Reliable discrimination of specific epidermal growth factor receptor (EGFR) gene mutations plays a critical role in guiding lung cancer therapeutics. Until now, convenient and accurate recognition of the specific deletion of EGFR exons has remained particularly challenging. Herein, we propose a palindromic-assisted self-annealing transcription amplification (PASTA) strategy for the reliable detection of circulating EGFR exon mutations. We designed a palindromic DNA hairpin nanorobot consisting of a palindromic tail, a T7 promoter, a target recognition region, and a transcription template. The nanorobot enabled prompt self-assembly into a target-hairpin/hairpin-target dimer in the presence of single-stranded DNA target and further triggered in vitro transcription. In a proof-of-concept experiment for detecting circulating 15n-del EGFR mutation, a detection limit of 0.8 fM and a linear detection range of 1 fM to 100 pM was achieved, and an accuracy of 100% was reached in clinical validation by analyzing 20 samples from clinical lung cancer patients. Empowered by the intrinsic sensitivity and selectivity, the proposed PASTA approach will lead to the development of a universal platform for reliable molecular subtyping.

对特定表皮生长因子受体 (EGFR) 基因突变的可靠区分在指导肺癌治疗中起着关键作用。到目前为止，方便和准确识别 EGFR 外显子的特定缺失仍然特别具有挑战性。在此，我们提出了一种回文辅助自退火转录扩增 (PASTA) 策略，用于可靠检测循环 EGFR 外显子突变。我们设计了一个回文 DNA 发夹纳米机器人，由回文尾、T7 启动子、目标识别区域和转录模板组成。纳米机器人能够在存在单链 DNA 靶标的情况下迅速自组装成靶标-发夹/发夹-靶标二聚体，并在体外进一步触发转录。在检测循环 15n-del EGFR 突变的概念验证实验中，实现了 0.8 fM 的检测限和 1 fM 至 100 pM 的线性检测范围，通过分析在临床验证中达到了 100% 的准确率来自临床肺癌患者的 20 个样本。凭借固有的灵敏度和选择性，提议的 PASTA 方法将导致开发用于可靠的分子亚型分型的通用平台。