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”

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ype

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nsity

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

0 1 2 3 4 5 6 7 8 9

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

0

20

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80

AU

120

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

etho

ds

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

etho

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50-330°Cramp-70°C

Inte

nsi

ty (

tota

l io

n ch

rom

ato

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m)

HO

O

OOH

O

O OO

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

etho

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$20 direct costs/sample

target vendor software Statistics Mapping

(4) Secondary metabolites < 1,500 Da by UPLC-MS/MSM

etho

ds

$60 direct costs/sample

(5) Complex lipids < 1,500 Da by nanoESI-MS/MSM

etho

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

0

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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

-5 .0

-2 .5

0 .0

2 .5

5 .0

7 .5

1 0 .0

1 2 .5

1 5 .0

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