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Generation of patient-derived pediatric pilocytic astrocytoma in-vitro models using SV40 large T: evaluation of a modeling workflow. | LitMetric
Purpose : Although pediatric low-grade gliomas (pLGG) are the most common pediatric brain tumors, patient-derived cell lines reflecting pLGG biology in culture are scarce. This also applies to the most common pLGG subtype pilocytic astrocytoma (PA). Conventional cell culture approaches adapted from higher-grade tumors fail in PA due to oncogene-induced senescence (OIS) driving tumor cells into arrest. Here, we describe a PA modeling workflow using the Simian Virus large T antigen (SV40-TAg) to circumvent OIS.Methods : 18 pLGG tissue samples (17 (94%) histological and/or molecular diagnosis PA) were mechanically dissociated. Tumor cell positive-selection using A2B5 was perfomed in 8/18 (44%) cases. All primary cell suspensions were seeded in Neural Stem Cell Medium (NSM) and Astrocyte Basal Medium (ABM). Resulting short-term cultures were infected with SV40-TAg lentivirus. Detection of tumor specific alterations (BRAF-duplication and BRAF V600E-mutation) by digital droplet PCR (ddPCR) at defined time points allowed for determination of tumor cell fraction (TCF) and evaluation of the workflow. DNA-methylation profiling and gene-panel sequencing were used for molecular profiling of primary samples.Results : Primary cell suspensions had a mean TCF of 55% (+/- 23% (SD)). No sample in NSM (0/18) and ten samples in ABM (10/18) were successfully transduced. Three of these ten (30%) converted into long-term pLGG cell lines (TCF 100%), while TCF declined to 0% (outgrowth of microenvironmental cells) in 7/10 (70%) cultures. Young patient age was associated with successful model establishment.Conclusion : A subset of primary PA cultures can be converted into long-term cell lines using SV40-TAg depending on sample intrinsic (patient age) and extrinsic workflow-related (e.g. type of medium, successful transduction) parameters. Careful monitoring of sample-intrinsic and extrinsic factors optimizes the process.
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