ArticleGenome medicine2016
iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes.
Article in Genome medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03192501 (Multicentre Perspective Non-interventional Study of Survival Benefits of iCAGES-guided Therapy in Contrast to Standard Therapy or IHC-guided Therapy for Advanced Cancers), which is not on this map. Cited by 33 papers.
What it found
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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.
Multicentre Perspective Non-interventional Study of Survival Benefits of iCAGES-guided Therapy in Contrast to Standard Therapy or IHC-guided Therapy for Advanced Cancers
Who cites it
33 citing papers in PubMed, 61 citations in OpenAlex.
- Advancing precision oncology with AI-powered genomic analysis.Frontiers in pharmacology · 2025Review
- SMG: self-supervised masked graph learning for cancer gene identification.Briefings in bioinformatics · 2023Article
- A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants.International journal of molecular sciences · 2023Article
- PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology.Bioinformatics (Oxford, England) · 2023Article
- Identifying cancer driver genes in individual tumours.Computational and structural biotechnology journal · 2023Review
- TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer.Life (Basel, Switzerland) · 2022Article
- Driver gene detection through Bayesian network integration of mutation and expression profiles.Bioinformatics (Oxford, England) · 2022Article
- CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.Science advances · 2022Article
- Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource.Genome biology · 2022Review
- Molecular-based precision oncology clinical decision making augmented by artificial intelligence.Emerging topics in life sciences · 2021Review
- driveR: a novel method for prioritizing cancer driver genes using somatic genomics data.BMC bioinformatics · 2021Article
- Pan-cancer detection of driver genes at the single-patient resolution.Genome medicine · 2021Article
- Machine learning analysis of TCGA cancer data.PeerJ. Computer science · 2021Article
- Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data.Scientific reports · 2020Article
- Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns.Genome medicine · 2020Article
- A novel nonsense mutation in TNNT2 in a Chinese pedigree with hypertrophic cardiomyopathy: A case report.Medicine · 2020Article
- Comprehensive characterization of driver genes in diffuse large B cell lymphoma.Oncology letters · 2020Article
- Targeted molecular profiling of genetic alterations in colorectal cancer using next-generation sequencing.Oncology letters · 2020Article
- Identification of osteosarcoma driver genes using a network method.Oncology letters · 2020Article
- Molecular Subtyping and Prognostic Assessment Based on Tumor Mutation Burden in Patients with Lung Adenocarcinomas.International journal of molecular sciences · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Cancer results from the acquisition of somatic driver mutations. Several computational tools can predict driver genes from population-scale genomic data, but tools for analyzing personal cancer genomes are underdeveloped. Here we developed iCAGES, a novel statistical framework that infers driver variants by integrating contributions from coding, non-coding, and structural variants, identifies driver genes by combining genomic information and prior biological knowledge, then generates prioritized drug treatment. Analysis on The Cancer Genome Atlas (TCGA) data showed that iCAGES predicts whether patients respond to drug treatment (P = 0.006 by Fisher's exact test) and long-term survival (P = 0.003 from Cox regression). iCAGES is available at http://icages.wglab.org .
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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.