Conclusions

For FA cancers, especially oral SCC, the use of multi-level dynamic mechanistic modelling provides a new perspective on early-stage diagnosis and decision support for the treatment of this rare disease. Such an approach is critical, since classical statistical models, using case studies and controls, cannot be applied due to the dearth of large patient groups. As such, we aim to build accurate computational models of tumorigenesis in a limited but representative number of FA patients. These mechanistic models will utilize pre-existing public knowledge on biochemical and regulatory pathways together with our knowledge of the life and disease course of more than 750 FA individuals, which will be essential for distinguishing the tumorigenesis process of FA cancer from that of regular cancer. In this way, our mechanistic models of FA cancer will take specific characteristics of this rare disease into account. Using longitudinal information about the lifestyles of FA individuals over years, together with multi-omics data at the genomic, transcriptomic, and proteomic levels, will lead to the construction of individual-specific models, or digital twins, that can be used to develop personalized treatment options. This approach has the potential to revolutionize the way FA individuals are treated clinically.