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CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data
Liqing Tian1, Yongjin Li1, Michael N Edmonson1
1Department of Computational Biology, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
Genome Biology
|May 30, 2020
Summary
CICERO, a novel algorithm, enhances the detection of cancer driver fusions, including complex non-canonical events, by integrating RNA-seq data and annotations. This tool improves diagnostic accuracy for pediatric cancers and precision oncology applications.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Canonical exon-to-exon gene fusions are established cancer drivers.
- Identifying non-canonical driver fusions, such as internal tandem duplications, remains challenging with existing methods.
Purpose of the Study:
- To develop and validate CICERO, a new algorithm for detecting diverse driver fusions beyond canonical transcripts.
- To improve the sensitivity and specificity of driver fusion detection in cancer transcriptomes.
Main Methods:
- CICERO employs a local assembly-based approach, integrating RNA-sequencing (RNA-seq) read support with comprehensive gene annotation for ranking candidate fusions.
- The algorithm was evaluated on 170 pediatric cancer transcriptomes, with 184 driver fusions independently validated.
Main Results:
- CICERO achieved a 95% detection rate for validated driver fusions, outperforming existing methods.
- The analysis identified previously unreported kinase fusions (KLHL7-BRAF) and revealed a 13% prevalence of EGFR C-terminal truncation in glioblastoma.
- The algorithm successfully detected non-canonical events, including internal tandem duplications.
Conclusions:
- CICERO significantly enhances the detection of driver fusions, particularly non-canonical events, in cancer transcriptomes.
- The algorithm offers a valuable tool for cancer research and precision oncology, with accessible implementation options.
- Findings highlight the importance of detecting diverse fusion types for comprehensive cancer analysis.
