Abstract 1651: Utilization of an ensemble approach for identification of driver fusions in pediatric cancer

Pediatric cancers, which make up ~1% of cancer diagnoses each year, comprise distinct genomic landscapes compared to adult cancers. Pediatric cancers typically exhibit a “quiet” genome with a reduced number of somatic mutations compared to adult tumors, which tend to carry a high mutational burden. In addition, pediatric tumors are often associated with fusion events, including fusions that involve potentially targetable kinases, as well as transcription factors which can often be targeted downstream. Fusion events can occur through chromosomal rearrangements, large deletions, or insertions. These events can lead to dysregulated gene expression, and can often become a driving event in pediatric cancer. Sensitive and specific detection of these fusion events through the utilization of RNA-seq data has proven to be a difficult task, given the complexities in tumor cellularity and clonality, and the numerous false positive identifications that are typically output from single fusion callers. Additionally, there are confounding issues surrounding memory usage and compute time required for certain fusion calling algorithms. The Institute for Genomic Medicine at Nationwide Children’s Hospital has implemented an ensemble approach, utilizing 7 fusion callers: FusionMap, JAFFA, STAR-Fusion, SOAPfuse, FusionCatcher, MapSplice, and TopHat-Fusion. This approach allows us to identify fusions that have been called by at least 2 of the above algorithms, and also allows for a prioritization approach based on the number of callers that have identified a specific fusion. We have employed this approach on 67 pediatric cancer cases that we have analyzed in a collaborative Institutional Review Board approved protocol which encompasses scientists, genetic counselors, oncologists, and pathologists at Nationwide Children’s Hospital. These cases are comprised of 52 central nervous system (CNS) tumors, 13 solid tumors, and 1 hematologic tumor, to date. To analyze these cases for fusion events, total RNA-seq with ribodepletion was performed on RNA, extracted from either flash frozen or Formalin-Fixed Paraffin-Embedded (FFPE) tissue samples, to generate libraries that were sequenced using 150bp paired-end reads. Through the employment of our ensemble fusion calling approach and a manual knowledge based filtering strategy, we have currently identified 18 clinically meaningful fusions, all of which we have confirmed in a CAP/CLIA laboratory. We have identified both known and novel fusion events, of which several have provided diagnostic value and/or provided targeted treatment options for patients. In conclusion, this method has the potential to offer a streamlined approach to uncover driver fusions in cancer, while also providing additional diagnostics and an opportunity to identify targeted treatment options for patients in a clinical setting. Citation Format: Stephanie LaHaye, Kyle Voytovich, James Fitch, Sean McGrath, Anthony Miller, Amy Wetzel, Vincent Magrini, Elaine R. Mardis, Richard K. Wilson, Peter White, Catherine E. Cottrell. Utilization of an ensemble approach for identification of driver fusions in pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1651.