Two Host Factors Regulate Persistence of H7a-Specific T Cells Injected in Tumor-Bearing Mice
Bibliographic record
Abstract
BACKGROUND: Injection of CD8 T cells primed against immunodominant minor histocompatibility antigens (MiHA) such as H7(a) can eradicate leukemia and solid tumors. To understand why MiHA-targeted T cells have such a potent antitumor effect it is essential to evaluate their in vivo behavior. In the present work, we therefore addressed two specific questions: what is the proliferative dynamics of H7(a)-specifc T cells in tumors, and do H7(a)-specific T cells persist long-term after adoptive transfer? METHODOLOGY/PRINCIPAL FINDINGS: By day 3 after adoptive transfer, we observed a selective infiltration of melanomas by anti-H7(a) T cells. Over the next five days, anti-H7(a) T cells expanded massively in the tumor but not in the spleen. Thus, by day 8 after injection, anti-H7(a) T cells in the tumor had undergone more cell divisions than those in the spleen. These data strongly suggest that anti-H7(a) T cells proliferate preferentially and extensively in the tumors. We also found that two host factors regulated long-term persistence of anti-H7(a) memory T cells: thymic function and expression of H7(a) by host cells. On day 100, anti-H7(a) memory T cells were abundant in euthymic H7(a)-negative (B10.H7(b)) mice, present in low numbers in thymectomized H7(a)-positive (B10) hosts, and undetectable in euthymic H7(a)-positive recipients. CONCLUSIONS/SIGNIFICANCE: Although in general the tumor environment is not propitious to T-cell invasion and expansion, the present work shows that this limitation may be overcome by adoptive transfer of primed CD8 T cells targeted to an immunodominant MiHA (here H7(a)). At least in some cases, prolonged persistence of adoptively transferred T cells may be valuable for prevention of late cancer relapse in adoptive hosts. Our findings therefore suggest that it may be advantageous to target MiHAs with a restricted tissue distribution in order to promote persistence of memory T cells and thereby minimize the risk of cancer recurrence.
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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".