P.097 Metabolomic and lipidomic profiling of high and low grade gliomas - a matched serum and tissue clinical study
Bibliographic record
Abstract
Background: It is well understood that gliomas require vast supply of energy to proliferate, invade and spread. We wished to identify novel biomarkers by comparing normal brain and plasma to high and low grade gliomas using newer techniques in laser ionization mass spectroscopy - precision metabolomics and lipidomics. Methods: Single center IRB approved tissue bank of “normal” brain and plasma (n=6) and IDH wild-type GBM tissue and plamsa (n=29), IDH mutant GBM tissue and plamsa (n=6), Low grade glioma (n=4) tissue and plamsa were analyzed for over 2000 endogenous metbolites and complex lipids. Unbiased clustering and Random Forest plots and pathway analysis were performed with appropriate statsitical analysis (significance p < 0.05). Results: IDH mutant GBM had higher levels of 2-HG, however, plasma 2-HG did not reflect IDH genotype. Changes in glucose and fatty acid utilization were observed in IDH WT and mutant gliomas compared to normal brain tissue. Lipidomics of plasma and tissue of normal and gliomas did not reveal a biomarker reaching statistical significance. Conclusions: We will continue to investigate if plasma and tissue biomarkers including hypotaurine, methionine, branched chain amino acid catabolites and pregnonolone can be used to predict tumor progression, response to treatment and clinical outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".