Thymic CD8+ T Cell Production Strongly Influences Tumor Antigen Recognition and Age-Dependent Glioma Mortality
Bibliographic record
Abstract
For unknown reasons, advanced age remains a dominant predictor of poor clinical outcome for nearly all cancers. A decrease in the production of T cells by the thymus accompanies normal aging and parallels the age-dependent increase in cancer progression, but the specific impact of immunity on tumor progression in general is unknown. Glioblastoma multiforme (GBM), the most common primary brain neoplasm, is characterized by rapid age-dependent rates of progression and death. In this study, we show levels of CD8(+) recent thymic emigrants (RTEs) accounted for the prognostic power of age on clinical outcome in GBM patients. CD8(+) RTEs, typically a tiny proportion of CD8(+) T cells, remarkably accounted for the majority of tumor Ag-binding small precursor cells in PBMC from these patients and from healthy individuals. Large blasting tumor Ag-binding cells comprised of CD8(+) RTEs and phenotypically related cells were predominantly expanded following experimental vaccination of GBM patients. Quantification of CD8(+) RTE expansion in vivo correlated strongly with vaccine-elicited cytokine responses, and estimated numbers of expanding CD8(+) RTEs were consistent predictors of clinical outcome in vaccinated GBM patients. Targeted mutant (CD8beta(-/-)) mice specifically deficient in thymic CD8(+) T cell production uniquely displayed an age-specific decrease in glioma host survival as well as a strong correlation between host survival and thymus cellular production. These findings suggest that levels and function of newly produced CD8(+) T cells critically influence age-dependent cancer mortality and exert one of the strongest known influences on GBM outcome by predominantly mediating clinically beneficial antitumor immune responses.
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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 | 0.000 |
| 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".