Medulloblastoma is the most common malignant brain tumor in childhood. Molecular studies from several groups around the world demonstrated that medulloblastoma is not one disease but comprises a collection of distinct molecular subgroups. However, all these studies reported on different numbers of subgroups. The current consensus is that there are only four core subgroups, which should be termed WNT, SHH, Group 3 and Group 4. Based on this, we performed a meta-analysis of all molecular and clinical data of 550 medulloblastomas brought together from seven independent studies. All cases were analyzed by gene expression profiling and for most cases SNP or array-CGH data were available. Data are presented for all medulloblastomas together and for each subgroup separately. For validation purposes, we compared the results of this meta-analysis with another large medulloblastoma cohort (n = 402) for which subgroup information was obtained by immunohistochemistry. Results from both cohorts are highly similar and show how distinct the molecular subtypes are with respect to their transcriptome, DNA copy-number aberrations, demographics, and survival. Results from these analyses will form the basis for prospective multi-center studies and will have an impact on how the different subgroups of medulloblastoma will be treated in the future.Electronic supplementary materialThe online version of this article (doi:10.1007/s00401-012-0958-8) contains supplementary material, which is available to authorized users.

Diffuse Intrinsic Pontine Glioma (DIPG) is a fatal childhood cancer. We performed a chemical screen in patient-derived DIPG cultures along with RNAseq analyses and integrated computational modeling to identify potentially effective therapeutic strategies. The multi-histone deacetylase inhibitor panobinostat demonstrated efficacy in vitro and in DIPG orthotopic xenograft models. Combination testing of panobinostat with histone demethylase inhibitor GSKJ4 revealed synergy. Together, these data suggest a promising therapeutic strategy for DIPG.

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We developed a DIPG survival prediction tool that can be used to predict the outcome of patients and for stratification in trials. Validation of the model is needed in a prospective cohort.