ArticleNucleic acids research2012
Functional impact bias reveals cancer drivers.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 205 papers, 3 of them syntheses that pooled it.
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Who cites it
205 citing papers in PubMed, 3 syntheses or guidelines pooled it, 358 citations in OpenAlex.
- Uncovering Oncogenic Mechanisms of Tumor Suppressor Genes in Breast Cancer Multi-Omics Data.International journal of molecular sciences · 2022Pooled it
- Multimodal Meta-Analysis of 1,494 Hepatocellular Carcinoma Samples Reveals Significant Impact of Consensus Driver Genes on Phenotypes.Clinical cancer research : an official journal of the American Association for Cancer Research · 2019Pooled it
- Computational Identification of Novel Stage-Specific Biomarkers in Colorectal Cancer Progression.PloS one · 2016Pooled it
- Identification of the Lynch syndrome and Lynch-like syndrome specific somatic mutations in microsatellite instability-high colorectal cancer cases.Surgery today · 2026Article
- TGBWDriver: A Cancer Driver Gene Identification Method Based on GraphSAGE and Bidirectional Weighted Feature Aggregation.International journal of molecular sciences · 2026Article
- PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data.PLoS computational biology · 2026Article
- Oncodrive3D: fast and accurate detection of structural clusters of somatic mutations under positive selection.Nucleic acids research · 2025Article
- Multimodal multiobjective optimization with structural network control principles to optimize personalized drug targets for drug discovery of individual patients.Briefings in bioinformatics · 2024Article
- The integrated molecular and histological analysis defines subtypes of esophageal squamous cell carcinoma.Nature communications · 2024Article
- CIBRA identifies genomic alterations with a system-wide impact on tumor biology.Bioinformatics (Oxford, England) · 2024Article
- A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer.International journal of molecular sciences · 2024Article
- Somatic mutations associate with clonal expansion of CD8Science advances · 2024Article
- A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer.bioRxiv : the preprint server for biology · 2024Article
- Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome.NAR cancer · 2024Article
- Haplotype-specific assembly of shattered chromosomes in esophageal adenocarcinomas.Cell genomics · 2024Article
- Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation.Molecular cancer research : MCR · 2024Article
- Exploring new roles for RNA-binding proteins in epigenetic and gene regulation.Current opinion in genetics & development · 2024Review
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.BMC bioinformatics · 2024Article
- Computational Approaches: A New Frontier in Cancer Research.Combinatorial chemistry & high throughput screening · 2024Review
145 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying cancer driver genes and pathways among all somatic mutations detected in a cohort of tumors is a key challenge in cancer genomics. Traditionally, this is done by prioritizing genes according to the recurrence of alterations that they bear. However, this approach has some known limitations, such as the difficulty to correctly estimate the background mutation rate, and the fact that it cannot identify lowly recurrently mutated driver genes. Here we present a novel approach, Oncodrive-fm, to detect candidate cancer drivers which does not rely on recurrence. First, we hypothesized that any bias toward the accumulation of variants with high functional impact observed in a gene or group of genes may be an indication of positive selection and can thus be used to detect candidate driver genes or gene modules. Next, we developed a method to measure this bias (FM bias) and applied it to three datasets of tumor somatic variants. As a proof of concept of our hypothesis we show that most of the highly recurrent and well-known cancer genes exhibit a clear FM bias. Moreover, this novel approach avoids some known limitations of recurrence-based approaches, and can successfully identify lowly recurrent candidate cancer drivers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.