Survivin as a therapeutic target in Sonic hedgehog-driven medulloblastoma.

Medulloblastoma (MB) is a highly malignant brain tumor that occurs primarily in children. Although surgery, radiation and high-dose chemotherapy have led to increased survival, many MB patients still die from their disease, and patients who survive suffer severe long-term side effects as a consequence of treatment. Thus, more effective and less toxic therapies for MB are critically important. Development of such therapies depends in part on identification of genes that are necessary for growth and survival of tumor cells. Survivin is an inhibitor of apoptosis protein that regulates cell cycle progression and resistance to apoptosis, is frequently expressed in human MB and when expressed at high levels predicts poor clinical outcome. Therefore, we hypothesized that Survivin may have a critical role in growth and survival of MB cells and that targeting it may enhance MB therapy. Here we show that Survivin is overexpressed in tumors from patched (Ptch) mutant mice, a model of Sonic hedgehog (SHH)-driven MB. Genetic deletion of survivin in Ptch mutant tumor cells significantly inhibits proliferation and causes cell cycle arrest. Treatment with small-molecule antagonists of Survivin impairs proliferation and survival of both murine and human MB cells. Finally, Survivin antagonists impede growth of MB cells in vivo. These studies highlight the importance of Survivin in SHH-driven MB, and suggest that it may represent a novel therapeutic target in patients with this disease

MM2S: personalized diagnosis of medulloblastoma patients and model systems

Abstract Background Medulloblastoma (MB) is a highly malignant and heterogeneous brain tumour that is the most common cause of cancer-related deaths in children. Increasing availability of genomic data over the last decade had resulted in improvement of human subtype classification methods, and the parallel development of MB mouse models towards identification of subtype-specific disease origins and signaling pathways. Despite these advances, MB classification schemes remained inadequate for personalized prediction of MB subtypes for individual patient samples and across model systems. To address this issue, we developed the Medullo-Model to Subtypes ( MM2S ) classifier, a new method enabling classification of individual gene expression profiles from MB samples (patient samples, mouse models, and cell lines) against well-established molecular subtypes [Genomics 106:96-106, 2015]. We demonstrated the accuracy and flexibility of MM2S in the largest meta-analysis of human patients and mouse models to date. Here, we present a new functional package that provides an easy-to-use and fully documented implementation of the MM2S method, with additional functionalities that allow users to obtain graphical and tabular summaries of MB subtype predictions for single samples and across sample replicates. The flexibility of the MM2S package promotes incorporation of MB predictions into large Medulloblastoma-driven analysis pipelines, making this tool suitable for use by researchers. Results The MM2S package is applied in two case studies involving human primary patient samples, as well as sample replicates of the GTML mouse model. We highlight functions that are of use for species-specific MB classification, across individual samples and sample replicates. We emphasize on the range of functions that can be used to derive both singular and meta-centric views of MB predictions, across samples and across MB subtypes. Conclusions Our MM2S package can be used to generate predictions without having to rely on an external web server or additional sources. Our open-source package facilitates and extends the MM2S algorithm in diverse computational and bioinformatics contexts. The package is available on CRAN, at the following URL: https://cran.r-project.org/web/packages/MM2S/ , as well as on Github at the following URLs: https://github.com/DGendoo and https://github.com/bhklab