1. Paolo Vineis Imperial College London and HuGeF Foundation Exposomics: a hybrid study design, and causal interpretation Centre Investigator’s seminar 26 November 2014

2. Conceptual framework: - Life course - Integration of experiment and observation - Improved exposure assessment (reduction of uncertainty) - Systematic use of omics - Meet in the middle

3. 10 20 30 0 50 ALSPAC EPIC- ESCAPE PICCOLI+ Critical stages of life Mid- and late-life Childhood Early Adulthood Adolescence Adulthood 60 Age Birth PISCINA INMA RHEA PISCINA RAPTES OXFORD ST MCC SAPALDI A EPICURO

4. The logical structure of EXPOsOMICS 4 Randomized trials (Oxford St, TAPAS2) provide stronger evidence on short-term effects (Paul Cullinan) Updated metabolomic database provides priors for Bayesian analysis (Augustin Scalbert) Pathway analysis, dynamic models and DAGs to reinforce causality (Marc Chadeau-Hyam) Integration of cross-sectional (PEM and fresh blood – John Gulliver) with longitudinal approaches (archived samples and LUR with back-extrapolation) to identify best omic candidates (examples from Piscina and ALSPAC)(Michelle Plusquin, Karin Van Veldhoven, Florence Guida)

5. Phase 1 – biological samples Samples (in brackets methylation)FreshArchived Oxford Street60x2 (-) TAPAS 2 60x2 (-)sent to labs Piscina60x2 (-)sent to labs INMA (PEM)35x2 (70) EPIC-ESCAPE, East Anglia, SAPALDIA (ALTS-PEM)40 each x2 probably only (320)100 archived Total Phase 1750100 Total to be analysed Phase 1 850 EPIGENETICS(390)(100)

6. Phase 2 Adults 200/200 asthma adults SAPALDIA and East Anglia 300/300 CVD EPIC 200/200 colon cancer MCC Total 700/700 (all undergoing epigenetics) Phase 2 Children 500 with continuous measurements Total 1900 samples in Phase 2 (plus 10% quality controls)

7. Phase 2 Children cohorts (excluding ALSPAC) Current plan for Phase 2 – based on continuous measurements 200 children with neurodevelopmental problems at age 4 from Rhea/INMA 200 birth weight and growth curves from EnvironAge 100 birth weight and growth curves from Piccoli+ (200 birth weight and growth curves in Rhea/INMA to be decided) Plus 10% quality control samples In principle: cord blood (and/or blood at age 4) In addition ALSPAC has 1,000 children with metabolomics, methylome and air pollution exposure assessment (including 500 with asthma)

8. UFP LUR models developed in EXPOsOMICS Exposure comparisons (UFP) Reference site PEM UFP MEASUREMENTS AT HOMES (UT) UFP MEASUREMENTS AT HOMES (UB) Backpack indoors Backpack outdoors Backpack outdoors AND OTHER LOCATIONS (work, leisure etc.)

9. Blood Processing Protocol for Exposome Studies Oxford Street Study and EXPOsOMICS 5.5 ml Blood Protocol Peripheral Blood (~5.5 ml) EDTA Tube (4 ml) Serum Tube (1.5 ml) Centrifugation Adductomics (1) -80 °C 0.8 ml1.5 ml 0.5 ml -80 °C 0.5 ml0.25 ml0.125 ml -80 °C Adductomics (2)MetabolomicsProteomics Destinations Adductomics (1) – King’s College, London Adductomics (2) – Berkeley Metabolomics – IARC, Lyon Proteomics – Utrecht mRNA/miRNA (1)/miRNA (2) – Maastricht Epigenetics (1) – IARC, Lyon Epigenetics (2) – Athens Allow to clot Whole blood + RNAlater 2 x 0.4 ml mRNA miRNA (1) 1.5 ml Plasma 6 x 0.5 ml miRNA (2)* * Excess plasma not required for miRNA analysis may be stored for potential adductomics use Buffy coat (1)Buffy coat (2) Epigenetics (1)Epigenetics (2) -80 °C

10. The “meet-in-the-middle” concept Advancing the application of omics-based biomarkers in environmental epidemiology. Vineis P et al Environ Mol Mutagen. 2013 Aug;54(7):461-7 Advancing the application of omics-based biomarkers in environmental epidemiology.

11. The idea of meet-in-the-middle has several roots: - the need to find biological plausibity to epidemiological observations - to create a “chain of causality” through intermediate events One of the major philosophers of causality in biology (Wesley Salmon) argued that simple statistical associations are not enough to establish causation, but we need to identify a chain of intermediate events called “the propagation of a mark” TIME IS THE KEY ISSUE!

12. Gallo V, Egger M, McCormack V, Farmer PB, et al. (2011) STrengthening the Reporting of OBservational studies in Epidemiology – Molecular Epidemiology (STROBE-ME): An Extension of the STROBE Statement. PLoS Med 8(10): e1001117. doi:10.1371/journal.pmed.1001117 http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001117

13. Heat map depicting the associations between 10 environmental pollutants and 750 features according to P-value 23 July 2013, London, UK From exposure characterization (metabolomic profiles) to early disease markers (14 to 18 translocations in follicular lymphoma)

14. Methylation in ex-smokers (Guida et al, HMG accepted)

15. These findings give origin to several important hypotheses: (a) hematopoietic stem cells are involved, since the persistence of altered gene methylation goes much beyond the half-life of mature white blood cells; (b) methylation changes in key genes (like AHRR) are likely to confer selective advantage to cells and lead to clonal cell selection. What we call “hypomethylation” is a change in the proportion of cells that are unmethylated at a certain CpG site compared to those that are methylated. Methylation at the single cell-single CpG site level is either 0 or 1.

16. Therefore, stem cells preserve a “memory” of past exposures in the form of a greater proportion of cells with unmethylated CpG sites vs methylated CpG sites. We speculate that exposure to toxic agents selects a clone of cells that are unmethylated in a CpG involved in the activation of a pathway reactive to environmental insults. This imprinting remains in the memory of stem cells and is comparable to immunological memory (?)

17. The studies we conducted in smokers were in white blood cells, i.e. “memory” involved hematopoietic stem cells. However, we also investigated the lung tissue of smokers and non-smokers (Shenker et al, 2011). Methylation levels in the AHRR gene probes were significantly decreased (P < 0.001) and expression increased (P=0.0047) in the lung tissue of current smokers compared with non-smokers. This was further validated in a mouse model of smoke exposure with similar results.

18. Cumulative risk of lung cancer mortality among men in the United Kingdom who smoke, according to the age when they stopped smoking. [Figure adapted from the original by permission of the British Medical Journal (56)]. Vineis P et al. JNCI J Natl Cancer Inst 2004;96:99-106 © Oxford University Press

19. Back to meet-in-the-middle The meet-in-the-middle concept is very rudimentary. The next challenge is how to incorporate markers in a time sequence with mathematical models - Need for new biostatistical tools and causal interpretation - repeat samples and intra-individual variation - validation of omics: Hebels et al, EHP - quality controls (e.g. nuisance parameters: Chadeau-Hyam et al,2014) - “cross-omics” - longitudinal models of causality Chadeau-Hyam M, et al. Deciphering the complex: Methodological overview of statistical models to derive OMICS-based biomarkers. Environ Mol Mutagen. 2013 Aug;54(7):542-57. Hebels et al. Performance in omics analyses of blood samples in long-term storage: opportunities for the exploitation of existing biobanks in environmental health research. Environ Health Perspect. 2013 Apr;121(4):480-7.Deciphering the complex: Methodological overview of statistical models to derive OMICS-based biomarkers. Environ Health Perspect.

20. But the real challenge is that of emergence of a new property, as described by the literature on complexity, i.e. how exogenous molecules (e.g. pollutants) modify endogenous molecules (DNA, RNA, proteins) and the latter make a difference in cells; then how cells with such changes emerge as having selective advantage or influencing surrounding or distant cells, etc., up to the level of the organism and the population.

21. “In philosophy, systems theory, science, and art, emergence is conceived as a process whereby larger entities, patterns, and regularities arise through interactions among smaller or simpler entities that themselves do not exhibit such properties. Emergence is central in theories of integrative levels and of complex systems. For instance, the phenomenon life as studied in biology is commonly perceived as an emergent property of interacting molecules (…)”. (Wikipedia)philosophysystems theoryscienceartintegrative levels complex systemsbiology