ArticleNature communications2020
Combined burden and functional impact tests for cancer driver discovery using DriverPower.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research.European journal of human genetics : EJHG · 2021Trial
- Global Geo-Pharmacogenomics: Environmental Mutational Signatures Drive Population-Level Heterogeneity in Anticancer Drug Response.Journal of xenobiotics · 2026Article
- Characterization of ADGRG6 as a potential molecular oncotarget of pancreatic cancer.Cell death & disease · 2026Article
- Topographical archetypes of somatic mutagenesis in cancer.bioRxiv : the preprint server for biology · 2026Article
- MODCAN: driver gene identification based on multi-omics features and differential co-association networks for tumor subtypes.BMC bioinformatics · 2026Article
- A new algorithm Precision OncoPanels (PrOPs) identifies short individualized actionable panels that can guide cancer treatment: a pan-cancer analysis of TCGA cohorts.NAR genomics and bioinformatics · 2025Article
- In silico generation of synthetic cancer genomes using generative AI.Cell genomics · 2025Article
- Mutation of CMTR2 in Lung Adenocarcinoma Alters RNA Alternative Splicing and Reveals Therapeutic Vulnerabilities.Nature communications · 2025Article
- Large-scale multiomic analysis identifies non-coding somatic driver mutations and nominatesGut · 2025Article
- The genomic landscape of relapsed infant and childhood KMT2A-rearranged acute leukemia.Nature communications · 2025Article
- The Noncoding Mutational Landscape of Pancreatic Cancer Reveals Recurrent Somatic Mutations in Enhancer Regions.Cancer research communications · 2025Article
- Whole-genome sequencing reveals three follicular lymphoma subtypes with distinct cell of origin and patient outcomes.Cell reports. Medicine · 2025Article
- A core driver gene set identified based on geMER reveals its potential driver mechanism in pan-cancer.NPJ precision oncology · 2025Article
- A comprehensive database for identifying and interpreting ctDNA driver genes and variants in cancer.Scientific data · 2025Article
- Integrating gene mutation spectra from tumors and the general population with gene expression topological networks to identify novel cancer driver genes.Human genetics · 2025Article
- Pan-cancer analysis shapes the understanding of cancer biology and medicine.Cancer communications (London, England) · 2025Review
- Element-specific estimation of background mutation rates in whole cancer genomes through transfer learning.NPJ precision oncology · 2025Article
- Domain-specific embeddings uncover latent genetics knowledge.bioRxiv : the preprint server for biology · 2025Article
- The mutational landscape and functional effects of noncoding ultraconserved elements in human cancers.Science advances · 2025Article
- Incorporating mutational heterogeneity to identify genes that are enriched for synonymous mutations in cancer.BMC bioinformatics · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower's background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.
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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 Socratic graph.