Hyperpolarized 13C MR imaging detects no lactate production in mutant IDH1 gliomas: Implications for diagnosis and response monitoring
Bibliographic record
Abstract
Metabolic imaging of brain tumors using (13)C Magnetic Resonance Spectroscopy (MRS) of hyperpolarized [1-(13)C] pyruvate is a promising neuroimaging strategy which, after a decade of preclinical success in glioblastoma (GBM) models, is now entering clinical trials in multiple centers. Typically, the presence of GBM has been associated with elevated hyperpolarized [1-(13)C] lactate produced from [1-(13)C] pyruvate, and response to therapy has been associated with a drop in hyperpolarized [1-(13)C] lactate. However, to date, lower grade gliomas had not been investigated using this approach. The most prevalent mutation in lower grade gliomas is the isocitrate dehydrogenase 1 (IDH1) mutation, which, in addition to initiating tumor development, also induces metabolic reprogramming. In particular, mutant IDH1 gliomas are associated with low levels of lactate dehydrogenase A (LDHA) and monocarboxylate transporters 1 and 4 (MCT1, MCT4), three proteins involved in pyruvate metabolism to lactate. We therefore investigated the potential of (13)C MRS of hyperpolarized [1-(13)C] pyruvate for detection of mutant IDH1 gliomas and for monitoring of their therapeutic response. We studied patient-derived mutant IDH1 glioma cells that underexpress LDHA, MCT1 and MCT4, and wild-type IDH1 GBM cells that express high levels of these proteins. Mutant IDH1 cells and tumors produced significantly less hyperpolarized [1-(13)C] lactate compared to GBM, consistent with their metabolic reprogramming. Furthermore, hyperpolarized [1-(13)C] lactate production was not affected by chemotherapeutic treatment with temozolomide (TMZ) in mutant IDH1 tumors, in contrast to previous reports in GBM. Our results demonstrate the unusual metabolic imaging profile of mutant IDH1 gliomas, which, when combined with other clinically available imaging methods, could be used to detect the presence of the IDH1 mutation in vivo.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".