Metabolomic profiling of childhood medulloblastoma: contributions and relevance to diagnosis and molecular subtyping.

The incidence of brain tumors among children is second only to acute lymphoblastic leukemia, but the mortality rate of brain tumors has exceeded that of leukemia, making it the most common cause of death among children. Medulloblastoma (MB) is the most common type of brain tumor among children. Malignant brain tumors have strong invasion and metastasis capabilities, can spread through cerebrospinal fluid, and have a high mortality rate. In 2010, the World Health Organization first divided MB into four molecular subtypes based on molecular markers: WNT, Sonic hedgehog (SHH), Group 3, and Group 4. MB is a highly heterogeneous tumor. Different molecular subtypes of MB have significantly different clinical, pathological, and molecular characteristics. The prognosis of MB varies significantly among patients with different subtypes of this cancer. Thus, it is needed to study new diagnostic and therapeutic strategies. Metabolomics is an advanced analytical technology that uses various spectroscopic, electrochemical, and data analysis technologies to study and analyze the body's metabolites. By detecting changes in metabolite types and quantities in different types of samples, it can sensitively discover the physiological and pathological changes in the body. It has great potential for clinical application and personalized medicine. It is promising and can help develop personalized treatment strategies based on the metabolic profiles of individuals. It can unravel the unique metabolic profiles of MB, which may revolutionize our understanding of the disease and improve patients' outcomes.

Medulloblastoma subgrouping at first sight.

Recent incorporation of the four primary medulloblastoma subgroups into the WHO Classification of Central Nervous System Tumors necessitates globally accessible methods to discern subgroups. In this issue of Cancer Cell, Wang et al. develop a rapid and reliable machine learning workflow for pre-operative subgroup determination using routine magnetic resonance imaging.

[Clinicopathological characteristics of the CD8+ T lymphocytes infiltration and its mechanism in distinct molecular subtype of medulloblastoma].

OBJECTIVE: To investigate the characteristics of the CD8+ T cells infiltration from the 4 subtypes in medulloblastoma (MB), to analyze the relationship between CD8+ T cells infiltration and prognosis, to study the function of C-X-C motif chemokine ligand 11 (CXCL11) and its receptor in CD8+ T cells infiltration into tumors and to explore the potential mechanism, and to provide the necessary clinicopathological basis for exploring the immunotherapy of MB. METHODS: In the study, 48 clinical MB samples (12 cases in each of 4 subtypes) were selected from the multiple medical center from 2012 to 2019. The transcriptomics analysis for the tumor of 48 clinical samples was conducted on the NanoString PanCancer IO360TM Panel (NanoString Technologies). Immunohistochemistry (IHC) staining of formalin-fixed, paraffin-embedded sections from MB was carried out using CD8 primary antibody to analyze diffe-rential quantities of CD8+ T cells in the MB four subtypes. Through bioinformatics analysis, the relationship between CD8+T cells infiltration and prognosis of the patients and the expression differences of various chemokines in the different subtypes of MB were investigated. The expression of CXCR3 receptor on the surface of CD8+T cells in MB was verified by double immunofluorescence staining, and the underlying molecular mechanism of CD8+T cells infiltration into the tumor was explored. RESULTS: The characteristic index of CD8+T cells in the WNT subtype of MB was relatively high, suggesting that the number of CD8+T cells in the WNT subtype was significantly higher than that in the other three subtypes, which was confirmed by CD8 immunohistochemical staining and Gene Expression Omnibus (GEO) database analysis by using R2 online data analysis platform. And the increase of CD8+T cells infiltration was positively correlated with the patient survival. The expression level of CXCL11 in the WNT subtype MB was significantly higher than that of the other three subtypes. Immunofluorescence staining showed the presence of CXCL11 receptor, CXCR3, on the surface of CD8+T cells, suggesting that the CD8+T cells might be attracted to the MB microenvironment by CXCL11 through CXCR3. CONCLUSION: The CD8+T cells infiltrate more in the WNT subtype MB than other subtypes. The mechanism may be related to the activation of CXCL11-CXCR3 chemokine system, and the patients with more infiltration of CD8+T cells in tumor have better prognosis. This finding may provide the necessary clinicopathological basis for the regulatory mechanism of CD8+T cells infiltration in MB, and give a new potential therapeutic target for the future immunotherapy of MB.

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.

Subtyping of Group 3/4 medulloblastoma as a potential prognostic biomarker among patients treated with reduced dose of craniospinal irradiation: a Japanese Pediatric Molecular Neuro-Oncology Group study.

BACKGROUND: One of the most significant challenges in patients with medulloblastoma is reducing the dose of craniospinal irradiation (CSI) to minimize neurological sequelae in survivors. Molecular characterization of patients receiving lower than standard dose of CSI therapy is important to facilitate further reduction of treatment burden. METHODS: We conducted DNA methylation analysis using an Illumina Methylation EPIC array to investigate molecular prognostic markers in 38 patients with medulloblastoma who were registered in the Japan Pediatric Molecular Neuro-Oncology Group and treated with reduced-dose CSI. RESULTS: Among the patients, 23 were classified as having a standard-risk and 15 as high-risk according to the classic classification based on tumor resection rate and presence of metastasis, respectively. The median follow-up period was 71.5 months (12.0-231.0). The median CSI dose was 18 Gy (15.0-24.0) in both groups, and 5 patients in the high-risk group received a CSI dose of 18.0 Gy. Molecular subgrouping revealed that the standard-risk cohort included 5 WNT, 2 SHH, and 16 Group 3/4 cases; all 15 patients in the high-risk cohort had Group 3/4 medulloblastoma. Among the patients with Group 3/4 medulloblastoma, 9 of the 31 Group 3/4 cases were subclassified as subclass II, III, and V, which were known to an association with poor prognosis according to the novel subtyping among the subgroups. Patients with poor prognostic subtype showed worse prognosis than that of others (5-year progression survival rate 90.4% vs. 22.2%; p < 0.0001). The result was replicated in the multivariate analysis (hazard ratio12.77, 95% confidence interval for hazard ratio 2.38-99.21, p value 0.0026 for progression-free survival, hazard ratio 5.02, 95% confidence interval for hazard ratio 1.03-29.11, p value 0.044 for overall survival). CONCLUSION: Although these findings require validation in a larger cohort, the present findings suggest that novel subtyping of Group 3/4 medulloblastoma may be a promising prognostic biomarker even among patients treated with lower-dose CSI than standard treatment.

Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance.

BACKGROUND: Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors. PURPOSE: To assess radiomics-based ML models' diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies. MATERIAL AND METHODS: A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies' diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed. RESULTS: Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively. CONCLUSION: Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability.

Unified rhombic lip origins of group 3 and group 4 medulloblastoma.

Medulloblastoma, a malignant childhood cerebellar tumour, segregates molecularly into biologically distinct subgroups, suggesting that a personalized approach to therapy would be beneficial1. Mouse modelling and cross-species genomics have provided increasing evidence of discrete, subgroup-specific developmental origins2. However, the anatomical and cellular complexity of developing human tissues3-particularly within the rhombic lip germinal zone, which produces all glutamatergic neuronal lineages before internalization into the cerebellar nodulus-makes it difficult to validate previous inferences that were derived from studies in mice. Here we use multi-omics to resolve the origins of medulloblastoma subgroups in the developing human cerebellum. Molecular signatures encoded within a human rhombic-lip-derived lineage trajectory aligned with photoreceptor and unipolar brush cell expression profiles that are maintained in group 3 and group 4 medulloblastoma, suggesting a convergent basis. A systematic diagnostic-imaging review of a prospective institutional cohort localized the putative anatomical origins of group 3 and group 4 tumours to the nodulus. Our results connect the molecular and phenotypic features of clinically challenging medulloblastoma subgroups to their unified beginnings in the rhombic lip in the early stages of human development.

Failure of human rhombic lip differentiation underlies medulloblastoma formation.

Medulloblastoma (MB) comprises a group of heterogeneous paediatric embryonal neoplasms of the hindbrain with strong links to early development of the hindbrain1-4. Mutations that activate Sonic hedgehog signalling lead to Sonic hedgehog MB in the upper rhombic lip (RL) granule cell lineage5-8. By contrast, mutations that activate WNT signalling lead to WNT MB in the lower RL9,10. However, little is known about the more commonly occurring group 4 (G4) MB, which is thought to arise in the unipolar brush cell lineage3,4. Here we demonstrate that somatic mutations that cause G4 MB converge on the core binding factor alpha (CBFA) complex and mutually exclusive alterations that affect CBFA2T2, CBFA2T3, PRDM6, UTX and OTX2. CBFA2T2 is expressed early in the progenitor cells of the cerebellar RL subventricular zone in Homo sapiens, and G4 MB transcriptionally resembles these progenitors but are stalled in developmental time. Knockdown of OTX2 in model systems relieves this differentiation blockade, which allows MB cells to spontaneously proceed along normal developmental differentiation trajectories. The specific nature of the split human RL, which is destined to generate most of the neurons in the human brain, and its high level of susceptible EOMES+KI67+ unipolar brush cell progenitor cells probably predisposes our species to the development of G4 MB.

Comparison of Clinical and Molecular Wnt and SHH Subgroups in Medulloblastoma Tumor Cases.

AIM: To determine the Wnt and SHH subtypes at the molecular level, and to compare them clinically by examining the changes in CTNNB1, AXIN, PTCH1, SMO, SUFU, and GLI1 mRNA expression in the medulloblastoma of a Turkish population determined according to patient selection criteria. In this context, the clinical distinction between Wnt and SHH groups are realized by considering the age, gender, survival time, location of the lesion, and radiological features of the patients. MATERIAL AND METHODS: Molecular separation was performed by RT-PCR analysis of CTNNB1, AXIN, PTCH1, SMO, SUFU, and GLI1 mRNA expression changes. RESULTS: About 17.8% and 22.2% of the cases were included in the Wnt and the SHH group, respectively. When comparing group differences based on clinical and molecular data, 72.7% and 66.6% of matches were observed in the Wnt and the SHH group, respectively. CONCLUSION: It has been revealed that molecular analysis and grouping of patients with medulloblastoma can provide support for clinically determined subgroups.

Diagnostic Accuracy of a Reduced Immunohistochemical Panel in Medulloblastoma Molecular Subtyping, Correlated to DNA-methylation Analysis.

Medulloblastomas (MBs) are the most frequent childhood malignant brain tumor. Four histopathologic variants and 4 genetic subgroups have been defined in the World Health Organization (WHO) 2016 Classification and constitute major risk stratification items directly affecting the patient management. Although MB subgroups have been molecularly defined, immunohistochemical surrogates are needed. The aim of our retrospective study was to evaluate the concordance between immunohistochemistry, using 4 antibodies (YAP1, GAB1, OTX2, and ß-catenin), and DNA-methylation profiling in MB subgrouping. From a series of 155 MBs, the κ coefficient of concordance was almost perfect (0.90), with only 8/152 discrepant cases (no DNA-methylation analysis was available in 3 cases). Interestingly, the discrepancies mostly concerned (7/8 cases) MBs with divergent differentiations (myogenic, melanotic, and others) with all of those classified into group 3 (n=6) and group 4 (n=1) by DNA-methylation profiling. Another discrepant case concerned a WNT-activated MB (showing only 1% of immunopositive tumor cell nuclei), highlighting the difficulties of determining an appropriate ß-catenin immunostaining cutoff. The high concordance of the routine immunohistochemical panel (YAP1, GAB1, OTX2, and ß-catenin) and DNA-methylation profiling confirm its utility as a reliable predictive marker of molecular subtype in MBs. We analyzed the accuracy of 10 different IHC combinations for the determination of MB subtype and found that a combination of 2 antibodies (YAP1 and OTX2) allows for the successful characterization of 144 cases of 152 cases. Finally, our series extends the molecular data of the rare morphologic variant of MBs with melanotic/myogenic differentiations.

Solid Component Volume as a Proxy to Identify Distinct Hemangioblastoma Populations.

BACKGROUND: Posterior fossa hemangioblastomas usually consist of a small solid nodule with a large cyst, while more rarely they present as a large solid mass with a small or absent cyst, which can be surgically challenging. We sought to investigate the potential existence of multiple distinct hemangioblastoma populations using tumor volumetric data as an indicator. METHODS: We conducted a retrospective review of surgically treated hemangioblastomas between 2005 and 2019 in our unit, including clinical notes, preoperative magnetic resonance imaging volumetric analysis of the solid component of the tumor, and pathology. Finite Gaussian mixture modeling was applied on the solid component volume dataset to identify potential underlying Gaussian distributions with their associated characteristics. Nonparametric Mann-Whitney U tests were used to investigate significance of differences (P < 0.05) in solid component volume and different variables (Von Hippel-Lindau disease, extent of resection, outcome). RESULTS: A total of 68 consecutive patients were included. Solid component volumes followed a multimodal distribution (median = 1287 mm3, interquartile range of 3428 mm3). The best-fit finite Gaussian mixture modeling model identified 3 statistically significant different (P = 0.001) potential mixture components: X1 (219 ± 187 mm3), X2 (2686 ± 1299 mm3), and X3 (10,800 ± 5514 mm3). The second-best model detected 2 significantly different (P = 9.99e-08) mixture components Y1 (222 ± 189 mm3) and Y2 (5391 ± 5094 mm3). A significant difference in solid component volume was found between patients with favorable and unfavorable outcome (P = 0.002). CONCLUSIONS: This study has shown preliminary evidence that large solid hemangioblastomas may constitute a completely distinct population, rather than a variant of one large group of hemangioblastomas.

Time, pattern, and outcome of medulloblastoma relapse and their association with tumour biology at diagnosis and therapy: a multicentre cohort study.

BACKGROUND: Disease relapse occurs in around 30% of children with medulloblastoma, and is almost universally fatal. We aimed to establish whether the clinical and molecular characteristics of the disease at diagnosis are associated with the nature of relapse and subsequent disease course, and whether these associations could inform clinical management. METHODS: In this multicentre cohort study we comprehensively surveyed the clinical features of medulloblastoma relapse (time to relapse, pattern of relapse, time from relapse to death, and overall outcome) in centrally reviewed patients who relapsed following standard upfront therapies, from 16 UK Children's Cancer and Leukaemia Group institutions and four collaborating centres. We compared these relapse-associated features with clinical and molecular features at diagnosis, including established and recently described molecular features, prognostic factors, and treatment at diagnosis and relapse. FINDINGS: 247 patients (175 [71%] boys and 72 [29%] girls) with medulloblastoma relapse (median year of diagnosis 2000 [IQR 1995-2006]) were included in this study. 17 patients were later excluded from further analyses because they did not meet the age and treatment criteria for inclusion. Patients who received upfront craniospinal irradiation (irradiated group; 178 [72%] patients) had a more prolonged time to relapse compared with patients who did not receive upfront craniospinal irradiation (non-irradiated group; 52 [21%] patients; p<0·0001). In the non-irradiated group, craniospinal irradiation at relapse (hazard ratio [HR] 0·27, 95% CI 0·11-0·68) and desmoplastic/nodular histology (0·23, 0·07-0·77) were associated with prolonged time to death after relapse, MYC amplification was associated with a reduced overall survival (23·52, 4·85-114·05), and re-resection at relapse was associated with longer overall survival (0·17, 0·05-0·57). In the irradiated group, patients with MBGroup3 tumours relapsed significantly more quickly than did patients with MBGroup4 tumours (median 1·34 [0·99-1·89] years vs 2·04 [1·39-3·42 years; p=0·0043). Distant disease was prevalent in patients with MBGroup3 (23 [92%] of 25 patients) and MBGroup4 (56 [90%] of 62 patients) tumour relapses. Patients with distantly-relapsed MBGroup3 and MBGroup4 displayed both nodular and diffuse patterns of disease whereas isolated nodular relapses were rare in distantly-relapsed MBSHH (1 [8%] of 12 distantly-relapsed MBSHH were nodular alone compared with 26 [34%] of 77 distantly-relapsed MBGroup3 and MBGroup4). In MBGroup3 and MBGroup4, nodular disease was associated with a prolonged survival after relapse (HR 0·42, 0·21-0·81). Investigation of second-generation MBGroup3 and MBGroup4 molecular subtypes refined our understanding of heterogeneous relapse characteristics. Subtype VIII had prolonged time to relapse and subtype II had a rapid time from relapse to death. Subtypes II, III, and VIII developed a significantly higher incidence of distant disease at relapse whereas subtypes V and VII did not (equivalent rates to diagnosis). INTERPRETATION: This study suggests that the nature and outcome of medulloblastoma relapse are biology and therapy-dependent, providing translational opportunities for improved disease management through biology-directed disease surveillance, post-relapse prognostication, and risk-stratified selection of second-line treatment strategies. FUNDING: Cancer Research UK, Action Medical Research, The Tom Grahame Trust, The JGW Patterson Foundation, Star for Harris, The Institute of Child Health - Newcastle University - Institute of Child Health High-Risk Childhood Brain Tumour Network (co-funded by The Brain Tumour Charity, Great Ormond Street Children's Charity, and Children with Cancer UK).

Medulloblastoma: "Onset of the molecular era".

Among brain tumors, Medulloblastoma (MB) is one of the most common, malignant, pediatric tumors of the cerebellum. It accounts for ~20% of all childhood central nervous system (CNS) tumors. Despite, tremendous advances in drug development processes, as well as novel drugs for MB the morbidity and mortality rates, remain high. Craniospinal radiation, high-dose chemotherapy, and surgical resection are the primary therapeutic strategies. Tremendous progress in the field of "genomics" with vast amounts of data has led to the identification of four distinct molecular subgroups in medulloblastoma: WNT group, SHH group, group-III, and group-IV. The identification of these subgroups has led to individualized treatment strategies for each subgroup. Here, we discuss the various molecular subgroups of medulloblastoma as well as the differences between them. We also highlight the latest treatment strategies available for medulloblastoma.

Clinical and mutational profiles of adult medulloblastoma groups.

Adult medulloblastomas are clinically and molecularly understudied due to their rarity. We performed molecular grouping, targeted sequencing, and TERT promoter Sanger sequencing on a cohort of 99 adult medulloblastomas. SHH made up 50% of the cohort, whereas Group 3 (13%) was present in comparable proportion to WNT (19%) and Group 4 (18%). In contrast to paediatric medulloblastomas, molecular groups had no prognostic impact in our adult cohort (p = 0.877). Most frequently mutated genes were TERT (including promoter mutations, mutated in 36% cases), chromatin modifiers KMT2D (31%) and KMT2C (30%), TCF4 (31%), PTCH1 (27%) and DDX3X (24%). Adult WNT patients showed enrichment of TP53 mutations (6/15 WNT cases), and 3/6 TP53-mutant WNT tumours were of large cell/anaplastic histology. Adult SHH medulloblastomas had frequent upstream pathway alterations (PTCH1 and SMO mutations) and few downstream alterations (SUFU mutations, MYCN amplifications). TERT promoter mutations were found in 72% of adult SHH patients, and were restricted to this group. Adult Group 3 tumours lacked hallmark MYC amplifications, but had recurrent mutations in KBTBD4 and NOTCH1. Adult Group 4 tumours harboured recurrent mutations in TCF4 and chromatin modifier genes. Overall, amplifications of MYC and MYCN were rare (3%). Since molecular groups were not prognostic, alternative prognostic markers are needed for adult medulloblastoma. KMT2C mutations were frequently found across molecular groups and were associated with poor survival (p = 0.002). Multivariate analysis identified histological type (p = 0.026), metastasis (p = 0.031) and KMT2C mutational status (p = 0.046) as independent prognosticators in our cohort. In summary, we identified distinct clinical and mutational characteristics of adult medulloblastomas that will inform their risk stratification and treatment.

A transcriptome-based classifier to determine molecular subtypes in medulloblastoma.

Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53-mutant, Sonic Hedgehog TP53-wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities.

Advances in the molecular classification of pediatric brain tumors: a guide to the galaxy.

Central nervous system (CNS) tumors are the most common solid tumor in pediatrics, accounting for approximately 25% of all childhood cancers, and the second most common pediatric malignancy after leukemia. CNS tumors can be associated with significant morbidity, even those classified as low grade. Mortality from CNS tumors is disproportionately high compared to other childhood malignancies, although surgery, radiation, and chemotherapy have improved outcomes in these patients over the last few decades. Current therapeutic strategies lead to a high risk of side effects, especially in young children. Pediatric brain tumor survivors have unique sequelae compared to age-matched patients who survived other malignancies. They are at greater risk of significant impairment in cognitive, neurological, endocrine, social, and emotional domains, depending on the location and type of the CNS tumor. Next-generation genomics have shed light on the broad molecular heterogeneity of pediatric brain tumors and have identified important genes and signaling pathways that serve to drive tumor proliferation. This insight has impacted the research field by providing potential therapeutic targets for these diseases. In this review, we highlight recent progress in understanding the molecular basis of common pediatric brain tumors, specifically low-grade glioma, high-grade glioma, ependymoma, embryonal tumors, and atypical teratoid/rhabdoid tumor (ATRT). © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Expression of GNAS, TP53, and PTEN Improves the Patient Prognostication in Sonic Hedgehog (SHH) Medulloblastoma Subgroup.

Medulloblastoma (MB) is the most common malignant brain tumor in children. It is currently classified in four main molecular subgroups with different clinical outcomes: sonic hedgehog, wingless, group 3, and group 4 (MBSHH, MBWNT, MBGRP3, or MBGRP4). Presently, a 22-gene expression panel has been efficiently applied for molecular subgrouping using nCounter technology. In this study, formalin-fixed, paraffin-embedded samples from 164 Brazilian medulloblastomas were evaluated, applying the 22-gene panel, and subclassified into the low and high expression of nine key medulloblastoma-related genes. In addition, TP53 mutation status was assessed using TruSight Tumor 15 Panel, and its correlation with expression and prognostic impact was evaluated. Samples from 149 of 164 patients (90%) were classified into MBSHH (47.7%), MBWNT (16.1%), MBGRP3 (15.4%), and MBGRP4 (20.8%). GNAS presented the highest expression levels, with higher expression in MBSHH. TP53, MYCN, SOX2, and MET were also up-regulated in MBSHH, whereas PTEN was up-regulated in MBGRP4. GNAS, TP53, and PTEN low expression was associated with the unfavorable patient outcome only for MBSHH (P = 0.04, P = 0.01, and P = 0.02, respectively). TP53 mutations were detected in 28.57% of MBSHH cases and exhibited association with lower expression and worse clinical outcome, although not statistically significant. The 22-gene panel for molecular classification of medulloblastoma associated with the expression of GNAS, TP53, and PTEN improves the patient prognostication in MBSHH subgroup and can be easily incorporated in the 22-gene panel without any additional costs.

Classification of childhood medulloblastoma into WHO-defined multiple subtypes based on textural analysis.

Childhood medulloblastoma is a case of a childhood brain tumour that requires close attention due to the low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture-based computer-aided categorization of childhood medulloblastoma samples. According to the World Health Organization, it has four subtypes (desmoplastic, classic, nodular and large). Classification is done in two levels: (i) normal and abnormal and (ii) its four subtypes. The system is evaluated on indigenous patient samples collected from the region. The main objective of database generation is to create a data set of childhood medulloblastoma samples since there exists no available benchmark data set. The proposed framework for automated classification is based on the architectural property and the distribution of cells. Five texture features were extracted for the feature set, namely: grey-level co-occurrence matrix, grey-level run length matrix, first-order histogram features, local binary pattern and Tamura features. The performance of each feature set was evaluated, both individually and in combinations, using five different classifiers. Fivefold cross-validation was used for training and testing the data set. Experiments on both individual feature sets and combinations (best-2, best-3, best-4 and all-5) of feature sets were evaluated based on the accuracy of performance. It was revealed that the combined best-4 feature set resulted in the highest accuracy of 91.3%. The precision, recall and specificity were 0.913, 0.913 and 0.97, respectively. Significantly, it implied that the all-5 feature set is not necessary to have a useful classification. Feature reduction by principal component analysis resulted in increased accuracy of 96.7%. LAY DESCRIPTION: Childhood medulloblastoma is a case of childhood brain tumour that requires high attention due to a low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture-based computer-aided categorization of childhood medulloblastoma samples. According to the World Health Organization (W.H.O), it has four subtypes (desmoplastic, classic, nodular, and large). Classification is done in two levels: i) normal and abnormal ii) its four subtypes. The system is evaluated on indigenous patient samples collected from the region. The main objective of database generation is to create a data set of childhood medulloblastoma samples since there exists no available benchmark data set. The proposed framework is a model for the automatic classification of the samples. The tissue samples obtained post-operation by doctors are converted into images, and then necessary algorithms are applied so that certain features describing each group of the image are known and studied for classification. Later these images are classified using the image features into the subtypes of abnormal samples.

Pathology, diagnostics, and classification of medulloblastoma.

Medulloblastoma (MB) is the most common CNS embryonal tumor. While the overall cure rate is around 70%, patients with high-risk disease continue to have poor outcome and experience long-term morbidity. MB is among the tumors for which diagnosis, risk stratification, and clinical management has shown the most rapid advancement. These advances are largely due to technological improvements in diagnosis and risk stratification which now integrate histomorphologic classification and molecular classification. MB stands as a prototype for other solid tumors in how to effectively integrate morphology and genomic data to stratify clinicopathologic risk and aid design of innovative clinical trials for precision medicine. This review explores the current diagnostic and classification of MB in modern neuropathology laboratories.