TMIC-07. HYPOXIC MICROENVIRONMENT CONFERS SPECIFIC ALTERATIONS IN DNA METHYLATION PROFILES IN GLIOBLASTOMA
Bibliographic record
Abstract
High cellularity and poorly organized tumour vasculature in high-grade gliomas leads to insufficient blood supply, hypoxic areas, and ultimately to the formation of necrosis. Thus, hypoxia is a hallmark of malignant glioma microenvironment and it is associated with aggressive tumor behavior such as growth, progression, and resistance to chemo-radiation. Current pathologic markers are insufficient to identify patients that may benefit from specific treatments. We therefore, hypothesized that underlying epigenetic alterations confer therapeutic resistance under hypoxic conditions. Twenty five GBM patients were consented and treated with pimonidazole (PIMO) 16–18 hours prior to surgery. Tumor sections were subjected to immunohistochemical analysis using antibodies against PIMO and other hypoxia markers such HIF1a and CAIX. Samples were subjected to laser capture microdissection followed by DNA isolation and DNA methylation profiling using the Illumina Human Methylation EPIC Array. Data was analyzed using minfi and conumee packages in Bioconductor, together with appropriate biostatistics tools. PIMO score was determined to range from 10–60% and positively correlated with other hypoxia markers such as CA IX and HIF1a (p4,000) were hypomethylated. Gene set enrichment analysis (GSEA) indicated that the majority of these CpGs are associated with genes involved in signalling cascades and oncogenic processes, including WNT and NOTCH. These were compared to DNA methylation profiles of glioma stem cells exposed to transient hypoxia and extensive overlap was found in proportion of hypomethylated CpG sites and cellular processes that were altered. These findings were correlated with complementary RNA expression data from RNA sequencing to establish the biological relevance of changes in DNA methylation profiles under hypoxia in GBM.
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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 | 0.000 |
| 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) | 0.002 | 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".