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Cell line-based xenograft mouse model of paediatric glioma stem cells mirrors the clinical course of the patient

Journal article
Authors Susanna Larsson
Anna Wenger
S. Dosa
Magnus Sabel
Teresia Kling
Helena Carén
Published in Carcinogenesis
Volume 39
Issue 10
Pages 1304-1309
ISSN 0143-3334
Publication year 2018
Published at Sahlgrenska Cancer Center
Institute of Biomedicine, Department of Pathology
Institute of Clinical Sciences, Department of Pediatrics
Pages 1304-1309
Language en
Keywords DNA methylation, glioblastoma, tumors, identification, mutations, h3f3a, tool
Subject categories Pediatrics, Cancer and Oncology


The leading cause of cancer-related mortality among children is brain tumour, and glioblastoma multiforme (GBM) has the worst prognosis. New treatments are urgently needed, but with few cases and clinical trials in children, pre-clinical models such as patient-derived tumour xenografts (PDTX) are important. To generate these, tumour tissue is transplanted into mice, but this yields highly variable results and requires serial passaging in mice, which is time-consuming and expensive. We therefore aimed to establish a cell line-based orthotopic mouse model representative of the patient tumour. Glioma stem cell (GSC) lines derived from paediatric GBM were orthotopically transplanted into immunodeficient mice. Overall survival data were collected and histological analysis of the resulting neoplasias was performed. Genome-wide DNA methylation arrays were used for methylation and copy-number alterations (CNA) profiling. All GSC lines initiated tumours on transplantation and the survival of the mice correlated well with the survival of the patients. Xenograft tumours presented histological hallmarks of GBM, and were also classified as GBM by methylation profiling. Each xenograft tumour clustered together with its respective injected GSC line and patient tumour based on the methylation data. We have established a robust and reproducible cell line-based xenograft paediatric GBM model. The xenograft tumours accurately reflected the patient tumours and mirrored the clinical course of the patient. This model can therefore be used to assess patient response in pre-clinical studies.

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