NIH Research Festival
Most mutations in cancer are neutral, with few mutations acting as drivers of cancer progression. To distinguish between mutations which reoccur due to high background mutation probability and mutations under selection in cancer progression, we developed probabilistic models to estimate DNA context-dependent background mutability per nucleotide and codon substitution as a result of different exogenous and endogenous mutagenic processes. We showed that observed frequency of cancer mutations follows expected mutability, especially for tumor suppressor genes. In oncogenes, highly recurring mutations were characterized by a lower mutability, showing a U-shape trend. Mutations not occurring in tumors were shown to have lower mutability than observed mutations, indicating that DNA mutability could be a limiting step in mutation occurrence. To check whether mutability helps to discriminate driver mutations from passengers, we compiled a set of missense mutations with experimentally validated functional and transforming impacts. Accounting for background mutability significantly improved performance of classification into cancer driver and passenger mutations compared to mutation recurrence alone, performing similar to state-of-the-art machine-learning methods, even though no training was involved.
Scientific Focus Area: Computational Biology
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