Memory T Cells in the Immunoprevention of Cancer: A Switch from Therapeutic to Prophylactic Approaches
Bibliographic record
Abstract
Cancer immunoprevention, the engagement of the immune system to prevent cancer, is largely overshadowed by therapeutic approaches to treating cancer after detection. Vaccines or, alternatively, the utilization of genetically engineered memory T cells could be methods of engaging and creating cancer-specific T cells with superb memory, lenient activation requirements, potent antitumor cytotoxicity, tumor surveillance, and resilience against immunosuppressive factors in the tumor microenvironment. In this review we analyze memory T cell subtypes based on their potential utility in cancer immunoprevention with regard to longevity, localization, activation requirements, and efficacy in fighting cancers. A particular focus is on how both tissue-resident memory T cells and stem memory T cells could be promising subtypes for engaging in immunoprevention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".