CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data
Open Access
- 28 May 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Genome Biology
- Vol. 21 (1), 1-18
- https://doi.org/10.1186/s13059-020-02043-x
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.Keywords
Funding Information
- Foundation for the National Institutes of Health (R01CA216391, P30CA021765)
- Foundation for the National Institutes of Health (P01CA09683)
- American Lebanese Syrian Associated Charities
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