SUMMARY

Genomic rearrangements are thought to occur progressively during tumor development. Recent findings, however, suggest an alternative mechanism, involving massive chromosome rearrangements in a one-step catastrophic event termed chromothripsis. We report the whole-genome sequencing-based analysis of a Sonic-Hedgehog medulloblastoma (SHH-MB) brain tumor from a patient with a germline TP53 mutation (Li-Fraumeni syndrome), uncovering massive, complex chromosome rearrangements. Integrating TP53 status with microarray and deep sequencing-based DNA rearrangement data in additional patients reveals a striking association between TP53 mutation and chromothripsis in SHH-MBs. Analysis of additional tumor entities substantiates a link between TP53 mutation and chromothripsis, and indicates a context-specific role for p53 in catastrophic DNA rearrangements. Among these, we observed a strong association between somatic TP53 mutations and chromothripsis in acute myeloid leukemia. These findings connect p53 status and chromothripsis in specific tumor types, providing a genetic basis for understanding particularly aggressive subtypes of cancer.

Embryonal tumor with multilayered rosettes (ETMR, previously known as ETANTR) is a highly aggressive embryonal CNS tumor, which almost exclusively affects infants and is associated with a dismal prognosis. Accurate diagnosis is of critical clinical importance because of its poor response to current treatment protocols and its distinct biology. Amplification of the miRNA cluster at 19q13.42 has been identified previously as a genetic hallmark for ETMR, but an immunohistochemistry-based assay for clinical routine diagnostics [such as INI-1 for atypical teratoid rhabdoid tumor (AT/RT)] is still lacking. In this study, we screened for an ETMR-specific marker using a gene-expression profiling dataset of more than 1,400 brain tumors and identified LIN28A as a highly specific marker for ETMR. The encoded protein binds small RNA and has been implicated in stem cell pluripotency, metabolism and tumorigenesis. Using an LIN28A specific antibody, we carried out immunohistochemical analysis of LIN28A in more than 800 childhood brain-tumor samples and confirmed its high specificity for ETMR. Strong LIN28A immunoexpression was found in all 37 ETMR samples tested, whereas focal reactivity was only present in a small (6/50) proportion of AT/RT samples. All other pediatric brain tumors were completely LIN28A-negative. In summary, we established LIN28A immunohistochemistry as a highly sensitive and specific, rapid, inexpensive diagnostic tool for routine pathological verification of ETMR.

Pediatric glioblastomas (GBM) including diffuse intrinsic pontine gliomas (DIPG) are devastating brain tumors with no effective therapy. Here, we investigated clinical and biological impacts of histone H3.3 mutations. Forty-two DIPGs were tested for H3.3 mutations. Wild-type versus mutated (K27M-H3.3) subgroups were compared for HIST1H3B, IDH, ATRX and TP53 mutations, copy number alterations and clinical outcome. K27M-H3.3 occurred in 71 %, TP53 mutations in 77 % and ATRX mutations in 9 % of DIPGs. ATRX mutations were more frequent in older children (p < 0.0001). No G34V/R-H3.3, IDH1/2 or H3.1 mutations were identified. K27M-H3.3 DIPGs showed specific copy number changes, including all gains/amplifications of PDGFRA and MYC/PVT1 loci. Notably, all long-term survivors were H3.3 wild type and this group of patients had better overall survival. K27M-H3.3 mutation defines clinically and biologically distinct subgroups and is prevalent in DIPG, which will impact future therapeutic trial design. K27M- and G34V-H3.3 have location-based incidence (brainstem/cortex) and potentially play distinct roles in pediatric GBM pathogenesis. K27M-H3.3 is universally associated with short survival in DIPG, while patients wild-type for H3.3 show improved survival. Based on prognostic and therapeutic implications, our findings argue for H3.3-mutation testing at diagnosis, which should be rapidly integrated into the clinical decision-making algorithm, particularly in atypical DIPG.

Electronic supplementary material

The online version of this article (doi:10.1007/s00401-012-0998-0) contains supplementary material, which is available to authorized users.

The surface marker PROM1 is considered one of the most important markers of tumor-initiating cells, and its expression is believed to be an adverse prognostic factor in gliomas and in other malignancies. To date, to our knowledge, no specific studies of its expression in medulloblastoma series have been performed. The aims of our study were to evaluate the expression profile of the PROM1 gene in medulloblastoma and to assess its possible role as a prognostic factor. The PROM1 gene expression was evaluated by quantitative– polymerase chain reaction on 45 medulloblastoma samples by using specific dye-labeled probe systems. A significantly higher expression of PROM1 was found both in patients with poorer prognosis (P= .007) and in those with metastasis (P= .03). Kaplan–Meier analysis showed that both overall survival (OS) and progression-free survival (PFS) were shorter in patients with higher PROM1 mRNA levels than in patients with lower expression, even when the desmoplastic cases were excluded (P= .0004 and P= .002, for OS and PFS for all cases, respectively; P= .002 and P= .008 for OS and PFS for nondesmoplastic cases, respectively). Cox regression model demonstrated that PROM1 expression is an independent prognostic factor (hazard ratio, 4.56; P= .008). The result was validated on an independent cohort of 42 cases by microarray-based analysis (P= .019). This work suggests that high mRNA levels of PROM1 are associated with poor outcome in pediatric medulloblastoma. Furthermore, high PROM1 expression levels seem to increase the likelihood of metastases. Such results need to be confirmed in larger prospective series to possibly incorporate PROM1 gene expression into risk classification systems to be used in the clinical setting.

Purpose

Medulloblastomas are heterogeneous tumors that collectively represent the most common malignant brain tumor in children. To understand the molecular characteristics underlying their heterogeneity and to identify whether such characteristics represent risk factors for patients with this disease, we performed an integrated genomic analysis of a large series of primary tumors.

Patients and Methods

We profiled the mRNA transcriptome of 194 medulloblastomas and performed high-density single nucleotide polymorphism array and miRNA analysis on 115 and 98 of these, respectively. Non-negative matrix factorization–based clustering of mRNA expression data was used to identify molecular subgroups of medulloblastoma; DNA copy number, miRNA profiles, and clinical outcomes were analyzed for each. We additionally validated our findings in three previously published independent medulloblastoma data sets.

Results

Identified are six molecular subgroups of medulloblastoma, each with a unique combination of numerical and structural chromosomal aberrations that globally influence mRNA and miRNA expression. We reveal the relative contribution of each subgroup to clinical outcome as a whole and show that a previously unidentified molecular subgroup, characterized genetically by c-MYC copy number gains and transcriptionally by enrichment of photoreceptor pathways and increased miR-183∼96∼182 expression, is associated with significantly lower rates of event-free and overall survivals.

Conclusion

Our results detail the complex genomic heterogeneity of medulloblastomas and identify a previously unrecognized molecular subgroup with poor clinical outcome for which more effective therapeutic strategies should be developed.

Purpose

Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis.

Patients and Methods

A Bayesian cumulative log-odds model of outcome was developed from a training cohort of 96 children treated for medulloblastoma, starting with the evidence provided by clinical features of metastasis and histology (model A) and incrementally adding the evidence from gene-expression–derived features representing disease subtype–independent (model B) and disease subtype–dependent (model C) pathways, and finally high-level copy-number genomic abnormalities (model D). The models were validated on an independent test cohort (n = 78).

Results

On an independent multi-institutional test data set, models A to D attain an area under receiver operating characteristic (au-ROC) curve of 0.73 (95% CI, 0.60 to 0.84), 0.75 (95% CI, 0.64 to 0.86), 0.80 (95% CI, 0.70 to 0.90), and 0.78 (95% CI, 0.68 to 0.88), respectively, for predicting relapse versus no relapse.

Conclusion

The proposed models C and D outperform the current clinical classification schema (au-ROC, 0.68), our previously published eight-gene outcome signature (au-ROC, 0.71), and several new schemas recently proposed in the literature for medulloblastoma risk stratification.

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 material

The online version of this article (doi:10.1007/s00401-012-0958-8) contains supplementary material, which is available to authorized users.

Medulloblastoma, a small blue cell malignancy of the cerebellum, is a major cause of morbidity and mortality in pediatric oncology. Current mechanisms for clinical prognostication and stratification include clinical factors (age, presence of metastases, and extent of resection) as well as histological subgrouping (classic, desmoplastic, and large cell/anaplastic histology). Transcriptional profiling studies of medulloblastoma cohorts from several research groups around the globe have suggested the existence of multiple distinct molecular subgroups that differ in their demographics, transcriptomes, somatic genetic events, and clinical outcomes. Variations in the number, composition, and nature of the subgroups between studies brought about a consensus conference in Boston in the fall of 2010. Discussants at the conference came to a consensus that the evidence supported the existence of four main subgroups of medulloblastoma (Wnt, Shh, Group 3, and Group 4). Participants outlined the demographic, transcriptional, genetic, and clinical differences between the four subgroups. While it is anticipated that the molecular classification of medulloblastoma will continue to evolve and diversify in the future as larger cohorts are studied at greater depth, herein we outline the current consensus nomenclature, and the differences between the medulloblastoma subgroups.

Poly ADP-ribose polymerase (PARP) is a protein involved in single strand break repair. Recently, PARP inhibitors have shown considerable promise in the treatment of several cancers, both in monotherapy and in combination with cytotoxic agents. Synthetic lethal action of PARP inhibitors has been observed in tumors with mutations in double strand break repair pathways. In addition, PARP inhibition potentially enhances sensitivity of tumor cells to DNA damaging agents, including radiotherapy. Aim of this study is to determine the radiosensitizing properties of the PARP inhibitor Olaparib in childhood medulloblastoma, ependymoma and high grade glioma (HGG). Increased PARP1 expression was observed in medulloblastoma, ependymoma and HGG, as compared to non-neoplastic brain tissue. Pediatric high grade glioma, medulloblastoma and ependymoma gene expression profiling revealed that high PARP1 expression is associated with poor prognosis. Cell growth inhibition assays with Olaparib resulted in differential sensitivity, with IC50 values ranging from 1.4 to 8.4 μM, irrespective of tumor type and PARP1 protein expression. Sensitization to radiation was observed in medulloblastoma, ependymoma and HGG cell lines with subcytotoxic concentrations of Olaparib, which coincided with persistence of double strand breaks. Combining PARP inhibitors with radiotherapy in clinical studies in childhood high grade brain tumors may improve therapeutic outcome.

The diagnosis of medulloblastoma likely encompasses several distinct entities, with recent evidence for the existence of at least four unique molecular subgroups that exhibit distinct genetic, transcriptional, demographic, and clinical features. Assignment of molecular subgroup through routine profiling of high-quality RNA on expression microarrays is likely impractical in the clinical setting. The planning and execution of medulloblastoma clinical trials that stratify by subgroup, or which are targeted to a specific subgroup requires technologies that can be economically, rapidly, reliably, and reproducibly applied to formalin-fixed paraffin embedded (FFPE) specimens. In the current study, we have developed an assay that accurately measures the expression level of 22 medulloblastoma subgroup-specific signature genes (CodeSet) using nanoString nCounter Technology. Comparison of the nanoString assay with Affymetrix expression array data on a training series of 101 medulloblastomas of known subgroup demonstrated a high concordance (Pearson correlation r = 0.86). The assay was validated on a second set of 130 non-overlapping medulloblastomas of known subgroup, correctly assigning 98% (127/130) of tumors to the appropriate subgroup. Reproducibility was demonstrated by repeating the assay in three independent laboratories in Canada, the United States, and Switzerland. Finally, the nanoString assay could confidently predict subgroup in 88% of recent FFPE cases, of which 100% had accurate subgroup assignment. We present an assay based on nanoString technology that is capable of rapidly, reliably, and reproducibly assigning clinical FFPE medulloblastoma samples to their molecular subgroup, and which is highly suited for future medulloblastoma clinical trials.

Electronic supplementary material

The online version of this article (doi:10.1007/s00401-011-0899-7) contains supplementary material, which is available to authorized users.

Both OTX2 and MYC are important oncogenes in medulloblastoma, the most common malignant brain tumor in childhood. Much is known about MYC binding to promoter regions, but OTX2 binding is hardly investigated. We used ChIP-on-chip data to analyze the binding patterns of both transcription factors in D425 medulloblastoma cells. When combining the data for all promoter regions in the genome, OTX2 binding showed a remarkable bi-modal distribution pattern with peaks around −250 bp upstream and +650 bp downstream of the transcription start sites (TSSs). Indeed, 40.2% of all OTX2-bound TSSs had more than one significant OTX2-binding peak. This OTX2-binding pattern was very different from the TSS-centered single peak binding pattern observed for MYC and other known transcription factors. However, in individual promoter regions, OTX2 and MYC have a strong tendency to bind in proximity of each other. OTX2-binding sequences are depleted near TSSs in the genome, providing an explanation for the observed bi-modal distribution of OTX2 binding. This contrasts to the enrichment of E-box sequences at TSSs. Both OTX2 and MYC binding independently correlated with higher gene expression. Interestingly, genes of promoter regions with multiple OTX2 binding as well as MYC binding showed the highest expression levels in D425 cells and in primary medulloblastomas. Genes within this class of promoter regions were enriched for medulloblastoma and stem cell specific genes. Our data suggest an important functional interaction between OTX2 and MYC in regulating gene expression in medulloblastoma.

Background

Medulloblastoma is the most common malignant brain tumor in children. Despite recent improvements in cure rates, prediction of disease outcome remains a major challenge and survivors suffer from serious therapy-related side-effects. Recent data showed that patients with WNT-activated tumors have a favorable prognosis, suggesting that these patients could be treated less intensively, thereby reducing the side-effects. This illustrates the potential benefits of a robust classification of medulloblastoma patients and a detailed knowledge of associated biological mechanisms.

Methods and Findings

To get a better insight into the molecular biology of medulloblastoma we established mRNA expression profiles of 62 medulloblastomas and analyzed 52 of them also by comparative genomic hybridization (CGH) arrays. Five molecular subtypes were identified, characterized by WNT signaling (A; 9 cases), SHH signaling (B; 15 cases), expression of neuronal differentiation genes (C and D; 16 and 11 cases, respectively) or photoreceptor genes (D and E; both 11 cases). Mutations in β-catenin were identified in all 9 type A tumors, but not in any other tumor. PTCH1 mutations were exclusively identified in type B tumors. CGH analysis identified several fully or partly subtype-specific chromosomal aberrations. Monosomy of chromosome 6 occurred only in type A tumors, loss of 9q mostly occurred in type B tumors, whereas chromosome 17 aberrations, most common in medulloblastoma, were strongly associated with type C or D tumors. Loss of the inactivated X-chromosome was highly specific for female cases of type C, D and E tumors. Gene expression levels faithfully reflected the chromosomal copy number changes. Clinicopathological features significantly different between the 5 subtypes included metastatic disease and age at diagnosis and histology. Metastatic disease at diagnosis was significantly associated with subtypes C and D and most strongly with subtype E. Patients below 3 yrs of age had type B, D, or E tumors. Type B included most desmoplastic cases. We validated and confirmed the molecular subtypes and their associated clinicopathological features with expression data from a second independent series of 46 medulloblastomas.

Conclusions

The new medulloblastoma classification presented in this study will greatly enhance the understanding of this heterogeneous disease. It will enable a better selection and evaluation of patients in clinical trials, and it will support the development of new molecular targeted therapies. Ultimately, our results may lead to more individualized therapies with improved cure rates and a better quality of life.

Background

Serial Analysis of Gene Expression (SAGE) and microarrays have found awidespread application, but much ambiguity exists regarding the evaluation of these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce the need for duplicate experiments and facilitate a more extensive exchange of data within the research community. This requires a measure for the correspondence of the different gene expression platforms. To date, a number of cross-platform evaluations (including a few studies using SAGE and Affymetrix GeneChips) have been conducted showing a variable, but overall low, concordance. This study evaluates these overall measures and introduces the between-ratio difference as a concordance measure pergene.

Results

In this study, gene expression measurements of Unigene clusters represented by both Affymetrix GeneChips HG-U133A and SAGE were compared using two independent RNA samples. After matching of the data sets the final comparison contains a small data set of 1094 unique Unigene clusters, which is unbiased with respect to expression level. Different overall correlation approaches, like Up/Down classification, contingency tables and correlation coefficients were used to compare both platforms. In addition, we introduce a novel approach to compare two platforms based on the calculation of differences between expression ratios observed in each platform for each individual transcript. This approach results in a concordance measure per gene (with statistical probability value), as opposed to the commonly used overall concordance measures between platforms.

Conclusion

We can conclude that intra-platform correlations are generally good, but that overall agreement between the two platforms is modest. This might be due to the binomially distributed sampling variation in SAGE tag counts, SAGE annotation errors and the intensity variation between probe sets of a single gene in Affymetrix GeneChips. We cannot identify or advice which platform performs better since both have their (dis)-advantages. Therefore it is strongly recommended to perform follow-up studies of interesting genes using additional techniques. The newly introduced between-ratio difference is a filtering-independent measure for between-platform concordance. Moreover, the between-ratio difference per gene can be used to detect transcripts with similar regulation on both platforms.