Estimation of Neutral Mutation Rates and Quantification of Somatic Variant Selection Using cancereffectsizeR.

Area of Science:

• Oncology
• Computational Biology
• Genetics

Background:

• Somatic mutations drive cancer progression, but quantifying their specific effects remains challenging.
• Distinguishing driver mutations from passenger mutations is crucial for understanding cancer biology.
• Existing methods may not fully capture the complex interplay of genetic variants in cancer.

Purpose of the Study:

• To develop a computational tool for estimating the effects of somatic mutations in cancer.
• To enable researchers to quantify oncogenic and epistatic effects of genetic variants.
• To provide a framework for analyzing mutation rates and selection models in cancer cohorts.

Main Methods:

• Developed `cancereffectsizeR`, an R package for organizing somatic variant data.
• Implemented functions for mutational signature analysis and site-specific mutation rate calculation.
• Enabled estimation of single nucleotide, gene-level, and epistatic effects, including custom models.

Main Results:

• Validated `cancereffectsizeR` using a pan-cancer dataset, showing pathogenic variants have significantly higher effects.
• Demonstrated that `cancer effect size` is a superior predictor of pathogenic status compared to variant prevalence or functional impact scores.
• Revealed that driver mutations in BRAF, EGFR, or KRAS can reduce selection for alterations in other key genes in lung adenocarcinoma.

Conclusions:

• `cancereffectsizeR` provides a robust method for comprehensive estimation of somatic mutation effects in cancer.
• The tool offers insights into oncogenic trajectories and can inform cancer prognosis and treatment strategies.
• The package supports various genome builds and species, with accompanying documentation and a public code repository.