期刊
ANALYTICAL CHEMISTRY
卷 -, 期 -, 页码 -出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.2c02419
关键词
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资金
- National Natural Science Foundation of China [51871246, 81702603]
- Hunan Provincial Science & Technology Department [2017XK2027]
- Open Sharing Fund for the Large-scale Instruments and Equipments of Central South University [CSUZC202201]
- Taishan Scholar Project of Shandong Province [tsqn201812135]
- China Postdoctoral Science Foundation [2021T140408, 2021M691934]
A machine learning-driven surface-enhanced Raman spectroscopy (SERS)-integrated strategy is developed for label-free detection of cellular HER2, achieving high accuracy in cell identification and classification for HER2+ breast cancer. The combination of label-free SERS detection and machine learning-driven analysis enables longitudinal monitoring of therapeutic efficacy, aiding in decision-making and management.
The expression of human epidermal growth factor receptor-2 (HER2) has important implications for pathogenesis, progression, and therapeutic efficacy of breast cancer. The detection of its variation during the treatment is crucial for therapeutic decision-making but remains a grand challenge, especially at the cellular level. Here, we develop a machine learning-driven surface-enhanced Raman spectroscopy (SERS)-integrated strategy for label-free detection of cellular HER2. Specifically, our method allows the extraction of cell-rich spectral signatures utilized for identification and classification of cancer cells with distinct HER2 expression with a high accuracy of 99.6%. By combining label-free SERS detection and machine learning-driven chemometric analysis, we are able to perform longitudinal monitoring of therapeutic efficacy at the cellular level during the treatment of HER2+ breast cancer, which aids in the subsequent decision-making and management. This work provides a promising technique capable of performing dynamic label-free spectroscopic detection for therapeutic surveillance of diseases.
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