Accurate and efficient detection of gene fusions from RNA sequencing data.
Accurate and efficient detection of gene fusions from RNA sequencing data.
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从RNA测序数据中准确且高效地检测基因融合。
DOI：
10.1101/gr.257246.119
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发表时间：
2021-03
期刊：
影响因子：
7
通讯作者：
Brors B
中科院分区：
文献类型：
--
作者：
Uhrig S;Ellermann J;Walther T;Burkhardt P;Fröhlich M;Hutter B;Toprak UH;Neumann O;Stenzinger A;Scholl C;Fröhling S;Brors B
从RNA测序数据中识别基因融合是癌症研究和精准肿瘤学中的一项常规任务。然而，尽管有许多计算工具可用，融合检测仍然具有挑战性。现有方法存在预测准确性差且对计算要求高的问题。我们开发了Arriba，这是一种具有高灵敏度和短运行时间的新型融合检测算法。当应用于大量已发表的胰腺癌样本（n = 803）时，Arriba识别出了多种驱动基因融合，其中许多影响可成药蛋白，包括ALK、BRAF、FGFR2、NRG1、NTRK1、NTRK3、RET和ROS1。这些融合与KRAS野生型肿瘤显著相关，并涉及刺激MAPK信号通路的蛋白质，表明它们替代了KRAS中的激活突变。此外，我们在细胞实验中证实了两种新型融合（RRBP1 - RAF1和RASGRP1 - ATP1A1）的转化潜力。这些结果显示了Arriba在基础癌症研究和临床转化方面的实用性。
The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (n = 803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS. In addition, we confirmed the transforming potential of two novel fusions, RRBP1-RAF1 and RASGRP1-ATP1A1, in cellular assays. These results show Arriba's utility in both basic cancer research and clinical translation.
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影响因子：
3.7
作者：
Guzmán C;Bagga M;Kaur A;Westermarck J;Abankwa D
通讯作者：
Abankwa D
DOI：
10.1056/nejmoa1714448
发表时间：
2018-02-22
期刊：
The New England journal of medicine
影响因子：
--
作者：
Drilon A;Laetsch TW;Kummar S;DuBois SG;Lassen UN;Demetri GD;Nathenson M;Doebele RC;Farago AF;Pappo AS;Turpin B;Dowlati A;Brose MS;Mascarenhas L;Federman N;Berlin J;El-Deiry WS;Baik C;Deeken J;Boni V;Nagasubramanian R;Taylor M;Rudzinski ER;Meric-Bernstam F;Sohal DPS;Ma PC;Raez LE;Hechtman JF;Benayed R;Ladanyi M;Tuch BB;Ebata K;Cruickshank S;Ku NC;Cox MC;Hawkins DS;Hong DS;Hyman DM
通讯作者：
Hyman DM
影响因子：
6.6
作者：
Kirby, Marie K.;Ramaker, Ryne C.;Myers, Richard M.
通讯作者：
Myers, Richard M.
影响因子：
8.8
作者：
Gao Q;Liang WW;Foltz SM;Mutharasu G;Jayasinghe RG;Cao S;Liao WW;Reynolds SM;Wyczalkowski MA;Yao L;Yu L;Sun SQ;Fusion Analysis Working Group;Cancer Genome Atlas Research Network;Chen K;Lazar AJ;Fields RC;Wendl MC;Van Tine BA;Vij R;Chen F;Nykter M;Shmulevich I;Ding L
通讯作者：
Ding L
DOI：
10.1158/1078-0432.ccr-17-2994
发表时间：
2018-03-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Aung KL;Fischer SE;Denroche RE;Jang GH;Dodd A;Creighton S;Southwood B;Liang SB;Chadwick D;Zhang A;O'Kane GM;Albaba H;Moura S;Grant RC;Miller JK;Mbabaali F;Pasternack D;Lungu IM;Bartlett JMS;Ghai S;Lemire M;Holter S;Connor AA;Moffitt RA;Yeh JJ;Timms L;Krzyzanowski PM;Dhani N;Hedley D;Notta F;Wilson JM;Moore MJ;Gallinger S;Knox JJ
通讯作者：
Knox JJ