HER2 Expression in Different Cell Lines at Different Inoculation Sites Assessed by [52Mn]mn-Dotaga(anhydride)-trastuzumab
Pathology oncology research POR(2025)
Abstract
Purpose:Positron emission tomography (PET) hybrid imaging targeting HER2 requires antibodies labelled with longer half-life isotopes. With a suitable radiation profile, 52Mn coupled with DOTAGA as a bifunctional chelator is a potential candidate. In this study, we investigated the tumor HER2 specificity and the temporal biodistribution of the [52Mn]Mn-DOTAGA(anhydride)-trastuzumab in preclinical models. Methods:PET/MRI and PET/CT were performed on SCID mice bearing orthotopic and ectopic HER2-positive and ectopic HER2-negative tumors at 4, 24, 48, 72, and 120 h post-injection with [52Mn]Mn-DOTAGA(anhydride)-trastuzumab. Melanoma xenografts were included for comparison of specificity. Results:In vivo biodistribution demonstrated strong contrast in HER2-positive tumors, particularly in orthotopic tumors, where uptake was significantly higher than in the blood pool and other organs from 24 h onwards and consistently higher than in ectopic HER2-positive tumors at all time points. Significantly higher tumor-to-blood and tumor-to-muscle ratios were observed in HER2-positive ectopic tumors compared to HER2-negative tumors but only at 4 and 24 h; the differences were likely due to non-specific binding of the tracer. The ratios for orthotopic HER2-positive tumors were significantly higher than those for ectopic HER2-negative tumors and melanoma at all time points. However, the differences between HER2-positive and HER2-negative tumors decreased at later time points. Conclusion:These results suggest that [52Mn]Mn-DOTAGA(anhydride)-trastuzumab demonstrates efficient tumor-to-background contrast, emphasize the higher tumor uptake observed in orthotopic tumors, and highlight the influence of tumor environment characteristics on uptake.
MoreTranslated text
Key words
breast cancer,HER2,trastuzumab,positron emission tomography,52Mn
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined