23 INVESTIGATING METABOLIC DEPENDENCIES DURING THE EVOLUTION OF GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Brain Tumour Foundation of Canada Travel Award Recipient Glioblastoma (GBM) is the most common and aggressive form of brain cancer in adults, with a five-year survival rate of less than 7%. GBMs are difficult to treat and often recur within a year of diagnosis. Despite many attempts, treatment options have remained stagnant over decades. Clinical trials have yet to reveal an effective therapy for GBMs. This stems, in part, from an incomplete understanding of tumour biology, and lack of appropriate preclinical models to capture patient specific tumour heterogeneity. There is an urgent need for novel approaches to identify new therapies. Patient-derived organoids (PDOs) have emerged as important models, maintaining the phenotypic and genetic features of the parent tumours. PDOs are used in precision medicine to explore new therapeutic applications. Altered metabolism is a hallmark of GBM and is necessary for the growth and survival of GBMs. Here, we aim to develop and characterize matched newly diagnosed and recurrent GBM (nd-rGBM) organoids to identify phenotypic, growth and metabolic properties of GBMs. Our data indicates that nd-rGBM organoids displayed similar features such as cells expressing cycling (Ki67), stem-or progenitor-like (NES, SOX2, OLIG2), and glial (GFAP) markers compared to their parent tumours. Notably, we observed distinct growth between matched nd-rGBM pairs, with slow-growing GBMs exhibiting increased oxidative phosphorylation (OxPhos). This is important because different metabolic subtypes of GBMs are linked to varied prognoses. We, therefore anticipate our work may lead to identification of new insights into the tumour evolution and can be further exploited to identify new therapeutic strategies.
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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 | 0.001 |
| 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".