ArticleGenome research2012
De novo discovery of mutated driver pathways in cancer.
Article in Genome research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 206 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
206 citing papers in PubMed, 2 syntheses or guidelines pooled it, 455 citations in OpenAlex.
- 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
- Review
- Oncogenic p53 induces mitotic errors in lung cancer cells by recopying DNA replication forks conferring targetable proliferation advantage.Cell death and differentiation · 2026Article
- Precision Medicine Through Network Language: Integrating Clinical Insight and Data Expertise.Genes · 2026Review
- IBI-DT: a novel approach combining individualized Bayesian inference and decision tree for identifying cancer drivers and their interactions.Briefings in bioinformatics · 2025Article
- Multi-context modeling of driver pathways reveals common and specific mechanisms across 23 cancer types.PLoS computational biology · 2025Article
- Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data.BMC bioinformatics · 2025Article
- ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response.Journal of translational medicine · 2025Article
- iSoMAs: Finding isoform expression and somatic mutation associations in human cancers.PLoS computational biology · 2025Article
- Identification of cancer driver genes based on hierarchical weak consensus model.Health information science and systems · 2024Article
- Lung-MAP Next-Generation Sequencing Analysis of Advanced Squamous Cell Lung Cancers (SWOG S1400).Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2024Article
- Article
- A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer.bioRxiv : the preprint server for biology · 2024Article
- Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.BMC bioinformatics · 2024Article
- Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways.American journal of human genetics · 2024Article
- MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules.Bioinformatics (Oxford, England) · 2023Article
- Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm.Entropy (Basel, Switzerland) · 2023Article
- Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm.BMC bioinformatics · 2023Article
- SWEET: a single-sample network inference method for deciphering individual features in disease.Briefings in bioinformatics · 2023Article
146 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
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Next-generation DNA sequencing technologies are enabling genome-wide measurements of somatic mutations in large numbers of cancer patients. A major challenge in the interpretation of these data is to distinguish functional "driver mutations" important for cancer development from random "passenger mutations." A common approach for identifying driver mutations is to find genes that are mutated at significant frequency in a large cohort of cancer genomes. This approach is confounded by the observation that driver mutations target multiple cellular signaling and regulatory pathways. Thus, each cancer patient may exhibit a different combination of mutations that are sufficient to perturb these pathways. This mutational heterogeneity presents a problem for predicting driver mutations solely from their frequency of occurrence. We introduce two combinatorial properties, coverage and exclusivity, that distinguish driver pathways, or groups of genes containing driver mutations, from groups of genes with passenger mutations. We derive two algorithms, called Dendrix, to find driver pathways de novo from somatic mutation data. We apply Dendrix to analyze somatic mutation data from 623 genes in 188 lung adenocarcinoma patients, 601 genes in 84 glioblastoma patients, and 238 known mutations in 1000 patients with various cancers. In all data sets, we find groups of genes that are mutated in large subsets of patients and whose mutations are approximately exclusive. Our Dendrix algorithms scale to whole-genome analysis of thousands of patients and thus will prove useful for larger data sets to come from The Cancer Genome Atlas (TCGA) and other large-scale cancer genome sequencing projects.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.