Deuterium magnetic resonance spectroscopy enables noninvasive metabolic imaging of tumor burden and response to therapy in low-grade gliomas
Bibliographic record
Abstract
BACKGROUND: The alternative lengthening of telomeres (ALT) pathway is essential for tumor proliferation in astrocytomas. The goal of this study was to identify metabolic alterations linked to the ALT pathway that can be exploited for noninvasive magnetic resonance spectroscopy (MRS)-based imaging of astrocytomas in vivo. METHODS: Genetic and pharmacological methods were used to dissect the association between the ALT pathway and glucose metabolism in genetically engineered and patient-derived astrocytoma models. 2H-MRS was used for noninvasive imaging of ALT-linked modulation of glycolytic flux in mice bearing orthotopic astrocytomas in vivo. RESULTS: The ALT pathway was associated with higher activity of the rate-limiting glycolytic enzyme phosphofructokinase-1 and concomitantly elevated flux of glucose to lactate in astrocytoma cells. Silencing the ALT pathway or treating with the poly(ADP-ribose) polymerase inhibitor niraparib that induces telomeric fusion in ALT-dependent astrocytoma cells abrogated glycolytic flux. Importantly, this metabolic reprogramming could be non-invasively visualized by 2H-MRS. Lactate production from [6,6'-2H]-glucose was higher in ALT-dependent astrocytoma tumors relative to the normal brain in vivo. Furthermore, treatment of orthotopic astrocytoma-bearing mice with niraparib reduced lactate production from [6,6'-2H]-glucose at early timepoints when alterations in tumor volume could not be detected by anatomical imaging, pointing to the ability of [6,6'-2H]-glucose to report on pseudoprogression in vivo. CONCLUSIONS: We have mechanistically linked the ALT pathway to elevated glycolytic flux and demonstrated the ability of [6,6'-2H]-glucose to non-invasively assess tumor burden and response to therapy in astrocytomas. Our findings point to a novel, clinically translatable method for metabolic imaging of astrocytoma patients.
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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 | 0.000 | 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".