Highly Sensitive Fusion Transcript Detection And Quantification In Cancer

Gene fusion detection in cancer samples can provide tumor-specific information for cancer research, clinical diagnosis and targeted treatment. Common fusion detection methods such as qPCR and FISH are restricted to known fusion junctions and limited in the number of genes that can be detected in parallel. In contrast, RNA sequencing is a powerful approach for simultaneous discovery of all possible fusion junctions in a single reaction. But, the sequencing depth required for sensitive detection of fusions from whole-transcriptome libraries can be cost-prohibitive. Here we describe a cancer-specific capture-based approach for fusion detection by RNA sequencing that requires only a fraction of the sequencing depth of whole-transcriptome methods. We designed oligo probes that densely target coding regions of over 200 clinically relevant gene fusions and cancer-associated genes. This oligo panel was used to capture cancer-specific fusions from total RNA-Seq libraries. We used commonly studied cancer cell lines including MCF-7, K562, PC-3, LnCAP, A431 and Universal Human Reference RNA (UHRR) to compare the sensitivity of fusion detection across three RNA-Seq library prep methods: (1) cancer panel library capture (2) whole-transcriptome library capture and (3) PolyA selection. We show that probes targeting individual exons can robustly capture well-characterized cancer gene fusions such as BCR-ABL and BCAS4-BCAS3, as well as translocations where fusion junctions are unknown. Furthermore, these comparisons demonstrate the enhanced sequencing efficiency of the targeted cancer panel, while maintaining highly accurate quantitation of gene expression. We show that selective enrichment of RNA-Seq libraries with cancer-specific capture probes enables high-resolution mapping of genomic rearrangements in patient cancer samples, even those derived from FFPE, facilitating sequencing studies that were not previously possible. Citation Format: Lisa C. Watson, Stephen M. Gross, Irina Khrevtukova, Smita Pathak, Claire Attwooll, Jason Goode, Anthony Mai, Gary P. Schroth. Highly sensitive fusion transcript detection and quantification in cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4884. doi:10.1158/1538-7445.AM2015-4884