作者
Xiaofeng He,Jiahui Wang,Junwei Li,Yangsi Zheng,Wenqi Shi,Jiatian Song,Jiayu Li,Zhiyong Wu,Menglong Zhao,T. Zhai,Siqi Qiu
摘要
Current intraoperative cancer diagnosis predominantly relies on frozen section histopathology, which is time-consuming and prone to sampling bias. Development of techniques for intraoperative cancer diagnosis is urgently needed. Notably, malignant tumors manifest distinctive microenvironmental signatures characterized by acidic pH gradients and elevated ATP concentrations, which are important biomarkers for tumor identification. Based on these signatures, a pH/ATP dual-sensitive bioluminescent nanoprobe, d-luciferase-liposome-PEOz (DLPNPs), was engineered using thin-film hydration. The nanoprobe displayed a spherical morphology (∼140 nm in diameter), low pH-dependent activation (pH <6.5), and good biocompatibility in vitro. Ex vivo bioluminescence imaging of freshly resected specimens from five murine tumor models (breast, liver, colon, lung, and tongue) and five human cancer types (breast, colorectal, endometrial, lung, and gastric) demonstrated significantly elevated tumor signals compared to normal tissues. The median tumor-to-background ratios (TBRs) were 21.49 (IQR, 8.86–105.7) in mice and 47.89 (IQR, 5.27–238.9) in humans. The nanoprobe achieved 100% sensitivity and 100% specificity in cancer diagnosis, with an area under the curve (AUC) of 1.00 across all cancer types evaluated in both mouse and human tissues. Microscopic analysis further confirmed precise colocalization between bioluminescent signals and malignant regions. Notably, the entire workflow, from nanoprobe application to image acquisition, requires less than 10 min. These findings indicate that DLPNPs enable rapid and accurate intraoperative tumor diagnosis by leveraging tumor microenvironmental signatures. The multicancer applicability, high TBR, and streamlined workflow indicate that DLPNPs are worthy of further evaluation for clinical translation in future studies with larger sample sizes and more diverse cancer cohorts.