Bioinformatic Treatment of Human Metabolome
Profile for Diagnostics
Dr. Petr Lokhov & Dr. Alexander Archakov
Institute of Biomedical Chemistry, RAMS
?
Set of small molecules (<1500Da)
in biosample
Visual or/and numerical profile
Biosample from human
of set of small molecules
Human metabolome profile for
diagnostics
Biofluid samples
(blood plasma)
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Methods for metabolome profiling
Technique
Protocols of sample preparation
LC (liquid chromatography)
•Ultrafiltration
CE (capillary electrophoresis)
•Proteins sedimentation with
GC
organic solvent
NMR
MS (Mass spectrometry)
LC-MS
Detection
GC-MS
CE-MS
Type of
LC-NMR Ionization
Mass spectrometry
…
mode
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Direct-infusion electrospray (ESI) mass
spectrometry of blood plasma metabolites
1. Add methanol
Mass
2. Centrifuge
spectrometry
3. Take supernatant
blood plasma
100 µl
Mass spectrometric
metabolome profile
Soft method for protein precipitation (with methanol)
Direct-infusion of plasma metabolites in ion source
Electrosprey ionization
High accuracy MS
Reproducible, rapid and cheap method for metabolome profiling
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Representative mass spectrum of blood
plasma metabolites
x106
~ 2000 metabolite
ions are detected
diagnostic metabolites
lipidome
~ 2000 main metabolites
in human organism
Beecher C.W.W., in: Metabolic
Profiling: Its Role in Biomarker
Discovery and Gene Function
Analysis, Springer, 2003 pp.
311–335.
Da
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Bioinformatic treament of
metabolome profile
1. Normalization (isn’t required)
2. Baseline subtraction (isn’t required)
3. Mass spectrometry peaks alignment (common
for mass spectrometry data processing)
4. Detection of ionic inconsistency in plasma
samples
5. Dimensionality reduction of mass
spectrometry data
6. Samples classification (diagnostics)
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Ions in blood plasma samples
that affect ESI-mass spectra
Ion
Level in blood plasma sample
Remark
H+
pH ~2.8
Na+
136–145 mM
physiological conditions
K+
3.5–60 mM
plasma level (3-5 mM)
plus K+ leaked from
erythrocytes (80–120 mM)
Other ions
-
sample + formic acid
levels too low for influencing
ESI-mass spectra
potassium leaks from cells when plasma is not immediately separated
from collected blood, or when blood has been temporarily stored, or
plasma is handled roughly
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Detection of ionic inconsistency in
plasma samples
K2Cl+ peaks in mass spectrum
Distribution of K+ in samples
good
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Dimensionality reduction
Metabolite profile is multivariable characteristic of an organism.
&
To avoid overfitting,
the rule of 10–15 samples per variable should be followed.
The dimensionality of the mass spectrometry data should be reduced.
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Dimensionality reduction by PCA
PCA
2000 variables
(peak’s intensities)
PC1
disease
pathogenesis
PC2
X
case
PC3
risk factors
control
PC3
PC1
PC4
PC5
X
X
nutrition
age, sex…
Only PCs useful for diagnostics should be used
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Samples classification
(diagnostics)
SVM
Diagnostics parameters
case
testing
PC3
• Sensitivity TP/(TP+FN)
• Specificity TN/(TN+FP)
• Accuracy
control
PC1
Support Vector Machine (SVM) may classify multidimensional data by
formation of a hyperplane in a multidimensional space.
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Biochemical context for
diagnostics
2000 variables
+
identification
Identified metabolites
0
PC3
accurate mass tag
isotopic pattern
MS/MS
MRM
-
0
Biochemical context for
metabolome-based
diagnostics
+
PC1
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Ten Leading Cancer Types, 2010
CA CANCER J CLIN 2010;60:277–300
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Example 1: Diagnostics of prostate
cancer II stage
Metabolome-based diagnostics
Sensitivity
Specificity
Accuracy
95.0%
96.7%
95.7%
PSA-based diagnostics
Sensitivity
Specificity
Accuracy
Lokhov, Archakov et al.
Metabolic Fingerprinting of Blood Plasma from Patients with Prostate Cancer. Biochemistry (Moscow), 2010.
Metabolite profiling of blood plasma of patients with prostate cancer. Metabolomics. 2010
35.0%
83.3%
51.4%
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Example 2: Diagnostics of lung
cancer
Diagnostics
Cancer stage
I
II
III
IV
I-IV
Sensitivity (%)
100.0
91.4
92.3
93.2
91.1
Selectivity (%)
Accuracy (%)
92.4
93.9
92.3
92.4
92.5
93.3
Lokhov, Archakov et al. Diagnosis of lung cancer based on direct-infusion electrospray mass spectrometry of blood
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plasma metabolites. International Journal of Mass Spectrometry. 2011
Diagnostics of lung cancer
(identified metabolites)
Identified metabolites
(PC1)
Exposure to tobacco
smoke
Biotin sulfone
Creatinine
R-benzene
ethylbenzoic acid
аcetanisol
dimethylbenzoic acid
benzenepropionate
Permethrin
Halfenprox
….
Metabolites reflecting exposure organism to tobacco smoke contribute in diagnostics
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Risk of lung cancer development
Cigarette consumption
Smoker/non-smoker
Cigarettes smoked per day
Individual differences in
how cigarettes are smoked
Ranges of nicotine intake per
cigarette
Metabolome-based approach
Levels of:
Biotin sulfone
Creatinine
R-benzene
Permethrin
Halfenprox
….
Age
Body weight
Averaged and inexactly
calculated exposure
to tobacco smoke
Objectively calculated
exposure to tobacco smoke
OR (odd ration) - Risk of disease development
OR (smokers/non-smokers)=4
OR (R-benzene)=38
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Conclusions
Metabolome profile for diagnostics can be obtained by direct-infusion
mass spectrometry of blood plasma sample.
The profile quality can be checked using data from profile itself.
Dimensionality reduction allows following to rule 10-15 samples per
variable and select groups of metabolites useful for diagnostics.
Metabolome profile can be used for early diagnostics of lung and
prostate cancers as well for calculation of risk of lung cancer
development.
One metabolome profile is needed for diagnostics and prognosis of
lung cancer.
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Acknowledgements
Program “Proteomics for Medicine and Biotechnology” of
Russian Academy of Medical Sciences.
Russian Foundation for Basic Research
Russian N.N. Blokhin Cancer Research Center
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