Diagnosing pediatric mitochondrial disease: lessons from 2,000 exomes

Abstract

Abstract Background The spectrum of mitochondrial disease is genetically and phenotypically diverse, resulting from pathogenic variants in over 400 genes, with aerobic energy metabolism defects as a common denominator. Such heterogeneity poses a significant challenge in making an accurate diagnosis, critical for precision medicine. Methods In an international collaboration initiated by the European Network for Mitochondrial Diseases (GENOMIT) we recruited 2,023 pediatric patients at 11 specialist referral centers between October 2010 and January 2021, accumulating exome sequencing and HPO-encoded phenotype data. An exome-wide search for variants in known and potential novel disease genes, complemented by functional studies, followed ACMG guidelines. Results 1,109 cases (55%) received a molecular diagnosis, of which one fifth have potential disease-modifying treatments (236/1,109, 21%). Functional studies enabled diagnostic uplift from 36% to 55% and discovery of 62 novel disease genes. Pathogenic variants were identified within genes encoding mitochondrial proteins or RNAs in 801 cases (72%), while, given extensive phenotype overlap, the remainder involved proteins targeted to other cellular compartments. To delineate genotype-phenotype associations, our data was complemented with registry and literature data to develop “GENOMITexplorer”, an open access resource detailing patient- (n=3,940), gene- (n=427), and variant-level (n=1,492) associations (prokischlab.github.io/GENOMITexplorer/). Conclusions Reaching a molecular diagnosis was essential for implementation of precision medicine and clinical trial eligibility, underlining the need for genome-wide screening given inability to accurately define mitochondrial diseases clinically. Key to diagnostic success were functional studies, encouraging early acquisition of patient- derived tissues and routine integration of high-throughput functional data to improve patient care by uplifting diagnostic rate.