TMET-30. Uncovering the metabolic programs underlying malignant cell state heterogeneity in glioblastoma
Bibliographic record
Abstract
Abstract BACKGROUND Glioblastoma (GBM) is an incurable and aggressive brain cancer marked by profound intra-tumoral heterogeneity. Malignant cells exist in four core transcriptional states: stem-like (OPC-like and NPC-like) and more differentiated astrocytic (AC-like) or mesenchymal (MES-like) states. Using spatial transcriptomics, we previously identified a layered tumor structure of these states, with hypoxia emerging as an organizing driver, implicating oxygen availability as a central factor in cell state dynamics. However, how these cellular states interact with each other and with the tumor microenvironment remains incompletely understood. METHODS To uncover state-specific vulnerabilities for reducing GBM cell state diversity, we conducted a small-molecule screen in gliomasphere models using over 1,600 cysteine-reactive covalent inhibitors, assessing their effects on cell states via RNA-seq. We then employed the Sonar metabolic reporter to visualize NAD+ and NADH levels in gliomaspheres. Through experiments with orthotopic xenografts in mice and co-culture with human cortical organoids, we characterized distinct redox profiles across GBM cell states by single-cell and bulk RNA-seq. Spatial single-cell transcriptomics and mass spectrometry imaging (MSI) metabolomics on adjacent sections of frozen patient samples were used to identify preferential metabolic programs in specific cell states. RESULTS Our small-molecule screen identified compounds that promote differentiation and are enriched for NRF2 activation, a master regulator of the antioxidant response. Spatial single-cell transcriptomics and mass spectrometry metabolomics experiments confirmed that MES-like cells exhibit a heightened glycolytic signature, whereas stem-like states preferentially depend on oxidative phosphorylation (OXPHOS) for their energetic needs. Finally, pharmacologic perturbations revealed that mitochondrial and glycolysis inhibition selectively deplete different cell state populations in gliomaspheres. CONCLUSION Our findings provide deeper insight into the metabolic underpinnings of GBM cell states and support the rationale for combinatorial therapies aimed at reducing intratumoral heterogeneity that exploit their divergent dependencies.
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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".