Reaction Pathway Differentiation Enabled Fingerprinting Signal for Single Nucleotide Variant Detection

Abstract

Accurate identification of single‐nucleotide variants (SNVs) is paramount for disease diagnosis. Despite the facile design of DNA hybridization probes, their limited specificity poses challenges in clinical applications. Here, a differential reaction pathway probe (DRPP) based on a dynamic DNA reaction network is presented. DRPP leverages differences in reaction intermediate concentrations between SNV and WT groups, directing them into distinct reaction pathways. This generates a strong pulse‐like signal for SNV and a weak unidirectional increase signal for wild‐type (WT). Through the application of machine learning to fluorescence kinetic data analysis, the classification of SNV and WT signals is automated with an accuracy of 99.6%, significantly exceeding the 80.7% accuracy of conventional methods. Additionally, sensitivity for variant allele frequency (VAF) is enhanced down to 0.1%, representing a ten‐fold improvement over conventional approaches. DRPP accurately identified D614G and N501Y SNVs in the S gene of SARS‐CoV‐2 variants in patient swab samples with accuracy over 99% (n = 82). It determined the VAF of ovarian cancer‐related mutations KRAS‐G12R, NRAS‐G12C, and BRAF‐V600E in both tissue and blood samples (n = 77), discriminating cancer patients and healthy individuals with significant difference (p < 0.001). The potential integration of DRPP into clinical diagnostics, along with rapid amplification techniques, holds promise for early disease diagnostics and personalized diagnostics.