Abstract 5289: Enhance both precision and sensitivity of fusion gene detection by hybrid sequencing
Authors: Weirather Jason L. , Clark Tyson A. , Tseng Elizabeth , Korlach Jonas , Au Kin Fai 
Published: July 2016

Background: New Third Generation Sequencing (TGS) techniques, such as PacBio, can provide very informative insights into the transcriptome, such as expression of fusion genes/fusion transcripts from cancer samples. However, the currently available fusion genes analysis tools are for Second Generation Sequencing (SGS) data, where the short read length and unreliable alignments can lead to uncertain accuracy of fusion gene detections. Hybrid-Seq, which integrates SGS short read data into the analysis of TGS long read data, can complement the strengths of both and thus improve the overall performance and resolution of the output data. It also reduces the required amount of TGS data and thus the sequencing cost. Recently, we developed and reported on a Hybrid-Seq approach, IDP-fusion and the results of the proof-of-concept application to MCF-7 data. We demonstrated that IDP-fusion can identify fusions genes with much higher precision than SGS-based approaches. Although the sensitivity is comparable to the most sensitive SGS-only method, a significant proportion of experimentally verified gold standard fusion genes had yet to be identified by IDP-fusion. It indicated an opportunity to enhance the sensitivity of IDP-fusion while retaining the unparalleled precision of the results.

Method: Here we present an innovative Hybrid-Seq approach which extends IDP-fusion, to filtered fusion gene candidates predicted by SGS short read alignments. The fusion gene candidates are verified by the presence of a TGS long read better aligned to an artificial chromosome created from the fusion candidate than any single genome locus. We applied IDP-fusion to a Hybrid-Seq data from the MCF-7 breast cancer cell line, including Illumina SGS data and a lately TGS data generated by PacBio P5-C3 sequencing chemistry. The new IDP-fusion considered fusion candidates reported by several popular SGS tools (TopHat-Fusion, SOAPfuse, TRUP, FusionMap, and deFuse). We compared performance of our new tool to the original IDP-fusion, and the SGS-only approaches.

Results: The new algorithm of IDP-fusion improved the sensitivity from 33.8% to 54.9%. This is higher than the most sensitive SGS-only tool (deFuse, 38.0%), which is achieved at the cost of a low precision of 13.8%. The improved IDP-fusion retains a precision of 60.9%, which is only down slightly from the original IDP-fusion at 68.6%. This tradeoff is acceptable when considering the overall accuracy described by F-score for IDP-fusion. The F-score has increased from 45.3% to 57.8%, which is also considerably better than the best F-score achieved by SGS-only methods (32.8%).

Conclusions: Fusion candidates identified directly from SGS reads can be screened using alignments of TGS long reads, and supplement fusion candidates detected from long reads. Comparing to SGS-only methods, this Hybrid-Seq approach provides much more sensitive and more accurate reports on the fusion genes.

Citation Format: Jason L. Weirather, Tyson A. Clark, Elizabeth Tseng, Jonas Korlach, Kin Fai Au. Enhance both precision and sensitivity of fusion gene detection by hybrid sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5289.