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