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<p>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 <i>STIL</i>::<i>TAL1</i> and <i>FIP1L1</i>::<i>PDGFRA</i>), lowly expressed fusions, and IGH fusions (<i>DUX4</i>::IGH and IGH::<i>NSD2</i>). 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.</p>

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Comparison of gene fusion detection algorithms reveals frequently overlooked driver fusions in hematologic malignancies

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月4日

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  • Zen Tamura,
  • Yuki Saito,
  • Yasunori Kogure,
  • Yuji Oshikawa-Kumade,
  • Suguru Fukuhara,
  • Sumito Shingaki,
  • Hirokazu Kariyazono,
  • Yoshiya Kikukawa,
  • Yuta Ito,
  • Kota Mizuno,
  • Yuichi Shiraishi,
  • Kotoe Katayama,
  • Seiya Imoto,
  • Koji Izutsu,
  • Koichi Murakami,
  • Junji Koya,
  • Keisuke Kataoka

摘要

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.