Glioblastoma gene expression based subtypes have defined metabolomic states
Bibliographic record
Abstract
ABSTRACT Glioblastoma (GBM) is a highly aggressive primary brain cancer with poor prognosis (<15 months), highlighting the urgent need for more effective therapies. As current treatments are not effective, the need for a deeper understanding of the biology of GBM cells, including how they reprogram their metabolism to support their aberrant and uncontrolled growth, is critical. To this end, we established a collection of 41 human glioma cell lines derived from freshly resected tumour tissues from 99 patients. We characterized 12 of these cell lines by combining histologic, genetic, stem cell derivation and self-renewal, and metabolomic analyses. Histological and genetic profiles included IDH mutation status, Ki-67 proliferation index, ATRX status, mutant TP53 expression, chromosome 10q loss, EGFR amplification, and MGMT promoter methylation. Of these, only p53 mutation expression status showed weak segregation of the cell lines into 2 separate metabolic groups based on amino acid levels, but none showed an effect on stem cell derivation or self-renewal. Further characterization of these 12 cell lines revealed significant metabolic and phenotypic differences when comparing mesenchymal versus proneural gene expression subtyping. We show significant increases in TCA cycle metabolites in mesenchymal-like GBM cells and higher overall metabolic activity compared to proneural-like cells. These findings highlight the complexity of GBM and the need for personalized treatments that consider the metabolome of each subtype as a potential therapeutic avenue.
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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 | 0.001 | 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".