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Previously : Head, Nutrition, Hormones and Cancer Group International Agency for

TROMS Ø. UMEÅ. AARHUS. MALM Ö. COPENHAGEN. UTRECHT. CAMBRIDGE. POTSDAM. BILTHOVEN. OXFORD. HEIDELBERG. PARIS. MILAN. LYON. TURIN. OVIEDO. FLORENCE. SAN SEBASTIAN. PAMPLONA. BARCELONA. NAPLES. ATHENS. MURCIA. RAGUSA. GRANADA. Elio Riboli, MD, ScM, MPH. Previously :

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Previously : Head, Nutrition, Hormones and Cancer Group International Agency for

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  1. TROMSØ UMEÅ AARHUS MALMÖ COPENHAGEN UTRECHT CAMBRIDGE POTSDAM BILTHOVEN OXFORD HEIDELBERG PARIS MILAN LYON TURIN OVIEDO FLORENCE SAN SEBASTIAN PAMPLONA BARCELONA NAPLES ATHENS MURCIA RAGUSA GRANADA 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@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. TROMSØ UMEÅ AARHUS MALMÖ COPENHAGEN UTRECHT CAMBRIDGE POTSDAM BILTHOVEN OXFORD HEIDELBERG PARIS IARC MILAN LYON TURIN OVIEDO FLORENCE SAN SEBASTIAN PAMPLONA BARCELONA NAPLES ATHENS MURCIA RAGUSA GRANADA EPIC Collaborating Centres and Participating Subjects

  4. ETIOLOGICAL STUDIES • FOLLOW-UP: • Cancer diagnosis • Vital status • Causes of death • Changes in Lifestyle EPIC Time Table • BASELINE • Subjects recruitment • Questionnaires data • Anthropometry data • Blood/DNA collection • Data Base & Biorepository Sweden Netherlands Germany Norway Greece Italy DK France Spain UK 1993…………………………..…….1999………… 2000…….2002……………………2006 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. EPIC 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) • 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 <2ug/straw 11 1.3% n <5ug/straw 21 2.5% ug/straw Min 0.08 Max 172 Median 50.3 Average 52.8

  9. GenEPIC 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 Austrian French Swedish Norwegian Czechoslovakian Portuguese Italian Spanish Hungarian Polish Russian Scottish,Irish Finnish Icelandic Basque Yugoslavian Greek Sardinian Saami EPIC’s 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. GenEPIC 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. Metabolite 1 Metabolite 2 Metabolite 3 Metabolite 4 Phenotype Enzyme A Enzyme B Enzyme C Polymorphism Gene A Gene B Gene C 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 20;111:762-71.

  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) P trend 0.0002 0.001 <0.0001 0.0004 <0.0001 0.004 <0.0001 <0.0001 RR 1.00 DHEAS 1.28 1.06 1.68 1.69 1.00 Androstenedione 1.47 1.35 1.70 1.73 Testosterone 1.00 1.14 1.33 1.56 1.85 1.00 Estrone 1.60 1.89 2.05 1.96 1.00 Estradiol 1.10 1.45 1.54 2.05 1.00 SHBG 0.98 0.72 0.87 0.61 Free testosterone 1.00 1.83 1.92 1.86 2.50 Free estradiol 1.00 1.30 1.34 1.71 2.00 0.5 1 2 Kaaks et al., Endocr Relat Cancer, (2006)

  20. 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 1.00 Androstenedione 0.01 1.11 1.14 1.64 1. 00 Estrone 0.76 1.13 0.73 1.22 1.00 Estradiol 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

  21. 3.06 2.67 2.50 1.49 1.20 1.00 Reference

  22. Serum SHBG by BMI level; EPIC study postmenopausal women (n = 1210)

  23. Serum estrone by BMI level; EPIC study postmenopausal women (n= 1171)

  24. Serum free estradiol by BMI level; EPIC study postmenopausal women (n=1204)

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

  26. 1999-2000: NCI-NIH Bypass programme “Exceptional Opportunities” for research in the Area of Gene-Environment interaction studies 2003: 1st Funded Project:Cohort Consortium on Hormone Metabolizing Gene Variants and Breast and Prostate cancer risk 2000: NCI Cohort Studies Consortiumon gene environment interaction

  27. Study Year started Subjects with blood samples Breast cancer cases Prostate cancer cases 1992 EPIC 397,256 2,050 900 39,000 ACS (CPS-II) 1998 500 1,450 20,500 ATBC 1991 - 1,000 20,000 - 1,500 PHS 1982 1989 NHS 32,826 945 - HPFS 1993 33,240 - 600 1993 WH 28,263 675 - Multi Ethnic USC 100,000 1,990 2,400 PLCO - 1,000 1993 75,000 Total 797,085 6,160 8,850 NCI Cohort Consortium on Hormone Metabolizing Gene Variants and Breast and Prostate cancer risk Harvard

  28. 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 FSHB 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 TCYP19 estadiol, estrone Breast tissue steroid receptors: ESR1, ESR2, PGR, AR ----------------------------- 4A, T CYP19 E1E2 HSD17B1, HSD17B2 CYP1A1, CYP1B1, CYP3A4, COMT hydroxy / methoxy estrogens Liver SHBG

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

  30. Hypothalamus GHSR IGF1R + SST GHRH + - SSTR GHRHR - Ghrelin Growth GHSR + - + Target tissues: Breast Prostate Colorectum etc. IGF1+ IGFBP3+ IGFALS POU1F1 - GH Pituitary Circulation Circulation Liver Circulation Regulation of IGF1 and related molecules IGFBP3 GH + IGF1 GHR Ghrelin IGFALS Stomach

  31. 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 30 Kb 10 Kb Exons 2Kb 2Kb Promoter & upstream 3’ UTR & downstream Human/Mouse conserved regions > 200 bp ; > 80% identity

  32. Genotype every SNP in trios from CEPH families (768 subjects) • Obtain precise reconstruction of all haplotypes in the population • Calculate haplotype frequencies in the population 71.0% 10.5% 9.4% 5.1% 2.9% 1.1% ATGCCG CATCCG CATTCC CATCCC CAGCTG CAGCCG SNP selection by haplotype tagging Phase II: Haplotype reconstruction 020523

  33. Reconstruction of phylogenetic tree • Selection of maximally informative SNPs CAGCCG CATTCC CAGCTG 1,AC2,TA 5,CT CATCCG CATCCG ATGCCG CATCCC 3,GT CATCCC ATGCCG CAGCTG 6,GC CATTCC CAGCCG 4,CT ATGCCG SNP selection by haplotype tagging Phase III: SNP selection 020523

  34. 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

  35. Cohort Consortium Work Flow Chart Study planning and gene choice Gene Resequencing Haplotype determination Identification of ht-SNPs Steering Group and Secretariat ? Advisory Committee Whitehead CEPH NCI PUBLIC ACCESS Web ht-SNP Database ICL, DKFZ, Cambridge UK NCI Genotyping Centres USC & Honolulu 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

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

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