Structure-based predictions broadly link transcription factor mutations to gene expression changes in cancers.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Thousands of transcription factor (TF) mutations occur in cancer, but their functional roles are often unknown.
• Understanding these mutations is crucial for cancer research and treatment development.

Purpose of the Study:

• To systematically predict the functional consequences of cancer-associated mutations in frequently mutated TFs.
• To develop and apply structure-based methods for analyzing TF mutations and their impact on protein-DNA interactions.

Main Methods:

• Utilized structure-based computational methods tailored for DNA-binding proteins.
• Analyzed mutations in TP53 and RUNX1, focusing on protein-DNA interactions and thermodynamic impacts.
• Validated predictions using The Cancer Genome Atlas (TCGA) data for TP53 genotype-expression associations.

Main Results:

• Structure-based analysis accurately explained mutation roles and prevalence in TP53 and RUNX1.
• Achieved higher specificity in identifying p53-regulated genes compared to existing methods.
• Demonstrated that TP53 missense mutation frequency correlates with thermodynamic impact on protein stability and DNA binding.

Conclusions:

• Structure and thermodynamics-based predictions are effective for inferring molecular phenotypes in cancer.
• These methods enhance the understanding of TF mutations and their contribution to cancer development.
• The approach offers a precise and scalable way to study aberrant molecular functions in complex diseases.