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

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

4 Direct-infusion electrospray (ESI) mass spectrometry of blood plasma metabolites Soft method for protein precipitation (with methanol) Direct-infusion of plasma metabolites in ion source Electrosprey ionization High accuracy MS blood plasma 100 µ l 1. Add methanol 2. Centrifuge Mass spectrometry 3. Take supernatant Mass spectrometric metabolome profile Reproducible, rapid and cheap method for metabolome profiling

5 Representative mass spectrum of blood plasma metabolites ~ 2000 metabolite ions are detected ~ 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. x10 6 Da diagnostic metabolites lipidome

6 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)

7 Ions in blood plasma samples that affect ESI-mass spectra Ion Level in blood plasma sample Remark H + pH ~2.8 sample + 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 K 2 Cl + peaks in mass spectrumDistribution 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 2000 variables (peak’s intensities) PCA PC1 PC2 PC3 PC4 PC5 X X X PC1 PC3 case control disease pathogenesis nutrition age, sex… risk factors Only PCs useful for diagnostics should be used

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

12 Biochemical context for diagnostics PC1 PC variables identification accurate mass tag isotopic pattern MS/MS MRM Identified metabolites Biochemical context for metabolome-based diagnostics

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

14 Example 1: Diagnostics of prostate cancer II stage Lokhov, Archakov et al. Metabolic Fingerprinting of Blood Plasma from Patients with Prostate Cancer. Biochemistry (Moscow), Metabolite profiling of blood plasma of patients with prostate cancer. Metabolomics Sensitivity 95.0% Specificity 96.7% Accuracy 95.7% PSA-based diagnostics Sensitivity 35.0% Specificity 83.3% Accuracy 51.4% Metabolome-based diagnostics

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

16 Diagnostics of lung cancer (identified metabolites) Exposure to tobacco smoke Identified metabolites (PC1) 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 Cigarettes smoked per day Individual differences in how cigarettes are smoked Ranges of nicotine intake per cigarette Body weight Age OR (odd ration) - Risk of disease development Metabolome-based approach Biotin sulfone Creatinine R-benzene Permethrin Halfenprox …. Objectively calculated exposure to tobacco smoke OR (smokers/non-smokers)=4OR (R-benzene)=38 Levels of: Cigarette consumption Smoker/non-smoker Averaged and inexactly calculated exposure to tobacco smoke

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 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