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

[11] IDENTIFICATION OF NOVEL GENE FUSIONS IN ACUTE MYELOID LEUKEMIA WITH COMPLEX KARYOTYPE BY TRANSCRIPTOME ANALYSIS USING RNA SEQUENCING
Ruecker, F. G.
Gong, X.
Dolnik, A.
Hoyos, M.
Grasedieck, S.
Biscocho, J.
Blaette, T.
Swoboda, J.
Pollack, A. S.
Turchiano, A.
Doehner, H.
Doehner, K.
Pollack, J. R.
Bullinger, L.
HAEMATOLOGICA, 2017, 102 : 39 - 40
[12] driveR: a novel method for prioritizing cancer driver genes using somatic genomics data
Ulgen, Ege
Sezerman, O. Ugur
BMC BIOINFORMATICS, 2021, 22 (01)
[13] driveR: a novel method for prioritizing cancer driver genes using somatic genomics data
Ege Ülgen
O. Uğur Sezerman
BMC Bioinformatics, 22
[14] Detection of multiple types of cancer driver mutations using targeted RNA sequencing in NSCLC.
Hong, Yuanyuan
Wang, Xiaoqing
Mu, Weina
Zeng, Xuexia
Su, Lin
Lin, Xiaojing
Zhang, Zhuo
Zhang, Qi
Chen, Weizhi
JOURNAL OF CLINICAL ONCOLOGY, 2022, 40 (16)
[15] Neoantigen prediction in human breast cancer using RNA sequencing data
Hashimoto, Sachie
Noguchi, Emiko
Bando, Hiroko
Miyadera, Hiroko
Morii, Wataru
Nakamura, Takako
Hara, Hisato
CANCER SCIENCE, 2021, 112 (01) : 465 - 475
[16] Detection of multiple types of cancer driver mutations using targeted RNA sequencing in nonsmall cell lung cancer
Ju, Sheng
Cui, Zihan
Hong, Yuanyuan
Wang, Xiaoqing
Mu, Weina
Xie, Zhuolin
Zeng, Xuexia
Su, Lin
Lin, Xiaojing
Zhang, Zhuo
Zhang, Qi
Song, Xiaofeng
You, Songxia
Chen, Ruixin
Chen, Weizhi
Xu, Chun
Zhao, Jun
CANCER, 2023, 129 (15) : 2422 - 2430
[17] Discovery of non-ETS gene fusions in human prostate cancer using next-generation RNA sequencing
Pflueger, Dorothee
Terry, Stephane
Sboner, Andrea
Habegger, Lukas
Esgueva, Raquel
Lin, Pei-Chun
Svensson, Maria A.
Kitabayashi, Naoki
Moss, Benjamin J.
MacDonald, Theresa Y.
Cao, Xuhong
Barrette, Terrence
Tewari, Ashutosh K.
Chee, Mark S.
Chinnaiyan, Arul M.
Rickman, David S.
Demichelis, Francesca
Gerstein, Mark B.
Rubin, Mark A.
GENOME RESEARCH, 2011, 21 (01) : 56 - 67
[18] Depth normalization of small RNA sequencing: using data and biology to select a suitable method
Dueren, Yannick
Lederer, Johannes
Qin, Li-Xuan
NUCLEIC ACIDS RESEARCH, 2022, 50 (10)
[19] A Model for Detecting Type 2 Diabetes Using Mixed Single-Cell RNA Sequencing with Optimized Data
Padmaja K.
Mukhopadhyay D.
SN Computer Science, 4 (6)
[20] Pan-Solid Tumor Identification of NRG1 Fusions Utilizing RNA Sequencing Identifies Diverse Fusion Partners and Highlights a Lack of Co-occurring Oncogenic Driver Alterations
Severson, E.
Nesline, M.
Achyut, B.
Pabla, S.
Kannan, G.
Chenn, A.
Zhang, S.
Klein, R.
Conroy, J.
Sausen, M.
Sathyan, P.
Saini, K.
Jensen, T.
Reddy, P.
Ramkissoon, S.
JOURNAL OF MOLECULAR DIAGNOSTICS, 2022, 24 (10): : S104 - S105

← 1 2 3 4 5 →