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

in Genome biology by Liqing Tian, Yongjin Li, Michael N Edmonson, Xin Zhou, Scott Newman, Clay McLeod, Andrew Thrasher, Yu Liu, Bo Tang, Michael C Rusch, John Easton, Jing Ma, Eric Davis, Austyn Trull, J Robert Michael, Karol Szlachta, Charles Mullighan, Suzanne J Baker, James R Downing, David W Ellison, Jinghui Zhang

TLDR

  • CICERO is a new tool that helps doctors find out if there are any problems with the way genes work in cancer patients. It looks at the way genes are put together in a patient's body and finds any changes that might be causing problems. The tool is really good at finding these changes and it outperforms other tools that doctors use. The study found that the tool could find changes that were previously unknown and that these changes could be important for treating cancer patients. The tool is easy to use and can be used in a doctor's office or on a computer.

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.

Overview

  • The study aims to develop a new algorithm, CICERO, for detecting driver fusions in cancer transcriptomes beyond the canonical exon-to-exon chimeric transcripts. The algorithm integrates RNA-seq read support with extensive annotation for candidate ranking. The study compares CICERO with commonly used methods and finds that CICERO outperforms them, achieving a 95% detection rate for 184 independently validated driver fusions in 170 pediatric cancer transcriptomes. The primary objective of the study is to enhance driver fusion detection for research and precision oncology. The CICERO source code is available at <https://github.com/stjude/Cicero>.

Comparative Analysis & Findings

  • CICERO outperforms commonly used methods for detecting driver fusions in cancer transcriptomes, achieving a 95% detection rate for 184 independently validated driver fusions in 170 pediatric cancer transcriptomes. The study identifies previously unreported kinase fusions (KLHL7-BRAF) and a 13% prevalence of EGFR C-terminal truncation in TCGA glioblastoma RNA-seq. The key findings of the study support the hypothesis that CICERO can detect driver fusions beyond the canonical exon-to-exon chimeric transcripts and enhance driver fusion detection for research and precision oncology.

Implications and Future Directions

  • The study's findings have significant implications for the field of research and clinical practice, as they demonstrate the potential of CICERO to detect driver fusions beyond the canonical exon-to-exon chimeric transcripts. The study identifies previously unreported kinase fusions and a high prevalence of EGFR C-terminal truncation in TCGA glioblastoma RNA-seq, which could have important clinical implications. The study suggests future research directions, such as integrating CICERO with other genomic and clinical data to improve driver fusion detection and develop personalized treatment strategies for cancer patients. The study also highlights the importance of developing novel algorithms and approaches for detecting driver fusions in cancer transcriptomes.
Read Full Article