Classification Of Medulloblastoma Research Articles

MDB-12. NANOPORE SEQUENCING AS A CUTTING-EDGE TECHNOLOGY FOR MEDULLOBLASTOMA CLASSIFICATION

Abstract INTRODUCTION Medulloblastoma (MB) is one of the most prevalent embryonal malignant brain tumors, divided into four distinct molecular subgroups (WNT, SHH, group 3, and group 4) with its individual prognosis. A more extensive classification of MB has recently been provided, identifying numerous subtypes, some with poor prognosis, leading to a significant number of patients succumbing to the disease while many survivors experience long-term side effects, highlighting the need for efficient subgrouping methods. Distinct genome-wide DNA methylation profiles characterize each MB subgroup, and the advent of Nanopore DNA sequencing has positioned itself as a reliable technique for comprehensive copy number and methylation profiling. MATERIAL AND METHODS We evaluated the ability of Nanopore sequencing to provide clinically relevant methylation and copy number profiles of MB, on a discovery cohort of 45 frozen MB samples, benchmarked against the gold standard EPIC array, and subsequently validated on a cohort of 116 MB samples. RESULTS The majority of MB in both the discovery cohort (43/45; 95.6%) and the validation cohort (110/116; 94.8%) were accurately subgrouped by Nanopore sequencing. Flongle flow cells for 18 MB allowed for a more rapid and cost-effective analysis, with 94.4% (17/18) being correctly classified. Additionally, Nanopore sequencing allowed us to correctly subtype 24/30 (80.0%) within MB groups. DISCUSSION We provided proof of concept that Nanopore sequencing can subgroup MB using DNA extracted from formalin-fixed paraffin-embedded (FFPE) samples and cell-free DNA obtained from cerebrospinal fluid. This first large-scale study establishes the proof of concept that this modern and innovative technology is well-suited for MB classification. We confirm the use of Nanopore sequencing on circulating DNA and present an initial exploration of its application on DNA extracted from FFPE samples. Its ability to deliver quick and cost-effective results firmly establishes Nanopore sequencing as a game-changer in the field of MB classification.

The Role of the Dysregulation of Long Non-Coding and Circular RNA Expression in Medulloblastoma: A Systematic Review.

Medulloblastoma (MB) is the most common malignant brain tumor in childhood. Although recent multi-omic studies have led to advances in MB classification, there is still room for improvement with regard to treatment response and survival. Therefore, identification of new and less invasive biomarkers is needed to refine the diagnostic process and to develop more personalized treatment strategies. In this context, non-coding RNAs (ncRNAs) could be useful biomarkers for MB. In this article, we reviewed the role of two types of ncRNAs, long non-coding (lncRNAs) and circular RNAs (circRNAs), as biomarkers for the diagnosis, subgroup classification, and prognosis of MB. We also reviewed potential candidates with specific functions and mechanisms of action in the disease. We performed a search in PubMed and Scopus using the terms ("long non coding RNAs" OR "lncRNAs") and ("circular RNAs" OR "circRNAs") AND "medulloblastoma" to identify biomarker discovery or functional studies evaluating the effects of these ncRNAs in MB. A total of 26 articles met the inclusion criteria. Among the lncRNAs, the tumorigenic effects of the upregulated lnc-IRX3-80 and lnc-LRRC47-78 were the most studied in MB. Among the circRNAs, the upregulation of circSKA3 and its functional impact in MB cell lines were the most consistent results, so this circRNA could be considered a potential biomarker in MB. Additional validation is required for many deregulated lncRNAs and circRNAs; therefore, further studies are warranted.

Research progress in molecular pathology markers in medulloblastoma.

Medulloblastoma (MB) is the commonest primary malignant brain cancer. The current treatment of MB is usually surgical resection combined with radiotherapy or chemotherapy. Although great progress has been made in the clinical management of MB, tumor metastasis and recurrence are still the main cause of death. Therefore, definitive and timely diagnosis is of great importance for improving therapeutic effects on MB. In 2016, the World Health Organization (WHO) divided MB into four subtypes: wingless-type mouse mammary tumor virus integration site (WNT), sonic hedgehog (SHH), non-WNT/non-SHH group 3, and group 4. Each subtype of MB has a unique profile in copy number variation, DNA alteration, gene transcription, or post-transcriptional/translational modification, all of which are associated with different biological manifestations, clinical features, and prognosis. This article reviewed the research progress of different molecular pathology markers in MB and summarized some targeted drugs against these molecular markers, hoping to stimulate the clinical application of these molecular markers in the classification, diagnosis, and treatment of MB.

Gene Expression Data Points to a Role for Hypoxia in Medulloblastoma Pathogenesis


 
 
 Background:
 Medulloblastoma (MB) is the most common intracranial tumor in children. While molecular classification of MB is well-established, detailing cell origin, biological properties, and biomarkers, little research has been performed concerning the MB tumor microenvironment. Hypoxia is significantly associated with tumor spread, poor prognosis, malignant phenotype, and resistance to radiotherapy and chemotherapy in numerous cancer types. The aim of the present study was to assess a possible role for hypoxia in MB and the potential effect on clinical outcomes.
 Methods:
 We, therefore, performed a systematic review examining the role of hypoxia in MB as well as pediatric brain tumors in general. In vitro studies have identified a role for HIF-1α in chemotherapy resistance, while patient samples suggest hypoxia-induced changes in gene expression as well as proteomic, metabolomic, and lipidomic profiles. Based on this literature review, 55 candidate hypoxia-related genes were identified. The PedcBioPortal for Integrated Childhood Cancer Genomics was used to assess expression differences in pediatric patient samples for these genes of interest.
 Results:
 RNA expression was analyzed for correlation with survival, molecular group, and Chang stage. Expression of DDAH1, HYOU1, MYC, and RBX1 were significantly associated with survival. ANOVA and Ttest with a Bonferroni correction were used to assess for expression differences between groups and Chang stage. Multiple hypoxia candidate genes (ARNT2, BHLHE40, CYP3A5, DDAH1, DDIT4L, EGLN3, MT3, MYC, MYCN, TGFBR2, TP53, VEGFA) were significantly correlated with molecular group. Expression levels of TGFBR3 and GPR37 were associated with Chang stage.
 Conclusion and Potential Impact:
 The results of our systematic review and gene expression analyses support a role for hypoxia in the pathogenesis and potentially the clinical outcomes of children with MB. Future studies comparing gene expression levels at normal oxygen tension (21%) and physiologic oxygen tension (1-3%) will allow us to assess the role of hypoxia in medulloblastoma pathogenesis.
 
 

Pathological implications of metabolic reprogramming and its therapeutic potential in medulloblastoma.

Tumor-specific alterations in metabolism have been recognized to sustain the production of ATP and macromolecules needed for cell growth, division and survival in many cancer types. However, metabolic heterogeneity poses a challenge for the establishment of effective anticancer therapies that exploit metabolic vulnerabilities. Medulloblastoma (MB) is one of the most heterogeneous malignant pediatric brain tumors, divided into four molecular subgroups (Wingless, Sonic Hedgehog, Group 3 and Group 4). Recent progresses in genomics, single-cell sequencing, and novel tumor models have updated the classification and stratification of MB, highlighting the complex intratumoral cellular diversity of this cancer. In this review, we emphasize the mechanisms through which MB cells rewire their metabolism and energy production networks to support and empower rapid growth, survival under stressful conditions, invasion, metastasis, and resistance to therapy. Additionally, we discuss the potential clinical benefits of currently available drugs that could target energy metabolism to suppress MB progression and increase the efficacy of the current MB therapies.

New Developments in the Pathogenesis, Therapeutic Targeting, and Treatment of Pediatric Medulloblastoma.

Simple SummaryMedulloblastoma is the most common pediatric brain tumor, comprising one-third of all pediatric brain tumors, and originating in the posterior fossa of the brain. The disease is categorized into four subtypes: WNT, Sonic hedgehog (SHH), Group 3, and Group 4. Each subtype has unique pathogenesis, biomarkers, prognosis, response to therapy, and potential for further pharmacologic investigation. For example, it has recently been found that tumors in the SHH group arise due to aberrant persistence of defective cells of the embryonic germinal layer of the cerebellum. Herein, we review the recent critical advancements in understanding the four molecular subtypes that continue to shape our diagnostic, surgical, radiotherapeutic, and chemotherapeutic intervention and management to aid in treating pediatric medulloblastoma. We describe preclinical studies and clinical trials that aim to improve risk-stratification of disease, reduce therapy toxicity, and optimize the treatment of pediatric patients.Pediatric medulloblastoma (MB) is the most common pediatric brain tumor with varying prognoses depending on the distinct molecular subtype. The four consensus subgroups are WNT, Sonic hedgehog (SHH), Group 3, and Group 4, which underpin the current 2021 WHO classification of MB. While the field of knowledge for treating this disease has significantly advanced over the past decade, a deeper understanding is still required to improve the clinical outcomes for pediatric patients, who are often vulnerable in ways that adult patients are not. Here, we discuss how recent insights into the pathogenesis of pediatric medulloblastoma have directed current and future research. This review highlights new developments in understanding the four molecular subtypes’ pathophysiology, epigenetics, and therapeutic targeting. In addition, we provide a focused discussion of recent developments in imaging, and in the surgery, chemotherapy, and radiotherapy of pediatric medulloblastoma. The article includes a brief explanation of healthcare costs associated with medulloblastoma treatment.

507 Rational Radiomic Design for Stepwise Diagnosis of Posterior Fossa Pediatric Tumors

INTRODUCTION: Medulloblastoma (MB), pilocytic astrocytoma (PA), and ependymoma (EP) compose the majority of posterior fossa (PF) pediatric tumors and share similar features on MRI. Pre-operative anticipation of pathology can inform the surgical approach, extent of resection, and potential complications. METHODS: We extracted 1800 Image features from T2- and gadolinium-enhanced T1-weighted images in a multinational cohort of 274 MB, 156 PA, and 97 EP. We designed a two-step classifier—first ruling out PA with a three-way classifier, and next distinguishing MB from EP with a binary classifier. For each step, we selected the best performing classifier model from six candidates following LASSO-feature reduction. Final multi-class classifier performance was measured on a holdout test set with the micro-averaged F1-score. RESULTS: Optimal diagnostic performance was achieved using two decision steps, each with their respective feature set and classifier method. An initial three-way (MB, PA, and EP) logistic regression classifier exhibited a micro-averaged F1-score of 0.739. From a reduced feature set of 61 features, T2-Uniformity and T1-Contrast were the most relevant for distinguishing PA. A subsequent two-way neural net classifier distinguished MB from EP with F1-score 0.9189. In the second reduced feature set of 39 features, T2-Sphericity and T1-Flatness were most relevant. Performing the two classifiers sequentially for MB, PA, and EP classification produced a micro-averaged F1-score of 0.9179. CONCLUSION: A sequential radiomic improved upon a single-step radiographic classifier for pediatric PF tumors. Strong overlap between MB and EP prevented a single-step classifier from distinguishing all three pathologies at once. PA-relevant features aligned with avid gadolinium-enhancement and non-enhancing cystic components. MB and EP-related features correlated to how each conform within and extrude from the fourth ventricle, respectively.

AI-Based Pipeline for Classifying Pediatric Medulloblastoma Using Histopathological and Textural Images.

Pediatric medulloblastomas (MBs) are the most common type of malignant brain tumors in children. They are among the most aggressive types of tumors due to their potential for metastasis. Although this disease was initially considered a single disease, pediatric MBs can be considerably heterogeneous. Current MB classification schemes are heavily reliant on histopathology. However, the classification of MB from histopathological images is a manual process that is expensive, time-consuming, and prone to error. Previous studies have classified MB subtypes using a single feature extraction method that was based on either deep learning or textural analysis. Here, we combine textural analysis with deep learning techniques to improve subtype identification using histopathological images from two medical centers. Three state-of-the-art deep learning models were trained with textural images created from two texture analysis methods in addition to the original histopathological images, enabling the proposed pipeline to benefit from both the spatial and textural information of the images. Using a relatively small number of features, we show that our automated pipeline can yield an increase in the accuracy of classification of pediatric MB compared with previously reported methods. A refined classification of pediatric MB subgroups may provide a powerful tool for individualized therapies and identification of children with increased risk of complications.

Medulloblastoma: WHO 2021 and Beyond.

In 2016, medulloblastoma classification was restructured to allow for incorporation of updated data about medulloblastoma biology, genomics, and clinical behavior. For the first time, medulloblastomas were classified according to molecular characteristics ("genetically defined" categories) as well as histologic characteristics ("histologically defined" categories). Current genetically-defined categories include WNT-activated, SHH-activated TP53 wildtype, SHH-activated TP53-mutant, and non-WNT/non-SHH. In this article, we review the most recent update to the classification of medulloblastomas, provide a practical approach to immunohistochemical and molecular testing for these tumors, and demonstrate how to use key molecular genetic findings to develop an integrated diagnosis.

Immunotherapy in Medulloblastoma: Current State of Research, Challenges, and Future Perspectives.

Medulloblastoma (MB), a primary tumor of the central nervous system, is among the most prevalent pediatric neoplasms. The median age of diagnosis is six. Conventional therapies include surgical resection of the tumor with subsequent radiation and chemotherapy. However, these therapies often cause severe brain damage, and still, approximately 75% of pediatric patients relapse within a few years. Because the conventional therapies cause such severe damage, especially in the pediatric developing brain, there is an urgent need for better treatment strategies such as immunotherapy, which over the years has gained accumulating interest. Cancer immunotherapy aims to enhance the body's own immune response to tumors and is already widely used in the clinic, e.g., in the treatment of melanoma and lung cancer. However, little is known about the possible application of immunotherapy in brain cancer. In this review, we will provide an overview of the current consensus on MB classification and the state of in vitro, in vivo, and clinical research concerning immunotherapy in MB. Based on existing evidence, we will especially focus on immune checkpoint inhibition and CAR T-cell therapy. Additionally, we will discuss challenges associated with these immunotherapies and relevant strategies to overcome those.

Clinical Characteristic and Molecular Subgroups of Thai Pediatric Medulloblastomas at Queen Sirikit National Institute of Child Health

Objective: To determine the correlation between clinical characteristics and molecular subgroups of medulloblastoma (MB) in Thai pediatric patients at the Queen Sirikit National Institute of Child Health (QSNICH), Thailand. Materials and Methods: MB specimens operated between 2004 and 2018 were classified by Nanostring into four molecular subgroups, including Wingless signaling pathway (WNT), Sonic Hedgehog signaling pathway (SHH), Group 3, and Group 4. For the present cases, the clinical records were retrospectively analyzed. Results: Twenty-two MB cases with complete clinical records were analyzed. Group 4 was the most common molecular subgroup (31.82%), followed by WNT (27.27%), SHH (22.73%), and Group 3 (18.18%). The histologic subtypes included 18, three, and one cases of classic MB, MB with extensive nodularity (MBEN), and large cell MB, respectively. All SHH MBs were found in infants. All MBENs belonged to SHH subgroup, and the large cell MB was Group 3. All six WNT MB cases did not experience tumor recurrence. Five-year cause specific survival rates were 100% in WNT, 60% in SHH, 57.1% in Group 4, and 0% in Group 3. Five-year recurrence-free survival rates were 100% in WNT, 42.9% in Group 4, and 0% in SHH and Group 3. Conclusion: MB is a heterogeneous disease. Classification of MB, especially at the molecular subtype, is helpful for the management and prognostication. Keywords: Medulloblastoma; Molecular subgroup

CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.

Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based on either deep learning (DL) or textural analysis feature extractions. Also, such studies employed distinct features to accomplish the classification procedure. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. The CoMB-Deep consists of a composite of DL techniques. Initially, it extracts deep spatial features from 10 convolutional neural networks (CNNs). It then performs a feature fusion step using discrete wavelet transform (DWT), a texture analysis method capable of reducing the dimension of fused features. Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. A bi-directional long-short term memory (Bi-LSTM) network; a DL-based approach that is utilized for the classification phase. CoMB-Deep maintains two classification categories: binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.

MBRS-38. MOLECULAR CLASSIFICATION AND CLINICAL CHARACTERISTICS OF 236 MEDULLOBLASTOMAS IN JAPAN

Recent intensive genomic and molecular biological analyses have made a consensus that medulloblastomas (MBs) are at least classified into four core subgroups, and the new 2016 WHO brain tumor classification has introduced the classification of MBs genetically defined in addition to classical histopathological diagnosis. To establish a nationwide network of a molecular diagnosis system for pediatric brain tumors, the JPMNG co-organized by the Japan Society for Neuro-Oncology and the Japanese Society for Pediatric Neurosurgery have started the clinical researches in 2012, and we have summarized results of molecular analysis of Japanese MBs. Total 236 primary MBs have been subclassified by gene expression profile using the NanoString nCounter system or DNA methylation array, and their single nucleotide mutations and copy number aberrations have been also examined. Mean follow up time was 68.9 months. Proportion of four core subgroups were WNT (16.9%), SHH (25.4%), Group 3 (17.4%) and Group 4 (40.3%), respectively. In cases of less than 3 years old, no WNT have been found and 63.2% cases were SHH. In cases between 3 to 17 years old, Group 4 is the most (47%), and these trends is almost consistent with published references. TP53 mutations were identified in 23.3% of SHH, and they were significantly poor prognosis. Metastatic or MYC gain Group 3 MBs were poor prognosis, while Group 4 MBs with loss of chromosome 11 or whole chromosomal aberration were good prognosis. These findings reveal molecular properties of Japanese MBs and will contribute to develop new therapeutic strategies.

MBRS-29. PROSPECTIVE MOLECULAR PROFILING IN PEDIATRIC MEDULLOBLASTOMA PATIENTS ENROLLED ON THE “HEAD START 4” PROTOCOL

Medulloblastoma is the most common malignant embryonal brain tumor in children with only modest improvements in outcomes achieved over the last 20 years. The implementation of irradiation-avoiding strategies, including trials by the “Head Start” consortium, have demonstrated improved cure rates along with enhanced quality of life. Simultaneously, the classification of medulloblastomas has undergone a dramatic shift as molecular testing has made it possible to divide these tumors into distinctive subtypes. Currently, the WHO recognizes four medulloblastoma molecular subgroups; however it remains unclear how patients within these subgroups respond to modern irradiation-avoiding therapies. This study aims to demonstrate the feasibility of prospective molecular profiling in medulloblastoma patients enrolled on the “Head Start 4” trial. Whole-exome sequencing (SureSelect Human All Exon V6+COSMIC) and DNA methylation (Illumina EPIC Array) profiling were performed on 10 paired tumor/blood samples and 4 tumor samples, respectively. High-quality mutational and copy number data were produced for each of the 10 subjects demonstrating well-described gene mutations (SUFU) and chromosomal losses (9q and 10q). Four subjects had methylation profiling which successfully separated them into the WHO subgroups (two SHH and two Group 3). These data showed the feasibility of prospective high-dimensional mutational and DNA methylation analysis using “Head Start 4” patients. Future work will focus on finalizing these profiling efforts, enabling the development of models that predict response to irradiation-avoiding treatment and, in general, a better understanding of the molecular mechanisms underlying treatment resistance and tumor progression, leading to more personalized approaches to treating children with medulloblastoma.

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.

Advances in Immunohistochemical Markers in Molecular Classification of Medulloblastoma

Advances in Immunohistochemical Markers in Molecular Classification of Medulloblastoma

Abstract A33: Molecular classification of Brazilian medulloblastoma by a NanoString gene panel

Abstract Background: Medulloblastoma is the most frequent malignant brain tumor in children. Currently, four distinct medulloblastoma molecular subgroups have been identified: SHH, WNT, Group 3, and Group 4. The nCounter® technology (NanoString) is a high-throughput platform, highly sensitive, robust and useful for analyses of highly degraded samples, such as formalin-fixed, paraffin-embedded (FFPE) samples. Recently, a gene panel of 22 genes employing the nCounter® technology was developed to distinguish meduloblastoma molecular subgroups. Aim: To apply and to validate a gene panel for classification of Brazilian medulloblastoma molecular subgroups using the nCounter® technology. Methods: We evaluated 47 tumor samples (FFPE) from patients diagnosed with medulloblastoma from Barretos Cancer Hospital, São Paulo, Brazil. Clinicopathologic features from all patients were collected and overall (OS) survival and 5-year OS were calculated. Gene expression profiling was carried out using the nCounter Elements™ custom panel (NanoString), and the custom CodeSet was composed of distinctive genes for each medulloblastoma molecular subgroup (WNT, SHH, Group 3, Group 4) plus housekeeping genes. Heatmap clustering was employed for initial classification of medulloblastoma molecular subgroups and the PAM method was applied for class prediction. hTERT hotspot mutations were screened by Sanger sequencing. Statistical analyses: Kappa index, Fisher's exact test, Kaplan-Meier method and log-rank test; level of significance: 95%. Results: Forty-seven patients diagnosed with medulloblastoma were analyzed (average age = 14.5 years). The median OS was 66.5 months and the 5-year OS was 54%. After heatmap clustering, we distinguished 44 tumors: 21 SHH, 7 WNT, 5 Group 3, 11 Group 4, and 3 of them were not distinguished. After class prediction by PAM method, we classified 44 tumors: 22 SHH, 6 WNT, 5 Group 3, 11 Group 4, and 3 of them were not classified. Among the 3 cases which were not distinguished by heatmap clustering, 2 of them were predicted as SHH and 1 remained unclassified and 2 cases distinguished by heatmap clustering as WNT and SHH subgroups were unclassified by PAM method prediction. Classifications by heatmap clustering and PAM method were highly concordant according to kappa index. WNT group presented the lowest 5-year OS and the Group 3 presented the highest 5-year OS. hTERT hotspot mutation (C228A) was mostly found in the SHH subgroup. Conclusion: We validated in a Brazilian cohort a modern approach for classification of medulloblastoma molecular subgroups. The nCounter® technology associated with PAM method is helpful to distinguish the medulloblastoma molecular subgroups and prospectively can be applied as a forthcoming diagnostic approach. Citation Format: Leticia Ferro Leal, Adriane Feijo Evangelista, Flavia Escremim de Paula, Adriana Cruvinel Carloni, Gisele Caravina de Almeida, Bruna Mancano, Rui Manuel Reis. Molecular classification of Brazilian medulloblastoma by a NanoString gene panel [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr A33.

Meduloblastoma: de la clasificación histológica a la molecular

Medulloblastoma is a common tumor in children, but is rare in adults. It is the second most common cause of cancer-related death in patients under 15 years. The presence of a median or paramedian enhancing cerebellar mass, often compressing the fourth ventricle may indicate the presence of this tumor. Genetic and molecular markers offer a new approach to the understanding and classification of medulloblastomas. Hence we have come to understand medulloblastoma not as a sole disease but rather, as a group of clinically and molecularly distinct pathologies. Magnetic resonance imaging of the spine and lumbar puncture must be performed in all patients as part of the assessment of the extent of disease, since spinal leptomeninges are common sites for metastatic dissemination. Ideally, its treatmeant involves an interdisciplinary group that provides surgery, radiotherapy, and chemotherapy. Prognosis depends upon variables such as age (under three years), metastatic dissemination, residual tumor after surgery, large cell/anaplastic histological variant, and group 3 tumor (MYC amplification group).

The evolution of medulloblastoma therapy to personalized medicine.

Recent advances in cancer genomics have revolutionized the characterization and classification of medulloblastomas. According to the current WHO guidelines, medulloblastomas are now classified into the following molecularly defined groups: Wnt signaling pathway (WNT)-activated, sonic hedgehog signaling pathway (SHH)-activated and tumor suppressor protein p53 (TP53)-mutant, SHH-activated and TP53-wildtype, and non-WNT/non-SHH (i.e. group 3 and group 4). Importantly, genomic, epigenomic, and proteomic advances have created a potential paradigm shift in therapeutic options. The challenge now is to (i) translate these observations into new therapeutic approaches and (ii) employ these observations in clinical practice, utilizing the classification following a molecular analysis for diagnosis and application of new subgroup-specific targeted therapeutics.

The ubiquitin-proteasome system and chromosome 17 in cerebellar granule cells and medulloblastoma subgroups.

Chromosome 17 abnormalities are often observed in medulloblastomas (MBs), particularly those classified in the consensus Groups 3 and 4. Herein we review MB signature genes associated with chromosome 17 and the relationship of these signature genes to the ubiquitin-proteasome system. While clinical investigators have not focused on the ubiquitin-proteasome system in relation to MB, a substantial amount of data on the topic has been hidden in the form of supplemental datasets of gene expression. A supplemental dataset associated with the Thompson classification of MBs shows that a subgroup of MB with 17p deletions is characterized by reduced expression of genes for several core particle subunits of the beta ring of the proteasome (β1, β4, β5, β7). One of these genes (PSMB6, the gene for the β1 subunit) is located on chromosome 17, near the telomeric end of 17p. By comparison, in the WNT group of MBs only one core proteasome subunit, β6, associated with loss of a gene (PSMB1) on chromosome 6, was down-regulated in this dataset. The MB subgroups with the worst prognosis have a significant association with chromosome 17 abnormalities and irregularities of APC/C cyclosome genes. We conclude that the expression of proteasome subunit genes and genes for ubiquitin ligases can contribute to prognostic classification of MBs. The therapeutic value of targeting proteasome subunits and ubiquitin ligases in the various subgroups of MB remains to be determined separately for each classification of MB.