CICERO – a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data

To discover driver fusions beyond canonical exon-to-exon chimeric transcripts, researchers at St. Jude Children’s Research Hospital have developed 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.

Fusion detection using CICERO

a Overview of CICERO algorithm which consists of fusion detection through analysis of candidate SV breakpoints and splice junction, fusion annotation, and ranking; key data sets used in each step are labeled. b Workflow of fusion detection. A user can submit an aligned BAM file or a raw fastq file as the input on a local computer cluster or on St. Jude Cloud. The raw output can be curated using FusionEditor and final results can be exported as a text file

Availability – The CICERO source code is available at https://github.com/stjude/Cicero.