ECOA-4. Hypoxia alters the DNA methylation profile of glioblastoma tumor cells
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the deadliest and most vascularized brain tumor in adults; however, blood circulation is highly inefficient in these tumors, contributing to areas of cell death (necrosis) within the tumor, which is likely due to oxygen deprivation (hypoxia). Hypoxia plays a major role in tumor growth, invasion, and resistance to therapy. Hypoxic stress has been linked to several changes that are fundamental to the malignant progression of GBM and other tumor types. Pimonidazole (PIMO) is an exogenous marker of hypoxia that is used to delineate hypoxic regions in several tumor types. To date, a clear hypoxia gene signature has not been specifically described for GBM. We hypothesize that specific cellular pathways are differentially regulated in hypoxic tumor niches and can serve as novel actionable targets for treatment-resistant tumor cells in GBM. Over the past 3 years, we have administered PIMO to 35 patients with primary IDH1/2 wild-type GBM and isolated PIMO-positive and PIMO-negative tumor cells by laser capture microdissection using a PIMO-specific antibody on frozen tumor specimens. Total genomic DNA was isolated and subjected to DNA methylation profiling using the Illumina Infinium Methylation EPIC array. Our preliminary results suggest that PIMO-positive (hypoxic) tumor cells display a distinct DNA methylation profile that corresponds to changes in expression of a set of genes associated with immune regulation, angiogenesis, and proliferation. Furthermore, multiple CpG sites within the promoter of some genes are differentially methylated in hypoxic cells, suggesting these genes may be epigenetically regulated under hypoxia. In conclusion, our results indicate that hypoxia is associated with distinct epigenetic alterations in tumor cells which may alter how these cells respond to low oxygen levels and can further be utilized to uncover the epigenomic vulnerabilities of hypoxic tumor cells in GBM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".