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

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

Metabolic phenotypes reflect multiple origins SNPs allelic variants gender racial disparities inherited methylations gut microbes calorie intake food composition life style / exercise disease history Background transport

gate to personalized medicine 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

Case study of a Finnish girl diagnosed with type 1 diabetes at age 9y Glutamate 13-fold increase g-aminobuyrate (GABA ) 9-fold increase g-aminobutyrate (GABA) Glutamate Glutamate decarboxylase antibody (GADA) GADA IAA Insulin autoantibody (IAA) Background % max. Diagnosis Normal level (GABA, Glu) + + 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

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

But cancers are solely due to mutations? Background

Cancer cell metabolism is linked to signaling and NADPH for rapid cell growth Sreekumar et al 2009 Nature 457: 910 Background Thompson & Thompson 2004 J. Clin. Onc. 22: 4217 Dang et al 2009 Nature 462: 739 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

Clinical validation of cancer biomarkers Sreekumar et al 2009 Nature 457: 910 ….this was not claimed by Sreekumar et al. Background ….this was not claimed by Sreekumar et al. 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 Debate on: urine sediment vs supernatant, normalization to creatinine vs alanine vs….)

Methods How many platforms do we need? UPLC-UV-MS/MS secondary metabolites oxylipids, anthocyanins, flavonoids, pigments acylcarnitines, folates, glucuronidated & glycosylated aglycones Twister-GC-TOF volatiles terpenes, alkanes, FFA, benzenes nanoESI-MS/MS polar & neutral lipids UPLC-MS/MS phosphatidylcholines, -serines, -ethanolamines, -inositols, ceramides, sphingomyelins, plasmalogens, triglycerides GCxGC-TOF primary small metabolites sugars, HO-acids, FFA, amino acids, sterols, phosphates, aromatics 350 ID 200 ID 100 ID 70 pyGC-MS monomers lignin, hemicellulose complex lipids How many platforms do we need? UC Davis Genome Center – Metabolomics Facility 3,000 sq.ft. 6 GC-MS, 6 LC-MS (TOFs, QTOF, FTMS, QQQ, ion traps) ~15 staff key card secured entrances, password-protected data Methods

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

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

(1+2) Databases are critical for success Methods

(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

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

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

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

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 grade1 grade2 grade3 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

Study Design (1) Can we identify metabolites or metabolic pathways that 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 cohort 284 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

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

Partial Least Square (multivariate stats) (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. Partial Least Square (multivariate stats) grade1 grade2 grade3 Results grade1 grade3 breast adipose grade2