Toward ‘digital twins’ of FA
individuals
Studies on SCC prevention in FA are limited by the small number of
individuals with the condition, who are spread around the world. In
addition to regular histopathological diagnosis of oral cancer
development and cytology-based screening methods (32), reliable
molecular markers are limited. Moreover, the scarcity of
genotype-phenotype associations in FA makes it highly likely that each
patient will respond in an individual way to drug treatments and/or
lifestyle changes. Since robust predictive in vitro and in
vivo FA models are lacking, drug screening and testing cannot be
generalized for all FA individuals. For example, in vitro analysis of radiation sensitivity of fibroblasts from FA individuals
does not correlate with the clinical response of the same patient to
radiotherapy (69, 70) and the amount of chromosomal breaks found in
lymphocyte cultures does not correlate with the severity of the disease, e.g., bone marrow function of the individuum. These issues
motivate the effort to create multi-level, dynamical computational
models of FA that can aid clinicians in tailoring therapies to each
specific FA patient. Models of this type have been termed “medical
digital twins” (71, 72).
Although a consensus definition of a medical digital twin does not yet
exist, the concept of a digital twin is common in engineering
disciplines (73). Often referred to as “industrial digital twins,”
these models are computational replicas of complex devices or processes,
such as jet engines or wind turbines, that are used to diagnose
technical problems and guide interventions. Industrial digital twins are
typically composed of multiple, interconnected computational models of
the constituent components of an engineered system. Critically, this
integrated “template” model of the base processes of the engineered
system in question is subsequently tuned, or “calibrated,” to a
specific instance of that system, e.g., a particular jet engine,
using performance data collected from sensors in real time. It is
this “twinning” process, involving consistent feedback from real-time
data streams, that differentiates a multi-level, computational model of
a dynamical system from a true digital twin (74). Construction of
digital twins for medical and clinical applications has been receiving
increased interest lately (71, 72). However, biological systems are far
more complex than engineered systems, making their practical
implementation much more challenging. Nevertheless, there have been
successful applications of medical digital twins for the treatment of
type 1 diabetes (75) and pediatric cardiac patients (76). Furthermore,
it is important to note that medical digital twins differ from
alternative approaches gaining popularity in biomedical sciences, such
as statistical and machine-learning models (77), in that they are based
on a mechanistic understanding of the underlying biological system. As
such, they are not constrained by the confines of the experimental data
on which they are constructed, which in FA is sparse.
The utility of a FA medical digital twin will be to aid clinicians in
determining best courses of action for individual patients in both the
prevention and treatment of malignant tumors. The template model for an
FA medical digital twin will comprise the biological processes mentioned
previously, including microbiome interactions, DNA damage sensing and
repair, EMT, cell cycle progression, and cell death, among others
(Fig. 3). Calibrating the template model to individual FA
patients will be challenging and require collecting spatially resolved,
single-cell resolution multi-omics data, epigenome profiling, and
metagenomics of the oral microbiome, from patients at regular intervals,e.g., every three months in accordance with clinical care
guidelines for FA individuals with potentially premalignant lesions in
the epithelial tissue. Additional patient data, such as blood draws,
genetic screens, and oral swabs, together with standard data from
electronic health records, can also be integrated into the calibration
data stream. Once the model is personalized in this way, it will be
possible to test, in silico, numerous preventive and/or
therapeutic options before applying them clinically to FA patients.
Furthermore, medical digital twins should be flexible and extensible,
able to grow in precision and predictive power as new knowledge is
accrued and experimental data sets are generated. In this way, the FA
digital twin will develop together with the patient and their clinician,
ultimately forecasting, with high accuracy and precision, responses to
novel personalized interventions and therapies.