HDAC inhibitors to the rescue in sonic hedgehog medulloblastoma

Stem Cells in Cancer: From Mechanisms to Therapeutic Strategies

Stem cells have emerged as a pivotal area of research in the field of oncology, offering new insights into the mechanisms of cancer initiation, progression, and resistance to therapy. This review provides a comprehensive overview of the role of stem cells in cancer, focusing on cancer stem cells (CSCs), their characteristics, and their implications for cancer therapy. We discuss the origin and identification of CSCs, their role in tumorigenesis, metastasis, and drug resistance, and the potential therapeutic strategies targeting CSCs. Additionally, we explore the use of normal stem cells in cancer therapy, focusing on their role in tissue regeneration and their use as delivery vehicles for anticancer agents. Finally, we highlight the challenges and future directions in stem cell research in cancer.

Identification of HDAC4 as a potential therapeutic target and prognostic biomarker for ZFTA-fused ependymomas

Members of the HDAC family are predictive biomarkers and regulate the tumorigenesis in several cancers. However, the role of these genes in the biology of intracranial ependymomas (EPNs) remains unexplored. Here, an analysis of eighteen HDACs genes in an EPN transcriptomic dataset, revealed significantly higher levels of HDAC4 in supratentorial ZFTA fusion (ST-ZFTA) compared with ST-YAP1 fusion and posterior fossa EPNs, while HDAC7 and SIRT2 were downregulated in ST-ZFTA. HDAC4 was also overexpressed in ST-ZFTA as measured by single-cell RNA-Seq, quantitative real time-polymerase chain reaction, and immunohistochemistry. Survival analyses showed a significantly worse outcome for EPNs with higher HDAC4 and SIRT1 mRNA levels. Ontology enrichment analysis showed an HDAC4-high signature consistent with viral processes while collagen-containing extracellular matrix and cell-cell junction were enriched in those with an HDAC4-low signature. Immune gene analysis demonstrated a correlation between HDAC4 expression and low levels of NK resting cells. Several small molecules compounds targeting HDAC4 and ABCG2, were predicted by in silico analysis to be effective against HDAC4-high ZFTA. Our results provide novel insights into the biology of the HDAC family in intracranial ependymomas and reveal HDAC4 as a prognostic marker and potential therapeutic target in ST-ZFTA.

Multidisciplinary Management of Medulloblastoma: Consensus, Challenges, and Controversies

Medulloblastoma is a highly aggressive "small round blue cell tumor" of the posterior fossa predominantly seen in children. Historically aggressive multimodality regimens have achieved encouraging outcomes with the caveat of severe long-term toxicities. The last decade has unleashed a revolution in terms of evolved understanding of this heterogeneous disease entity in terms of molecular biology. Medulloblastoma as of today is grouped into one of four canonical molecular subgroups (WNT, SHH, Group 3, and Group 4) each characterized by different putative cells of origin, characteristic aberrations at the molecular level, radiogenomics, and outcomes. Our understanding continues to grow in this regard. The future promises much in terms of personalized medicine in tailoring therapy to the needs of individual patients based on their clinical and molecular profile in order to maximize individual and population based outcomes at the cost of minimizing toxicity.

NBM-BMX, an HDAC8 Inhibitor, Overcomes Temozolomide Resistance in Glioblastoma Multiforme by Downregulating the β-Catenin/c-Myc/SOX2 Pathway and Upregulating p53-Mediated MGMT Inhibition

Although histone deacetylase 8 (HDAC8) plays a role in glioblastoma multiforme (GBM), whether its inhibition facilitates the treatment of temozolomide (TMZ)-resistant GBM (GBM-R) remains unclear. By assessing the gene expression profiles from short hairpin RNA of HDAC8 in the new version of Connectivity Map (CLUE) and cells treated by NBM-BMX (BMX)-, an HDAC8 inhibitor, data analysis reveals that the Wnt signaling pathway and apoptosis might be the underlying mechanisms in BMX-elicited treatment. This study evaluated the efficacy of cotreatment with BMX and TMZ in GBM-R cells. We observed that cotreatment with BMX and TMZ could overcome resistance in GBM-R cells and inhibit cell viability, markedly inhibit cell proliferation, and then induce cell cycle arrest and apoptosis. In addition, the expression level of β-catenin was reversed by proteasome inhibitor via the β-catenin/ GSK3β signaling pathway to reduce the expression level of c-Myc and cyclin D1 in GBM-R cells. BMX and TMZ cotreatment also upregulated WT-p53 mediated MGMT inhibition, thereby triggering the activation of caspase-3 and eventually leading to apoptosis in GBM-R cells. Moreover, BMX and TMZ attenuated the expression of CD133, CD44, and SOX2 in GBM-R cells. In conclusion, BMX overcomes TMZ resistance by enhancing TMZ-mediated cytotoxic effect by downregulating the β-catenin/c-Myc/SOX2 signaling pathway and upregulating WT-p53 mediated MGMT inhibition. These findings indicate a promising drug combination for precision personal treating of TMZ-resistant WT-p53 GBM cells.

The multilayer community structure of medulloblastoma

Multilayer networks allow interpreting the molecular basis of diseases, which is particularly challenging in rare diseases where the number of cases is small compared with the size of the associated multi-omics datasets. In this work, we develop a dimensionality reduction methodology to identify the minimal set of genes that characterize disease subgroups based on their persistent association in multilayer network communities. We use this approach to the study of medulloblastoma, a childhood brain tumor, using proteogenomic data. Our approach is able to recapitulate known medulloblastoma subgroups (accuracy >94%) and provide a clear characterization of gene associations, with the downstream implications for diagnosis and therapeutic interventions. We verified the general applicability of our method on an independent medulloblastoma dataset (accuracy >98%). This approach opens the door to a new generation of multilayer network-based methods able to overcome the specific dimensionality limitations of rare disease datasets.

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The molecular interpretation of rare diseases is a challenging task

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Multilayer networks allow patient stratification and explainability

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We identify subgroup-specific genes and multilayer associations in medulloblastoma

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Multilayer community analysis enables the molecular interpretation of rare diseases