Cooperative action of CD8 T lymphocytes and natural killer cells controls tumour growth under conditions of restricted T‐cell receptor diversity
Bibliographic record
Abstract
In mice expressing a transgenic T-cell receptor (TCR; TCRP1A) of DBA/2 origin with reactivity towards a cancer-germline antigen P1A, the number of TCRP1A CD8+ T cells in lymphoid organs is lower in DBA/2 than in B10.D2 or B10.D2(x DBA/2)F1 mice. This reduction results from haemopoietic cell autonomous differences in the differentiation of the major histocompatibility complex class I-restricted TCRP1A thymocytes controlled by DBA/2 versus B10.D2-encoded genes. We report here that the lower number of TCRP1A CD8+ T cells in DBA/2 mice correlated with their poor resistance to P1A-expressing mastocytoma solid tumours. Functional potency of CD8+ cytolytic T lymphocytes (CTL) from the above strains was not compromised, but their number after expansion appeared to be influenced by their genetic background. Intriguingly, non-transgenic DBA/ 2 mice resisted P1A+ tumours more efficiently despite poor representation of P1A-specific CTL. This was partly the result of their more heterogeneous TCR repertoire, including reactivity to non-P1A tumour antigens because mice that had rejected a P1A+ tumour became resistant to a P1A) variant of the tumour. Such 'cross-resistance' did not develop in the TCRP1A transgenic mice. Nonetheless, reconstitution of RAGo/o mice with TCRP1A CD8+ T cells, with or without CD4+ T cells, or exclusive representation of TCRP1A CD8+ T cells in RAGo/o TCRP1A transgenic mice efficiently resisted the growth of P1A-expressing tumours. Natural killer cells present at a higher number in RAGo/o mice also contributed to tumour resistance, in part through an NKG2D-dependent mechanism. Hence, in the absence of a polyclonal T-cell repertoire, precursor frequencies of natural killer cells and tumour-specific CTL affect tumour resistance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".