IMMU-03. TUMOR NECROSIS FACTOR OVERCOMES IMMUNE EVASION IN P53-MUTANT MEDULLOBLASTOMA
Bibliographic record
Abstract
Many immunotherapies act by enhancing the ability of T cells to kill tumor cells. But T cell killing depends on recognition of antigens presented by Class I MHC (MHC-I) on the tumor cell surface; if a tumor lacks MHC-I, it cannot be recognized by T cells. To study the efficacy of immunotherapy for Group 3 medulloblastoma, we have used mouse models driven by overexpression of Myc and dominant negative p53 (“MP tumors”), or by overexpression of Myc and Gfi1 (“MG tumors”). While MP tumors grow in immunocompetent mice, MG tumors don’t grow, due to T-cell rejection. To understand this difference, we analyzed expression of immunoregulatory molecules, and found that MP tumors completely lack surface MHC-I. Mechanistically, this is because two key proteins required for MHC-I trafficking – TAP1 and ERAP1 – are targets of p53; since MP tumors lack functional p53, they also lack TAP1 and ERAP1, and surface MHC-I. These studies suggest that p53 plays a critical role in determining immunogenicity, and that p53-mutant tumors are resistant to immune attack. To overcome this resistance, we treated tumor cells with agents that regulate MHC-1. We found that low doses of tumor necrosis factor alpha (TNF) can rescue expression of ERAP, TAP and MHC-I on MP tumor cells. In vivo, TNF prolongs survival of tumor-bearing mice, and markedly augments the efficacy of immune checkpoint inhibitors. These results raise the possibility that TNF could be used prior to immunotherapy to render tumors more sensitive to T cell attack.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".