SeekFusion - A Clinically Validated Fusion Transcript Detection Pipeline for PCR-Based Next-Generation Sequencing of RNA

• Published in: Frontiers in genetics Vol. 12; p. 739054
• Main Authors: Balan, Jagadheshwar, Jenkinson, Garrett, Nair, Asha, Saha, Neiladri, Koganti, Tejaswi, Voss, Jesse, Zysk, Christopher, Barr Fritcher, Emily G., Ross, Christian A., Giannini, Caterina, Raghunathan, Aditya, Kipp, Benjamin R., Jenkins, Robert, Ida, Cris, Halling, Kevin C., Blackburn, Patrick R., Dasari, Surendra, Oliver, Gavin R., Klee, Eric W.
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 22.10.2021
• Subjects: bioinformatics; gene fusion; Genetics; QiaSeq; RNA; sarcoma; UMI consensus
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2021.739054

Detecting gene fusions involving driver oncogenes is pivotal in clinical diagnosis and treatment of cancer patients. Recent developments in next-generation sequencing (NGS) technologies have enabled improved assays for bioinformatics-based gene fusions detection. In clinical applications, where a small number of fusions are clinically actionable, targeted polymerase chain reaction (PCR)-based NGS chemistries, such as the QIAseq RNAscan assay, aim to improve accuracy compared to standard RNA sequencing. Existing informatics methods for gene fusion detection in NGS-based RNA sequencing assays traditionally use a transcriptome-based spliced alignment approach or a de-novo assembly approach. Transcriptome-based spliced alignment methods face challenges with short read mapping yielding low quality alignments. De-novo assembly-based methods yield longer contigs from short reads that can be more sensitive for genomic rearrangements, but face performance and scalability challenges. Consequently, there exists a need for a method to efficiently and accurately detect fusions in targeted PCR-based NGS chemistries. We describe SeekFusion, a highly accurate and computationally efficient pipeline enabling identification of gene fusions from PCR-based NGS chemistries. Utilizing biological samples processed with the QIAseq RNAscan assay and in-silico simulated data we demonstrate that SeekFusion gene fusion detection accuracy outperforms popular existing methods such as STAR-Fusion, TOPHAT-Fusion and JAFFA-hybrid. We also present results from 4,484 patient samples tested for neurological tumors and sarcoma, encompassing details on some novel fusions identified.