Identifying fusion transcripts using next generation sequencing

Abstract

Fusion transcripts (i.e. chimeric RNAs) resulting from gene fusions have been used successfully for cancer diagnosis, prognosis, and therapeutic applications. In addition, many fusion transcripts are found in normal human cell lines and tissues, with some data supporting their role in normal physiology. Besides chromosomal rearrangement, intergenic splicing can generate them. Global identification of fusion transcripts becomes possible with the help of Next Generation Sequencing (NGS) technology like RNA-Seq. In the past decade, major advancements have been made for chimeric RNA discovery due to the development of advanced sequencing platform and software packages. However, current software tools behave differently in terms of specificity, sensitivity, time, and computational memory usage. Recent benchmarking studies showed that none of the tools are inclusive. The development of high performance (accurate and fast), and user-friendly fusion detection tool/pipeline is still an open quest. In this article, we review the existing software packages for fusion detection. We explain the methods of the tools, and discuss various factors that affect fusion detection. We summarize conclusions drawn from several comparative studies, and then discuss some of the pitfalls of these studies. We also describe the limitations of current tools, and suggest directions for future development.

Applications of Immunogenomics to Cancer

“…These can result from inversion or deletion of a chromosomal segment, or from chromosomal translocations. Detecting these alterations from exome sequencing data is quite challenging and error-prone, but RNA-based analysis can identify the resulting fusion transcript (Li et al 2011; Scolnick et al 2015; Zhang et al 2016; Kumar et al 2016), and compare the predicted fusion sequence to NGS data from DNA (whole genome or exome sequencing) to identify supporting evidence of the genomic event causing the fusion. Recently, we adapted this approach for neoantigen prediction with a process called IntegrateNEO, using the TMPRSS2-ERG fusions common in prostate cancer to evaluate its ability to identify fusion peptide neoantigens (Zhang et al, 2016b).…”

Targeted RNA sequencing: A routine ancillary technique in the diagnosis of bone and soft tissue neoplasms

“…Although the majority of RNA-Seq applications, whether targeted or whole transcriptome, are used for the determination of differential gene expression, fusion detection by this method establishes only the presence or absence of the fusion transcript; these techniques will also detect "intergenically" spliced fusions that arise at the RNA level. 7 Whole transcriptome RNA-Seq is the optimal discovery tool for the detection of novel fusions; however, it requires fresh frozen tissue and is not currently feasible using formalin-fixed paraffin-embedded (FFPE) tissue, and its cost renders it impractical as a routine diagnostic assay. By narrowing the scope of the assay to targeted genes, multiple samples may be assessed on a benchtop instrument for a fraction of the cost of whole transcriptome sequencing.…”

“…The role, if any, these events contribute to neoplasia remains to be elucidated; indeed, some of these events may have a normal physiologic basis. 7 It would be confusing, and potentially deleterious, to report such a fusion as a disease-defining event; consequently, each newly encountered fusion must be dutifully investigated. This task can be facilitated by maintaining this information (eg, fusion events and annotations) in a prospective database ( Figure 3).…”

“…Although DNA‐based assays (ie, conventional karyotyping and FISH) are not affected by this variation, RNA‐based assays (RT‐PCR and RNA‐Seq) obviate the need to sequence variable and sometimes lengthy intronic sequences and rely instead on the detection of the relatively consistent exon‐exon junction. Although the majority of RNA‐Seq applications, whether targeted or whole transcriptome, are used for the determination of differential gene expression, fusion detection by this method establishes only the presence or absence of the fusion transcript; these techniques will also detect “intergenically” spliced fusions that arise at the RNA level …”

“…These calls are presumably increased in broader fusion panels, and likely reflect the result of secondary “passenger” events, as well as “read‐through” fusions across adjacent genes. The role, if any, these events contribute to neoplasia remains to be elucidated; indeed, some of these events may have a normal physiologic basis . It would be confusing, and potentially deleterious, to report such a fusion as a disease‐defining event; consequently, each newly encountered fusion must be dutifully investigated.…”