Title |
iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes
|
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Published in |
Genome Medicine, December 2016
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DOI | 10.1186/s13073-016-0390-0 |
Pubmed ID | |
Authors |
Chengliang Dong, Yunfei Guo, Hui Yang, Zeyu He, Xiaoming Liu, Kai Wang |
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 . |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 15 | 33% |
United Kingdom | 4 | 9% |
India | 3 | 7% |
Saudi Arabia | 2 | 4% |
Italy | 2 | 4% |
Ireland | 1 | 2% |
Germany | 1 | 2% |
Canada | 1 | 2% |
Switzerland | 1 | 2% |
Other | 5 | 11% |
Unknown | 10 | 22% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 30 | 67% |
Scientists | 13 | 29% |
Science communicators (journalists, bloggers, editors) | 1 | 2% |
Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | <1% |
United States | 1 | <1% |
Brazil | 1 | <1% |
Unknown | 108 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 21 | 19% |
Student > Ph. D. Student | 18 | 16% |
Student > Master | 15 | 14% |
Other | 9 | 8% |
Student > Bachelor | 6 | 5% |
Other | 25 | 23% |
Unknown | 17 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 32 | 29% |
Agricultural and Biological Sciences | 21 | 19% |
Computer Science | 11 | 10% |
Medicine and Dentistry | 9 | 8% |
Engineering | 4 | 4% |
Other | 9 | 8% |
Unknown | 25 | 23% |