MODL-05. Age-appropriate models of pediatric brain tumors reveal age-dependent tumor-immune interactions.
Bibliographic record
Abstract
Abstract The developing immune system of a child is distinct to that of an adult. These immunological differences are often ignored in preclinical pediatric cancer research, where adult mice are more commonly used, potentially overlooking developmental influences on cancer-microenvironment interactions. This is of particular importance when testing immunotherapeutic agents for pediatric cancers. To address this issue, we have developed pediatric brain cancer mouse models which reflect the developing microenvironment in which these tumors arise. By doing so, we sought to understand the impact of age on tumor progression and immune interactions. Using flow cytometry, RNA sequencing, and immunohistochemistry, we have characterized differences in the tumor-immune microenvironment of multiple orthotopically-implanted murine brain tumor models in juvenile mice compared to adults. We found that identical brain tumor cells elicited tumors that grew faster in juvenile mice and had fewer immune cell infiltrates. Moreover, these immune infiltrates were markedly distinct between juvenile and adult mice. Specifically, juvenile mice possessed more naïve-like CD8 T cells with reduced effector, resident, and exhausted-like CD8 T cells. Tumor-associated macrophages in juvenile mice had reduced MHC II expression and appeared polarized towards an anti-inflammatory state, potentially suppressing effective anti-tumour immune responses. Importantly, we demonstrate that repolarization of macrophages using immune-modulating agents changed the pediatric tumor-infiltrating immune microenvironment towards a more “adult-like state”, that may enhance immunotherapy effectiveness. Acknowledging the challenges in finding an appropriate match for human developmental stage in mice, our findings highlight that preclinical model age significantly influences cancer-immune interactions. These data strongly support the use of age-relevant models in preclinical pediatric cancer studies, especially when evaluating microenvironment-targeting agents.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".