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<p>An affordable, precise detection of mutations is critical for guiding targeted cancer therapies and improving patient outcomes. Epidermal growth factor receptor (EGFR), a protein on the surface of cells that regulates growth and division, is frequently mutated in non–small cell lung cancer (NSCLC). Early identification of these mutations enables clinicians to select the most effective tyrosine kinase inhibitors, thereby enhancing treatment response and survival rates. Recent studies have focused on developing CRISPR-based detection strategies incorporating nanomaterials to achieve more accurate results. In this study, we present a CRISPR-based “turn-on” detection platform that leverages the cleavage of a novel enhanced bimetallic DNA nanocluster to measure <i>EGFR</i> exon 19 deletion in non-small cell lung cancer (NSCLC). The system is innovatively designed using guide RNAs (gRNAs) rationally derived from the normal <i>EGFR</i> gene, enabling the determination of exon 19 deletion through CRISPR-Cas activation in samples containing the normal and mutant. Upon recognition of the normal <i>EGFR</i> gene, the Cas12a enzyme induces cleavage of the Spermiform-designed Ag/Au DNA nanocluster and fluorescence quenching. At the same time, fluorescence signal retention depends on mutation frequency, with higher mutation frequencies resulting in greater or “turn-on” fluorescence signals. This approach achieves a detection limit (LOD) of approximately 0.35 nM, which is capable of detecting about 1.5% mutation, offering a cost-effective, label-free diagnostic tool and a promising strategy for future detection of deletion-related subtypes in PCR products by targeting normal sequences. The integration of bimetallic nanocluster-based reporters with CRISPR precision provides an emerging platform for next-generation molecular diagnostics targeting <i>EGFR</i> and other clinically relevant mutations.</p>

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A “turn-on” CRISPR-mediated method using enhanced fluorescent bimetallic DNA nanoclusters for EGFR mutation detection in non-small cell lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Fatemeh Goudarzi,
  • Pouya Salehipour,
  • Mohammad Hossein Modarressi,
  • Morteza Hosseini

摘要

An affordable, precise detection of mutations is critical for guiding targeted cancer therapies and improving patient outcomes. Epidermal growth factor receptor (EGFR), a protein on the surface of cells that regulates growth and division, is frequently mutated in non–small cell lung cancer (NSCLC). Early identification of these mutations enables clinicians to select the most effective tyrosine kinase inhibitors, thereby enhancing treatment response and survival rates. Recent studies have focused on developing CRISPR-based detection strategies incorporating nanomaterials to achieve more accurate results. In this study, we present a CRISPR-based “turn-on” detection platform that leverages the cleavage of a novel enhanced bimetallic DNA nanocluster to measure EGFR exon 19 deletion in non-small cell lung cancer (NSCLC). The system is innovatively designed using guide RNAs (gRNAs) rationally derived from the normal EGFR gene, enabling the determination of exon 19 deletion through CRISPR-Cas activation in samples containing the normal and mutant. Upon recognition of the normal EGFR gene, the Cas12a enzyme induces cleavage of the Spermiform-designed Ag/Au DNA nanocluster and fluorescence quenching. At the same time, fluorescence signal retention depends on mutation frequency, with higher mutation frequencies resulting in greater or “turn-on” fluorescence signals. This approach achieves a detection limit (LOD) of approximately 0.35 nM, which is capable of detecting about 1.5% mutation, offering a cost-effective, label-free diagnostic tool and a promising strategy for future detection of deletion-related subtypes in PCR products by targeting normal sequences. The integration of bimetallic nanocluster-based reporters with CRISPR precision provides an emerging platform for next-generation molecular diagnostics targeting EGFR and other clinically relevant mutations.