Horizon Cancer Mission: Virtual Human Twins
Funds up to four €8-9M grants for teams building AI-driven digital models of a patient's cancer to predict how it develops and guide personalised treatment.
Cancer research usually works from broad patterns across many patients. That approach struggles hardest on the cancers this topic targets: ones with few treatment options, biology that is not well understood, low five-year survival, or cancers that appear early in life. This topic funds a different tool — a computer model built from one patient's own data, detailed enough to help predict how their…
Building and testing multiscale virtual human twins — AI-driven digital models of a patient's own cancer biology, from cells and tissues up to organs — to understand how a cancer starts and develops, and to personalise treatment. Priority goes to cancers with few treatment options or unclear biology: refractory cancers, rare cancers, early-onset cancers, cancers with low five-year survival, and paediatric or adolescent cancers. Models are validated against real patient data — new or existing, multi-omics, imaging, and real-world data — with age, sex and gender differences built into the modelling itself. Results are published as FAIR data and made available through the UNCAN.eu and Advanced Virtual Human Twin platforms.
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