Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis

Abstract

Purpose: A primary barrier to improving exome sequencing diagnostic rates is the interpretation of variants of uncertain clinical significance. We aimed to determine the contribution of integrated untargeted metabolomics in the analysis of exome sequencing data by retrospective analysis of patients evaluated by both whole exome sequencing and untargeted metabolomics within the same clinical laboratory. Methods: Exome sequencing and untargeted metabolomic data were collected and analyzed for 170 patients. Pathogenic variants, likely pathogenic variants, and variants of uncertain significance in genes associated with a biochemical phenotype were extracted. Metabolomic data were evaluated to determine if these variants resulted in biochemical abnormalities which could be used to support their interpretation using current ACMG guidelines. Results: Metabolomic data contributed to the interpretation variants in 74 individuals (43.5%) over 73 different genes. The data allowed for the re-classification of 9 variants as likely benign, 15 variants as likely pathogenic, and 3 variants as pathogenic. Metabolomic data confirmed a clinical diagnosis in 21 cases, for a diagnostic rate of 12.3% in this population. Conclusion: Untargeted metabolomics can serve as a useful adjunct to exome sequencing by providing valuable functional data that may not otherwise be clinically available, resulting in improved variant classification.

Comparison of Untargeted Metabolomic Profiling vs Traditional Metabolic Screening to Identify Inborn Errors of Metabolism

“…Details of positive diagnosed cases and known and novel disease-related secondary analytes revealed by metabolomics 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 are reported in Table 2 and eTable 3 in the Supplement . Among the 655 families in the suggestive category ( Figure 1 B), 40 families (6.1%) were confirmed by further testing, including 36 families (90.0%) confirmed via molecular testing.…”

“… 12 , 13 , 14 , 21 , 39 Therefore, plasma metabolomic analysis should be considered as an initial screening approach when screening for IEMs, and it may also be useful to provide functional evidence for genomic variants of uncertain significance. 22 …”

Genetic basis of mitochondrial diseases

“…Based on mass spectrometry of small molecules, this OMICS technology enables the detection and quantification of thousands of small molecule metabolites in a single assay [133]. Although in a clinical setting, metabolomics has been applied for biomarker discovery [134], developments in its analytics and methods for data analysis expanded its application to the studies of disturbed metabolic pathways [135], as well as the variant interpretation [136] (Fig. 4).…”

Prioritizing disease-causing metabolic genes by integrating metabolomics with whole exome sequencing data

“…For this purpose, metabolomics is catching more and more interest since it has the potential to resolve the degree of pathogenicity for genetic variants which are expected to have an effect on the patient's metabolism, i.e. inborn errors of metabolism (IEM) (Kerkhofs, et al, 2020) (Alaimo, et al, 2020) (Linck, et al, 2020). Some strategies have already been developed for this purpose; Haijes et al applied expert knowledge to develop an algorithm that matches metabolic signatures obtained from metabolomics with expected metabolic signatures caused by each IEM, thereby ranking potential enzymatic deficiencies (Haijes, et al, 2020).…”