Systems perspectives of the exposome
Wednesday 8 June - 14:40-15:00
photo intervenant Paolo VINEIS
Chair in Environmental Epidemiology, Centre for environment and Health, School of Public Health, Imperial College London, United Kingdom

Paolo Vineis is a leading researcher in the fields of molecular epidemiology and exposomics. His latest research activities mainly focus on examining biomarkers of disease risk, complex exposures and intermediate biomarkers from omic platforms (including metabolomics and epigenetics) in large epidemiological studies as well as studying the effects of climate change on noncommunicable diseases. He has more than 700 publications (many as leading author) in journals such as Nature, Nature Genetics, The Lancet and The Lancet Oncology. He is a member of various international scientific and ethics committees (including the Committee of the US National Academy of Sciences on 21st Century Risk Assessment) and vice-Chair of the ethics Committee at the International Agency for Research on Cancer. He has been a member of the Scientific Council of IARC. Professor Vineis has extensive experience in leading international projects. He is currently the coordinator of the European Commission-funded Exposomics project (valued at 8.7m, started in 2012) and the Horizon 2020-funded project Lifepath (valued at 6m, started in 2015). He is a Principal Investigator/Co-investigator of numerous international research projects, such as the European Commission-funded geNAIR, eCNIS2, envirogenomarkers, hypergenes, ESCAPE and Transphorm networks, in which he has led Work Packages. In addition, he has attracted grants from the Leverhulme Trust, MRC, Cancer Research UK, Huge Foundation and the US National Cancer Institute. He is the director of the unit of Molecular and genetic epidemiology, Huge Foundation, Turin, Italy and leads the exposome and health theme of the MRC-Phe Centre for Environment and Health at Imperial College.

ABSTRACT

Systems biology has been driven by technology (the development of omics) and by statistical modelling and bioinformatics. It is time to bring biological thinking back. We need to make at least three traditions of thought compatible: (a) causality in epidemiology, e.g. the “sufficient-component-cause framework”, and causality in other sciences, e.g. the Salmon and Dowe approach; (b) new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c) the burgeoning of omic research, with a large number of “signals” that need to be interpreted. To address the new challenges of epidemiology, the concept of the “exposome” has been proposed, initially by Wild [2005], with more recent detailed development in relation to its application to population-based studies [Wild, 2012]. The original concept was expanded by others, particularly Rappaport and Smith [2010] who functionalized the exposome in terms of chemical signals detectable in biospecimens. The canonical exposome concept refers to the totality of exposures from a variety of sources including chemical agents, biological agents, radiation, and psychosocial components from conception onward, over a complete lifetime [Rappaport and Smith, 2010; Wild, 2012]. I will try to offer a unifying framework to incorporate omic data into causal models, referring to a position called “evidential pluralism”, according to which causal reasoning is based on both “difference-making” and the underlying biological mechanisms (Russo and Williamson). I will show examples from recent projects in the field, namely: new omic approaches such as adductomics; new long-term methylation biomarkers (in relation to smoking and air pollution); markers related to early life exposure and the role of socio-economic differentials. In particular, Illari and Russo suggest to conceptualize the detecting and tracing of signals in terms of information transmission, which is a development of Salmon’s and Dowe’s mark transmission theory. One advantage of information transmission is that it is potentially widely applicable and capable of explaining how heterogeneous factors such as micro and macro – biological and social – are linked; this is arguably a pressing issue in the light of results of omic studies and also for the design of public health policies.