Perspectives of metabolomics towards personalized medicine
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Transcript of Perspectives of metabolomics towards personalized medicine
Perspectives of metabolomicstowards personalized medicine
Oliver FiehnGenome Center, University of California, Davis
PI Prof Carsten Denkert, Charite, Berlinfiehnlab.ucdavis.edu
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ndMetabolism is the endpoint
of non-linear cellular regulationGenotype x Environment
mRNA expression
protein expression
metabolite levels & fluxes
temporal x spatial resolution
phenotypeFiehn 2001 Comp. Funct. Genomics 2: 155
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nd
transport
SNPsallelic variantsgenderracial disparitiesinherited methylations
gut microbes
calorie intakefood compositionlife style / exercisedisease history
Metabolic phenotypes reflect multiple origins
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ndMetabotypes:
gate to personalized medicine
Disease
Healthy
Time
“Intervention”
Met
abot
ype
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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
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ndCase study of a Finnish girl
diagnosed with type 1 diabetes at age 9y
Orešič et al. 2008 J. Exp. Med. 205: 2975
GADA
Normal level (GABA, Glu)
Age (years)
Diagnosis
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Insulin autoantibody (IAA)
Glutamate decarboxylase antibody (GADA)
Glutamate-aminobutyrate (GABA)
Glutamate13-fold increase
-aminobuyrate (GABA )9-fold increase
IAA
% m
ax.
++
BCAA++, ketoleucine - -
before GADA, IAA
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ndChallenge tests tell more
if clinical chemistry is advanced to metabolomicsOral Glucose Tolerance Test
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0 40 80 120min
individual subjects
free palmitic acid
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nd
But cancers are solely due to mutations?
Dang et al 2009 Nature 462: 739
Many tumors produce NADPH via glutaminegln glu akg succ fum mal pyr lactate
Mutation in IDH1 in brain tumors leads to pro-oncogenic factor 2-hydroxyglutarate -ketoglutarate + NADPH 2HO-glutarate + NADP+
NADP+ NADPH
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ndCancer cell metabolism is linked to signaling and
NADPH for rapid cell growth
Thompson & Thompson 2004 J. Clin. Onc. 22: 4217 Sreekumar et al 2009 Nature 457: 910
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ndClinical validation of cancer biomarkers
Sreekumar et al 2009 Nature 457: 910
….this was not claimed by Sreekumar et al.
….this was not claimed by Sreekumar et al.
Lessons learned: (a)authors should disclose all data and metadata, not just graphs(b)biomarkers will be more robust as panel, not as single variable (c) validation should follow guidelines as given in the EDRN network of NCI
Debate on: urine sediment vs supernatant, normalization to creatinine vs alanine vs….)
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UPLC-UV-MS/MS secondary metabolites
oxylipids, anthocyanins, flavonoids, pigmentsacylcarnitines, folates, glucuronidated & glycosylated aglycones
Twister-GC-TOF volatilesterpenes, alkanes,FFA, benzenes
nanoESI-MS/MS polar & neutral lipidsUPLC-MS/MS phosphatidylcholines, -serines, -ethanolamines, -inositols, ceramides, sphingomyelins, plasmalogens, triglycerides
GCxGC-TOFprimary small metabolites
sugars, HO-acids, FFA, amino acids, sterols, phosphates, aromatics
350 ID
200 ID
200 ID
100 ID 70
pyGC-MSmonomerslignin, hemicellulosecomplex lipids
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
50-250°C
50-330°Cramp
70 eV
20 spectra/s
20 mg breast tissue homogenization
-20°C cold extraction(iPrOH, ACN, water)
Dry down, derivatizeto increase volatility
(1) Primary metabolites < 550 Da by ALEX-CIS-GC-TOF MSM
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Fiehnlab BinBase DB Statistics Mapping
$60 direct costs/sample
70 eV
20 spectra/s
Exhale breath on Twister
Fiehnlab vocBinBase DB Statistics Mapping
(2) Volatiles < 450 Da by Twister TDU GC-TOF MSM
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50-330°Cramp-70°C
Inte
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tota
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HO
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OOH
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400 500 600 700 Time (s)
$60 direct costs/sample
(1+2) Databases are critical for success M
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(1+2) Databases are critical for success M
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1. discard poor quality signals (low signal to noise ratio )2. cross reference multiple chromatograms3. compound identification (mass spectra + RI matching by FiehnLib)4. store and compare all metabolites against all 24,368 samples in 373 studies
FiehnLib: Mass spectral and retention index libraries Anal. Chem. 2009, 81: 10038
Chemical translation service cts.fiehnlab.ucdavis.edu
AMDIS / SpectConnect Statistics Mapping
(3) Polymers by pyrolysis GC-MSM
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$20 direct costs/sample
target vendor software Statistics Mapping
(4) Secondary metabolites < 1,500 Da by UPLC-MS/MSM
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$60 direct costs/sample
(5) Complex lipids < 1,500 Da by nanoESI-MS/MSM
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ds$60 direct costs/sample
nanoESI infusionchip robot
LTQ-FT-ICR-MSHigh resolution
Statistics
Genedata Refiner MS
Mapping
Fiehnlab LipidBLAST
Experimental MS/MS listExperimental MS/MS list
Library hit scoresLibrary hit scores
exp. MS/MSexp. MS/MS
in-silico MS/MSin-silico MS/MS
in-silico MS/MSin-silico MS/MS
exp. MS/MSexp. MS/MS
Experimental MS/MS listExperimental MS/MS list
Library hit scoresLibrary hit scores
exp. MS/MSexp. MS/MS
in-silico MS/MSin-silico MS/MS
in-silico MS/MSin-silico MS/MS
exp. MS/MSexp. MS/MS
Experimental MS/MS listExperimental MS/MS list
Library hit scoresLibrary hit scores
exp. MS/MSexp. MS/MS
in-silico MS/MSin-silico MS/MS
in-silico MS/MSin-silico MS/MS
exp. MS/MSexp. MS/MS
Experimental MS/MS listExperimental MS/MS list
Library hit scoresLibrary hit scores
exp. MS/MSexp. MS/MS
in-silico MS/MSin-silico MS/MS
in-silico MS/MSin-silico MS/MS
exp. MS/MSexp. MS/MS
exp. MS/MS
in silico MS/MS
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ndBreast Cancer:Therapeutic success depends on hormonal receptor status
Tumors without expression of hormone receptors (‘Triple negative’) are more likely progress to invasive states; patients have higher 5y mortality
In combination with surgery, endocrine therapy can treat ER+ (estrogen), PR+ (progesteron) or HER+ (Herceptin) tumors
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
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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 cohort113 Samples Jan 2009
23 normal samples 90 tumors (10 grade 1, 46 grade 2, 30 grade 3)
EU FP7, PI Prof Carsten Denkert, Berlin
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E+P+H- E+P+H+ E+P-H- E+P-H+ E-P+H- E-P+H+ E-P-H- E-P-H+
grade 1 grade 2 grade 3
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Hormone receptor status vs grade
% o
f pati
ents
Estrogen negativeEstrogen positive
triple neg.
Resu
lts S co re sca tte rp l o t (t1 vs. t2 )
S ta n d a rd d e via ti o n o f t1 : 4 .9 9 2
S ta n d a rd d e via ti o n o f t2 : 7 .0 8 9
+ /-3 .0 0 0 *S td .De v-1 2 .5 -1 0 .0 -7 .5 -5 .0 -2 .5 0 .0 2 .5 5 .0 7 .5 1 0 .0
t1
-2 0 .0
-1 7 .5
-1 5 .0
-1 2 .5
-1 0 .0
-7 .5
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-2 .5
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t2
grade1grade2
grade3
(1) Can we identify metabolites or metabolic pathways that are associated with breast cancer clinical parameters?
Alex-CIS-GCTOF MS w/ BinBase: 470 detected compounds161 known metabolites, 309 without identified structure.
Partial Least Square (multivariate stats)
grade3
grade2
grade1
breast adipose