Dataset Information

Development of a Rare Earth Nanoprobe Enables In Vivo Real-Time Detection of Sentinel Lymph Node Metastasis of Breast Cancer Using NIR-IIb Imaging.

ABSTRACT: Sentinel lymph node (SLN) biopsy plays a critical role in axillary staging of breast cancer. However, traditional SLN mapping does not accurately discern the presence or absence of metastatic disease. Detection of SLN metastasis largely hinges on examination of frozen sections or paraffin-embedded tissues post-SLN biopsy. To improve detection of SLN metastasis, we developed a second near-infrared (NIR-II) in vivo fluorescence imaging system, pairing erbium-based rare-earth nanoparticles (ErNP) with bright down-conversion fluorescence at 1,556 nm. To visualize SLNs bearing breast cancer, ErNPs were modified by balixafortide (ErNPs@POL6326), a peptide antagonist of the chemokine receptor CXCR4. The ErNPs@POL6326 probes readily drained into SLNs when delivered subcutaneously, entering metastatic breast tumor cells specifically via CXCR4-mediated endocytosis. NIR fluorescence signals increased significantly in tumor-positive versus tumor-negative SLNs, enabling accurate determination of SLN breast cancer metastasis. In a syngeneic mouse mammary tumor model and a human breast cancer xenograft model, sensitivity for SLN metastasis detection was 92.86% and 93.33%, respectively, and specificity was 96.15% and 96.08%, respectively. Of note, the probes accurately detected both macrometastases and micrometastases in SLNs. These results overall underscore the potential of ErNPs@POL6326 for real-time visualization of SLNs and in vivo screening for SLN metastasis.

Significance

NIR-IIb imaging of a rare-earth nanoprobe that is specifically taken up by breast cancer cells can accurately detect breast cancer macrometastases and micrometastases in sentinel lymph nodes.

SUBMITTER: Zhu YY

PROVIDER: S-EPMC10570679 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Development of a Rare Earth Nanoprobe Enables In Vivo Real-Time Detection of Sentinel Lymph Node Metastasis of Breast Cancer Using NIR-IIb Imaging.

Zhu Yuan-Yuan YY Song Liang L Zhang Yong-Qu YQ Liu Wan-Ling WL Chen Wei-Ling WL Gao Wen-Liang WL Zhang Li-Xin LX Wang Jia-Zheng JZ Wang Jia-Zheng JZ Ming Zi-He ZH Zhang Yun Y Zhang Guo-Jun GJ

Cancer research 20231001 20

Sentinel lymph node (SLN) biopsy plays a critical role in axillary staging of breast cancer. However, traditional SLN mapping does not accurately discern the presence or absence of metastatic disease. Detection of SLN metastasis largely hinges on examination of frozen sections or paraffin-embedded tissues post-SLN biopsy. To improve detection of SLN metastasis, we developed a second near-infrared (NIR-II) in vivo fluorescence imaging system, pairing erbium-based rare-earth nanoparticles (ErNP) w ...[more]

PMID: 37540231

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Project description:Purpose: Sentinel lymph node biopsy (SLNB) has emerged as the preferred standard procedure in patients with breast cancer, melanoma and other types of cancer. Herein, we developed a method to intra-operatively map SLNs and differentiate tumor metastases within SLNs at the same time, with the aim to provide more accurate and real-time intraoperative guidance. Experimental Design: Hyaluronic acid (HA), a ligand of lymphatic vessel endothelial hyaluronan receptor (LYVE)-1, is employed as a SLN mapping agent after being conjugated with a near-infrared fluorophore (Cy5.5). Different sized HAs (5, 10 and 20K) were tested in normal mice and mice with localized inflammation to optimize LN retention time and signal to background ratio. Cetuximab, an antibody against epidermal growth factor receptor (EGFR), and trastuzumab, an antibody against human epidermal growth factor receptor 2 (HER2), were labeled with near-infrared fluorophore (IRDye800) for detecting metastatic tumors. LN metastasis model was developed by hock injection of firefly luciferase engineered human head neck squamous carcinoma cancer UM-SCC-22B cells or human ovarian cancer SKOV-3 cells. The metastases within LNs were confirmed by bioluminescence imaging (BLI). IRDye800-Antibodies were intravenously administered 24 h before local administration of Cy5.5-HA. Optical imaging was then performed to identify nodal metastases. Results: Binding of HA with LYVE-1 was confirmed by ELISA and fluorescence staining. HA with a size of 10K was chosen based on the favorable migration and retention profile. After sequential administration of IRDye800-antibodies intravenously and Cy5.5-HA locally to a mouse model with LN metastases and fluorescence optical imaging, partially metastasized LNs were successfully distinguished from un-metastasized LNs and fully tumor occupied LNs, based on the different signal patterns. Conclusions: Fluorophore conjugated HA is a potential lymphatic mapping agent for SLNB. Dual-tracer imaging with the combination of lymphatic mapping agents and tumor targeting agents can identify tumor metastases within SLNs, thus may provide accurate and real-time intra-operative guidance to spare the time spent waiting for a biopsy result.

Project description:BackgroundThe rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy. It is great important to explore the possibility of omitting axillary lymph node dissection (ALND) in early-stage breast cancer patients with 1 or 2 positive sentinel lymph nodes (SLNs) in China. The aim of this study was to develop a nomogram based on elastography for the prediction of the risk of non-SLN (NSLN) metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs.MethodsA total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. In the training cohort, the independent predictors were screened by logistic regression analysis and then were used to conducted the nomogram for the prediction of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The calibration curves, concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and Decision curve analysis (DCA) were used to verified the performance of the nomogram.ResultsThe multivariable analysis showed that the enrolled patients with positive HER2 expression (OR=6.179, P=0.013), Ki67≥14% (OR=8.976, P=0.015), larger lesion size (OR=1.038, P=0.045), and higher Emean (OR=2.237, P=0.006) were observed to be the independent factors of NSLN metastasis. Based on the above four independent predictors, a nomogram was conducted to predict the risk of the NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The nomogram showed good discrimination in the prediction of NSLN metastasis, with bias-corrected C-index of 0.855 (95% CI, 0.754-0.956) and 0.853 (95% CI, 0.724-0.983) in the training and validation cohorts, respectively. Furthermore, the AUC was 0.877 (95%CI: 0.776- 0.978) and 0.861 (95%CI: 0.732-0.991), respectively, indicating a good performance of the nomogram. The calibration curve suggested a satisfactory agreement between the predictive and actual risk in both the training (χ2 = 11.484, P=0.176, HL test) and validation (χ2 = 6.247, p = 0.620, HL test) cohorts, and the obvious clinical nets were revealed by DCA.ConclusionsWe conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND.

Dataset Information

Development of a Rare Earth Nanoprobe Enables In Vivo Real-Time Detection of Sentinel Lymph Node Metastasis of Breast Cancer Using NIR-IIb Imaging.

Significance

Publications

Development of a Rare Earth Nanoprobe Enables In Vivo Real-Time Detection of Sentinel Lymph Node Metastasis of Breast Cancer Using NIR-IIb Imaging.

Similar Datasets

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