TMET-13. DNA DAMAGE SIGNALING ACTIVATES GTP SYNTHESIS TO PROMOTE GLIOBLASTOMA TREATMENT RESISTANCE
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common type of invasive brain tumor in adults and is uniformly fatal due to inherent resistance to radiation therapy (RT) and chemotherapy. Our group and others have found that metabolites can regulate DNA repair and therapy resistance in brain tumors, but little is known about how DNA damage regulates metabolic pathway activity in cancer. Here we show that following treatment with RT, GBMs increase rates of de novo guanylate synthesis in vitro and in orthotopic patient-derived xenograft models via signaling through the DNA repair protein DNA-PK. To determine if disrupting this regulation can augment GBM treatment efficacy, we combined an FDA-approved inhibitor of purine synthesis (mycophenolate mofetil, MMF) with chemoradiation in a variety of mouse models of GBM. Critically, targeting GTP synthesis improved the efficacy of both RT alone and chemoradiation in multiple patient-derived and syngeneic intracranial models. Our translational studies have shown that mycophenolic acid, the active metabolite of MMF, penetrates the blood-brain barrier at concentrations sufficient to inhibit GTP synthesis in GBMs of human patients. The phase 1 arms of our study, which are ongoing, are testing the safety and efficacy of combining MMF with standard of care chemoradiation for patients with primary and recurrent GBM.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".