Transcript 5 MBFiehn_Seattle_October_2010_Part_1

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
Background
mRNA expression
protein expression
metabolite
levels & fluxes
temporal x spatial resolution
phenotype
Fiehn 2001 Comp. Funct. Genomics 2: 155
Background
Metabolic phenotypes
reflect multiple origins
SNPs
allelic variants
gender
racial disparities
inherited methylations
gut microbes
calorie intake
food composition
life style / exercise
disease history
transport
Metabotype intensity
Background
Metabotypes:
gate to personalized medicine
“Intervention”
Disease
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
g-aminobuyrate (GABA )
9-fold increase
g-aminobutyrate (GABA)
Glutamate
Glutamate decarboxylase
antibody (GADA)
Insulin autoantibody (IAA)
GADA
% max.
Background
Glutamate
13-fold increase
IAA
Diagnosis
Normal level
(GABA, Glu)
0
BCAA++, ketoleucine - before GADA, IAA
+
1
+
2
3
4
5
Age (years)
6
7
8
9
Orešič et al. 2008 J. Exp. Med. 205: 2975
Challenge tests tell more
if clinical chemistry is advanced to metabolomics
Oral Glucose Tolerance Test
individual subjects
Background
free palmitic acid
120
AU
80
60
40
20
0
0
40
80
min 120
Background
But cancers are
solely due to mutations?
Background
Cancer 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
Many tumors produce NADPH via glutamine
gln glu  akg  succ  fum  mal  pyr  lactate
NADP+
NADPH
Mutation in IDH1 in brain tumors leads to
pro-oncogenic factor 2-hydroxyglutarate
a-ketoglutarate + NADPH  2HO-glutarate + NADP+
Dang et al 2009 Nature 462: 739
Clinical validation of cancer biomarkers
Background
….this was not claimed by Sreekumar et al.
….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:
(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
How many platforms do we need?
Methods
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
pyGC-MS
monomers
Twister-GC-TOF
volatiles
lignin, hemicellulose
complex lipids
100 ID
70
sugars, HO-acids, FFA, amino acids,
sterols, phosphates, aromatics
polar & neutral lipids
phosphatidylcholines, -serines,
-ethanolamines, -inositols, ceramides,
sphingomyelins, plasmalogens, triglycerides
350 ID
terpenes, alkanes,
FFA, benzenes
200 ID
GCxGC-TOF
primary small metabolites
nanoESI-MS/MS
UPLC-MS/MS
200 ID
UPLC-UV-MS/MS secondary metabolites
oxylipids, anthocyanins, flavonoids, pigments
acylcarnitines, folates, glucuronidated & glycosylated
aglycones
(1) Primary metabolites < 550 Da by ALEX-CIS-GC-TOF MS
Methods
20 mg breast tissue
homogenization
-20°C cold extraction
(iPrOH, ACN, water)
50250°C
70 eV
50-330°C
ramp
20 spectra/s
$60 direct
costs/sample
Dry down, derivatize
to increase volatility
Fiehnlab BinBase DB
Statistics
Mapping
(2) Volatiles < 450 Da by Twister TDU GC-TOF MS
Exhale breath
on Twister
70 eV
-70°C
50-330°C
ramp
Intensity (total ion chromatogram)
Methods
20 spectra/s
$60 direct
costs/sample
HO
O
400
Fiehnlab vocBinBase DB
500
Statistics
600
Mapping
700
Time (s)
Methods
(1+2) Databases are critical for success
(1+2) Databases are critical for success
Methods
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
Chemical translation service
cts.fiehnlab.ucdavis.edu
Methods
(3) Polymers by pyrolysis GC-MS
$20 direct
costs/sample
AMDIS / SpectConnect
Statistics
Mapping
(4) Secondary metabolites < 1,500 Da by UPLC-MS/MS
Methods
$60 direct
costs/sample
target vendor software
Statistics
Mapping
(5) Complex lipids < 1,500 Da by nanoESI-MS/MS
$60 direct
costs/sample
Methods
nanoESI infusion
chip robot
Experimental MS/MS list
Experimental MS/MS list
ry hit scores
ary hit scores
LTQ-FT-ICR-MS
High resolution
Genedata Refiner MS
exp. MS/MS
exp. MS/MS
Fiehnlab LipidBLAST
exp.MS/MS
MS/MS
exp.
exp. MS/MS
Statistics
inin-silico
silico MS/MS
MS/MS
Experimental
MS/MS list
in-silico MS/MS
Experimental
MS/MS list
exp. MS/MS
exp. MS/MS
Mapping
exp. MS/MS
exp. MS/MS
Background
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
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
EU FP7, PI Prof Carsten Denkert, Berlin
Methods
(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
Second cohort
113 Samples
Nov 2008
74 normal samples
210 tumors (20 grade 1, 101 grade 2, 71 grade 3)
Jan 2009
23 normal samples
90 tumors (10 grade 1, 46 grade 2, 30 grade 3)
60
Hormone receptor status
vs grade
% of patients
Methods
50
40
grade 1
30
grade 2
grade 3
20
triple
neg.
10
0
E+P+H-
E+P+H+
E+P-H-
Estrogen positive
E+P-H+
E-P+H-
E-P+H+
E-P-H-
Estrogen negative
E-P-H+
(1) Can we identify metabolites or metabolic pathways
that are associated with breast cancer clinical parameters?
Partial Least Square
(multivariate stats)
Score scatterplot (t1 vs. t2)
Standard deviation of t1: 4.992
Standard deviation of t2: 7.089
15.0
grade2
12.5
grade1
grade1
10.0
grade3
7.5
5.0
2.5
0.0
t2
Results
Alex-CIS-GCTOF MS w/ BinBase: 470 detected compounds
161 known metabolites, 309 without identified structure.
breast
adipose
grade2
-2.5
-5.0
-7.5
-10.0
-12.5
grade3
-15.0
-17.5
-20.0
-12.5 -10.0 -7.5
-5.0
-2.5
t1
0.0
2.5
5.0
7.5
10.0