Metabolomic analysis of Human Hepatocellular
Carcinoma by proton NMR spectroscopy
Palaiseau november 2010
Aicha DemidemINRA, Theix
Colloquium LIX, Ecole Polytechnique.
Context
• Hepatocarcinoma – HCC– Deadly disease: 3rd position (WHO)
• Diagnostic– Relatively late
Diagnostic of Hepatocarcinoma(HCC)
– Marker Serique• AFP (α-fœto protein) weak sensibility
– Anatomo-pathologie, mutation of β-catenin after hepatic resection
AIMS OF STUDY
• Identify new biomarkers from tissues and sera (from patients having hepatocarcinoma) to improve the early detection of this deadly disease
• Identify metabolic pathway of cellular transformation in the aim to determine
new therapeutic targets
Method
• Metabolomic : approach without a priori * Nuclear Magnetic Resonance (NMR)* Mass Spectroscopy (SM)
– Identification and quantification of metabolites from tissues and biological fluids
• Literature dataMetabonomic studies of human hepatocellular carcinoma using 1H NMR spectroscopy
Yang et al., J of Proteome Res, 2007
Material & Method: NMR
• Spectral acquisition
– NMR 400 MHz– 1H Spectra– 31P Spectra
• Software : MestReNova
• Reference : creatine 3,035 ppm
Spectral Treatments
Materials and methods: Experimental protocol
• Collection of liver tissues (+ 4°C)
• Collection of blood (serum sample)
• Preparation of tissues for extract and acquisition of NMR spectra
• Histology report
Prelevement of pathological and healthy tissues
Comparative metabolic profile from tumoral tissue versus adjacent tissue from the same patient by 1H-NMR
Ten patients underwent liver resection for tumor
Biopsies of both tumor and non-tumoral tissues were obtained from these patients
Clinical MaterialAge and
sexesNon tumoral tissue Tumoral tissue Serum
Patient 1 F61 years
healthy Endocrine carcinoma yes
Patient 2 M52 years
healthy Neuroendocrine carcinoma yes
Patient 3 M67 years
healthy non
Patient 4 M78 years
Cirrhosis post-hepatitis B HCC grade III Edmonson and Steinert
yes
Patient 5 M72 years
Non cirrhoticSteatose 25% of hepatocytes
HCC grade III Edmonson and Steinert
yes
Patient 6 M 72 years
Cirrhoses macro-nodular and hepatic steatose
HCC grade II Edmonson and Steinert
yes
Patient 7 M73 years
Cirrhosis alcoholic HCC grade II Edmonson and Steinert
yes
Patient 8 M77 years
Steatosis 30% of hepatocytes Benin Hemangiome yes
Patient 9 M67 years
Fibrosis and excess in iron HCC grade II Edmonson and Steinert
yes
Patient 10 F70 years
Steatosis 10% of hepatocytes HCC grade II Edmonson and Steinert
yes
Results: Identification of Metabolites
PublicationsHMDB
Results: Identification of Metabolites
Results: 2D spectral to identify metabolites
Results : Spectrum difference
Patient 7 TP/TS
-3000
-2000
-1000
0
1000
2000
3000
4000
5000
6000
7000
11.21.41.61.822.22.42.62.83
lactate
alanine
glutathione
Healthy Tissue
Pathological Tissue
Difference PT / HT
glutamate
PT/HT
Results : Spectrum difference
patient 7 TP/TS
-3500
-1500
500
2500
4500
6500
33,23,43,63,844,24,44,6
glucose glucose / glycogen
ascorbic acid
Healthy Tissue
Pathological Tissu
Difference PT / HT
PT/HT
Analysis of Relative Concentrations( Univariate data analysis)
Non-Tumoral Group Tumoral Group
meanStandard deviation mean
Standard deviation p-value
hydroxybutyrate 0,0015 0,0013 0,0023 0,0019 0,2226
lactate 0,0910 0,0140 0,1372 0,0610 0,02564 *
alanine 0,0162 0,0033 0,0214 0,0087 0,0507 *
acetate 0,0034 0,0059 0,0014 0,0007 0,4178
glutamate 0,0083 0,0043 0,0144 0,0060 0,002331 **
succinate 0,0065 0,0033 0,0035 0,0025 0,03671 *
glutamine 0,0062 0,0052 0,0142 0,0112 0,01748 *
glutathione 0,0008 0,0006 0,0038 0,0020 0,004662 **
ascorbic acid 0,0012 0,0004 0,0035 0,0020 0,00407 **
PE 0,0017 0,0010 0,0055 0,0026 0,00058 ***
Mann-Whitney test = 5%
Cho 0,0018 0,0010 0,0014 0,0010 0,2226
lactate CH 0,0288 0,0052 0,0401 0,0186 0,1474
GPC 0,0066 0,0027 0,0065 0,0078 0,1474
signal 4.35 0,0010 0,0006 0,0019 0,0011 0,0507 *
signal 4.43 0,0015 0,0004 0,0013 0,0008 0,4726
creatine 0,0101 0,0009 0,0119 0,0045 0,4178
β-glucose 0,0305 0,0055 0,0198 0,00470,00058
28 ***
α-glucose 0,0223 0,0043 0,0155 0,0041 0,02005 *
glycogen 0,0023 0,0030 0,0006 0,0007 0,02673 *
fumarate 0,0003 0,0001 0,0003 0,0002 0,2669
Non TumoralGroup Tumoral Group
mean SD mean SD p-value
Analysis of relative concentrations
Glycolysis is predominant: Warburg effect
Anaerobic creatine system / phosphocreatine is predominant
Results from Principal Component Analysis (PCA)• Allow to describe the information from a
set of data (linear combinations of variables)
The 2 dimension analysis represents
51% of total variance
Correlation circle
Ros increasedDifferentiation Ros increasedDifferentiation Ros increasedDifferentiation
Correlation between the grade of HCC and the increase of glycolysis?
Interpretation1- Increase of lactate and alanine content (final products of glycolysis) Biomarkers of Warburg effect : correlation with progression/stage of disease
2- Increase of glutamate (aggressive form) and glutamine level (less aggressive form ) : Promoters of the tumoral progression
Actual target: Glutaminase Inhibitors Convert an aggressive form to an non aggressive form
3 -Decrease of glucose and glycogen level : Result from the conversion of glucose to lactate: hyperactive glycoysis Phenomena common to tumor cells
Conclusion
• Comparative metabolomic analysis of healthy hepatic tissue and HCC allowed generating assumptions on the redox status and bioenergetics of HCC
Metabolomic analysis of HCC allows to propose :
- Metabolic tumoral biomarker (s ) as candidate HCC
- Hypothesis (ses) on the metabolic pathways implicated in HCC pathological evolution (aerobic glycolysis, glutamine and creatine system)
Conclusion
Transfer to in vivo studies using Magnetic Resonance Imaging (MRI)
Metabolomic study represents an good way of generating experimental data for metabolic modeling in integrative biology
Hospital TeamArmand AbergelEmmanuel BucDenis Pezet
INRAAnne FagesDaniel MorvanPascale RioGeorges Stepien
MNR/MS TeamGuy Bielicki Jean Pierre RenouEstelle Pujos