Measuring what changes
Capturing the pancreatic cancer exposome is exceptionally challenging. Diet, medications, pollutants, and microbial communities change over time, often in concert. Their effects may depend on age, exposure duration, or biological context.
Questionnaires can miss relevant exposure windows. Molecular measurements provide information but usually offer only snapshots. A metabolite measured at diagnosis may reflect an earlier exposure, altered metabolism, or the effects of the cancer itself. Distinguishing among these explanations is crucial for prevention.
Integrative molecular epidemiology provides a framework for investigating them. It integrates exposure histories and phenotypes with the genome, epigenome, metabolome, and microbiome. Each layer reveals aspects of susceptibility, biological responses, or potential disease markers.
Our published work on genetic susceptibility, including a multilayer post-genome wide association study analysis, shows how association signals can be examined in their functional context. A risk locus – a specific chromosomal region where genetic variation is linked to an increased susceptibility or probability of developing a particular disease – initiates an inquiry into the genes and regulatory processes through which it might act.
The microbiome offers another avenue. In collaboration with Peer Bork’s laboratory, we identified a fecal microbial signature associated with PDAC. This supports further investigation of stool-based markers, while leaving an essential question open: can a signature detected in patients with cancer identify risk before diagnosis?
Putting the pieces into place
Collecting several molecular layers does not automatically establish causation. A biological feature could mediate an exposure’s effect, modify that effect, confound an association, or emerge as a consequence of disease. Each role has implications for prevention.
DNA methylation, for example, may help link environmental exposures to biological responses. An association with PDAC does not establish that a methylation change causes cancer. Its interpretation depends on when it occurred, which cells were measured, and how it relates to other risk factors.
We need study designs that place these observations in time. Prospective cohorts with repeated exposure assessments and samples collected before diagnosis are crucial. Standardised collection procedures and compatible exposure definitions will help researchers compare findings across populations.
Artificial intelligence can support the analysis of complex, multimodal data. Its value depends on the questions we ask and on how we test its predictions. Independent validation and rigorous causal inference remain essential, especially when the intended application is to identify people who currently appear healthy.
