TMIC-31. MULTI-PLATFORM GENOMIC LANDSCAPE OF INTRA-TUMORAL HYPOXIA IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Hypoxia is a prominent characteristic of aggressive tumors, including glioblastoma (GB), and is linked to therapy resistance. Understanding the molecular mechanisms underlying hypoxia-induced changes in neoplastic and non-neoplastic cells is crucial for developing targeted therapies. In this study, we took a comprehensive approach to investigate the genomic, epigenomic, and spatial transcriptomic profiles of intra-tumoral hypoxia in GB at bulk and single-cell levels. Our objectives were to identify a GB-specific hypoxia gene signature and uncover novel mechanisms of GB's response to hypoxia. Pimonidazole (PIMO), a hypoxia marker, was administered to 77 GB patients. We utilized immune-guided laser microdissection to extract DNA and perform methylome profiling of PIMO+ (hypoxic) and PIMO- regions. Visium 10X spatial transcriptomics was employed to identify differentially expressed genes in PIMO+ tumor regions. Through single-nuclear multi-omics profiling, we examined gene expression and chromatin accessibility of the hypoxic neoplastic and non-neoplastic cells at the single-cell level. The hypoxia transcriptome in GB exhibited general commonalities, in addition to unique features, compared to other reported hypoxia signatures. Some of these alterations were accompanied by changes at the DNA methylome level. Importantly, we found a heterogenous molecular landscape across PIMO+ regions within each sample. While a general transition to a MES-like state was observed, we found a mixture of cell states across the different PIMO+ regions. We also found distinct sub-hypoxic microenvironments harboring unique immune cell populations that overlapped with specific cell type states, cell cycle states, and deregulation of specific cellular pathways. These sub-regions were associated with specific anatomic features and displayed prognostic utility in GBM patient cohorts. Our single-cell multi-omics analysis further revealed intra-tumoral copy number aberrations and changes in chromatin accessibility associated with hypoxic cells. Our findings demonstrate that the hypoxic microenvironment in GB is heterogenous with unique targetable features that could lead to novel treatment modalities.
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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) | 0.002 | 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".