Literature DB >> 35230688

RNA-seq Fusion Detection in Clinical Oncology.

Dale J Hedges1.   

Abstract

Gene fusions play a prominent role in the oncogenesis of many cancers and have been extensively targeted as biomarkers for diagnostic, prognostic, and therapeutic purposes. Detection methods span a number of platforms, including cytogenetics (e.g., FISH), targeted qPCR, and sequencing-based assays. Before the advent of next-generation sequencing (NGS), fusion testing was primarily targeted to specific genome loci, with assays tailored for previously characterized fusion events. The availability of whole genome sequencing (WGS) and whole transcriptome sequencing (RNA-seq) allows for genome-wide screening for the simultaneous detection of both known and novel fusions. RNA-seq, in particular, offers the possibility of rapid turn-around testing with less dedicated sequencing than WGS. This makes it an attractive target for clinical oncology testing, particularly when transcriptome data can be multi-purposed for tumor classification and additional analyses. Despite considerable efforts and substantial progress, however, genome-wide screening for fusions solely based on RNA-seq data remains an ongoing challenge. A host of technical artifacts adversely impact the sensitivity and specificity of existing software tools. In this chapter, the general strategies employed by current fusion software are discussed, and a selection of available fusion detection tools are surveyed. Despite its current limitations, RNA-seq-based fusion detection offers a more comprehensive and efficient strategy as compared to multiple targeted fusion assays. When thoughtfully employed within a wider ecosystem of diagnostic assays and clinical information, RNA-seq fusion detection represents a powerful tool for precision oncology.
© 2022. Springer Nature Switzerland AG.

Entities:  

Keywords:  Alignment; Breakpoint; Chimeric; Discordant reads; Ensemble; Fusion; Homology; Internal tandem duplication (ITD); Junction; Mapping; RNA-seq; Screening; Sensitivity; Sequencing artifact; Soft clip; Specificity

Mesh:

Year:  2022        PMID: 35230688     DOI: 10.1007/978-3-030-91836-1_9

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   2.622


  6 in total

1.  Transcription-mediated gene fusion in the human genome.

Authors:  Pinchas Akiva; Amir Toporik; Sarit Edelheit; Yifat Peretz; Alex Diber; Ronen Shemesh; Amit Novik; Rotem Sorek
Journal:  Genome Res       Date:  2005-12-12       Impact factor: 9.043

Review 2.  Next-generation transcriptome assembly.

Authors:  Jeffrey A Martin; Zhong Wang
Journal:  Nat Rev Genet       Date:  2011-09-07       Impact factor: 53.242

3.  Exploring genomic alteration in pediatric cancer using ProteinPaint.

Authors:  Xin Zhou; Michael N Edmonson; Mark R Wilkinson; Aman Patel; Gang Wu; Yu Liu; Yongjin Li; Zhaojie Zhang; Michael C Rusch; Matthew Parker; Jared Becksfort; James R Downing; Jinghui Zhang
Journal:  Nat Genet       Date:  2016-01       Impact factor: 38.330

Review 4.  Pathogenesis of ETV6/RUNX1-positive childhood acute lymphoblastic leukemia and mechanisms underlying its relapse.

Authors:  Congcong Sun; Lixian Chang; Xiaofan Zhu
Journal:  Oncotarget       Date:  2017-05-23

5.  SQUID: transcriptomic structural variation detection from RNA-seq.

Authors:  Cong Ma; Mingfu Shao; Carl Kingsford
Journal:  Genome Biol       Date:  2018-04-12       Impact factor: 13.583

6.  Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods.

Authors:  Brian J Haas; Alexander Dobin; Bo Li; Nicolas Stransky; Nathalie Pochet; Aviv Regev
Journal:  Genome Biol       Date:  2019-10-21       Impact factor: 13.583

  6 in total

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