Previously:

1
Elio Riboli, MD, ScM, MPH
Previously Head, Nutrition, Hormones and Cancer
Group International Agency for Research on
Cancer World Health Organization Lyon, France
London
Since November 2005 Chair, Cancer Epidemiology
and Prevention, Faculty of Medicine Imperial
Collage, London e.riboli_at_imperial.ac.uk
2
Westernization of lifestyle and cancer.

• Western Lifestyle
• Energy dense diet, rich in
• - fat,
• - refined carbohydrates
• - animal protein
• - Low physical activity
• - Smoking and drinking
• Early menarche, late menopause
• Consequences
• - Obesity
• - Diabetes
• - Cardiovascular disease
• - Hypertension

and cancer !
3
EPIC
Collaborating Centres and Participating Subjects
4
EPIC Time Table

• BASELINE
• Subjects recruitment
• Questionnaires data
• Anthropometry data
• Blood/DNA collection
• Data Base Biorepository

Sweden
Norway
Netherlands
Germany
Greece
Italy
DK
France
Spain
UK
1993...1999 2000.2002200
6
Development of common/standardized Nutrient and
lifestyle Data Bases Setting up of lab facilities
for sample handling / DNA extraction etc
5
EPIC Organizational Structure
EPIC Steering Committee
Coordination E. Riboli (Imperial College,
London) IARC R. Kaaks, N.
Slimani France F. Clavel, MC Boutron
(I.G.R-INSERM, Paris) Greece A. Trichopoulou, D.
Trochopoulos (U. Athens/Harvard) Germany J.
Linseisen (DKFZ), H. Boeing (DIFE) Danemark
A.Tjonneland (DK Cancer Soc.), K. Overvad (U.
Aarhus) Netherlands P. Peeters (U. Utrecht), B.
Bueno de Mesquita (RIVM) Norway E. Lund (U.
Tromso) Spain C. Gonzalez (I.C.O.), C. Martinez,
C. Navarro, M. Doronsoro Sweden G. Berglund (U.
Lund), G. Hallmans (U.Umea) UK S. Bingham, K-T
Khaw (U.Cambridge), T. Key (CRUK Oxford)
Italy F. Berrino, D. Palli,
P.Vineis, S.Panico, R.Tumino, R.Saracci
6
EPIC Organizational Structure
EPIC Steering Committee
Working groups on risk factors, end-points
other than cancer, methodological issues
Coordinators EPIC-Elderly-EC (Aging)
Antonia Trichopoulou (Athens) EPIC-Heart-EC
(M.I.) John Danesh (Cambridge U.)
EPIC-Diabetes Nick Wareham (MRC Cambridge)
Anthropometry Heiner Boeing
(DIFE-Potsdam) Total Mortality Kim
Overvad (U. Aaarhus) Dietary Patterns
Nadia Slimani (IARC) Phytoestrogens
Petra Peeters (U. Utrecht)

7
Blood Collection and Storage

EPIC

• 30 ml venous blood
• 20 ml citrated 10 ml dry
• 28 aliquots of 500 ?l
• plasma 12 (red straws)
• serum 8 (yellow straws)
• buffy coat 4 (blue straws)
• RBC 4 (green straws)

28 aliquots x 300.000 subjects 8.4 Million
aliquots stored, half in each EPIC centre, half
at IARC Plus 12 x 110,000 1.3 Million in
Sweden and Denmark
8
DNA Extraction EPIC subjects who developed
Prostate Cancer
DNA Yield
n
848
n lt2ug/straw
11
1.3
n lt5ug/straw
2.5
21
ug/straw
Min
0.08
Max
172
Median
50.3
Average
52.8
9
The EPIC study from a genetics point of view
Advantages

• Population-based
• Ethnic and geographic diversity within Europe

• Large sample size within each ethnic/geographic
  region
• Excellent data on lifestyle on each individual
• Pre diagnostic bank of biological samples

10
Dutch Danish English Swiss German Belgian Austria
n French Swedish Norwegian Czechoslovakian Portug
uese Italian Spanish Hungarian Polish Russian Sco
ttish,Irish Finnish Icelandic Basque Yugoslavian G
reek Sardinian Saami
EPICs Ethnic Groups
From Cavalli-Sforza et al, The history and
geography of human genes, Princeton University
Press, 1994
Genetic distance (FST)
0.04
0.03
0.02
0.01
0
11
The EPIC study from a genetics point of view
Disadvantages

• No families !!
• Cohort study-must wait until sufficient number
  of cases of disease occur to study genetic
  effects
• Limited amount of blood (no viable cells).
  Need careful plans on use
• Collection of cancer tissues possible, but
  complex

12
Studies on candidate genes
Selection of candidate genes

• Biological plausibility

• Some data from previous epi studies

• Possibility to study intermediate markers
  (gene - biomarker - disease)

Selection of candidate polymorphisms

• Established knowledge of functional meaning

• Allele frequencies (function of the available
  sample size)

• Linkage disequilibrium data

1800 DNAs, cross-sectionally selected from EPIC
cohorts are used for these purposes
13
Pathway scanning
Single gene approach

• Measure phenotype
• Genotype one polymorphism in the coding region
  of one gene
• Correlate or Mandelian randomization analyses

Pathway approach

• Measure phenotype
• Measure metabolites 1,2, 3, 4
• Genotype all polymorphisms in all genes 1,2, 3,
  4
• Correlate genotypes biomarkers with phenotype

14

• Factors associated with breast cancer aetiology
• Attained Height
• Sexual maturation
• Childbearing (age at first last and n. of FTP)
• Breast feeding
• Overweight
• Physical activity
• Diet composition
• Exogenous Hormones ( Steroids, Insulin, IGF..)
• and GENETICS !

15
Trends Towards Greater Adult Body Height
16
Int J Cancer. 2004 Sep 20111762-71.
17
Trends Towards Earlier Menarche
From J.M. Tanner Nature 243 95-96 (1973)
18
Breast Cancer Risk Associated with Menstrual
Characteristics
Age at menarche OR (95 CI) ? 12 years
1.0 (reference) 13 1.1 (0.8-1.5) 14 0.9
(0.7-1.2) 15 0.9 (0.7-1.3) 16 0.8
(0.6-1.1) ? 17 0.6 (0.5-0.9)
From Gao et al. Int. J. Cancer 87 295-300
(2000).
19

Postmenopausal Serum Sex Steroids and Breast
Cancer Risk The EPIC Study (677 cases / 1309
controls)

Kaaks et al., Endocr Relat Cancer, (2006)
20
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21
Premenopausal Serum Sex Steroids and Breast
Cancer Risk The EPIC Study (416 cases, 815
controls)



Ptrend

OR






1.00
Testosterone
0.02


1.33


1.36



1.58




1.00
SHBG


1.05
0.98



0.97


1.02





1.00
DHEAS
0.17


1.34


1.15



1.37



Androstenedione
1.00


0.01

1.11


1.14


1.64




Estrone
1.
00
0.76


1.13



0.73


1.22




Estradiol
1.00


0.75

0.76


0.96


0.99





1.00
Progesterone


1.16
0.07



1.07

0.63
Kaaks et al., JNCI (2005)



0.5
1
2
22
3.06
2.67
2.50
1.49
1.20
1.00
Reference
23
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24
Serum SHBG by BMI level EPIC study postmenopausal
women (n 1210)
25
Serum estrone by BMI level EPIC
study postmenopausal women (n 1171)
26
Serum free estradiol by BMI level EPIC
study postmenopausal women (n1204)
27
Serum free testosterone by BMI level EPIC
study postmenopausal women (n1192)
28
2003 1st Funded Project Cohort Consortium on
Hormone Metabolizing Gene Variants and Breast
and Prostate cancer risk
1999-2000 NCI-NIH Bypass programme Exceptional
Opportunities for research in the Area of
Gene-Environment interaction studies
2000 NCI Cohort Studies Consortiumon gene
environment interaction
29
NCI Cohort Consortium on Hormone Metabolizing
Gene Variants and Breast and Prostate cancer risk
Harvard
30
Genes encoding enzymes that are central to the
synthesis,
conversions and hydroxylation/methoxylation of
sex steroids, or encoding steroid-binding
proteins and receptors,
Blood DHEA(S) ?4A T E1 E2 SHBG
Hypotha lamus GNRH Pituitary GNRHR CGA LHB FSH
B POMC LH FSH ACTH
Blood
Ovary / Adrenal gland receptors LHCGR, FSHR,
ACTHR cholesterol STAR, CYP11A1,
CYP17, HSD3B, pregnenolone,
DHEA progesterone, ?4A HSD17B
Ovary Adipose tissue T
CYP19 estadiol,
estrone
Breast tissue steroid receptors ESR1, ESR2,
PGR, AR ----------------------------- ?4A, T
CYP19 E1 E2
HSD17B1, HSD17B2 CYP1A1, CYP1B1,
CYP3A4, COMT hydroxy / methoxy estrogens
Liver SHBG
31
Steroidogenesis pathway
Cholesterol
CYP11A1
Pregnolone
3bHSD
Progesterone
CYP17
17-hydroxy-progesterone
Female specific
Male specific
CYP17
Androstenedione
CYP19
Testosterone
CYP19
Estrone
Testosterone
5a-reductase
CYP19
17bHSD
Estradiol
Dihydrostestosterone
Estradiol
Estradiol
Testosterone
Inactive form in the circulation
SHBG
SHBG
Active form in the cell
Androgen receptor
Estrogen receptor
Active form in the nucleus
000511
32
Regulation of IGF1 and related molecules
33
Re-sequencing Strategy
4 x sequencing of exons, promoter, intronic
regions of high homology with mouse. Gap filling
with SNPs from data bases
Extended gene region
Critical region
Start transcription
Stop translation
Exons
30 Kb
10 Kb
2Kb
2Kb
Promoter upstream
3 UTR downstream
Human/Mouse conserved regions gt 200 bp gt 80
identity
34
SNP selection by haplotype tagging
Phase II Haplotype reconstruction
020523
35
SNP selection by haplotype tagging
Phase III SNP selection
020523
36
Project flowchart
Selection of candidate genes (53 genes involved
in metabolism of IGF-I and steroid hormones)
SNP discovery by gene resequencing (CEPH, WI-MIT)
Haplotype tagging (CEPH, WI-MIT)
Genotyping (IARC, Cambridge, Harvard, USC,
Hawaii, NCI)
Hormone measurement (IARC, Harvard)
Statistical analysis main effects of SNPs and
haplotypes, gene-environment interactions Breast
at IARC Prostate at Harvard
37
Cohort Consortium Work Flow Chart
Study planning and gene choice Gene
Resequencing Haplotype determination Identificatio
n of ht-SNPs
Steering Group and Secretariat
?
Advisory Committee
Whitehead
CEPH
NCI
PUBLIC ACCESS
Web ht-SNP Database
ICL, DKFZ, Cambridge UK
NCI
USC Honolulu
Genotyping Centres
Harvard
Multiethnic Cohort
Harvard Cohorts
ACS
PLCO
ATBC
EPIC
Exposure Data
Breast Cancer Database IARC
Prostate Cancer Database Harvard
Database consolidation
Collaborative Statistical Analysis
Web and Journal Publications
PUBLIC ACCESS
38
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39
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40
RR of prostate cancer for the CAGC haplotype of
HSD 17B1