1. Previously: Head, Nutrition, Hormones and Cancer Group International Agency for Research on Cancer World Health Organization Lyon, 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 NAPLES BARCELONA OVIEDO GRANADA MURCIA PAMPLONA SAN SEBASTIAN CAMBRIDGE OXFORD BILTHOVEN UTRECHT ATHEN S HEIDELBERG POTSDA M MALMÖ UMEÅ AARHUS COPENHAGEN TROMSØ London

2. 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.

3. IARC LYON PARIS FLORENCE MILAN RAGUSA TURIN NAPLES BARCELONA OVIEDO GRANADA MURCIA PAMPLONA SAN SEBASTIAN CAMBRIDGE OXFORD BILTHOVEN UTRECHT ATHEN S HEIDELBERG POTSDA M MALMÖ UMEÅ AARHUS COPENHAGEN TROMSØ Collaborating Centres and Participating Subjects EPIC

4. BASELINE Subjects recruitment Questionnaires data Anthropometry data Blood/DNA collection Data Base & Biorepository 1993…………………………..…….1999………… 2000…….2002……………………2006 EPIC Time Table Spain Norway France Italy UK Netherlands Germany Greece FOLLOW-UP: Cancer diagnosis Vital status Causes of death Changes in Lifestyle Development of common/standardized Nutrient and lifestyle Data Bases Setting up of lab facilities for sample handling / DNA extraction etc ETIOLOGICAL STUDIES Sweden DK

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) GermanyJ. Linseisen (DKFZ), H. Boeing (DIFE) Danemark A.Tjonneland (DK Cancer Soc.), K. Overvad (U. Aarhus) NetherlandsP. Peeters (U. Utrecht), B. Bueno de Mesquita (RIVM) NorwayE. Lund (U. Tromso) SpainC. Gonzalez (I.C.O.), C. Martinez, C. Navarro, M. Doronsoro Sweden G. Berglund (U. Lund), G. Hallmans (U.Umea) UKS. Bingham, K-T Khaw (U.Cambridge), T. Key (CRUK Oxford) Italy F. Berrino, D. Palli, P.Vineis, S.Panico, R.Tumino, R.Saracci

6. 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-Diabetes EPIC-Diabetes Nick Wareham (MRC Cambridge) Anthropometry Anthropometry Heiner Boeing (DIFE-Potsdam) Total Mortality Total Mortality Kim Overvad (U. Aaarhus) Dietary Patterns Dietary Patterns Nadia Slimani (IARC) Phytoestrogens Phytoestrogens Petra Peeters (U. Utrecht) EPIC Steering Committee EPIC: Organizational Structure

7. Blood Collection and Storage 30 ml venous blood: –20 ml citrated +10 ml dry 28 aliquots of 500  l : –plasma 12 (red straws) (yellow 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 EPIC

8. n n <2ug/straw 1.3% n <5ug/straw 2.5% Min Max Median Average 172 50.3 52.8 21 0.08 11 ug/straw DNA Yield 848 DNA Extraction EPIC subjects who developed Prostate Cancer

9. The EPIC study from a 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

10. EPIC’sEthnicGroups Dutch Danish English Swiss German Belgian Austrian French Swedish Norwegian Czechoslovakian Portuguese Italian Spanish Hungarian Polish Russian Scottish,Irish Finnish Icelandic Basque Yugoslavian Greek Sardinian Saami 0.040.030.020.010 Genetic distance (F ST ) From: Cavalli-Sforza et al, The history and geography of human genes, Princeton University Press, 1994

11. 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 from a genetics point of view Disadvantages GenEPIC

12. Studies on candidate genes Selection of candidate genes Selection of candidate polymorphisms 1800 DNAs, cross-sectionally selected from EPIC cohorts are 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

13. Pathway scanning Enzyme AEnzyme BEnzyme C Metabolite 1Metabolite 2Metabolite 3Metabolite 4 Phenotype Gene AGene BGene C Polymorphism Single gene approach Single gene approach Pathway 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

14. 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 !

15. Trends Towards Greater Adult Body Height

16. Int J Cancer. 2004 Sep 20;111:762-71.

17. From: J.M. Tanner Nature 243: 95-96 (1973) Trends Towards Earlier Menarche

18. Breast Cancer Risk Associated with Menstrual Characteristics From: Gao et al. Int. J. Cancer 87: 295-300 (2000). Age at menarcheOR (95% CI)  12 years 1.0 (reference) 131.1 (0.8-1.5) 140.9 (0.7-1.2) 150.9 (0.7-1.3) 160.8 (0.6-1.1)  170.6 (0.5-0.9)

19. Postmenopausal Serum Sex Steroids and Breast Cancer Risk The EPIC Study; (677 cases / 1309 controls) Kaaks et al., Endocr Relat Cancer, (2006)

21. Premenopausal Serum Sex Steroids and Breast Cancer Risk The 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 1.00 1.13 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)

22. 3.06 2.50 2.67 1.00 1.20 1.49 Reference

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 (n=1204)

27. Serum free testosterone by BMI level; EPIC study postmenopausal women (n=1192)

28. 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

29. NCI Cohort Consortium on Hormone Metabolizing Gene Variants and Breast and Prostate cancer risk StudyYear started Subjects with blood samples Breast cancer cases Prostate cancer cases EPIC 1992 397,256 2,050 900 ACS (CPS-II)1998 39,000 5001,450 Harvard PHS1982 20,000-1,500 NHS 1989 32,826945- HPFS199333,240-600 WH 1993 28,263675- Multi Ethnic USC100,000 1,990 2,400 PLCO 199375,000 - 1,000 Total797,0856,1608,850 ATBC1991 20,500 - 1,000

30. Hypotha lamus GNRH Pituitary GNRHR CGA LHB FSHB POMC LH FSH ACTH BloodOvary / Adrenal gland receptors: LHCGR, FSHR, ACTHR cholesterol STAR, CYP11A1, CYP17, HSD3B, pregnenolone, DHEA progesterone,  4A HSD17B Ovary & Adipose tissue T CYP19 estadiol, estrone Blood DHEA(S)  4A T E1 E2 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,

31. Cholesterol Pregnolone Progesterone 17-hydroxy-progesterone Androstenedione EstroneTestosterone Estradiol Testosterone Dihydrostestosterone Estradiol Female specific Male specific CYP11A1 3  HSD CYP17 CYP19 17  HSD Testosterone SHBG Estradiol SHBG Androgen receptor Estrogen receptor Inactive form in the circulation Active form in the cell Steroidogenesis pathway Active form in the nucleus 000511 5  -reductase

32. Regulation of IGF1 and related molecules Target tissues: Breast Prostate Colorectum etc. IGF1R Hypothalamus SSTGHRH Stomach Ghrelin - Pituitary SSTRGHRHR - - GH -+ POU1F1 GH Circulation Growth + Ghrelin Circulation + + GHSR Liver GHR + IGF1 IGFBP3 IGFALS IGF1+ IGFBP3+ IGFALS Circulation

33. Re-sequencing Strategy Exons 2Kb Human/Mouse conserved regions > 200 bp ; > 80% identity 30 Kb10 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

34. 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 ATGCCG CATCCG CATCCC CATTCC CAGCCG CAGCTGCAGCTG Calculate haplotype frequencies in the population 71.0% 10.5% 9.4% 5.1% 2.9% 1.1% 020523

35. SNP selection by haplotype tagging Phase III: SNP selection Selection of maximally informative SNPs 020523 Reconstruction of phylogenetic tree ATGCCG CATCCG CATCCC CATTCC CAGCCG CAGCTGCAGCTG ATGCCG CATCCG CATCCC CATTCC CAGCCG CAGCTGCAGCTG ATGCCG 1,A  C 2,T  A 3,G  T 6,G  C 4,C  T 5,C  T

36. 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 analysis main effects of SNPs and haplotypes, gene-environment interactions Breast at IARC Prostate at Harvard Selection of candidate genes (53 genes involved in metabolism of IGF-I and steroid hormones)

37. WhiteheadCEPH 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 PLCOATBC Breast Cancer Database IARC Collaborative Statistical Analysis Web and Journal Publications Exposure Data Cohort Consortium Work Flow Chart Prostate Cancer Database Harvard Genotyping Centres Database consolidation Steering Group and Secretariat Advisory Committee PUBLIC ACCESS NCI ?

40. RR of prostate cancer for the CAGC haplotype of HSD 17B1