Pre‐Encoded IFN‐I Sensitivity Exacerbates Memory T Cell Senescence in Solid Tumors
Bibliographic record
Abstract
Solid tumors often suppress antitumor immune responses by promoting various dysfunctional CD8+ T cell states, which limit the effectiveness of T-cell-based immunotherapy. However, the mechanisms that promote these states have not been fully characterized. It is demonstrated that spontaneous priming responses during tumor growth can produce memory T cell reservoirs that are conducive to poor proliferative responsiveness during boosting vaccination. Surprisingly, when type I interferon (IFN-I) signaling is impeded, boosting vaccination can elicit robust proliferative responses from tumor-primed memory T cells and promote tumor control. This is observed in multiple tumor types and target antigens. In contrast to conventional memory T cells, tumor-primed memory T cells are unique in their pre-encoded responsiveness to IFN-I and show enrichment of pathways pertaining to DNA repair and cell cycle arrest. Tumor-primed memory T cells up-regulate p21 expression and blockade of either p21 or IFN-I can alleviate this effect to improve their proliferative capacity during boosting vaccination. Characterization of tumor-primed memory T cells revealed transcriptional and phenotypic features of cellular senescence, where higher senescence severity correlated with higher responsiveness to IFNα/β receptor blockade. Overall, IFN-I hyperresponsiveness may be a unique feature of senescent tumor-primed memory T cells that can exacerbate their dysfunction during cancer vaccination.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".