Reannotation of cancer mutations based on expressed RNA transcripts reveals functional non-coding mutations in...

Area of Science:

• Genomics
• Cancer Biology
• Molecular Oncology

Background:

• The role of synonymous mutations in cancer development is not well understood.
• Accurate mutation annotation is crucial for understanding cancer pathogenesis.
• Current annotation methods may misclassify non-coding mutations as coding.

Purpose of the Study:

• To develop a method for detecting synonymous and missense mutation clusters in cancer genomics data.
• To identify and characterize misannotated mutations in melanoma.
• To investigate the functional impact and clinical relevance of these mutations.

Main Methods:

• Development of a computational method to detect mutation clusters.
• Analysis of public cancer genomics (DNA) and transcriptomics (RNA) data.
• Functional validation using CRISPR-Cas9 in primary melanocyte models.
• Correlation analysis with clinical data on immunotherapy response.

Main Results:

• 22% of mutation clusters in melanoma were misannotated as coding due to unexpressed reference transcripts.
• Identified functional non-coding mutations targeting the promoter of IRF3 and BCL2L12, leading to downregulation of IRF3, BCL2L12, and TP53.
• These mutations were associated with a poorer response to immunotherapy in melanoma patients.

Conclusions:

• Synonymous mutations can be functional non-coding mutations, impacting cancer gene expression and clinical outcomes.
• Integrating DNA and RNA sequencing data is essential for accurate mutation annotation.
• A novel method for automated mutation annotation based on expressed transcripts is proposed.