Establishment and characterization of the first patient-derived radiation-induced angiosarcoma xenograft model (RT-AS5).
Publication year
2023Source
Scientific Reports, 13, 1, (2023), pp. 2653, article 2653ISSN
Publication type
Article / Letter to editor
Display more detailsDisplay less details
Organization
Medical Oncology
Central Animal Laboratory
Human Genetics
Pathology
Journal title
Scientific Reports
Volume
vol. 13
Issue
iss. 1
Page start
p. 2653
Subject
Radboudumc 2: Cancer development and immune defence Pathology; Radboudumc 7: Neurodevelopmental disorders DCMN: Donders Center for Medical Neuroscience; Radboudumc 9: Rare cancers Medical Oncology; Radboudumc 9: Rare cancers Pathology; Radboud University Medical CenterAbstract
Angiosarcomas are a heterogeneous group of rare endothelial malignancies with a complex, not completely unravelled biology. They encompass primary (sporadically occurring) angiosarcomas of several origins and secondary angiosarcomas, which often arise due to DNA damaging factors including radiotherapy or ultraviolet light exposure. The optimal treatment of metastatic angiosarcomas is unclear and the prognosis is poor. In order to discover novel treatment strategies for angiosarcomas it is important to take the heterogeneity of these tumors into account. For this reason it is also important to have preclinical models available for the different clinical subtypes. Owing to the rarity of angiosarcomas, models are scarce. So far, only five human cell lines of angiosarcomas (all of the scalp after UV exposure) are available worldwide. In this paper we describe a novel established patient-derived xenograft model of a radiotherapy-induced angiosarcoma of the breast. The tumor was characterized by a MYC amplification, CD31 and ERG immunohistochemical positivity and was further characterized by using next generation sequencing (TruSight Oncology 500) in combination with the R-package XenofilteR to separate mouse from human sequence reads.
This item appears in the following Collection(s)
- Academic publications [246515]
- Electronic publications [134157]
- Faculty of Medical Sciences [93308]
- Open Access publications [107690]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.