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.

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.