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Genomics in cancer molecular epidemiology: The EnviroGenomarkers project Soterios A. Kyrtopoulos National Hellenic Research Foundation, Institute of Biological Research and Biotechnology, Athens, Greece Biomarkers in environmental cancer research Biomarkers of exposure: improve exposure assessment Biomarkers of early effects: provide evidence of early, biologically relevant changes Biomarkers of individual susceptibility: help recognise individuals with high susceptibility to specific stages of carcinogenic process 1st generation of biomarkers in environmental carcinogenesis research exposure internal dose chemicals / metabolites / in body fluids or tissues clinical disease biologically effective dose protein adducts / DNA adducts gene or chromosome mutations / modified gene expression mutation spectra in tumours or pre-cancerous cells biomarkers of effect (risk) biomarkers of exposure metabolism altered structure/ function early biological effects DNA repair / genetic instability individual susceptibility: diet / lifestyle / genetic makeup immune defence Pros and cons of “1st generation biomarkers” Advantages - highly sensitive and chemical-specific biomarkers of exposure - information on specific stages of carcinogenesis (e.g. gene or chromosome mutagenesis) Disadvantages - collect information one item at a time (but “adductomics”) - different assays/technologies for different types of endpoints - limited mechanistic information Potential advantages of –omics biomarkers Genomics Epigenomics (DNA methylation) Transcriptomics Metabonomics Proteomics - can be derived from global, untargeted searches - use generic technology regardless of disease or exposure of interest -provide mechanistic information on multiple endpoints - combined use of multiple –omics technologies, plus bioinformatics, can integrate multi-level information and provide a systems biology approach to biomarker discovery Current evidence of potential of –omics in environmental health research 1. Genomics: widespread use in GWAS studies Massive SNP analysis and search for association with disease risk Case-control studies, some hundreds to a few thousands of subjects ORs for individual alleles tend to be rather small (<1.5) Tenesa et al., Nat Genet. May;40 (2008) 631-7 colorectal cancer: OR = 2.6 for combination of six alleles Amos et al., Nat Genet. 40 (2008) 616-22 lung cancer: ΟR = 1.32 for combination of two alleles 2. Εpigenomics Bulk hypomethylation, promoter (CpG island) hypermethylation - Most studies targeted on candidate genes (p16, MGMT, RASSF1A etc) or on surrogate sequences (alu, LINES) -Few genome-wide studies, mostly in relation with diseased states Clear evidence of effects of exposures on epigenetic status in blood mononuclear cells Emerging evidence of analogous effects in relation to disease risk Perera et al., PLOS One 2009, e4488 Cord blood, children with maternal high/low PAH exposure; ACSL3 CpG island Breton et al., AJRCCM 2009 Maternal pregnancy exposure to tobacco smoke vs methylation in buccal cells of children; Illumina Goldengate platform; 8 CGI with changes detected; changes in AluYb8 but not LINE1; Widschwendter et al., PLoS One 2008, e2656 Epigenotyping in peripheral blood cell DNA and breast cancer risk DNA methylation of peripheral blood cell DNA provides good prediction of BC risk 3. Transcriptomics: Global analysis of gene expression, search for association with exposure or early effects Few, rather small studies to date van Leeuwen et al., Mutat Res. 600 (2006 ), 12-22 Genome-wide differential gene expression in PBMCs of children exposed to air pollution in the Czech Republic 24 high exposure / 23 low exposure subjects Forrest et al., Environ Health Perspect. 113 (2005), 801-7 Microarray analysis of PBMC gene expression in benzene-exposed workers 6 exposed & 6 controls 4. Proteomics - Adsorption on protein chips / SELDI-TOF MS - Multiplex ELISAs Luminex LabMAP technology Few studies, limited to a few tens of subjects Vermeulen et al., PNAS 102 (2005 ), 17041-6 10 exposed & 10 controls Decreased levels of CXC-chemokines in serum of benzeneexposed workers identified by array-based proteomics 5. Μetabolomics full-profile analysis of serum/urine by NMR OR HPLC/MS/MS Studies so far limited to diseased states Holmes et al., Nature 453 (2008) 396-400 Human metabolic phenotype diversity and its association with diet and blood pressure ΙΝΤΕΡΜΑP project: metabolomic profiles in relation to blood pressure & influence of diet & other factors 4,630 subjects, 17 populations Urine analysis using NMR Current state of the art in application of –omics in the search for biomarkers of environmental disease Conclusion so far: Even with small studies, distinct profiles, with biological meaning, can be identified, reflecting toxic exposure or predictive of disease risk Problems: existing studies based on very small populations limited information on exposure uncertainty regarding the potential of use of –omics with samples already stored in existing biobanks and in large-scale studies FP7 integrated project Main aims: -Application of wide range of –omics technologies, in combination with Genomics biomarkers health advanced bioinformatics, in the contextof of environmental molecular epidemiology studies, for (EnviroGenomarkers) the discovery of new biomarkers of exposure to toxic environmental agents, new biomarkers of disease risk, and exploration of their relationships - Exploration of technical potential and problems in the application of –omics technologies in large-scale population studies using biosamples in long-term storage * *Currently more than 1 million samples are stored in biobanks in Europe 11 partners from 7 European countries no. Participant organisation name Country 1 National Hellenic Research Foundation; co-ordination; epigenomics; SNPs; PAH adducts Greece 2 University of Maastricht; transcriptomics Netherlands 3 Imperial College London; exposure assessment; risk assessment; metabolomics UK 4 Umeå University; NHSDS biosamples & data; epidemiology; exposure assessment Sweden 5 Centro per lo Studio e la Prevenzione Oncologia, Florence; EPIC Italy biosamples & data; epidemiology; exposure assessment Italy 6 University of Crete; Rhea cohort samples & data; phthalate & PBDE analyses Greece 7 University of Utrecht; proteomics Netherlands 8 Istituto Superiore di Sanita, Rome; exposure assessment (GIS) Italy 9 National Public Health Institute (KTL), Kuopio; PCB analyses Finland 10 University of Leeds; data warehousing; bioinformatics UK 11 University of Lund; Cd analyses Sweden start March 2009, 4 yrs Epidemiologic design: case-control nested within prospective cohorts prospective study exposure biomarkers of exposure intermediate –omics biomarkers of early effects relationship of environmental exposures vs risk biomarkers disease risk-predictive biomarkers “meet-in-the-middle” approach [P. Vineis & F. Perera, Cancer Epidemiol. Biomarkers. Prev 16 (2007):1954–65] EPIC Collaborating centres and cohort subjects The EnviroGenomarkers project cohorts Subjects included TROMSØ Questionnaire Q + Blood France 74 524 21 053 UME Italy 47 749 47 725 Å Spain 41 440 39 579 UK 87 942 43 141 AARHUS MALMÖ Netherlands 40 072 36 318 COPENHAGEN UTRECHT CAMBRIDGE POTSD Greece 28 555 28 483 OXFORD BILTHOVEN AM NHSDS: Population based cohorts Germany 53 091 50 678 HEIDELBERG Questionnaires andPARIS blood sampling Sweden 53 826 53 781 MILAN IARCLYON TURIN OVIEDO Denmark 57 054 56 131 • The Västerbotten Intervention Project Cohort (VIP) 1985 FLORENCE SAN SEBASTIAN Norway 37 215 11 000 PAMPLO BARCELONA NAPLES NA •AllThe Northern Sweden Monica Cohort 521 468 387 889 ATHE MURCI GRANADA A RAGUSA 85 000 13 000 NS from 1986, 1990, 1994, 1999 (+ follow up), 2004 • The Västerbotten Mammary Screening Cohort 1995 Unique individuals Rhea• mother-child cohort, Crete Approx. 1,700 mothers & their children borne since 2007 in the Heraklion area, Crete; Questionnaires, maternal blood & urine, cord blood & its components - (+ 4 500) 50 000 90 000 Diseases and exposures 1. breast cancer vs PCBs PAHs cadmium 2. B-cell lymphoma vs PCBs OR~2-4.5 OR~1.5 OR~2.3 OR~5-13 Case-control nested within EPIC Italy and NSHDS 3. chronic diseases of the nervous and immune system & allergies establishing themselves in early childhood vs early life exposure to endocrine disruptors (PCBs, PAHs, phthalates, polybrominated diphenyl ethers) Prospective, Rhea cohort (Crete), children followed up at age 4 years vs exposure during pregnancy Samples to be analysed samples cohort NSHDS, EPIC-Italy disease end-point subjects original repeats breast ca 600 600 ~ 30 breast ca controls 600 600 ~ 30 BCL 300 300 BCL controls 300 300 ~ 30 ~ 30 allergy/ asthma Rhea immune neurological 600 600 cord blood, mother urines 600 blood & urine at age 4 yrs Intermediate biomarker analyses In general use 2-phase approach: a) discovery phase (genome-wide); 10-20% of samples b) validation phase (targeted): all samples 1. Metabolomics Start off with pilot study to technically validate applicability of plasma, HPLC/MS; all samples –omics technologies to existing biobank samples 2. Epigenomics discovery: genome-wide (Illumina Infinium 27k CpG microarray platform) validation: 10 selected sequences, pyrosequencing 3. Proteomics discovery: Luminex Multianalyte Profiling (47 inflammationrelated proteins) validation: 10 selected proteins; same method 4. Transcriptomics discovery: genome-wide (Agilent 44k microarray platform) validation: 10 selected sequences, RT-PCR 5. Genotyping a) Genome-wide SNP data already available for many of the samples from previous studies b) RT-PCR on selected targets and in selected sub-groups Exposure assessment Biomarkers PCBs: plasma concentrations of selected congeners, using GC/MS cadmium: erythrocyte levels, using inductively-coupled plasma mass spectrometry phthalates: urine metabolites, using HPLC/MS BDPEs: plasma concentrations of selected congeners, using HPLC/MS PAHs: DNA adducts (ELISA) Additional data on biomarkers of carcinogen exposure available from other projects Other exposure information FFQs and environmental exposure questionnaires Data on exposure to air, water and food toxicants available from other projects GIS Data management & analysis Centralised data repository Use existing & construct new bioinformatics tools Aim for integrated, multi-level analysis 1. Intermediate biomarkers vs disease: biomarkers with risk predictivity 2. Biomarkers of risk vs exposure 3. Intermediate biomarkers vs exposure Bioinformatics – integrated analysis of multilevel data 1 2 3 4 5 6 7 8 9 10 genomics (SNPs) diseased controls transcriptomics diseased individual genomic profile controls epigenomics diseased controls proteomics metabolomics diseased controls diseased controls combined risk biomarker adapted from Butcher & Beck, 2008) Challenges 1. Suitability of old biobank samples for certain –omics analysis (e.g. transcriptomics)? Pilot study ongoing 2. Detection of risk biomarkers in surrogate tissues (PBLs)? 3. Systems-level bioinformatics analysis Funded by the European Union FP7, Theme: Environment (including climate change) (Grant no. 226756) Nature, Vol. 458 (9 April 2009), p. 458