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

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA 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