Pilot 1.4

The introduction of genomic analysis techniques in clinical practice is revolutionizing the diagnostic landscape of rare genetic diseases. Specifically, next-generation tests capable of analyzing the entire genome currently stand as the singular molecular tests with the highest diagnostic yield compared to other techniques. This type of analysis represents a crucial tool in identifying genes responsible for certain phenotypic traits and, notably, the genetic variants accountable for specific clinical conditions. For this new diagnostic tool to be fully utilized, it is crucial to accompany the rapid progress in understanding the genetic causes of diseases with new support tools and processes for physicians.

The Pilot 1.4 involves collaboration between the Laboratory of Immunology and Genetics of Rare Diseases of the Mario Negri Institute-IRCCS, the Molecular Genetics Laboratory of ASST Papa Giovanni XXIII

the University of Milan-Bicocca, and the University of Bergamo, and has three main objectives:

• Improve the understanding of the progression mechanisms of rare diseases and the effects of therapeutic interventions, adopting an integrated approach that combines various diagnostic methodologies, including clinical observation, genetic analysis, imaging diagnostics, and data analysis.
• Explore innovative artificial intelligence-based solutions to support genomic diagnostics of rare diseases. This will involve the development of next-generation tools to speed up the diagnostic process and identify correlations between genetic variants and phenotypic manifestations.
• Develop and validate mathematical models for the simulation of physiological phenomena using numerical techniques based on individual patient data, in order to characterize the alterations induced by pathologies, monitor their evolution over time and evaluate the effects of treatments.

To achieve these objectives, genetic, omic, and clinical data will be collected, using a variety of instruments, to better characterize the pathophysiology of rare diseases. Additionally, specific infrastructures and pipelines will be developed to store, manage, and analyze the produced digital data.