Rapid, multiplex, one-pot CRISPR/Dx system for detecting cancer fusion genes

Abstract

Targeted therapies directed at fusion genes have proven remarkably effective against cancers. Therefore, the rapid and reliable identification of cancer fusion genes can guide subsequent therapeutic treatment and predict prognosis. By integrating the RT-RPA and CRISPR/Cas12a approaches, we developed a one-pot CRISPR/Dx system for the rapid and multiplex detection of cancer fusion genes. A tube with unique assemblies was created using 3D printing technology to realize this application. As proof of principle, we demonstrated the feasibility of the one-pot CRISPR/Dx system in detecting lung cancer by targeting ROS1 fusions. The performance of the one-pot CRISPR/Dx detection system was comparable to a two-tube-based testing platform. When tested with synthetic RNA fusions, both approaches efficiently detected all 14 ROS1 fusions with an LOD in the range of 5–10 copies per μL, without generating a background signal, even in the presence of a large excess of wild-type RNA. The total reaction time for both approaches was 30 minutes. Notably, the one-pot CRISPR/Dx detection system minimized the operation steps and aerosol contamination without compromising detection sensitivity and specificity. Furthermore, its diagnostic power was validated using clinical samples. Thus, we successfully developed a rapid, multiplex, one-pot CRISPR/Dx detection system for detecting 14 clinically relevant ROS1 fusions with high sensitivity and specificity. It is also cost-effective and simple to operate, thereby realizing the ultimate goal of establishing CRISPR/Dx as the paragon of cancer diagnostics for home self-testing and point-of-care testing.

Graphical abstract: Rapid, multiplex, one-pot CRISPR/Dx system for detecting cancer fusion genes

Supplementary files

Article information

Article type
Paper
Submitted
08 May 2025
Accepted
11 Jul 2025
First published
11 Jul 2025

Anal. Methods, 2025, Advance Article

Rapid, multiplex, one-pot CRISPR/Dx system for detecting cancer fusion genes

J. Li, C. Lau, J. Wang, W. Wu, Z. Huang, X. Chen, J. Li, Y. Huang, T. Wang, Y. Li, Z. Zhao, M. Xu, G. Chen, S. Tong and H. Zhu, Anal. Methods, 2025, Advance Article , DOI: 10.1039/D5AY00783F

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