Proteomics in IDH-mutated diffuse lower-grade glioma: a scoping review
Bibliographic record
Abstract
Background: Therapeutic options and biomarkers for isocitrate dehydrogenase-mutated (IDHmut) diffuse lower-grade glioma (dLGG), WHO grade 2-3, are limited. Global quantitative proteomics has aided the discovery of novel markers and drug targets across various pathologies. This review aimed to summarize current proteomic findings in IDHmut dLGG. Methods: PubMed, Embase, and Scopus were searched following PRISMA-ScR guidelines. Studies examining quantitative proteomics in IDHmut dLGG with liquid chromatography-mass spectrometry in adult human samples were included. Studies with only high-grade gliomas, without IDHmut, using xenografts, or cell line samples, and reviews were excluded. Results: In total, 1,902 records were identified; 85 full-texts were retrieved, and 13 met the inclusion criteria. Twelve studies were cross-sectional and one longitudinal. Two studies used cerebrospinal fluid samples, while seven used fresh frozen and five formalin-fixed paraffin-embedded (FFPE) tissue samples. There was a large heterogeneity in aims, sample types, and analytical techniques. The most recurrent finding was altered energy metabolism, mostly related to the tricarboxylic acid cycle, compared to IDH-wildtype gliomas. IDHmut dLGG proteomic profile was distinct from other brain tumors, including IDH-wildtype glioblastoma, IDHmut grade 4 astrocytomas, and grade 1 gliomas or normal brain. Conclusions: IDHmut dLGG has a unique proteome that may be leveraged for biomarkers and therapeutic discovery. Proteomic findings indicate a particular dependency on glutamate metabolism to sustain the citric acid cycle and energy production. Although current proteomic knowledge is limited and fragmented, technological advancements present an opportunity for large-scale studies using FFPE samples, advancing proteomic knowledge and precision medicine in IDHmut dLGG.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".