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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".