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.