Characterizing the brain tumor microenvironment
Bibliographic record
Abstract
Background: Targeting the tumor microenvironment has become a promising avenue of oncology, however little is known about this aspect in medulloblastoma.The tumor microenvironment includes many components which may either inhibit or promote malignant growth.Tumor-associated macrophages (TAMs) and neutrophils have demonstrated both pro-and anti-tumoral activity that can vary between tumor types.Senescent cells exhibit a senescent associated secretory phenotype (SASP) which can affect neighboring tumor cells, immune cell infiltration, and cell differentiation.We aimed to characterize the tumor microenvironment in the pediatric brain tumor Sonic Hedgehog (Shh) medulloblastoma.Methods/Results: Using the Ptch1 +/-mouse model of Shh medulloblastoma, we have shown that the tumor microenvironment evolves as the tumor develops.In preneoplasia there are macrophages and senescent cells.Peripheral macrophage depletion with clodronate liposomes did not affect TAM density early in tumorigenesis, suggesting that at the preneoplastic stage TAMs originate from the microglia rather than the periphery.Through ablation with Navitoclax, we found that senescent cells restrict preneoplasia growth.Advanced tumors have a more complex microenvironment including macrophages and neutrophils.Both M1-like and M2-like macrophages are present in tumors, and they aggregate in regions of high apoptosis and low proliferation.Through comparing tissues of bypassed senescence or potentially-unrestricted SASP activity, we show that neither macrophage, neutrophil, nor stem cell densities are affected by senescent cells. Conclusion:Our results demonstrate that the tumor microenvironment of medulloblastoma evolves, becoming more complex as the tumor progresses to advanced stage.Senescent cells restrict tumor growth during early stages, suggesting their SASP is inducing paracrine senescence.Understanding the tumor microenvironment of medulloblastoma may lend insights into how this tumor can be targeted with immunotherapy 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 | 0.000 | 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 | 0.000 |
| 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".