Medulloblastoma: Molecular Classification-Based Personal Therapeutics.
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ABSTRACT:
SUBMITTER: Archer TC
PROVIDER: S-EPMC5398996 | biostudies-literature | 2017 Apr
REPOSITORIES: biostudies-literature
Publications
Medulloblastoma: Molecular Classification-Based Personal Therapeutics.
Archer Tenley C TC Mahoney Elizabeth L EL Pomeroy Scott L SL
Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 20170401 2
Recent advances in cancer genomics have revealed 4 distinct subgroups of medulloblastomas, each with unique transcription profiles, DNA alterations and clinical outcome. Molecular classification of medulloblastomas improves predictions of clinical outcome, allowing more accurate matching of intensity of conventional treatments with chemotherapy and radiation to overall prognosis and setting the stage for the introduction of targeted therapies. ...[more]
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Project description:IntroductionMedulloblastoma is an aggressive but potentially curable central nervous system tumor that remains a treatment challenge. Analysis of therapeutic targets can provide opportunities for the selection of agents.MethodsUsing multiplatform analysis, 36 medulloblastomas were extensively profiled from 2009 to 2015. Immunohistochemistry, next generation sequencing, chromogenic in situ hybridization, and fluorescence in situ hybridization were used to identify overexpressed proteins, immune checkpoint expression, mutations, tumor mutational load, and gene amplifications.ResultsHigh expression of MRP1 (89%, 8/9 tumors), TUBB3 (86%, 18/21 tumors), PTEN (85%, 28/33 tumors), TOP2A (84%, 26/31 tumors), thymidylate synthase (TS; 80%, 24/30 tumors), RRM1 (71%, 15/21 tumors), and TOP1 (63%, 19/30 tumors) were found in medulloblastoma. TOP1 was found to be enriched in metastatic tumors (90%; 9/10) relative to posterior fossa cases (50%; 10/20) (p = 0.0485, Fisher exact test), and there was a positive correlation between TOP2A and TOP1 expression (p = 0.0472). PD-1 + T cell tumor infiltration was rare, PD-L1 tumor expression was uncommon, and TML was low, indicating that immune checkpoint inhibitors as a monotherapy should not necessarily be prioritized for therapeutic consideration based on biomarker expression. Gene amplifications such as those of Her2 or EGFR were not found. Several unique mutations were identified, but their rarity indicates large-scale screening efforts would be necessary to identify sufficient patients for clinical trial inclusion.ConclusionsTherapeutics are available for several of the frequently expressed targets, providing a justification for their consideration in the setting of medulloblastoma.
| S-EPMC6132970 | biostudies-literature
Project description: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.
| S-EPMC7317787 | biostudies-literature
Project description:BackgroundMedulloblastoma (MB) has been classified into four core subgroups according to the transcriptional profile in recent years. However, some disagreement among researchers remains regarding the prognoses and most effective treatments of the different subgroups with different age distributions.ObjectiveThe objective of this study was to analyze MB prognosis in children population based on the classification of four molecular subgroups.MethodsFrom January 2011 to January 2013, 84 consecutive MB patients aged underwent tumor removal at Beijing Tiantan Hospital. A total of 55 patients who ranged in age from 4 to 18 years underwent detailed follow-up. Molecular subgrouping was performed using RT-PCR.ResultsThe 2-year progression-free survival (PFS) and overall survival (OS) rates for the entire cohort were 76.2 ± 5.8 and 81.8 ± 5.2%, respectively. Univariate analysis revealed that the Group 4 patients had a better survival (2-year OS, 90.6 ± 5.2%) than the SHH subgroup (P = 0.002) and Group 3 patients (P = 0.008). Only two of the 23 non-metastasized Group 4 patients relapsed, and chemotherapy did significantly affect these patients (PFS, P = 0.685). One out of five WNT patients had tumor relapse and died at last. Large cell/anaplastic (LC/A) histology and chemotherapy were independent risk factors in multivariate analysis.ConclusionIn our study, the non-metastasized Group 4 patients had an excellent prognosis. The SHH subgroup and Group 3 patients had worst prognoses. LC/A histology had a dismal prognosis in our cohorts, which warrants intensive treatment.
| S-EPMC5427081 | biostudies-literature
Project description:BackgroundInternational consensus recognises four medulloblastoma molecular subgroups: WNT (MBWNT), SHH (MBSHH), group 3 (MBGrp3), and group 4 (MBGrp4), each defined by their characteristic genome-wide transcriptomic and DNA methylomic profiles. These subgroups have distinct clinicopathological and molecular features, and underpin current disease subclassification and initial subgroup-directed therapies that are underway in clinical trials. However, substantial biological heterogeneity and differences in survival are apparent within each subgroup, which remain to be resolved. We aimed to investigate whether additional molecular subgroups exist within childhood medulloblastoma and whether these could be used to improve disease subclassification and prognosis predictions.MethodsIn this retrospective cohort study, we assessed 428 primary medulloblastoma samples collected from UK Children's Cancer and Leukaemia Group (CCLG) treatment centres (UK), collaborating European institutions, and the UKCCSG-SIOP-PNET3 European clinical trial. An independent validation cohort (n=276) of archival tumour samples was also analysed. We analysed samples from patients with childhood medulloblastoma who were aged 0-16 years at diagnosis, and had central review of pathology and comprehensive clinical data. We did comprehensive molecular profiling, including DNA methylation microarray analysis, and did unsupervised class discovery of test and validation cohorts to identify consensus primary molecular subgroups and characterise their clinical and biological significance. We modelled survival of patients aged 3-16 years in patients (n=215) who had craniospinal irradiation and had been treated with a curative intent.FindingsSeven robust and reproducible primary molecular subgroups of childhood medulloblastoma were identified. MBWNT remained unchanged and each remaining consensus subgroup was split in two. MBSHH was split into age-dependent subgroups corresponding to infant (<4·3 years; MBSHH-Infant; n=65) and childhood patients (≥4·3 years; MBSHH-Child; n=38). MBGrp3 and MBGrp4 were each split into high-risk (MBGrp3-HR [n=65] and MBGrp4-HR [n=85]) and low-risk (MBGrp3-LR [n=50] and MBGrp4-LR [n=73]) subgroups. These biological subgroups were validated in the independent cohort. We identified features of the seven subgroups that were predictive of outcome. Cross-validated subgroup-dependent survival models, incorporating these novel subgroups along with secondary clinicopathological and molecular features and established disease risk-factors, outperformed existing disease risk-stratification schemes. These subgroup-dependent models stratified patients into four clinical risk groups for 5-year progression-free survival: favourable risk (54 [25%] of 215 patients; 91% survival [95% CI 82-100]); standard risk (50 [23%] patients; 81% survival [70-94]); high-risk (82 [38%] patients; 42% survival [31-56]); and very high-risk (29 [13%] patients; 28% survival [14-56]).InterpretationThe discovery of seven novel, clinically significant subgroups improves disease risk-stratification and could inform treatment decisions. These data provide a new foundation for future research and clinical investigations.FundingCancer Research UK, The Tom Grahame Trust, Star for Harris, Action Medical Research, SPARKS, The JGW Patterson Foundation, The INSTINCT network (co-funded by The Brain Tumour Charity, Great Ormond Street Children's Charity, and Children with Cancer UK).
| S-EPMC5489698 | biostudies-literature
Project description: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.
| S-EPMC7654754 | biostudies-literature
Project description: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.
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Project description:BackgroundMedulloblastoma (MB) is the most common malignant paediatric brain tumour and a leading cause of cancer-related mortality and morbidity. Existing treatment protocols are aggressive in nature resulting in significant neurological, intellectual and physical disabilities for the children undergoing treatment. Thus, there is an urgent need for improved, targeted therapies that minimize these harmful side effects.MethodsWe identified candidate drugs for MB using a network-based systems-pharmacogenomics approach: based on results from a functional genomics screen, we identified a network of interactions implicated in human MB growth regulation. We then integrated drugs and their known mechanisms of action, along with gene expression data from a large collection of medulloblastoma patients to identify drugs with potential to treat MB.ResultsOur analyses identified drugs targeting CDK4, CDK6 and AURKA as strong candidates for MB; all of these genes are well validated as drug targets in other tumour types. We also identified non-WNT MB as a novel indication for drugs targeting TUBB, CAD, SNRPA, SLC1A5, PTPRS, P4HB and CHEK2. Based upon these analyses, we subsequently demonstrated that one of these drugs, the new microtubule stabilizing agent, ixabepilone, blocked tumour growth in vivo in mice bearing patient-derived xenograft tumours of the Sonic Hedgehog and Group 3 subtype, providing the first demonstration of its efficacy in MB.ConclusionsOur findings confirm that this data-driven systems pharmacogenomics strategy is a powerful approach for the discovery and validation of novel therapeutic candidates relevant to MB treatment, and along with data validating ixabepilone in PDX models of the two most aggressive subtypes of medulloblastoma, we present the network analysis framework as a resource for the field.
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Project description:BackgroundMedulloblastoma (MB) comprises four subtypes of which group 3 MB are the most aggressive. Although overall survival for MB has improved, the outcome of group 3 MB remains dismal. C-MYC (MYC) amplification or MYC overexpression which characterizes group 3 MB is a strong negative prognostic factor and is frequently associated with metastases and relapses. We previously reported that MYC expression alone promotes highly aggressive MB phenotypes, in part via repression of thrombospondin-1 (TSP-1), a potent tumor suppressor.MethodsIn this study, we examined the potential role of TSP-1 and TSP-1 peptidomimetic ABT-898 in MYC-amplified human MB cell lines and two distinct murine models of MYC-driven group 3 MBs.ResultsWe found that TSP-1 reconstitution diminished metastases and prolonged survival in orthotopic xenografts and promoted chemo- and radio-sensitivity via AKT signaling. Furthermore, we demonstrate that ABT-898 can recapitulate the effects of TSP-1 expression in MB cells in vitro and specifically induced apoptosis in murine group 3 MB tumor cells.ConclusionOur data underscore the importance of TSP-1 as a critical tumor suppressor in MB and highlight TSP-1 peptidomimetics as promising novel therapeutics for the most lethal subtype of MB.
| S-EPMC7890793 | biostudies-literature
Project description:Molecular classification of medulloblastoma is critical for the correct treatment of this malignant paediatric brain tumour. The analysis of genome-wide DNA methylation patterns has profoundly improved diagnostic precision and classification of brain tumours. However, the implementation of DNA methylation microarrays in daily clinical practice can be time-consuming, costly and inaccessible for many centres worldwide. We aimed to develop a machine-learning decision support system for rapid and cost-effective prediction of medulloblastoma methylation class directly from quantitative PCR data.
2023-08-23 | GSE210723 | GEO
Project description:BACKGROUND AND PURPOSE:Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma. MATERIALS AND METHODS:In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract MR imaging-based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve to evaluate model performance. RESULTS:Of 590 MR imaging-derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70-0.73) and group 4 (area under the curve = 0.76-0.80) medulloblastoma. CONCLUSIONS:This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups.
| S-EPMC6330121 | biostudies-literature