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 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".