycosylation of glycosphingolipids in colorectal cancer tissues using liquid 817chromatography and matrix-assisted laser desorption time-of-flight mass spectrometry (MALDI-TOF-818MS)., Mol. Cell. Proteomics. 12 (2013) 3081–93. doi:10.1074/mcp.M113.030387.819
[85] B. Tan, Y. Qiu, X. Zou, T. Chen, G. Xie, Y. Cheng, et al., Metabonomics identifies serum metabolite 820markers of colorectal cancer., J. Proteome Res. 12 (2013) 3000–9. doi:10.1021/pr400337b.821
[86] L.C. Phua, X.P. Chue, P.K. Koh, P.Y. Cheah, H.K. Ho, E.C.Y. Chan, Non-invasive fecal metabonomic 822detection of colorectal cancer., Cancer Biol. Ther. 15 (2014) 389–97. doi:10.4161/cbt.27625.823
[87] S.K. Manna, N. Tanaka, K.W. Krausz, M. Haznadar, X. Xue, T. Matsubara, et al., Biomarkers of 824coordinate metabolic reprogramming in colorectal tumors in mice and humans., Gastroenterology. 146 825(2014) 1313–24. doi:10.1053/j.gastro.2014.01.017.826
Page 27 of 31
Accep
ted
Man
uscr
ipt
27
[88] Y. Wang, D. Gao, Z. Chen, S. Li, C. Gao, D. Cao, et al., Acridone derivative 8a induces oxidative 827stress-mediated apoptosis in CCRF-CEM leukemia cells: application of metabolomics in mechanistic 828studies of antitumor agents., PLoS One. 8 (2013) e63572. doi:10.1371/journal.pone.0063572.829
[89] Y. Wang, L. Zhang, W.-L. Chen, J.-H. Wang, N. Li, J.-M. Li, et al., Rapid diagnosis and prognosis of de 830novo acute myeloid leukemia by serum metabonomic analysis., J. Proteome Res. 12 (2013) 4393–401. 831doi:10.1021/pr400403p.832
[90] A. Lodi, S. Tiziani, F.L. Khanim, U.L. Günther, M.R. Viant, G.J. Morgan, et al., Proton NMR-based 833metabolite analyses of archived serial paired serum and urine samples from myeloma patients at 834different stages of disease activity identifies acetylcarnitine as a novel marker of active disease., PLoS 835One. 8 (2013) e56422. doi:10.1371/journal.pone.0056422.836
[91] L. Puchades-Carrasco, R. Lecumberri, J. Martínez-López, J.-J. Lahuerta, M.-V. Mateos, F. Prósper, et 837al., Multiple myeloma patients have a specific serum metabolomic profile that changes after achieving 838complete remission., Clin. Cancer Res. 19 (2013) 4770–9. doi:10.1158/1078-0432.CCR-12-2917.839
[92] T. Abaffy, M.G. Möller, D.D. Riemer, C. Milikowski, R.A. DeFazio, Comparative analysis of volatile 840metabolomics signals from melanoma and benign skin: a pilot study., Metabolomics. 9 (2013) 998–8411008. doi:10.1007/s11306-013-0523-z.842
[93] J. Feng, N.G. Isern, S.D. Burton, J.Z. Hu, Studies of Secondary Melanoma on C57BL/6J Mouse Liver 843Using 1H NMR Metabolomics., Metabolites. 3 (2013) 1011–35. doi:10.3390/metabo3041011.844
[94] T.A. Fedele, A.C. Galdos-Riveros, H. Jose de Farias e Melo, A. Magalhães, D.A. Maria, Prognostic 845relationship of metabolic profile obtained of melanoma B16F10., Biomed. Pharmacother. 67 (2013) 846146–56. doi:10.1016/j.biopha.2012.10.013.847
[95] T.A. Clayton, J.C. Lindon, O. Cloarec, H. Antti, C. Charuel, G. Hanton, et al., Pharmaco-metabonomic 848phenotyping and personalized drug treatment., Nature. 440 (2006) 1073–7. doi:10.1038/nature04648.849
[96] X. Tang, C.-C. Lin, I. Spasojevic, E.S. Iversen, J.-T. Chi, J.R. Marks, A joint analysis of metabolomics 850and genetics of breast cancer., Breast Cancer Res. 16 (2014) 415. doi:10.1186/s13058-014-0415-9. 851
852
853
854
855
856
857
858
859
860
861
862
Page 28 of 31
Accep
ted
Man
uscr
ipt
28
Fig. 1. Potential drug targets in cancer and role of metabolomics in diagnosis, prognosis and therapy863
864
Page 29 of 31
Accep
ted
Man
uscr
ipt
29
Table. Conventional cancer biomarkers and nvel biomarkers discovered by metabolomics864865
Cancer Coventional markers Novel markers from metabolomics study Metabolomic tool used
Reference
Breast BRCA1CA 15-3, CEAHER2 (HER2/neu, erbB-2, EGFR2)ER/PR
Phosphocholine, total choline, glycerophosphocholineThreonine, isoleucine, glutamine, linolenic acidβ-alanine, 2-hydroyglutarate, glutamate, xanthine and glutamineHistidine, acetoacetate, glycerol, pyruvate, glycoproteins (N-acetyl), mannose, glutamate and phenylalanine
1H-NMRLC-MSLC-ESI-MSGC-TOF-MS
Lefort et al.[45]Wei et al.[46]Mimmi et al.[48]Jobard et al.[50]
Colorectal CEA, CA 19-9EGFRBRAFUGT1A1KRAS
Taurine, isoglutamine, choline, lactate, phenylalanine, tyrosine, lipids and triglyceridesGlycosylationsTCA cycle, urea cycle, glutamine, fatty acids, and gut flora metabolism2-hydroxybutyrate, 2-oxobutyrate and 2-aminobutyrate5-hydroxytryptamine, N-acetyl-5-hydroxytryptamine, indoxyl, and indoxyl sulphateLinoleic acid and nicotinic acidProline, threonine, glutamic acid, arginine, N1-acetylspermidine, xanthine, uracil, betaine, symmetric dimethylarginine andasymmetric dimethylarginine
HR-MAS-NMRMALDI-TOF-MS 2D-LC-MS/MSGC-TOF-MS UPLC-Q-TOF-MS
Jimenez et al.[79]Holst et al.[80]Tan et al.[81]Phua et al.[82]Manna et al.[83]
Gastric (oesophagus, GIST)
HER2 (HER2/neu, erbB-2 or EGFR2)c-kit
Lysophosphatidylcholines, fatty acids, L-carnitine, acylcarnitinesLipid metabolism, amino acid metabolism, glycolysis, ketogenesis, TCA cycle and energy metabolismGlucose, adenosine monophosphate (AMP), NAD and up-regulation of formate
LC-MS1H-NMR UHPLC
Xu et al.[53]Zhang et al.[54]Wang et al.[55]
Head, neck Bmi-1FAP
Warburg effect, oxidative phosphorylation, energy metabolism, TCA cycle, glutaminolysis, hexosamine pathway, osmo-regulatory and antioxidant mechanismPhosphatidylcholine/lysophosphatidylcholine Phosphocholine/glycerophosphocholineArachidonic acidLactic acid and 2-aminopimelic acidCitric acid, isocitric acidValine, thyrosine, serine and methionine
1H-NMRGC-MS
Tiziani et al.[35]Pan et al.[36]Tripathi et al.[37]Nakamizo et al.[38]Yonezawa et al.[39]
Kidney and bladder
BTANMP22
Tryptophan-quinolinic acidHistidine, phenylalanine, tyrosine and tryptophan
GC-GC-TOF-MSHR-MAS-NMRLC-MS CE-TOF-MS
Pasikanti et al.[69]Alberice et al.[71]
Leukaemia BCR-ABLB2MCD20CD30PDGFR
Glutathione L-cysteinyl-glycine, glutamate Phosphatidylcholines, lysophosphatidylcholines
UPLC/Q-TOF-MS
Wang et al.[84]Wang et al.[85]
Lungs (NSCLC,mesothelioma)
PAPSMRPALKKRASEGFR
N-succinyl-2,6-diaminopimelate, deoxycholic acid glycine conjugate, octanoylcarnitine, lysophosphatidylcholines, lysophosphatidylcholines, phosphatidylcholines, phosphatidylserine and cholesteryl acetate, dipeptidesGlutamine metabolismCholine, free fatty acids, lysophosphatidylcholines.
GC-MS LC-MSCE-TOF-MSUPLC-Q-TOF MS
Gao et al.[40]Wu et al.[41]Serizawa et al.[43]Li et al.[44]
Melanoma S-100BRAF
Lauric acid and palmitic acidGlutamate, creatine, fumarate, cholesterolSuccinate, glycine, glucose, long chain fatty acids, total choline,acyl glycerolGlycerophosphocholine, phosphocholineLactate, aspartate, glycerol, alanine, myo-inositol, phosphocholine, acetate, taurine
HR-MAS-NMR
Abaffy et al.[88]Feng et al.[89]Fedele et al[90]
Multiple myeloma
ImmunoglobulinsFree light chainsPAP
Carnitine, acetylcarnitine Isoleucine, arginine, acetate, phenylalanine, tyrosine, 3-hydroxybutyrate, lysine, glutamine
1H-NMR Lodi et al.[86]Puchades-Carrasco et al.[87]
Ovary, uterus, cervix
CA 125HE-4Inhibin
Nucleotide, histidine, tryptophan and mucin metabolism N4-acetylcytidine, pseudouridine, urate-3-ribonucleoside, and succinic acidKetones and lactateProline betaine, pyridylacetic acid, urocanic acid, 1-methylguanine, uric acid, methylxanthine, tryptophan, theophylline, carnitine
UPLC-Q-TOF-MSHR-NMRLC-MS
Zhang et al.[72]Vettukattil et al.[73]Chen et al.[74]
Pancreas and liver
CA 19-9CEAAFP
Choline, taurine, alanine, isoleucine, leucine, valine, lactate and glutamic acidPhosphocholine, glycerophosphocholine, betainepalmitoleic acidPhosphatidylcholines, lysophosphatidylcholines and
NMRHR-MAS-NMRGC-MSUPLC-Q-
Wen et al.[56]He et al.[57]Yabushita et al.[58]Ritchie et al.[61]Xiao et al.[62]
Page 30 of 31
Accep
ted
Man
uscr
ipt
30
sphingomyelinsGlycholic acid, glycodeoxycholic acid, 3β, 6β-dihydroxy-5β-cholan-24-oic acid, oleoyl carnitine, peptide Phe-PheMedium-chain acylcarnitines and branched-chain amino acidlong-chain acylcarnitines and aromatic amino acidGlucose, glycerol 3- and 2-phosphate, malate, alanine, myo-inositol, and linoleic acidL-phenylalanine, glycerophosphocholine, chenodeoxycholic acid Glycine conjugate, β-sitosterol, quinaldic acid, arachidyl carnitine, tetradecanal oleamideSynthesis of ketone bodies, citrate cycle, phospholipid metabolism, sphingolipid metabolism, fatty acid oxidation, amino acid catabolism and bile acid metabolism
TOF-MSUHPLC-QQQ-MRM-MS
Chen et al.[63]Beyoglu et al.[64]Liu et al.[8]Bowers et al.[67]Lit et al. [68]
Prostate PSA Phosphatidylethanolamine,Ether-linked phosphatidylethanolamine Ether-linked phosphatidylcholineNAD+ and kynurenineSpermine and citrate
ESI-MS1H-NMRHR-MAS-NMRGC-MS and UHPLC-MS/MS
Zhou et al[75]McDunn et al.[78]Raina et al.[76]Giskeodegrad et al.[77]
866867868869870871872873874