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Historical risk stratification criteria for medulloblastoma rely primarily on clinicopathological variables pertaining to age, presence of metastases, extent of resection, histological subtypes and in some instances individual genetic aberrations such as MYC and MYCN amplification. In 2010, an international panel of experts established consensus defining four main subgroups of medulloblastoma (WNT, SHH, Group 3 and Group 4) delineated by transcriptional profiling. This has led to the current generation of biomarker-driven clinical trials assigning WNT tumors to a favorable prognosis group in addition to clinicopathological criteria including MYC and MYCN gene amplifications. However, outcome prediction of non-WNT subgroups is a challenge due to inconsistent survival reports. In 2015, a consensus conference was convened in Heidelberg with the objective to further refine the risk stratification in the context of subgroups and agree on a definition of risk groups of non-infant, childhood medulloblastoma (ages 3–17). Published and unpublished data over the past 5 years were reviewed, and a consensus was reached regarding the level of evidence for currently available biomarkers. The following risk groups were defined based on current survival rates: low risk (>90 % survival), average (standard) risk (75–90 % survival), high risk (50–75 % survival) and very high risk (<50 % survival) disease. The WNT subgroup and non-metastatic Group 4 tumors with whole chromosome 11 loss or whole chromosome 17 gain were recognized as low-risk tumors that may qualify for reduced therapy. High-risk strata were defined as patients with metastatic SHH or Group 4 tumors, or MYCN -amplified SHH medulloblastomas. Very high-risk patients are Group 3 with metastases or SHH with TP53 mutation. In addition, a number of consensus points were reached that should be standardized across future clinical trials. Although we anticipate new data will emerge from currently ongoing and recently completed clinical trials, this consensus can serve as an outline for prioritization of certain molecular subsets of tumors to define and validate risk groups as a basis for future clinical trials.

Charles Roberts, Peter Park, Bradley Bernstein and colleagues examine the consequences of SMARCB1 loss on enhancer landscapes in human rhabdoid tumors. They show that SMARCB1 is essential for the integrity and abundance of SWI/SNF complexes and facilitates their targeting to appropriate enhancers. SMARCB1 (also known as SNF5, INI1, and BAF47), a core subunit of the SWI/SNF (BAF) chromatin-remodeling complex 1 , 2 , is inactivated in nearly all pediatric rhabdoid tumors 3 , 4 , 5 . These aggressive cancers are among the most genomically stable 6 , 7 , 8 , suggesting an epigenetic mechanism by which SMARCB1 loss drives transformation. Here we show that, despite having indistinguishable mutational landscapes, human rhabdoid tumors exhibit distinct enhancer H3K27ac signatures, which identify remnants of differentiation programs. We show that SMARCB1 is required for the integrity of SWI/SNF complexes and that its loss alters enhancer targeting—markedly impairing SWI/SNF binding to typical enhancers, particularly those required for differentiation, while maintaining SWI/SNF binding at super-enhancers. We show that these retained super-enhancers are essential for rhabdoid tumor survival, including some that are shared by all subtypes, such as SPRY1 , and other lineage-specific super-enhancers, such as SOX2 in brain-derived rhabdoid tumors. Taken together, our findings identify a new chromatin-based epigenetic mechanism underlying the tumor-suppressive activity of SMARCB1.

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets. microarray

PurposeMedulloblastoma, the most common primary pediatric malignant brain tumor, originates in the posterior fossa of the brain. Pineoblastoma, which originates within the pineal gland, is a rarer malignancy that also presents in the pediatric population. Medulloblastoma and pineoblastoma exhibit overlapping clinical features and have similar histopathological characteristics. Histopathological similarities confound rapid diagnoses of these two tumor types. We have conducted a pilot feasibility study analyzing the molecular profile of archived frozen human tumor specimens using mass spectrometry imaging (MSI) to identify potential biomarkers capable of classifying and distinguishing between medulloblastoma and pineoblastoma.MethodsWe performed matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry imaging on eight medulloblastoma biopsy specimens and three pineoblastoma biopsy specimens. Multivariate statistical analyses were performed on the MSI dataset to generate classifiers that distinguish the two tumor types. Lastly, the molecules that were discriminative of tumor type were queried against the Lipid Maps database and identified.ResultsIn this pilot study we show that medulloblastoma and pineoblastoma can be discriminated using molecular profiles determined by MSI. The highest-ranking discriminating classifiers of medulloblastoma and pineoblastoma were glycerophosphoglycerols and sphingolipids, respectively.ConclusionWe demonstrate proof-of-concept that medulloblastoma and pineoblastoma can be rapidly distinguished by using MSI lipid profiles. We identified biomarker candidates capable of distinguishing these two histopathologically similar tumor types. This work expands the current molecular knowledge of medulloblastoma and pineoblastoma by characterizing their lipidomic profiles, which may be useful for developing novel diagnostic, prognostic and therapeutic strategies.

Medulloblastomas are the most common malignant brain tumors in children. Several large-scale genomic studies have detailed their heterogeneity, defining multiple subtypes with unique molecular profiles and clinical behavior. Increased expression of the miR-183~96~182 cluster of microRNAs has been noted in several subgroups, including the most clinically aggressive subgroup associated with genetic amplification of MYC . To understand the contribution of miR-183~96~182 to the pathogenesis of this aggressive subtype of medulloblastoma, we analyzed global gene expression and proteomic changes that occur upon modulation of miRNAs in this cluster individually and as a group in MYC -amplified medulloblastoma cells. Knockdown of the full miR-183~96~182 cluster results in enrichment of genes associated with apoptosis and dysregulation of the PI3K/AKT/mTOR signaling axis. Conversely, there is a relative enrichment of pathways associated with migration, metastasis and epithelial to mesenchymal transition, as well as pathways associated with dysfunction of DNA repair in cells with preserved miR-183 cluster expression. Immunocytochemistry and FACS analysis confirm induction of apoptosis upon knockdown of the miR-183 cluster. Importantly, cell-based migration and invasion assays verify the positive regulation of cell motility/migration by the miR-183 cluster, which is largely mediated by miR-182. We show that the effects on cell migration induced by the miR-183 cluster are coupled to the PI3K/AKT/mTOR pathway through differential regulation of AKT1 and AKT2 isoforms. Furthermore, we show that rapamycin inhibits cell motility/migration in medulloblastoma cells and phenocopies miR-183 cluster knockdown. Thus, the miR-183 cluster regulates multiple biological programs that converge to support the maintenance and metastatic potential of medulloblastoma.

Polyubiquitination of the tumor suppressor p53 (encoded by TP53 ) regulates its stability by targeting it for degradation. Wu et al . now report that UBE4B, an E3 and E4 ligase, is a key enzyme in this process and that overexpression of UBE4B in some brain tumors is associated with reduced p53 abundance, suggesting a previously unknown mechanism blocking p53 function in cancer. The TP53 gene (encoding the p53 tumor suppressor) is rarely mutated, although frequently inactivated, in medulloblastoma and ependymoma. Recent work in mouse models showed that the loss of p53 accelerated the development of medulloblastoma. The mechanism underlying p53 inactivation in human brain tumors is not completely understood. We show that ubiquitination factor E4B (UBE4B), an E3 and E4 ubiquitin ligase, physically interacts with p53 and Hdm2 (also known as Mdm2 in mice). UBE4B promotes p53 polyubiquitination and degradation and inhibits p53-dependent transactivation and apoptosis. Notably, silencing UBE4B expression impairs xenotransplanted tumor growth in a p53-dependent manner and overexpression of UBE4B correlates with decreased expression of p53 in these tumors. We also show that UBE4B overexpression is often associated with amplification of its gene in human brain tumors. Our data indicate that amplification and overexpression of UBE4B represent previously undescribed molecular mechanisms of inactivation of p53 in brain tumors.

Tumour cells release an abundance of microvesicles containing a selected set of proteins and RNAs. Here, we show that tumour microvesicles also carry DNA, which reflects the genetic status of the tumour, including amplification of the oncogene c-Myc . We also find amplified c-Myc in serum microvesicles from tumour-bearing mice. Further, we find remarkably high levels of retrotransposon RNA transcripts, especially for some human endogenous retroviruses, such as LINE-1 and Alu retrotransposon elements, in tumour microvesicles and these transposable elements could be transferred to normal cells. These findings expand the nucleic acid content of tumour microvesicles to include: elevated levels of specific coding and non-coding RNA and DNA, mutated and amplified oncogene sequences and transposable elements. Thus, tumour microvesicles contain a repertoire of genetic information available for horizontal gene transfer and potential use as blood biomarkers for cancer. Microvesicles containing RNA are released from tumour cells. Here, the authors show that microvesicles released from tumour cells in culture have amplified levels of the c-Myc oncogene, which is also found in the cell of origin, suggesting that microvesicles could be used as biomarkers.

Background Computational phenotypes are most often combinations of patient billing codes that are highly predictive of disease using electronic health records (EHR). In the case of rare diseases that can only be diagnosed by genetic testing, computational phenotypes identify patient cohorts for genetic testing and possible diagnosis. This article details the validation of a computational phenotype for PTEN hamartoma tumor syndrome (PHTS) against the EHR of patients at three collaborating clinical research centers: Boston Children's Hospital, Children's National Hospital, and the University of Washington. Methods A combination of billing codes from the International Classification of Diseases versions 9 and 10 (ICD-9 and ICD-10) for diagnostic criteria postulated by a research team at Cleveland Clinic was used to identify patient cohorts for genetic testing from the clinical data warehouses at the three research centers. Subsequently, the EHR—including billing codes, clinical notes, and genetic reports—of these patients were reviewed by clinical experts to identify patients with PHTS. Results The PTEN genetic testing yield of the computational phenotype, the number of patients who needed to be genetically tested for incidence of pathogenic PTEN gene variants, ranged from 82 to 94% at the three centers. Conclusions Computational phenotypes have the potential to enable the timely and accurate diagnosis of rare genetic diseases such as PHTS by identifying patient cohorts for genetic sequencing and testing.

Snf5 (Ini1/Baf47/Smarcb1), a core member of the Swi/Snf chromatin remodeling complex, is a potent tumor suppressor whose mechanism of action is largely unknown. Biallelic loss of Snf5 leads to the onset of aggressive cancers in both humans and mice. We have developed an innovative and widely applicable analytical technique for cross-species validation of cancer models and show that the gene expression profiles of our Snf 5 murine models closely resemble those of human Snf5-deficient rhabdoid tumors. We exploit this system to produce what we believe to be the first report documenting the effects on gene expression of inactivating a Swi/Snf subunit in normal mammalian cells and to identify the transcriptional pathways regulated by Snf5. We demonstrate that the tumor suppressor activity of Snf5 depends on its regulation of cell cycle progression; Snf5 inactivation leads to aberrant upregulation of E2F targets and increased levels of p53 that are accompanied by apoptosis, polyploidy, and growth arrest. Further, conditional mouse models demonstrate that inactivation of p16lnk4a or Rb (retinoblastoma) does not accelerate tumor formation in Snf5 conditional mice, whereas mutation of p53 leads to a dramatic acceleration of tumor formation.