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Updated: Sep 22, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach
Published on: November 5, 2019
Structural bioinformatics enhances the interpretation of somatic mutations in KDM6A found in human cancers
Young-In Chi1,2, Timothy J Stodola1, Thiago M De Assuncao1,2
1Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, United States.
Abstract:
The histone demethylase KDM6A has recently elicited significant attention because its mutations are associated with a rare congenital disorder (Kabuki syndrome) and various types of human cancers. However, distinguishing KDM6A mutations that are deleterious to the enzyme and their underlying mechanisms of dysfunction remain to be fully understood. Here, we report the results from a multi-tiered approach evaluating the impact of 197 KDM6A somatic mutations using information derived from combining conventional genomics data with computational biophysics. This comprehensive approach incorporates multiple scores derived from alterations in protein sequence, structure, and molecular dynamics. Using this method, we classify the KDM6A mutations into 136 damaging variants (69.0%), 32 tolerated variants (16.2%), and 29 variants of uncertain significance (VUS, 14.7%), which is a significant improvement from the previous classification based on the conventional tools (over 40% VUS). We further classify the damaging variants into 15 structural variants (SV), 88 dynamic variants (DV), and 33 structural and dynamic variants (SDV). Comparison with variant scoring methods used in current clinical diagnosis guidelines demonstrates that our approach provides a more comprehensive evaluation of damaging potential and reveals mechanisms of dysfunction. Thus, these results should be taken into consideration for clinical assessment of the damaging potential of each mutation, as they provide hypotheses for experimental validation and critical information for the development of mutant-specific drugs to fight diseases caused by KDM6A dysfunctions.
Insights
This study introduces a computational method to assess KDM6A mutations, identifying 136 damaging variants linked to Kabuki syndrome and cancer. This approach improves mutation classification accuracy for disease research.
Area of Science:
- Genetics
- Biophysics
- Computational Biology
Background:
- Mutations in the histone demethylase KDM6A are linked to Kabuki syndrome and human cancers.
- Understanding the functional impact and mechanisms of KDM6A dysfunction is crucial but challenging.
Purpose of the Study:
- To develop and apply a multi-tiered computational approach to comprehensively evaluate the impact of KDM6A somatic mutations.
- To improve the classification accuracy of KDM6A variants compared to conventional methods.
Main Methods:
- Integrated conventional genomics data with computational biophysics for 197 KDM6A somatic mutations.
- Incorporated scores from protein sequence, structure, and molecular dynamics alterations.
- Classified variants into damaging, tolerated, and variants of uncertain significance (VUS).
Main Results:
- Classified 136 (69.0%) damaging, 32 (16.2%) tolerated, and 29 (14.7%) VUS KDM6A mutations.
- Significantly reduced VUS rate compared to conventional tools (improved from >40% VUS).
- Further categorized damaging variants into structural, dynamic, and combined structural/dynamic types.
Conclusions:
- The developed computational approach offers a more comprehensive evaluation of KDM6A mutation damaging potential.
- Provides insights into mechanisms of KDM6A dysfunction.
- Results aid clinical assessment, experimental validation, and development of targeted therapies for KDM6A-related diseases.
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