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

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

in Accuracy / Algorithms / Animal Genetics and Genomics / Bioinformatics / Biomedical and Life Sciences / Brain cancer / Cloud computing / Epidermal growth factor receptors / Evolutionary Biology / Exports / Fusion visualization / Gene expression / Gene Fusion / genome / Genomes / Glioblastoma / Human Genetics / Humans / Interfaces / Kinases / Leukemia / Life Sciences / Method / Microbial Genetics and Genomics / Molecular Sequence Annotation - methods / Neoplasms - genetics / Oncology / Pediatrics / Plant Genetics and Genomics / Precision medicine / Precision oncology / Proteins / Quality / Ribonucleic acid / RNA / RNA-seq / sequence analysis / Sequence Analysis, RNA / Software / transcriptome / Tumors

2020

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

2020

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

2020

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Journal Article

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

Davis, Eric,

Newman, Scott,

Edmonson, Michael N.,

Liu, Yu,

Rusch, Michael C.,

Baker, Suzanne J.,

Downing, James R.,

Ellison, David W.,

Zhou, Xin,

McLeod, Clay,

Li, Yongjin,

Easton, John,

Zhang, Jinghui,

Ma, Jing,

Thrasher, Andrew,

Tang, Bo,

Trull, Austyn,

Szlachta, Karol,

Michael, J. Robert,

Mullighan, Charles,

Tian, Liqing

2020

Overview

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 .

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Publisher

BioMed Central,Springer Nature B.V,BMC

Subject

Accuracy

/ Algorithms

/ Animal Genetics and Genomics

/ Bioinformatics

/ Biomedical and Life Sciences

/ Brain cancer

/ Cloud computing