Identifying risk factors and improving prevention and early detection

Preventing cancer begins with understanding who is at risk and why. Work Stream 1 develops the scientific foundation for cancer prevention and risk prediction by investigating how genetic, biological, environmental, reproductive, lifestyle, and socioeconomic factors interact across the life course to influence the development of women’s cancers.

We integrate genomic and clinical information with data from Swedish population cohorts, biobanks, healthcare and socioeconomic registers, and environmental datasets to develop comprehensive models of cancer risk. By combining polygenic risk scores with non-genetic determinants, including reproductive history, lifestyle, immune function, environmental exposures, and social factors, we seek to better understand the complex interactions that drive cancer development and improve the accuracy of risk prediction.

A key focus is to understand how life experiences across the life course — from early life to adulthood, including pregnancy, family formation, and changing social and healthcare environments — influence cancer risk. By evaluating these factors together, Work Stream 1 aims to better understand cancer etiology and the mechanisms underlying disparities in cancer incidence.

The knowledge generated will be translated into robust, clinically relevant risk prediction models and decision-support tools for healthcare. These models will support personalized prevention, risk-stratified screening, and earlier detection, helping to ensure that preventive strategies are both effective and equitable across the population.

Participating investigators

Camila Consiglio, Carina Bernardo, Ingrid Hedenfalk, Lisa Rydén, Niklas Loman, Päivi Kannisto, Sophia Zackrisson, Simon Timpka, Therese Nilsson 

Research focus

• Identify biological, environmental, and social determinants of women’s cancer risk across the life course.
• Develop and validate personalized risk models that combine genetic and non-genetic factors.
• Ensure risk prediction models are equitable and applicable across diverse populations.
• Translate research into practical tools that support personalized prevention, risk-stratified screening, earlier detection, and evidence-based clinical and public health decision-making.