Integrative Bayesian Analysis Identifies Rhabdomyosarcoma Disease Genes.
Integrative Bayesian Analysis Identifies Rhabdomyosarcoma Disease Genes.
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DOI:
10.1016/j.celrep.2018.06.006
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发表时间:
2018-07-03
期刊:
影响因子:
8.8
通讯作者:
Skapek SX
中科院分区:
文献类型:
--
作者:
Xu L;Zheng Y;Liu J;Rakheja D;Singleterry S;Laetsch TW;Shern JF;Khan J;Triche TJ;Hawkins DS;Amatruda JF;Skapek SX
Identifying oncogenic drivers and tumor suppressors remains a challenge for many forms of cancer, including rhabdomyosarcoma. Anticipating gene expression alterations encrypted by DNA copy-number variants to be particularly important, we developed a computational and experimental strategy incorporating a Bayesian algorithm and CRISPR/Cas9 “mini-pool” screen enabling both genome-scale assessment of disease genes and functional validation. The algorithm, called iExCN, identified 29 rhabdomyosarcoma drivers and suppressors enriched for cell cycle and nucleic acid binding activities. Functional studies showed many iExCN genes to represent rhabdomyosarcoma line-specific or shared vulnerabilities. Complementary experiments addressed modes of action and demonstrated coordinated repression of multiple iExCN genes during skeletal muscle differentiation. Analysis of two separate cohorts revealed that the number of iExCN genes harboring copy-number alterations correlates with survival. Our findings highlight rhabdomyosarcoma as a cancer in which multiple drivers influence disease biology and demonstrate a generalizable capacity for iExCN to unmask previously-unrecognized disease genes in cancer.
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影响因子:
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