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Previously:
Head, Nutrition, Hormones and Cancer Group
International Agency forResearch on Cancer
World Health OrganizationLyon, France
Since November 2005
Chair,
Cancer Epidemiology and Prevention,
Faculty of Medicine
Imperial Collage, London
e.riboli@imperial.ac.uk
Elio Riboli, MD, ScM, MPH
LYON
PARIS
FLORENCE
MILAN
RAGUSA
TURIN
NAPLESBARCELONA
OVIEDO
GRANADAMURCIA
PAMPLONA
SAN SEBASTIAN
CAMBRIDGE
OXFORD BILTHOVEN
UTRECHT
ATHENS
HEIDELBERG
POTSDAM
MALMÖ
UMEÅ
AARHUS
COPENHAGEN
TROMSØ
London
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 !
“Westernization” of lifestyle and cancer.
IARC LYON
PARIS
FLORENCE
MILAN
RAGUSA
TURIN
NAPLESBARCELONA
OVIEDO
GRANADAMURCIA
PAMPLONA
SAN SEBASTIAN
CAMBRIDGE
OXFORD BILTHOVEN
UTRECHT
ATHENS
HEIDELBERG
POTSDAM
MALMÖ
UMEÅ
AARHUS
COPENHAGEN
TROMSØ
Collaborating Centres and Participating Subjects
Participating Subjects Questionnaire
s Q + Blood
France 74 524 28 053 Italy 47 749 47 725 Spain 41 440 39 579 U.K. 87 942 43 141 Netherlands 40 072 36 318 Greece 28 555 28 483 Germany 53 091 50 678 Sweden 53 826 53 781 Denmark 57 054 56 131 Norway 37 215 31 000 Total 521 468 414 889
EPIC
BASELINE•Subjects recruitment •Questionnaires data•Anthropometry data•Blood/DNA collection•Data Base & Biorepository
1993…………………………..…….1999………… 2000…….2002……………………2006
EPIC Time Table
SpainNor
way
France
Italy
UKNeth
erlan
d
sGer
man
y
Greec
e
FOLLOW-UP:• Cancer diagnosis• Vital status • Causes of death• Changes in Lifestyle
Development of common/standardized Nutrient and lifestyle Data BasesSetting up of lab facilities for sample handling / DNA extraction etc
ETIOLOGICAL STUDIES
Swed
en
DK
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
Working groups on risk factors, end-points other than cancer, methodological issues: Coordinators:
EPIC-Elderly-EC (Aging)EPIC-Elderly-EC (Aging) Antonia Trichopoulou (Athens)
EPIC-Heart-EC (M.I.)EPIC-Heart-EC (M.I.) John Danesh (Cambridge U.)
EPIC-DiabetesEPIC-Diabetes Nick Wareham (MRC Cambridge)
Anthropometry Anthropometry Heiner Boeing (DIFE-
Potsdam)
Total MortalityTotal Mortality Kim Overvad (U. Aaarhus)
Dietary PatternsDietary Patterns Nadia Slimani
(IARC)
PhytoestrogensPhytoestrogens Petra Peeters (U.
Utrecht)
EPIC Steering Committee
EPIC: Organizational Structure
Blood Collection and Storage
• 30 ml venous blood:
– 20 ml citrated +10 ml dry
• 28 aliquots of 500 l :
– plasma 12 (red straws)– serum 8 (yellow straws)(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
EPIC
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
DNA extracted, ug/straw
Fre
qu
en
cy
nn <2ug/straw 1.3%n <5ug/straw 2.5%
MinMaxMedianAverage
17250.352.8
21
0.08
11
ug/straw
DNA Yield
848
DNA Extraction EPIC subjects who developed Prostate Cancer
The EPIC study fromThe EPIC study froma genetics point of viewa genetics point of view
Advantages
• Large sample size within each ethnic/geographic region• Excellent data on lifestyle on each individual• Pre –diagnostic bank of biological samples
GenEPIC
• Population-based • Ethnic and geographic diversity within Europe
EPIC’sEPIC’sEthnicEthnicGroupGroup
ss
DutchDanishEnglish
SwissGermanBelgianAustrianFrenchSwedishNorwegianCzechoslovakian
PortugueseItalianSpanishHungarianPolish
RussianScottish,IrishFinnishIcelandicBasqueYugoslavianGreekSardinianSaami
0.04 0.03 0.02 0.01 0Genetic distance (FST)
From: Cavalli-Sforza et al,The history and geography of human genes,Princeton University Press, 1994
• 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
The EPIC study fromThe EPIC study froma genetics point of viewa genetics point of view
Disadvantages GenEPIC
Studies on candidate genesStudies on candidate genes
Selection of candidate genes
Selection of candidate polymorphisms
1800 DNAs, cross-sectionally selected from EPIC cohortsare used for these purposes
• Biological plausibility
• Some data from previous epi studies• Possibility to study intermediate markers (gene - biomarker - disease)
• Established knowledge of functional meaning
• Allele frequencies (function of the available sample size)
• Linkage disequilibrium data
Pathway scanning
Enzyme A Enzyme B Enzyme C
Metabolite 1 Metabolite 2 Metabolite 3 Metabolite 4
Phenotype
Gene A Gene B Gene C
Polymorphism
Single gene approachSingle gene approach
Pathway approachPathway approach
• Measure phenotype• Genotype one polymorphism in the coding region of one gene• Correlate or Mandelian randomization analyses
• Measure phenotype• Measure metabolites 1,2, 3, 4…• Genotype all polymorphisms in all genes 1,2, 3, 4…• Correlate genotypes & biomarkers with phenotype
Factors associated with breast cancer aetiology:
1. Attained Height
2. Sexual maturation
3. Childbearing (age at first & last and n. of FTP)
4. Breast feeding
5. Overweight
6. Physical activity
7. Diet composition
8. Exogenous Hormones ( Steroids, Insulin, IGF..)
9. and GENETICS !
Trends Towards Greater Adult Body Height
Int J Cancer. 2004 Sep 20;111:762-71.
From: J.M. Tanner Nature 243: 95-96 (1973)
Trends Towards Earlier Menarche
Breast Cancer Risk Associated with Menstrual Characteristics
FromFrom: Gao : Gao et al.et al. Int. J. Cancer 87: 295-300 (2000). Int. J. Cancer 87: 295-300 (2000).
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)
Postmenopausal Serum Sex Steroids and Breast Cancer RiskThe EPIC Study; (677 cases / 1309 controls)
DHEAS
Androstenedione
Testosterone
Estrone
Estradiol
SHBG
Freetestosterone
Freeestradiol
1.001.281.061.681.69
1.001.471.351.701.73
1.001.141.331.561.85
1.001.601.892.051.96
1.001.101.451.542.05
1.000.980.720.870.61
1.001.831.921.862.50
1.001.301.341.712.00
RR
0.5 1 2
P trend
0.0002
0.001
<0.0001
0.0004
<0.0001
0.004
<0.0001
<0.0001
Kaaks et al., Endocr Relat Cancer, (2006)
Premenopausal Serum Sex Steroids and Breast Cancer RiskThe EPIC Study; (416 cases, 815 controls)
Testosterone
SHBG
DHEAS
Androstenedione
Estrone
Estradiol
Progesterone
1.00 1.33 1.36 1.58
1.00 1.05 0.97 1.02
1.00 1.34 1.15 1.37
1.00 1.11 1.14 1.64
1.001.13 0.73 1.22
1.00 0.76 0.96 0.99
1.00 1.16 1.07 0.63
OR
0.5 1 2
Ptrend
0.02
0.98
0.17
0.01
0.76
0.75
0.07
Kaaks et al., JNCI (2005)
0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
+/-2 kg2-15 kg
>15 kg
no
yes
Re
lati
ve
Ris
k
Weight gain (kg)
Current hormone use
3.06
2.50 2.67
1.001.20
1.49
Re
fere
nc
e
Kaaks / IARC / 98
Available IGF-I
IGFBP1 in plasma, target-tissues
Plasma insulin
Insulin resistance
Plasma SHBG
Ovarian androgen production
Plasma testosterone
Estrogen binding to SHBG
Free estrogen
Regulation of plasma steroid hormones by insulin / IGF-I in women
20
30
40
50
60
70
80
< 20 20-22.5 22.5-25 25-27.5 >27.5
BMI
SH
BG
(n
mo
l/l)
Serum SHBG by BMI level; EPIC studypostmenopausal women (n = 1210)
110
120
130
140
150
160
170
180
< 20 20-22.5 22.5-25 25-27.5 >27.5
BMI
Est
ron
e (p
mo
l/l)
Serum estrone by BMI level; EPIC studypostmenopausal women (n= 1171)
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
3
< 20 20-22.5 22.5-25 25-27.5 >27.5
BMI
Fre
e es
trad
iol
(pm
ol/
l)
Serum free estradiol by BMI level; EPIC studypostmenopausal women (n=1204)
8
10
12
14
16
18
20
22
24
< 20 20-22.5 22.5-25 25-27.5 >27.5
BMI
Fre
e te
sto
ster
on
e (p
mo
l/l)
Serum free testosterone by BMI level; EPIC studypostmenopausal women (n=1192)
2003: 1st Funded Project:
Cohort Consortium on Hormone Metabolizing Gene
Variants and Breast and Prostate cancer risk
2000: NCI Cohort Studies Consortium
on gene environment interaction
1999-2000: NCI-NIH Bypass programme “Exceptional Opportunities” for research in the Area of Gene-Environment interaction studies
NCI Cohort Consortium on Hormone Metabolizing Gene Variants and Breast and Prostate cancer risk
Study Yearstarted
Subjects withblood samples
Breast cancercases
Prostate cancercases
EPIC 1992 397,256 2,050 900
ACS (CPS-II) 1998 39,000 500 1,450
Harvard
PHS 1982 20,000 - 1,500
NHS 1989 32,826 945 -
HPFS 1993 33,240 - 600
WH 1993 28,263 675 -
Multi Ethnic USC 100,000 1,990 2,400
PLCO 1993 75,000 - 1,000
Total 797,085 6,160 8,850
ATBC 1991 20,500 - 1,000
Hypothalamus
GNRH
Pituitary
GNRHRCGALHBFSHBPOMC
LH
FSH
ACTH
Blood Ovary / Adrenal gland
receptors: LHCGR, FSHR, ACTHR
cholesterol STAR, CYP11A1, CYP17, HSD3B,
pregnenolone, DHEAprogesterone, 4A
HSD17B
Ovary & Adipose tissue T CYP19
estadiol, estrone
Blood
DHEA(S)4ATE1E2
SHBG
Liver
SHBG
Breast tissue
steroid receptors: ESR1, ESR2, PGR, AR-----------------------------4A, T
CYP19
E1 E2
HSD17B1, HSD17B2
CYP1A1, CYP1B1, CYP3A4, COMT
hydroxy / methoxy estrogens
Genes encoding enzymes that are central to the synthesis, conversions and hydroxylation/methoxylation
of sex steroids, or encoding steroid-binding proteins and receptors,
Cholesterol
Pregnolone
Progesterone
17-hydroxy-progesterone
Androstenedione
Estrone Testosterone
Estradiol
Testosterone
DihydrostestosteroneEstradiol
Femalespecific
Malespecific
CYP11A1
3HSD
CYP17
CYP17
CYP19
CYP19
CYP19
17HSD
TestosteroneSHBG
Estradiol
SHBG
Androgenreceptor
Estrogenreceptor
Inactive formin the circulation
Active formin the cell
Steroidogenesis pathwaySteroidogenesis pathway
Active formin the nucleus
000511
5-reductase
Regulation of IGF1 and related moleculesRegulation of IGF1 and related molecules
Target tissues: Breast Prostate Colorectum etc.
IGF1RHypothalamus
SST GHRH
Stomach
Ghrelin
-
Pituitary
SSTR GHRHR -
-GH
- +POU1F1
GH
Circulation
Growth
+Ghrelin
Circulation
+
+
GHSR
GHSR
Liver
GHR + IGF1
IGFBP3
IGFALS
IGF1+IGFBP3+IGFALS
Circulation
Re-sequencing Strategy
Exons
2Kb 2Kb
Human/Mouse conserved regions> 200 bp ; > 80% identity
30 Kb 10 Kb
Promoter &upstream
3’ UTR &downstream
Start transcription
Stop translation
Critical region
Extended gene region
4 x sequencing of exons, promoter, intronic regions of high homology with mouse. Gap filling with SNPs from data bases
SNP selection by haplotype tagging
Phase II: Haplotype reconstruction
• Genotype every SNP in trios from CEPH families (768 subjects)
• Obtain precise reconstruction of all haplotypes in the population
ATGCCGCATCCG
CATCCCCATTCC
CAGCCGCAGCTG
• Calculate haplotype frequencies in the population
71.0%10.5% 9.4% 5.1% 2.9% 1.1%
020523
SNP selection by haplotype taggingSNP selection by haplotype taggingPhase III: SNP selection
• Selection of maximally informative SNPs
020523
• Reconstruction of phylogenetic tree
ATGCCGCATCCG
CATCCC
CATTCC
CAGCCGCAGCTG ATGCCG
CATCCGCATCCCCATTCC
CAGCCGCAGCTG
ATGCCG
1,AC2,TA
3,GT
6,GC4,CT
5,CT
Project flowchart
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 analysismain effects of SNPs and haplotypes,
gene-environment interactionsBreast at IARC
Prostate at Harvard
Selection of candidate genes(53 genes involved in metabolism of IGF-I and steroid hormones)
Whitehead
CEPH
Web ht-SNP Database
Study planning and gene choice
Gene Resequencing
Haplotype determination
Identification of ht-SNPs
Harvard USC & Honolulu
ICL, DKFZ, Cambridge
UK
NCI
Harvard
Cohorts
Multiethnic
Cohort
ACS
EPIC PLCO
ATBC
Breast Cancer
Database IARC
Collaborative Statistical Analysis
Web and Journal
Publications
Exposure Data
Cohort Consortium Work Flow Chart
Prostate Cancer
DatabaseHarvard
Genotyping
Centres
Database consolidation
Steering Group and
Secretariat
Advisory Committee
PUBLIC ACCESS
PUBLIC ACCESS
NCI
?
RR of prostate cancer for the CAGC haplotype of HSD 17B1
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