iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes

被引：34
|
作者：
Dong, Chengliang [1 ,2 ]
Guo, Yunfei [1 ,2 ]
Yang, Hui [1 ,3 ]
He, Zeyu [4 ]
Liu, Xiaoming [5 ,6 ]
Wang, Kai [1 ,7 ]
机构：
[1] Univ Southern Calif, Zilkha Neurogenet Inst, Los Angeles, CA 90089 USA
[2] Univ Southern Calif, Dept Prevent Med, Biostat Grad Program, Los Angeles, CA 90089 USA
[3] Univ Southern Calif, Neurosci Grad Program, Los Angeles, CA 90089 USA
[4] NYU, Dept Comp Sci, New York, NY 10012 USA
[5] Univ Texas Hlth Sci Ctr Houston, Human Genet Ctr, Houston, TX 77030 USA
[6] Univ Texas Hlth Sci Ctr Houston, Div Epidemiol Human Genet & Environm Sci, Houston, TX 77030 USA
[7] Columbia Univ, Inst Genom Med, 630 W 168th St,Room 11-451, New York, NY 10032 USA
来源：
GENOME MEDICINE | 2016年 / 8卷
关键词：
Cancer genomics; Machine learning; Precision medicine; Precision oncology; TCGA; HIGH-THROUGHPUT ANNOTATION; SOMATIC MUTATIONS; KRAS MUTATIONS; PREDICTION; VARIANTS; RESOURCE; IDENTIFICATION; EVOLUTIONARY; CONSEQUENCES; LANDSCAPE;
D O I：
10.1186/s13073-016-0390-0
中图分类号：
Q3 [遗传学];
学科分类号：
071007 ; 090102 ;
摘要：
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).
引用
收藏
页数：22
相关论文
共 50 条
  • [41] Combing the genome for cancer genes
    Patrick Goymer
    Nature Reviews Genetics, 2007, 8 : 910 - 910
  • [42] Combing the genome for cancer genes
    Goymer, Patrick
    NATURE REVIEWS GENETICS, 2007, 8 (12) : 910 - 910
  • [43] Combing the genome for cancer genes
    Patrick Goymer
    Nature Reviews Cancer, 2007, 7 : 891 - 891
  • [44] Systematic illumination of druggable genes in cancer genomes
    Jiang, Junjie
    Yuan, Jiao
    Hu, Zhongyi
    Zhang, Youyou
    Zhang, Tianli
    Xu, Mu
    Long, Meixiao
    Fan, Yi
    Tanyi, Janos L.
    Montone, Kathleen T.
    Tavana, Omid
    Vonderheide, Robert H.
    Chan, Ho Man
    Hu, Xiaowen
    Zhang, Lin
    CELL REPORTS, 2022, 38 (08):
  • [45] Characterization of survival influential genes in cancer genomes
    Lin, Chen-Ching
    Chang, Yu-Lin
    Lin, Yin-Chen
    CANCER SCIENCE, 2018, 109 : 1053 - 1053
  • [46] Comprehensively analyzing the genetic alterations, and identifying key genes in ovarian cancer
    Tang, Qingling
    Atiq, Warda
    Mahnoor, Shaista
    Abdel-Maksoud, Mostafa A.
    Aufy, Mohammed
    Yaz, Hamid
    Zhu, Jianyu
    ONCOLOGY RESEARCH, 2023, 31 (02) : 141 - 156
  • [47] An integrated analysis of cancer genes in thyroid cancer
    Chai, Li
    Li, Jia
    Lv, Zhongwei
    ONCOLOGY REPORTS, 2016, 35 (02) : 962 - 970
  • [48] The search for cis-regulatory driver mutations in cancer genomes
    Poulos, Rebecca C.
    Sloane, Mathew A.
    Hesson, Luke B.
    Wong, Jason W. H.
    ONCOTARGET, 2015, 6 (32) : 32509 - 32525
  • [49] Identifying cancer driver genes in individual tumours
    Gillman, Rhys
    Field, Matt A.
    Schmitz, Ulf
    Karamatic, Rozemary
    Hebbard, Lionel
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2023, 21 : 5028 - 5038
  • [50] Cancer driver genes: a guilty by resemblance doctrine
    Ramsahai, Emilie
    Tripathi, Vrijesh
    John, Melford
    PEERJ, 2019, 7
12345