Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.
Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.
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DOI：
10.1016/j.cels.2016.06.006
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发表时间：
2016-08
期刊：
影响因子：
9.3
通讯作者：
Baliga NS
中科院分区：
文献类型：
--
作者：
Plaisier CL;O'Brien S;Bernard B;Reynolds S;Simon Z;Toledo CM;Ding Y;Reiss DJ;Paddison PJ;Baliga NS
我们开发了转录因子(TF)靶基因数据库和系统遗传网络分析(SYGNAL)管道，以从多组和临床患者数据中破译转录调控网络，并将这些工具应用于422例多形性胶质母细胞瘤(GBM)患者。由此产生的gbmSYGNAL网络预测了112个体细胞突变基因或途径，这些基因或途径通过74个TF和37个miRNAs(67个以前不与GBM相关)来失调237个与患者生存或致癌过程相关的不同的共同调节基因模块。通过CRISPR-Cas9和Small-RNA扰动研究，这些调控预测与癌症表型相关，也证明了GBM的特异性。GbmSYGNAL网络预测的两个成对组合(ETV6-NFKB1和romidessin-miR-486-3p)具有协同抗增殖作用。最后，该网络揭示了NF1和PIK3CA的突变调节了IRF1介导的MHC-I类抗原处理和递呈基因，从而增加了肿瘤淋巴细胞的侵袭并恶化了预后。重要的是，SYGNAL广泛适用于整合来自其他人类队列的基因组和转录组测量。
We developed the transcription factor (TF)-target gene database and the SYstems Genetics Network AnaLysis (SYGNAL) pipeline to decipher transcriptional regulatory networks from multi-omic and clinical patient data, and applied these tools to 422 patients with glioblastoma multiforme (GBM). The resulting gbmSYGNAL network predicted 112 somatically mutated genes or pathways that act through 74 TFs and 37 miRNAs (67 not previously associated with GBM) to dysregulate 237 distinct co-regulated gene modules associated with patient survival or oncogenic processes. The regulatory predictions were associated to cancer phenotypes using CRISPR-Cas9 and small-RNA perturbation studies, and also demonstrated GBM specificity. Two pairwise combinations (ETV6–NFKB1 and romidepsin–miR-486-3p) predicted by the gbmSYGNAL network had synergistic anti-proliferative effects. Finally, the network revealed that mutations in NF1 and PIK3CA modulate IRF1-mediated regulation of MHC class I antigen processing and presentation genes to increase tumor lymphocyte infiltration and worsen prognosis. Importantly, SYGNAL is widely applicable for integrating genomic and transcriptomic measurements from other human cohorts.
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