AbstractFanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the “hallmarks of cancer in FA”, which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.IntroductionRare diseases are disorders that affect less than one case in 2000 people, i.e., only a small percentage of the population. However, there are more than 6000 known rare diseases, affecting over 300 million people worldwide (1, 2). In 80% of the cases, the origin of a rare disease is one or multiple disadvantageous inherited variations of the genome (3). These are present in all cell types of the affected individual. Nevertheless, most rare diseases, which are not already prenatally lethal, are rather tissue specific. Pediatricians are more likely confronted with rare diseases than healthcare specialists from other disciplines, as those diseases frequently present symptoms early in life. Rare diseases are often referred to as “orphan diseases”, since in comparison to common non-communicable diseases, such as cardiovascular diseases and type 2 diabetes, there is less research and development of therapies for them. Fanconi anemia (FA) belongs to a small group of rare diseases that are investigated more intensively than most others (4). This is also the result of significant contributions from patient organizations in the United States, Germany and many other countries (5).In general, evidence-based medicine aims to make optimal medical decisions by integrating the experience of a clinician with data from the individual patient and available scientific information on the respective disease (6). The latter information often derives from randomized clinical trials involving large numbers of cases and controls. Those trials are the source for the construction of statistical models, i.e., to quantify mathematical relationships between non-random variables measured from the study participants (Fig. 1, left). For common diseases, there is no problem identifying a sufficiently large number of cases to achieve acceptable statistical power of the applied statistical model, e.g., reflected by the p-value. However, this approach cannot be used for rare diseases due to the small number of cases. An alternative approach is to study a few individuals in very high detail by collecting longitudinal samples for many biological parameters (7) (Fig. 1, right). For example, multi-omic analyses provide many thousands of data points per individual, such as genome-wide DNA methylation, histone modifications and gene expression. These data, together with mechanistic information on biochemical and regulatory pathways from public databases, such as KEGG (Kyoto Encyclopedia of Genes and Genomes) (8), Wikipathways (9) and SPOKE (Scalable Precision Medicine Open Knowledge Engine) (10), can then be used to construct multi-level dynamical computational models (11, 12). These models can act as virtual platforms for identifying novel therapeutic targets and designing treatment and preventative protocols to improve individual patient outcomes. In this Perspective article, we introduce the concept of mechanistic modelling as a clinical decision support tool in FA, using the example of the multi-step tumorigenesis of squamous cell carcinoma (SCC) in FA individuals.