CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data
Liqing Tian, Yongjin Li, Michael N. Edmonson, Xin Zhou, Scott Newman, Clay McLeod, Andrew Thrasher, Yu Liu, Bo Tang, Michael Rusch, John Easton, Jing Ma, Eric M. Davis, Austyn Trull, J. Robert Michael, Karol Szlachta, Charles G. Mullighan, Suzanne J. Baker, James R. Downing, David W. Ellison, Jinghui Zhang
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.