Differential Regulation of Simultaneous Antitumor and Alloreactive CD8+ T-Cell Responses in the Same Host by Rapamycin
Bibliographic record
Abstract
Rapamycin is an immunosuppressive agent routinely used in organ transplantation but also paradoxically exerts antiviral and antitumor activities. Pathogen-specific memory CD8(+) T-cell (T(CD8) ) responses were recently found to be augmented by rapamycin. However, whether rapamycin influences the magnitude and quality of anticancer T(CD8) responses is unknown. Importantly, how rapamycin may regulate simultaneous virus/tumor-specific and alloreactive T(CD8) in the same host remains unexplored. To answer these questions, we primed wild-type mice with allogeneic cells concomitantly expressing simian virus 40 large tumor antigen (T Ag), a viral oncoprotein with well-defined epitopes. Rapamycin selectively enhanced the cross-priming of T(CD8) specific for T Ag's most immunodominant epitope called site IV but not T(CD8) alloreactivity. Rapamycin-treated mice also had a high percentage of splenic CD127(high) KLRG1(low) T(CD8) and an increased frequency of site IV-specific T cells long after the peak of their primary response. When site IV was presented as a cytosolic minigene encoded by a recombinant vaccinia virus, rapamycin failed to boost the site IV-specific response. Therefore, the nature and presentation mode of antigen determine the susceptibility to the adjuvant effect of rapamycin. Our findings reveal the unexpected benefit of rapamycin treatment in recipients of allografts co-expressing tumor/viral Ags.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".