Potential Use of γδ T Cell-Based Vaccines in Cancer Immunotherapy
Bibliographic record
Abstract
IMMUNOTHERAPY IS A FAST ADVANCING METHODOLOGY INVOLVING ONE OF TWO APPROACHES: (1) compounds targeting immune checkpoints and (2) cellular immunomodulators. The latter approach is still largely experimental and features in vitro generated, live immune effector cells, or antigen-presenting cells. γδ T cells are known for their efficient in vitro tumor killing activities. Consequently, many laboratories worldwide are currently testing the tumor killing function of γδ T cells in clinical trials. Reported benefits are modest; however, these studies have demonstrated that large γδ T-cell infusions were well tolerated. Here, we discuss the potential of using human γδ T cells not as effector cells but as a novel cellular vaccine for treatment of cancer patients. Antigen-presenting γδ T cells do not require to home to tumor tissues but, instead, need to interact with endogenous, tumor-specific αβ T cells in secondary lymphoid tissues. Newly mobilized effector αβ T cells are then thought to overcome the immune blockade by creating proinflammatory conditions fit for effector T-cell homing to and killing of tumor cells. Immunotherapy may include tumor antigen-loaded γδ T cells alone or in combination with immune checkpoint inhibitors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".