Efficacy of DNA Methyltransferase Inhibitor Immune Priming Therapy in Combination with PD-1 Inhibitors to Treat High-Risk Pediatric Brain Tumors
Bibliographic record
Abstract
ABSTRACT Background Despite intensive therapies, outcomes for high-risk pediatric brain tumors (PBTs) remain dismal, prompting the search for novel treatments. DNA methyltransferase inhibitors (DNMTi) have been shown to prime tumors to improve response to checkpoint inhibition. The aim of this study was to investigate the potential of decitabine (DAC), in combination with a PD-1 inhibitor, to improve survival in pediatric high-risk brain tumor models. Methods Analysis of human PBT datasets was performed to determine gene expression levels of immune cell associated markers. Tumor response to DAC, with or without a PD-1 inhibitor, was tested in murine models representing H3-wildtype diffuse intrinsic pontine glioma (DIPG), H3K27-mutant diffuse midline glioma (DMG), atypical teratoid rhabdoid tumor (ATRT), and medulloblastoma (MB). CyTOF analysis of allograft tumors was performed to characterize changes within the tumor microenvironment. Results Analysis of PBT subtypes revealed heterogeneous expression of immune cell markers, checkpoint receptors, and MHC molecules. DAC treatment decreased DNA methylation and increased neoantigen expression in human and mouse tumor cells. DAC alone or in combination with a PD-1 inhibitor resulted in prolonged survival in syngeneic mouse models of DIPG and ATRT but not DMG and MB models. CyTOF analysis of mouse tumors revealed changes in local immune cell infiltration upon combination treatment. Conclusions DAC in combination with a PD-1 inhibitor can alter the immune microenvironment in mouse tumor models. Changes were observed in H3-wildtype DIPG and ATRT models, suggesting that certain tumor subtypes may respond to checkpoint blockade after immune augmentation with DNMTi. Key Points PBTs show heterogenous expression of immune cell infiltrates DAC or DAC plus a PD-1 inhibitor shows extension of survival in H3-wildtype DIPG and ATRT mouse models Myeloid-derived suppressor cell abundance could be a major contributing factor to treatment response Importance of the Study Children with high-risk PBTs face dismal outcomes. Immune checkpoint inhibitor (ICI) successes have been demonstrated in a variety of adult malignancies; however, such beneficial outcomes have not been realized in PBTs. Here we investigate single and combination treatment of DNMTi and PD-1 checkpoint inhibition in syngeneic mouse models of high-risk PBTs. Our results suggest that some H3-wildtype DIPG and ATRT tumor types may be responsive to checkpoint therapy post immunomodulation and warrant further investigation.
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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) | 0.001 | 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) | 0.001 | 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".