3. 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
Includes peptides, oligonucleotides, sugars, nucelosides, organic acids,
ketones, aldehydes, amines, amino acids, lipids, steroids, alkaloids and
drugs (xenobiotics)
Includes human & microbial products
Metabolome refers to complete set
of small-molecule metabolites to be
found within a biological sample,
such as a single organism.
4. 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
6. Bioiformatics:
Using techniques
developed in fields
of computational
science & statistics
Key element in data
management &
analysis of collected
data sets
GENOMICS
TRANSCRIPTOMICS
PROTEOMICS
METABOLOMICS
7. 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
8. 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
9. Some More Comparisons
Genomics Transcriptomics Proteomics Metabolomics
Target number 40,000
genes
150000 transcripts 1,000,000
proteins
2500
metabolites
Specimen tissue, cells Tissue, cells Biofluids,
tissue, cells
Biofluids,
tissue, cells
Technique SNP arrays DNA arrays 2DE1 &
MALDI2-TOF
MS3
NMR4,
GC-MS5
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
11. 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
12. How does Metabolomics work?
• ? Samples
• ? Methods
• ? Data collection
• ? Determination of significance
13. Sample collection,
treatment and
processing
Detection technique:
• Nuclear Magnetic
Resonance Spectroscopy
(NMR)
• Mass Spectrometry (MS)
Separation technique:
•Gas Chromatography (GC)
•High Performance Liquid
Chromatography (HPLC)
•Ultra Performance Liquid
Chromatography (UPLC)
•Capillary Electrophoresis (CE)
Data analysis using multivariate
analysis e.g.
• Principle Component Analysis
(PCA)
• Partial Least-Squares (PLS)
Method
• Orthogonal PLS (OPLS)
Basic Workflow Validation followed by clinical
application
15. 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
– Plasma
– Urine
– Ascitic fluid/pleural fluid
– Saliva
– Bronchial washes
– Prostatic secretions
Maximum experience
with serum and urine
samples
Currently, interest is
evolving to use tissue
samples directly
16. 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)
17. Sample collection,
treatment and
processing
Separation technique:
•Gas Chromatography (GC)
•High Performance Liquid
Chromatography (HPLC)
•Ultra Performance Liquid
Chromatography (UPLC)
•Capillary Electrophoresis (CE)
Basic Workflow Both approaches involve an initial
chromatographic stage in which
metabolites are separated either in the
gas or solution phase, resp.
19. Detection Techniques
• Mass spectrometry (MS)
• Nuclear magnetic resonance (NMR) spectroscopy
• Others:
• Ion-mobility spectrometry,
• Electrochemical detection (coupled to HPLC)
• Radiolabelling techniques (when combined with thin-
layer chromatography)
• MRSI (Magnetic resonance spectroscopic imaging)
• PET scan
Qualitative &
quantitative
assessment
MS NMR
20. 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
• Highly reproducible
A variant of NMR called high resolution magic angle
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
21. 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
-hydroxybutyrate Succinate Phosphocholine Glycine
-ketoisovalerate Asparate Glycerophospho-
choline
Glycerol
-Glucose Fumarate Histidine NAD and NADH
-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-
choline
Phosphatidyl-
ethanolamine
Phosphatidyl-
glycerol
Plasmalogen Triacylglycerol
22. 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
23. Comparison of NMR &MS
MASS SPECTROMETRY
– More sensitive for metabolite
detection
• Mass spectrometers can detect
analytes routinely in
femtomolar to attomolar range
– Requires more tissue
destruction
– Difficulty in quantification
NMR SPECTROSCOPY
– Less sensitive for metabolite
detection
– Non-destructive, requires little
sample handling & preparation:
• Metabolites in liquid state (serum,
urine and so on),
• Intact tissues (e.g., tumors) or in vivo
– Quantification easy:
• Peak area of compound in NMR
spectrum directly related to conc. of
specific nuclei (e.g., 1H, 13C), making
quantification of compounds in
complex mixture very precise
24. Sample collection,
treatment and
processing
Detection technique:
• Nuclear Magnetic
Resonance Spectroscopy
(NMR)
• Mass Spectrometry (MS)
Separation technique:
•Gas Chromatography (GC)
•Capillary Electrophoresis (CE)
•High Performance Liquid
Chromatography (HPLC)
•Ultra Performance Liquid
Chromatography (UPLC)
Data analysis using multivariate
analysis e.g.
• Principle Component Analysis
(PCA)
• Partial Least-Squares (PLS)
Method
• Orthogonal PLS (OPLS)
Basic Workflow
27. • 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
DATAAnalysis
28. DATAAnalysis
STEP 1
Formation of spectral
data set for pattern
recognition
STEP 2
Linking specific spectral
region to specific
metabolite based on its
NMR chemical shifts
29. STEP 1
Formation of spectral
data set for pattern
recognition
STEP 2
Linking specific spectral region
causing group clustering to specific
metabolite based on its NMR
chemical shifts
STEP 3
Quantitation & association
of putative biomarkers
with respect to particular
characteristic or outcome
DATAAnalysis
30. • 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
32. 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’
33. • 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
Human Metabolome Project
34. 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
35. 1. High glycolytic enzyme activities
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
6. A high rate of fatty acid de novo synthesis
7. A low (ATP+GPT) : (CTP+UTP) ratio
8. Low AMP levels
9. A high glutaminolytic capacity
10. Release of immunosuppressive metabolites
11. A high methionine dependency
Characterization of Tumor
Metabolome
Warburg effect
1) Mazurek S et al. Anticancer Res 2003;23:1149–54
M2-PK of particular
interest as its inactive
dimeric form is
dominant in tumors
& named tumor M2-
PK (tM2-PK) 1
Quantification of this
tumor M2-PK in
plasma & stool allows
early detection of
tumors/ therapy
36. 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?
37. 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
38. 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
Diagnosis: Carcinoma Prostate
39. 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
Lokhov, Archakov et al. Biomedical Chemistry. 2009 May-Jun;55(3):247-54.
Sensitivity 95.0%
Specificity 96.7%
Accuracy 95.7%
PSA-based diagnostics
Sensitivity 35.0%
Specificity 83.3%
Accuracy 51.4%
Metabolome-based diagnostics
40. 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.
41. 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
42. 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.
44. 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
45. 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
46. 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
47. 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
48. 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.
49. 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
50. 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
51. 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
52. 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
– Total choline (P = 0.03)
– Taurine (P = 0.03)
– 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
Significantly higher in
cancer versus control
samples
Tesiram et al. Pancreas. 2012 Apr;41(3):474-80.
53. 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
54. 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
55. 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
56. • 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
Application in Therapeutics contd….
Neckers L. Heat shock protein 90: the cancer chaperone. J Biosci 2007;32:517–30
