ONCOBIOME focuses on microbiome-cancer links across breast, colon, lung cancer and melanoma; LIFEPATH studied biological pathways in large phenotyped cohorts.
IIGM FOUNDATION
Torino-based genomic medicine institute combining computational biology and molecular network modeling with cancer, microbiome, and precision medicine research.
Their core work
IIGM Foundation (Italian Institute for Genomic Medicine) is a Torino-based research centre focused on computational and molecular approaches to understanding human disease, particularly cancer. They develop mathematical models and statistical inference methods to analyze large-scale biological data, including multi-scale molecular networks and protein-protein interactions. Their work spans from population-level epidemiological cohorts studying social determinants of healthy ageing to precision medicine approaches linking the gut microbiome to cancer incidence, diagnosis, and immunotherapy response.
What they specialise in
INFERNET, which they coordinated, developed algorithms for inference and optimization from large-scale biological data, including molecular network modeling.
ONCOBIOME investigates gut microbiome signatures associated with cancer prognosis and treatment prediction, including immunotherapy response.
LIFEPATH involved cohorts with intense phenotyping and repeat biological samples totalling over 33,000 individuals.
INFERNET addressed co-evolution, metabolic networks, regulatory networks, and protein-protein interaction modeling.
How they've shifted over time
IIGM's H2020 journey shows a clear shift from population-level epidemiology toward computational oncology and precision medicine. Their early work (LIFEPATH, 2015) centred on large phenotyped cohorts studying social determinants of ageing, while their middle period (INFERNET, 2017) built up computational and algorithmic capacity for analyzing molecular networks. By 2019, ONCOBIOME brought these threads together — applying computational tools to microbiome-cancer interactions for diagnostic and therapeutic purposes.
IIGM is converging its computational biology strengths with cancer microbiome research, positioning itself at the intersection of data science and translational oncology.
How they like to work
IIGM operates primarily as a specialist partner in large consortia, having coordinated only one of three projects (the smaller, methodological INFERNET). Their 41 unique partners across 16 countries indicate they are well-networked and trusted within large European health research consortia. They tend to contribute deep analytical and computational expertise rather than lead consortium management, making them a reliable technical partner for data-intensive biomedical projects.
IIGM has collaborated with 41 distinct partners across 16 countries, reflecting broad European reach through participation in large health research consortia. Their network spans major biomedical research hubs across Western and Southern Europe.
What sets them apart
IIGM bridges computational biology and clinical oncology in a way few genomic medicine institutes do — they build the mathematical tools (network inference, statistical modeling) and then apply them directly to cancer and microbiome datasets. Based in Torino's biomedical research cluster, they combine algorithmic development capacity with access to large clinical cohorts. For consortium builders, they offer a rare dual capability: both the methods expertise and the domain knowledge in cancer genomics to apply those methods meaningfully.
Highlights from their portfolio
- ONCOBIOMELargest funding (EUR 888K) and most translational project — linking gut microbiome to cancer diagnosis, prognosis, and immunotherapy prediction across multiple cancer types.
- INFERNETTheir only coordinated project, focused on building foundational computational methods for biological data analysis — represents their core methodological identity.
- LIFEPATHLargest single EC contribution (EUR 1.05M) and involved population cohorts exceeding 33,000 individuals, demonstrating capacity to handle large-scale epidemiological data.