Exploring cellular subpopulations in glioblastoma and matched organoids using single-cell RNA-seq
Bibliographic record
Abstract
Background: Gliomas are the most common and fatal adult brain tumor with distinct genomic subgroups defined by isocitrate dehydrogenase (IDH) mutation status. Mutations in IDH result in overproduction of the oncometabolite 2-hydroxyglutarate (2HG). The landscape of metabolic changes that define gliomas has not previously been explored. Methods: We performed liquid chromotography-mass spectrometry (LC-MS) to examine over 700 metabolites on 90 fresh-frozen glioma samples (30 IDH-wildtype, 30 IDH-mutant 1p/19q codeleted, 30 IDH-mutant 1p/19q non-codeleted) from our institutional biobank. R and S enantiomers of 2HG were quantified using high pressure liquid chromatography tandem mass spectrometry coupled with a CHIROBIOTIC R column. Genome wide DNA methylation was performed on all tumors using Illumina 850k EPIC array. Unsupervised consensus clustering of differentially expressed metabolites and methylated post-processed probes was performed. Copy number variations were determined based on intensity values of the methylation array. Survival of unsupervised cluster groups was determined using the Kaplan-Meier Estimate. Results: Unsupervised clustering of 689 metabolites revealed 2 distinct subgroups of gliomas associated with recurrence-free survival (RFS, P = 0.021). IDH mutant tumours were found in both cluster groups where as IDH-wildtype tumors were found only in Group 2. Group 2 IDH-mutant tumors had unfavourable PFS, higher R/S-2HG levels, and higher proportion of copy number alterations (4q, 9p, 13q, 17q) compared to group 1 IDH-mutant tumors (P=0.048, P=0.0194, P<0.0001 respectively) compared to group 1 IDH-mutant tumors. (P= p=0.048). . Conclusions: Metabolic profiling of gliomas reveals 2 distinct subtypes of IDH-mutant independent of 1p/19q codeletion status with differing survival patterns and large scale chromosomal alterations that may be driven by varying levels of R/S-2HG.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".