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

被引：69

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.

引用

页数：18

共 50 条

[1] CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data
Liqing Tian
Yongjin Li
Michael N. Edmonson
Xin Zhou
Scott Newman
Clay McLeod
Andrew Thrasher
Yu Liu
Bo Tang
Michael C. Rusch
John Easton
Jing Ma
Eric Davis
Austyn Trull
J. Robert Michael
Karol Szlachta
Charles Mullighan
Suzanne J. Baker
James R. Downing
David W. Ellison
Jinghui Zhang
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