Spatiotemporal Identification of Therapeutic Markers via Dual-targeted SERS Bioprobes During Breast Cancer Progression

The precise breast cancer treatment heavily relies on the accurate identification of therapeutic markers. Single surface-enhanced Raman spectroscopy (SERS) bioprobes have been demonstrated for tumor primary screening. However, single biomarker detection is insufficient due to tumor heterogeneity and drug resistance with cancer progression. In this study, we have developed a dual-targeted SERS sensing platform using IO NPs@AR@PDA@aPD-L1 and Au NPs@4-MBA@PDA@aHER2 bioprobes for simultaneous detection of dual therapeutic markers (PD-L1 and HER2) in breast cancer, achieving single-cell resolution imaging with a detection limit of 2 cells/mL. Specifically, by digitizing SERS spectra using machine learning, the dual-targeted strategy improved spatial resolution and effectively distinguished the heterogeneous expression of HER2 and PD-L1 in SK-BR-3, MCF-7, and MDA-MB-231 cell lines, improving detection accuracy by ~20% compared to single-marker methods. In the temporal dimension, transforming growth factor-β (TGF-β) was used to induce epithelial-mesenchymal transition (EMT) in MCF-7, simulating the highly invasive and metastatic characteristics of advanced tumors, thereby enabling dynamic monitoring of cancer progression from locally advanced to metastatic disease. This continuous monitoring capability of the dual-targeted strategy offers critical insights into tumor evolution, metastasis risk assessment, and recurrence prediction, thereby playing a vital role in guiding precision cancer management and treatment adaptation.