Microb-AI-ome | Federated artificial intelligence for privacy-preserving international stratification of colorectal cancer patients

Original description

In the EU, 1 in 35 women and 1 in 23 men will be diagnosed with colorectal cancer (CRC) in their life span (ca. 340,000 cases and 156,000 deaths in 2020) causing an annual economic burden of ca. 20 billion EUR. Identifying CRC early enables better treatment options. Screening usually entails a quantitative faecal immunological test (FIT) to predict the need of colonoscopy for the detection of colorectal lesions, an expensive and invasive procedure. We aim to predict this need with specificity increased by >20 percentage points by using metagenomic microbiomes. We hypothesise that computational microbiome profiles extracted using artificial intelligence (Al) technology will allow for optimised personal therapy stratification. However, clinicians do not have access to broad microbiome data. With Microb-AI-ome, we will develop a novel kind of computational stratification technology to enable microbiome-enhanced precision medicine of CRC. Metagenomic microbiome data to date is distributed over many national registries, and privacy regulations are hindering its effective integration. With Microb-AI-ome, we will overcome this barrier by establishing the first privacy-preserving federated big data network in CRC research. We will integrate isolated, national databases into one international federated database network - rather than a cloud - covering metagenomes for over 5,000 individuals screened for CRC, and an expected total of 100,000 by 2026. Microb-AI-ome ensures that no sensitive patient data will leave the safe harbours of the local databases while still allowing for the classification of clinical CRC phenotypes, which we will demonstrate in clinical practice allowing regulatory bodies to adopt evidence-based guidelines. Our consortium combines expertise in CRC and its treatment, microbiomics, artificial intelligence, software development, and privacy protection to close the gap between privacy and big data in international medical research.

Status

SIGNED

Url

https://cordis.europa.eu/project/id/101079777

Call topic

HORIZON-HLTH-2022-TOOL-12-01-two-stage

Update Date

31-07-2023