Consortia, big data and the future of population research
Friday 10 June - 15:00-15:30
photo intervenant Elio RIBOLI
Director, School of Public Health, Imperial College London, United Kingdom

Professor Elio Riboli's career started at the Department of Epidemiology of the National Institute of Cancer, Milan (1978-1983). In 1983 Dr Riboli was appointed Medical Officer in Epidemiology at the International Agency for Research on Cancer (IARC). While at IARC he engaged in a novel area of research focusing on the role of diet, nutrition and endogenous hormones in cancer aetiology. In 1990 this materialized into the initiation of the european Prospective Investigation into Cancer and Nutrition (ePIC), and its subsequent funding by the "europe Against Cancer" programme of the European Commission, from 1992 onward. Over the past decade, Dr Riboli has led research contributing to the discovery of the role of metabolic factors (obesity, insulin resistance and other components of the so-called "metabolic syndrome") in cancer causation. These results have translated into worldwide public health guidance by international bodies such as the World Health Organization and the World Cancer Research Fund. While working at IARC, during the period 1990-2005, he received joint appointments as Adjunct Professor in the Department of Environmental Medicine at New York University and as Senior Visiting Scientist at the National Cancer Institute, National Institutes of Health, in the USA. In 2006 Dr Riboli was appointed Professor and Chair in Cancer epidemiology and Prevention and in 2008 Director of the School of Public Health. He is also Chair of the Interventional Public Health Clinical Programme group of the Imperial College Academic Health Science Centre (AHSC) and Director of Research in Public Health of the Imperial College National Health Service Healthcare Trust, providing a direct link between academic research, public health and clinical translation.

ABSTRACT
The role of large prospective cohort studies in cancer research


Over the past decades, a growing number of large population cohort studies with extensive exposure information and stored biosamples have been developed in different regions of the world.  These studies include early cohorts with extensive follow-up of up to 30 years, as well as more recently established cohorts with shorter follow-up time but often with richer baseline phenotype and exposure information. The two largest European cohort studies are EPIC and the more recently established UK Biobank. Both cohorts have recruited over 500,000 participants.  EPIC was initiated in the 1990s, coordinated by IARC in collaboration with 23 collaborating centre in 10 countries. After 20 years of follow-up EPIC has accrued over 80,000 incident cases of cancer, over 26,000 cases of CHD and stroke and over 16,000 cases of type 2 diabetes. The EPIC database has become a shared research infrastructure used by hundreds of researchers in Europe and across the world (http://epic.iarc.fr/).  EPIC and a number of other European cohorts have built a collaboration network that has materialized in the “Large Population Cohort” FP7 programme of the BBMRI infrastructure (http://www.bbmri-lpc.org/).
Population cohorts have greatly contributed to the advancement of our scientific understanding of the causes of chronic diseases. The first historical example was the demonstration of the carcinogenicity of tobacco smoke by the British Doctors Cohort in the 1950s. The list of causes of cancer and other chronic diseases discovered thanks to cohort studies is very long and include environmental, occupational, behavioural, pharmacological, nutritional exposures as well as endogenous metabolic and hormonal characteristics.  A more recent development is the unique role of cohorts in research focused on the interaction between genetic and epigenetic characteristics in combination with environmental exposures and endogenous/metabolic characteristics. By supporting the investigation of the biological interplay of inherited and acquired risk factors, cohort studies have the potential to help epidemiology to move beyond “associations” into the realm of “causation”.