ArticlePLoS computational biology2019
Finding driver mutations in cancer: Elucidating the role of background mutational processes.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Interpreting cancer genetics through a two-step "evolutionary cascade hypothesis": bridging neutral and selective perspectives.Journal of translational medicine · 2026Review
- An interpretable molecular framework for predicting cancer driver missense mutations.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Integrating evidence from protein domains to identify cancer driver mutations.Protein science : a publication of the Protein Society · 2026Article
- G34R cancer mutation alters the conformational ensemble and dynamics of the histone H3.3 tails.Nucleic acids research · 2026Article
- Implementation of Apriori Algorithm to Biomedical Data: Silent Mutations in GWAS-GAD Edition.Advances in experimental medicine and biology · 2026Article
- Cancer histone mutations impact protein binding and DNA repair with possible links to genomic instability.Nucleic acids research · 2025Article
- Noise-driven morphogenesis independent of transcriptional regulatory programs.bioRxiv : the preprint server for biology · 2025Article
- Comprehensive Analysis of Human Colorectal Cancers Harboring Polymerase Epsilon Mutations.International journal of molecular sciences · 2025Article
- Primary exploration of cell-free DNA in the plasma of patients with parathyroid neoplasms using next-generation sequencing.Cancer cell international · 2025Article
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- Transforming Cancer Care: Genomic Profiling, AI, and Next-Generation Drug Delivery Systems.Current genomics · 2025Review
- Understanding the characteristic behaviour of the wild-type and mutant structure of FLT3 protein by computational methods.Computational and structural biotechnology journal · 2025Article
- Advances in predicting breast cancer driver mutations: Tools for precision oncology (Review).International journal of molecular medicine · 2025Review
- Somatic mutations in 3929 HPV positive cervical cells associated with infection outcome and HPV type.Nature communications · 2024Article
- Tertiary Lymphoid Structures Gene Signature Predicts Prognosis and Immune Infiltration Analysis in Head and Neck Squamous Cell Carcinoma.Current genomics · 2024Article
- Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.BMC bioinformatics · 2024Article
- DNA polymerase ε and δ variants drive mutagenesis in polypurine tracts in human tumors.Cell reports · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Methods for Estimating Personal Disease Risk and Phylogenetic Diversity of Hematopoietic Stem Cells.Molecular biology and evolution · 2024Article
- Network analysis of driver genes in human cancers.Frontiers in bioinformatics · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation in patients remains one of the most reliable markers of mutation driver status. However, some mutations are more likely to occur than others due to differences in background mutation rates arising from various forms of infidelity of DNA replication and repair machinery, endogenous, and exogenous mutagens. We calculated nucleotide and codon mutability to study the contribution of background processes in shaping the observed mutational spectrum in cancer. We developed and tested probabilistic pan-cancer and cancer-specific models that adjust the number of mutation recurrences in patients by background mutability in order to find mutations which may be under selection in cancer. We showed that mutations with higher mutability values had higher observed recurrence frequency, especially in tumor suppressor genes. This trend was prominent for nonsense and silent mutations or mutations with neutral functional impact. In oncogenes, however, highly recurring mutations were characterized by relatively low mutability, resulting in an inversed U-shaped trend. Mutations not yet observed in any tumor had relatively low mutability values, indicating that background mutability might limit mutation occurrence. We compiled a dataset of missense mutations from 58 genes with experimentally validated functional and transforming impacts from various studies. We found that mutability of driver mutations was lower than that of passengers and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver mutation prediction. Even though no training on existing data was involved, our approach performed similarly or better to the state-of-the-art methods.
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