Abstract 3562: Building workflows for gene fusion detection by RNA-seq

Many cancers are associated with chromosomal rearrangements, which in some cases yield fusions between disparate genes and impact cancer phenotypes. Rapid discovery of gene fusions in cancer can facilitate a deeper understanding of specific cancers and may become an important diagnostic tool in clinical settings informing decisions on cancer treatment. An attractive and cost-effective method to identify and study gene fusions is by massively-parallel RNA sequencing (RNA-seq), which provides rich transcriptome profiling data and, with appropriate computational analysis, can discover fusions between transcribed genes. Herein we present data exploring the robustness of gene fusion detection by RNA-seq on Illumina sequencing platforms with TopHat-Fusion, a RNA-seq read aligner specifically designed to detect expressed gene fusions. Using a set of synthetic fusion transcripts to spike in to human RNA samples, we demonstrate quantitative detection of gene fusions by RNA-seq. Additionally, we report the sensitivity of gene fusion discovery across different RNA-seq library preparation methods, including mRNA enrichment by poly-A selection, rRNA-depletion, and exome enrichment by targeted capture. This data helps build workflow recommendations for the detection and study of gene fusions by RNA-seq. Citation Format: Stephen Gross, Lisa Watson, Smita Pathak, Irina Khrebtukova, Gary Schroth. Building workflows for gene fusion detection by RNA-seq. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3562. doi:10.1158/1538-7445.AM2014-3562