Aiming for the Sweet Spot: Glyco-Immune Checkpoints and γδ T Cells in Targeted Immunotherapy
Bibliographic record
Abstract
Though a healthy immune system is capable of recognizing and eliminating emergent cancerous cells, an established tumor is adept at escaping immune surveillance. Altered and tumor-specific expression of immunosuppressive cell surface carbohydrates, also termed the "tumor glycocode," is a prominent mechanism by which tumors can escape anti-tumor immunity. Given their persistent and homogeneous expression, tumor-associated glycans are promising targets to be exploited as biomarkers and therapeutic targets. However, the exploitation of these glycans has been a challenge due to their low immunogenicity, immunosuppressive properties, and the inefficient presentation of glycolipids in a conventional major histocompatibility complex (MHC)-restricted manner. Despite this, a subset of T-cells expressing the gamma and delta chains of the T-cell receptor (γδ T cells) exist with a capacity for MHC-unrestricted antigen recognition and potent inherent anti-tumor properties. In this review, we discuss the role of tumor-associated glycans in anti-tumor immunity, with an emphasis on the potential of γδ T cells to target the tumor glycocode. Understanding the many facets of this interaction holds the potential to unlock new ways to use both tumor-associated glycans and γδ T cells in novel therapeutic interventions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".