Content

• Introduction
• Methods
• Applications
• Challenges & problems
• Future directions
• Conclusion
 Introduction
Emerging Field of ‘Omics' Research
• Unbiased global survey of all low molecular-weight molecules or
metabolites in biofluid, cell, tissue, organ, or organism

• Study of range of metabolites in cells or organs & ways they are

altered in disease states and their changes over time as consequence
of stimuli (including biological perturbation such as diet, disease or
intervention)
Any organic molecule detectable in body with MW < 1000 Dalton with
concentration ≥ 1 µM Metabolome refers to complete set
of sugars,
Includes peptides, oligonucleotides, small-molecule metabolites
nucelosides, to
organic acids,
ketones, aldehydes, amines, aminobeacids,
found within
lipids, a alkaloids
steroids, biologicaland
drugs (xenobiotics) sample, such as a single organism.
Includes human & microbial products
 Introduction contd…
• The name ‘metabolomics’ was coined in the late 1990s
– The first paper using the word was by Oliver, S. G., Winson, M.
K., Kell, D. B. & Baganz, F. (1998). Systematic functional analysis
of the yeast genome.Trends Biotechnol.1998 Sep;16(9):373-8.
• Study of metabolome, started decades ago with early applications
in field of toxicology, inborn metabolic errors & nutrition

• Original report to mention metabolomics approach in oncology dates

back 25 years ago where authors claimed that cancer could be
identified from nuclear magnetic resonance (NMR) spectra generated
from blood samples*

*Fossel et al. N Engl J Med 1986;315:1369–76

 THE ‘OMIC’ WORLD
Transcriptomics
Genomics

Pharmacogenomics Spliceomics

Epigenomics Proteomics
Metabolomics

‘OMICS’ REFERS TO LARGE SCALE ANALYSIS

 GENOMICS

TRANSCRIPTOMICS Bioiformatics:
Using techniques
developed in fields of
computational science
& statistics
PROTEOMICS Key element in data
management & analysis
of collected data sets

METABOLOMICS
 Why Metabolomics ?.....!!!!!
Since metabolome is closely tied to
genotype of an organism, its
physiology and its environment (what
the organism eats or breathes),
metabolomics offers a unique
opportunity to look at genotype-
phenotype as well as genotype-
envirotype relationships
 In Other Words……..
• Not all changes or abnormalities
detected in genome or transcriptome
may be causing abnormality or disease
e.g. silent mutations
• Similarly not all enzymes & protein
products detected via proteomics are
functional
• Also they do not take into account
environmental influences occurring at
later stage
• Can be used to monitor changes in
genome or to measure effects of
downregulation or upregulation of
specific gene transcript
• Metabolites are ultimate result of
cellular pathways (taking into account
changes in genome, trancriptome,
proteome as well as metabolic
influences)
Direct correlation with abnormalities being caused
 Some More Comparisons
Genomics Transcriptomics Proteomics Metabolomics

Target number 40,000 genes 150000 transcripts 1,000,000 2500

proteins metabolites

Specimen tissue, cells Tissue, cells Biofluids, Biofluids,

tissue, cells tissue, cells

Technique SNP arrays DNA arrays 2DE1 & NMR4,

MALDI2-TOF GC-MS5
MS3

1:Two-dimensional gel electrophoresis

2:Matrix-assisted laser desorption/ionization
3:Time-of-flight mass spectrometry
4:Nuclear magnetic resonance
5:Gas chromatography–mass spectrometry
 Trends
Published papers

Genomics

Proteomics

Metabolomics

Genomics, Proteomics : 5 folds / 5yrs

 Definitions
• Metabolic profiling :
– Quantitative study of a group of metabolites, known or unknown,
within or associated with a particular metabolic pathway

• Metabolic fingerprinting:
– Measures a subset of the whole profile with little differentiation or
quantitation of metabolites

• Target isotope-based analysis:

– Focuses on particular segment of metabolome by analysing only few
selected metabolites comprising specific biochemical pathway
How does Metabolomics work?
• ? Samples
• ? Methods
• ? Data collection
• ? Determination of significance
Basic Workflow Validation followed by clinical
application

Data
Dataanalysis
analysisusing
usingmultivariate
multivariate
analysis
analysise.g.
e.g.
Sample
Samplecollection,
collection, ••Principle
PrincipleComponent
ComponentAnalysis
Analysis
treatment
treatmentand
and (PCA)
(PCA)
processing
processing ••Partial
PartialLeast-Squares
Least-Squares(PLS)
(PLS)
Method
Method
••Orthogonal
OrthogonalPLSPLS(OPLS)
(OPLS)

Separation
Separationtechnique:
technique:
••Gas Detection
Detectiontechnique:
technique:
GasChromatography
Chromatography(GC)
(GC)
••High •• Nuclear
NuclearMagnetic
Magnetic
HighPerformance
PerformanceLiquid
Liquid
Chromatography Resonance
ResonanceSpectroscopy
Spectroscopy
Chromatography(HPLC)
(HPLC)
••Ultra (NMR)
(NMR)
UltraPerformance
PerformanceLiquid
Liquid
•• Mass
MassSpectrometry
Spectrometry(MS)
(MS)
Chromatography
Chromatography(UPLC)
(UPLC)
••Capillary
CapillaryElectrophoresis
Electrophoresis(CE)
(CE)
Basic Workflow

Sample
Samplecollection,
collection,
treatment
treatmentand
and
processing
processing
 Metabolomic Samples
• Metabolomic assessment can be pursued both in vitro and in
vivo using cells, fluids, or tissues
• Biofluids are easiest to work with:
– Serum Maximum
Maximumexperience
experience
– Plasma with
withserum
serumand
andurine
urine
– Urine samples
samples
– Ascitic fluid/pleural fluid
– Saliva Currently,
Currently,interest
interestisis
– Bronchial washes evolving
evolvingto
touse
usetissue
tissue
– Prostatic secretions samples
samplesdirectly
directly
 Sample Collection & Handling
• All biological samples collected for metabolic analysis require careful
sample handling, special requirements for diet, physical activities, &
other patient validation

• Due to high susceptibility of metabolic pathways to exogenous

environment, maintaining low temperature and consistent sample
extraction is essential

• For biofluids, standard sample volume: 0.1 to 0.5 mL

• For NMR, minimal sample preparation is required (including direct

analysis of intact tissue specimen)
Basic Workflow Both approaches involve an initial
chromatographic stage in which
metabolites are separated either in the
Sample
Samplecollection,
collection, gas or solution phase, resp.
treatment
treatmentand
and
processing
processing

Separation
Separationtechnique:
technique:
••Gas
GasChromatography
Chromatography(GC)
(GC)
••High
HighPerformance
PerformanceLiquid
Liquid
Chromatography
Chromatography(HPLC)
(HPLC)
••Ultra
UltraPerformance
PerformanceLiquid
Liquid
Chromatography
Chromatography(UPLC)
(UPLC)
••Capillary
CapillaryElectrophoresis
Electrophoresis(CE)
(CE)
Basic Workflow

Sample
Samplecollection,
collection,
treatment
treatmentand
and
processing
processing

Separation
Separationtechnique:
technique:
••Gas
GasChromatography
Chromatography(GC)
(GC) Detection
Detectiontechnique:
technique:
••Capillary
CapillaryElectrophoresis
Electrophoresis(CE)
(CE) •• Nuclear
NuclearMagnetic
Magnetic
••High
HighPerformance
PerformanceLiquid
Liquid Resonance
ResonanceSpectroscopy
Spectroscopy
Chromatography
Chromatography(HPLC)
(HPLC) (NMR)
(NMR)
••Ultra
UltraPerformance
PerformanceLiquid
Liquid •• Mass
MassSpectrometry
Spectrometry(MS)
(MS)
Chromatography
Chromatography(UPLC)
(UPLC)
 Detection Techniques
• Mass spectrometry (MS) Qualitative &
quantitative
• Nuclear magnetic resonance (NMR) spectroscopy assessment
• Others:
• Ion-mobility spectrometry,
• Electrochemical detection (coupled to HPLC)
• Radiolabelling techniques (when combined with thin-
layer chromatography)
• MRSI (Magnetic resonance spectroscopic imaging)
• PET scan

MS NMR
Nuclear Magnetic Resonance (NMR)
Spectroscopy
• Uses isotopes possessing property of magnetic spin
• Isotopes usually used : 1H and 13C NMR spectroscopy, although 31P
NMR spectroscopy used to measure high-energy phosphate
metabolites and phosphorylated lipid intermediates.
• Relatively insensitive technique: Current detection limits are of
order of 100 µM in a tissue extract or biofluid
• Can be used in a non-invasive manner, making it possible to
metabolically profile intact tissue or whole organ
• Typical acquisition times: about 10 minutes
A variant of NMR called high resolution magic angle
• Highly reproducible
spinning NMR spectroscopy (HR-MAS) developed to
improve spectral resolution in solids such as intact tissue
samples
It preserves tissue architecture so pathological
evaluation is not compromised
 Metabolites detected in cancer by NMR
Leucine Acetate Lysine Taurine

Isoleucine Glutamine Creatine Phosphoethanol-amine

Valine Glutamate Phosphocreatine Myo-inositol

Lactate Glutathione Free choline Scyllo-inositol

β-hydroxybutyrate Succinate Phosphocholine Glycine

α-ketoisovalerate Asparate Glycerophospho- Glycerol

choline
β-Glucose Fumarate Histidine NAD and NADH

α-Glucose Tyrosine Phenylalanine Glycerophospho-

ethanolamine
Formate Dimethylamine Betaine Inosine

Alanine Aspargine ADP and ATP Threonine

UTP and UDP Inorganic phosphate Sugar Phosphates Cholesterols and esters

Phosphatidyl- Phosphatidyl- Phosphatidyl-

choline ethanolamine glycerol
Plasmalogen Triacylglycerol
 Gas Chromatography– & Liquid
Chromatography–Mass Spectrometry
(MS)
• Both approaches involve an initial chromatographic stage followed
by separation according to their mass to charge ratio
• Current detection limits for MS-based approaches are of the order
of 100 nM, allowing detection of large no. of metabolites.
• However, not all metabolites can be ionized to an equal extent,
potentially biasing the information produced.
• Typical acquisition times of about 30 minutes
 Comparison of NMR &MS
MASS SPECTROMETRY NMR SPECTROSCOPY
– More sensitive for metabolite – Less sensitive for metabolite
detection detection
• Mass spectrometers can detect – Non-destructive, requires little
analytes routinely in sample handling & preparation:
femtomolar to attomolar range
• Metabolites in liquid state (serum,
– Requires more tissue urine and so on),
destruction • Intact tissues (e.g., tumors) or in vivo

– Difficulty in quantification – Quantification easy:

• Peak area of compound in NMR
spectrum directly related to conc. of
specific nuclei (e.g., 1H, 13C), making
quantifi-cation of compounds in
complex mixture very precise
Basic Workflow
Data
Dataanalysis
analysisusing
usingmultivariate
multivariate
analysis
analysise.g.
e.g.
••Principle
PrincipleComponent
ComponentAnalysis
Analysis
Sample
Samplecollection,
collection, (PCA)
(PCA)
treatment
treatmentand
and ••Partial
PartialLeast-Squares
Least-Squares(PLS)
(PLS)
processing
processing Method
Method
••Orthogonal
OrthogonalPLSPLS(OPLS)
(OPLS)

Separation
Separationtechnique:
technique:
••Gas
GasChromatography
Chromatography(GC)
(GC) Detection
Detectiontechnique:
technique:
••Capillary
CapillaryElectrophoresis
Electrophoresis(CE)
(CE) •• Nuclear
NuclearMagnetic
Magnetic
••High
HighPerformance
PerformanceLiquid
Liquid Resonance
ResonanceSpectroscopy
Spectroscopy
Chromatography
Chromatography(HPLC)
(HPLC) (NMR)
(NMR)
••Ultra
UltraPerformance
PerformanceLiquid
Liquid •• Mass
MassSpectrometry
Spectrometry(MS)
(MS)
Chromatography
Chromatography(UPLC)
(UPLC)
DATA Analysis & Interpretation
DATA Analysis
 DATA Analysis
• NMR/MS spectra from biofluids or tumor tissue contain hundreds of
signals from endogenous metabolites: converted to spectral data sets,
reduced to 100 to 500 spectral segments, & their respective signal
intensities are directly entered into statistical programs
• This first step of metabolomics analysis facilitates pattern recognition, or
group clustering, such as normal versus cancer or responders versus
nonresponders,
• Multivariate statistics (e.g. Principle Component Analysis) designed for
large data sets are then applied
DATA Analysis
DATA Analysis
• Quantitation & association of putative biomarkers with respect to particular
characteristic or outcome, such as tumor grade or response to therapy
• Statistical approach represented by standard Student’s t test or ANOVA, depending on
group number & size
APPLICATIONS
 Applications
• Increasingly being used in a variety of health applications including
– Pharmacology & pre-clinical drug trials
– Toxicology
– Transplant monitoring
– New-born screening
– Clinical chemistry
– Tool for functional genomics

• However, a key limitation to metabolomics

– ‘The human metabolome is not at all well characterized’
Human Metabolome Project

• On 23 January 2007, Human Metabolome Project, led by Dr. David

Wishart of the University of Alberta, Canada, completed first draft of
human metabolome, consisting of database of approximately 2500
metabolites

• Project mandate: identify, quantify, catalogue & store all metabolites

that can potentially be found in human tissues and biofluids at
concentrations greater than one micromolar
Wishart DS et al. "HMDB: the Human Metabolome Database". Nucleic Acids Research 35 :
D521–6
 Applications in the Field of Oncology
• Goal of these omics-based studies is more effective, more specific,
safer, more “personalized” medical care
• Biomarker in cancer diagnosis, prognosis, & therapeutic response
evaluation (including detection of residual tumor cells)
• Screening tool
• Detection of micrometastases
• As both predictive & pharmacodynamic marker of drug effect
including search for new drugs
• In Nutrigenomics, to see effect of diet on cancer prevention as well as
response to treatment
• As translational research tool, can provide link between laboratory &
clinic
• Molecular analyses of cancers can reveal information about
mechanisms of initiation, progression & provide foundation for
clinical tests
Characterization of Tumor
Metabolome

1. High glycolytic enzyme activities Warburg effect

2. The expression of the pyruvate kinase isoenzyme type M2(M2PK))
3. High phosphometabolite levels
4. A high channelling of glucose carbons to synthetic processes
5. A high rate of pyrimidine and purine de novo synthesis
M2-PK of particular
6. A high rate of fatty acid de novo synthesis interest as its
7. A low (ATP+GPT) : (CTP+UTP) ratio Quantification of
inactive this dimeric
8. Low AMP levels tumor M2-PK in
form is dominant in
9. A high glutaminolytic capacity plasma & stool
tumorsallows
& named
10. Release of immunosuppressive metabolites early detection
tumor of M2-PK
11. A high methionine dependency tumors/ therapy
(tM2-PK) 1

1) Mazurek S et al. Anticancer Res 2003;23:1149–54

 Diagnosis
Carcinoma Prostate: Making a difference
• Traditional biomarker: Prostate specific antigen (PSA)
• Shortcomings:
– Low specificity of PSA,
– Inability to specify a cut-point below which cancer is unlikely
– Non-trivial false negative rate for prostate biopsy
– Over-diagnosis and over-treatment of relatively indolent tumors with
low potential for morbidity or death if left untreated

• Small percentage of cancers account for the mortality: those which are
invasive and metastasize. What are the molecular markers and mediators for
such cellular behaviors?

• How can we tell apart the lethal cancers from the relatively innocuous
cancers that look the same by histology and stage?
 Carcinoma Prostate
• Metabolomic profiles delineate potential role for sarcosine in
prostate cancer progression
Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Mehra R et al
• Using a combination of LC & GC based MS, profiling of more than 1,126
metabolites across 262 clinical samples related to prostate cancer (42 tissues
and 110 each of urine and plasma)
• Sarcosine (can be detected non-invasively in urine):
– Highly increased during prostate cancer progression to metastasis
– Levels also increased in invasive prostate cancer cell lines relative to benign
prostate epithelial cells
– Knockdown of glycine-N-methyl transferase, the enzyme that generates sarcosine
from glycine, attenuated prostate cancer invasion

Sreekumar A et al. Nature 2009, 457:910-914

 Diagnosis: Carcinoma Prostate

CONCLUSION : Sarcosine could be a potentially promising biomarker for

early detection of prostate cancer as well as cut-off levels can

be defined to mark biological aggressiveness of the disease
 Diagnostics of Prostate Cancer
• Application of blood plasma metabolites fingerprinting for
diagnosis of II stage of prostate cancer has been investigated

• Area under the ROC-curve (0.994) suggests that the proposed

approach is effective and can be used for clinical applications
Metabolome-based diagnostics PSA-based diagnostics
Sensitivity 95.0% Sensitivity 35.0%
Specificity 96.7% Specificity 83.3%
Accuracy 95.7% Accuracy 51.4%

Lokhov, Archakov et al. Biomedical Chemistry. 2009 May-Jun;55(3):247-54.

 Lung Cancer
Exhaled breath analysis with a colorimetric sensor array for
the identification and characterization of lung cancer.
Mazzone PJ, Wang XF, Xu Y, Mekhail T, Beukemann MC, Na J, Kemling JW, Suslick
KS, Sasidhar M
•Pattern of exhaled breath volatile organic compounds represents
metabolic biosignature with potential to identify & characterize lung
cancer
•Reported accuracy exceeding 80% in lung cancer detection, which is
comparable to CT scan
•Also colorimetric sensor array could identify subtype of lung cancer
(small cell versus adenocarcinoma versus squamous cell) with accuracy
approaching 90%
•Combining breath biosignature with clinical risk factors may improve
accuracy of signature
Mazzone PJ et al. J Thorac Oncol. 2012 Jan;7(1):137-42.
 Diagnosis: Breast Cancer
• Several NMR studies analyzed breast biopsy sample identifying over 30
endogenous metabolites in breast tissue1,2
• Cancers reliably showed elevated phosphocholine, low glycer-
ophosphocholine, & low glucose compared with benign tumors or healthy
tissue
• Also, when 91breast cancers & 48 adjacent normal tissue specimens examined
after surgical resection using HR-MAS 1H-NMR
– Malignant phenotype could reliably be differentiated from normal tissue with sensitivity &
specificity between 83% and 100% for tumor size, lymph node, and hormonal status, as well
as histology1
• In vivo, when MRSI of breast is performed on patients before biopsy, precise
differentiation of cancer and benign tissue possible based on choline
detection, with a sensitivity of 100%3
• Importantly, biopsy could have been prevented 68% of the time if only
performed on the choline-positive tissue
1.Bathen TF et al. Breast Cancer Res Treat 2007;104:181-9
2.Sitter B et al. Biomed 2006;19:30-40
3.Bartella L et al. Radiology 2007;245: 80-7
 Diagnosis in Ovarian Cancer
• Metabolomic differences between
healthy women & ovarian cancer
investigated.
• 1H-NMR spectroscopy done on serum
from
– 38 preoperative ovarian cancer
patients,
– 12 women with benign ovarian cysts,
– 53 samples from healthy women
• Separation rates were:
– In premenopausal group: cancer vs
normal/benign disease: 100 %
– In postmenopausal group: cancer vs
normal/benign diease: 97.4 %

Odunsi K et al. Int J Cancer 2005;113:782-8.

Metabolic Biomarkers of Tumors
 Metabolomics: Predictive Markers of
Response to Therapy
• Evolving innovative cancer drugs, many with cytostatic rather
than cytotoxic mechanism of action, challenges our traditional
way to asses tumor response based on volumetric changes as
performed by standard imaging techniques

• Compelling interest to develop new tools to monitor outcomes

of therapeutic intervention

• More specifically, non-invasive imaging techniques reporting on

tissue function and metabolism such as PET scan, functional MRI
studies & NMR spectroscopy hold great potential
 Assessment of Response to Therapy
• Use of metabolomics for assessment of treatment effect, as
predictive measure of efficacy & as pharmacodynamic marker, has
been shown in vitro for traditional chemotherapy as well as
hormonal agents.
• Goal is to define pretreatment metabolic profile based on which
we can choose subgroup of patients who will benefit maximum from
given therapy
• Can be assessed both in vitro as well as in vivo
• In vitro, use of 1H-NMR on human glioma cell culture successfully
predicted separation into drug-resistant & drug-sensitive groups
before treatment with nitrosoureas

El-Deredy et al. Cancer Res 1997;57:4196–9

 Assessment of Response contd…
• In vivo, 1H-NMR,used to investigate metabolic changes associated with nitrosourea
treatment of B16 melanoma in mice
• During growth-inhibitory phase, significant accumulation of glucose, glutamine,
aspartate, and serine-derived metabolites occurred
• Growth recovery reflected activation of energy production systems and increased
nucleotide synthesis thus characterising drug resistance

Morvan D et al. Cancer Res 2007;67:2150–9

 Application in Novel Therapeutics
• Therapeutics in oncology now targeting aberrant pathways involved in
growth, proliferation, and metastases
• Biomarkers are being increasingly used in the early clinical
development of such agents
– To identify, validate, and optimize therapeutic targets and agents
– To determine and confirm mechanism of drug action
– As a pharmacodynamic end point
– In predicting or monitoring responsiveness to treatment, toxicity, and
resistance
• Current examples of using metabolomics in developmental therapeutics
are with tyrosine kinase inhibitors, proapoptotic agents, heat shock
protein inhibitors and PIK3 inhibitors
 Application in Therapeutics contd….
• Treatment with targeted therapies results in distinct metabolic profile
between sensitive & resistant cells
• Metabolic detection of imatinib resistance:
– Decrease in mitochondrial glucose oxidation
– Nonoxidative ribose synthesis from glucose
– Highly elevated phosphocholine levels
• These data indicate that NMR metabolomics may provide way for
monitoring changes reflecting early resistance to novel targeted agents
• Early metabolomic markers of resistance may dictate therapy adjustments
that prevent overt phenotypic progression (clinical failure)

Gottschalk S et al. Clin Cancer Res 2004;10:6661–8.

 Detection of Chemotoxicity

• Chemotherapy drugs capable to cause significant, irreversible, life

threatening organ damage
• Bothersome and distressing for patients and might affect the
optimal delivery of treatment
• Various studies have predicted the risk factors for drug induced
organ damage
• However, lack of biomarker to pick-up these changes in early phase
causes potential morbidity and mortality
• Metabolomics can more thoroughly address interplay between
gene, drugs environment and thus increase our ability to predict
individual variation in drug response phenotypes
This approach has been coined pharmacometabolomics
 As Biomarker for Chemotoxicity
Metabolomic study of cisplatin-induced nephrotoxicity
Portilla D,Li S, Nagothu KK, Megyesi J, Kaissling B, Schnackenberg L, Safirstein RL, Beger
RD
•Samples from mice treated with single injection of cisplatin were collected for 3 days and
analyzed by 1H-NMR spectroscopy
•Biochemical analysis of endogenous metabolites performed in serum, urine,& kidney tissue
•Presence of glucose, amino acids, & trichloacetic acid cycle metabolites in urine after 48 h
of cisplatin administration was demonstrated in mice subsequently developing renal failure
•These metabolic alterations precede changes in serum creatinine
•Study shows that cisplatin induces a unique NMR metabolic profile in urine of mice
developing acute renal failure
•Injury-induced metabolic profile may be used as a biomarker of cisplatin-induced
nephrotoxicity

Kidney Int 2006 Jun;69(12):2194-204

 Problems & Challenges in Metabolomics
•Metabolites have wide range of
molecular weights & large variations in
concentration

•Metabolome is much more dynamic

than proteome & genome, which makes
metabolome more time sensitive

•Loss of various metabolites during

tissue extraction e.g. glutathione

•Number of metabolites existing far

smaller than the no. of transcripts
Therefore, given metabolite pattern can
reflect several genomic changes
Not All Metabolites can be Identified
Carcinoma Pancreas
•Tesiram et al. tried to determine NMR characteristics & metabolite
profiles of serum samples from patients with pancreatic cancer
compared with noncancerous control samples
•Data showed that
Significantly higher in
– Total choline (P = 0.03)
cancer versus control
– Taurine (P = 0.03) samples
– Glucose plus triglycerides (P = 0.01)
• Also detected were species that could not be individually identified
and that were designated UCM (unresolved complex matter)
•Levels of UCM were significantly higher in subjects with cancer,
being almost double those of control samples

Tesiram et al. Pancreas. 2012 Apr;41(3):474-80.

 Problems contd….
• Metabolic profiles are complex & highly susceptible to
endogenous and exogenous factors (hormones, race, age, sex, rate
of metabolism, diet, physical activities, xenobiotics)

• Therefore samples collected for metabolic analysis require careful

sample handling and information regarding diet, physical
activities, and other patient validation

• Marked heterogeneity across studies

• Among distinct tumor types, profiles vary with respect to many

metabolites, including alanine, citrate, glycine, lactate, nucleotides,
and lipids, making it difficult to generalize findings across tumor
groups
 Future Directions
• If pathognomonic metabolic profiles of various cancers/diseases can be
identified & validated in various body fluids, metabolomics may save
time, cost, & effort in obtaining definitive diagnosis in situations where
no other test can provide answers

• Future role as minimally invasive screening tool

• Most of recent research into tumour metabolomics comes from NMR-

based studies, studies aiming at using combination of NMR & MS so as
to improve upon sensitivity, specificity & reproducibility

• Improved sensitivity will also be possible using cryogenically cooled

NMR probes, known as CRYOPROBES
 Conclusion
• Metabolomics is a novel discipline encompassing comprehensive metabolite
evaluation, pattern recognition & statistical analyses
• May provide ability to diagnose cancer in curative state, determine
aggressiveness of cancer to help direct prognosis, therapy, & predict drug
efficacy
• Still in its infancy & has lagged behind other ‘omic’ sciences due to technical
limitations, database challenges
• It is a long path of discovery, confirmation, clinical trials, and approval to
establish test validity and utility
• Urgent need to establish spectral databases of metabolites, as well as cross-
validation of NMR- or MS-obtained metabolites & correlation with other
quantitative assays
• Important to integrate it with other ‘omics’ technology so that the entire spectrum
of the malignant phenotype can be characterized
THANK YOU
Application in Therapeutics contd….
• Another interesting application of metabolomics is in area of heat shock
protein 90 (Hsp90) inhibitors
• Although their mechanism of action is not fully elucidated, current data
suggest that this family of agents increase cellular destruction of client
oncogenic proteins
• In one study, colon cancer xenografts were treated with an Hsp90 inhibitor
and extracts of these tumors were analyzed by 31P-NMR, reflecting a
significant increase in phosphocholine, valine and phosphoethanolamine
levels, indicating altered phospholipid metabolism
• These results, although preliminary, address that metabolic changes could
be used as pharmacodynamic biomarkers of Hsp90 inhibitors, class of
agents that do not seem to result in classic antitumor effects

Neckers L. Heat shock protein 90: the cancer chaperone. J Biosci 2007;32:517–30