M&G 15 dec 2006 1

Toxicogenomics and Toxicogenetics

Maastricht UniversityJ. van Delft, D. van Leeuwen, H.

Ketelslegers, R. Vlietinck, J. Kleinjans

M&G 15 dec 2006 2

General concept

toxicogenomicstoxicogenetics

M&G 15 dec 2006 3

Goals

Development, validation and application of:1. biomarkers of effect as health indicator for

exposure to carcinogenic compounds2. biomarkers for genetic susceptibility related to

those indicators

Based on the newest genomic technologies:1. Gene expression profiles as biomarker for

effect, by Danitsja van Leeuwen2. Multiplex genotyping as biomarker for genetic

susceptibility, Hans Ketelslegers

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Phases Toxicogenomics

Studies to select genes using DNA microarrays:

1. In vitro studies in human peripheral blood cells exposed to carcinogenic compounds

2. Small scale field study in monozygotic twins disconcordant in smoking

Application in Environment & Health field study of “Luik III” on adults by quantitative RT-PCR

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Example of a DNA microarray

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Human 600 Toxarrays of Phase-1 Molecular Toxicology

Gene Categories Types of Genes in CategoryApoptosis Caspases, BAK, Bax, Fas, Cyclins,

TNFsCell Cycle Cyclins, DNA Binding Protein, Waf 1Cell Proliferation Kinases, Transcription Factors,

Growth Factors and Receptors, Connexins

DNA Damage/ Repair DNA Repair Genes, ERCC’s, GADDs, Helicases, Topoisomerases

Inflammation Serum Amyloids, Interleukins, Adhesion Molecules, Chemokines

Metabolism P450s, Glucuronidation Enzymes, Glutathione Enzymes, Methyltransferases, Redox Enzymes

Oxidative Stress O2 Response Genes, Superoxide Dimutase, Redox Enzymes

Peroxisome Proliferators Peroxisomal EnzymesTransport Multi-drug Resistance Proteins,

Organic Anion and Cation Transporters

Cell-Environment Connexins, Integrins, Selectins, Cadherins

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In vitro study in human peripheral blood cells

Model carcinogenic compounds:o Cigarette smoke condensateo Benzo[a]pyreneo Tabaco specific nitrosamine (NNK)o 4-amino biphenyl

o H2O2

Possible biomarker genes:o Deregulated by all compoundso Correlating with DNA adducts

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Deregulated by CSC

CSC

-6

-4

-2

0

2

4

6

IL1β

*

IL6*

CS

F1R

*

CX

CL2

*

SP

P1*

SE

RP

INB

2*

TG

M2*

XR

CC

1*

PT

GS

2

AT

F3

AC

O1

EN

O1

VE

GF

MM

P12

Diff

eren

ce (l

og2)

lowmediumhigh

M&G 15 dec 2006 9

Deregulated by all compoundsCSC 169

H2O2 49

BaP68

4-ABP 51

NNK 78

109

87

95 91

50

50

48 4816

38

19

26 2534

19

2554

34

ATF4GLULGSTA2GAPDVMP1HPRTIL1IL6

CXCL2MMP12VDAC2MIG2PECAM1SATF3COPEB34

5226

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Small scale field study

Monozygotic twins discordant in smokingo Total peripheral blood cellso Analysis of:

Gene expressionDNA adducts (post labelling) Plasma cotinin levels

Data analyses of gene expression :o Smokers vs non-smokerso Correlations with DNA-adductso Validation with RT-PCR

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Differentially expressed genes in smokers vs non-smokers

WILCOXON 2 RELATED SAMPLES TEST

p<0.05

CONFIDENCE ANALYSIS (>99%,

regulation level >0.1)

SIGNAL-TO-NOISEratio >0.5

ATF4BNIP2CANXCASP4CATIFRD1MAPK14MCL1MRPS11

OATPLATPLEKHC1PTENPXNS100A9SERPINA1SOD2VDAC2

WILCOXON 2 RELATED SAMPLES TEST

p<0.05

CONFIDENCE ANALYSIS (>99%,

regulation level >0.1)

SIGNAL-TO-NOISEratio >0.5

ATF4BNIP2CANXCASP4CATIFRD1MAPK14MCL1MRPS11

OATPLATPLEKHC1PTENPXNS100A9SERPINA1SOD2VDAC2

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Validation with RT-PCR

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Czech study

Another relevant field study, though not related to current program

Compared children from polluted versus clean area in Czech republic

Identified:o Differentially expressed genes o Genes correlating with micronuclei

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Deregulated and correlating genes

Microarray Real-time PCR

Gene Difference

Correlation

Difference

Correlation

CXCL1 0.52 (6.7*10-5)

0.37 (0.011)

0.60 (0.0009)

0.36 (0.014)

PINK1 0.34 (2.3*10-5)

0.30 (0.038)

0.37 (0.009)

0.16(0.27)

DGAT2 0.43 (1.5*10-4)

0.32 (0.027)

0.78 (0.005)

0.25(0.09)

TIGD3 0.033 (1.9*10-4)

0.41 (0.004)

0.52 (0.008)

0.39 (0.008)

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Genes selected for field study

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Gene name (abbreviation)1 Biological summary1

Cytochrome p450 1B1 (CYP1B1) Catalyzes many reactions involved in drug metabolism; metabolism of procarcinogens, e.g. polycyclic aromatic hydrocarbons (PAHs).

Superoxide dismutase 2 (SOD2) Associated with oxidative stress; encodes a mitochondrial protein that binds to the superoxide byproducts of oxidative phosphorylation and converts them to hydrogen peroxide and diatomic oxygen.

Activating transcription factor 4 (ATF4)

Encodes a transcription factor that belongs to a family of DNA-binding proteins, including the AP-1 family of transcription factors, cAMP-response element binding proteins (CREBs) and CREB-like proteins.

Mitogen-activated protein kinase 14 (MAPK14)

Activated by various environmental stressors and proinflammatory cytokines. Integration point for multiple biochemical signals, involved in a wide variety of cellular processes such as proliferation, differentiation, transcription regulation and development. The substrates of this kinase include transcription regulator ATF2, MEF2C, and MAX, cell cycle regulator CDC25B, and tumor suppressor p53, which suggest the roles of this kinase in stress related transcription and cell cycle regulation, as well as in genotoxic stress response.

Chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity, alpha) (CXCL1)

Regulate cell trafficking of various types of leukocytes through interactions with a subset of 7-transmembrane, G protein-coupled receptors. Chemokines also play fundamental roles in the development, homeostasis, and function of the immune system, and have effects on cells of the central nervous system as well as on endothelial cells involved in angiogenesis or angiostasis.

PTEN-induced putative kinase-1 (PINK1)

Gene Ontology: ATP binding, magnesium ion binding, nucleotide binding, protein serine/threonine kinase activity, transferase activity, protein kinase cascade, response to stress.

Diacylglycerol O-acyltransferase homolog 2 (mouse) (DGAT2)

DGAT is responsible for the synthesis of triglycerides. It catalyzes a reaction in which diacylglycerol is covalently joined to long chain fatty acyl-CoAs.

Tigger transpo-sable element derived 3 (TIGD3)

The protein encoded by this gene belongs to the tigger subfamily of the pogo superfamily of DNA-mediated transposons in humans. These proteins are related to DNA transposons found in fungi and nematodes, and more distantly to the Tc1 and mariner transposases. They are also very similar to the major mammalian centromere protein B. The exact function of this gene is not known.

1) See NCBI at http://www.ncbi.nlm.nih.gov/Gene

Based on smokers study with MZ twins

CYP1B1

SOD2

ATF4

MAPK14

Based on Czech air pollution study

CXCL1

PINK1

DGAT2

TIGD3

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Field study on elderly people

aged 50-65 years, n = 398RNA from total peripheral blood cellsQuantitative RT-PCR of 8 genes vs 2

house keeping genesReference RNA sample: pool from 20

randomly selected individualsCompared data with:

o COMET o MN frequencieso 8-OH-dGin urineo Tumor markers in serum (p53, CEA, PSA)

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Effect of region

Antwerp

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Harbor

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Fruit

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Olen

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Gent

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Incinerators

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Rural

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Canal

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Never + former smokers

Never + former +current smokers

Antwerp

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Harbor

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Fruit

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Olen

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Gent

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Incinerators

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Rural

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Canal

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Antwerp

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Harbor

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Fruit

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Olen

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Gent

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Incinerators

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Rural

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Canal

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

CYP1B1

ATF4

MAPK14

SOD2

CXCL1

DGAT2

TIGD3

PINK1

Never + former smokers

Never + former +current smokers

Never + former smokers

Never + former +current smokers

Non-smokers

All subjects

M&G 15 dec 2006 19

Effect of seasonCYP1B1

-1,0

-0,5

0,0

0,5

1,0

1,5

0 5 10 15 20 25

A H F O G I R C

March 22nd 2005 - June 6th 2005

Sep 27th 2004 - Dec 21st 2004

Dec 22nd 2004 - March 21st 2005

ATF4

-1,0

-0,5

0,0

0,5

1,0

1,5

A H F OG

I R C

MAPK14

-1,0

-0,5

0,0

0,5

1,0

1,5

A H F O G I R C

SOD2

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

0 5 10

15

20

25

A H F O G I R C

CXCL1

-1,0

-0,5

0,0

0,5

1,0

1,5

A H F O G I R C

DGAT2

-1,0

-0,5

0,0

0,5

1,0

1,5

A H F O G I R C

TIGD3

-1,0

-0,5

0,0

0,5

1,0

1,5

A H F O G I R C

SUM

-5,0

-4,0

-3,0

-2,0

-1,0

0,0

1,0

2,0

3,0

4,0

5,0

0 5 10 15 20 25

A H F O G I R C

PINK1

-1,0

-0,5

0,0

0,5

1,0

1,5

0 5 10 15 20 25

A H F O G I R C

M&G 15 dec 2006 20

Comparison of regions

Incinerato

rs

Ca

na

l

Ru

ral

Fru

it

An

twe

rp

Ole

n

Ge

nt

Ha

rbor

A

Incinerato

rs

Ca

na

l

Ru

ral

Fru

it

An

twe

rp

Ole

n

Ge

nt

Ha

rbor

Incinerato

rs

Ca

na

l

Ru

ral

Fru

it

An

twe

rp

Ole

n

Ge

nt

Ha

rbor

Incinerato

rs

Ca

na

l

Ru

ral

Fru

it

An

twe

rp

Ole

n

Ge

nt

Ha

rbor

A

Incin

era

tors

Ca

na

l

Ru

ral

Fru

it

An

twe

rp

Ole

n

Ge

nt

Ha

rbor

B

Incin

era

tors

Ca

na

l

Ru

ral

Fru

it

An

twe

rp

Ole

n

Ge

nt

Ha

rbor

B

M&G 15 dec 2006 21

Correlations with effect biomarkers

ATF4MAPK14 SOD2

CXCL1 DGAT2TIGD3PINK1 SUM

CYP1B1

B

PSA p53COMET

countCEA

COMET P90

ATF4MAPK14 SOD2

CXCL1 DGAT2TIGD3PINK1 SUM

CYP1B1

B

PSA p53COMET

countCEA

COMET P90

M&G 15 dec 2006 22

Comparison with classical biomarkers

Majority of gene expressions differed significantly between 2 or more regions

Classical biomarkers did not always differ and if so, with lower significance

Magnitude of differences o gene expression: 1.2 (DGAT2) – 2.0 (ATF4) o classical biomarkers: 1.10 (COMET count) – 2.43

(COMET median)

Smoking significantly affected:o CYP1B1 and ATF4o MN, CEA and p53

Correlations with exposure markers not yet done

M&G 15 dec 2006 23

Conclusions

Gene expression profiling as possible biomarker has been developed and applied

More in-dept analyses are required in order to establish relevance:o Exposure markerso Effect markerso Susceptibility markerso Confounding factors

Gene expression profiling is promising for molecular epidemiology on the risks of environmental exposures for humans

M&G 15 dec 2006 24

General concept

toxicogenomicstoxicogenetics

M&G 15 dec 2006 25

Phases Toxicogenetics

Select genes and polymorphism to be included

Develop and validate methods for multiplex genotyping

Apply in Environment & Health field studies of “Luik III” on newborns, adolescents, elderly

M&G 15 dec 2006 26

Selection criteria of genes and polymorphisms

Genes must be relevant for endpoints / biomarkers in filed studies:o Asthma and allergyo Cancer

Polymorphisms must be relevant:o Highly frequent (>5%)o Cause a phenotypic effect (proven or highly

likely)

M&G 15 dec 2006 27

SNP Database: Database: 66 SNPs in 41 genes

Biotransformation (Set 1&7)o E.g. CYP1A1, -1A2, -1B1, GSTs, NATs, mEH

etc.

DNA repair (Set 2)o E.g. XRCC, XPD, BRCA2, OGG1 etc.

Oxidative stress related (Set 3)o E.g. CAT, SOD, NQO, GPX etc.

Inflammation (Set 4&5)o E.g. Interleukins, TNFα, PAFAH etc.

Apoptosis & Cell Cycle control (Set 6)o E.g. p53, p21, Cylin D, CDKs etc.

M&G 15 dec 2006 28

Examples

Allele Nucleotide change

Amino Acid Change

Allele frequency (%)

Phenotype Disease Association

SOD2*1 C47T Val16Ala

C:38.5;T:61.5

Altered expression

Ala variant: incr. Risk for breast cancer

NQO1*2 C609T Pro187Ser

C:80; T:20

Less mRNA, no protein or activity

CC: higher risk for smoking related bladder cancer

MPO*1 G-463A

G:74.5; A:25.5

AA:lower transcription, mRNA and protein

Lung , larynx and esophageal cancer, CAD and alzheimer

XPA*1 A23G +/- 50 GG: decr. risk for lung cancer

mEH*2 C/T Tyr113His

C:36.1; T:63.9

40% decrease in hydrolase activity

His: protective against colon cancer

mEH*3 A/G His139Arg

A:80.1; G:19..9

25% increase in hydrolase activity

No relationship

M&G 15 dec 2006 29

Examples for genotyping by Single Base Extension

1 4 5 6 7 8 92 3

C/C

C/C G/A G/G

G/G C/A

C/C

A/AA/0

0/0

C/TG/A A/AA/0

A/AA0

C/C G/G

G/GC/T

C/T

1 4 5 6 7 8 92 31 4 5 6 7 8 92 3

C/C

C/C G/A G/G

G/G C/A

C/C

A/AA/0

0/0

C/TG/A A/AA/0

A/AA0

C/C G/G

G/GC/T

C/T

M&G 15 dec 2006 30

Validation of genotyping method (1)

SNP N % OK Alternativ method CYP1A2*1C 10 100 sequencing

CYP1A2*1F 20 100 PCR-RFLP

GSTM1*0 68 100 PCR

GSTT1*0 68 100 PCR

GSTP1*2 20 100 PCR-RFLP

NAT2*5 68 100 PCR-RFLP (+ sequencing)

NAT2*6 10 100 PCR-RFLP

NAT2*7 10 100 PCR-RFLP

M&G 15 dec 2006 31

Validation of genotyping method(2)

M&G 15 dec 2006 32

Adolescent study Population: +/- 450

adolescents (age: 16 years old)

Biomarkers: o Effect: Comet Analysis (DNA

damage)o Exposure: 1-OHP (PAHs), PCBs,

DDE, Cd, Pb

Genotyping: Biotransformation, DNA repair and oxidative stress related

M&G 15 dec 2006 33

Statistical approaches

Univariate analyses: e.g. Mann Whitney or Kruskall Wallis

NAT2*6: p=0,006

M&G 15 dec 2006 34

Statistical approaches

Multivariate analyses: e.g. Multiple Linear Regression, Discriminant Analyses or Binary Logistic Regression

M&G 15 dec 2006 35

Exposure Marker

(Confounding) Effect of Smoking?

Remove Smokers from Analysis

In Non-Smokers

Relationship Exposure with Effect Marker?

Dose-Response

2 groups based on Regression Line: 0 & 1

Logistic RegressionGroup as Dependent;

Sex, Cig/Day*, Smoking Y/N*, SNPs as Independents

Most important

predictors?

+-

Total Population

M&G 15 dec 2006 36

Linear Relationships-Adolescents

Smoker Y/N Comet tt-MAp'p'DDE p=0.858 p=0.521HCB p=0.343 p=0.552Cd114 p=0.000 p=0.822 (0.353)*Cd_Crea p=0.209 p=0.146Pb p=0.375 p=0.080PCB p=0.548 p=0.7551OHP p=0.013 p=0.580 (0.303)*ttMA p=0.256 p=0.602Benzene p=0.165 p=0.462 p=0.808ClBenzene p=0.837 p=0.046 p=0.747EthylBenzene p=0.808 p=0.022 p=0.892Comet p=0.032 *** p=0.602

* Non-Smokers

M&G 15 dec 2006 37

Ethylbenzene * CometLinear Regression

M&G 15 dec 2006 38

Ethylbenzene * CometLogistic Regression

P=0.201 P=0.131

Catalase (p=0.027) GSTT1 (p=0.035)

M&G 15 dec 2006 39

Adult study Population: +/- 400

adolescents (age: 65 years old) Biomarkers:

o Effect: Comet Analysis (DNA damage), 8-OHdG, PSA, CEA, p53

o Exposure: 1-OHP (PAHs), PCBs, DDE, Cd, Pb

Genotyping: Biotransformation, DNA repair and oxidative stress related

M&G 15 dec 2006 40

Linear Relationships-Adults

Smoker Y/N Comet 8-OHdG PSA CEA p53PCB p=0.544 p=0.676 p=0.755 p=0.843 p=0.520 p=0.230HCB p=0.517 p=0.239 p=0.003 p=0.623 p=0.546 p=0.082p'p'DDE p=0.562 p=0.651 p=0.613 p=0.873 p=0.836 p=0.150Cd114 p=0.000 p=0.380 (0.156)* p=0.355 (0.666)* p=0.091 (0.139)* p=0.000 (0.530)* p=0.463 (0.908)*Cd_Crea p=0.000 p=0.816 (0.790)* p=0.004 (0.007)* p=0.943 (0.158)* p=0.000 (0.546)* p=0.102 (0.030)*Pb p=0.510 p=0.438 p=0.088 p=0.663 p=0.004 p=0.3561OHP p=0.000 p=0.313 (0.777)* p=0.007 (0.029)* p=0.256 (0.078)* p=0.000 (0.371)* p=0.375 (0.680)*Comet p=0.400 *** p=0.683 p=0.122 p=0.141 p=0.6948-OHdG p=0.887 p=0.683 *** p=0.861 p=0.136 p=0.703PSA p=0.358 p=0.122 p=0.861CEA p=0.000 p=0.141 (0.248)* p=0.136 (0.859)*p53 p=0.004 p=0.694 (0.514)* p=0.703 (0.620)*

* Non-Smokers: Significant Relationship: Only Significant in Total Population: Only Significant in Non-Smokers

M&G 15 dec 2006 41

Cadmium (Urine) * 8-OHdG Linear Regression non-smokers

M&G 15 dec 2006 42

Cadmium (Urine) * 8-OHdGLogistic Regression

P=0.224

GSTT1 (p=0.041)

M&G 15 dec 2006 43

1-OH-pyrene (Urine) * 8-OHdGLinear Regression non-smokers

M&G 15 dec 2006 44

Adults: 1-OH-pyrene (Urine) * 8-OHdGLogistic Regression

P=0.003 P=0.035 P=0.098

Gender CYP1A1*m4 mEH*3 (p=0.001) (p=0.05) (p=0.023)

M&G 15 dec 2006 45

Comparison with classical biomarkers

Majority of gene expressions differed significantly between 2 or more regions

Classical biomarkers did not always differ and if so, with lower significance

Magnitude of differences o gene expression: 1.2 (DGAT2) – 2.0 (ATF4) o classical biomarkers: 1.10 (COMET count) – 2.43

(COMET median)

Smoking significantly affected:o CYP1B1 and ATF4o MN, CEA and p53

Correlations with exposure markers not yet done

M&G 15 dec 2006 46

Conclusions

Genetic polymorphisms affect susceptibility for effect biomarkers related to exposure

Sensitive populations can be genotyping for relevant polymorphisms

More in-dept analyses are required on order to establish relevance:o Interactions between genotypeso Univariate analyseso Effect of / interaction with smokingo Relations with gene expression

Genotyping enables to identify sensitive populations for specific exposure – effect relations

M&G 15 dec 2006 47

Demonstrated the value for molecular epidemiology

toxicogenomicstoxicogenetics