A Novel Method for Rapid Molecular Subgrouping of Medulloblastoma

Singular Value Decomposition-Based Penalized Multinomial Regression for Classifying Imbalanced Medulloblastoma Subgroups Using Methylation Data

Medulloblastoma (MB) is a molecularly heterogeneous brain malignancy with large differences in clinical presentation. According to genomic studies, there are at least four distinct molecular subgroups of MB: sonic hedgehog (SHH), wingless/INT (WNT), Group 3, and Group 4. The treatment and outcomes depend on appropriate classification. It is difficult for the classification algorithms to identify these subgroups from an imbalanced MB genomic data set, where the distribution of samples among the MB subgroups may not be equal. To overcome this problem, we used singular value decomposition (SVD) and group lasso techniques to find DNA methylation probe features that maximize the separation between the different imbalanced MB subgroups. We used multinomial regression as a classification method to classify the four different molecular subgroups of MB using the reduced DNA methylation data. Coordinate descent is used to solve our loss function associated with the group lasso, which promotes sparsity. By using SVD, we were able to reduce the 321,174 probe features to just 200 features. Less than 40 features were successfully selected after applying the group lasso, which we then used as predictors for our classification models. Our proposed method achieved an average overall accuracy of 99% based on fivefold cross-validation technique. Our approach produces improved classification performance compared with the state-of-the-art methods for classifying MB molecular subgroups.

EpiGe: A machine-learning strategy for rapid classification of medulloblastoma using PCR-based methyl-genotyping

Molecular classification of medulloblastoma is critical for the treatment of this brain tumor. Array-based DNA methylation profiling has emerged as a powerful approach for brain tumor classification. However, this technology is currently not widely available. We present a machine-learning decision support system (DSS) that enables the classification of the principal molecular groups-WNT, SHH, and non-WNT/non-SHH-directly from quantitative PCR (qPCR) data. We propose a framework where the developed DSS appears as a user-friendly web-application-EpiGe-App-that enables automated interpretation of qPCR methylation data and subsequent molecular group prediction. The basis of our classification strategy is a previously validated six-cytosine signature with subgroup-specific methylation profiles. This reduced set of markers enabled us to develop a methyl-genotyping assay capable of determining the methylation status of cytosines using qPCR instruments. This study provides a comprehensive approach for rapid classification of clinically relevant medulloblastoma groups, using readily accessible equipment and an easy-to-use web-application.t.

MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches

Childhood medulloblastoma is a malignant form of brain tumor that is widely classified into four subgroups based on molecular and genetic characteristics. Accurate classification of these subgroups is crucial for appropriate treatment, monitoring plans, and targeted therapies. However, misclassification between groups 3 and 4 is common. To address this issue, an AI-based R package called MBMethPred was developed based on DNA methylation and gene expression profiles of 763 medulloblastoma samples to classify subgroups using machine learning and neural network models. The developed prediction models achieved a classification accuracy of over 96% for subgroup classification by using 399 CpGs as prediction biomarkers. We also assessed the prognostic relevance of prediction biomarkers using survival analysis. Furthermore, we identified subgroup-specific drivers of medulloblastoma using functional enrichment analysis, Shapley values, and gene network analysis. In particular, the genes involved in the nervous system development process have the potential to separate medulloblastoma subgroups with 99% accuracy. Notably, our analysis identified 16 genes that were specifically significant for subgroup classification, including EP300, CXCR4, WNT4, ZIC4, MEIS1, SLC8A1, NFASC, ASCL2, KIF5C, SYNGAP1, SEMA4F, ROR1, DPYSL4, ARTN, RTN4RL1, and TLX2. Our findings contribute to enhanced survival outcomes for patients with medulloblastoma. Continued research and validation efforts are needed to further refine and expand the utility of our approach in other cancer types, advancing personalized medicine in pediatric oncology.

Omission of Upfront Craniospinal Irradiation in Patients with Low-Risk WNT-Pathway Medulloblastoma Is Associated with Unacceptably High Risk of Neuraxial Failure

PURPOSE

Medulloblastoma is a heterogenous disease comprising four molecular subgroups: wingless (WNT), sonic hedgehog (SHH), group 3, and group 4, respectively. Excellent long-term outcomes have prompted deintensification of therapy in WNT-pathway medulloblastoma. We assessed the safety of avoiding upfront craniospinal irradiation (CSI) in children with low-risk WNT-pathway medulloblastoma.

PATIENTS AND METHODS

Children with low-risk WNT-pathway medulloblastoma were treated with postoperative focal conformal radiotherapy, avoiding upfront CSI, followed by six cycles of adjuvant systemic chemotherapy. A group-sequential design (triangular test) with predefined stopping rules if the rate of relapse exceeded 15% at 2 years was incorporated to ensure the safety of study participants.

RESULTS

7 children with low-risk WNT-pathway medulloblastoma were accrued after written informed consent/assent and treated as per protocol. One child died of neutropenic sepsis and multiorgan dysfunction during chemotherapy. Three children were detected with neuraxial failure (supratentorial brain and/or spine) on surveillance neuro-imaging within 2 years from index diagnosis, leading to premature study termination. At relapse, children were treated with salvage CSI plus boost irradiation of metastatic deposits followed by second-line chemotherapy. Two of them continue to be in remission (32 and 26 months after first relapse), while one child developed a second relapse, necessitating further systemic chemotherapy and craniospinal reirradiation, resulting in excellent clinico-radiologic response. At a median follow-up of 42 months, the 2-year Kaplan-Meier estimates of event-free survival, recurrence-free survival, and overall survival were 42.9%, 50%, and 85.7% respectively.

CONCLUSIONS

Omission of upfront CSI in low-risk WNT-pathway medulloblastoma is associated with an unacceptably high risk of neuraxial failure. See related commentary by Remke and Ramaswamy, p. 4161.

Medulloblastoma and Down Syndrome: An Extremely Rare Association

Medulloblastoma has a reduced incidence in Down syndrome (DS). This protective characteristic has not been clarified yet. Here, we report the second case of SHH medulloblastoma and DS documented in the literature. A complete surgery was performed followed by reduced craniospinal irradiation dose and adjuvant chemotherapy. No evidence of tumor recurrence was observed. The overall survival was 9.1 years. No family history or physical stigma of other hereditary predisposition syndrome was found. In the elucidation of this extremely rare association, future case reports play an important role in defining the spectrum of brain tumors and their peculiar features in DS.

Enrichment of Tumor-Infiltrating B Cells in Group 4 Medulloblastoma in Children

Medulloblastoma (MB) is the most common malignant brain tumor in children. It is classified into core molecular subgroups (wingless activated (WNT), sonic hedgehog activated (SHH), Group 3 (G3), and Group 4 (G4)). In this study, we analyzed the tumor-infiltrating immune cells and cytokine profiles of 70 MB patients in Taiwan using transcriptome data. In parallel, immune cell composition in tumors from the SickKids cohort dataset was also analyzed to confirm the findings. The clinical cohort data showed the WNT and G4 MB patients had lower recurrence rates and better 5-year relapse-free survival (RFP) compared with the SHH and G3 MB patients, among the four subgroups of MB. We found tumor-infiltrating B cells (TIL-Bs) enriched in the G4 subgroups in the Taiwanese MB patients and the SickKids cohort dataset. In the G4 subgroups, the patients with a high level of TIL-Bs had better 5-year overall survival. Mast cells presented in G4 MB tumors were positively correlated with TIL-Bs. Higher levels of CXCL13, IL-36γ, and CCL27 were found compared to other subgroups or normal brains. These three cytokines, B cells and mast cells contributed to the unique immune microenvironment in G4 MB tumors. Therefore, B-cell enrichment is a G4-subgroup-specific immune signature and the presence of B cells may be an indicator of a better prognosis in G4 MB patients.

Development of a Machine Learning Classifier for Brain Tumors Diagnosis Based on DNA Methylation Profile

Background: More than 150 types of brain tumors have been documented. Accurate diagnosis is important for making appropriate therapeutic decisions in treating the diseases. The goal of this study is to develop a DNA methylation profile-based classifier to accurately identify various kinds of brain tumors.

Methods: Thirteen datasets of DNA methylation profiles were downloaded from the Gene Expression Omnibus (GEO) database, of which GSE90496 and GSE109379 were used as the training set and the validation set, respectively, and the remaining 11 sets were used as the independent test set. The random forest algorithm was used to select the CpG sites based on the importance of the features and a multilayer perceptron (MLP) model was trained to classify the samples. Deconvolution with the debCAM package was used to explore the cellular composition difference among tumors.

Results: From training datasets with 2,801 samples, 396,568 CpG sites were retained after preprocessing, of which 767 were selected as the modeling features. A three-layer MLP model was developed, which consists of 1,320 nodes in the hidden layer, to predict the histological types of brain tumors. The prediction accuracy is 99.2, 87.0, and 96.58%, respectively, on the training, validation and test sets. The results of deconvolution analysis showed that the cell proportions of different tumor subtypes were different, and it is approximately enough to distinguish different tumor entities.

Conclusion: We developed a classifier that is robust for the classification of central nervous system tumors, and tried to analyze the reasons for the classification performance.

Molecular Subgroup Is the Strongest Predictor of Medulloblastoma Outcome in a Resource-Limited Country

PURPOSE

Medulloblastoma is composed of four clinically and prognostically distinct molecular subgroups (WNT, SHH, group 3, and group 4). However, the clinical implications of these subgroups in the context of the unique challenges of low- to middle-income countries are rarely reported.

METHODS

We assembled an institutional cohort of children (3-18 years) diagnosed with medulloblastoma and treated in Jordan between 2003 and 2016. Tumors were subgrouped by NanoString and correlated with clinical and radiologic characteristics.

RESULTS

Eighty-eight patients were identified (63% male); median age was 6.9 years (interquartile range 4.8-9.2) and median symptom duration was 6 weeks (interquartile range 4-11). Radiotherapy was implemented as standard-risk in 41 patients (47%) and high-risk in 47 patients (53%). Subgrouping revealed 17 WNT (19%), 22 SHH (25%), 21 group 3 (24%), and 28 group 4 tumors (32%). Median time between craniotomy and radiotherapy was 45 days (17-155); 44% of them > 49 days. Median duration of radiotherapy was 44 days (36-74). Seventy-two patients (82%) received chemotherapy afterward. With a median follow-up of 4.6 years (0.2-14.9), 5-year progression-free survival (PFS) and overall survival were 73.5% and 69.4%, respectively, with no statistically significant survival difference between standard-risk and high-risk patients. Metastasis was significant for overall survival (P = .011). Patients with SHH and group 4 tumors had very good PFS (83.4% and 87.0%, respectively) and those with group 3 tumors had dismal outcomes (PFS 44.9%), whereas WNT tumors had less-than expected PFS (70.5%). PFS was statistically significant in patients with nonmetastatic tumors receiving radiotherapy ≤ 49 days (P = .011), particularly group 3 tumors.

CONCLUSION

Patients with SHH and group 4 medulloblastoma had excellent survival comparable with high-income countries. Compliance with treatment protocols and avoiding radiotherapy delays are important in achieving adequate survival in low- to middle-income country settings. Subgroup-driven treatment protocols should be considered in countries with limited resources.

Classifying Medulloblastoma Subgroups Based on Small, Clinically Achievable Gene Sets

As treatment protocols for medulloblastoma (MB) are becoming subgroup-specific, means for reliably distinguishing between its subgroups are a timely need. Currently available methods include immunohistochemical stains, which are subjective and often inconclusive, and molecular techniques—e.g., NanoString, microarrays, or DNA methylation assays—which are time-consuming, expensive and not widely available. Quantitative PCR (qPCR) provides a good alternative for these methods, but the current NanoString panel which includes 22 genes is impractical for qPCR. Here, we applied machine-learning–based classifiers to extract reliable, concise gene sets for distinguishing between the four MB subgroups, and we compared the accuracy of these gene sets to that of the known NanoString 22-gene set. We validated our results using an independent microarray-based dataset of 92 samples of all four subgroups. In addition, we performed a qPCR validation on a cohort of 18 patients diagnosed with SHH, Group 3 and Group 4 MB. We found that the 22-gene set can be reduced to only six genes (IMPG2, NPR3, KHDRBS2, RBM24, WIF1, and EMX2) without compromising accuracy. The identified gene set is sufficiently small to make a qPCR-based MB subgroup classification easily accessible to clinicians, even in developing, poorly equipped countries.

Topoisomerase IIβ immunoreactivity (IR) co-localizes with neuronal marker-IR but not glial fibrillary acidic protein-IR in GLI3-positive medulloblastomas: an immunohistochemical analysis of 124 medulloblastomas from the Japan Children's Cancer Group

We previously reported observing GLI3 in medulloblastomas expressing neuronal markers (NM) and/or glial fibrillary acidic protein (GFAP). Furthermore, patients with medulloblastomas expressing NM or GFAP tended to show favorable or poor prognosis, respectively. In the present study, we focused on the role of topoisomerase IIβ (TOP2β) as a possible regulator for neuronal differentiation in medulloblastomas and examined the pathological roles of GLI3, NM, GFAP, and TOP2β expressions in a larger population. We divided 124 medulloblastomas into three groups (NM-/GFAP-, NM +/GFAP-, and GFAP +) based on their immunoreactivity (IR) against NM and GFAP. The relationship among GLI3, NM, GFAP, and TOP2β was evaluated using fluorescent immunostaining and a publicly available single-cell RNA sequencing dataset. In total, 87, 30, and 7 medulloblastomas were classified as NM-/GFAP-, NM + /GFAP-, and GFAP +, and showed intermediate, good, and poor prognoses, respectively. GLI3-IR was frequently observed in NM +/GFAP-  and GFAP + , and TOP2β-IR was frequently observed only in NM +/GFAP-  medulloblastomas. In fluorescent immunostaining, TOP2β-IR was mostly co-localized with NeuN-IR but not with GFAP-IR. In single-cell RNA sequencing, TOP2β expression was elevated in CMAS/DCX-positive, but not in GFAP-positive, cells. NM-IR and GFAP-IR are important for estimating the prognosis of patients with medulloblastoma; hence they should be assessed in clinical practice.

Immunohistochemical and nanoString-Based Subgrouping of Clinical Medulloblastoma Samples

The diagnosis of medulloblastoma incorporates the histologic and molecular subclassification of clinical medulloblastoma samples into wingless (WNT)-activated, sonic hedgehog (SHH)-activated, group 3 and group 4 subgroups. Accurate medulloblastoma subclassification has important prognostic and treatment implications. Immunohistochemistry (IHC)-based and nanoString-based subgrouping methodologies have been independently described as options for medulloblastoma subgrouping, however have not previously been directly compared. We describe our experience with nanoString-based subgrouping in a clinical setting and compare this with our IHC-based results. Study materials included FFPE tissue from 160 medulloblastomas. Clinical data and tumor histology were reviewed. Immunohistochemical-based subgrouping using β-catenin, filamin A and p53 antibodies and nanoString-based gene expression profiling were performed. The sensitivity and specificity of IHC-based subgrouping of WNT and SHH-activated medulloblastomas was 91.5% and 99.54%, respectively. Filamin A immunopositivity highly correlated with SHH/WNT-activated subgroups (sensitivity 100%, specificity 92.7%, p < 0.001). Nuclear β-catenin immunopositivity had a sensitivity of 76.2% and specificity of 99.23% for detection of WNT-activated tumors. Approximately 23.8% of WNT cases would have been missed using an IHC-based subgrouping method alone. nanoString could confidently predict medulloblastoma subgroup in 93% of cases and could distinguish group 3/4 subgroups in 96.3% of cases. nanoString-based subgrouping allows for a more prognostically useful classification of clinical medulloblastoma samples.

Molecular-Clinical Correlation in Pediatric Medulloblastoma: A Cohort Series Study of 52 Cases in Taiwan

In 2016, a project was initiated in Taiwan to adopt molecular diagnosis of childhood medulloblastoma (MB). In this study, we aimed to identify a molecular-clinical correlation and somatic mutation for exploring risk-adapted treatment, drug targets, and potential genetic predisposition. In total, 52 frozen tumor tissues of childhood MBs were collected. RNA sequencing (RNA-Seq) and DNA methylation array data were generated. Molecular subgrouping and clinical correlation analysis were performed. An adjusted Heidelberg risk stratification scheme was defined for updated clinical risk stratification. We selected 51 genes for somatic variant calling using RNA-Seq data. Relevant clinical findings were defined. Potential drug targets and genetic predispositions were explored. Four core molecular subgroups (WNT, SHH, Group 3, and Group 4) were identified. Genetic backgrounds of metastasis at diagnosis and extent of tumor resection were observed. The adjusted Heidelberg scheme showed its applicability. Potential drug targets were detected in the pathways of DNA damage response. Among the 10 patients with SHH MBs analyzed using whole exome sequencing studies, five patients exhibited potential genetic predispositions and four patients had relevant germline mutations. The findings of this study provide valuable information for updated risk adapted treatment and personalized care of childhood MBs in our cohort series and in Taiwan.

Molecular Heterogeneity and Cellular Diversity: Implications for Precision Treatment in Medulloblastoma

Medulloblastoma, the most common pediatric malignant brain tumor, continues to have a high rate of morbidity and mortality in childhood. Recent advances in cancer genomics, single-cell sequencing, and sophisticated tumor models have revolutionized the characterization and stratification of medulloblastoma. In this review, we discuss heterogeneity associated with four major subgroups of medulloblastoma (WNT, SHH, Group 3, and Group 4) on the molecular and cellular levels, including histological features, genetic and epigenetic alterations, proteomic landscape, cell-of-origin, tumor microenvironment, and therapeutic approaches. The intratumoral molecular heterogeneity and intertumoral cellular diversity clearly underlie the divergent biology and clinical behavior of these lesions and highlight the future role of precision treatment in this devastating brain tumor in children.

EORTC SPECTA-AYA: A unique molecular profiling platform for adolescents and young adults with cancer in Europe

For most adolescent and young adult (AYA) cancers, age‐specific molecular features are poorly understood. EORTC‐SPECTA, an academic translational research infrastructure for biomaterial collection, will explicitly recruit AYA patients and will therefore collect empirical data to bridge the molecular gap between pediatric and adult oncology. The initial pilot study, activated in February 2019 across Europe, will recruit 100 AYA patients (aged 12–29 years) with newly diagnosed or relapsed high‐grade gliomas and high‐grade bone and soft tissue sarcomas. The primary objective of the pilot is to determine feasibility and recruitment rates. Formalin‐fixed tumor tissue and whole blood from study participants will be prospectively collected with clinical data and stored centrally at the Integrated BioBank of Luxembourg. Whole exome sequencing of matched tumor and blood, and tumor RNA sequencing and DNA methylation profiling will be performed at the German Cancer Research Center, Heidelberg, Germany. Virtual central pathology review of scanned diagnostic slides will be undertaken by an international expert panel, and diagnostic material returned to the participating centers. A multidisciplinary molecular tumor board will release a clinically validated report to referring clinicians within 4–6 weeks after biopsy. SPECTA‐AYA constitutes a major opportunity to gain knowledge about the tumor biology of this unique age group. It incorporates notable innovative aspects: AYA specificity, pan‐European academic collaboration, centralized biobanking, comprehensive molecular profiling and virtual central pathology review, among others. SPECTA‐AYA will help untangle the tumor particularities of AYAs with cancer and aims to improve their access to novel drugs and personalized medicine.

What's new?

To date, age‐specific molecular features remain poorly understood for most adolescent and young adult (AYA) cancers. This paper presents how SPECTA, a pan‐European academic translational research infrastructure for biomaterial collection, will specifically recruit AYA patients to bridge the molecular gap between pediatric and adult oncology. Further notable innovative aspects include centralized biobanking, comprehensive molecular profiling, and virtual central pathology review. SPECTA‐AYA, whose initial pilot study was launched in February 2019, constitutes a major opportunity to gain knowledge about the tumor biology of this unique age group and aims to improve the access of AYAs to novel drugs and personalized medicine.

Longitudinal study of leukocyte DNA methylation and biomarkers for cancer risk in older adults

Background

Changes in DNA methylation over the course of life may provide an indicator of risk for cancer. We explored longitudinal changes in CpG methylation from blood leukocytes, and likelihood of future cancer diagnosis.

Methods

Peripheral blood samples were obtained at baseline and at follow-up visit from 20 participants in the Health, Aging and Body Composition prospective cohort study. Genome-wide CpG methylation was assayed using the Illumina Infinium Human MethylationEPIC (HM850K) microarray.

Results

Global patterns in DNA methylation from CpG-based analyses showed extensive changes in cell composition over time in participants who developed cancer. By visit year 6, the proportion of CD8+ T-cells decreased (p-value = 0.02), while granulocytes cell levels increased (p-value = 0.04) among participants diagnosed with cancer compared to those who remained cancer-free (cancer-free vs. cancer-present: 0.03 ± 0.02 vs. 0.003 ± 0.005 for CD8+ T-cells; 0.52 ± 0.14 vs. 0.66 ± 0.09 for granulocytes). Epigenome-wide analysis identified three CpGs with suggestive p-values ≤10^− 5 for differential methylation between cancer-free and cancer-present groups, including a CpG located in MTA3, a gene linked with metastasis. At a lenient statistical threshold (p-value ≤3 × 10^− 5), the top 10 cancer-associated CpGs included a site near RPTOR that is involved in the mTOR pathway, and the candidate tumor suppressor genes REC8, KCNQ1, and ZSWIM5. However, only the CpG in RPTOR (cg08129331) was replicated in an independent data set. Analysis of within-individual change from baseline to Year 6 found significant correlations between the rates of change in methylation in RPTOR, REC8 and ZSWIM5, and time to cancer diagnosis.

Conclusion

The results show that changes in cellular composition explains much of the cross-sectional and longitudinal variation in CpG methylation. Additionally, differential methylation and longitudinal dynamics at specific CpGs could provide powerful indicators of cancer development and/or progression. In particular, we highlight CpG methylation in the RPTOR gene as a potential biomarker of cancer that awaits further validation.

Electronic supplementary material

The online version of this article (10.1186/s40364-019-0161-3) contains supplementary material, which is available to authorized users.

Old drugs still work! Oral etoposide in a relapsed medulloblastoma

Medulloblastoma is the most common malignant brain tumor in children. Approximately 30% of children with medulloblastoma will progress or relapse despite being treated. New therapies have been proposed in recent years, including high-dose chemotherapy, immunotherapy, and targeted therapy. However, the best treatment for these patients remains unclear, and in this situation prognosis is poor. Oral etoposide has been used as a single agent or in combination for treating relapsed brain tumors since the 1990s. We report an 8-year-old patient with recurrent metastatic medulloblastoma who had an excellent response after treatment with oral etoposide, maintaining a great quality of life. As clinicians, we must always try to include our patients in clinical trials; however, when this is not possible, we should not forget that "old drugs" such as oral etoposide may work in some patients, with a good response of the tumor, and what is most important, providing the patient with a good quality of life.

Medulloblastoma: optimizing care with a multidisciplinary approach

Medulloblastoma is a malignant tumor of the cerebellum and the most frequent malignant brain tumor in children. The standard of care consists of maximal resection surgery, followed by craniospinal irradiation and chemotherapy. Such treatment allows long-term survival rates of nearly 70%; however, there are wide disparities among patient outcomes, and in any case, major long-term morbidity is observed with conventional treatment. In the last two decades, the molecular understanding of medulloblastoma has improved drastically, allowing us to revolutionize our understanding of medulloblastoma pathophysiological mechanisms. These advances led to an international consensus in 2010 that defined four prognostic molecular subgroups named after their affected signaling pathways, that is, WNT, SHH, Group 3 and Group 4. The molecular understanding of medulloblastoma is starting to translate through to clinical settings due to the development of targeted therapies. Moreover, recent improvements in radiotherapy modalities and the reconsideration of craniospinal irradiation according to the molecular status hold promise for survival preservation and the reduction of radiation-induced morbidity. This review is an overview of the current knowledge of medulloblastoma through a molecular approach, and therapeutic prospects currently being developed in surgery, radiotherapy and targeted therapies to optimize the treatment of medulloblastoma with a multidisciplinary approach will also be discussed.

Molecular markers and potential therapeutic targets in non-WNT/non-SHH (group 3 and group 4) medulloblastomas

Childhood medulloblastomas (MB) are heterogeneous and are divided into four molecular subgroups. The provisional non-wingless-activated (WNT)/non-sonic hedgehog-activated (SHH) category combining group 3 and group 4 represents over two thirds of all MBs, coupled with the highest rates of metastases and least understood pathology. The molecular era expanded our knowledge about molecular aberrations involved in MB tumorigenesis, and here, we review processes leading to non-WNT/non-SHH MB formations.

The heterogeneous group 3 and group 4 MBs frequently harbor rare individual genetic alterations, yet the emerging profiles suggest that infrequent events converge on common, potentially targetable signaling pathways. A mutual theme is the altered epigenetic regulation, and in vitro approaches targeting epigenetic machinery are promising. Growing evidence indicates the presence of an intermediate, mixed signature group along group 3 and group 4, and future clarifications are imperative for concordant classification, as misidentifying patient samples has serious implications for therapy and clinical trials.

To subdue the high MB mortality, we need to discern mechanisms of disease spread and recurrence. Current preclinical models do not represent the full scale of group 3 and group 4 heterogeneity: all of existing group 3 cell lines are MYC-amplified and most mouse models resemble MYC-activated MBs. Clinical samples provide a wealth of information about the genetic divergence between primary tumors and metastatic clones, but recurrent MBs are rarely resected. Molecularly stratified treatment options are limited, and targeted therapies are still in preclinical development. Attacking these aggressive tumors at multiple frontiers will be needed to improve stagnant survival rates.

A simplified approach using Taqman low-density array for medulloblastoma subgrouping

Next-generation sequencing platforms are routinely used for molecular assignment due to their high impact for risk stratification and prognosis in medulloblastomas. Yet, low and middle-income countries still lack an accurate cost-effective platform to perform this allocation. TaqMan Low Density array (TLDA) assay was performed using a set of 20 genes in 92 medulloblastoma samples. The same methodology was assessed in silico using microarray data for 763 medulloblastoma samples from the GSE85217 study, which performed MB classification by a robust integrative method (Transcriptional, Methylation and cytogenetic profile). Furthermore, we validated in 11 MBs samples our proposed method by Methylation Array 450 K to assess methylation profile along with 390 MB samples (GSE109381) and copy number variations. TLDA with only 20 genes accurately assigned MB samples into WNT, SHH, Group 3 and Group 4 using Pearson distance with the average-linkage algorithm and showed concordance with molecular assignment provided by Methylation Array 450 k. Similarly, we tested this simplified set of gene signatures in 763 MB samples and we were able to recapitulate molecular assignment with an accuracy of 99.1% (SHH), 94.29% (WNT), 92.36% (Group 3) and 95.40% (Group 4), against 97.31, 97.14, 88.89 and 97.24% (respectively) with the Ward.D2 algorithm. t-SNE analysis revealed a high level of concordance (k = 4) with minor overlapping features between Group 3 and Group 4. Finally, we condensed the number of genes to 6 without significantly losing accuracy in classifying samples into SHH, WNT and non-SHH/non-WNT subgroups. Additionally, we found a relatively high frequency of WNT subgroup in our cohort, which requires further epidemiological studies. TLDA is a rapid, simple and cost-effective assay for classifying MB in low/middle income countries. A simplified method using six genes and restricting the final stratification into SHH, WNT and non-SHH/non-WNT appears to be a very interesting approach for rapid clinical decision-making.

Liquid biopsy for pediatric central nervous system tumors

Central nervous system (CNS) tumors are the most common solid tumors in children, and the leading cause of cancer-related death. Over the past decade, molecular profiling has been incorporated into treatment for pediatric CNS tumors, allowing for a more personalized approach to therapy. Through the identification of tumor-specific changes, it is now possible to diagnose, assign a prognostic subgroup, and develop targeted chemotherapeutic treatment plans for many cancer types. The successful incorporation of informative liquid biopsies, where the liquid biome is interrogated for tumor-associated molecular clues, has the potential to greatly complement the precision-based approach to treatment, and ultimately, to improve clinical outcomes for children with CNS tumors. In this article, the current application of liquid biopsy in cancer therapy will be reviewed, as will its potential for the diagnosis and therapeutic monitoring of pediatric CNS tumors.

Genetic Abnormalities, Clonal Evolution, and Cancer Stem Cells of Brain Tumors

Brain tumors are highly heterogeneous and have been classified by the World Health Organization in various histological and molecular subtypes. Gliomas have been classified as ranging from low-grade astrocytomas and oligodendrogliomas to high-grade astrocytomas or glioblastomas. These tumors are characterized by a peculiar pattern of genetic alterations. Pediatric high-grade gliomas are histologically indistinguishable from adult glioblastomas, but they are considered distinct from adult glioblastomas because they possess a different spectrum of driver mutations (genes encoding histones H3.3 and H3.1). Medulloblastomas, the most frequent pediatric brain tumors, are considered to be of embryonic derivation and are currently subdivided into distinct subgroups depending on histological features and genetic profiling. There is emerging evidence that brain tumors are maintained by a special neural or glial stem cell-like population that self-renews and gives rise to differentiated progeny. In many instances, the prognosis of the majority of brain tumors remains negative and there is hope that the new acquisition of information on the molecular and cellular bases of these tumors will be translated in the development of new, more active treatments.

Malignant Brain Tumours in Children : Present and Future Perspectives

In contrast to many of the malignant tumors that occur in the central nervous system in adults, the management, responses to therapy, and future perspectives of children with malignant lesions of the brain hold considerable promise. Within the past 5 years, remarkable progress has been made with our understanding of the basic biology of the molecular genetics of several pediatric malignant brain tumors including medulloblastoma, ependymoma, atypical teratoid rhabdoid tumour, and high grade glioma/diffuse intrinsic pontine glioma. The recent literature in pediatric neuro-oncology was reviewed, and a summary of the major findings are presented. Meaningful sub-classifications of these tumors have arisen, placing children into discrete categories of disease with requirements for targeted therapy. While the mainstay of therapy these past 30 years has been a combination of central nervous system irradiation and conventional chemotherapy, now with the advent of high resolution genetic mapping, targeted therapies have emerged, and less emphasis is being placed on craniospinal irradiation. In this article, the present and future perspective of pediatric brain malignancy are reviewed in detail. The progress that has been made offers significant hope for the future for patients with these tumours.