Biology subjects

Velazquez Vega, J. E.

Publications and source records attributed to Velazquez Vega, J. E..

4 recordsLinked to original sources

bioRxiv2025

ONC206 demonstrates potent anti-tumorigenic activity and is a potential novel therapeutic strategy for high-risk medulloblastoma

BackgroundMedulloblastoma is the most common malignant pediatric brain tumor, and has an urgent need for novel treatment approaches. Dordaviprone (ONC201) and its chemical derivative with nanomolar potency, ONC206, induce apoptosis of cancer cells by activation of the mitochondrial caseinolytic protease P (ClpP). ONC206 is currently in Phase I clinical trials for pediatric patients with primary brain tumors. MethodsIn this study, we evaluated the preclinical therapeutic effects of ONC206 in medulloblastoma and investigated its mechanism of action. ResultsWe found evidence for high expression of ClpP at both the RNA and protein level in medulloblastoma tumors, compared to very low expression in normal brain tissue. In addition, we saw a pronounced reduction in cell viability of human Group 3 and Group 4 and murine SHH-driven and Group 3 medulloblastoma cells treated with ONC206 with low IC-50s. After treatment with ONC206, we observed an induction of integrated stress response and mitochondrial damage. To test the efficacy of ONC206 in vivo, we used murine models of SHH-driven and Group 3 medulloblastoma as well as Group 3 and Group 4 patient-derived xenografts (PDXs). ONC206 led to a significant prolongation of survival in both murine models, with the SHH mice demonstrating survival extension from 70 to 140 days. PDX-bearing mice also responded to ONC206, which led to a significant survival benefit. ConclusionOur results highlight ONC206 as a novel therapeutic option for patients with high-risk medulloblastoma and provide strong rationale for testing the efficacy of ONC206 in the treatment of these patients. Key points (2-3)ONC206 potently kills medulloblastoma cells by inducing integrated stress response and mitochondrial damage. ONC206 prolongs survival of medulloblastoma-bearing mice in both murine and patient-derived xenograft models. Importance of studyThere is an unmet need for better therapies for high-risk medulloblastoma patients. ONC201 has shown promising responses and recently received FDA approval for diffuse midline glioma. ONC206 is a chemical derivative with higher potency and better brain penetrance. In this study, we analyzed the therapeutic potential of ONC206 for high-risk medulloblastoma and found that the drug effectively killed mouse and human medulloblastoma cells with high nanomolar potency. We also saw that ONC206 very significantly prolonged survival of medulloblastoma-bearing mice, both in genetically engineered mouse models and patient-derived xenografts. Our study provides a strong rationale for testing the efficacy of ONC206 in the treatment of patients with medulloblastoma and has set the stage for a clinical trial with this agent in pediatric patients with recurrent malignant brain tumors, including medulloblastoma (NCT04732065).

cancer biology↗

bioRxiv2024

Medulloblastoma Spatial Transcriptomics Reveals Tumor Microenvironment Heterogeneity with High-Density Progenitor Cell Regions Correlating with High-Risk Disease

The tumor microenvironment (TME) of medulloblastoma (MB) influences progression and therapy response, presenting a promising target for therapeutic advances. Prior single-cell analyses have characterized the cellular components of the TME but lack spatial context. To address this, we performed spatial transcriptomic sequencing on sixteen pediatric MB samples obtained at diagnosis, including two matched diagnosis-relapse pairs. Our analyses revealed inter- and intra-tumoral heterogeneity within the TME, comprised of tumor-associated astrocytes (TAAs), macrophages (TAMs), stromal components, and distinct subpopulations of MB cells at different stages of neuronal differentiation and cell cycle progression. We identified dense regions of quiescent progenitor-like MB cells enriched in patients with high-risk (HR) features and an increase in TAAs, TAMs, and dysregulated vascular endothelium following relapse. Our study presents novel insights into the spatial architecture and cellular landscape of the medulloblastoma TME, highlighting spatial patterns linked to HR features and relapse, which may serve as potential therapeutic targets.

cancer biology↗

bioRxiv2022

PRC2 disruption in cerebellar progenitors produces cerebellar hypoplasia and aberrant myoid differentiation without blocking medulloblastoma growth

We show that the Polycomb Repressive Complex 2 (PRC2) maintains neural identity in Sonic Hedgehog (SHH)-driven cerebellar granule neuron progenitors (CGNPs) and SHH-driven medulloblastoma, a cancer of CGNPs. Proliferating CGNPs and medulloblastoma cells pass the neural fate commitment to their progeny through epigenetic mechanisms. The PRC2 mediates epigenetic regulation through histone methylation, and PRC2 inhibitors have been proposed for medulloblastoma therapy. We investigated PRC2 function in CGNPs and medulloblastoma by conditionally deleting PRC2 components Eed or Ezh2 in CGNPs and analyzing cerebellar growth, cellular gene expression (scRNA-seq), and tumorigenesis in medulloblastoma-prone Smo-mutant mice. Eed-deleted CGNPs showed reduced growth, with decreased proliferation, increased apoptosis and inappropriate myoid differentiation. Ezh2-deleted CGNPs also showed myoid differentiation without reduced growth. Eed-deleted and Ezh2-deleted medulloblastomas similarly demonstrated myoid differentiation, but progressed more rapidly than PRC2-intact controls. The PRC2 thus maintained neural fate in CGNPs and medulloblastoma, but PRC2 disruption did not block SHH medulloblastoma progression.

neuroscience↗

bioRxiv2017

Predicting cancer outcomes from histology and genomics using convolutional networks

Cancer histology reflects underlying molecular processes and disease progression, and contains rich phenotypic information that is predictive of patient outcomes. In this study, we demonstrate a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrate performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma. We also provide techniques to visualize the tissue patterns learned by these deep learning survival models, and establish a framework for addressing intratumoral heterogeneity and training data deficits.

bioinformatics↗