Clinically accessible drug-based nano-assemblies with self-targeting ability for NIR-II fluorescence imaging-guided surgery in triple-negative breast cancer.
Accurate intraoperative visualization is critical for reducing margin positivity during breast-conserving surgery for triple-negative breast cancer (TNBC). Second near-infrared (NIR-II) fluorescence imaging represents a promising approach for precision surgery by combining lesion detection with real-time guidance. Nevertheless, the clinical translation of most fluorescence agents remains hampered by carrier-related toxicity and complex synthesis. Therefore, a "green" drug-repurposing strategy was adopted here to construct carrier-free pure-drug nano-assemblies (PDNAs), aiming to provide a biocompatible and precise intraoperative navigation tool for TNBC resection.
We developed a novel PDNA system (CF-ICG) formed by the simple self-assembly of two clinically employed drugs: calcium folinate and indocyanine green. The targeting specificity of CF-ICG and the feasibility of NIR-II fluorescence-guided surgery were validated using MDA-MB-231-Luc xenograft and MMTV-PyVT transgenic models. A rapid ex vivo incubation protocol was developed to differentiate breast cancer from para-cancer tissues.
Driven by intrinsic Ca2+ from CF, CF-ICG was assembled through π-π stacking and electrostatic interactions, demonstrating stable physicochemical properties and FRα self-targeting ability. In vivo imaging provided high-contrast NIR-II signals for real-time surgical navigation and enabled precise identification of residual submillimeter tumor lesions (diameter ~0.9 mm) in MDA-MB-231-Luc xenograft models. It also clearly differentiated malignant from normal breast tissues in MMTV-PyVT transgenic mice (AUC = 0.941). Furthermore, the diagnostic performance of the rapid ex vivo incubation protocol was preliminarily validated using surgical specimens from TNBC patients (n = 11). Notably, this approach effectively differentiated tumors from para-cancer tissues within 12 min (AUC = 0.926).
By combining a "green" fabrication process with a drug-repurposing strategy, we developed CF-ICG as a carrier-free PDNA with tumor self-targeting capability, enabling precise intraoperative navigation in preclinical models and ex vivo tissues. These findings support the further development of this approach for more accurate tumor visualization and surgical decision-making in TNBC.
We developed a novel PDNA system (CF-ICG) formed by the simple self-assembly of two clinically employed drugs: calcium folinate and indocyanine green. The targeting specificity of CF-ICG and the feasibility of NIR-II fluorescence-guided surgery were validated using MDA-MB-231-Luc xenograft and MMTV-PyVT transgenic models. A rapid ex vivo incubation protocol was developed to differentiate breast cancer from para-cancer tissues.
Driven by intrinsic Ca2+ from CF, CF-ICG was assembled through π-π stacking and electrostatic interactions, demonstrating stable physicochemical properties and FRα self-targeting ability. In vivo imaging provided high-contrast NIR-II signals for real-time surgical navigation and enabled precise identification of residual submillimeter tumor lesions (diameter ~0.9 mm) in MDA-MB-231-Luc xenograft models. It also clearly differentiated malignant from normal breast tissues in MMTV-PyVT transgenic mice (AUC = 0.941). Furthermore, the diagnostic performance of the rapid ex vivo incubation protocol was preliminarily validated using surgical specimens from TNBC patients (n = 11). Notably, this approach effectively differentiated tumors from para-cancer tissues within 12 min (AUC = 0.926).
By combining a "green" fabrication process with a drug-repurposing strategy, we developed CF-ICG as a carrier-free PDNA with tumor self-targeting capability, enabling precise intraoperative navigation in preclinical models and ex vivo tissues. These findings support the further development of this approach for more accurate tumor visualization and surgical decision-making in TNBC.
Authors
Yang Yang, Lou Lou, Bao Bao, Yang Yang, Gai Gai, Chen Chen, Yang Yang, Qiu Qiu, Lin Lin, Zhao Zhao, Li Li
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