Shifting the equilibrium in cancer immunoediting: from tumor tolerance to eradication
Bibliographic record
Abstract
The continual interaction of the immune system with a developing tumor is thought to result in the establishment of a dynamic state of equilibrium. This equilibrium depends on the balance between effector and regulatory T-cell compartments. Whereas regulatory T cells can infiltrate and accumulate within tumors, effector T cells fail to efficiently do so. Furthermore, effector T cells that do infiltrate the tumor become tightly controlled by different regulatory cellular subsets and inhibitory molecules. The outcome of this balance is critical to survival, and whereas in some cases the equilibrium can rapidly result in the elimination of the transformed cells by the immune system, in many other cases the tumor manages to escape immune control. In this review, we discuss relevant work focusing on the establishment of the intratumor balance, the dynamic changes in the populations of effector and regulatory T cells within the tumor, and the role of the tumor vasculature and its activation state in the recruitment of different T-cell subsets. Finally, we also discuss work associated to the manipulation of the immune response to tumors and its impact on the infiltration, accumulation, and function of tumor-reactive lymphocytes within the tumor microenvironment.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".