Accurate detection of driver gene fusions is essential for the diagnosis, treatment, and prognostic prediction of hematologic malignancies. Despite increasing reliance on RNA sequencing (RNA-seq) for fusion detection, its algorithms have not been sufficiently examined for their ability to detect clinically relevant driver fusions. We evaluated 12 algorithms using conventional RNA-seq from 170 cell lines and targeted RNA-seq from 26 cell lines and 165 clinical samples. The true positive rate, based on 61 and 24 driver fusion-cell line pairs for conventional and targeted RNA-seq, varied between 0.41-1 (median 0.81) and 0-1 (median 0.85), respectively. Many algorithms failed to detect fusions resulting from small deletions (including STIL::TAL1 and FIP1L1::PDGFRA), lowly expressed fusions, and IGH fusions (DUX4::IGH and IGH::NSD2). Targeted RNA-seq more sensitively detected driver fusions than conventional RNA-seq, especially lowly expressed ones. One algorithm, Arriba, detected all driver fusions. These findings will inform algorithm selection in clinical settings.
Tamura et al. (Sat,) studied this question.