Clinical Cancer Research · 2020 · 27 citations · 40 references
MedicineMolecular GeneticsOncologyCancer GeneticsMolecular OncologyTranscriptomicsCancer GenomicsGene Fusions In CancerNext-generation SequencingOncogenic Fusion GenesTranscriptomic ProfilingVariant DetectionCancer Structural Variant AnalysisFusion Gene DetectionFusion Gene DiscoveryNext-generation Sequencing PanelsRna Sequencing-based Fusion DetectionStructural Variant DetectionTranscriptomic ProfilesTumor-specific Fusion Genes
Abstract Purpose: Gene fusions are important oncogenic drivers and many are actionable. Whole-genome and transcriptome (WGS and RNA-seq, respectively) sequencing can discover novel clinically relevant fusions. Experimental Design: Using WGS and RNA-seq, we reviewed the prevalence of fusions in a cohort of 570 patients with cancer, and compared prevalence to that predicted with commercially available panels. Fusions were annotated using a consensus variant calling pipeline (MAVIS) and required that a contig of the breakpoint could be constructed and supported from ≥2 structural variant detection approaches. Results: In 570 patients with advanced cancer, MAVIS identified 81 recurrent fusions by WGS and 111 by RNA-seq, of which 18 fusions by WGS and 19 by RNA-seq were noted in at least 3 separate patients. The most common fusions were EML4-ALK in thoracic malignancies (9/69, 13%), and CMTM8-CMTM7 in colorectal cancer (4/73, 5.5%). Combined genomic and transcriptomic analysis identified novel fusion partners for clinically relevant genes, such as NTRK2 (novel partners: SHC3, DAPK1), and NTRK3 (novel partners: POLG, PIBF1). Conclusions: Utilizing WGS/RNA-seq facilitates identification of novel fusions in clinically relevant genes, and detected a greater proportion than commercially available panels are expected to find. A significant benefit of WGS and RNA-seq is the innate ability to retrospectively identify variants that becomes clinically relevant over time, without the need for additional testing, which is not possible with panel-based approaches.
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