Metabolomics and Cancer Research
Syed Ghulam Musharraf
Dr. Panjwani Center for Molecular Medicine and Drug Research,
International Centre for Chemical and Biological Sciences (ICCBS)
University of Karachi, Karachi-75270
E mail: musharraf1977@yahoo.com
The Omic Sciences: side by side comparison
Journal of Surgical Oncology
Volume 103,Issue 5, pages 451-459, 28, 2011
Yearly Increase in Metabolomics Publications
Number of published paper
1200
1000
800
600
400
200
0
20022003200420052006200720082009201020112012
Year
Metabolomics is….
Metabolomics is the comparative analysis
of endogenous metabolites found in
biological samples:





Compare two or more biological groups
Find and identify potential biomarkers
Look for biomarkers of toxicology
Understand biological pathways
Discover new metabolites
Metabolites are the by-products of
metabolism
 Range of physiochemical properties
 Classes: Amino acids, lipids, fatty acid, organic
acids, sugars
Classification of Endogenous Metabolite Analysis
Metabolome analysis
Metabolite
target analysis
specific
metabolites
Metabolite
profiling
Metabolomics
group of related
compounds or
metabolites in
specific metabolic
pathways
all metabolites
present in a
cell/sample
Metabolic
fingerprinting
Sample
classification
by rapid, global
analysis
Plant Molecular Biology 2002, 48, (155- 171).
Metabolomics in Oncology
Potential applications of metabolomics in
the field of cancer research:
Early diagnosis
Cancer Staging
Refining tumor characterization
Predictive biomarkers of cancer
Personalized drug discovery
Some Examples from Published Data
Metabolomic profiling of B16 melanoma (top) and 3LL pulmonary
carcinoma tumors (bottom) showing variations in multiple metabolites
before (red) and after (blue) chloroethylnitrosurea treatment.
*, P <0.05; **, P < 0.01; ***, P < 0.001.
Cancer Research 2004, 64, 4270–4276.
Examples of Key Metabolite Differences
Key Cancer Types
Healthy Controls/ Benign
Disease vs Malignancy
Journal of Surgical
Oncology
Volume 103,Issue 5, 451-459,
2010
Challenges in Metabolomics Study

Number of samples to analyze (for proper
statistical treatment of the data)

Metabolites have a wide range of molecular
weights and large variations in concentration

The metabolome is much more dynamic than
proteome and genome, which makes the
metabolome more time sensitive

Detection

Identification and quantification

Efficient and unbiased separation of analytes
A General Methodology for Metabolomics Study
Yearly Increase in Metabolomics Publications
1200
Number of published
papers
1000
800
600
GC/MS
LC/MS
NMR
Total
400
200
0
Year
Mass Spectrometry
High Vacuum System
Ion
source
Inlet
EI
CI
FAB
ESI
MALDI
Mass
Analyzer
Magnetic Sector
Electrostatic Sector
Quadrupole
Iontrap
Time-of-Flight
FT-ICR
Turbo
molecular
pumps
Detector
Data
System
Most commonly used methods for Metabolomics
NMR
MS (LC-MS and GCMS)
Sample
volume
Large sample is
required (500 microL)
Less sample is required
(1–10 microL)
Intervention
Nondestructive to
the sample
Destructive, requires
derivatization (GC-MS)
Sample preparation
Simple
Extensive
Reproducibility
Very reproducible
Possible variation
introduced by
preparation
Sensitivity
Low
High
Structural
information
High
Low
Chemophysical
information
Less information
More information (time
separation)
Characterization
GC-MS: A tool for high-throughput phytochemicals analysis
GC-MS: A tool for high-throughput phytochemicals analysis
Mass Spectrometers as a GC Detector
GC-MS: A tool for high-throughput phytochemicals analysis
Methods in GC-MS
GC-MS: A tool for high-throughput phytochemicals analysis
GC-MS: A tool for high-throughput phytochemicals analysis
Normal Operation
GC-MS: A tool for high-throughput phytochemicals analysis
Two Different Chromatogram:
GC-MS: A tool for high-throughput phytochemicals analysis
Use of Extracted Ions:
GC-MS: A tool for high-throughput phytochemicals analysis
GC-MS: A tool for high-throughput phytochemicals analysis
Data Refinement:
GC-MS: A tool for high-throughput phytochemicals analysis
Data Refinement:
GC-MS: A tool for high-throughput phytochemicals analysis
Data Refinement:
GC-MS: A tool for high-throughput phytochemicals analysis
Data Refinement:
GC-MS: A tool for high-throughput phytochemicals analysis
Data Refinement:
GC-MS: A tool for high-throughput phytochemicals analysis
Data Refinement:
GC-MS: A tool for high-throughput phytochemicals analysis
AMDIS =
Automated
Mass Spectral
Deconvolution and
Identification
System
-Developed by NIST (National Institute of Standards
and Technology) in USA
-An automated mass spectrometric data analysis
software
GC-MS: A tool for high-throughput phytochemicals analysis
GC-MS: A tool for high-throughput phytochemicals analysis
Important points need to consider:
GC-MS analysis of Plant extract
AlO (Oil-01)
GC-MS analysis of Plant extract
GC-MS analysis of Plant extract
GC-MS analysis of Plant extract
10.26= (-)-β-Pinene
GC-MS analysis of Plant extract
Peak No. RT
Name
1
3.104
Heptane
24
14.115
α-Terpineol
2
4.607
Toluene
3
7.475
3,5-Octadiyne
25
26
14.238
14.578
4
7.698
3,5-Octadiyne
5
8.297
3,5-Octadiyne
6
9.095
Cumene
7
9.305
3-Carene
8
9.633
Camphene
9
10.257
(-)-β-Pinene
10
10.566
.(-)-β-Pinene
27
28
29
30
31
32
14.757
14.918
15.555
16.414
16.81
16.89
11
11.234
o-Cymene
33
17.787
12
11.314
D-Limonene
13
11.364
Cineole
14
11.877
Crithmene
15
16
12.427
12.588
Fenchone
Linalol
17
12.86
Fenchol
34
35
36
37
38
39
18.275
18.386
18.491
18.627
18.714
19.524
18
13.299
L-pinocarveol
19
13.404
Alcanfor
20
13.465
4-Terpineol
21
13.737
Isoborneol
22
13.917
4-Terpineol
23
14.016
Alpha,alpha,4-trimethylbenzyl
carbanilate
40
41
42
43
44
45
46
19.672
20.049
20.154
20.408
20.488
20.587
21.978
(1R)-(-)-Myrtenal
Acetic acid, 1,7,7-trimethyl-bicyclo[2.2.1]hept2-yl ester
Methyl thymyl ether
Benzylacetone
(-)-Bornyl acetate
α-Terpineol acetate
Dysoxylonene
2-(2-Ethylphenoxy)-N'-[(E)-(4isopropylphenyl)methylidene]acetohydrazide
cis-4,11,11-Trimethyl-8methylenebicyclo(7.2.0)undeca-4-ene
γ-Selinene
Valencen
α-Himachalene
γ-Muurolene
Eudesma-3,7(11)-diene
9-Isopropyl-1-methyl-2-methylene-5oxatricyclo[5.4.0.0(3,8)]undecane
(+)-Carotol
γ-Eudesmol
Daucol
Juniper camphor
Elemol
Farnesyl bromide
6-[1-(Hydroxymethyl)vinyl]-4,8a-dimethyl4a,5,6,7,8,8a-hexahydro-2(1H)-naphthalenone
Fraction of ALO (Oil-02)
ALO (Oil-01)
GC-MS analysis of Plant extract
GC-MS analysis of Plant extract
Peak Number
RT
Name
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
3.104
4.607
7.475
7.691
8.303
9.305
9.639
9.868
10.257
11.314
11.364
13.404
13.744
13.917
14.121
15.555
16.81
16.89
19.672
20.488
22.621
Heptane
Toluene
Ethylbenzene
p-Xylene
m-Xylene
3-Carene
Camphene
2-Hexyl hydroperoxide
(-)-β-Pinene
D-Limonene
Eucalyptol
Alcanfor
Borneol
4-Carvomenthenol
α-Terpineol
Acetic acid, 1,7,7-trimethyl-bicyclo[2.2.1]hept-2-yl ester
γ-Selinene
α-Elemene
Carotol
Columbin
4-Chloro-2,5-dimethoxyamphetamine
GC-MS
Advancement in the sample preparation
Advancement in GC system
1-D SDS-PAGE Analysis
Male Plasma
Electrophoretic
Conditions:
Female Plasma
Smoker Plasma
Cancer Plasma
0.2µL Crude Plasma , NuPAGE 12 % precast gel ,
MES SDS Running Buffer.
200 volts, 90-120 mA , colloidal blue staining solution
38
Comparative Analysis of Healthy, Smoker and Cancer
Comparative gel view of the three
classes comprises of
120 mass spectra of 40 of each class
of nonsmokers, smokers, and
lung cancer patients
Differentially expressed peaks: Gel
view in chromatic mode representing
the comparison of average intensity of
the individual signature peptide. Lung
cancer (red), smokers (green), and
nonsmokers (blue)
Metabolomics studies: Sample Collection
and Pooling Strategy
Age Groups
20-30 (code)
Normal healthy
30-40 (code)
40-50 (code)
Above 50 (code)
20 (HMPG1-1-20) 10 (HMPG2-1-10) 10 (HMPG3-1-10)
10 (HMPG4-1-10)
20 (HFPG1-1-20)
10 (HFPG4-1-10)
male
Normal healthy
10 (HFPG2-1-10)
10 (HFPG3-1-10)
female
HMPG1-G4 (all samples)
HMPG1-G4-P
Pooling 1
HFPG1-G4 (all samples)
HMP-P
Pooling 2
HFPG1-G4-P
Pooling 3
HPP-P
HFP-P
HMP-P (Healthy Male Plasma-Pool), HFP-P (Healthy Female Plasma-Pool), HPP-P (Healthy Pakistani Plasma-Pool)
Ping et al., Proteomics 2005, 5, 3442-3453
Data Processing and Statistical Analysis
Agilent Mass Hunter
Qualitative Analysis (version
B.04.00)
Data acquisition
XCMS online
Spectral alignment
 NIST mass spectral library
(Wiley registry)
 Fiehn RTL library
Metabolite identification
 Minitab software (version
11.12 )
 Qlucore Omics Explorer
Software (version 2.3)
Multivariate statistical
analysis
GC/MS Total Ion Chromatogram (TIC) of Healthy Pakistani
Plasma-Pool (HPP-P) by different fractionation techniques
 Metabolite features from each
spectrum were analyzed by XCMS
online software. A metabolite
feature was defined as a mass
spectral peak in the mass region of
m/z 100-1000 with a signal-tonoise ratio exceeding 10:1.
 %RSD of each metabolite
feature intensity was obtained
from three replicates, where Avg.
% RSD reports the average value
for all detected features within
each method.
 Using XCMS software, between
1000 to 2000 reproducible
metabolite features were detected
for each fractionation technique.
Method Reproducibility
An example of a well-aligned metabolite feature
detected from all 30 runs
Reproducibility of Four Selected
Compounds in Each Method
500000
450000
400000
350000
300000
250000
200000
150000
100000
50000
0
Methyl oleate
1D-anion
1D-C18
Tetratriacontane
2D-anion
2D-C18
Palmitic acid
1D-sol ppt
1D-cation
Dioctyl phthalate
1D-Si
2D-cation
2D-Si
Each bar represents standard deviation of the average of
three independent run in each method.
Clustering of Fractionation Techniques
Out of total 7,299 metabolite features from all 10
fractionation techniques, overall 52% distinct features were
observed by applying different fractionation techniques.
Comparative Analysis between Male and
Female Plasma Samples
At p<10-5
200 metabolite features in 2D-C18 out of 1,076
6 in 1D-anion out of 1,201
39 in 2D-anion out of 1,533
56 in 2D-cation out of 1,441
15 in 2D-Si out of 1,522
16 in 1D-C18 out of 2,287
1 in 1D-MWCOT out of 1,741
176 in 1D-Si out of 3,268
32 in solvent precipitation out of 2,068
No differentiative metabolite in 1D-cation out of 1,010
Comparative Analysis between Male and
Female Plasma Samples
Box and whisker plots of
metabolite features with
threshold value of p<10-5 from
10 fractionation techniques
except 1D-cation at p<0.001.
Comparison of Pooled and Individual Samples
Loading plot of A. HPP-P, HMP-P and HFP-P samples (1615 metabolite
features) B. HPP-P and individual samples (900 metabolite features) C. HMP-P
and individual male samples (1,442 metabolite features) D. HFP-P and
individual female samples, (1,903 metabolite feature).
Metabolite Identification
39%
153
173
27%
155
26%
25%
21%
19%
84
130
158
25%
25%
21%
12%
7%
139
145
10%
7%
NIST
11%
7%
77
29%
27%
25%
20%
9%
Fiehn
The percentage of metabolites identified by each fractionation
techniques by NIST and Fiehn GC/MS data base
64
The intensities
of some of the
compounds
through NIST
and Fiehn data
base are shown
by a heat map
pattern
Venn Diagram of Metabolite Features
Percent distribution of different classes
from each fractionation technique