Computational Approaches Integrated in a Digital Ecosystem Platform for a Rare Disease - Visibelli, Anna; Cicaloni, Vittoria; Spiga, Ottavia; Santucci, Annalisa (Frontiers in Molecular Medicine, 2022)
Alkaptonuria (AKU) is an ultra-rare autosomal recessive disease caused by a mutation in the homogentisate 1,2-dioxygenase gene. One of the main obstacles in studying AKU and other ultra-rare diseases, is the lack of a standardized methodology to assess disease severity or response to treatment. Based on that, a multi-purpose digital platform, called ApreciseKUre, was implemented to facilitate data collection, integration and analysis for patients affected by AKU. It includes genetic, biochemical, histopathological, clinical, therapeutic resources and Quality of Life (QoL) scores that can be shared among registered researchers and clinicians to create a Precision Medicine Ecosystem. The combination of machine learning applications to analyse and re-interpret data available in the ApreciseKUre clearly indicated the potential direct benefits to achieve patients’ stratification and the consequent tailoring of care and treatments to a specific subgroup of patients. In order to generate a comprehensive patient profile, computational modeling and database construction support the identification of potential new biomarkers, paving the way for more personalized therapy to maximize the benefit-risk ratio. In this work, different Machine Learning implemented approaches were described: ● predictive model for the estimation of oxidative status trend of each AKU patient based on different biochemical predictors [Cicaloni et al., 2019]. ● prediction of QoL scores based on clinical AKU patients’ clinical data to perform patients’stratification [Spiga et al., 2020]. ● a tool able to investigate the most suitable treatment in accordance with AKU patients’ QoL scores [Spiga et al., 2021 A]. ● the comparison of different algorithms to explore the phenotype–genotype relationships unknown in AKU so far [Spiga et al., 2021 B]. We also implemented an ApreciseKUre plugin, called AKUImg [Rossi et al., 2021], dedicated to the storage and analysis of AKU histopathological slides, where images can be shared to extend the AKU knowledge network. The outcomes of these predictions highlight the necessity of development databases for rare diseases like ApreciseKUre. We believe this is not limited to the study of AKU, but it could be applied to other rare diseases, allowing data management, analysis, and interpretation.