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, rapid and reliable identification of cancer fusion genes can inform subsequent therapeutic treatment and predict prognosis. By integrating RT-RPA and CRISPR/Cas12a approaches, we have developed a one-pot CRISPR/Dx system for 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 a one-pot CRISPR/Dx system in detecting lung cancer by targeting ROS1 fusions. The performance of our one-pot CRISPR/Dx detection system was comparable to the two-tube-based testing platform. When tested with the synthetic RNA fusions, both approaches efficiently detected all 14 ROS1 fusions with the LOD in the range of 5-10 copies/μL, without generating a background signal even in the presence of great excess wild-type RNA. The total reaction time for both approaches was 30 minutes. Notably, the one-pot CRISPR/Dx detection system minimizes the operation steps and aerosol contamination without compromising the detection sensitivity and specificity. Its diagnostic power has been validated with the clinical samples. We have successfully developed a rapid, multiplex, one-pot CRISPR/Dx detection system for detecting clinically relevant 14 ROS1 fusions at 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.

Supplementary files

Article information

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

Anal. Methods, 2025, Accepted Manuscript

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, Accepted Manuscript , DOI: 10.1039/D5AY00783F

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