eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Millions of somatic mutations in cancer lack functional understanding.
• The expression-based variant impact phenotyping (eVIP) method uses gene expression to characterize variant function.
• eVIP distinguishes between gain-of-function, loss-of-function, change-of-function, and neutral variants.

Purpose of the Study:

• Introduce eVIP2, an enhanced software for variant impact phenotyping.
• Enable pathway analysis (eVIP Pathways) and RNA-seq data integration.
• Characterize functional impacts of RNF43 frameshift variants in cancer.

Main Methods:

• Overexpressed wild-type (WT) and mutant RNF43 cDNAs in HEK293T cells.
• Utilized eVIP2's decision tree algorithm and RNA-seq data for variant impact prediction.
• Validated eVIP2 predictions using reporter assays and Western blot analysis.

Main Results:

• eVIP2 predicted RNF43 R117fs as loss-of-function and G659fs as change-of-function.
• eVIP Pathways identified altered signaling in KRAS, TNF-α, and hypoxia for G659fs.
• Reporter assays and Western blots confirmed G659fs as a change-of-function mutation impacting specific pathways.
• Patient data showed RNF43 variants correlate with activated RAS/MAPK signaling.

Conclusions:

• eVIP2 provides a comprehensive approach to determine gene variant function.
• Identified RNF43 G659fs as a change-of-function mutation with significant pathway alterations.
• eVIP2 is a valuable tool for variant interpretation in precision medicine.