Discovery of clinically relevant fusions in pediatric cancer

BMC Genomics

Krzysztof Mrózek¹  Selene C. Koo²  Daniel R. Boué²  Jeffrey R. Leonard³  Ajay Gupta⁴  Diana S. Osorio⁵  Mohamed S. Abdelbaki⁵  Bhuvana Setty⁵  Jonathan L. Finlay⁵  Ann-Kathrin Eisfeld⁶  Anthony R. Miller⁷  Kathleen M. Schieffer⁷  Kristen Leraas⁷  Amy Wetzel⁷  Jeremy A. Arbesfeld⁷  Elizabeth A. Varga⁷  Daniel C. Koboldt⁷  Kristy Lee⁷  Kyle J. Voytovich⁷  Saranga Wijeratne⁷  Katherine E. Miller⁷  Benjamin J. Kelly⁷  Adam C. Herman⁷  Samuel J. Franklin⁷  Grant E. Lammi⁷  Natalie Bir⁷  Tracy A. Bedrosian⁷  James R. Fitch⁷  Stephanie LaHaye⁷  Sean D. McGrath⁷  Vincent Magrini⁸  Peter White⁸  Richard K. Wilson⁸  Elaine R. Mardis⁸  Alex H. Wagner⁹  Catherine E. Cottrell^1,10 

[1] Clara D. Bloomfield Center for Leukemia Outcomes Research, The Ohio State University, Columbus, OH, USA;The Ohio State Comprehensive Cancer Center, Columbus, OH, USA;Department of Pathology, The Ohio State University, Columbus, OH, USA;Department of Pathology, Nationwide Children’s Hospital, Columbus, OH, USA;Department of Pediatrics, The Ohio State University, Columbus, OH, USA;Section of Neurosurgery, Nationwide Children’s Hospital, Columbus, OH, USA;Division of Hematology, Oncology, Blood and Marrow Transplant, Nationwide Children’s Hospital, Columbus, OH, USA;Division of Hematology, Oncology, Blood and Marrow Transplant, Nationwide Children’s Hospital, Columbus, OH, USA;Department of Pediatrics, The Ohio State University, Columbus, OH, USA;Division of Hematology, The Ohio State University, Columbus, OH, USA;Clara D. Bloomfield Center for Leukemia Outcomes Research, The Ohio State University, Columbus, OH, USA;The Ohio State Comprehensive Cancer Center, Columbus, OH, USA;The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA;The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA;Department of Pediatrics, The Ohio State University, Columbus, OH, USA;The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA;Department of Pediatrics, The Ohio State University, Columbus, OH, USA;Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA;The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA;Department of Pediatrics, The Ohio State University, Columbus, OH, USA;Department of Pathology, The Ohio State University, Columbus, OH, USA;

关键词: Transcriptomics;    Genomics;    Pediatric neoplasms;    Gene fusions;    Cancer;    RNA-Seq;   

DOI  :  10.1186/s12864-021-08094-z

来源: Springer

【 摘 要 】

BackgroundPediatric 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.ResultsOur 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 report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information.ConclusionsThe EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

【 授权许可】

CC BY