CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data

被引：77

作者：

Tian, Liqing ^{[1
]}

Li, Yongjin ^{[1
]}

Edmonson, Michael N. ^{[1
]}

Zhou, Xin ^{[1
]}

Newman, Scott ^{[1
]}

McLeod, Clay ^{[1
]}

Thrasher, Andrew ^{[1
]}

Liu, Yu ^{[1
,2
]}

Tang, Bo ^{[3
]}

Rusch, Michael C. ^{[1
]}

Easton, John ^{[1
]}

Ma, Jing ^{[3
]}

Davis, Eric ^{[1
]}

Trull, Austyn ^{[1
]}

Michael, J. Robert ^{[1
]}

Szlachta, Karol ^{[1
]}

Mullighan, Charles ^{[3
]}

Baker, Suzanne J. ^{[4
]}

Downing, James R. ^{[3
]}

Ellison, David W. ^{[3
]}

Zhang, Jinghui ^{[1
]}

机构：

[1] St Jude Childrens Res Hosp, Dept Computat Biol, 262 Danny Thomas Pl, Memphis, TN 38105 USA

[2] Shanghai Jiao Tong Univ, Sch Med, Shanghai Childrens Med Ctr, Pediat Translat Med Inst, Shanghai, Peoples R China

[3] St Jude Childrens Res Hosp, Dept Pathol, 262 Danny Thomas Pl, Memphis, TN 38105 USA

[4] St Jude Childrens Res Hosp, Dept Dev Neurobiol, 262 Danny Thomas Pl, Memphis, TN 38105 USA

来源：

GENOME BIOLOGY | 2020年 / 21卷 / 01期

基金：

美国国家卫生研究院;

关键词：

Gene fusion; Precision oncology; Fusion visualization; RNA-seq; Cloud computing; GENE FUSIONS; TANDEM DUPLICATION; GENOMIC LANDSCAPE; REARRANGEMENT; MUTANTS; KINASE;

D O I：

10.1186/s13059-020-02043-x

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

To discover driver fusions beyond canonical exon-to-exon chimeric transcripts, we develop CICERO, a local assembly-based algorithm that integrates RNA-seq read support with extensive annotation for candidate ranking. CICERO outperforms commonly used methods, achieving a 95% detection rate for 184 independently validated driver fusions including internal tandem duplications and other non-canonical events in 170 pediatric cancer transcriptomes. Re-analysis of TCGA glioblastoma RNA-seq unveils previously unreported kinase fusions (KLHL7-BRAF) and a 13% prevalence of EGFR C-terminal truncation. Accessible via standard or cloud-based implementation, CICERO enhances driver fusion detection for research and precision oncology. The CICERO source code is available at https://github.com/stjude/Cicero.

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