Over the past decade, multiple bioinformatics methods and software tools have been developed to identify candidate fusion transcripts from RNA-seq (reviewed in ), with select methods leveraged in recent efforts to build catalogs of fusions across thousands of tumor samples. RNA-seq yields the “expressed exome” of the tumor, capturing only the transcriptionally active regions of the genome, and thus provides a cost-effective means to acquire evidence for both mutations and structural rearrangements involving transcribed sequences, which can reflect on functionally relevant changes in the cancer genome. While point mutations and indels can be readily captured from whole exome sequencing (WES), detecting genome rearrangements typically requires whole genome sequencing (WGS). Transcriptome sequencing (RNA-seq) has emerged as an effective method to detect fusion transcripts in the precision medicine pipeline. For example, tyrosine kinase inhibitors have been highly effective in the treatment of tumors harboring kinase fusions in leukemia and other cancers. Determining the driver of a given tumor is important to inform diagnosis and therapeutic strategies. These include BCR–ABL1, found in ~ 95% of chronic myelogenous leukemia (CML) patients TMPRSS2–ERG in ~ 50% of prostate cancers and DNAJB1–PRKACA, the hallmark and likely driver of fibrolamellar carcinoma. Chromosomal rearrangements leading to the formation of fusion transcripts are a frequent driver in certain cancer types, including leukemia and prostate cancer, and contribute to many others.
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