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Anchored random reverse primer sequencing for quantitative detection of novel gene fusions

Nature Biomedical Engineering (2026) Cite this article

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Abstract

Identifying novel gene fusions is critical for cancer diagnosis and drug development. While a few advanced methods have shown the capability to detect gene fusions involving unknown partners, comprehensive detection of gene fusions, especially of those with low copy numbers, remains a challenge. Indeed, most current panel-based sequencing methods fall short in reliability and cost efficiency. Here we present a method for detecting potentially novel gene fusions using anchored random reverse primers (ARRP) during PCR-based library construction, allowing the simultaneous capture of mutations and RNA splicing variants. Furthermore, the combination with blocker displacement amplification technology enables a median of 22-fold allele enrichment for gene fusions, achieving a limit of detection ~10-fold lower than that of current technologies and resulting in an 8-fold cost reduction. Using ARRP-seq, we identify numerous novel fusions in 98 clinical tissue samples, showcasing its diagnostic potential in prostate cancer and capacity for personalized diagnostics in cervical cancer.

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Fig. 1: ARRP-seq analyses of gene fusions.
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Fig. 2: Quantitative analysis of gene fusions using ARRP-seq on commercial samples.
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Fig. 3: Custom bioinformatics pipeline of gene fusions for ARRP-seq.
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Fig. 4: ARRP-seq combined with mBDA technique for gene fusion detection.
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Fig. 5: ARRP-seq for the detection of cervical cancer tissue RNA samples.
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Fig. 6: ARRP–mBDA-seq for the detection of prostate cancer tissue RNA samples.
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Data availability

The raw sequencing data have been deposited in the Genome Sequence Archive82 at the National Genomics Data Center83, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA012318). Source data are available in figshare at https://figshare.com/authors/NABME_SJTU/19729816 (ref. 84). Source data are also provided with this paper.

Code availability

The code used in this study is available in GitHub at https://github.com/NABMElab/ARRP-seq (ref. 85).

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Acknowledgements

This work was financially supported by the National Key R&D Program of China (2022YFF1201801), the National Natural Science Foundation of China (Nos. 22174094, T2188102, 21991134, 92059205, 22025404, 22404109), the Fundamental Research Funds for the Central Universities (YG2023QNA33, YG2025ZD28) to P.S., X.Z. and C.F., the Program of Shanghai Academic Research Leader (No. 22XD1403500), Central Guidance on Local Science and Technology Development Fund of Shanghai Province (No. YDZX20223100003006), and the Shanghai Science and Technology Committee (24Y22800300) to Y. Wang. We thank F. Yin for providing clinical prostate samples. We also thank the patient donors from The International Peace Maternity and Child Health Hospital and Renji Hospital for their contribution to medical research. The language of this manuscript was polished from the original text by ChatGPT.

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  1. These authors contributed equally: Xuehao Xiu, Yi Wu, Jiangxue Li.

Authors and Affiliations

  1. School of Biomedical Engineering, Department of Gynecologic Oncology, The International Peace Maternity and Child Health Hospital, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

    Xuehao Xiu, Yi Wu, Jiangxue Li, Dongfa Lin, Xinglei Su, Zhi Weng & Ping Song

  2. Department of Gynecologic Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Xiao Sun & Yudong Wang

  3. Institute of Molecular Medicine Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital School of Medicine, School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

    Xiaolei Zuo

  4. School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

    Xiurong Yang & Chunhai Fan

  5. Biostate AI, Inc., Houston, TX, USA

    David Yu Zhang

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Contributions

P.S. designed and supervised this research. X.X., Y. Wu, J.L., D.L. and X. Su contributed to experiments. Y. Wang, X. Sun, X.Z., X.Y. and C.F. contributed to providing the clinical samples. X.X., Y. Wu, Z.W., P.S. and D.Y.Z. contributed to analysing the data. X.X. and P.S. wrote and edited the manuscript. All authors read and contributed to the final version of the manuscript.

Corresponding authors

Correspondence to Yudong Wang or Ping Song.

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Competing interests

There are patents pending on the BDA (patent number EP3146080B1), mBDA (patent number WO2019164885A1) and RRP (patent number US20250011833A1) methods used in this work. D.Y.Z. declares a competing interest in the form of substantial equity ownership in Nuprobe, Torus Biosystems, Pana Bio, Biostate AI and Pupil Bio. The other authors declare no competing interests.

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Xiu, X., Wu, Y., Li, J. et al. Anchored random reverse primer sequencing for quantitative detection of novel gene fusions. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-025-01564-9

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