Mutant IDH and non-mutant chondrosarcomas display distinct cellular metabolomes
Bibliographic record
Abstract
BACKGROUND: Majority of chondrosarcomas are associated with a number of genetic alterations, including somatic mutations in isocitrate dehydrogenase 1 (IDH1) and IDH2 genes, but the downstream effects of these mutated enzymes on cellular metabolism and tumor energetics are unknown. As IDH mutations are likely to be involved in malignant transformation of chondrosarcomas, we aimed to exploit metabolomic changes in IDH mutant and non-mutant chondrosarcomas. METHODS: Here, we profiled over 69 metabolites in 17 patient-derived xenografts by targeted mass spectrometry to determine if metabolomic differences exist in mutant IDH1, mutant IDH2, and non-mutant chondrosarcomas. UMAP (Uniform Manifold Approximation and Projection) analysis was performed on our dataset to examine potential similarities that may exist between each chondrosarcoma based on genotype. RESULTS: UMAP revealed that mutant IDH chondrosarcomas possess a distinct metabolic profile compared with non-mutant chondrosarcomas. More specifically, our targeted metabolomics study revealed large-scale differences in organic acid intermediates of the tricarboxylic acid (TCA) cycle, amino acids, and specific acylcarnitines in chondrosarcomas. Lactate and late TCA cycle intermediates were elevated in mutant IDH chondrosarcomas, suggestive of increased glycolytic metabolism and possible anaplerotic influx to the TCA cycle. A broad elevation of amino acids was found in mutant IDH chondrosarcomas. A few acylcarnitines of varying carbon chain lengths were also elevated in mutant IDH chondrosarcomas, but with minimal clustering in accordance with tumor genotype. Analysis of previously published gene expression profiling revealed increased expression of several metabolism genes in mutant IDH chondrosarcomas, which also correlated to patient survival. CONCLUSIONS: Overall, our findings suggest that IDH mutations induce global metabolic changes in chondrosarcomas and shed light on deranged metabolic pathways.
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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 | 0.000 | 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".