Statistical algorithms improve accuracy of gene fusion detection.
Statistical algorithms improve accuracy of gene fusion detection.
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DOI：
10.1093/nar/gkx453
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发表时间：
2017-07-27
影响因子：
14.9
通讯作者：
Salzman J
中科院分区：
文献类型：
--
作者：
Hsieh G;Bierman R;Szabo L;Lee AG;Freeman DE;Watson N;Sweet-Cordero EA;Salzman J
已知基因融合在肿瘤发病机制中发挥关键作用。然而，目前还不存在检测癌症基因融合的敏感且特定的算法。在本文中，我们提出了一种新的统计算法，MACHETE（错配比对 CHimEra 跟踪引擎），它实现了对 RNA-Seq 数据中基因融合的高度灵敏和特异性检测，包括与当前最先进技术相比的最高阳性预测值 (PPV)，如模拟数据中评估的那样。我们表明，性能最佳的已发布算法要么在阴性对照数据中发现大量融合，要么在黄金标准设置（例如 EWSR1-FLI1）中检测已知驱动融合的灵敏度较低。作为 MACHETE 在体内高精度发现新基因融合的原理证明，我们挖掘公共数据以发现并随后通过 PCR 验证卵巢癌细胞系 OVCAR3 中其他算法遗漏的新基因融合。这些结果凸显了通过将统计模型引入融合检测所实现的准确性的提高，并为公正地发现原发性肿瘤中潜在驱动和可药物基因融合铺平了道路。
Gene fusions are known to play critical roles in tumor pathogenesis. Yet, sensitive and specific algorithms to detect gene fusions in cancer do not currently exist. In this paper, we present a new statistical algorithm, MACHETE (Mismatched Alignment CHimEra Tracking Engine), which achieves highly sensitive and specific detection of gene fusions from RNA-Seq data, including the highest Positive Predictive Value (PPV) compared to the current state-of-the-art, as assessed in simulated data. We show that the best performing published algorithms either find large numbers of fusions in negative control data or suffer from low sensitivity detecting known driving fusions in gold standard settings, such as EWSR1-FLI1. As proof of principle that MACHETE discovers novel gene fusions with high accuracy in vivo, we mined public data to discover and subsequently PCR validate novel gene fusions missed by other algorithms in the ovarian cancer cell line OVCAR3. These results highlight the gains in accuracy achieved by introducing statistical models into fusion detection, and pave the way for unbiased discovery of potentially driving and druggable gene fusions in primary tumors.
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影响因子：
4.6
作者：
Kumar S;Vo AD;Qin F;Li H
通讯作者：
Li H
影响因子：
48
作者：
Engstrom, Par G.;Steijger, Tamara;Sipos, Botond;Grant, Gregory R.;Kahles, Andre;Raetsch, Gunnar;Goldman, Nick;Hubbard, Tim J.;Harrow, Jennifer;Guigo, Roderic;Bertone, Paul
通讯作者：
Bertone, Paul
影响因子：
3.7
作者：
Ortiz de Mendíbil I;Vizmanos JL;Novo FJ
通讯作者：
Novo FJ
影响因子：
64.8
作者：
Patch, Ann-Marie;Christie, Elizabeth L.;Bowtell, David D. L.
通讯作者：
Bowtell, David D. L.
MYB-QKI重排在血管中心神经胶质瘤中通过三方机制驱动肿瘤性。
影响因子：
30.8
作者：
Bandopadhayay P;Ramkissoon LA;Jain P;Bergthold G;Wala J;Zeid R;Schumacher SE;Urbanski L;O'Rourke R;Gibson WJ;Pelton K;Ramkissoon SH;Han HJ;Zhu Y;Choudhari N;Silva A;Boucher K;Henn RE;Kang YJ;Knoff D;Paolella BR;Gladden-Young A;Varlet P;Pages M;Horowitz PM;Federation A;Malkin H;Tracy AA;Seepo S;Ducar M;Van Hummelen P;Santi M;Buccoliero AM;Scagnet M;Bowers DC;Giannini C;Puget S;Hawkins C;Tabori U;Klekner A;Bognar L;Burger PC;Eberhart C;Rodriguez FJ;Hill DA;Mueller S;Haas-Kogan DA;Phillips JJ;Santagata S;Stiles CD;Bradner JE;Jabado N;Goren A;Grill J;Ligon AH;Goumnerova L;Waanders AJ;Storm PB;Kieran MW;Ligon KL;Beroukhim R;Resnick AC
通讯作者：
Resnick AC