Mutation impact on mRNA versus protein expression across human cancers.

Area of Science:

• Genomics and Proteomics
• Cancer Biology
• Molecular Oncology

Background:

• Cancer mutations are assumed to alter proteins, but their impact on protein expression is understudied.
• mRNA and protein levels often correlate moderately due to translation and degradation.
• Proteogenomic data allows systematic analysis of mutation effects on both mRNA and protein levels.

Purpose of the Study:

• To systematically analyze the effects of somatic mutations on mRNA and protein abundance.
• To identify mutations with distinct impacts on molecular expression levels.
• To leverage proteogenomic datasets for cancer mutation analysis.

Main Methods:

• Comprehensive analysis of mutation impacts on mRNA and protein expression in 953 cancer cases.
• Utilized paired genomics and global proteomic profiling across 6 cancer types.
• Developed a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs).

Main Results:

• Validated protein-level impacts for 47.2% of somatic expression quantitative trait loci (seQTLs).
• Identified mutations (e.g., NF1, MAP2K4, TP53) with disproportionate effects on protein abundance.
• TP53 missenses linked to high tumor protein levels were more likely functional based on MAVE data.

Conclusions:

• Somatic mutations can have distinct impacts on mRNA versus protein levels.
• Integrating proteogenomic data is crucial for identifying functionally significant cancer mutations.
• Provides a framework for prioritizing mutations for validation and therapeutic targeting.