Microbiome for early cancer prediction before the onset of disease

 

SCOPE & EXPECTED OUTCOMES :

 

The call focuses on the development and validation of microbiome-based tools capable of identifying individuals at risk of developing cancer before the onset of disease, ideally up to two years in advance. The aim is to enable earlier intervention, personalised prevention strategies, and higher treatment success rates.

Cancer often develops silently over many years. The deterioration of the human microbiome (dysbiosis) is increasingly associated with oncogenesis and tumour progression. The call seeks to exploit this correlation to develop minimally invasive, reliable predictive biomarkers based on microbiome analysis.

Funded projects are expected to:

• Develop validated microbiome-based predictive tools for earlier and personalised cancer prevention, covering several cancer types where possible.
• Use longitudinal data (large numbers of samples collected over multiple years) to ensure robustness and predictive power.
• Integrate AI-based risk modelling approaches, including simulations of organs or body systems.
• Collaborate with large existing microbiome biobanks, cohorts and registries, combining existing data with newly generated clinical and microbiome data.
• Compare microbiome tools with other minimally invasive methods (e.g. liquid biopsy) in terms of predictive accuracy, simplicity, cost-effectiveness and commercial potential.
• Validate results in an independent cohort.
• Develop guidelines for risk management, including lifestyle and dietary interventions.

 

The consortium should gather at least 3 independent legal entities. These partners must be from 3 different countries (EU or Associated), with at least one from an EU Member State.

Must be established in an EU Member State or Associated Country, or a third country as explicitly eligible in Horizon Europe.

Consortia should include complementary expertise in:

• Microbiome science
• Oncology and clinical research
• Biobanking and cohort management
• AI and risk modelling
• Data governance and regulatory compliance

Involvement of clinical partners and access to large longitudinal cohorts are essential.