Discovery of clinically relevant fusions in pediatric cancer

作者：Stephanie LaHaye, James Fitch, Kyle Voytovich, Adam Herman, Benjamin Kelly, Grant Lammi, Jeremy A. Arbesfeld, Saranga Wijeratne, Samuel J. Franklin, Kathleen M. Schieffer, Natalie Bir, Sean McGrath, Anthony R. Miller, Amy Wetzel, Katherine E. Miller, Tracy A. Bedrosian, Kristen Leraas, Liz Varga, Kristy Lee, Ajay Gupta, Bhuvana A. Setty, Daniel R. Boué, Jeffrey R. Leonard, Jonathan L. Finlay, Mohamed S Abdelbaki, Diana S. Osorio, Selene C. Koo, Daniel C. Koboldt, Alex H. Wagner, Ann‐Kathrin Eisfeld, Krzysztof Mrózek, Vincent Magrini, Catherine E. Cottrell, Elaine R. Mardis, Richard K. Wilson, Peter White · 发表于：BMC Genomics · 年份：2021 · DOI：10.1186/s12864-021-08094-z · 被引用次数：32 · 研究领域：Cancer Genomics and Diagnostics、Neuroblastoma Research and Treatments、Genomics and Rare Diseases

BACKGROUND: Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions. RESULTS: Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a "known fusion list" prevents failure to ...