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Perspectives of metabolomics towards personalized medicine

This article explores the advancements in metabolomics towards personalized medicine, discussing the impact of genotype, environment, and phenotype on metabolism. It highlights the potential of metabolite levels and fluxes as biomarkers for personalized diagnostics and treatment. The article also discusses case studies and challenges in clinical validation of cancer biomarkers.

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Perspectives of metabolomics towards personalized medicine

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  1. Perspectives of metabolomicstowards personalized medicine Oliver Fiehn Genome Center, University of California, Davis fiehnlab.ucdavis.edu PI Prof Carsten Denkert, Charite, Berlin

  2. Metabolism is the endpoint of non-linear cellular regulation Genotype x Environment mRNA expression Background protein expression metabolite levels & fluxes temporal x spatial resolution phenotype Fiehn 2001 Comp. Funct. Genomics 2: 155

  3. Metabolic phenotypes reflect multiple origins SNPsallelic variantsgenderracial disparitiesinherited methylations gut microbes calorie intakefood compositionlife style / exercisedisease history Background transport

  4. Metabotypes: gate to personalized medicine “Intervention” Metabotype intensity Disease Background Healthy Time Metabotype = personal sum of metabolic data, e.g. biomarker panel. Analyzed over time or in response to treatment vd Greef et al. 2004 Curr. Opin.Chem.Biol. 8: 559

  5. Case study of a Finnish girl diagnosed with type 1 diabetes at age 9y g-aminobuyrate (GABA )9-fold increase Glutamate13-fold increase g-aminobutyrate (GABA) Glutamate Glutamate decarboxylase antibody (GADA) GADA IAA Insulin autoantibody (IAA) % max. Background Diagnosis Normal level (GABA, Glu) + + 0 1 2 3 4 5 6 7 8 9 Age (years) BCAA++, ketoleucine - - before GADA, IAA Orešič et al. 2008 J. Exp. Med. 205: 2975

  6. Challenge tests tell more if clinical chemistry is advanced to metabolomics Oral Glucose Tolerance Test individual subjects free palmitic acid 120 AU 80 Background 60 40 20 0 min 0 40 80 120

  7. But cancers are solely due to mutations? Background

  8. Cancer cell metabolism is linked to signaling and NADPH for rapid cell growth Background Thompson & Thompson 2004 J. Clin. Onc. 22: 4217 Sreekumar et al 2009 Nature 457: 910 Many tumors produce NADPH via glutamine gln glu  akg  succ  fum  mal  pyr  lactate Mutation in IDH1 in brain tumors leads to pro-oncogenic factor 2-hydroxyglutarate a-ketoglutarate + NADPH  2HO-glutarate + NADP+ NADP+ NADPH Dang et al 2009 Nature 462: 739

  9. Clinical validation of cancer biomarkers ….this was not claimed by Sreekumar et al. Background ….this was not claimed by Sreekumar et al. Sreekumar et al 2009 Nature 457: 910 Debate on: urine sediment vs supernatant, normalization to creatinine vs alanine vs….) • Lessons learned: • authors should disclose all data and metadata, not just graphs • biomarkers will be more robust as panel, not as single variable • validation should follow guidelines as given in the EDRN network of NCI

  10. How many platforms do we need? UC Davis Genome Center – Metabolomics Facility3,000 sq.ft. 6 GC-MS, 6 LC-MS (TOFs, QTOF, FTMS, QQQ, ion traps) ~15 staffkey card secured entrances, password-protected data pyGC-MS monomerslignin, hemicellulosecomplex lipids nanoESI-MS/MS polar & neutral lipidsUPLC-MS/MSphosphatidylcholines, -serines, -ethanolamines, -inositols, ceramides, sphingomyelins, plasmalogens, triglycerides Twister-GC-TOF volatilesterpenes, alkanes,FFA, benzenes Methods 70 100 ID 350 ID 200 ID 200 ID GCxGC-TOFprimary small metabolitessugars, HO-acids, FFA, amino acids, sterols, phosphates, aromatics UPLC-UV-MS/MS secondary metabolitesoxylipids, anthocyanins, flavonoids, pigmentsacylcarnitines, folates, glucuronidated & glycosylated aglycones

  11. (1) Primary metabolites < 550 Da by ALEX-CIS-GC-TOF MS 50-250°C 20 mg breast tissue homogenization 70 eV 50-330°Cramp -20°C cold extraction(iPrOH, ACN, water) 20 spectra/s Methods Dry down, derivatizeto increase volatility $60 direct costs/sample Fiehnlab BinBase DB Statistics Mapping

  12. (2) Volatiles < 450 Da by Twister TDU GC-TOF MS Exhale breath on Twister 70 eV 50-330°Cramp -70°C 20 spectra/s O Methods $60 direct costs/sample Intensity (total ion chromatogram) O OH O 400 500 600 700 O Time (s) O HO O Fiehnlab vocBinBase DB Statistics Mapping

  13. (1+2) Databases are critical for success Methods

  14. (1+2) Databases are critical for success FiehnLib: Mass spectral and retention index libraries Anal. Chem. 2009, 81: 10038 1. discard poor quality signals (low signal to noise ratio ) 2. cross reference multiple chromatograms 3. compound identification (mass spectra + RI matching by FiehnLib) 4. store and compare all metabolites against all 24,368 samples in 373 studies Methods Chemical translation service cts.fiehnlab.ucdavis.edu

  15. (3) Polymers by pyrolysis GC-MS Methods $20 direct costs/sample AMDIS / SpectConnect Statistics Mapping

  16. (4) Secondary metabolites < 1,500 Da by UPLC-MS/MS $60 direct costs/sample Methods target vendor software Statistics Mapping

  17. (5) Complex lipids < 1,500 Da by nanoESI-MS/MS $60 direct costs/sample LTQ-FT-ICR-MS High resolution nanoESI infusion chip robot Genedata Refiner MS Methods Fiehnlab LipidBLAST exp. MS/MS Statistics in silico MS/MS Mapping

  18. Breast Cancer:Therapeutic success depends on hormonal receptor status • lifetime risk of breast cancer in the U.S. ~ 12% • lifetime risk of dying from breast cancer 3% • in U.S., around 200k invasive plus 60k in-situ breast cancers. • in U.S., around 40k deaths by breast cancer annually. • cancer grades (1, 2, 3) reflect lack of cellular differentiation ; indicate progression grade2 grade3 grade1 Background In combination with surgery, endocrine therapy can treat ER+ (estrogen), PR+ (progesteron) or HER+ (Herceptin) tumors • Tumors without expression of hormone receptors (‘Triple negative’) are more likely progress to invasive states; patients have higher 5y mortality

  19. Study Design (1) Can we identify metabolites or metabolic pathwaysthat are associated with breast cancer clinical parameters? (2) Once we have identified those metabolic aberrations, can we validate these in a fully independent study? First cohort284 samples Nov 2008 74 normal samples 210 tumors (20 grade 1, 101 grade 2, 71 grade 3) Second cohort 113 Samples Jan 2009 23 normal samples 90 tumors (10 grade 1, 46 grade 2, 30 grade 3) EU FP7, PI Prof Carsten Denkert, Berlin Methods

  20. Hormone receptor status vs grade % of patients Methods triple neg. Estrogen positive Estrogen negative

  21. (1) Can we identify metabolites or metabolic pathways that are associated with breast cancer clinical parameters? Alex-CIS-GCTOF MS w/ BinBase: 470 detected compounds 161 known metabolites, 309 without identified structure. grade2 grade1 Partial Least Square (multivariate stats) Results grade3 grade1 grade3 breast adipose grade2

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