Advances in the molecular biology of medulloblastoma revealed four genetically and clinically distinct subgroups. Group 3 medulloblastomas are characterized by frequent amplifications of the oncogene MYC, a high incidence of metastasis, and poor prognosis despite aggressive therapy. We investigated several potential small molecule inhibitors to target Group 3 medulloblastomas based on gene expression data using an in silico drug screen. The Connectivity Map (C-MAP) analysis identified piperlongumine as the top candidate drug for non-WNT medulloblastomas and the cyclin-dependent kinase (CDK) inhibitor alsterpaullone as the compound predicted to have specific antitumor activity against Group 3 medulloblastomas. To validate our findings we used these inhibitors against established Group 3 medulloblastoma cell lines. The C-MAP predicted drugs reduced cell proliferation in vitro and increased survival in Group 3 medulloblastoma xenografts. Alsterpaullone had the highest efficacy in Group 3 medulloblastoma cells. Genomic profiling of Group 3 medulloblastoma cells treated with alsterpaullone confirmed inhibition of cell cycle-related genes, and down-regulation of MYC. Our results demonstrate the preclinical efficacy of using a targeted therapy approach for Group 3 medulloblastomas. Specifically, we provide rationale for advancing alsterpaullone as a targeted therapy in Group 3 medulloblastoma.
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http://dx.doi.org/10.18632/oncotarget.4304 | DOI Listing |
Neuro Oncol
December 2024
Genetics Department, Institut Curie, Paris, France.
Background: Medulloblastoma (MB) is one of the most prevalent embryonal malignant brain tumors. Current classification organizes these tumors into four molecular subgroups (WNT, SHH, Group 3, and Group 4 MB). Recently, a comprehensive classification has been established, identifying numerous subtypes, some of which exhibit a poor prognosis.
View Article and Find Full Text PDFMedulloblastoma (MB) is the most prevalent malignant brain tumor in children, exhibiting clinical and genomic heterogeneity. Of the four major subgroups, Group 3 tumors (MYC-MB), display high levels of MYC and metastasis rates. Despite treatment with surgery, radiation, and chemotherapy, patients with Group 3 MB are more likely to develop aggressive recurrent tumors with poor survival.
View Article and Find Full Text PDFGenome Biol
December 2024
Group Genome Instability in Tumors, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Single-cell DNA sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple and accessible assay to profile low-coverage DNA and RNA in thousands of cells in parallel.
View Article and Find Full Text PDFNeurooncol Adv
October 2024
Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.
Background: Medulloblastoma (MB) is the most common high-grade pediatric brain tumor, comprised of 4 main molecular subgroups-sonic-hedgehog (SHH), Wnt, Group 3, and Group 4. Group 3 and Group 4 tumors are the least characterized MB subgroups, despite Group 3 having the worst prognosis (~50% survival rate), and Group 4 being the most prevalent. Such poor characterization can be attributed to high levels of inter- and intratumoral heterogeneity, making it difficult to identify common therapeutic targets.
View Article and Find Full Text PDFEur Radiol
December 2024
Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
Background: This study aimed to establish a voxel-based map to predict the occurrence of cerebellar mutism syndrome (CMS) and investigate the relationship between CMS and motor dysfunction.
Method: This multicenter study cohort included 224 patients diagnosed with medulloblastoma at Beijing Children's Hospital (n = 88) and Beijing Tiantan Hospital (n = 136). The dataset was randomly divided into training (n = 95), test (n = 41), and validation (n = 88) datasets.
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