Abstract 752: Hypoxia-driven spatial dynamics of epigenetic and transcriptional cell states in glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is an aggressive disease with poor overall survival rates that have not substantially changed in decades. A key feature underlying the lack of therapeutic success in GBM is its inherent morphological and phenotypic diversity. To better understand intra-tumor heterogeneity, single cell sequencing studies have delineated core transcriptional states to characterize GBM cell phenotypes. This study investigates the role of hypoxia in driving the establishment and transformation of GBM cell state. To characterize the spatial organization of cell states relative to hypoxia, spatial transcriptomics (ST) was performed on samples from GBM patients administered pimonidazole prior to surgery as part of a clinical trial. By overlaying serial pimonidazole-stained sections with ST, we constructed a hypoxia gene signature to identify hypoxic regions in ST. Our ST data was integrated with public datasets to create the largest global GBM dataset. In this dataset, we identified novel, ST-specific states that represent recurring cellular phenotypes. We used geographically weighted models to assess hypoxia’s spatial colocalization with previously defined cell states and our independently derived states. Finally, we investigated underlying mechanisms by which hypoxia may influence cell state using patient derived glioma stem cells (GSCs) cultured under hypoxic conditions. Using ATAC-seq, ChIP-seq, and bulk RNA-seq, we identified specific genes and chromatin regions differentially regulated under hypoxia. We find that hypoxia demonstrates remarkable spatial correlation with mesenchymal-like cell states, while states reminiscent of neural and glial lineages are notably absent in hypoxic regions. We also identify a leukocyte migratory phenotype strongly spatially correlated with hypoxia, which may indicate immune infiltration in these regions. Epigenetic profiling in GSCs identified specific genomic regions epigenetically regulated by hypoxia. Chromatin regions open under hypoxia with corresponding increased expression are enriched for known hypoxia-associated transcription factors. Conversely, chromatin regions closed and with corresponding decreased expression are enriched for neurodevelopment and differentiation transcription factors. These epigenetically regulated genes are strongly spatially correlated with hypoxic regions in patient ST data and are prognostic as a signature in data from TCGA. Together, our work demonstrates that hypoxia drives remodeling of the transcriptional and epigenetic landscape in GBM. In patients, hypoxia and genes epigenetically regulated under hypoxic conditions associate with specific GBM cell states, suggesting that hypoxia-driven epigenetic remodeling may contribute to these state phenotypes and, consequently, to malignant phenotypes in tumors. Citation Format: Phoebe Lombard, Mark Zaidi, Ronald Wu, Sheila Mansouri, Gelareh Zadeh, Bradly G. Wouters. Hypoxia-driven spatial dynamics of epigenetic and transcriptional cell states in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 752
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".