Bioinformatic Treatment of Human Metabolome Profile for Diagnostics

1. Bioinformatic Treatment of Human Metabolome Profile for Diagnostics Dr. Petr Lokhov & Dr. Alexander Archakov Institute of Biomedical Chemistry, RAMS

2. Human metabolome profile for diagnostics ? Set of small molecules (<1500Da) in biosample Visual or/and numerical profile of set of small molecules Biosample from human Biofluid samples (blood plasma)

3. Detection Type of Mass spectrometry Methods for metabolome profiling LC (liquid chromatography) CE (capillary electrophoresis) GC NMR MS (Mass spectrometry) LC-MS GC-MS CE-MS LC-NMR … Technique Protocols of sample preparation • Ultrafiltration • Proteins sedimentation with organic solvent Ionization mode

4. Direct-infusion electrospray (ESI) mass spectrometry of blood plasma metabolites 1. Add methanol Mass spectrometry 2. Centrifuge 3. Take supernatant Mass spectrometric metabolome profile blood plasma 100 µl 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

5. 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

6. Bioinformatic treamentof metabolome profile • Normalization (isn’t required) • Baseline subtraction (isn’t required) • Mass spectrometry peaks alignment(common for mass spectrometry data processing) • Detection of ionic inconsistency in plasma samples • Dimensionality reduction of mass spectrometry data • Samples classification (diagnostics)

7. Ions in blood plasma samples that affect ESI-mass spectra IonLevel in blood plasma sample Remark H+ pH ~2.8sample + formic acid 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 - 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

8. Detection of ionic inconsistency in plasma samples K2Cl+ peaks in mass spectrum Distribution of K+ in samples good

9. 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.

10. Dimensionality reduction by PCA disease pathogenesis case PCA PC1 PC3 X PC2 2000 variables (peak’s intensities) control risk factors PC3 PC1 PC4 X nutrition PC5 age, sex… X Only PCs useful for diagnostics should be used

11. Samples classification(diagnostics) SVM Diagnostics parameters case testing • Sensitivity TP/(TP+FN) • Specificity TN/(TN+FP) • Accuracy PC3 control PC1 Support Vector Machine (SVM) may classify multidimensional data by formation of a hyperplane in a multidimensional space.

12. Biochemical context for diagnostics 2000 variables + identification Identified metabolites 0 PC3 accurate mass tag isotopic pattern MS/MS MRM Biochemical context for metabolome-based diagnostics - + - 0 PC1

13. Ten Leading Cancer Types, 2010 CA CANCER J CLIN 2010;60:277–300

14. Example 1: Diagnostics of prostate cancer II stage Metabolome-based diagnostics Sensitivity 95.0% Specificity 96.7% Accuracy 95.7% PSA-based diagnostics Sensitivity 35.0% Specificity 83.3% Accuracy 51.4% 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

15. Example 2: Diagnostics of lung cancer Diagnostics Cancer stage Sensitivity (%) Selectivity (%) Accuracy (%) I 100.0 93.9 II 91.4 92.3 III 92.3 92.4 IV 93.2 92.5 I-IV 91.1 93.3 92.4 Lokhov, Archakov et al.Diagnosis of lung cancer based on direct-infusion electrospray mass spectrometry of blood plasma metabolites. International Journal of Mass Spectrometry. 2011

16. 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

17. Risk of lung cancer development Cigaretteconsumption Metabolome-based approach Levels of: Smoker/non-smoker Cigarettes smoked per day Biotin sulfone Creatinine R-benzene Permethrin Halfenprox …. Individual differences in how cigarettes are smoked Ranges of nicotine intake per cigarette Age Body weight Objectively calculated exposure to tobacco smoke Averaged and inexactly calculated exposure to tobacco smoke OR (odd ration) - Risk of disease development OR (smokers/non-smokers)=4 OR (R-benzene)=38

18. 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.

19. 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