Background: Medulloblastoma is a cerebellar neoplasia of the central nervous system. Four molecular subgrups have been identified (MBWNT, MBSHH, MBgroup3 and MBgroup4) with distinct genetics and clinical outcome. Among these, MBgroup3-4 are highly metastatic with the worst prognosis. The current standard therapy includes surgery, radiation and chemotherapy. Thus, specific treatments adapted to cure those different molecular subgroups are needed. The use of orthotopic xenograft models, together with the non-invasive in vivo biolumiscence imaging (BLI) technology, is emerging during preclinical studies to test novel therapeutics for medulloblastoma treatment.
Methods: Orthotopic MB xenografts were performed by injection of Daoy-luc cells, that had been previously infected with lentiviral particles to stably express luciferase gene, into the fourth right ventricle of the cerebellum of ten nude mice. For the implantation, specific stereotactic coordinates were used. Seven days after the implantation the mice were imaged by acquisitions of bioluminescence imaging (BLI) using IVIS 3D Illumina Imaging System (Xenogen). Tumor growth was evaluated by quantifying the bioluminescence signals using the integrated fluxes of photons within each area of interest using the Living Images Software Package 3.2 (Xenogen-Perkin Elmer). Finally, histological analysis using hematoxylin-eosin staining was performed to confirm the presence of tumorigenic cells into the cerebellum of the mice.
Results: We describe a method to use the in vivo bioluminescent imaging (BLI) showing the potential to be used to investigate the potential antitumorigenic effects of a drug for in vivo medulloblastoma treatment. We also discuss other studies in which this technology has been applied to obtain a more comprehensive knowledge of medulloblastoma using orthotopic xenograft mouse models.
Conclusions: There is a need to develop patient's derived-xenograft (PDX) model systems to test novel drugs for medulloblastoma treatment within each molecular sub-groups with a higher predictive value. Here we show how this technology should be applied with hopes on generations of new treatments to be applied then in human.