CICERO

CICERO detects diverse classes of driver gene fusions from RNA sequencing (RNA-seq) data by performing assembly-based local reconstruction to identify canonical and non-canonical fusion events in cancer transcriptomes.

Key Features:

• Assembly-based algorithm: Performs local assembly of RNA-seq reads to reconstruct fusion breakpoints.
• Integration of read support and annotation: Integrates RNA-seq read support with comprehensive annotation for candidate ranking.
• Broad fusion detection: Detects canonical exon-to-exon chimeric transcripts and extends detection to non-canonical events.
• Internal tandem duplication (ITD) detection: Identifies internal tandem duplications and other non-canonical fusion events.
• Validated performance: Achieved a 95% detection rate for 184 independently validated driver fusions across 170 pediatric cancer transcriptomes.
• TCGA glioblastoma findings: Re-analysis of TCGA glioblastoma RNA-seq identified novel kinase fusions including KLHL7-BRAF and reported a 13% prevalence of EGFR C-terminal truncations.

Scientific Applications:

• Pediatric cancer fusion discovery: Identification and validation of driver fusions in pediatric cancer transcriptomes (170 samples; 184 validated fusions).
• Glioblastoma re-analysis: Re-analysis of TCGA glioblastoma RNA-seq to discover previously unreported kinase fusions such as KLHL7-BRAF.
• EGFR truncation detection: Detection and prevalence estimation of EGFR C-terminal truncations (13%) within glioblastoma cohorts.
• Precision oncology candidate prioritization: Prioritizes fusion candidates that may inform therapeutic targeting in cancer.

Methodology:

Local assembly of RNA-seq reads combined with integration of read support and comprehensive annotation for candidate ranking, using an assembly-based approach that extends detection to non-canonical events including internal tandem duplications.