Transcript Presentation - Envirogenomarkers

Genomics in cancer molecular epidemiology:
The EnviroGenomarkers project
Soterios A. Kyrtopoulos
National Hellenic Research Foundation, Institute of
Biological Research and Biotechnology, Athens,
Greece
Biomarkers in environmental cancer research
 Biomarkers of exposure: improve exposure assessment
 Biomarkers of early effects: provide evidence of early, biologically
relevant changes
 Biomarkers of individual susceptibility: help recognise individuals
with high susceptibility to specific stages of carcinogenic process
1st generation of biomarkers in environmental
carcinogenesis research
exposure
internal dose
chemicals /
metabolites /
in body fluids or
tissues
clinical
disease
biologically
effective dose
protein adducts /
DNA adducts
gene or chromosome
mutations /
modified gene expression
mutation spectra
in tumours or
pre-cancerous cells
biomarkers of effect (risk)
biomarkers of exposure
metabolism
altered
structure/
function
early
biological effects
DNA repair / genetic instability
individual susceptibility:
diet / lifestyle / genetic makeup
immune defence
Pros and cons of “1st generation biomarkers”
Advantages
- highly sensitive and chemical-specific biomarkers of exposure
- information on specific stages of carcinogenesis (e.g. gene or
chromosome mutagenesis)
Disadvantages
- collect information one item at a time (but “adductomics”)
- different assays/technologies for different types of endpoints
- limited mechanistic information
Potential advantages of –omics biomarkers
Genomics
Epigenomics (DNA methylation)
Transcriptomics
Metabonomics
Proteomics
- can be derived from global, untargeted searches
- use generic technology regardless of disease or exposure of
interest
-provide mechanistic information on multiple endpoints
- combined use of multiple –omics technologies, plus bioinformatics,
can integrate multi-level information and provide a systems biology
approach to biomarker discovery
Current evidence of potential of –omics in
environmental health research
1. Genomics: widespread use in GWAS studies
Massive SNP analysis and search for association with disease risk
Case-control studies, some hundreds to a few thousands of subjects
ORs for individual alleles tend to be rather small (<1.5)
Tenesa et al., Nat Genet. May;40 (2008) 631-7
colorectal cancer: OR = 2.6 for combination of six alleles
Amos et al., Nat Genet. 40 (2008) 616-22
lung cancer: ΟR = 1.32 for combination of two alleles
2. Εpigenomics
Bulk hypomethylation, promoter (CpG island)
hypermethylation
- Most studies targeted on candidate genes (p16, MGMT, RASSF1A etc)
or on surrogate sequences (alu, LINES)
-Few genome-wide studies, mostly in relation with diseased states
Clear evidence of effects of exposures on epigenetic status in blood
mononuclear cells
Emerging evidence of analogous effects in relation to disease risk
 Perera et al., PLOS One 2009, e4488
Cord blood, children with maternal high/low PAH exposure;
ACSL3 CpG island
 Breton et al., AJRCCM 2009
Maternal pregnancy exposure to tobacco smoke vs methylation in
buccal cells of children;
Illumina Goldengate platform; 8 CGI with changes detected; changes in
AluYb8 but not LINE1;
 Widschwendter et al., PLoS One 2008, e2656
Epigenotyping in peripheral blood cell DNA and breast cancer risk
DNA methylation of peripheral blood cell DNA provides good prediction of
BC risk
3. Transcriptomics: Global analysis of gene expression,
search for association with exposure or early effects
Few, rather small studies to date
van Leeuwen et al., Mutat Res. 600 (2006 ), 12-22
Genome-wide differential gene expression in PBMCs of children exposed
to air pollution in the Czech Republic
24 high exposure / 23 low exposure subjects
Forrest et al., Environ Health Perspect. 113 (2005), 801-7
Microarray analysis of PBMC gene expression in benzene-exposed
workers
6 exposed & 6 controls
4. Proteomics
- Adsorption on protein chips / SELDI-TOF MS
- Multiplex ELISAs
Luminex LabMAP technology
Few studies, limited to a few tens of subjects
Vermeulen et al., PNAS 102 (2005 ), 17041-6
10 exposed & 10 controls
Decreased levels of CXC-chemokines in serum of benzeneexposed workers identified by array-based proteomics
5. Μetabolomics
full-profile analysis of serum/urine by NMR OR HPLC/MS/MS
Studies so far limited to diseased states
Holmes et al., Nature 453 (2008) 396-400
Human metabolic phenotype diversity and its association with diet and blood
pressure
ΙΝΤΕΡΜΑP project: metabolomic profiles
in relation to blood pressure
& influence of diet & other factors
4,630 subjects, 17 populations
Urine analysis using NMR
Current state of the art in application of –omics in the
search for biomarkers of environmental disease
Conclusion so far: Even with small studies, distinct profiles, with
biological meaning, can be identified, reflecting toxic exposure or
predictive of disease risk
Problems:
 existing studies based on very small populations
 limited information on exposure
 uncertainty regarding the potential of use of –omics with samples already
stored in existing biobanks and in large-scale studies
FP7 integrated project
Main aims:
-Application of wide range of –omics technologies, in combination with
Genomics
biomarkers
health
advanced
bioinformatics,
in the contextof
of environmental
molecular epidemiology
studies, for
(EnviroGenomarkers)
the discovery of
new biomarkers of exposure to toxic environmental agents,
new biomarkers of disease risk, and
exploration of their relationships
- Exploration of technical potential and problems in the application of –omics
technologies in large-scale population studies using biosamples in long-term
storage *
*Currently more than 1 million samples are stored in biobanks in Europe
11 partners from 7 European countries
no.
Participant organisation name
Country
1
National Hellenic Research Foundation; co-ordination; epigenomics; SNPs; PAH
adducts
Greece
2
University of Maastricht; transcriptomics
Netherlands
3
Imperial College London; exposure assessment; risk assessment; metabolomics
UK
4
Umeå University; NHSDS biosamples & data; epidemiology; exposure assessment
Sweden
5
Centro per lo Studio e la Prevenzione Oncologia, Florence; EPIC Italy biosamples
& data; epidemiology; exposure assessment
Italy
6
University of Crete; Rhea cohort samples & data; phthalate & PBDE analyses
Greece
7
University of Utrecht; proteomics
Netherlands
8
Istituto Superiore di Sanita, Rome; exposure assessment (GIS)
Italy
9
National Public Health Institute (KTL), Kuopio; PCB analyses
Finland
10
University of Leeds; data warehousing; bioinformatics
UK
11
University of Lund; Cd analyses
Sweden
start March 2009, 4 yrs
Epidemiologic design: case-control nested within
prospective cohorts
prospective study
exposure
biomarkers
of exposure
intermediate
–omics
biomarkers of
early effects
relationship of environmental exposures vs
risk biomarkers
disease
risk-predictive biomarkers
“meet-in-the-middle” approach
[P. Vineis & F. Perera, Cancer Epidemiol. Biomarkers. Prev 16 (2007):1954–65]
EPIC
Collaborating centres and cohort subjects
The EnviroGenomarkers
project
cohorts
Subjects included
TROMSØ
Questionnaire Q + Blood
France
74 524
21 053
UME
Italy
47 749
47 725
Å
Spain
41 440
39 579
UK
87 942
43 141
AARHUS MALMÖ
Netherlands
40 072
36 318
COPENHAGEN
UTRECHT
CAMBRIDGE
POTSD
Greece
28 555
28 483
OXFORD BILTHOVEN AM
NHSDS:
Population based cohorts
Germany
53 091
50 678
HEIDELBERG
Questionnaires
andPARIS
blood
sampling
Sweden
53 826
53 781
MILAN
IARCLYON
TURIN
OVIEDO
Denmark
57
054
56
131
• The Västerbotten Intervention
Project Cohort
(VIP) 1985
FLORENCE
SAN SEBASTIAN
Norway
37 215
11 000
PAMPLO
BARCELONA NAPLES
NA
•AllThe Northern
Sweden
Monica
Cohort
521 468
387 889
ATHE
MURCI
GRANADA
A
RAGUSA
85 000
13 000
NS
from 1986, 1990, 1994, 1999 (+ follow up), 2004
• The Västerbotten Mammary Screening Cohort 1995 Unique individuals
Rhea• mother-child
cohort, Crete
Approx. 1,700 mothers & their children borne
since 2007 in the Heraklion area, Crete;
Questionnaires, maternal blood & urine, cord
blood & its components
-
(+ 4 500)
50 000
90 000
Diseases and exposures
1. breast cancer vs PCBs
PAHs
cadmium
2. B-cell lymphoma vs PCBs
OR~2-4.5
OR~1.5
OR~2.3
OR~5-13
Case-control nested within EPIC Italy and NSHDS
3. chronic diseases of the nervous and immune system & allergies
establishing themselves in early childhood
vs early life exposure to endocrine disruptors
(PCBs, PAHs, phthalates, polybrominated diphenyl ethers)
Prospective, Rhea cohort (Crete), children followed up at age 4 years
vs exposure during pregnancy
Samples to be analysed
samples
cohort
NSHDS,
EPIC-Italy
disease
end-point
subjects
original
repeats
breast ca
600
600
~ 30
breast ca
controls
600
600
~ 30
BCL
300
300
BCL
controls
300
300
~ 30
~ 30
allergy/
asthma
Rhea
immune
neurological
600
600 cord blood,
mother urines
600 blood
& urine at
age
4 yrs
Intermediate biomarker analyses
In general use 2-phase approach:
a) discovery phase (genome-wide); 10-20% of samples
b) validation phase (targeted): all samples
1. Metabolomics
Start off with pilot study to technically validate applicability of
plasma, HPLC/MS;
all samples
–omics
technologies to existing biobank samples
2. Epigenomics
discovery: genome-wide (Illumina Infinium 27k CpG microarray platform)
validation: 10 selected sequences, pyrosequencing
3. Proteomics
discovery: Luminex Multianalyte Profiling (47 inflammationrelated proteins)
validation: 10 selected proteins; same method
4. Transcriptomics
discovery: genome-wide (Agilent 44k microarray platform)
validation: 10 selected sequences, RT-PCR
5. Genotyping
a) Genome-wide SNP data already available for many of the samples from
previous studies
b) RT-PCR on selected targets and in selected sub-groups
Exposure assessment
Biomarkers
PCBs: plasma concentrations of selected congeners, using GC/MS
cadmium: erythrocyte levels, using inductively-coupled plasma mass
spectrometry
phthalates: urine metabolites, using HPLC/MS
BDPEs: plasma concentrations of selected congeners, using HPLC/MS
PAHs: DNA adducts (ELISA)
Additional data on biomarkers of carcinogen exposure available from
other projects
Other exposure information
FFQs and environmental exposure questionnaires
Data on exposure to air, water and food toxicants available from other
projects
GIS
Data management & analysis
Centralised data repository
Use existing & construct new bioinformatics tools
Aim for integrated, multi-level analysis
1. Intermediate biomarkers vs disease: biomarkers with risk predictivity
2. Biomarkers of risk vs exposure
3. Intermediate biomarkers vs exposure
Bioinformatics – integrated analysis of multilevel data
1 2 3 4 5 6 7 8 9 10
genomics
(SNPs)
diseased
controls
transcriptomics
diseased
individual
genomic profile
controls
epigenomics
diseased
controls
proteomics
metabolomics
diseased
controls
diseased
controls
combined
risk
biomarker
adapted from Butcher & Beck, 2008)
Challenges
1. Suitability of old biobank samples for certain –omics analysis (e.g.
transcriptomics)?
Pilot study ongoing
2. Detection of risk biomarkers in surrogate tissues (PBLs)?
3. Systems-level bioinformatics analysis
Funded by the European Union FP7,
Theme: Environment (including climate change)
(Grant no. 226756)
Nature, Vol. 458 (9 April 2009), p. 458