P10.21 2-hydroxyglutarate as a biomarker for IDH mutation in low grade gliomas
Bibliographic record
Abstract
Glioma patients continue to carry a very poor prognosis despite maximal therapy. Several genetic alterations are linked with better survival outcome in glioma patients, including mutations in isocitrate dehydrogenase 1 and 2 (IDH1/2). The product of IDH mutation is the chemical 2-hydroxyglutarate (2-HG), which is a metabolite linked to cancer development. Currently, there is no method by which we can determine the IDH status of glioma during surgery. A much-needed area of progress is the application of innovative methods to enable detection of IDH mutation intraoperatively to guide extent of resection while avoiding the need for a second high-risk cranial surgery. High performance liquid chromatography tandem mass spectrometry (HPLC-MS) was developed, validated and utilized to quantify the R-2-HG and S-2-HG enantiomers and total 2-HG levels in 38 frozen brain tumor samples (IDH WT n=14, IDH mutated n=24). Brain tumor samples were retrieved from the Toronto Western Foundation brain tumor bank. Glioma diagnosis and IDH status were determined by microscopy and immunohistochemistry. 30 patients had paired serum that was included in our analysis. Progression-free survival data for all patients was collected. We found that the R:S ratio of 2-HG within patient tumors corresponds with IDH status, with our IDH-mutant patients having an elevated R:S ratio of 1551 ± 457 in contrast to IDH-WT patients = 6.2 ± 4.4 (P<0.001). This trend was also confirmed when comparing total 2-HG levels (P<0.001). Of the patients with IDH-mutated tumors, a higher R:S ratio of 2-HG appears to be associated with worse survival, in contrast to total 2-HG levels, where there was no obvious correlation to survival. Finally, serum 2-HG levels did not correspond with IDH status. We believe the R:S 2-HG ratio can potentially be used as an intraoperative biomarker for IDH mutation detection to personalize glioma surgery.
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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 | 0.001 | 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".