Age-dependent tumor-immune interactions underlie immunotherapy response in pediatric cancer
Bibliographic record
Abstract
SUMMARY Pediatric cancers originate in rapidly growing tissues within the context of a developing host. However, the interactions between cancer cells and the developing immune system are incompletely understood. Here, we established a suite of pediatric syngeneic mouse cancer models across diverse anatomical sites and compared their tumor immune microenvironment with that in adult mice. Tumors in pediatric mice exhibited significantly accelerated growth and diminished leukocyte infiltration, dominated by naïve-like PD-1 low /CD8 + T cells, and proliferative MHCII low /PD-L1 hi /CD86 low macrophages. Tumor-infiltrating leukocytes in pediatric mice were enriched for MYC targets, which was also observed in pediatric patient samples. Furthermore, pediatric mice displayed poor responses to anti-PD-1/PD-L1 or bispecific T cell engager antibodies, which could be reversed by inducing a proinflammatory microenvironment via MYC inhibition or inducing macrophage polarization to an MHCII hi phenotype. These findings underscore the significant influence of young age on cancer immune responses and reveal potential new therapeutic opportunities for pediatric cancers. HIGHLIGHTS Allograft tumors exhibit markedly accelerated growth in pediatric hosts compared to adults. Tumors growing in pediatric mice have reduced leukocyte infiltration, dominated by naïve-like PD-1 low /CD8 + T cells, and MHCII low /M2-like macrophages. Enrichment of MYC target genes is observed in pediatric mouse tumors and confirmed in primary patient tumor samples. Pediatric mice display reduced response to anti-PD-1/PD-L1 and BiTE immunotherapy, which can be reversed by remodeling the TIME, using either MYC inhibition or macrophage polarization.
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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) | 0.001 | 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".