期刊
GENOME MEDICINE
卷 8, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s13073-016-0390-0
关键词
Cancer genomics; Machine learning; Precision medicine; Precision oncology; TCGA
资金
- NIH [HG006465, MH108728]
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).
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