RBIO-02. DEVELOPMENT OF FLASH-CAR RADIOIMMUNOTHERAPY FOR PEDIATRIC BRAIN TUMORS
Bibliographic record
Abstract
Abstract Pediatric brain tumors including medulloblastoma and high-grade glioma are generally refractory to T cell-based immunotherapy, largely due to an immune-hostile microenvironment infiltrated extensively with immunosuppressive macrophages. Ultra-high dose rate (FLASH) radiotherapy holds promise for treating solid tumors, given the potential lower toxicity in normal tissues and possible favorable impact on tumor immunity. Using a genetically engineered mouse model of medulloblastoma and glioma, we show that FLASH radiation stimulates pro-inflammatory polarization in tumor macrophages. Single-cell transcriptome analysis shows that FLASH proton beam radiation skews macrophages towards proinflammatory phenotypes and increases T cell infiltration in the medulloblastoma model. Further bulk transcriptome analyses reveal that FLASH radiation reduces peroxisome proliferator-activated receptor (PPAR)-γ and arginase-1 expression and inhibits immunosuppressive macrophage polarization under stimulus-inducible conditions. Mechanistically, FLASH radiation abrogates lipid oxidase expression and oxidized low-density lipid (oxLDL) generation to reduce PPARγ activity, while standard radiation induces reactive oxygen species (ROS)-dependent PPARγ activation in macrophages. Notably, FLASH radiotherapy improves infiltration and activation of chimeric antigen receptor (CAR) T cells and sensitizes tumors to GD2 CAR T immunotherapy in the autochthonous medulloblastoma and syngeneic glioma models. These findings suggest that FLASH radiotherapy may reprogram macrophage lipid metabolism to reverse tumor immunosuppression and that combination FLASH-CAR radioimmunotherapy may offer exciting opportunities for treatment of pediatric brain tumor.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".