SORS: Integrative data approaches towards a personalized prevention of cancer: The epidemiological vision

Date: Tuesday, 26 June, 2018 – 11:00

Speakers: Núria Malats, MD, MPH, PhD Genetic & Molecular Epidemiology Group Spanish National Cancer Research Centre (CNIO) Madrid, Spain.

Venue: Sala d’Actes, FIB Building (B6), Campus Nord, Barcelona

Abstract: Disease prevention can highly benefit of a personalized medicine approach through the accurate discrimination of individuals at high risk of developing a specific disease from those at moderate and low risk. To this end precise risk prediction models need to be built. This endeavour requires a precise characterization of the individual exposome, genome, and phenome. Massive molecular omics data representing the different layers of the biological processes of the host and the non-host will enable to build more accurate risk prediction models. Epidemiologists aim to integrate omics data along with important information coming from other sources (questionnaires, candidate markers) that has been proved to be relevant in the risk assessment of complex diseases.

The vast proportion of pancreatic cancer is named sporadic because it does not aggregate within families and its aetiology is complex. Both genetic and non-genetic factors have been associated with sporadic pancreatic cancer though the magnitude of their risk is small/moderate. Therefore, cost-efficient primary and secondary prevention programs for sporadic pancreatic cancer should be based on multifactorial integrative scores to define high-risk populations. Steps towards the integration of omics and non-omics factors selected through an appropriate methodology are ongoing using the PanGenEU study resources. However, the integrative models in large-scale epidemiologic research still face numerous challenges, some of them at the analytical stage. I will comment on the efforts we do to better characterize pancreatic cancer risk factors and the strategies we plan to apply to build integrative predictive risk scores.