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Orozco, J. I. J.

Publications and source records attributed to Orozco, J. I. J..

2 recordsLinked to original sources

Glioblastoma embryonic-like stem cells exhibit immune-evasive phenotype

Glioma stem cells (GSCs) are a subset of cells with self-renewal and tumor-initiating capacities that are thought to participate in drug resistance and immune evasion mechanisms in glioblastoma (GBM). Given GBM heterogeneity, we hypothesized that GSCs might also display cellular hierarchies associated with different degrees of stemness. We evaluated a single-cell RNA-seq glioblastoma dataset (n = 28) and identified a stem cell population co-expressing high levels of embryonic pluripotency markers, named core glioma stem cells (c-GSCs). This embryonic-like population represents 4.22% {+/-} 0.59 of the tumor cell mass, and pathway analysis revealed an upregulation of stemness and downregulation of immune-associated pathways. Using induced pluripotent stem cell technology, we generated an in vitro model of c-GSCs by reprogramming glioblastoma patient-derived cells into induced c-GSCs (ic-GSCs). Immunostaining of ic-GSCs showed high expression of embryonic pluripotency markers and downregulation of antigen presentation HLA proteins, mimicking its tumoral counterpart. Transcriptomic analysis revealed a strong agreement of enriched biological pathways between tumor c-GSCs and in vitro ic-GSCs ({kappa} = 0.71). Integration of ic-GSC DNA methylation and gene expression with chromatin state analysis of epigenomic maps (n = 833) indicated that polycomb repressive marks downregulate HLA genes in stem-like phenotype. Together, we identified c-GSCs as a GBM cell population with embryonic signatures and poor immunogenicity. Genome-scale transcriptomic and epigenomic profiling provide a valuable resource for studying immune evasion mechanisms governing c-GSCs and identifying potential therapeutic targets for GBM immunotherapy.

cancer biology↗

Epigenetic Profiling for the Molecular Classification of Metastatic Brain Tumors

Optimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet these challenges, we generated genome-scale DNA methylomes of the three most frequent types of brain metastases: melanoma, breast, and lung cancers (n=96). Using supervised machine learning and integration of multiple DNA methylomes from normal, primary, and metastatic tumor specimens (n=1,860), we unraveled epigenetic signatures specific to each type of metastatic brain tumor and constructed a three-step DNA methylation-based classifier (BrainMETH) that categorizes brain metastases according to the tissue of origin and therapeutically-relevant subtypes. BrainMETH predictions were supported by routine histopathologic evaluation. We further characterized and validated the most predictive genomic regions in a large cohort of brain tumors (n=165) using quantitative methylation-specific PCR. Our study highlights the importance of brain tumor-defining epigenetic alterations, which can be utilized to further develop DNA methylation profiling as a critical tool in the histomolecular stratification of patients with brain metastases.

cancer biology↗