Level of Expression of MHCI-Presented Neoepitopes Influences Tumor Rejection by Neoantigen-Specific CD8+ T Cells
Bibliographic record
Abstract
Neoantigen-targeted therapy holds an array of benefits for cancer immunotherapy, but the identification of peptide targets with tumor rejection capacity remains a limitation. To better define the criteria dictating tumor rejection potential, we examined the capacity of high-magnitude T-cell responses induced toward several distinct neoantigen targets to regress MC38 tumors. Despite their demonstrated immunogenicity, vaccine-induced T-cell responses were unable to regress established MC38 tumors or prevent tumor engraftment in a prophylactic setting. Although unable to kill tumor cells, T cells showed robust killing capacity toward neoantigen peptide-loaded cells. Tumor-cell killing was rescued by saturation of target peptide-loaded MHCs on the cell surface. Overall, this study demonstrates a pivotal role for target protein expression levels in modulating the tumor rejection capacity of neoantigens. Thus, inclusion of this metric, in addition to immunogenicity analysis, may benefit antigen prediction techniques to ensure the full antitumor effect of cancer vaccines.
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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".