Frontiers in Genetics (Oct 2021)

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

  • Jagadheshwar Balan,
  • Garrett Jenkinson,
  • Asha Nair,
  • Neiladri Saha,
  • Tejaswi Koganti,
  • Jesse Voss,
  • Christopher Zysk,
  • Emily G. Barr Fritcher,
  • Christian A. Ross,
  • Caterina Giannini,
  • Aditya Raghunathan,
  • Benjamin R. Kipp,
  • Robert Jenkins,
  • Cris Ida,
  • Kevin C. Halling,
  • Patrick R. Blackburn,
  • Surendra Dasari,
  • Gavin R. Oliver,
  • Eric W. Klee

DOI
https://doi.org/10.3389/fgene.2021.739054
Journal volume & issue
Vol. 12

Abstract

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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.

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