Cancer immunotherapy with γδ T cells: many paths ahead of us
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
γδ T cells play uniquely important roles in stress surveillance and immunity for infections and carcinogenesis. Human γδ T cells recognize and kill transformed cells independently of human leukocyte antigen (HLA) restriction, which is an essential feature of conventional αβ T cells. Vγ9Vδ2 γδ T cells, which prevail in the peripheral blood of healthy adults, are activated by microbial or endogenous tumor-derived pyrophosphates by a mechanism dependent on butyrophilin molecules. γδ T cells expressing other T cell receptor variable genes, notably Vδ1, are more abundant in mucosal tissue. In addition to the T cell receptor, γδ T cells usually express activating natural killer (NK) receptors, such as NKp30, NKp44, or NKG2D which binds to stress-inducible surface molecules that are absent on healthy cells but are frequently expressed on malignant cells. Therefore, γδ T cells are endowed with at least two independent recognition systems to sense tumor cells and to initiate anticancer effector mechanisms, including cytokine production and cytotoxicity. In view of their HLA-independent potent antitumor activity, there has been increasing interest in translating the unique potential of γδ T cells into innovative cellular cancer immunotherapies. Here, we discuss recent developments to enhance the efficacy of γδ T cell-based immunotherapy. This includes strategies for in vivo activation and tumor-targeting of γδ T cells, the optimization of in vitro expansion protocols, and the development of gene-modified γδ T cells. It is equally important to consider potential synergisms with other therapeutic strategies, notably checkpoint inhibitors, chemotherapy, or the (local) activation of innate immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it