Related Experiment Video
Updated: Jul 9, 2025
Genome Editing and Directed Differentiation of hPSCs for Interrogating Lineage Determinants in Human Pancreatic Development
Published on: March 5, 2017
Saturation genome editing of DDX3X clarifies pathogenicity of germline and somatic variation
Elizabeth J Radford1,2, Hong-Kee Tan1, Malin H L Andersson1
1Wellcome Sanger Institute, Hinxton, CB10 1SA, UK.
Abstract:
Loss-of-function of DDX3X is a leading cause of neurodevelopmental disorders (NDD) in females. DDX3X is also a somatically mutated cancer driver gene proposed to have tumour promoting and suppressing effects. We perform saturation genome editing of DDX3X, testing in vitro the functional impact of 12,776 nucleotide variants. We identify 3432 functionally abnormal variants, in three distinct classes. We train a machine learning classifier to identify functionally abnormal variants of NDD-relevance. This classifier has at least 97% sensitivity and 99% specificity to detect variants pathogenic for NDD, substantially out-performing in silico predictors, and resolving up to 93% of variants of uncertain significance. Moreover, functionally-abnormal variants can account for almost all of the excess nonsynonymous DDX3X somatic mutations seen in DDX3X-driven cancers. Systematic maps of variant effects generated in experimentally tractable cell types have the potential to transform clinical interpretation of both germline and somatic disease-associated variation.
Insights
Loss-of-function mutations in the DDX3X gene cause neurodevelopmental disorders (NDD) and influence cancer. This study mapped variant effects, creating a classifier to accurately identify disease-causing DDX3X mutations for NDD and cancer.
Area of Science:
- Genetics
- Molecular Biology
- Bioinformatics
Background:
- Loss-of-function mutations in DDX3X are a primary cause of neurodevelopmental disorders (NDD) in females.
- DDX3X is also implicated as a cancer driver gene with proposed tumor-promoting and suppressing roles.
Purpose of the Study:
- To systematically map the functional impact of DDX3X variants using saturation genome editing.
- To develop a machine learning classifier for identifying DDX3X variants relevant to NDD and cancer.
Main Methods:
- Performed saturation genome editing of DDX3X to test the in vitro functional impact of 12,776 nucleotide variants.
- Trained a machine learning classifier to identify functionally abnormal variants associated with NDD.
- Analyzed the contribution of functionally abnormal variants to somatic mutations in DDX3X-driven cancers.
Main Results:
- Identified 3,432 functionally abnormal DDX3X variants across three distinct classes.
- Developed a classifier with at least 97% sensitivity and 99% specificity for detecting NDD-pathogenic variants, outperforming existing predictors and resolving variants of uncertain significance.
- Demonstrated that functionally abnormal variants explain nearly all excess nonsynonymous DDX3X somatic mutations in relevant cancers.
Conclusions:
- Systematic mapping of DDX3X variant effects provides a powerful tool for clinical interpretation.
- The developed classifier significantly improves the identification of pathogenic DDX3X variants for NDD.
- These findings have implications for understanding DDX3X's role in both germline and somatic diseases, including cancer.
