Abstract

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.

Details

Title
CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data
Author
Tian, Liqing; Li, Yongjin; Edmonson, Michael N; Zhou, Xin; Newman, Scott; McLeod, Clay; Thrasher, Andrew; Liu, Yu; Tang, Bo; Rusch, Michael C; Easton, John; Ma, Jing; Davis, Eric; Trull, Austyn; Michael, J Robert; Szlachta, Karol; Mullighan, Charles; Baker, Suzanne J; Downing, James R; Ellison, David W; Zhang, Jinghui  VIAFID ORCID Logo 
Pages
1-18
Section
Method
Publication year
2020
Publication date
2020
Publisher
BioMed Central
ISSN
14747596
e-ISSN
1474760X
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1186/s13059-020-02043-x
ProQuest document ID
2414833312
Copyright
© 2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.