Comparison of four next generation sequencing platforms for fusion detection: Oncomine by ThermoFisher, AmpliSeq by illumina, FusionPlex by ArcherDX, and QIAseq by QIAGEN
Introduction
The significance of gene fusions has been well recognized for both diagnosis and treatment of cancer, including hematologic malignancies and solid tumors [1], [2], [3], [4]. This highlights the importance of accurate detection of gene fusions for patient care.
Multiple techniques are used clinically to detect gene fusions. G-banding cytogenetics detects chromosomal translocations that may be used to infer gene fusions. Reverse transcription polymerase chain reaction (RT-PCR) is used to detect fusion transcripts if the breakpoints are well established and are concentrated to a small region that can be adequately amplified by routine polymerase. However, gene fusions with heterogeneous breakpoints and highly variable partners often occur, e.g. ALK, MLL, and FGFR1/2 rearrangements. A significant portion of clinically important gene rearrangements do not result in a known fusion transcript, e.g. MYC and EVI1 rearrangements. Immunohistochemistry (IHC) detecting the overexpression of the protein that results from a fusion event is sometimes used, e.g. CCND1 fusions and cyclin D1 (BCL1) IHC for mantle cell lymphoma. Fluorescent in situ hybridization (FISH), using either dual-fusion or break-apart probes, is most frequently used to detect gene fusions clinically. Although regarded as the gold standard, FISH can be challenged by complex and cryptic rearrangements. In addition, FISH is typically carried out as multiple rounds of reflex studies, which add cost and turn-around time to patient care.
Advancements in next generation sequencing (NGS) enables the assessment of numerous potential fusions in a single experiment. RNA-based NGS is currently the most common approach in multi-target fusion detection. The library preparation can be achieved through amplicon-based or capture-based methods. Empirically, for DNA-based NGS assays, capture-based methods are more labor-intensive, require more input material but can be used for larger panels compared with amplicon-based methods. Among amplificon-based methods, earlier versions could detect fusions only if both partners are targeted by the assay. These methods use the traditional PCR approach, where both forward and reverse primers are gene-specific. In the event of a fusion, the library will contain amplicons that span the known fusion junction. Fusion identification then relies on the ability of the analysis software to match sequence reads to a reference database of fusion junction sequences, known as the fusion manifest file. These are exemplified by the RNA based AmpliSeq panels (Table 1). In this study, we compared Oncomine™ Comprehensive Assay v3 (Ion Torrent™, ThermoFisher, CA) and AmpliSeq for Illumina Comprehensive Panel v3 (Illumina, CA).
Our comparison also included newer techniques that allowed open-ended PCR to detect fusions as long as one of the fusion partners is targeted by the assay. The Anchored Multiplex PCR (AMP™, ArcherDX, CO) method and the Single Primer Extension Chemistry (SPE, QIAseq, QIAGEN, Hilden, Germany) both use specific primers for one partner gene and universal primers for the other (Table 1): AMP™ by ArcherDX performs nested PCR while SPE by QIAGEN carries out one round of PCR. Bioinformatically, unlike the alignment process of traditional amplicon-based fusion assays, sequencing reads from open-ended PCR technologies are assembled without a reference but de novo as contigs; and the contigs are aligned with the reference genome/transcriptome to identify fusions. In addition, the PCR amplification of these two assays incorporate molecular barcodes, enabling the assays to determine the number of cDNA molecules, termed “unique reads”, that contain the fusion before PCR. The analysis process following AMP™ by ArcherDX also calculates the number of unique start sites that are represented by each gene specific primer as a result of the random priming on the opposite end of the cDNA fragment. Unique reads and unique start sites are important parameters to assess for library complexity of the whole sample as well as the accuracy of each fusion call. As these techniques are adopted by clinical laboratories, assay performance comparison is urgently needed. The current study compared the above four assays on the same set of specimens conducted in a blinded manner.
Section snippets
Samples, library preparation, and sequencing
The study was approved by the institutional review boards at Fred Hutchinson Cancer Research Center (FHCRC) and the University of Washington (UW). The study used frozen metastatic tumor samples of men with castration-resistant prostate cancer (n = 24, University of Washington Prostate Cancer Donor Autopsy Program). Samples were from individual patients and included 13 lymph node metastases, 8 liver metastases, and one each of diaphragm, spleen, and periaortic metastasis. The frozen samples had
The quality of RNA samples and libraries
The frozen samples yielded RNA specimens with integrity numbers (RIN score) ranging from 7 to 8.9 (Median = 7.5) with sufficient quantity for all assays. Libraries by OCAv3, AICv3, and FusionPlex contained numbers of reads that met or exceeded the manufactures’ recommendations (Table 1). Libraries by QIAseq had lower numbers of reads than the recommendation because of the high number of samples sequenced in parallel. Based on data from this study, this did not lead to missed calls by QIAseq.
ETS fusions detected in our study
In
Discussion
RNA-based fusion NGS panels are beginning to be used clinically and assay comparisons are urgently needed. In the current study, we compared the calls by four commercial assays with different chemistry for the detection of different fusion transcripts. We used a pilot cohort of frozen tissues with high tumor content, which yielded RNA with decent quality and sufficient quantity. Our observation led us to select OCAv3 and the solid tumor of FusionPlex for further in-house clinical validation.
CRediT authorship contribution statement
Xiaoyu Qu: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Cecilia Yeung: Conceptualization, Methodology, Writing - review & editing, Resources. Ilsa Coleman: Methodology, Resources, Validation, Data curation, Writing - review & editing. Peter S Nelson: Resources, Validation, Writing - review & editing, Funding acquisition. Min Fang: Conceptualization, Methodology, Resources, Writing
Declarations of Competing Interest
ArcherDX supplied the ArcherDX library preparation reagents through an ArcherDX challenge grant. ThermoFisher, Illumina, and QIAGEN supplied reagents and performed the library preparation, sequencing, and preliminary data processing of these panels respectively: Oncomine™ Comprehensive Assay v3 (OCAv3) by ThermoFisher, the AmpliSeq for Illumina comprehensive panel v3 (AICv3) panel, and the QIAseq Targeted RNAscan Human Oncology by QIAGEN.
Acknowledgments
We thank Colm Morrissey and Bryce Lakely of the department of Urology at University of Washington for providing the RNA samples used in this study. We thank the patients and their families, Celestia Higano, Evan Yu, Elahe Mostaghel, Heather Cheng, Bruce Montgomery, Mike Schweizer, Funda Vakar-Lopez, Lawrence True and the rapid autopsy teams for their contributions to the University of Washington Medical Center Prostate Cancer Donor Rapid Autopsy Program. We thank the following individuals and
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