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蛋白质相互作用网络中的畸变中心突出了癌症中可操作的靶点。

Aberration hubs in protein interaction networks highlight actionable targets in cancer.

作者信息

Karimzadeh Mehran, Jandaghi Pouria, Papadakis Andreas I, Trainor Sebastian, Rung Johan, Gonzàlez-Porta Mar, Scelo Ghislaine, Vasudev Naveen S, Brazma Alvis, Huang Sidong, Banks Rosamonde E, Lathrop Mark, Najafabadi Hamed S, Riazalhosseini Yasser

机构信息

Department of Human Genetics, McGill University, Montreal, QC H3A 1B1, Canada.

McGill University and Genome Quebec Innovation Centre, Montreal, QC H3A 0G1, Canada.

出版信息

Oncotarget. 2018 May 18;9(38):25166-25180. doi: 10.18632/oncotarget.25382.

DOI:10.18632/oncotarget.25382
PMID:29861861
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5982744/
Abstract

Despite efforts for extensive molecular characterization of cancer patients, such as the international cancer genome consortium (ICGC) and the cancer genome atlas (TCGA), the heterogeneous nature of cancer and our limited knowledge of the contextual function of proteins have complicated the identification of targetable genes. Here, we present Aberration Hub Analysis for Cancer (AbHAC) as a novel integrative approach to pinpoint aberration hubs, i.e. individual proteins that interact extensively with genes that show aberrant mutation or expression. Our analysis of the breast cancer data of the TCGA and the renal cancer data from the ICGC shows that aberration hubs are involved in relevant cancer pathways, including factors promoting cell cycle and DNA replication in basal-like breast tumors, and Src kinase and VEGF signaling in renal carcinoma. Moreover, our analysis uncovers novel functionally relevant and actionable targets, among which we have experimentally validated abnormal splicing of spleen tyrosine kinase as a key factor for cell proliferation in renal cancer. Thus, AbHAC provides an effective strategy to uncover novel disease factors that are only identifiable by examining mutational and expression data in the context of biological networks.

摘要

尽管国际癌症基因组联盟(ICGC)和癌症基因组图谱(TCGA)等机构为全面分子表征癌症患者付出了诸多努力,但癌症的异质性以及我们对蛋白质背景功能的有限了解,使得可靶向基因的识别变得复杂。在此,我们提出癌症畸变枢纽分析(AbHAC),这是一种全新的综合方法,用于精准定位畸变枢纽,即与显示异常突变或表达的基因广泛相互作用的单个蛋白质。我们对TCGA的乳腺癌数据和ICGC的肾癌数据进行分析后发现,畸变枢纽参与了相关癌症通路,包括在基底样乳腺肿瘤中促进细胞周期和DNA复制的因子,以及在肾癌中涉及的Src激酶和VEGF信号传导。此外,我们的分析还揭示了新的功能相关且可操作的靶点,其中我们通过实验验证了脾酪氨酸激酶的异常剪接是肾癌细胞增殖的关键因素。因此,AbHAC提供了一种有效的策略,以揭示那些只有在生物网络背景下通过检查突变和表达数据才能识别的新型疾病因子。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fa9/5982744/5639c5d4319e/oncotarget-09-25166-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fa9/5982744/7ed749c2d334/oncotarget-09-25166-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fa9/5982744/a12eaca68896/oncotarget-09-25166-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fa9/5982744/535056e176d8/oncotarget-09-25166-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fa9/5982744/5639c5d4319e/oncotarget-09-25166-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fa9/5982744/7ed749c2d334/oncotarget-09-25166-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fa9/5982744/a12eaca68896/oncotarget-09-25166-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fa9/5982744/535056e176d8/oncotarget-09-25166-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fa9/5982744/5639c5d4319e/oncotarget-09-25166-g004.jpg

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Expression of DRD2 Is Increased in Human Pancreatic Ductal Adenocarcinoma and Inhibitors Slow Tumor Growth in Mice.DRD2 在人胰腺导管腺癌中表达增加,抑制剂可减缓小鼠肿瘤生长。
Gastroenterology. 2016 Dec;151(6):1218-1231. doi: 10.1053/j.gastro.2016.08.040. Epub 2016 Aug 28.
2
Proteogenomics connects somatic mutations to signalling in breast cancer.蛋白质基因组学将体细胞突变与乳腺癌中的信号传导联系起来。
Nature. 2016 Jun 2;534(7605):55-62. doi: 10.1038/nature18003. Epub 2016 May 25.
3
A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome.
一种疾病模块检测(DIAMOnD)算法,源自对人类相互作用组中疾病蛋白连接模式的系统分析。
PLoS Comput Biol. 2015 Apr 8;11(4):e1004120. doi: 10.1371/journal.pcbi.1004120. eCollection 2015 Apr.
4
limma powers differential expression analyses for RNA-sequencing and microarray studies.limma为RNA测序和微阵列研究提供差异表达分析的动力。
Nucleic Acids Res. 2015 Apr 20;43(7):e47. doi: 10.1093/nar/gkv007. Epub 2015 Jan 20.
5
Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes.泛癌网络分析确定了跨通路和蛋白质复合物的罕见体细胞突变组合。
Nat Genet. 2015 Feb;47(2):106-14. doi: 10.1038/ng.3168. Epub 2014 Dec 15.
6
Resistance to sunitinib in renal cell carcinoma: From molecular mechanisms to predictive markers and future perspectives.肾细胞癌中对舒尼替尼的耐药性:从分子机制到预测标志物及未来展望
Biochim Biophys Acta. 2015 Jan;1855(1):1-16. doi: 10.1016/j.bbcan.2014.11.002. Epub 2014 Nov 11.
7
Variation in genomic landscape of clear cell renal cell carcinoma across Europe.欧洲透明细胞肾细胞癌基因组景观的变异。
Nat Commun. 2014 Oct 29;5:5135. doi: 10.1038/ncomms6135.
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Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis.通过网络富集分析区分个体癌症基因组中的驱动突变和乘客突变。
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9
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Br J Cancer. 2014 May 27;110(11):2700-7. doi: 10.1038/bjc.2014.225. Epub 2014 May 1.
10
Principles and methods of integrative genomic analyses in cancer.癌症综合基因组分析的原则和方法。
Nat Rev Cancer. 2014 May;14(5):299-313. doi: 10.1038/nrc3721.