BeNeXT: Biomarker enhanced diagnostic and prognostic tools for rare disorders. Using X-chromosome alterations in Turner syndrome as a model

XAVIER SEVILLANO; ESTHER ESTEBAN; AROA CASADO; CARMEN GARRIDO; ALEJANDRO GONZÁLEZ; MARC FREIXES; JOAN CLAUDI SOCORÓ; ISABELLA MONLLEÓ; DEBORA MICHELATTO; ESTEPHANIA CANDELO; HARRY PACHAJOA; ROLANDO GONZÁLEZ-JOSÉ; CARINA ARGÜELLES; CAROLA CHEROKI; PAULA GONZÁLEZ; YANN HEUZÉ; NEUS MARTÍNEZ-ABADÍAS

Congreso; World Orphan Drug Congress 2025; 2025

There are more than 2,000 rare diseases (RD) associated with facial dysmorphologies. Despite affecting 200 million people in the world, RD have been systematically neglected due to their low prevalence. People with RD often face delayed and inaccurate diagnosis that leads to poor prognosis and quality of life, particularly in underrepresented populations of non-European descent that have been even less investigated. Research in RD is thus an emerging public health priority. However, current research still lacks a comprehensive approach to account for the complex, multisystemic and heterogenic etiology of RD. We hypothesize that a multi-omic approach, combining phenomics, genomics and machine learning-based (ML) in large diverse samples, is the optimal approach to decipher the complex phenotype-genotype correspondence in RD and develop efficient and unbiased diagnostic and prognostic tools. In the BeNeXT project, we propose to use Turner syndrome (TS) as a model for RD. TS strategically fulfils all the requirements needed to achieve the goals of the BeNeXT multi-omic approach. TS is a rare condition that only affects females, with a prevalence of 1-5/10,000 live births, and presents with high genetic and phenotypic heterogeneity due to alterations of the X-chromosome. To unravel the heterogeneity of the syndrome, we will assemble an exceptionally large sample of patients from Spanish and admixed Latin-American populations (Brazil, Colombia, Argentina), along with first-degree relatives and unrelated controls from their reference populations. In this sample, an extensive set of phenomic and genomic data with diagnostic and prognostic potential will be gathered, including facial and body dysmorphologies, voice features, dermatoglyphs, clinical signs and symptoms, chromosomal alterations, genetic ancestry, as well as common and rare genetic and epigenetic variants. Deep phenomic and genomic characterization of the syndrome will enable us to analyze the phenotype-genotype correlation of TS. Our multi-omic approach will use this knowledge to i) identify clinical subtypes within TS, ii) combine phenomic, clinical and genomic biomarkers using ML, and iii) develop an end-to-end tool for automatic phenotyping and optimized diagnosis and prognosis of TS. This tool will be created as a smartphone application that could be useful and readily translated into clinical practice. Following an inter- and multidisciplinary research, BeNeXT will lead to the development of personalized and population-optimized algorithms for earlier treatment and improved clinical management through accurate prognosis of TS. Since RD present common characteristics and problems, our hypothesis is that the approach and tools developed in this project for TS could be adapted and applied in the future to other RD to improve their diagnosis and prognosis, reducing the diagnostic odyssey and inequalities that currently exist across the world.