Abstract IA11: Overcoming immune evasion in pediatric brain tumors
Bibliographic record
Abstract
Abstract Many immunotherapies act by enhancing T-cell killing of tumor cells. Cytotoxic T cells recognize antigens presented by class I major histocompatibility complex (MHC-I) proteins on tumor cells. Our studies suggest that medulloblastomas and high-grade gliomas lacking the p53 tumor suppressor do not express surface MHC-I and are therefore resistant to immune rejection. Mechanistically, this is because p53 regulates expression of the peptide transporter Tap1 and the aminopeptidase Erap1, which are required for MHC-I trafficking to the cell surface. Treatment with tumor necrosis factor or lymphotoxin beta receptor agonist rescues expression of Erap1, Tap1, and MHC-I on p53 mutant tumor cells. In vivo, TNF treatment prolongs survival and markedly augments the efficacy of the immune checkpoint inhibitor anti-PD-1. These studies identify p53 as a key regulator of immune evasion in vivo and suggest that TNF could be used to enhance sensitivity of p53-mutant tumors to immunotherapy. Citation Format: Alexandra Garancher, Hiromichi Suzuki, Svasti Haricharan, Meher B. Masihi, Jessica M. Rusert, Paula S. Norris, Florent Carrette, Megan M. Romero, Sorana A. Morrissy, Patryk Skowron, Florence M.G. Cavalli, Hamza Farooq, Vijay Ramaswamy, Alaide Morcavallo, Jacob J. Henderson, James M. Olson, Yoon-Jae Cho, Xiao-Nan Li, Louis Chesler, Marco A. Marra, Oren J. Becher, Linda M. Bradley, Carl F. Ware, Michael D. Taylor, Robert J. Wechsler-Reya. Overcoming immune evasion in pediatric brain tumors [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA11.
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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) | 0.003 | 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 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".